Search

Background Several breast cancer risk-assessment models exist. Few studies have evaluated predictive accuracy of multiple models in large screening populations. Methods We evaluated the performance of the BRCAPRO, Gail, Claus, Breast Cancer Surveillance Consortium (BCSC), and Tyrer-Cuzick models in predicting risk of breast cancer over 6 years among 35 921 women aged 40–84 years who underwent mammography screening at Newton-Wellesley Hospital from 2007 to 2009. We assessed model discrimination using the area under the receiver operating characteristic curve (AUC) and assessed calibration by comparing the ratio of observed-to-expected (O/E) cases. We calculated the square root of the Brier score and positive and negative predictive values of each model. Results Our results confirmed the good calibration and comparable moderate discrimination of the BRCAPRO, Gail, Tyrer-Cuzick, and BCSC models. The Gail model had slightly better O/E ratio and AUC (O/E = 0.98, 95% confidence interval [CI] = 0.91 to 1.06, AUC = 0.64, 95% CI = 0.61 to 0.65) compared with BRCAPRO (O/E = 0.94, 95% CI = 0.88 to 1.02, AUC = 0.61, 95% CI = 0.59 to 0.63) and Tyrer-Cuzick (version 8, O/E = 0.84, 95% CI = 0.79 to 0.91, AUC = 0.62, 95% 0.60 to 0.64) in the full study population, and the BCSC model had the highest AUC among women with available breast density information (O/E = 0.97, 95% CI = 0.89 to 1.05, AUC = 0.64, 95% CI = 0.62 to 0.66). All models had poorer predictive accuracy for human epidermal growth factor receptor 2 positive and triple-negative breast cancers than hormone receptor positive human epidermal growth factor receptor 2 negative breast cancers. Conclusions In a large cohort of patients undergoing mammography screening, existing risk prediction models had similar, moderate predictive accuracy and good calibration overall. Models that incorporate additional genetic and nongenetic risk factors and estimate risk of tumor subtypes may further improve breast cancer risk prediction.

Background Growing evidence shows that clinical and molecular subtypes of prostate cancer (PCa) have specific risk factors. Observational studies suggest that physical activity may lower the risk of aggressive PCa. To our knowledge, the association between physical activity and PCa defined by TMPRSS2:ERG has not been evaluated. Objective To prospectively examine the association between physical activity and risk of PCa defined by clinical features and TMPRSS2:ERG. Design, setting, and participants We studied 49 160 men aged 40–75 yr in the Health Professionals Follow-up Study from 1986 to 2012. Data was collected at baseline and every 2 yr with >90% follow-up. Total and vigorous physical activity were measured in metabolic equivalent of task (MET)-h/wk. Outcome measures and statistical analysis Advanced PCa was defined as stage T3b, T4, N1, or M1 at diagnosis and lethal PCa as distant metastases or death due to disease over follow-up. Presence of TMPRSS2:ERG was estimated by immunohistochemistry of ERG protein expression. Cox proportional hazards models were used to obtain multivariable hazard ratios (HRs) and 95% confidence intervals (CIs) for incidence of subtype-specific PCa. Results and limitations During 26 yr of follow-up, 6411 developed PCa overall and 888 developed lethal disease. There were no significant associations between total physical activity and risk of PCa in the overall cohort. In multivariable-adjusted models, men in the highest quintile of vigorous activity had a significant 30% lower risk of advanced PCa (HR: 0.70, 95% CI: 0.53–0.92) and 25% lower risk of lethal PCa (HR: 0.75, 95% CI: 0.59–0.94) than men in the lowest quintile of vigorous activity. The association was independent of screening history. Vigorous activity was not associated with total PCa in the overall cohort but was inversely associated among highly screened men (top vs bottom quintile, HR: 0.83, 95% CI: 0.70–0.97). Of all cases, 945 were assayed for ERG (48% ERG-positive). Men with higher vigorous activity had a lower risk of ERG-positive PCa (top vs bottom quintile, HR: 0.71, 95% CI: 0.52–0.97). There was no significant association with the risk of ERG-negative disease (p heterogeneity = 0.09). Conclusions Our study confirms that vigorous physical activity is associated with lower risk of advanced and lethal PCa and provides novel evidence for a lower risk of TMPRSS2:ERG-positive disease. Patient summary The identification of modifiable lifestyle factors for prevention of clinically important prostate cancer (PCa) is needed. In this report, we compared risk of PCa in men with different levels of physical activity. Men with higher vigorous activity had a lower risk of developing advanced and lethal PCa and PCa with the common TMPRSS2:ERG gene fusion.

Tissue microarrays (TMAs) have been used in thousands of cancer biomarker studies. To what extent batch effects, measurement error in biomarker levels between slides, affects TMA-based studies has not been assessed systematically. We evaluated 20 protein biomarkers on 14 TMAs with prospectively collected tumor tissue from 1,448 primary prostate cancers. In half of the biomarkers, more than 10% of biomarker variance was attributable to between-TMA differences (range, 1–48%). We implemented different methods to mitigate batch effects (R package batchtma), tested in plasmode simulation. Biomarker levels were more similar between mitigation approaches compared to uncorrected values. For some biomarkers, associations with clinical features changed substantially after addressing batch effects. Batch effects and resulting bias are not an error of an individual study but an inherent feature of TMA-based protein biomarker studies. They always need to be considered during study design and addressed analytically in studies using more than one TMA.

Introduction Identifying families with an underlying inherited cancer predisposition is a major goal of cancer prevention efforts. Mendelian risk models have been developed to better predict the risk associated with a pathogenic variant of developing breast/ovarian cancer (with BRCAPRO) and the risk of developing pancreatic cancer (PANCPRO). Given that pathogenic variants involving BRCA2 and BRCA1 predispose to all three of these cancers, we developed a joint risk model to capture shared susceptibility. Methods We expanded the existing framework for PANCPRO and BRCAPRO to jointly model risk of pancreatic, breast, and ovarian cancer and validated this new model, BRCAPANCPRO on three data sets each reflecting the common target populations. Results BRCAPANCPRO outperformed the prior BRCAPRO and PANCPRO models and yielded good discrimination for differentiating BRCA1 and BRCA2 carriers from non-carriers (AUCs 0.79, 95% CI: 0.73-0.84 and 0.70, 95% CI: 0.60-0.80) in families seen in high-risk clinics and pancreatic cancer family registries, respectively. In addition, BRCAPANCPRO was reasonably well calibrated for predicting future risk of pancreatic cancer (observed-to-expected (O/E) ratio = 0.81 [0.69, 0.94]). Discussion The BRCAPANCPRO model provides improved risk assessment over our previous risk models, particularly for pedigrees with a co-occurrence of pancreatic cancer and breast and/or ovarian cancer.

Since the beginning of the coronavirus disease-2019 (COVID-19) pandemic in 2020, there has been a tremendous accumulation of data capturing different statistics including the number of tests, confirmed cases and deaths. This data wealth offers a great opportunity for researchers to model the effect of certain variables on COVID-19 morbidity and mortality and to get a better understanding of the disease at the epidemiological level. However, in order to draw any reliable and unbiased estimate, models also need to take into account other variables and metrics available from a plurality of official and unofficial heterogenous resources. In this study, we introduce covid19census, an R package that extracts from many different repositories and combines together COVID-19 metrics and other demographic, environment- and health-related variables of the USA and Italy at the county and regional levels, respectively. The package is equipped with a number of user-friendly functions that dynamically extract the data over different timepoints and contains a detailed description of the included variables. To demonstrate the utility of this tool, we used it to extract and combine different county-level data from the USA, which we subsequently used to model the effect of diabetes on COVID-19 mortality at the county level, taking into account other variables that may influence such effects. In conclusion, it was observed that the ‘covid19census’ package allows to easily extract area-level data from both the USA and Italy using few functions. These comprehensive data can be used to provide reliable estimates of the effect of certain variables on COVID-19 outcomes. Database URL: https://github.com/c1au6i0/covid19census

Background: Men engaged in high physical activity have lower risks of advanced and fatal prostate cancer. Mechanisms underlying this association are not well understood but may include systemic and tumor-specific effects. We investigated potential mechanisms linking physical activity and gene expression in prostate tissue from men with prostate cancer. Methods: We included a subset of 118 men in the Health Professionals Follow-up Study diagnosed with prostate cancer between 1986 and 2005 with whole-transcriptome gene expression profiling on tumor and adjacent normal prostate tissue and physical activity data. Long-term vigorous physical activity was self-reported as the average time spent engaged in various forms of recreational physical activity at baseline and biennially until prostate cancer diagnosis. Gene set enrichment analysis was performed among KEGG and Hallmark gene sets to identify pathways with differential expression based on vigorous physical activity. Results: In adjacent normal tissue, we identified 25 KEGG gene sets enriched (downregulated) in the highest compared with lowest quintile of vigorous physical activity at an FDR Conclusions: These findings suggest that physical activity may influence the tumor microenvironment. Future studies are needed to confirm these findings and further investigate potential mechanisms linking physical activity to lethal prostate cancer. Impact: Identification of gene expression alterations in the prostate associated with physical activity can improve our understanding of prostate cancer etiology.

Purpose: Identifying cancers with high PI3K pathway activity is critical for treatment selection and eligibility into clinical trials of PI3K inhibitors. Assessments of tumor signaling pathway activity need to consider intratumoral heterogeneity and multiple regulatory nodes. Experimental Design: We established a novel, mechanistically informed approach to assessing tumor signaling pathways by quantifying single-cell–level multiplex immunofluorescence using custom algorithms. In a proof-of-concept study, we stained archival formalin-fixed, paraffin-embedded (FFPE) tissue from patients with primary prostate cancer in two prospective cohort studies, the Health Professionals Follow-up Study and the Physicians’ Health Study. PTEN, stathmin, and phospho-S6 were quantified on 14 tissue microarrays as indicators of PI3K activation to derive cell-level PI3K scores. Results: In 1,001 men, 988,254 tumor cells were assessed (median, 743 per tumor; interquartile range, 290–1,377). PI3K scores were higher in tumors with PTEN loss scored by a pathologist, higher Gleason grade, and a new, validated bulk PI3K transcriptional signature. Unsupervised machine-learning approaches resulted in similar clustering. Within-tumor heterogeneity in cell-level PI3K scores was high. During long-term follow-up (median, 15.3 years), rates of progression to metastases and death from prostate cancer were twice as high in the highest quartile of PI3K activation compared with the lowest quartile (hazard ratio, 2.04; 95% confidence interval, 1.13–3.68). Conclusions: Our novel pathway-focused approach to quantifying single-cell–level immunofluorescence in FFPE tissue identifies prostate tumors with PI3K pathway activation that are more aggressive and may respond to pathway inhibitors.

COVID-19 mortality rate is higher in the elderly and in those with preexisting chronic medical conditions. The elderly also suffer from increased morbidity and mortality from seasonal influenza infection, and thus annual influenza vaccination is recommended for them.In this study, we explore a possible area-level association between influenza vaccination coverage in people aged 65 years and older and the number of deaths from COVID-19. To this end, we used COVID-19 data until June 10, 2020 together with population health data for the United States at the county level. We fit quasi-Poisson regression models using influenza vaccination coverage in the elderly population as the independent variable and the number of deaths from COVID-19 as the outcome variable. We adjusted for a wide array of potential confounding variables using both county-level generalized propensity scores for influenza vaccination rates, as well as direct adjustment.Our results suggest that influenza vaccination coverage in the elderly population is negatively associated with mortality from COVID-19. This finding is robust to using different analysis periods, different thresholds for inclusion of counties, and a variety of methodologies for confounding adjustment.In conclusion, our results suggest a potential protective effect of the influenza vaccine on COVID-19 mortality in the elderly population. The significant public health implications of this possibility point to an urgent need for studying the relationship between influenza vaccination and COVID-19 mortality at the individual level, to investigate both the epidemiology and any underlying biological mechanism.

BACKGROUND: A few papers have considered reproducibility of a posteriori dietary patterns across populations, as well as pattern associations with head and neck cancer risk when multiple populations are available. METHODS: We used individual-level pooled data from seven case-control studies (3844 cases; 6824 controls) participating in the International Head and Neck Cancer Epidemiology consortium. We simultaneously derived shared and study-specific a posteriori patterns with a novel approach called multi-study factor analysis applied to 23 nutrients. We derived odds ratios (ORs) and 95% confidence intervals (CIs) for cancers of the oral cavity and pharynx combined, and larynx, from logistic regression models. RESULTS: We identified three shared patterns that were reproducible across studies (75% variance explained): the Antioxidant vitamins and fiber (OR = 0.57, 95% CI = 0.41, 0.78, highest versus lowest score quintile) and the Fats (OR = 0.80, 95% CI = 0.67, 0.95) patterns were inversely associated with oral and pharyngeal cancer risk. The Animal products and cereals (OR = 1.5, 95% CI = 1.1, 2.1) and the Fats (OR = 1.8, 95% CI = 1.4, 2.3) patterns were positively associated with laryngeal cancer risk, whereas a linear inverse trend in laryngeal cancer risk was evident for the Antioxidant vitamins and fiber pattern. We also identified four additional study-specific patterns, one for each of the four US studies examined. We named them all as Dairy products and breakfast cereals, and two were associated with oral and pharyngeal cancer risk. CONCLUSION: Multi-study factor analysis provides insight into pattern reproducibility and supports previous evidence on cross-country reproducibility of dietary patterns and on their association with head and neck cancer risk. See video abstract at, http://links.lww.com/EDE/B430.

Background Lynch syndrome, the most common colorectal cancer (CRC) syndrome, is caused by germline mismatch repair (MMR) genes. Precise estimates of age-specific risks are crucial for sound counseling of individuals managing a genetic predisposition to cancer, but published risk estimates vary. The objective of this work is to provide gene-, sex-, and age-specific risk estimates of CRC for MMR mutation carriers that comprehensively reflect the best available data. Methods We conducted a meta-analysis to combine risk information from multiple studies on Lynch syndrome–associated CRC. We used a likelihood-based approach to integrate reported measures of CRC risk and deconvolved aggregated information to estimate gene- and sex-specific risk. Results Our comprehensive search identified 10 studies (8 on MLH1, 9 on MSH2, and 3 on MSH6). We estimated the cumulative risk of CRC by age and sex in heterozygous mutation carriers. At age 70 years, for male and female carriers, respectively, risks for MLH1 were 43.9% (95% confidence interval [CI] = 39.6% to 46.6%) and 37.3% (95% CI = 32.2% to 40.2%), for MSH2 were 53.9% (95% CI = 49.0% to 56.3%) and 38.6% (95% CI = 34.1% to 42.0%), and for MSH6 were 12.0% (95% CI = 2.4% to 24.6%) and 12.3% (95% CI = 3.5% to 23.2%). Conclusions Our results provide up-to-date and comprehensive age-specific CRC risk estimates for counseling and risk prediction tools. These will have a direct clinical impact by improving prevention and management strategies for both individuals who are MMR mutation carriers and those considering testing.

Background Use of risk-reducing Salpingo-oophorectomy (RRSO) substantially reduces the risk of ovarian and breast cancer for women who carry a BRCA1/2 mutation. It is important to adjust for RRSO use in the estimation of BRCA1/2 penetrance of breast and ovarian cancer. Methods We searched PubMed for penetrance estimates of breast and ovarian cancer from studies that genotyped individual patients and explicitly adjusted for RRSO use by censoring follow-up at the age of RRSO. We meta-analyzed penetrance estimates from 7 identified studies. We implemented the resulting penetrance estimates in a Mendelian risk prediction model as iplemented in the software package BRCAPRO, which we applied to estimate carrier probabilities in 2 BRCA cohorts. Results Penetrance estimates by age 70 years for breast cancer were 64.6% (95% confidence interval [CI] = 59.5% to 69.4%) for BRCA1 mutation carriers and 61.0% (95% CI = 48.1% to 72.5%) for BRCA2 mutation carriers, and for ovarian cancer they were 48.3% (95% CI = 38.8% to 57.9%) and 20.0% (95% CI = 13.3% to 29.0%), respectively. When integrated into BRCAPRO, our estimates led to good calibration and different estimates of carrier probabilities for some individuals when evaluating the models in 2 cohorts. Conclusions The report updates penetrance estimates for BRCA1/2-associated cancer. We report higher estimates than previously reported, which did not adjust for RRSO. Differential use of RRSO may partially explain heterogeneity in the currently available penetrance estimates. For some individuals, using our estimates in BRCAPRO may result in changes in estimated carrier probabilities, which warrants validation in future studies.

Purpose:While various studies have highlighted the prognostic significance of pathologic complete response (pCR) after neoadjuvant chemotherapy (NAT), the impact of additional adjuvant therapy after pCR is not known.Experimental Design:PubMed was searched for studies with NAT for breast cancer and individual patient-level data was extracted for analysis using plot digitizer software. HRs, with 95% probability intervals (PI), measuring the association between pCR and overall survival (OS) or event-free survival (EFS), were estimated using Bayesian piece-wise exponential proportional hazards hierarchical models including pCR as predictor.Results:Overall, 52 of 3,209 publications met inclusion criteria, totaling 27,895 patients. Patients with a pCR after NAT had significantly better EFS (HR = 0.31; 95% PI, 0.24–0.39), particularly for triple-negative (HR = 0.18; 95% PI, 0.10–0.31) and HER2+ (HR = 0.32; 95% PI, 0.21–0.47) disease. Similarly, pCR after NAT was also associated with improved survival (HR = 0.22; 95% PI, 0.15–0.30). The association of pCR with improved EFS was similar among patients who received subsequent adjuvant chemotherapy (HR = 0.36; 95% PI, 0.19–0.67) and those without adjuvant chemotherapy (HR = 0.36; 95% PI, 0.27–0.54), with no significant difference between the two groups (P = 0.60).Conclusions:Achieving pCR following NAT is associated with significantly better EFS and OS, particularly for triple-negative and HER2+ breast cancer. The similar outcomes with or without adjuvant chemotherapy in patients who attain pCR likely reflects tumor biology and systemic clearance of micrometastatic disease, highlighting the potential of escalation/deescalation strategies in the adjuvant setting based on neoadjuvant response.See related commentary by Esserman, p. 2771

Importance Patients with cancer are at increased risk for severe COVID-19, but it is unknown whether SARS-CoV-2 vaccination is effective for them. Objective To determine the association between SARS-CoV-2 vaccination and SARS-CoV-2 infections among a population of Veterans Affairs (VA) patients with cancer. Design, Setting, and Participants Retrospective, multicenter, nationwide cohort study of SARS-CoV-2 vaccination and infection among patients in the VA health care system from December 15, 2020, to May 4, 2021. All adults with solid tumors or hematologic cancer who received systemic cancer-directed therapy from August 15, 2010, to May 4, 2021, and were alive and without a documented SARS-CoV-2 positive result as of December 15, 2020, were eligible for inclusion. Each day between December 15, 2020, and May 4, 2021, newly vaccinated patients were matched 1:1 with unvaccinated or not yet vaccinated controls based on age, race and ethnicity, VA facility, rurality of home address, cancer type, and treatment type/timing. Exposures Receipt of a SARS-CoV-2 vaccine. Main Outcomes and Measures The primary outcome was documented SARS-CoV-2 infection. A proxy for vaccine effectiveness was defined as 1 minus the risk ratio of SARS-CoV-2 infection for vaccinated individuals compared with unvaccinated controls. Results A total of 184 485 patients met eligibility criteria, and 113 796 were vaccinated. Of these, 29 152 vaccinated patients (median [IQR] age, 74.1 [70.2-79.3] years; 95% were men; 71% were non-Hispanic White individuals) were matched 1:1 to unvaccinated or not yet vaccinated controls. As of a median 47 days of follow-up, 436 SARS-CoV-2 infections were detected in the matched cohort (161 infections in vaccinated patients vs 275 in unvaccinated patients). There were 17 COVID-19-related deaths in the vaccinated group vs 27 COVID-19-related deaths in the unvaccinated group. Overall vaccine effectiveness in the matched cohort was 58% (95% CI, 39% to 72%) starting 14 days after the second dose. Patients who received chemotherapy within 3 months prior to the first vaccination dose were estimated to have a vaccine effectiveness of 57% (95% CI, -23% to 90%) starting 14 days after the second dose vs 76% (95% CI, 50% to 91%) for those receiving endocrine therapy and 85% (95% CI, 29% to 100%) for those who had not received systemic therapy for at least 6 months prior. Conclusions and Relevance In this cohort study, COVID-19 vaccination was associated with lower SARS-CoV-2 infection rates in patients with cancer. Some immunosuppressed subgroups may remain at early risk for COVID-19 despite vaccination, and consideration should be given to additional risk reduction strategies, such as serologic testing for vaccine response and a third vaccine dose to optimize outcomes.

Purpose: Breast and prostate cancer co-occur in families, and women with a family history of prostate cancer are at increased breast cancer risk. Prostate cancer is among the most heritable cancers, but few studies have investigated its association with familial breast cancer. The objective of this study is to investigate the extent to which familial breast or prostate cancer in first-degree relatives increases prostate cancer risk. Experimental Design: A prospective study of 37,002 U.S. men in the Health Professionals Follow-up Study. During the 16-year follow-up to 2012, 4,208 total and 344 lethal cases were diagnosed. Using cause-specific hazards regression, we estimated the multivariable HRs and 95% confidence intervals (CI) for associations between familial breast or prostate cancer and total and lethal prostate cancer. Results: Those with familial breast cancer had a 21% greater risk of prostate cancer overall (95% CI, 1.10–1.34), and a 34% greater risk of lethal disease (HR 1.34; 95% CI, 0.96–1.89). Family history of prostate cancer alone was associated with a 68% increased risk of total disease (95% CI, 1.53–1.83) and a 72% increased risk of lethal disease (95% CI, 1.25–2.38). Men with a family history of both cancers were also at elevated risk. Conclusions: Our study found that men with a family history of breast or prostate cancer had elevated prostate cancer risks, including risk of lethal disease. These findings have translational relevance for cancer risk prediction in men.

Purpose: The majority of ovarian carcinomas are of high-grade serous histology, which is associated with poor prognosis. Surgery and chemotherapy are the mainstay of treatment, and molecular characterization is necessary to lead the way to targeted therapeutic options. To this end, various computational methods for gene expression–based subtyping of high-grade serous ovarian carcinoma (HGSOC) have been proposed, but their overlap and robustness remain unknown. Experimental Design: We assess three major subtype classifiers by meta-analysis of publicly available expression data, and assess statistical criteria of subtype robustness and classifier concordance. We develop a consensus classifier that represents the subtype classifications of tumors based on the consensus of multiple methods, and outputs a confidence score. Using our compendium of expression data, we examine the possibility that a subset of tumors is unclassifiable based on currently proposed subtypes. Results: HGSOC subtyping classifiers exhibit moderate pairwise concordance across our data compendium (58.9%–70.9%; P < 10−5) and are associated with overall survival in a meta-analysis across datasets (P < 10−5). Current subtypes do not meet statistical criteria for robustness to reclustering across multiple datasets (prediction strength < 0.6). A new subtype classifier is trained on concordantly classified samples to yield a consensus classification of patient tumors that correlates with patient age, survival, tumor purity, and lymphocyte infiltration. Conclusions: A new consensus ovarian subtype classifier represents the consensus of methods and demonstrates the importance of classification approaches for cancer that do not require all tumors to be assigned to a distinct subtype. Clin Cancer Res; 24(20); 5037–47. ©2018 AACR.

The rapid drop in the cost of DNA sequencing led to the availability of multi-gene panels, which test 25 or more cancer susceptibility genes for a low cost. Clinicians and genetic counselors need a tool to interpret results, understand risk of various cancers, and advise on a management strategy. This is challenging as there are multiple studies regarding each gene, and it is not possible for clinicians and genetic counselors to be aware of all publications, nor to appreciate the relative accuracy and importance of each. Through an extensive literature review, we have identified reliable studies and derived estimates of absolute risk. We have also developed a systematic mechanism and informatics tools for (1) data curation, (2) the evaluation of quality of studies, and (3) the statistical analysis necessary to obtain risk. We produced the risk prediction clinical decision support tool ASK2ME (All Syndromes Known to Man Evaluator). It provides absolute cancer risk predictions for various hereditary cancer susceptibility genes. These predictions are specific to patients' gene carrier status, age, and history of relevant prophylactic surgery. By allowing clinicians to enter patient information and receive patient-specific cancer risks, this tool aims to have a significant impact on the quality of precision cancer prevention and disease management activities relying on panel testing. It is important to note that this tool is dynamic and constantly being updated, and currently, some of its limitations include (1) for many gene-cancer associations risk estimates are based on one study rather than meta-analysis, (2) strong assumptions on prior cancers, (3) lack of uncertainty measures, and (4) risk estimates for a growing set of gene-cancer associations which are not always variant specific. All of these concerns are being addressed on an ongoing basis, aiming to make the tool even more accurate.

On an average, 1% of monoclonal gammopathy of undermined significance (MGUS) and 10% of smoldering Multiple Myeloma (SMM) progress to symptomatic MM every year within the first five years of diagnosis. The probability of progression significantly decreases for SMM patients after first 5 years. However, a distinct subset of SMM patients progress within 2 years and are re-classified as high-risk patients based on risk markers such as 20/2/20 or certain genomic features. Although recent studies have evaluated the high-risk genomic features for SMM but genomic background of SMM patients who do not progress to MM after long-term follow-up (>= 5 years) has not been described. Here, we evaluated transcriptomic and genomic changes enriched in non-progressor (NP) (no progression after 5 years of follow-up) precursor conditions (N=31) with those progressed within short period of time (N=71) and compared them with changes observed in newly diagnosed MM (N=192). Additionally, using transcriptome, epigenome and whole genome profiling we also studied additional unique samples from 18 patients at their precursor stage as well as when progressed to MM. Overall, we have observed significantly lower mutational load for NP SMM from progressor SMM (median SNV 4900 vs. 7881 p < 3e-04) with high sensitivity (0.83) and specificity (0.65) to separate NP from progressors. We have further developed a deep learning model by using more than 4500 genome wide features using ten-fold cross validation. This model indicated that not only the load but also the patterns of mutations (type, location, frequency) are different between two conditions. We also found that NP samples have significantly lower heterogeneity (p < 0.05). However, progressed samples showed similar mutational load and heterogeneity at precursor stage and MM. Among CNA differences, absence of gain or deletion of chr8 (not involving MYC region) were strong predictor of NP (OR=7.2 95% CI 2.2-24). Focal genomic loss was also significantly lower for NP (p=0.004) which was also reflected by low genome scar score (GSS) (p=0.07). Structural variant and copy number signature analysis also showed that NPs were showing significantly low exposure to non-clustered variable size genomic deletions. We observed similar frequency of primary translocations [t(11;14), t(4;14), and t(14;16)] in both progressor and NP samples as well as newly diagnosed MM. MYC translocation with any partner was not observed in NP samples, whereas 37% of progressor samples had a MYC translocations (OR=12.8). Adding all these differences including chr8 CNAs, MYC translocations, mutation burden, GSS, focal deletions, all driver mutations as well as primary translocations into recursive partitioning model to predict non-progressor SMM, we have identified a simple genomic model only involving chr8 CN changes and overall mutational burden to achieve a high sensitivity (0.82) and specificity (74%). Our transcriptomic analysis measured the distance between progressor and NP SMM as well as MM and found that NP SMM has greater difference with MM which is closer to progressor SMM. We quantified transcriptomic heterogeneity by using molecular degree of perturbation. This analysis showed that consistent with DNA changes, DNA repair pathway and MYC target genes are expressed similarly in NP SMM as in normal plasma cells compared to progressor SMM. Epigenomic analysis yielded 75 SEs regions differentially utilized between precursor and symptomatic MM stage using paired samples. The targeted genes included BMP6, PRDM1, STAT1, SERTAD2 and RAB21 and possibly regulating genes related to oncogenic KRAS activities. In conclusion, we define genomic characterization of non-progressor SMM and our results now provide the basis to develop molecular definition of SMM as well as risk driving features. Disclosures Munshi: Janssen: Consultancy; Pfizer: Consultancy; Legend: Consultancy; Novartis: Consultancy; Adaptive Biotechnology: Consultancy; Oncopep: Consultancy, Current equity holder in publicly-traded company, Other: scientific founder, Patents & Royalties; Takeda: Consultancy; Abbvie: Consultancy; Karyopharm: Consultancy; Amgen: Consultancy; Celgene: Consultancy; Bristol-Myers Squibb: Consultancy.

Introduction Coronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2 virus, is particularly serious in patients with multiple myeloma (MM), with estimated mortality of over 30% in several studies. In the general population, SARS-CoV-2 vaccination has been demonstrated to be an effective approach to preventing infection. However, patients with MM were not included in vaccination trials. Recent studies suggest that patients with compromised immune systems exhibit reduced antibody response to SARS-CoV-2 vaccination, and MM patients are often immunocompromised both due to MM itself and due to MM treatment. Thus, the objective of this retrospective cohort study in the national Veterans Affairs (VA) healthcare system was to evaluate the real-world effectiveness of SARS-CoV-2 vaccination to prevent COVID-19 infection in MM patients during the 140-day period following initial vaccine availability. Methods This is a multicenter study of SARS-CoV-2 infection among vaccinated and unvaccinated patients at VA hospitals nationwide during the period from 12/15/2020 to 5/4/2021. We identified a cohort of MM patients who were alive and without prior SARS-CoV-2 infection on their date of vaccination or inclusion as a control. For added comparison with a less immunocompromised population, we also identified a cohort of cancer survivors, defined as patients with any solid or hematologic malignancy who had been treated with systemic cancer-directed therapy subsequent to 8/15/2010, but had not been treated with such therapy in the 6 months prior to vaccination or inclusion as a control, and were alive and without prior SARS-CoV-2 infection on that date. Vaccinated patients were exactly matched 1:1 to unvaccinated controls on race, VA facility, rurality of home address, cancer type, and treatment timing and modality with minimum distance matching on age. The primary exposure was receipt of a SARS-CoV-2 vaccine. The primary outcome was laboratory-confirmed SARS-CoV-2 infection. Vaccination effectiveness was defined as 1 minus the risk ratio of SARS-CoV-2 infection for vaccinated individuals compared to unvaccinated controls. Results 6,891 MM patients met eligibility criteria and 4,367 were vaccinated during the study period. Of those, 1,606 vaccinated MM patients were matched 1:1 to 1,606 unvaccinated or not yet vaccinated controls. In addition, for comparison, 2,476 vaccinated cancer survivors were matched 1:1 to 2,476 unvaccinated or not yet vaccinated controls. Median follow-up was 44 days among MM patients and 46 days among cancer survivors. Vaccine effectiveness in the matched cohort of MM patients was 22.2% (95% CI, -133 to 82.7%) starting 14 days after the second dose. In contrast, effectiveness was 82.3% (95% CI 16.4 to 100%) starting 14 days after the second dose in the matched cohort of cancer survivors. Among vaccinated MM patients in the matched cohort, 14 (8.7 per 1000 patients) were infected with SARS-CoV-2 subsequent to vaccination. Among vaccinated cancer survivors in the matched cohort, 10 (4.0 per 1000 patients) were infected subsequent to vaccination. Conclusion Vaccination is an effective strategy for preventing SARS-CoV-2. However, effectiveness may be reduced in patients with MM, likely due to a co-existing immunosuppression both due to the disease process as well as associated therapy. Future studies are needed to evaluate the relationship between MM disease states, types of therapy used and treatment timing that may impact vaccine effectiveness, and to also determine if MM patients would benefit from post-vaccination serologies or a booster vaccination. Disclosures Branch-Elliman: Gilead Pharmaceuticals: Research Funding. Brophy: Novartis: Research Funding. Munshi: Pfizer: Consultancy; Legend: Consultancy; Novartis: Consultancy; Adaptive Biotechnology: Consultancy; Karyopharm: Consultancy; Celgene: Consultancy; Oncopep: Consultancy, Current equity holder in publicly-traded company, Other: scientific founder, Patents & Royalties; Abbvie: Consultancy; Takeda: Consultancy; Amgen: Consultancy; Janssen: Consultancy; Bristol-Myers Squibb: Consultancy.

Background On average 10% of SMM patients progress to symptomatic MM per year with in first 5 years of diagnosis. However, a subset of SMM patients re-classified as high risk patients on the basis of risk markers which identify risk of progression within 2 years. Although recent studies have evaluated the high-risk SMM, genomic background of SMM patients who do not progress to MM after long-term follow-up (>=5 years) has not been described. Methods Here, we evaluated transcriptomic and genomic changes enriched in non-progressor (NP) (no progression after 5 years of follow-up) precursor conditions (N=31) with those progressed within short period time (N=71) and compared them with changes observed in newly diagnosed MM (N=192). Additionally, using transcriptome, epigenome and whole genome profiling we also studied additional unique samples from 18 patients at their precursor stage as well as when progressed to MM. Results Overall, we have observed significantly lower mutational load for NP SMM (median SNV 5460 vs. 7018, p 90% accuracy and >0.80 area under the curve on ROC using ten-fold cross validation. This indicated that not only the load but also the patterns of mutations (type, location, frequency) are different between two conditions. We also found that NP samples have significantly lower heterogeneity (p 80%) with AUC score 0.80. Our transcriptomic analysis measured the distance between progressor and NP SMM as well as MM and found that NP SMM are less similar to MM which is closer to progressor SMM. Epigenomic analysis yield that 75 SEs regions were differentially utilized between precursor and symptomatic MM stage. The targeted genes included BMP6, PRDM1, STAT1 and RAB21 and possibly regulating genes related to oncogenic KRAS activities. Conclusions In conclusion, our results now provide the basis to develop genomic definition of SMM as well as risk driving features.

Background: Data is lacking about SARS-CoV-2 vaccination effectiveness in patients with cancer, particularly those on systemic therapy. This retrospective cohort study in the US national Veterans Affairs (VA) healthcare system reports the effectiveness of SARS-CoV-2 vaccination in cancer patients on and off active therapy during the first 140 days following administration. Methods: This is a multicenter study of SARS-CoV-2 infection among vaccinated and unvaccinated Veterans vaccinated during the period from 12/15/2020 to 5/4/2021. Veterans with solid or hematologic malignancy who received systemic cancer-directed therapy at the VA at least one time between 8/15/2010 to 5/4/2021 were included. Vaccinated patients were exactly matched 1:1 to an unvaccinated control on race, VA facility, rurality of home address, cancer type, and treatment timing and modality with minimum distance matching on age. The primary exposure was receipt of a SARS-CoV-2 vaccine. The primary outcome was laboratory-confirmed SARS-CoV-2 infection. Vaccination effectiveness was defined as 1 minus the risk ratio of SARS-CoV-2 infection for vaccinated individuals compared to unvaccinated controls. Results: 184,485 patients met eligibility criteria and 113,796 were vaccinated during the study period. Of these, 29,152 vaccinated patients were matched 1:1 to 29,152 unvaccinated or not yet vaccinated controls. As of a median 47 days of follow-up, overall vaccine effectiveness in the matched cohort was 58% (95% CI, 39 to 72%) starting 14 days after the second dose. Patients on chemotherapy within three months prior to first vaccination dose exhibited a 14-day post-second dose effectiveness of 57% (95% CI -23 to 90%), versus 76% (95% CI 50 to 91%) for those on endocrine therapy and 85% (95% CI 29 to 100%) for those off systemic therapy for at least six months prior. Conclusions: Vaccination is an effective strategy for preventing COVID-19 in cancer patients. However, effectiveness may be reduced in patients actively receiving immunosuppressive systemic therapy. Future study is needed to determine if these patients would benefit from post-vaccination serologies and/or a booster vaccination following completion of therapy. Legal entity responsible for the study: Nathanael Fillmore. Funding: Has not received any funding. Disclosure: W. Branch-Elliman: Financial Interests, Institutional, Funding: Gilead. G. Parmigiani: Financial Interests, Personal, Leadership Role: Phaeno Biotechnology. M. Brophy: Financial Interests, Personal, Research Grant: Novartis. N. Munshi: Financial Interests, Personal, Advisory Role: Celgene;Financial Interests, Personal, Advisory Role: Janssen;Financial Interests, Personal, Advisory Role: AbbVie;Financial Interests, Personal, Advisory Role: Takeda;Financial Interests, Personal, Member of the Board of Directors: OncoPep. All other authors have declared no conflicts of interest.

Purpose The majority of randomized oncology trials are two-arm studies that test the efficacy of new therapies against a standard of care, thereby assigning a large proportion of patients to nonexperimental therapies. In contrast, multiarm studies efficiently share a common control arm while evaluating multiple experimental therapies. A major bottleneck for traditional multiarm trials is the requirement that all therapies—often drugs from different companies—have to be available at the same time when the trial starts. We evaluate the potential gains of a platform design—the rolling-arms design—that adds and removes arms on a rolling basis. Methods We define the rolling-arms design with the goal of minimizing the complexity of random assignment and data analyses of a platform trial. We then evaluate its potential advantages in hormone receptor–positive, human epidermal growth factor receptor 2–negative advanced breast cancer. Multiple pharmaceutical companies currently test CDK4/6 inhibitors in combination with letrozole in independent two-arm trials. We conducted a simulation study to quantify the reduction in sample size, number of patients treated with the standard of care, and the average time to treatment discovery if these therapies had been tested in a rolling-arms trial. Results A rolling-arms platform design with two to five experimental treatments can reduce the overall sample size requirement by up to 30% compared with standard two-arm studies. It assigns up to 60% fewer patients to the control arm compared with five independent trials that test distinct treatments. Moreover, under realistic scenarios, effective experimental treatments are discovered up to 15 months earlier compared with separate two-arm trials. Conclusion The rolling-arms platform design is applicable to a broad variety of diseases, and under realistic scenarios, it is substantially more efficient than standard two-arm randomized trials.

The aim of this study was to investigate the influence of age at diagnosis of atypical hyperplasia (“atypia”, ductal [ADH], lobular [ALH], or severe ADH) on the risk of developing subsequent invasive breast cancer or ductal carcinoma in situ (DCIS). Using standard survival analysis methods, we retrospectively analyzed 1353 women not treated with chemoprevention among a cohort of 2370 women diagnosed with atypical hyperplasia to determine the risk relationship between age at diagnosis and subsequent breast cancer. For all atypia diagnoses combined, our cohort showed a 5-, 10-, and 15-year risk of invasive breast cancer or DCIS of 0.56, 1.25, and 1.30, respectively, with no significant difference in the (65,75] year age group. For women aged (35,75] years, we observed no significant difference in the 15-year risk of invasive breast cancer or DCIS after atypical hyperplasia, although the baseline risk for a 40-year-old woman is approximately 1/8 the risk of a 70-year-old woman. The risks associated with invasive breast cancer or DCIS for women in our cohort diagnosed with ADH, severe ADH, or ALH, regardless of age, were 7.6% (95% CI 5.9–9.3%) at 5 years, 25.1% (20.7–29.2%) at 10 years, and 40.1% (32.8–46.6%) at 15 years. In contrast to current risk prediction models (e.g., Gail, Tyrer-Cuzick) which assume that the risk of developing breast cancer increases in relation to age at diagnosis of atypia, we found the 15-year cancer risk in our cohort was not significantly different for women between the ages of 35 (excluded) and 75. This implies that the “hits” received by the breast tissue along the “high-risk pathway” to cancer might possibly supersede other factors such as age.

We recently shown that high-dose melphalan (HDM) followed by autologous stem cell transplant (ASCT) as first line therapy in young ( A significant change in frequency of driver mutations including RAS/RAF, FAM46C, TP53, and DIS3 was not observed at the time of relapse. Clonality level was increased only for KRAS (p=0.054), while all other specific driver genes had similar clonality level at diagnosis and relapse. Interestingly, a significant increase in mutations involving MYO16 and SLC7A8 genes was observed at relapse in both arms, implicating components of the induction regimen (RVD). Investigating the mutational signature utilization in only newly acquired mutations identified 4 signatures: APOBEC, HR Double Strand Repair, clock-like signature, and unknown. k-means clustering analysis of samples based on signature utilization showed four distinct clusters. All patients clustering with high DNA repair signature utilization were in the HDM arm (65% HDM patients), the majority of whom achieved CR or sCR (74%); these patients acquired 8308 (range 3302-19107) new mutations between diagnosis and relapse. None of the RVD only treated patients were in this cluster. The remaining 35% HDM group patients were clustered with RVD samples and showed unknown signature utilization. Furthermore, motif enrichment analysis identified CYWR and ATGAGATV (p < 1e-130) as enriched motifs around the new mutations in HDM compared to RVD cohort. Importantly and as expected, DNA damage repair pathway genes were frequently targeted in the HDM group: 72% HDM samples accumulated DDR gene mutations vs. only 17% in the RVD alone arm (p < 0.001). At the time of relapse, 100% HDM arm patients had at least one DDR gene mutation and 80% had two or more, while only 37% RVD only group had one or more such mutation. Finally, we have reconstructed phylogenetic and evolutionary trajectories based on mutation and copy-number data from samples at diagnosis and relapse. The clonal composition in both arms was similar at diagnosis; however, HDM caused a significant shift to more subclonal mutations at relapse. chromothripsis and chromoplexy events were detected in 30% patients at diagnosis, which remained constant at relapse regardless of treatment. In summary, we describe significant accumulation of mutations following high dose melphalan. This fundamental molecular change in the disease at relapse, suggests the need for reappraisal of the optimal use and sequencing of high dose melphalan in the era of novel agents. Disclosures Fulciniti: NIH: Research Funding. Richardson:Celgene/BMS, Oncopeptides, Takeda, Karyopharm: Research Funding. Thakurta:Oxford University: Other: visiting professor; Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Perrot:Amgen, BMS/Celgene, Janssen, Sanofi, Takeda: Consultancy, Honoraria, Research Funding. Moreau:Sanofi: Consultancy, Honoraria; Celgene/Bristol-Myers Squibb: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Takeda: Honoraria; Novartis: Honoraria; Abbvie: Consultancy, Honoraria; Amgen: Consultancy, Honoraria. Anderson:Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Oncopep and C4 Therapeutics.: Other: Scientific Founder of Oncopep and C4 Therapeutics.; Sanofi-Aventis: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Millenium-Takeda: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees. Parmigiani:Phaeno Biotehnologies: Current equity holder in publicly-traded company; CRA Health: Current equity holder in publicly-traded company; Foundation Medicine Institute: Consultancy; Delphi Diagnostics: Consultancy; BayesMendel Laboratory: Other: Co-lead. Munshi:Amgen: Consultancy; AbbVie: Consultancy; Karyopharm: Consultancy; Takeda: Consultancy; Adaptive: Consultancy; Janssen: Consultancy; C4: Current equity holder in private company; OncoPep: Consultancy, Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties; BMS: Consultancy; Legend: Consultancy.

Background & Aims Lynch syndrome is the most common inherited cause of gastrointestinal cancer and increases risk for a variety of malignancies, including gastric cancer. We aimed to identify clinical factors associated with gastric cancer in carriers of germline variants causing Lynch syndrome. Methods We collected data from 52,758 consecutive individuals tested for genetic variants associated with Lynch syndrome from June 2006 through July 2013 at a commercial laboratory. We obtained clinical and demographic data, as well as information on personal and family histories of cancer (first- and second-degree relatives) from forms completed by ordering providers. We performed multivariate logistic regression to identify clinical factors associated with gastric cancer in carriers of mutations that cause Lynch syndrome (pathogenic mutations). Results After we excluded individuals with missing clinical data (n=1664) or with multiple pathogenic mutations (n=8), we analyzed data from 51,086 individuals. Of these, 3828 persons carried pathogenic mutations (1346 with mutations in MLH1 , 1639 with mutations in MSH2 , 670 with mutations in MSH6 , 145 with mutations in PMS2 , and 28 with mutations in EPCAM ). Of the 3828 carriers of pathogenic mutations, 41 (1.1%) had a previous gastric cancer and 350 (9.1%) had 1 or more first- or second-degree relatives with gastric cancer. In multivariate analysis, male sex (odds ratio [OR], 2.82; 95% CI, 1.48–5.38), older age (OR, 2.07 per 10 years; 95% CI, 1.64–2.61), mutations in MLH1 (OR, 6.53; 95% CI, 1.50–28.42) or MSH2 (OR, 5.23 compared to mutations in MSH6 , PMS2 , or EPCAM ; 95% CI, 1.21–22.71), and first-degree relative with gastric cancer (OR, 2.52; 95% CI, 1.42–4.45), but not second-degree relatives (OR, 1.12; 95% CI, 0.40–3.18) were independently associated with gastric among carriers of pathogenic mutations. Conclusions In an analysis of data from almost 4000 carriers of Lynch syndrome-associated mutations, we found history of gastric cancer to be independently associated with male sex, older age, mutations in MLH1 or MSH2 , and with having a first-degree relative with gastric cancer. These findings suggest that personalized, risk-stratified approaches to gastric cancer surveillance may be appropriate for individuals with Lynch syndrome-associated mutations.

Introduction: Identifying families with an underlying inherited cancer predisposition is a major goal of cancer prevention efforts. Mendelian risk models have been developed to better predict the risk associated with a pathogenic variant of developing breast/ovarian cancer (with BRCAPRO), and the risk of developing pancreatic cancer (PANCPRO). Given that pathogenic variants involving BRCA2 and BRCA1 predispose to all three of these cancers, we developed a joint risk model to capture shared susceptibility. Methods: We expanded the existing framework for PANCPRO and BRCAPRO to model underlying risk of pancreatic, breast, and ovarian cancer. We then validated this model on three datasets each reflecting the target populations for these models, including patients referred to a high-risk genetics clinic, patients with multiple family members with pancreatic cancer who underwent genetic testing for BRCA1/2, as well as a prospective registry of pancreatic cancer families. The new model is designated BRCAPANCPRO. Results: BRCAPANCPRO yielded good discrimination for differentiating BRCA1 and BRCA2 carriers from non-carriers (AUC = 0.84, 95% CI: 0.78, 0.87) and was reasonably well-calibrated for predicting future risk of pancreatic cancer (observed-to-expected (O/E) ratio = 0.82 [0.69, 0.94]). In these families, BRCAPANCPRO outperformed both the prior BRCAPRO and PANCPRO models. Discussion: The BRCAPANCPRO model improves the identification of families with a pathogenic variant in BRCA2 or BRCA1, particularly for families where there is the co-occurrence of pancreatic cancer in addition to breast and/or ovarian cancer. The model also improves the prediction of pancreatic cancer among families with a pancreatic cancer family history, with or without breast and/or ovarian cancer. Citation Format: Amanda L. Blackford, Erica J. Childs, Nancy Porter, Gloria Petersen, Kari Rabe, Steven Gallinger, Ayelet Borgida, Sapna Syngal, Ann Schwartz, Michele Cote, Ralph Hruban, Michael Goggins, Alison P. Klein, Giovanni Parmigiani. A risk prediction tool for individuals with a family history of breast, ovarian, or pancreatic cancer: BRCAPANCPRO [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 885.

Multiple Myeloma is preceded by precursor states of monoclonal gammopathy of undermined significance (MGUS) and smoldering multiple myeloma (SMM). Studies have shown that progression to symptomatic MM five years after diagnosis is 1% for MGUS and 10% for SMM. However, based on the genomic background, this rate is highly variable, especially for SMM patients. Recent studies have evaluated the high-risk genomic features for SMM, but the genomic background of SMM patients who do not progress to MM after long-term follow-up (>= 5 years) has not been described. Here, we evaluated genomic changes enriched in non-progressor (NP) (no progression after 5 years of follow-up) precursor conditions (N=31) with those progressed within a short time (N=71) as well as newly diagnosed Myeloma (N=192). We also studied additional unique samples from 18 patients at their precursor stage as well as when progressed to Myeloma. We report a similar large-scale CN alteration in non-progressor SMM compared to progressor SMMs or MM at diagnosis. However, whole-genome sequencing data showed that the overall mutational load for non-progressor SMM samples was lower than Progressor MGUS/SMM (median SNV 5460 vs. 7018). This difference significantly increased for mutations affecting the coding regions. NP samples at diagnosis had 26% and 53% less coding mutations (missense, nonsense, and frameshift mutations) compared to progressor MGUS/SMM (p=0.008) and newly diagnosed MM (p < 0.001) respectively. We observed very low NRAS (3%, OR=8.86) and BRAF (3%, OR=2.17), mutation frequency in non-progressor SMM samples compared to newly diagnosed MM. We did not observe driver mutations in FAM46C, TTN, CYLD, TP53, KMT2C, IRF4, HIST1H1E that are otherwise frequently mutated in high-risk SMM or symptomatic MM. None of the non-progressor SMM samples had MYC alteration. We observed t(11;14), t(4;14), and t(14;16) translocations at similar frequency compared to newly diagnosed MM samples. We also observed a significant difference in non-recurrent focal deletions. Based on our recent data in newly-diagnosed MM, we quantified genomic scar score, and observed that non-progressor SMM have lower GSS (median=3,IQR=[1-9]) compared to progressor MGUS/SMM (median=11,IQR=[5-15] / median=9,IQR=[9-15], respectively) as well as MM samples at diagnosis (median=9, IQR= [5-16],p=0.002). We further validated this observation in an independent cohort with 69 SMM samples in whom progressor SMM patients had high GSS (median =4, IQR=[2-7.75]), compared to delayed progressor (> four years) or non-progressor SMM (median =1.5, IQR= [0-3.5]; p=0.029). Moreover, non-progressor SMM had significantly low utilization of APOBEC and DNA repair mutational processes. Next, we compared non- progressor SMM with progressor SMM using RNAseq data. We identified 1653 differentially expressed genes (DEG) (762 up-regulated and 891 down-regulated). Genes that were upregulated in non-progressor SMM samples were enriched in IL6/JAK/STAT3 and IL2/STAT5 signaling and the regulation of cytokine secretion. Whereas genes up-regulated in progressor SMM were enriched in MYC targets, DNA repair, and mTOR pathways. Moreover, genes that control the translational initiation, translational elongation, mitochondrial translation, and ATP control were among the top highly expressed genes in progressor SMM. We used our MGUS/SMM to MM paired samples and showed that the E2F target, MYC target, and G2/M checkpoint pathways are more active at MM. We measured the distance between progressor and non-progressor SMM as well as MM and found that non-progressor SMM is less similar to MM compared to progressor SMM. In conclusion, the global CNA and translocations are similar between progressor and non-progressor SMM and symptomatic MM and confirm their role in the development of precursor condition but not adequate for progression to MM, which requires additional hits. On the other hand, lower GSS score reflecting genomic stability along with lower SNVs, low DNA damage and APOBEC mutational processes, down-regulated MYC target genes, and low DNA repair activation define low-risk SMM. These results now provide the basis to develop a genomic definition of SMM. Disclosures Fulciniti: NIH: Research Funding. Parmigiani:Phaeno Biotehnologies: Current equity holder in publicly-traded company; CRA Health: Current equity holder in publicly-traded company; Foundation Medicine Institute: Consultancy; Delphi Diagnostics: Consultancy; BayesMendel Laboratory: Other: Co-lead. Munshi:Janssen: Consultancy; Adaptive: Consultancy; Legend: Consultancy; Amgen: Consultancy; AbbVie: Consultancy; Karyopharm: Consultancy; Takeda: Consultancy; C4: Current equity holder in private company; BMS: Consultancy; OncoPep: Consultancy, Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties.

In 2019 there will be 22,500 new cases of ovarian cancer, resulting in approximately 14,000 deaths. Ovarian cancer remains the highest case fatality rate of any gynecologic cancer. Despite significant improvement in the surgical and chemotherapeutic managements of ovarian cancer patients, the overall survival has not changed in 30 years. However, these new therapies have resulted in a significant improvement in median survival, resulting in increasing numbers of patients surviving greater than 10 years with disease. The clinical and molecular features of these unique patients remain uncharacterized. The Department of Defense Ovarian Cancer Research Program (DOD OCRP) funded the creation of our interdisciplinary Long-term Survivor Consortium (LTSC), composed of 6 research sites including MDACC, UAB, IU, UCI, Ludwig Cancer Research, and University Hospitals of Canton Vaud (CHUV) and INOVA, a bioinformatic center at DFCI, and administrative centers at UCI and UAB. The purpose of the consortium is to identify characteristics and predictors of long-term survival in advanced ovarian cancer patients. The consortium is overseen by both a scientific and advocacy advisory board to guide direction and efforts. 285 specimens of advanced-stage high-grade serous ovarian cancers with full clinical annotation including greater than 12 years of follow-up were analyzed for this project. In addition, 340 early-stage high-grade ovarian cancers were available for one of the AIMs of the project. These latter samples were obtained through the creation of an international consortium funded by the DOD OCRP. All specimens were first reviewed for tumor context by H&E staining to determine percentage of tumor. The scientific platforms include whole transcriptome, microRNAs, global proteomics, immune profiling, and methylation. All specimens were analyzed on each individual platform and the results will be integrated in a global process. In parallel with these efforts, a systematic analysis of quality of life of patients at the time of diagnosis and later during the course of their disease based upon patient-reported outcome surveys has been undertaken. These data will also be integrated with genomic results to produce signatures that can further characterize long-term survivors of ovarian cancer. Citation Format: Michael J. Birrer, Lari Wenzel, Austin Miller, Heather Lankes, Nilsa Ramirez, Giovanni Parmigiani, Samuel Mok, Kenneth Nephew, Thomas Conrads, George Coukos, George L. Maxwell, Giulia Fulci, Mary Scroggins. The Long-Term Ovarian Cancer Survivor Project: A Department of Defense initiative [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr IA06.

1520 Background: It is critical for oncologists to be aware of unbiased and interpretable cancer risks (i.e., penetrance) in carriers with germline pathogenic variants in cancer susceptibility genes. However, relevant literature is large and varies significantly in study design, patient ascertainment, and types of risk estimates reported. This heterogeneity can cause inconsistent conclusions between studies and create barriers for clinicians to understand and apply them in practice. To further understand the current literature, we assessed penetrance studies associated with non-BRCA breast cancer susceptibility genes based on study design and ascertainment adjustment. Methods: We used a validated natural language processing-based abstract classifier to identify all penetrance studies regarding eleven genes: ATM, BARD1, CDH1, CHEK2, NBN, NF1, PALB2, PTEN, RECQL, STK11, and TP53. Relevant studies were then manually annotated as “with ascertainment adjustment” if a study was based on: (1) a general population; (2) a pedigree analysis or a family-based study with appropriate ascertainment adjustment; or (3) a hospital-based study or a panel testing analysis with well-matched cases and controls. Results: A total of 49 penetrance studies were identified, with a median of nine studies for each gene (range: 4-16). The case-control study was the dominant study type, accounting for over 80% in five genes, 50% in two genes, and 18% to 43% in the other four genes. The proportion of studies with ascertainment adjustment was generally low (mean: 33%) and varied widely between different genes (7% to 80%). Contradictory breast cancer risks (no increased risk vs. significantly increased risk) were found in eight genes (73%) (Table). The most common ascertainment bias identified was a case-control study with cases (patients) who had a strong family history but using general population controls. Conclusions: Ascertainment bias is common in penetrance studies, but few studies adjust for it appropriately. Clinicians should be aware of this issue, and new methods are warranted to select unbiased risk estimates, synthesize them, and provide the accurate general-population penetrance. [Table: see text]

Family history is among the strongest known risk factors for prostate cancer (PCa). Emerging data suggest molecular subtypes of PCa, including two somatic genetic aberrations: fusions of androgen-regulated promoters with ERG and, separately, phosphatase and tensin homolog (PTEN) loss. We examined associations between family history and incidence of these subtypes in 44,126 men from the prospective Health Professionals Follow-up Study. ERG and PTEN status were assessed by immunohistochemistry. Multivariable competing risks models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for associations between self-reported family history of PCa and molecular subtypes of disease. Thirteen percent of men had a positive family history of PCa at baseline. During a median follow-up of 18.5 years, 5,511 PCa cases were diagnosed. Among them, 888 were assayed for ERG status (47% ERG-positive) and 715 were assayed for PTEN loss (14% PTEN null). Family history was more strongly associated with risk of ERG-negative (HR: 2.15; 95% CI: 1.71-2.70) than ERG-positive (HR: 1.49; 95% CI: 1.13-1.95) disease (pheterogeneity : 0.04). The strongest difference was among men with an affected father (HRERG-negative : 2.09; 95% CI: 1.64-2.66; HRERG-positive : 1.30; 95% CI: 0.96-1.76; pheterogeneity : 0.01). Family history of PCa was positively associated with both PTEN null (HR: 2.10; 95% CI: 1.26-3.49) and PTEN intact (HR: 1.72; 95% CI: 1.39-2.13) PCa (pheterogeneity : 0.47). Our results indicate that PCa family history may be positively associated with PCa in all ERG and PTEN subtypes, suggesting a role of genetic susceptibility in their development. It is possible that ERG-negative disease could be especially associated with positive family history.

Background There have been few treatment advances for patients with glioblastoma (GBM) despite increasing scientific understanding of the disease. While factors such as intrinsic tumor biology and drug delivery are challenges to developing efficacious therapies, it is unclear whether the current clinical trial landscape is optimally evaluating new therapies and biomarkers. Methods We queried ClinicalTrials.gov for interventional clinical trials for patients with GBM initiated between January 2005 and December 2016 and abstracted data regarding phase, status, start and end dates, testing locations, endpoints, experimental interventions, sample size, clinical presentation/indication, and design to better understand the clinical trials landscape. Results Only approximately 8%-11% of patients with newly diagnosed GBM enroll on clinical trials with a similar estimate for all patients with GBM. Trial duration was similar across phases with median time to completion between 3 and 4 years. While 93% of clinical trials were in phases I-II, 26% of the overall clinical trial patient population was enrolled on phase III studies. Of the 8 completed phase III trials, only 1 reported positive results. Although 58% of the phase III trials were supported by phase II data with a similar endpoint, only 25% of these phase II trials were randomized. Conclusions The clinical trials landscape for GBM is characterized by long development times, inadequate dissemination of information, suboptimal go/no-go decision making, and low patient participation.

International audience; Although long intergenic non-coding RNAs (lincRNA) role in various cancers is described, their significance in Multiple Myeloma (MM) remains poorly defined. Here we have studied the lincRNA profile and their clinical impact in MM. We performed RNA-seq on MM cells from 308 newly diagnosed and uniformly treated patients, 16 normal plasma cells and utilized RNA-seq data from 532 newly diagnosed patients from CoMMpass study to analyze for lincRNAs. We observed 869 differentially expressed lincRNAs in MM compared to normal plasma cells. We identified 14 lincRNAs associated with PFS and calculated a risk score to stratify patients. The median PFS between high vs low-risk groups was 17 months vs not-reached (NR); and OS 30 months vs NR, respectively (p < 0.0001 for both). In the independent validation dataset between high and low-risk groups, PFS was 27 vs 42 months (HR 2.06 [1.44−2.96]; p < 0.0005); and 4-year OS 62% vs 86% (HR 2.76 [1.51–5.05]; p < 0.0005) confirming significant clinical relevance of lincRNA in MM. Importantly, lincRNA signature was able to further identify patients with significant differential outcomes within each low and high-risk categories identified using standard risk categorization including cytogenetic/FISH, ISS, and MRD negative or positive. Our results suggest that lincRNAs have an independent effect on MM outcome and provide a rationale to evaluate its molecular and biological impact.

Background: While the prognostic significance of pathological complete response (pCR) after neoadjuvant chemotherapy is relatively well established, the impact of adjuvant therapy in modulating relationship between pCR and long term outcomes is less clear. The primary objective of this study was to conduct a systematic review of published neoadjuvant chemotherapy studies to comprehensively evaluate the association between pCR with subsequent breast cancer recurrence and mortality, stratified by breast cancer subtypes and adjuvant chemotherapy usage. Methods: Based on PRISMA guidelines, a search of PubMed from inception until September 2016 was performed to identify eligible studies. Inclusion criteria were clinical trials or studies featuring neoadjuvant chemotherapy that reported pCR results as well as recurrence and/or survival. Hazard Ratios (HRs) and 95% probability intervals (PI) were estimated for endpoints using hierarchical models. We obtained the individual patient-level data for statistical analysis using plot digitizer software. Hazard ratios (HRs), with 95% PIs, measuring the association between pCR and OS or recurrence, were estimated using Bayesian piecewise-exponential proportional hazards hierarchical models including pCR as a predictor. Random effects model was utilized to account for between-study variability in baseline hazards and the variability of the pCR effect between studies. P-value of 0.05 was considered statistically significant. Results: A total of 3,209 citations with associated abstracts were reviewed, and 27,895 patients from 52 studies met inclusion criteria. Attainment of pCR, as compared to absence of pCR, was associated with significantly reduced disease recurrence overall (HR 0.31, 95% PI: 0.24-0.39), and in triple negative (HR 0.18, 95% PI: 0.10-0.31), human epidermal growth factor 2-positive (HER2+) (HR 0.32, 95% PI: 0.21-0.47), and trended towards significance for HR+ breast cancer (HR 0.15, 95% PI: 0.02-1.10). Similarly, pCR after neoadjuvant chemotherapy was also associated with reduced mortality overall (HR 0.22, 95% PI: 0.15-0.30), and among all three major disease subtypes. The association of pCR with reduced recurrence was similar among studies where patients received subsequent adjuvant chemotherapy (HR 0.34, 95% PI: 0.18-0.61) and those without adjuvant chemotherapy (95% HR 0.36, PI: 0.27-0.54). The association between magnitude of pCR change and corresponding change in survival will be presented at the meeting. Conclusion: Achieving pCR following neoadjuvant chemotherapy is associated with significantly improved disease recurrence and survival, particularly for triple negative and HER2+ breast cancer. The similar outcomes with/without adjuvant chemotherapy in patients who attain pCR after neoadjuvant chemotherapy likely reflects tumor biology and suggests adjuvant chemotherapy could potentially be abbreviated in certain circumstances, and highlights the need for further research to evaluate clinical utility of escalation/de-escalation strategies in the adjuvant setting based on neoadjuvant response for patients with localized breast cancer. Citation Format: Spring LM, Fell G, Arfe A, Trippa L, Greenup R, Reynolds K, Smith BL, Moy B, Isakoff SJ, Parmigiani G, Bardia A. Pathological complete response after neoadjuvant chemotherapy and impact on breast cancer recurrence and mortality, stratified by breast cancer subtypes and adjuvant chemotherapy usage: Individual patient-level meta-analyses of over 27,000 patients [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr GS2-03.

Genetic risk prediction models such as BRCAPRO are used routinely in genetic counseling for identification of potential BRCA1 and BRCA2 mutation carriers. They require extensive information on the counselee and her family history, and thus are not practical for primary care. To address this gap, we develop and test a two-stage approach to genetic risk assessment by balancing the tradeoff between the amount of information used and accuracy achieved. The first stage is intended for primary care wherein limited information is collected and analyzed using a simplified version of BRCAPRO. If the assessed risk is sufficiently high, more extensive information is collected and the full BRCAPRO is used (stage two: intended for genetic counseling). We consider three first-stage tools: BRCAPROLYTE, BRCAPROLYTE-Plus, and BRCAPROLYTE-Simple. We evaluate the two-stage approach on independent clinical data on probands with family history of breast and ovarian cancers, and BRCA genetic test results. These include population-based data on 1344 probands from Newton-Wellesley Hospital and mostly high-risk family data on 2713 probands from Cancer Genetics Network and MD Anderson Cancer Center. We use discrimination and calibration measures, appropriately modified to evaluate the overall performance of a two-stage approach. We find that the proposed two-stage approach has very limited loss of discrimination and comparable calibration as BRCAPRO. It identifies a similar number of carriers without requiring a full family history evaluation on all probands. We conclude that the two-stage approach allows for practical large-scale genetic risk assessment in primary care.

BACKGROUND We previously identified a protein tumor signature of PTEN, SMAD4, SPP1, and CCND1 that, together with clinical features, was associated with lethal outcomes among prostate cancer patients. In the current study, we sought to validate the molecular model using time-dependent measures of AUC and predictive values for discriminating lethal from non-lethal prostate cancer. METHODS Using data from the initial study, we fit survival models for men with prostate cancer who were participants in the Physicians' Health Study (PHS; n = 276). Based on these models, we generated prognostic risk scores in an independent population, the Health Professionals Follow-up Study (HPFS; n = 347) to evaluate external validity. In each cohort, men were followed prospectively from cancer diagnosis through 2011 for development of distant metastasis or cancer mortality. We measured protein tumor expression of PTEN, SMAD4, SPP1, and CCND1 on tissue microarrays. RESULTS During a median of 11.9 and 14.3 years follow-up in the PHS and HPFS cohorts, 24 and 32 men (9%) developed lethal disease. When used as a prognostic factor in a new population, addition of the four markers to clinical variables did not improve discriminatory accuracy through 15 years of follow-up. CONCLUSIONS Although the four markers have been identified as key biological mediators in metastatic progression, they do not provide independent, long-term prognostic information beyond clinical factors when measured at diagnosis. This finding may underscore the broad heterogeneity in aggressive prostate tumors and highlight the challenges that may result from overfitting in discovery-based research. Prostate 75:1926–1933, 2015. © 2015 Wiley Periodicals, Inc.

The I-SPY 2 breast cancer trials assess activity of new agents by adaptive randomization, applying criteria for success on a surrogate end point to predict success in confirmatory trials. Innovative designs may speed progress and eliminate waste in drug development.

Background. The use of auxiliary endpoints may provide efficiencies for clinical trial design, but such endpoints may not have intrinsic clinical relevance orclear linkage to more meaningful endpoints. The purpose of this study was to generate a novel endpoint that considers both overall survival (OS) and earlier events such as progression-free survival (PFS) and determine whether such an endpoint could increase efficiency in the design of glioblastoma clinical trials. Methods. Recognizing that the association between PFS and OS varies depending on therapy and tumor type, we developed a statistical model to predict OS based on PFS as the trial progresses. We then evaluated the efficiency of our model using simulations of adaptively randomized trials incorporating PFS and OS distributions from prior published trials in neuro-oncology. Results. When treatment effects on PFS and OS are concordant, our proposed approach results in efficiency gains compared with randomization based on OS alone while sacrificing minimal efficiency compared with using PFS as the primary endpoint. When treatment effects are limited to PFS, our approach provides randomization probabilities that are close to those based on OS alone. Conclusion. Use of OS as the primary endpoint, combined with statistical modeling of the relationship between OS and PFS during the course of the trial, results in more robust and efficient trial designs than using either endpoint alone.

Background Biomarkers can improve clinical trial efficiency, but designing and interpreting biomarker-driven trials require knowledge of relationships among biomarkers, clinical covariates, and endpoints. We investigated these relationships across genomic subgroups of glioblastoma (GBM) within our institution (DF/BWCC), validated results in The Cancer Genome Atlas (TCGA), and demonstrated potential impacts on clinical trial design and interpretation. Methods We identified genotyped patients at DF/BWCC, and clinical associations across 4 common GBM genomic biomarker groups were compared along with overall survival (OS), progression-free survival (PFS), and survival post-progression (SPP). Significant associations were validated in TCGA. Biomarker-based clinical trials were simulated using various assumptions. Results Epidermal growth factor receptor (EGFR)(+) and p53(-) subgroups were more likely isocitrate dehydrogenase (IDH) wild-type. Phosphatidylinositol-3 kinase (PI3K)(+) patients were older, and patients with O6-DNA methylguanine-methyltransferase (MGMT)-promoter methylation were more often female. OS, PFS, and SPP were all longer for IDH mutant and MGMT methylated patients, but there was no independent prognostic value for other genomic subgroups. PI3K(+) patients had shorter PFS among IDH wild-type tumors, however, and no DF/BWCC long-term survivors were either EGFR(+) (0% vs 7%, P = .014) or p53(-) (0% vs 10%, P = .005). The degree of biomarker overlap impacted the efficiency of Bayesian-adaptive clinical trials, while PFS and OS distribution variation had less impact. Biomarker frequency was proportionally associated with sample size in all designs. Conclusions We identified several associations between GBM genomic subgroups and clinical or molecular prognostic covariates and validated known prognostic factors in all survival periods. These results are important for biomarker-based trial design and interpretation of biomarker-only and nonrandomized trials.

Cancer arises through the sequential accumulation of mutations in oncogenes and tumor suppressor genes. However, how many such mutations are required for a normal human cell to progress to an advanced cancer? The best estimates for this number have been provided by mathematical models based on the relation between age and incidence. For example, the classic studies of Nordling [Nordling CO (1953) Br J Cancer 7(1):68-72] and Armitage and Doll [Armitage P, Doll R (1954) Br J Cancer 8(1):1-12] suggest that six or seven sequential mutations are required. Here, we describe a different approach to derive this estimate that combines conventional epidemiologic studies with genome-wide sequencing data: incidence data for different groups of patients with the same cancer type were compared with respect to their somatic mutation rates. In two well-documented cancer types (lung and colon adenocarcinomas), we find that only three sequential mutations are required to develop cancer. This conclusion deepens our understanding of the process of carcinogenesis and has important implications for the design of future cancer genome-sequencing efforts.

Multiple myeloma (MM) is a malignancy of the plasma cell in which clonal plasma cells infiltrate the bone marrow. Although increasingly effective treatment has significantly improved management of the disease, most MM patients ultimately relapse. Previous studies suggest that miRNA expression patterns may function as biomarkers for diagnosis, subtyping, and prognosis of the disease. However, the role of miRNAs in MM relapse and prognosis is not sufficiently understood. Therefore, in this study, we sought to understand the biological and prognostic role of dysregulated miRNAs between newly diagnosed and relapsed MM. We performed RNA-seq as well as microarray-based miRNA profiling on 319 MM samples from newly diagnosed MM patients enrolled in the 2009 IFM/DFCI clinical trial. This included 40 samples from patients with paired samples from diagnosis and at relapse while we had additional 239 samples collected at the time of diagnosis. Using the 40 diagnosis-relapse paired samples, we identified 48 miRNAs that are differentially expressed between diagnosis and relapse (FDR < 0.15). Of these 48 miRNAs, we found five miRNAs (miR-4484, miR-663b, miR-3687, miR-670-5p, miR-4417) whose expression level at diagnosis, expression level at relapse, and log2FC from diagnosis to relapse were significantly associated with overall survival or progression-free survival among the 40 patients with paired samples (p < 0.05). We next investigated the prognostic role of these five miRNAs through unsupervised hierarchical clustering of 239 independent, newly diagnosed MM samples. The two groups identified by the unsupervised hierarchical clustering differed significantly in both overall survival (log rank test, p = 0.009) and progression-free survival (log rank test, p = 0.012), suggesting that these five miRNAs are predictive of disease prognosis. We then performed an integrative analysis of the miRNA and mRNA expression data in our cohort of newly diagnosed samples and identified the potential protein-coding target genes of these five miRNAs. We further filtered the target gene list using TargetScan which predicts the biological targets of miRNAs by searching for the presence of conserved 8mer, 7mer, and 6mer sites that match the seed region of each miRNA. After filtering the target genes, we found that each miRNA targets 126 mRNAs [range 26 - 258] and key MM genes, including MAF, BCL2, 14-3-3 and MYEOV, were among the top candidates. The target genes of these select miRNAs are involved in such biological pathways as NF-kB signaling, antigen processing and presentation of endogenous peptide antigen via MHC class I via ER pathway, and positive regulation of cell death and apoptosis. In conclusion, this integrative analysis of miRNA and mRNA expression data from uniformly treated MM samples offers a unique perspective on the role of miRNAs in MM biology and prognosis. The study identified five miRNAs that target key MM genes, play a role in the biological processes associated with MM progression, and carry prognostic information predictive of overall and progression-free survival with prospect for future therapeutic application. Disclosures Richardson: Amgen: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Membership on an entity's Board of Directors or advisory committees, Research Funding; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees. Moreau:AbbVie: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Anderson:Sanofi-Aventis: Other: Advisory Board; Bristol-Myers Squibb: Other: Scientific Founder; Oncopep: Other: Scientific Founder; Amgen: Consultancy, Speakers Bureau; Janssen: Consultancy, Speakers Bureau; Takeda: Consultancy, Speakers Bureau; Celgene: Consultancy, Speakers Bureau. Munshi:Celgene: Consultancy; Amgen: Consultancy; Adaptive: Consultancy; Abbvie: Consultancy; Janssen: Consultancy; Takeda: Consultancy; Oncopep: Consultancy.

Somatic alterations including single nucleotide variants (SNVs), copy number alterations (CNAs) and structural variants (SVs) are an important component and the hallmark of Multiple Myeloma (MM) with certain alterations having clinical implications. Recently, the chronological order of such events was explained in detailed studies however previous study has compared the frequency and clonality of such events in early stage plasma cell disorder (MGUS or SMM) and their comparison with MM.Here we performed whole genome (WGS) and RNA sequencing (RNAseq) on 67 samples (8 Unique paired samples from patients with MGUS and when progressed to MM, 10 paired samples at SMM stage and then progressed to MM, as well as non-progressor SMM (NP-SMM) patients who have not progressed after at least 5 years of follow-up. In addition, we profiled CNAs and their clonality level in 164 MGUS, 147 SMM and 336 newly diagnosed MM patients to construct the clonal structure and frequency of somatic alterations.We did not observe significant difference in overall frequency of mutations at MGUS (median=5662 SNVs, IQR= [5252 - 7864]) and SMM stage (median=5308 SNVs, IQR=[4236 - 6125] ) and at progression to MM (median= 5983 SNVs, IQR=[5097 - 8916]). Overall mutational load increased from precursor stage to MM by only 5% with no recurrent change and the difference was not statistically significant (p > 0.05). Similar trend was also observed for SVs [median=36, IQR= [25.25 -40.75] at MGUS, median=34, IQR= [12-52] at SMM, median=27, IQR= [27-41] at MM]. In fact, some of the samples had lower SNV and SV load in MM compared to precursor stages even though that had increased cancer cell fraction. Copy number analysis on paired samples also showed >90% similarity only with few non-recurrent focal CNAs. We have compared WGS data from non-progressor SMM with progressor SMM and newly-diagnosed MM. The non-progressor SMM patients showed identical CNAs and not significantly different Ig translocation frequencies to both other groups. Except for NRAS mutations, mutation frequency in NP-SMM was similar to others. Finally, by using data from 647 patients at three stages (MGUS, SMM and MM), we compared the frequency of CNAs at each time point. Similar to MM, we observed almost equal frequency of hyperdiploid and non-hyperdiploid SMM cases. In hyperdiploid SMM, clonal copy number gain was observed in 95% of patients involving two of the three chromosomes 9, 15 and 19. Moreover, a similar pattern was observed in other hyperdiploid chromosomes including chromosomes 3,5,7,11 and 21. The observed event frequency and clonality levels in SMM were identical to one observed in MM as well as MGUS. Non-hyperdiploid SMM group had early and clonal events targeting chromosome 13 deletion with gain of 1q, 11 and/or 9 at the same frequency level as in MM. The only significant difference was observed in subclonal deletions targeting some of the infrequent deletion events observed in HMM and NHMM. In conclusion, our data from longitudinal analysis of paired precursor and symptomatic MM samples as well as cross sectional data highlight lack of significant differences in genomic changes between precursor conditions and symptomatic myeloma. This lack of progressive and unique genomic change suggests the need for additional transcriptomic, epigenomic or tumor micro environment changes to accelerate the tumor development. Disclosures Bolli: Celgene: Honoraria; Gilead: Other: travel expenses; Novartis: Honoraria. Moreau:Amgen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Anderson:Sanofi-Aventis: Other: Advisory Board; Bristol-Myers Squibb: Other: Scientific Founder; Oncopep: Other: Scientific Founder; Janssen: Consultancy, Speakers Bureau; Takeda: Consultancy, Speakers Bureau; Celgene: Consultancy, Speakers Bureau; Amgen: Consultancy, Speakers Bureau. Munshi:Celgene: Consultancy; Oncopep: Consultancy; Janssen: Consultancy; Takeda: Consultancy; Amgen: Consultancy; Adaptive: Consultancy; Abbvie: Consultancy.

Multiple Myeloma (MM) is a plasma cells malignancy with number of recent therapeutic options that has improved outcomes with median survival now stretching beyond 8 years. There has been an intense search to identify genomic and laboratory correlates of outcome for high risk patients. However, a subgroup of patients have a long survival but genomic segmentation of this important group which can be potentially cured has not been identified. We here described first such attempt at identifying a subset of patients with long survival. We have analyzed data from 205 newly-diagnosed uniformly-treated MM patients using both deep whole genome (WGS) (80x) and whole transcriptome sequencing (RNAseq). Median number of SNVs, small insertion and deletion per megabase were 2.18 [0.49 - 14.52], 0.077 [0.024 - 0.17] and 0.12 [0.03 - 0.26] respectively. MM subgroups defined by FISH or copy number alterations (CNAs) had significantly different mutational load (Kruskal-Wallis p-value = 0.003). In general, Hyperdiploid MM (HMM) patients had lower SNV load compared to other subgroups and t(14;16) had the highest mutational load per megabase (Dunn test FDR < 0.25). These subgroups also showed significant differences for mutational process utilization. Mutational processes associated spontaneous deamination of 5-methylcytosine to thymine was significantly high in HMM, APOBEC activity was very strong in t(14;16) MM and DNA repair was significantly different in t(6;14), del17p and t(11;14). Importantly, the mutations associated with clonal cell population and thus in early phases of MM cell progression were driven by spontaneous or enzymatic deamination (Signature 1), somatic hypermutation in lymphoid cells (Signature 9) and Signature 17 (ANOVA p value < 1e-16) and surprisingly APOBEC activity was constant among the clones (ANOVA p value > 0.05). However, mutations associated with homologous recombination (HR) and nucleotide excision repair (NER) activity were significantly enriched in the subclonal mutations (ANOVA p value < 1e-16) suggesting their role in later stages of MM progression. Based on this information, we next calculated the homologous recombination repair defects using copy number alterations from WGS data. We identified that HR score generated using WGS copy number information predicted outcome in newly diagnosed myeloma. Further analysis of this data identified that newly diagnosed MM patients with hyperdiploid MM (HMM) and no HR- repair deficiency have superior outcome with 6 year overall survival (OS) probability for this of 100% in IFM/DFCI 2009 cohort (Logrank test p value = 0.0012). We next validated this finding using MMRF CoMMpass dataset and we confirmed 6-yr OS probability of 95% (Logrank test p value = 0.0016) in this independent dataset (Figure 1). We further investigated RNAseq data between these genomically defined groups and identified that chromosome and telomere maintenance pathways (FDR < 0.01) and low bone disease associated genes described by Zhan et al. (FDR = 0.004) were up regulated in low-risk group. Finally, patients who were able to maintain HR, had lower mutation rate for TP53 (0% vs 9%, FDR < 0.05), CSMD1 (0% vs 6%, FDR < 0.05), FAT3/FAT4 mutations (1.7% vs 15%, FDR < 0.05), however surprisingly higher mutation rate for NRAS (42% vs 14%, p value < 0.001). In addition, DNA damage associated processes predicted by SNVs trinucleotides were significantly lower in the low-risk group (12% vs. 20%, p value < 1e-05) providing further support to this data. In conclusion, we report a detailed genomic profile using deep DNA and RNA sequencing and identify a genomically-defined sub group that is predicted to have a long survival. This study also identifies the role of HR in myeloma with potential for its translational application in both prognosis as well as therapy. Figure 1 Disclosures Richardson: Janssen: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol-Myers Squibb: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Membership on an entity's Board of Directors or advisory committees. Moreau:Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria. Thakurta:Celgene: Employment, Equity Ownership. Anderson:Sanofi-Aventis: Other: Advisory Board; Bristol-Myers Squibb: Other: Scientific Founder; Oncopep: Other: Scientific Founder; Amgen: Consultancy, Speakers Bureau; Janssen: Consultancy, Speakers Bureau; Takeda: Consultancy, Speakers Bureau; Celgene: Consultancy, Speakers Bureau. Avet-Loiseau:celgene: Consultancy, Other: travel fees, lecture fees, Research Funding; takeda: Consultancy, Other: travel fees, lecture fees, Research Funding. Munshi:Takeda: Consultancy; Oncopep: Consultancy; Adaptive: Consultancy; Amgen: Consultancy; Celgene: Consultancy; Abbvie: Consultancy; Janssen: Consultancy.

The traditional clinical trials infrastructure may not be ideally suited to evaluate the numerous therapeutic hypotheses that result from the increasing number of available targeted agents combined with the various methodologies to molecularly subclassify patients with glioblastoma. Additionally, results from smaller screening studies are rarely translated to successful larger confirmatory studies, potentially related to a lack of efficient control arms or the use of unvalidated surrogate endpoints. Streamlining clinical trials and providing a flexible infrastructure for biomarker development is clearly needed for patients with glioblastoma. The experience developing and implementing the I-SPY studies in breast cancer may serve as a guide to developing such trials in neuro-oncology.

Multi-arm clinical trials use a single control arm to evaluate multiple experimental treatments. In most cases this feature makes multi-arm studies considerably more efficient than two-arm studies. A bottleneck for implementation of a multi-arm trial is the requirement that all experimental treatments have to be available at the enrollment of the first patient. New drugs are rarely at the same stage of development. These limitations motivate our study of statistical methods for adding new experimental arms after a clinical trial has started enrolling patients. We consider both balanced and outcome-adaptive randomization methods for experimental designs that allow investigators to add new arms, discuss their application in a tuberculosis trial, and evaluate the proposed designs using a set of realistic simulation scenarios. Our comparisons include two-arm studies, multi-arm studies, and the proposed class of designs in which new experimental arms are added to the trial at different time points.

Women with atypical ductal hyperplasia (ADH), atypical lobular hyperplasia (ALH), lobular carcinoma in situ (LCIS), and severe ADH are at increased risk of breast cancer, but a systematic quantification of this risk and the efficacy of chemoprevention in the clinical setting is still lacking. The objective of this study is to evaluate a woman’s risk of breast cancer based on atypia type and to determine the effect of chemoprevention in decreasing this risk. Review of 76,333 breast pathology reports from three institutions within Partners Healthcare System, Boston, from 1987 to 2010 using natural language processing was carried out. This approach identified 2,938 women diagnosed with atypical breast lesions. The main outcome of this study is breast cancer occurrence. Of the 2,938 patients with atypical breast lesions, 1,658 were documented to have received no chemoprevention, and 184/1,658 (11.1 %) developed breast cancer at a mean follow-up of 68 months. Estimated 10-year cancer risks were 17.3 % with ADH, 20.7 % with ALH, 23.7 % with LCIS, and 26.0 % with severe ADH. In a subset of patients treated from 1999 on (the chemoprevention era), those who received no chemoprevention had an estimated 10-year breast cancer risk of 21.3 %, whereas those treated with chemoprevention had a 10-year risk of 7.5 % (p

The BRCAPRO model estimates carrier probabilities for the BRCA1 and BRCA2 genes, and was recently enhanced to use estrogen receptor (ER) and progesterone receptor (PR) status of breast cancer. No independent assessment of the added value of these markers exists. Moreover, earlier versions of BRCAPRO did not use human epidermal growth factor receptor 2 (Her-2/neu) status of breast cancer. Here, we incorporate Her-2/neu in BRCAPRO and validate all the markers. We trained the enhanced model on 406 germline tested individuals, and validated on a separate clinical cohort of 796 individuals for whom test results and family history are available. For model-building, we estimated joint probabilities of ER, PR, and Her-2/neu status for carriers and non-carriers of BRCA1/2 mutations. For validation, we obtained BRCAPRO predictions with and without markers. We calculated area under the receiver operating characteristic curve (AUC), sensitivity, specificity, predictive values, and correct reclassification rates. The AUC for predicting BRCA1 status among individuals who are carriers of at least one mutation improved when ER and PR were used. The AUC for predicting the presence of either mutation improved when Her-2/neu was added. Use of markers also produced highly significant correct reclassification improvements in both cases. Breast tumor markers are useful for prediction of BRCA1/2 mutation status. ER and PR improve discrimination between BRCA1 and BRCA2 mutation carriers while Her-2/neu helps discriminate between carriers and non-carriers, particularly among women who are ER positive and Her-2/neu negative. These results support the use of the enhanced version of BRCAPRO in clinical settings.

Several studies indicate that screening in combination with lifestyle modification may produce a greater reduction in colorectal cancer rates than screening alone. To identify national opportunities for the primary prevention of colorectal cancer, we assessed the prevalence of modifiable lifestyle risk factors in the United States. We used nationally representative, cross-sectional data from 5 NHANES cycles (1999–2000, n = 2,753; 2001–2002, n = 3,169; 2003–2004, n = 2,872; 2005–2006, n = 2,993; 2007–2008, n = 3,438). We evaluated the 5 colorectal cancer risk factors deemed “convincing” by the World Cancer Research Fund (obesity, physical inactivity, intake of red meat, processed meat, alcohol), and cigarette smoking, a “suggestive” risk factor in the Surgeon General's report. We estimated the prevalence of each risk factor separately and jointly, and report it overall, and by sex, race/ethnicity, age, and year. In 2007 to 2008, 81% percent of U.S. adults, aged 20 to 69 years, had at least one modifiable risk factor for colorectal cancer. More than 15% of those younger than 50 years had 3 or more risk factors. There was no change in the prevalence of risk factors between 1999 and 2008. The most common risk factors were risk factors for other chronic diseases. Our findings provide additional support for the prioritization of preventive services in health care reform. Increasing awareness, especially among young adults, that lifestyle factors influence colorectal cancer risk, and other chronic diseases, may encourage lifestyle changes and adherence to screening guidelines. Complementary approaches of screening and lifestyle modification will likely provide the greatest reduction of colorectal cancer. Cancer Prev Res; 5(1); 138–45. ©2011 AACR.

Multiple Myeloma (MM) is a complex disease with distinct molecular and clinical characteristics. Recent large collaborative efforts have identified number of driver genes. However over 95% of all somatic alterations occur in non-coding regions and very little is known about how they affect the disease. We performed a deep (average coverage > 80X) whole genome sequencing (WGS) on 260 MM samples (208 newly diagnosed and 52 first relapse after uniform treatment) to comprehensively analyze recurrent somatic alterations in non-coding regions. We detected median 11,852 (Range 4,802-87,396) mutations and indels per sample with overall more than 3.9M total somatic mutations. Introns (3.6 mutations/per Mb) and intergenic regions (4.06 mutations/per Mb) had significantly higher number of mutations per megabase compared to Exons (2.7 mutations/per Mb) (p < 1e-5). Mutations in coding regions in our data was similar to published whole exome sequencing studies. We observed 46 [range 7 - 219] structural variants (SVs) per sample with 98% involving non-coding regions. We found that number of SVs significantly correlated with overall survival (p value = 1.7e-5). We detected chromothripsis (>=7 oscillating copy number change and significant clustered SVs and/or clustered translocations) in 24% of newly diagnosed samples; and kataegis hotspots on chromosome 3q27-3q28 (24%), 11q13 (5.8%) and 12q24 (5.3%). By clustering SV breakpoints across the genome we have identified 3 SV hotspots on chromosome 17q21, 7q34, and 11q13. We next interrogated the non-coding regions to identify genomic loci with higher than expected mutation count compared to background mutation rate. We have identified 456 loci that are significantly enriched in non-coding regions (5' UTR, 3'UTR, promoter, intergenic, intronic, and distal regulatory regions) [adjusted p value < 1e-5 and observed in >=10% newly diagnosed MM]. These loci are then assigned to genes or gene neighborhoods to evaluate their potential impact. We have identified the most frequently involved genes affected by perturbation in neighboring non-coding region and integrate their expression using our matching deep RNA-seq data from the same patients. Of these the most prominent examples are 1.) 3'UTR mutations are enriched in CD93 gene, which plays critical role in B cell development with loss of expression in CD138+ MM cells compared to normal plasma cells (p value < 1e-5); 2.) Promoter region - we have identified 635 mutations in 2kb region in BCL6 coming from 76% of all newly diagnosed samples. BCL6 (p value < 1e-5) has significantly downregulated expression in MM. Interestingly, but not surprisingly this hypermutated region showed high intensity of H3K27Ac activity in normal cells; 3.) 5'UTR - BCL7A (27.9%) and LPP (11.7%) were top two 5' UTR mutated target genes and RNA-seq data confirmed significant downregulation of their expression (p values < 1e-5 and 0.0048 respectively) in the MM cells. Additionally, BCL7A (48%) also showed significant enrichment of intronic mutations. A similar mutational hotspots were observed within the vicinity of additional functionally important genes in myeloma including ROBO1/2, ILF2, IRF8 and BCL2A1. Our data also showed that these frequent mutations have higher cancer cell fraction (CCF) [median CCF > 0.75] suggesting their occurrence earlier in the disease development. To validate the function of these mutations, we have started to carry out gain/loss of function studies. Our analysis with BCL7A shows that BCL7A knockdown increases the cell viability while its overexpression decreased growth, colony formation and increased apoptosis. This tumor suppressor function of BCL7A is being further analyzed in light of our mutational data in the nearby non-coding region. In conclusion, this large deep whole genome sequencing data from newly-diagnosed MM patients identifies a vast majority of non-coding mutations with potentially significant functional and biological role in MM. Our integrative approach using both WGS and RNA-seq data from the patients now provides us important tools to further characterize the impact of these mutations and develop opportunities for targeted therapeutics. Disclosures Richardson: Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; Amgen: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Karyopharm: Membership on an entity's Board of Directors or advisory committees. Moreau:Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees. Thakurta:Celgene Corporation: Employment, Equity Ownership. Anderson:Millennium Takeda: Consultancy; Gilead: Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb: Consultancy; OncoPep: Equity Ownership, Other: Scientific founder; C4 Therapeutics: Equity Ownership, Other: Scientific founder; Celgene: Consultancy. Avet-Loiseau:Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding. Munshi:OncoPep: Other: Board of director.

Purpose: To derive a gene expression score of transcriptomic homogeneity in localized prostate tumor specimens, and assess its prognostic utility for metastatic or lethal disease. Background: At the time of prostate cancer diagnosis, improved biomarkers are needed to distinguish tumors with metastatic potential versus those likely to follow an indolent course. Previous evidence suggests that gene expression aberrations are homogeneous in nonaggressive tumors, while transcriptomic profiles are disregulated in widely varying patterns across metastasis-prone tumors. Here, we investigate whether a measure of transcriptomic homogeneity has prognostic utility. Study Population: Gene expression profiles of noncancerous prostate tissue from the Genotype-Tissue Expression (GTEx) project (n = 106) were used to develop the homogeneity measure. Prognostic utility of the score was assessed in a study nested in the Health Professionals Follow-up Study (HPFS) and Physicians’ Health Study (PHS), in which whole-transcriptome gene expression was quantified from archival surgical tumor tissue. Cases (n = 113) were men who died of prostate cancer or developed metastatic disease, and controls (n = 291) had at least 8 years of metastasis-free survival. Men were diagnosed with prostate cancer between 1982 and 2005, and median follow-up time was 14.0 years. TCGA data (n = 333) were further used to investigate associations of the expression score with Gleason. Methods: RNA-seq profiles from GTEx were filtered to retain genes persistently expressed across noncancerous prostate tissue specimens. Remaining genes were placed into decile bins on the basis of median expression across samples. The 25 lowest variance genes within each bin were selected to generate a 250-gene panel. Transcriptome homogeneity scores for patients in HPFS and PHS were calculated by summing the standard deviations over the decile bins, where standard deviations were calculated using patient-specific data across 25 genes in each bin. Associations between the score and lethal disease were assessed through logistic regression and AUC analyses. Associations between the homogeneity score and Gleason score in TCGA samples were evaluated via Pearson correlations. Results: High transcriptomic heterogeneity was strongly associated with lethal or metastatic outcomes. A crude odds ratio of 3.36 (95% CI 2.12-5.41; p Conclusions: Transcriptomic homogeneity, as measured by variation across gene sets that are typically stable in normal prostate tissue, is able to distinguish indolent from lethal prostate cancers. Further investigation and discovery of gene sets using this novel approach is likely to deliver new biologic insights and clinically actionable biomarker sets. Citation Format: Travis Gerke, Svitlana Tyekucheva, Jordan H. Creed, Kathryn L. Penney, Jennifer A. Sinnott, Ericka Ebot, Anders E. Berglund, Massimo Loda, Meir J. Stampfer, Peter Kraft, Giovanni Parmigiani, Lorelei A. Mucci. All happy families are alike: Transcriptomic homogeneity in indolent prostate tumors is a useful prognostic biomarker [abstract]. In: Proceedings of the AACR Special Conference: Prostate Cancer: Advances in Basic, Translational, and Clinical Research; 2017 Dec 2-5; Orlando, Florida. Philadelphia (PA): AACR; Cancer Res 2018;78(16 Suppl):Abstract nr A045.

Introduction: Family history is among the strongest known risk factors for prostate cancer. Emerging data suggest unique molecular subtypes of disease, including common somatic genetic aberrations: fusions of androgen-regulated promoters with ERG and, separately, PTEN loss. We examined associations between family history and incidence of these subtypes under the hypothesis that family history could be differentially associated with their occurrence. Methods: We studied 47,115 men in the prospective Health Professionals Follow-up Study from 1986 to 2009. ERG and PTEN status were assessed with immunohistochemistry. Multivariable competing risk models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for associations between self-reported family history of prostate cancer and disease defined by molecular subtypes. Results: During a median follow-up of 22.3 years, 5,606 prostate cancer cases were diagnosed. Among them, 889 and 711 were assayed for ERG status (48% ERG+) and PTEN loss (14% PTEN null), respectively. Prostate cancer family history was positively associated with both ERG+ (HR=1.85; 95% CI: 1.47-2.33) and ERG- (HR=2.20; 95% CI: 1.78-2.72) disease (P-heterogeneity=0.29). High-grade ERG+ disease suggested a pronounced association (HR=2.58; 95% CI: 1.85-3.61; P-heterogeneity versus high-grade ERG-: 0.08). Family history was also positively associated with PTEN null (HR=2.39; 95% CI: 1.53-3.71) and PTEN intact (HR=1.88; 95% CI: 1.54-2.28) prostate cancer (P-heterogeneity=0.34). Conclusion: Prostate cancer family history is positively associated with prostate cancer defined by ERG and PTEN, suggesting a similar role of genetic susceptibility for these subtypes. It is possible that high-grade ERG+ disease could be uniquely associated with positive family history. Citation Format: Dana Hashim, Amparo G. Gonzalez-Feliciano, Thomas U. Ahearn, Andreas Pettersson, Lauren Barber, Claire H. Pernar, Ericka M. Ebot, Stephen Finn, Edward L. Giovannucci, Rosina T. Lis, Massimo Loda, Giovanni Parmigiani, Kathryn L. Penney, Tamara Lotan, Philip W. Kantoff, Lorelei A. Mucci, Rebecca E. Graff. Family history of prostate cancer and the incidence of ERG- and PTEN-defined prostate cancer [abstract]. In: Proceedings of the AACR Special Conference: Prostate Cancer: Advances in Basic, Translational, and Clinical Research; 2017 Dec 2-5; Orlando, Florida. Philadelphia (PA): AACR; Cancer Res 2018;78(16 Suppl):Abstract nr B024.

e13569Background: Breast reduction surgery removes a random sample of breast tissue in otherwise asymptomatic women and thus provides a method to evaluate the background incidence of breast patholo...