Search

Supplementary Figure from Incident Cancer Risk and Signatures Among Older MUTYH Carriers: Analysis of Population-Based and Genomic Cohorts

MUTYH carriers have an increased colorectal cancer risk in case–control studies, with loss of heterozygosity (LOH) as the presumed mechanism. We evaluated cancer risk among carriers in a prospective, population-based cohort of older adults. In addition, we assessed if cancers from carriers demonstrated mutational signatures (G:C>T:A transversions) associated with early LOH. We calculated incident risk of cancer and colorectal cancer among 13,131 sequenced study participants of the ASPirin in Reducing Events in the Elderly cohort, stratified by sex and adjusting for age, smoking, alcohol use, BMI, polyp history, history of cancer, and aspirin use. MUTYH carriers were identified among 13,033 participants in The Cancer Genome Atlas and International Cancer Genome Consortium, and somatic signatures of cancers were analyzed. Male MUTYH carriers demonstrated an increased risk for overall cancer incidence [multivariable HR, 1.66; 95% confidence interval (CI), 1.03–2.68; P = 0.038] driven by increased colorectal cancer incidence (multivariable HR, 3.55; 95% CI, 1.42–8.78; P = 0.007), as opposed to extracolonic cancer incidence (multivariable HR, 1.40; 95% CI, 0.81–2.44; P = 0.229). Female carriers did not demonstrate increased risk of cancer, colorectal cancer, or extracolonic cancers. Analysis of mutation signatures from cancers of MUTYH carriers revealed no significant contribution toward early mutagenesis from widespread G:C>T:A transversions among gastrointestinal epithelial cancers. Among cancers from carriers, somatic transversions associated with base-excision repair deficiency are uncommon, suggestive of diverse mechanisms of carcinogenesis in carriers compared with those who inherit biallelic MUTYH mutations.Prevention Relevance:Despite absence of loss of heterozygosity in colorectal cancers, elderly male MUTYH carriers appeared to be at increased of colorectal cancer.

Purpose:To define dominant molecular and cellular features associated with PD-1/PD-L1 blockade resistance in metastatic urothelial cancer.Experimental Design:We pursued an unbiased approach using bulk RNA sequencing data from two clinical trials to discover (IMvigor 210) and validate (CheckMate 275) pretreatment molecular features associated with resistance to PD-1/PD-L1 blockade in metastatic urothelial cancer. We then generated single-cell RNA sequencing (scRNA-seq) data from muscle-invasive bladder cancer specimens to dissect the cellular composition underlying the identified gene signatures.Results:We identified an adaptive immune response gene signature associated with response and a protumorigenic inflammation gene signature associated with resistance to PD-1/PD-L1 blockade. The adaptive immune response:protumorigenic inflammation signature expression ratio, coined the 2IR score, best correlated with clinical outcomes, and was externally validated. Mapping these bulk gene signatures onto scRNA-seq data uncovered their underlying cellular diversity, with prominent expression of the protumorigenic inflammation signature by myeloid phagocytic cells. However, heterogeneity in expression of adaptive immune and protumorigenic inflammation genes was observed among single myeloid phagocytic cells, quantified as the myeloid single cell immune:protumorigenic inflammation ratio (Msc2IR) score. Single myeloid phagocytic cells with low Msc2IR scores demonstrated upregulation of proinflammatory cytokines/chemokines and downregulation of antigen presentation genes, were unrelated to M1 versus M2 polarization, and were enriched in pretreatment blood samples from patients with PD-L1 blockade–resistant metastatic urothelial cancer.Conclusions:The balance of adaptive immunity and protumorigenic inflammation in individual tumor microenvironments is associated with PD-1/PD-L1 resistance in urothelial cancer with the latter linked to a proinflammatory cellular state of myeloid phagocytic cells detectable in tumor and blood.See related commentary by Drake, p. 4139

Background: Aromatase inhibitors (AI) are standard of care in the treatment of early-stage hormone positive (HR+) breast cancer (BC). Commonly, these medications cause significant side effects which limit tolerability and often require a change in therapy. Supportive therapies are an essential part of treatment plans to improve adherence and quality of life. Studies in the last decade have demonstrated the critical role of supportive management in mitigating side effects from AI, such as the reduction in AI related joint pain with acupuncture or use of low dose oxybutynin for hot flashes. The aim of this study was to evaluate the real world use of supportive therapies for AI toxicities over the last decade and to determine if they were associated with patient adherence to initial AI treatment. Methods: We performed a retrospective chart review of all female patients at our academic institution with early-stage, HR+ BC who were initiated on adjuvant AI therapy between 2011-2020. From the electronic medical record, we collected information on patient demographics, AI side effects (hot flashes, vaginal dryness, joint pains, osteopenia/osteoporosis), use of supportive therapies, and duration of first AI therapy. Primary endpoint was the rate of discontinuation of AI at 1 year among those who used supportive therapies compared with those who did not. The Wilcoxon rank sum test, Fisher's exact test, Kruskal-Wallis rank sum test, and Pearson's Chi-squared test methods were used to compare rates of discontinuation of front-line AI therapy for each group. Results: We identified 990 patients (pts) with early-stage, HR+ BC who were started on adjuvant AI between 2011-2020. Of these patients, 97% (n= 963) had AI related side effects yet only 51% (n=504) received supportive therapies. The overall discontinuation rate within 1 year was 14.8% (147 of 990 pts). Patients who received at least 1 supportive therapy were more likely to remain on AI and had a lower 1 year discontinuation rate of 10% (51 of 504 pts) compared to 20% (96 of 486 pts) for those who did not receive any supportive therapy (p1 year AI treatment group, p=0.003). Race also appeared to have an impact on AI tolerance. Although only 31% (39 of 126 pts; p < 0.001) of Black patients received supportive therapy, this group had the lowest 1 year discontinuation rate at 8.7% (11 of 126 pts). Conversely, 56% (277 of 491 pts) of White patients received at least one supportive therapy, yet had the highest 1 year discontinuation rate at 17% (83 of 491 pts). Conclusions: Supportive therapies are essential to help patients mitigate side effects of AI. Patients who received at least one supportive therapy had increased rates of AI adherence beyond 1 year compared to those who did not receive any supportive treatment. The most commonly used therapies were bone strengthening agents and acupuncture. Use of acupuncture and low dose oxybutynin have increased in the past 3 years and 2 years, respectively, following publication of their efficacy. It is critical to further evaluate the impact that race and age have in relation to duration of treatment and use of supportive management. Further studies on how to improve patient tolerability and adherence to AI therapy are needed. Citation Format: Melanie W Kier, Zhiqiang Li, Brittney S Zimmerman, Rima Patel, Yunchen Yang, Mark Y Fink, Jason D Wells, Xiang Zhou, Scott Newman, Rong Chen, Eric Schadt, William Oh, Amy Tiersten. Impact of supportive therapies on tolerance of aromatase inhibitors in patients with early-stage, hormone-positive breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P5-18-11.

Higher levels of estrogen in obese patients may lead to incomplete inhibition by aromatase inhibitors (AIs). The aim of this study was to determine the impact of body mass index (BMI) on efficacy of AIs in patients with metastatic hormone receptor (HR)-positive breast cancer (BC).We performed a retrospective chart review of all female patients with metastatic HR-positive BC on an AI in first- or second-line settings and seen at our academic institution between 2001 and 2020. The primary endpoint was progression-free survival (PFS), defined as the time from start of AI to disease progression or death from any cause.We identified 219 patients who had received an AI in the first- or second-line settings for metastatic HR-positive BC and with documented information on BMI. Of the 219 patients, 56% (123) had a low BMI (defined as 27 kg/mWhile BMI influences efficacy of AIs in the adjuvant setting, our results suggest that in the metastatic setting, BMI may not impact the efficacy of AIs. This discrepancy could be due to other differences in disease characteristics that make complete aromatase inhibition more important in the adjuvant setting when disease burden is the lowest.

BACKGROUND Ground-glass opacities (GGOs) appearing in computed tomography (CT) scans may indicate potential lung malignancy. Proper management of GGOs based on their features can prevent lung cancer (LCA) development. Electronic health records (EHRs) are rich sources of information on GGO nodules and their granular features, but most of the valuable information is embedded in unstructured clinical notes OBJECTIVE To develop, test, and validate a deep learning-based natural language processing (NLP) tool that automatically extracts GGO features to inform the longitudinal trajectory of GGO status from large-scale radiology notes. METHODS We developed a bidirectional-long-short-term memory with a conditional-random-field-based deep-learning NLP pipeline to extract GGO and granular features of GGO retrospectively from radiology notes of 13,216 lung cancer patients. We evaluated the pipeline with quality assessments and cohort characterization was analyzed on the distribution of nodule features longitudinally to assess changes in size and solidity over time. RESULTS Our NLP pipeline, built upon the GGO ontology we developed, achieved 95-100% precision, 89-100% recall, and 92-100% F1 scores on different GGO features. We deployed this GGO NLP model to extract and structure comprehensive characteristics of GGOs from 29,496 radiology notes of 4,521 lung cancer patients. Longitudinal analysis revealed that size increased in 17.5% of patients, decreased in 15.1%, and remained unchanged in 67.4% in their last note compared to the first note. Among 1,127 patients who had longitudinal radiology notes of GGO status, 815 patients (72.3%) were reported to have stable status and 259 patients (23%) had increased/progressed status in the subsequent notes. CONCLUSIONS Our deep learning-based NLP pipeline can automatically extract granular GGO features at scale from EHRs when such information is documented in radiology notes and inform the natural history of GGO, which opens the way for a new paradigm in lung cancer prevention and early detection.

Abundant volumes of clinical laboratory test results available within Electronic health records (EHRs) are essential for differential diagnosis, treatment monitoring, and outcome evaluation. LOINC2HPO is a recently developed deep phenotyping approach to transform laboratory test results into the Human Phenotype Ontology (HPO) terms. Here, we deployed the approach on a large EHR dataset from the Sema4 Data Warehouse to build patient phenotypic journeys at scale. Among 1.07 billion laboratory test results, we successfully transformed 774 million (72.5%) into HPO-coded phenotypes and built a patient phenotypic journey for over 2.2 million patients. First, a global analysis of patient phenotypic journeys revealed a longitudinal increase in patients with genitourinary system abnormality. The analysis also revealed abnormal phenotypes with strong racial patterns. Second, using severe asthma as an example case, we identified abnormal phenotypes in the past three years that were correlated with asthma progression to severe state. Lastly, we demonstrated that converting laboratory test results into HPO terms resulted in limited information loss. Our study demonstrated that the phenotypic journey framework opens the way to characterize phenotypic trajectories in population level and screen biomarkers for translational research.

We characterize the clinical utility and economic benefits of a comprehensive pan-ethnic carrier screening panel that spans 282 monogenic disease conditions in a large, diverse population of 397,540 reproductive health patients. For 142,049 of these patients, we were able to accurately estimate genetic ancestries across 7 major population groups. We examined individual carrier and at-risk carrier couple (ARCC) rates with respect to self-reported and genetic ancestries across ancestry-specific and pan-ethnic panels. Our results show that this comprehensive panel identified >10-times the ARCCs compared with a two-gene pan-ethnic panel and provided a substantial benefit over ancestry-specific screening panels across the major population groups. Finally, we generated a universal cost-of-care model across the monogenic disease conditions represented on the comprehensive pan-ethnic carrier screening panel to demonstrate potential healthcare savings in addition to the demonstrated clinical benefits that could be realized adopting this type of panel as standard of care for all.

Gene therapy is a promising approach in the treatment of cardiovascular diseases. The vectors available for cardiovascular gene therapy have significantly improved over time. Cardiac tropism is a primary characteristic of an ideal vector along with a long-term expression profile and a minimal risk of cellular immune response. Preclinical and clinical studies have demonstrated that adeno-associated viral (AAV) vectors are one of the most attractive vehicles for gene transfer. AAV has gained great popularity in the last years because of its biological properties and advantages over other viral vector systems. In this chapter we will describe methods for intracardiac delivery of AAV vector in rats.

MUTYH carriers have an increased colorectal cancer risk in case–control studies, with loss of heterozygosity (LOH) as the presumed mechanism. We evaluated cancer risk among carriers in a prospective, population-based cohort of older adults. In addition, we assessed if cancers from carriers demonstrated mutational signatures (G:C>T:A transversions) associated with early LOH. We calculated incident risk of cancer and colorectal cancer among 13,131 sequenced study participants of the ASPirin in Reducing Events in the Elderly cohort, stratified by sex and adjusting for age, smoking, alcohol use, BMI, polyp history, history of cancer, and aspirin use. MUTYH carriers were identified among 13,033 participants in The Cancer Genome Atlas and International Cancer Genome Consortium, and somatic signatures of cancers were analyzed. Male MUTYH carriers demonstrated an increased risk for overall cancer incidence [multivariable HR, 1.66; 95% confidence interval (CI), 1.03–2.68; P = 0.038] driven by increased colorectal cancer incidence (multivariable HR, 3.55; 95% CI, 1.42–8.78; P = 0.007), as opposed to extracolonic cancer incidence (multivariable HR, 1.40; 95% CI, 0.81–2.44; P = 0.229). Female carriers did not demonstrate increased risk of cancer, colorectal cancer, or extracolonic cancers. Analysis of mutation signatures from cancers of MUTYH carriers revealed no significant contribution toward early mutagenesis from widespread G:C>T:A transversions among gastrointestinal epithelial cancers. Among cancers from carriers, somatic transversions associated with base-excision repair deficiency are uncommon, suggestive of diverse mechanisms of carcinogenesis in carriers compared with those who inherit biallelic MUTYH mutations. Prevention Relevance: Despite absence of loss of heterozygosity in colorectal cancers, elderly male MUTYH carriers appeared to be at increased of colorectal cancer.

Introduction: Immunotherapies such as checkpoint blockade, have demonstrated remarkable clinical efficacy yet a large percentage of patients do not respond, potentially due to a paucity of pre-existing immune priming against neoantigens. We developed a personalized genome vaccine (PGV_001) platform to generate neoantigen vaccines targeting each patient’s unique mutanome. Primary objectives of the study were to determine 1) the safety and tolerability; 2) the feasibility of PGV_001 production and administration; and 3) the immunogenicity of PGV_001. Secondary objectives included immunophenotyping vaccine driven cellular and soluble immune milieu in peripheral blood. We previously reported on the clinical efficacy, and here we report, analysis of vaccine-driven immune responses in all treated patients. Methods: The study (Trial Registration NCT02721043) enrolled patients with resected malignancies, including Head and neck squamous cell carcinomas, breast cancer and bladder cancer, or, in the case of multiple myeloma treated with autologous stem cell transplant; all patients determined to have a high risk of disease recurrence (>30% over 5 years). Tumor-derived and germline RNA and DNA was sequenced to predict neoantigens utilizing our custom computation pipeline, OpenVax. Approximately 10 peptides were synthesized per patient, and a mixture of these peptides was administered as 10 subcutaneous and intradermal vaccines over 27 weeks in combination with poly-ICLC and helper Tetanus peptide as adjuvants. Immune responses were analyzed utilizing assays including IFN-gamma ELISPOT, antigen specific T cell expansion followed by flow cytometry, etc. Results: In total 148 neoantigen peptides were manufactured for 15 patients. Overall, 136 PGV_001 doses were administered to 13 patients. Vaccine-specific T cell immunity was observed against multiple vaccine neoepitopes in all 13 subjects. Of the peptides administered, 45% of vaccine antigens (57/126) induced de novo immunity, starting as early as Week8 and often sustaining past last vaccination. Notably, while the vaccine driven T cell immunity was CD4 T cell dominant, most evaluated subjects also displayed vaccine induced polyfunctional CD8-T cell responses. Additional studies are ongoing to define qualities of reactive T cells, evaluate vaccine-induced humoral responses and probe the circulating inflammatory immune milieu. These will be presented. Conclusions: We have established a platform for generating personalized neoantigen vaccines. 100% of the vaccinated patients developed an immune response specific to the vaccine neoantigens predicted by OpenVax. Subjects who received treatment experienced mild Grade 1 or 2 adverse reactions as per the CTEP v 4.0 NCI CTCAE. This vaccine trial reached the primary endpoint of safety, tolerability, feasibility and immunogenicity. Based on the PGV_001 platform two clinical trials, one in patients with glioblastoma multiforme (NCT03223103) in combination with TT fields and second in patients with urothelial carcinoma (NCT03359239) in combination with Atezolizumab have been performed. Data from these trials is under evaluation. Citation Format: Mansi Saxena, Thomas Marron, Julia Kodysh, Alex Rubinsteyn, John Finnigan, Ana Blasquez, Marcia Meseck, Tim O'Donnell, Daniela Delbeau, Mathew Galsky, Deborah Doroshow, Brett Miles, Krzysztof Misiukiewicz, Hanna Irie, Amy Tiersten, Samir Parekh, Marshall Posner, Andrea Wolf, John Mandeli, Rachel Brody, Sacha Gnjatic, Eric Schadt, Philip Friedlander, Nina Bhardwaj. Immunogenicity of PGV_001 neoantigen vaccine in a Phase-I clinical trial, across various types of cancers in adjuvant setting [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr CT270.

BACKGROUND Very early onset inflammatory bowel disease (VEOIBD) is defined as disease onset prior to age 6. VEOIBD is monogenic in >5% of cases; however, in the remaining cases, molecular basis of disease is unknown. We aimed to utilize bulk RNA-seq of blood to 1) define transcriptional signatures in established monogenic IBD and 2) to inform novel molecular diagnoses. METHODS We sequenced the transcriptomes of 115 whole blood samples, comprising patients with VEOIBD of a known monogenic cause (n=35), VEOIBD without a known monogenic cause (n=70), and healthy controls (n=10). We focused on genes whose normalized expression was at least 5 standard deviations away from the population mean in at least one sample. We comprehensively characterized clinical phenotypes, to assess concordance with known pathway and gene function, and compared to whole exome sequencing data where available. We interrogated Bayesian IBD networks to assess for enrichment of genes of interest. RESULTS First, we report on a novel transcriptional signature shared by two patients with VEOIBD secondary to X-linked agammaglobulinemia (pathogenic BTK mutations). The signature is defined by under-expression of CXCR5 and FCRL5 (Fig 1) and suggests reduced transcripts secondary to B-cell depletion or novel BTK-dependent mechanisms of gene transcription. Second, we report on three novel candidate VEOIBD genes identified through our approach: MSN, SLC39A4, and BTN3A2 (Fig 2). MSN expression was below threshold in one patient with VEOIBD complicated by fistulae, recurrent infections, and death from pneumonia at age 62. Review of his exome revealed a novel loss of function mutation in MSN. MSN encodes moesin, an actin cytoskeleton modulator, which regulates lymphocyte migration and adhesion. Mutations in MSN are associated with X-linked moesin-associated immunodeficiency, which was first reported in a patient with IBD this year. SLC39A4, which encodes an intestinal zinc transporter, was below threshold in two patients. Mutations in SLC39A4 are associated with acrodermatitis enteropathica, characterized by dermatitis and diarrhea. One patient has VEOIBD with dermatitis; the second has VEOIBD complicated by toxic megacolon and colectomy. Coding mutations in SLC39A4 have not been identified. Further molecular assessment of zinc transporter function is underway. Lastly, BTN3A2 expression was below threshold in two patients with infantile-onset IBD. BTN3A2 plays a role in T cell receptor interactions and NF-KB signaling. Bayesian IBD networks, built from independent cohorts, revealed BTN3A2 and SLC39A4 subnetworks to be enriched in predicted key driver genes of inflammation, further supporting these genes’ potential disease association. CONCLUSIONS Bulk RNA-seq in blood can be used as a diagnostic tool to aid in identifying novel molecular pathways and monogenic diagnoses in VEOIBD.

Background Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms. Methods Using measurements of ~4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction. In the discovery cohort (N= 437), we identified 413 higher plasma abundances of protein targets and 40 lower plasma abundances of protein targets associated with COVID-AKI (adjusted p Results We demonstrate that COVID-AKI is associated with increased markers of tubular injury (NGAL) and myocardial injury. Using estimated glomerular filtration (eGFR) measurements taken after discharge, we also find that 25 of the 62 AKI-associated proteins are significantly associated with decreased post-discharge eGFR (adjusted p desmocollin-2, trefoil factor 3, transmembrane emp24 domain-containing protein 10, and cystatin-Cindicating tubular dysfunction and injury. Conclusions Using clinical and proteomic data, our results suggest that while both acute and long-term COVID-associated kidney dysfunction are associated with markers of tubular dysfunction, AKI is driven by a largely multifactorial process involving hemodynamic instability and myocardial damage.

A few copy number variations (CNVs) have been reported for Alzheimer's disease (AD). However, there is a lack of a systematic investigation of CNVs in AD based on whole genome sequencing (WGS) data.We used four methods to identify consensus CNVs from the WGS data of 1,411 individuals and further investigated their functional roles in AD using the matched transcriptomic and clinicopathological data.We identified 3,012 rare AD-specific CNVs whose residing genes are enriched for cellular glucuronidation and neuron projection pathways. Genes whose mRNA expressions are significantly correlated with common CNVs are involved in major histocompatibility complex class II receptor activity. Integration of CNVs, gene expression, and clinical and pathological traits further pinpoints a key CNV that potentially regulates immune response in AD.We identify CNVs as potential genetic regulators of immune response in AD. The identified CNVs and their downstream gene networks reveal novel pathways and targets for AD.

e18837 Background: There are several FDA-approved targeted therapies for NSCLC with driver gene rearrangements (DGRs) involving ALK, ROS1, RET, NTRK1-3, and MET exon 14. DNA-based sequencing panels miss 15-20% of DGRs compared to RNA-based methods (Benayed et al., 2019; Solomon et al., 2020). We modelled the years of life gained by using RNA- instead of DNA-based methods for DGR detection. Methods: We used the IVI-NSCLC open-source model to analyze the relationship between stable disease, progression, and death as previously described in a multi-state model. Previously reported PFS hazard ratios for targeted therapies (versus chemotherapy) treating DGRs range from 0.17 to 0.52 (Hoang et al., 2020). Therefore, we assumed that the HRs corresponding to the parameter estimates for the targeted therapy option built into the model would approximate treatment-specific relative effects of targeted therapies for DGRs (PFS = 0.3; OS = 0.8). We simulated life-years and quality-adjusted life years (QALYs) for a cohort of 1,000 patients with DGRs matched to appropriate targeted therapies over a 5-year time horizon. We used population-level prevalence data to estimate the number of patients with metastatic NSCLC and DGRs. We then estimated the number of false negatives generated by DNA-only sequencing and calculated the additional life-years gained if RNA-based methods were used instead. Results: In a simulated cohort of 1,000 patients with DGRs detected through RNA sequencing and receiving targeted therapy, median PFS and OS were 1.8 and 3.7 years, respectively. The use of targeted therapy (instead of chemotherapy) in these patients increased life-years by 3.46 years (95% CI, 3.04-3.90) and QALYs by 2.27 years (95% CI 1.91-2.71) on average per patient. There were approximately 235,768 new lung cancers in the US in 2021, 56% of which were metastatic at diagnosis (SEER). Approximately 50% of lung cancers are adenocarcinomas, and approximately 12% of adenocarcinomas have DGRs: ALK (5%) , MET (4%) , ROS1 (2%) , RET (1%) , and NTRK1-3 (< 1%). Thus, there were approximately 7,922 new metastatic NSCLC cases with DGRs in 2021. Prior studies have shown that DNA-only methods have a sensitivity of 80-85% for DGRs compared to RNA-based methods. At the level of the US population, this implies 1,188-1,584 false negative cases in 2021. The clinical impact of eliminating these false negatives by using RNA- instead of DNA-based methods to identify DGRs amounts to 4,110–5,480 life-years or 2,696–3,595 QALYs gained by appropriately matching patients with DGRs to targeted therapy over chemotherapy. Conclusions: DNA-only sequencing methods have limited sensitivity for DGRs in lung cancer, leading to false negatives and missed opportunities for targeted therapy. This modeling study suggests that the elimination of such false negatives using RNA-based detection methods would lead to meaningful gains in patient outcomes.

e13545 Background: The ASCO Quality Oncology Practice Initiative (QOPI) was established in 2006 to facilitate quality measurement and continuous improvement of cancer care.1 ASCO and QOPI leadership encourage a continuous learning environment to develop new tools and methodologies that improve quality and maximize outcomes and value in oncology practice.2 Parallel advances in tumor molecular profiling, bioinformatics, and EMR data curation are adding new insights to support the real-world research of longitudinal patient journeys. Methods: Referencing the QOPI Certification Track 2021 Measures Summary, we employed data visualization techniques to create a precision oncology dashboard for breast cancer. This evaluation included 409 breast cancer patients for whom genomic testing was performed and presented at the Avera molecular tumor board. Patients were then matched to appropriate biomarker-based targeted therapies. Results: The dashboard identified and prioritized areas for enhanced data capture to inform strategies for care-coordination, research, and delivery of specialized care. Conclusions: An interactive, visual platform may help facilitate the integration of QOPI reporting into CancerLinQ, thereby fostering a real-time, bidirectional system for quality reporting, clinical decision support, and rapid learning.3,4 The next stage will (1) expand the tumor list; and (2) apply NLP methods to assess accuracy, outcomes and temporal reasoning to create interactive graphs for patient referral patterns and surgery decision drivers. References: 1. Neuss MN, Desch CE, McNiff KK, et al: A process for measuring the quality of cancer care: The Quality Oncology Practice Initiative. J Clin Oncol. 2005 2. Blayney DW, McNiff K, Eisenberg PD, Gilmore T, Jacobsen PB, Jacobson JO, Kadlubek PJ, Neuss MN, Simone J. Development and future of the American Society of Clinical Oncology's Quality Oncology Practice Initiative. J Clin Oncol. 2014 3. Blayney DW: Enhancing quality through innovation: American Society of Clinical Oncology presidential address 2010. J Clin Oncol. 2010 4. Schilsky R, Hauser R, Mann J, et al: Lessons learned from the development of the CancerLinQ prototype: Clinical decision support. J Clin Oncol. 31, 2013[Table: see text]

TPS6605 Background: Precision medicine represents a new era in oncology and population health. Comprehensive molecular profiling and hereditary cancer testing are increasingly important in the routine management of many malignancies, yet may not be widely adopted in many practices and leads to patients not consistently receiving care recommended by current national guidelines such as NCCN and ASCO. In addition, available targeted DNA sequencing panels may be limited to only defined drivers and lack detection in precancerous conditions. Whole exome sequencing (WES) combined with whole transcriptome sequencing (WTS) represent an expanded testing approach that identifies additional genetic changes beyond standard commercial panel tests. Current evidence suggests that WES/WTS is feasible to carry out in routine clinical practice, with improved detection of somatic alterations compared to smaller panels, and the promise to enhance access to targeted therapies and clinical trials. The primary purpose of the ASAP study is to understand the breadth of molecular characteristics present in participants cared for in a large integrated community-based health system, by linking comprehensive molecular profiles with clinical data extracted and curated from the electronic medical record. Methods: Avera Health is a large, Midwestern, predominantly rural vertically integrated health system with six regional cancer centers and over 40 oncology outreach sites across South Dakota and surrounding states. The ASAP study was initiated in November 2021 in GYN and as of February, the study is enrolling in the Otolaryngology clinic and expected to open in medical oncology and hematology in March/April 2022. The study will expand to include additional cancer centers outside of Sioux Falls later in 2022 and is anticipated to be open for accrual for 5 years. Enrolled participants are expected to receive WES/WTS and proteomics on their primary tumor and metastatic sites were feasible as well as hereditary testing, pharmacogenomics (PGx) and microbiome profiling. Liquid biopsies will be performed every 3 months while on treatment or when treatment is changed, in follow up to coincide with scheduled radiographic testing, and as part of annual routine surveillance. For participants not diagnosed with cancer, each participant will receive hereditary testing, PGx, microbiome, and a liquid biopsy at least annually. Participants also consent to sharing their electronic health information. Future stages of this work will employ natural language processing (NLP) methods to develop algorithms to help identify opportunities to improve patient outcomes. Projected enrollment is 1000 participants in year 1, 1500 participants in year 2, and up to 3000 annually thereafter. Approximately 50 patients have consented as of February 1 2022. Clinical trial information: NCT05142033.

e15036 Background: Advances in tumor molecular profiling and bioinformatics are adding new insights to support real-world research. In this study, paired tumor NGS testing data was curated and analyzed from electronic medical records (EMR) to understand mutational and targeted therapy landscape. Methods: A database initiated in 2013 to collect data, including outcomes, on all patients referred to the molecular tumor board at the Avera Cancer Institute was curated and analyzed. A data pipeline that combined results from curated NGS data with curated EMR data from Avera was used to correlate targetable mutations to a corresponding therapy and assessed treatment patterns. Results: Data were from 1804 patients with 44 cancer types including breast (462; 26%), lung (338;19%), colorectal (198;11%), ovarian (168;9%), and uterine (100;5%). Overall, 83% of patients had a positive NGS result and 67% had a targetable mutation per NCCN guidelines. 32% of patients with a targetable mutation received targeted therapy per guideline. Overall, 381 patients with 28 tumor types received targeted therapies based on NGS results and targetable biomarkers. Of these, 288/381 (76%) were stage III-IV and 93/381 (24%) early stage/stage unknown. In non-small cell lung cancer, 76% of stage III-IV patients with mutations in EGFR (exon 19 deletion, L858, L861, S768, G719), MET (exon 14 skipping, amplification), BRAF V600 and ALK/ ROS1/ RET fusions received a targeted therapy per NCCN guidelines. In advanced breast cancer, 97% with ERBB2 amplification received targeted therapy (trastuzumab, pertuzumab, neratinib, lapatinib, tucatinib). 8% of advanced breast cancer patients with PIK3CA mutation received alpelisib, with all patients receiving the drug since 2019. 38% advanced breast cancer patients with BRCA1/2 mutation received Olaparib. In ovarian cancer, 48% of advanced stage patients received targeted therapy for BRCA 1/2, ATM, CDK12, CHEK2, ERBB2, FANCL, KRAS, ALK, BRAF alterations. Olaparib was used in 70% of advanced prostate cancer patients with BRCA1/2 mutations. Dabrafenib combined with Trametinib were most preferred for BRAF V600 mutation in melanoma and colorectal tumors respectively. Off label use of targeted therapy was observed in about 6% of advanced patients; examples include trametinib for KRAS/NRAS mutation [non-small cell lung cancer, colorectal, pancreatic, ovarian], PARP inhibitors for BRCA1/2, ATM mutations [urothelial, peritoneal], anti-ERBB2 inhibitors for ERBB2 in-frame, missense, exon 20 insertion mutations [lung and breast]. Conclusions: These study results demonstrate that automated data mining can provide insights into the genomic and therapeutic landscape of a real-world patient population. Across all tumor types, most patients are being given appropriate targeted therapies as per guidelines. Off-label use of targeted therapy was also observed.

e13563 Background: Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous disease with multiple phenotypes and endotypes and associated with lung cancer development. In this study, we analyzed the clinical features of COPD including various phenotypic and endotypic features from the electronic health records (EHR) as the potential risk factors for lung cancer development in a large cohort of COPD patients. Methods: We identified a COPD cohort based on electronic Medical Records and Genomics (eMERGE) network COPD cohort identification algorithms with minor modifications from the Mount Sinai Data Warehouse (2000-2020) and followed the patients on the first diagnosis of lung cancer. The development of lung cancer in COPD patients was confirmed by manual chart review. We retrieved the clinical features from EHRs and conducted Kaplan Meier (KM) analysis and multivariable Cox-regression modeling for hazard ratio (HR) analysis. Results: We found that 3.8 % of COPD patients (824 out of 21,658) developed lung cancer. While COPD patients with emphysema and smoking history (former or current) showed an increased risk of lung cancer onset, patients with concurrent asthma and corticosteroid (ICS) inhalation history showed a reduced risk of lung cancer onset (adjusted HR in Table). Interestingly, COPD patients with higher eosinophil counts showed late onset of lung cancer (>300 cells/ul, cancer rate at 5y=2.4%, p=< 0.002; 150-300 cells/ul, cancer rate at 5y=2.9%, p=0.003) when compared to patients with low eosinophils count (300 cells/ul; HR:0.72, 95% CI: 0.57-0.89, p=0.003) when compared to those with low eosinophils count (

523 Background: Socioeconomic disparities impact breast cancer survival with significantly higher mortality among women of lower socioeconomic status. Little is known about how disparities affect patients with early-stage hormone receptor positive (HR+) breast cancer (BC) and patients’ tolerance and adherence to standard of care adjuvant aromatase inhibitor (AI) therapy. AI-related adverse effects are common and can cause therapy intolerance and early discontinuation. Supportive therapies have been shown to improve symptom tolerability when utilized by patients. This study assessed socioeconomic disparities in utilization of supportive therapies and adherence to initial AI therapy. Methods: We performed a retrospective chart review of all female patients at our academic institution with early-stage, HR+, BC who were initiated on adjuvant AI between 2011-2020. We collected information on side effects, duration of first AI and use of supportive therapies. We linked median family income with zip codes based on national census data and sorted them based on the Pew Research Center categorization. Primary endpoints were the rate of discontinuation of AI at 1-year and utilization of supportive therapies in relation to income, insurance coverage and primary language. The Fisher's exact test, Pearson's Chi-squared test, and Wald test methods were used to compare rates of discontinuation of front-line AI therapy and use of supportive therapies for each group. Results: We identified 1006 patients of whom 95% (n = 954) had AI related side effects yet only 31% (n = 311) received supportive therapies in the first year of AI treatment. The majority (59%) of patients were in the middle-income range ($52,200-$156,000), followed by upper (24%) and lower (17%) income. Upper-income was associated with higher use of supportive therapies (OR 1.46, p = 0.031) but was not associated with lower 1-year discontinuation rate. Medicare was the most common insurance coverage (45%), followed by Commercial (32%) and Medicaid (23%). English was the primary language for 86% of patients. Neither insurance coverage nor primary language was associated with either endpoint. In evaluating race, Black patients had the least use of supportive therapies (p < 0.001), yet this group had the lowest 1-year discontinuation rate (p = 0.005). Conclusions: Our results demonstrate that income and race were associated with use of crucial supportive therapies that are proven to help patients mitigate AI toxicities. The etiology of these disparities is likely multifactorial and requires further study to ensure equitable care and access for all patients.

ImportancePreliminary reports indicate that acute kidney injury (AKI) is common in coronavirus disease (COVID)-19 patients and is associated with worse outcomes. AKI in hospitalized COVID-19 patients in the United States is not well-described.ObjectiveTo provide information about frequency, outcomes and recovery associated with AKI and dialysis in hospitalized COVID-19 patients.DesignObservational, retrospective study.SettingAdmitted to hospital between February 27 and April 15, 2020.ParticipantsPatients aged ≥18 years with laboratory confirmed COVID-19ExposuresAKI (peak serum creatinine increase of 0.3 mg/dL or 50% above baseline).Main Outcomes and MeasuresFrequency of AKI and dialysis requirement, AKI recovery, and adjusted odds ratios (aOR) with mortality. We also trained and tested a machine learning model for predicting dialysis requirement with independent validation.ResultsA total of 3,235 hospitalized patients were diagnosed with COVID-19. AKI occurred in 1406 (46%) patients overall and 280 (20%) with AKI required renal replacement therapy. The incidence of AKI (admission plus new cases) in patients admitted to the intensive care unit was 68% (553 of 815). In the entire cohort, the proportion with stages 1, 2, and 3 AKI were 35%, 20%, 45%, respectively. In those needing intensive care, the respective proportions were 20%, 17%, 63%, and 34% received acute renal replacement therapy. Independent predictors of severe AKI were chronic kidney disease, systolic blood pressure, and potassium at baseline. In-hospital mortality in patients with AKI was 41% overall and 52% in intensive care. The aOR for mortality associated with AKI was 9.6 (95% CI 7.4-12.3) overall and 20.9 (95% CI 11.7-37.3) in patients receiving intensive care. 56% of patients with AKI who were discharged alive recovered kidney function back to baseline. The area under the curve (AUC) for the machine learned predictive model using baseline features for dialysis requirement was 0.79 in a validation test.Conclusions and RelevanceAKI is common in patients hospitalized with COVID-19, associated with worse mortality, and the majority of patients that survive do not recover kidney function. A machine-learned model using admission features had good performance for dialysis prediction and could be used for resource allocation.Key PointsQuestionWhat is incidence and outcomes of acute kidney injury (AKI) in patients hospitalized with COVID-19?FindingsIn this observational study of 3,235 hospitalized patients with COVID-19 in New York City, AKI occurred in 46% of patients and 20% of those patients required dialysis. AKI was associated with increased mortality. 44% of patients discharged alive had residual acute kidney disease. A machine learned predictive model using baseline features for dialysis requirement had an AUC Of 0.79.MeaningAKI was common in patients with COVID-19, associated with increased mortality, and nearly half of patients had acute kidney disease on discharge.