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The prognostic relevance of diagnosis to treatment interval (DTI) in patients with newly diagnosed mantle cell lymphoma (MCL) is unknown. Hence, we sought to evaluate the impact of DTI on outcomes in MCL using 3 large datasets (1) the University of Iowa/Mayo Clinic Specialized Program of Research Excellence Molecular Epidemiology Resource, (2) patients enrolled in the ALL Age Asthma Cohort/CALGB 50403, and (3) a multisitecohort of patients with MCL. Patients were a priori divided into 2 groups, 0 to 14 days (short DTI) and 15 to 60 days (long DTI). The patients in whom observation was deemed appropriate were excluded. One thousand ninety-seven patients newly diagnosed with MCL and available DTI were included in the study. The majority (73%) had long DTI (n=797). Patients with short DTI had worse eastern cooperative oncology group performance status (ECOG PS ≥2), higher lactate dehydrogenase, bone marrow involvement, more frequent B symptoms, higher MCL International Prognostic Index (MIPI ≥6.2), and were less likely to receive intensive induction therapy than long DTI group. The median progression-free survival (2.5 years vs 4.8 years, p

PURPOSE Brexucabtagene autoleucel (brexu-cel) is an autologous CD19-directed chimeric antigen receptor (CAR) T-cell therapy approved for relapsed/refractory mantle cell lymphoma (MCL). This therapy was approved on the basis of the single-arm phase II ZUMA-2 trial, which showed best overall and complete response rates of 91% and 68%, respectively. We report clinical outcomes with brexu-cel in the standard-of-care setting for the approved indication. PATIENTS AND METHODS Patients who underwent leukapheresis between August 1, 2020 and December 31, 2021, at 16 US institutions, with an intent to manufacture commercial brexu-cel for relapsed/refractory MCL, were included. Patient data were collected for analyses of responses, outcomes, and toxicities as per standard guidelines. RESULTS Of 189 patients who underwent leukapheresis, 168 (89%) received brexu-cel infusion. Of leukapheresed patients, 79% would not have met ZUMA-2 eligibility criteria. Best overall and complete response rates were 90% and 82%, respectively. At a median follow-up of 14.3 months after infusion, the estimates for 6- and 12-month progression-free survival (PFS) were 69% (95% CI, 61 to 75) and 59% (95% CI, 51 to 66), respectively. The nonrelapse mortality was 9.1% at 1 year, primarily because of infections. Grade 3 or higher cytokine release syndrome and neurotoxicity occurred in 8% and 32%, respectively. In univariable analysis, high-risk simplified MCL international prognostic index, high Ki-67, TP53 aberration, complex karyotype, and blastoid/pleomorphic variant were associated with shorter PFS after brexu-cel infusion. Patients with recent bendamustine exposure (within 24 months before leukapheresis) had shorter PFS and overall survival after leukapheresis in intention-to-treat univariable analysis. CONCLUSION In the standard-of-care setting, the efficacy and toxicity of brexu-cel were consistent with those reported in the ZUMA-2 trial. Tumor-intrinsic features of MCL, and possibly recent bendamustine exposure, may be associated with inferior efficacy outcomes.

An enhanced understanding of the molecular heterogeneity of diffuse large B-cell lymphoma (DLBCL) has opened the door to clinical trials evaluating novel agents with subtype-specific activity. It is an emerging question whether core needle biopsies (CNB) can adequately meet the increasing tissue requirements of these clinical trials. This can potentially lead to selective enrollment of patients who can undergo excisional biopsy (EB). It is also important to know whether patients who can undergo extensive diagnostic work up differ in their disease characteristics and outcomes from those who cannot. In this observational study, we describe the characteristics, outcomes, and adequacy of diagnostic tissue in patients with newly diagnosed DLBCL and primary mediastinal large B-cell lymphoma who underwent EB vs CNB. Of the 1061 patients, 532 (49.8%) underwent EB and 529 (50.1%) underwent CNB. A significantly higher proportion of patients with CNB had advanced stage disease, an international prognostic index of ≥3, and inadequate tissue for molecular analyses. Patients with CNB had significantly worse 5-year event-free survival (67.6% vs 56.9%; hazard ratio [HR], 0.76; confidence interval [CI]95, 0.6-0.9, P < .001) and 5-year overall survival (76.4% vs 69.2%; HR, 0.8; CI95, 0.6-0.9, P < .001). Thus, patients who underwent CNB have poor-risk features and inferior outcomes on frontline chemoimmunotherapy, are more likely to have inadequate tissue for molecular analyses, and might not meet the tissue requirements of biomarker-driven clinical trials. Thus, the increasing tissue requirements of biomarker-driven clinical trials may result in the exclusion of patients with high-risk DLBCL who need novel agents.

Activated B cell (ABC) type diffuse large B cell lymphoma (DLBCL), double hit lymphoma (DHL) and double expressor lymphoma (DEL) have poor outcomes to frontline R-CHOP but impact of these molecular features on outcomes of relapsed/refractory (R/R) disease is not well-characterized. We evaluated the association of diagnostic cell of origin (COO), double hit and double expressor status with overall survival after first relapse in DLBCL patients who were enrolled into the Molecular Epidemiology Resource (MER) cohort. COO was available from immunohistochemistry (IHC) using Hans criteria or gene expression profiling (GEP) (Nanostring) on the diagnostic FFPE biopsy. Of 373 pts with R/R DLBCL, 278 had COO by IHC: 152 were GCB, 107 were non-GCB. One hundred and fourty had COO by GEP: 44 were ABC, 65 were GCB and 13 were unclassifiable. Nineteen out of 163 (12%) were DHL; 30 out of 135 (22%) had DEL. COO, either by IHC (2 years OS GCB: 45% [CI

Cancer clinical trial eligibility criteria may create patient populations studied in trials that do not reflect the patient populations treated in the real-world setting. Follicular lymphoma (FL) is an indolent lymphoma with heterogeneous presentations across a broad range of individuals, resulting in many acceptable management strategies. We evaluated how first-line clinical trial eligibility criteria impacted the demographic makeup and outcomes of patients with FL for whom systemic therapy might be considered. We compared the characteristics of 196 patients with FL from a single institution to eligibility criteria from 10 first-line FL trials on clinicaltrials.gov. Next, we tabulated eligibility criteria from 24 first-line FL protocols and evaluated their impact on 1198 patients with FL with stages II to IV disease from the prospective Molecular Epidemiology Resource (MER) and Lymphoma Epidemiology of Outcomes (LEO) cohort studies. We found that 39.8% and 52.7% of patients with FL might be excluded from clinical trials based on eligibility criteria derived from clinicaltrials.gov and protocol documents, respectively. Patients excluded because of renal function, prior malignancy, and self-reported serious health conditions tended to be older. Expanding stage requirement from III-IV to II-IV, and platelet requirement from ≥150 000 to ≥75 000 increased population size by 21% and 8%, respectively, in MER and by 16% and 13%, respectively, in LEO, without impacting patient demographics or outcomes. These data suggest that management of older individuals with FL may not be fully informed by recent clinical trials. Moreover, liberalizing stage and platelet criteria might expand the eligible population and allow for quicker trial accrual without impacting outcomes.

PDF file - 157K, Supplemental Table 1. Regulatory T cell genes included in this study (N=25). Supplemental Table 2. Regulatory T cell SNPs included in this study. Supplemental Table 3. Participating invasive epithelial ovarian cancer studies. Supplemental Table 4. Association between clinical variables and overall survival.

The presence of regulatory T cells (Treg) in solid tumors is known to play a role in patient survival in ovarian cancer and other malignancies. We assessed inherited genetic variations via 749 tag single-nucleotide polymorphisms (SNP) in 25 Treg-associated genes (CD28, CTLA4, FOXP3, IDO1, IL10, IL10RA, IL15, 1L17RA, IL23A, IL23R, IL2RA, IL6, IL6R, IL8, LGALS1, LGALS9, MAP3K8, STAT5A, STAT5B, TGFB1, TGFB2, TGFB3, TGFBR1, TGRBR2, and TGFBR3) in relation to ovarian cancer survival. We analyzed genotype and overall survival in 10,084 women with invasive epithelial ovarian cancer, including 5,248 high-grade serous, 1,452 endometrioid, 795 clear cell, and 661 mucinous carcinoma cases of European descent across 28 studies from the Ovarian Cancer Association Consortium (OCAC). The strongest associations were found for endometrioid carcinoma and IL2RA SNPs rs11256497 [HR, 1.42; 95% confidence interval (CI), 1.22–1.64; P = 5.7 × 10−6], rs791587 (HR, 1.36; 95% CI, 1.17–1.57; P = 6.2 × 10−5), rs2476491 (HR, = 1.40; 95% CI, 1.19–1.64; P = 5.6 × 10−5), and rs10795763 (HR, 1.35; 95% CI, 1.17–1.57; P = 7.9 × 10−5), and for clear cell carcinoma and CTLA4 SNP rs231775 (HR, 0.67; 95% CI, 0.54–0.82; P = 9.3 × 10−5) after adjustment for age, study site, population stratification, stage, grade, and oral contraceptive use. The rs231775 allele associated with improved survival in our study also results in an amino acid change in CTLA4 and previously has been reported to be associated with autoimmune conditions. Thus, we found evidence that SNPs in genes related to Tregs seem to play a role in ovarian cancer survival, particularly in patients with clear cell and endometrioid epithelial ovarian cancer. Cancer Immunol Res; 2(4); 332–40. ©2014 AACR.

Purpose: Due to variation of outcome among cases, we sought to examine whether overall survival in ovarian cancer was associated with common inherited variants in 227 candidate genes from ovarian cancer–related pathways including angiogenesis, inflammation, detoxification, glycosylation, one-carbon transfer, apoptosis, cell cycle regulation, and cellular senescence.Experimental Design: Blood samples were obtained from 325 women with invasive epithelial ovarian cancer diagnosed at the Mayo Clinic from 1999 to 2006. During a median follow-up of 3.8 years (range, 0.1-8.6 years), 157 deaths were observed. Germline DNA was analyzed at 1,416 single nucleotide polymorphisms (SNP). For all patients, and for 203 with serous subtype, we assessed the overall significance of each gene and pathway, and estimated risk of death via hazard ratios (HR) and 95% confidence intervals (CI), adjusting for known prognostic factors.Results: Variation within angiogenesis was most strongly associated with survival time overall (P = 0.03) and among patients with serous cancer (P = 0.05), particularly for EIF2B5 rs4912474 (all patients HR, 0.69; 95% CI, 0.54-0.89; P = 0.004), VEGFC rs17697305 (serous subtype HR, 2.29; 95% CI, 1.34-3.92; P = 0.003), and four SNPs in VHL. Variation within the inflammation pathway was borderline significant (all patients, P = 0.09), and SNPs in CCR3, IL1B, IL18, CCL2, and ALOX5 which correlated with survival time are worthy of follow-up.Conclusion: An extensive multiple-pathway assessment found evidence that inherited differences may play a role in outcome of ovarian cancer patients, particularly in genes within the angiogenesis and inflammation pathways. Our work supports efforts to target such mediators for therapeutic gain. Clin Cancer Res; 16(3); 995–1007

Supplementary Table 1. Key clinical characteristics of public cohort with respect to individual 14 studies Supplementary Table 2. Key clinical characteristics of Mayo Clinic HGSOC cohort Supplementary Table 3. Expression centroids of five de novo subtypes Supplementary Table 4. Expression centroids of four Tothill's HGSOC subtypes (C1/C2/C4/C5) Supplementary Table 5. Cross-tab between de novo subtypes and two other subtyping systems in public cohort Supplementary Table 6. The differential expression analysis results between s1.MES and rest de novo subtypes in public cohort Supplementary Table 7. The differential expression analysis results between s2.IMM and rest de novo subtypes in public cohort Supplementary Table 8. The differential expression analysis results between s3.RPO and rest de novo subtypes in public cohort Supplementary Table 9. The differential expression analysis results between s4.DIF and rest de novo subtypes in public cohort Supplementary Table 10. The differential expression analysis results between s5.ANM and rest de novo subtypes in public cohort Supplementary Table 11. Distribution tables of HGSOC molecular subtypes vs. surgical outcomes (optimal- and sub-optimal debulking) in Public and Mayo Clinic cohorts Supplementary Table 12. Previously reported debulking-predictive genes and their differential expressions related to s1.MES subtype in HGSOC public cases Supplementary Table 13. Enriched pathways (q-value

PDF file - 298K, 1) Ki67 IHC staining of patient & Avatars; 2) pan-CK, CD45, vimentin staining of Avatars; 3) graph of gains/losses by aCGH in Avatars; 4) Ca125 levels in serum of patients/Avatars.

Supplementary Figure 1. Analytical workflow of the present study. Supplementary Figure 2. Principal component analysis plots of public expression datasets before (A) and after (B) batch-correction Supplementary Figure 3. Consensus clustering results of non-negative matrix factorization from K= 2 to K = 9. The stable clustering relationships can be observed from K=2 to K=5. Supplementary Figure 4. Overall survival plots according to late-stage HGSOC cases in public cohort, n=1946. Supplementary Figure 5. Overall survival plots according to late-stage HGSOC cases with optimal debulking surgeries, in (A) public cohort, n=936; (B) Mayo Clinic cohort, n=250. Supplementary Figure 6. Overall survival plots according to late-stage HGSOC cases with sub-optimal debulking surgeries, in (A) public cohort, n=660; (B) Mayo Clinic cohort, n=88. Supplementary Figure 7. Overall survival plots according to surgical outcomes in Mayo clinic cohorts, for (A) all the late-stage HGSOC patients, (B-F) for late-stage patients belonging to each subtype. Supplementary Figure 8. Volcano plot of S1.MES subtype vs. other molecular subtypes. X-axis is the log-scaled expression fold-change (FC) and Y-axis is the significance level of differential expression analysis, defined as -log10(q-value). Two identical volcano plots were presented, where the larger one plotting according to gene symbols, and the smaller one plotting using dots. Red-color and green-color are used to indicate significantly up-regulated (FC{greater than or equal to}0.5 & q-value{less than or equal to}0.01) and down-regulated genes (FC{less than or equal to} -0.5 & q-value{less than or equal to}0.01), respectively. Supplementary Figure 9. Volcano plot of S2.IMM subtype vs. other molecular subtypes. X-axis is the log-scaled expression fold-change (FC) and Y-axis is the significance level of differential expression analysis, defined as -log10(q-value). Two identical volcano plots were presented, where the larger one plotting according to gene symbols, and the smaller one plotting using dots. Red-color and green-color are used to indicate significantly up-regulated (FC{greater than or equal to}0.5 & q-value{less than or equal to}0.01) and down-regulated genes (FC{less than or equal to} -0.5 & q-value{less than or equal to}0.01), respectively. Supplementary Figure 10. Volcano plot of S3.PRO subtype vs. other molecular subtypes. X-axis is the log-scaled expression fold-change (FC) and Y-axis is the significance level of differential expression analysis, defined as -log10(q-value). Two identical volcano plots were presented, where the larger one plotting according to gene symbols, and the smaller one plotting using dots. Red-color and green-color are used to indicate significantly up-regulated (FC{greater than or equal to}0.5 & q-value{less than or equal to}0.01) and down-regulated genes (FC{less than or equal to} -0.5 & q-value{less than or equal to}0.01), respectively. Supplementary Figure 11. Volcano plot of S4.DIF subtype vs. other molecular subtypes. X-axis is the log-scaled expression fold-change (FC) and Y-axis is the significance level of differential expression analysis, defined as -log10(q-value). Two identical volcano plots were presented, where the larger one plotting according to gene symbols, and the smaller one plotting using dots. Red-color and green-color are used to indicate significantly up-regulated (FC{greater than or equal to}0.5 & q-value{less than or equal to}0.01) and down-regulated genes (FC{less than or equal to} -0.5 & q-value{less than or equal to}0.01), respectively. Supplementary Figure 12. Volcano plot of S5.ANM subtype vs. other molecular subtypes. X-axis is the log-scaled expression fold-change (FC) and Y-axis is the significance level of differential expression analysis, defined as -log10(q-value). Two identical volcano plots were presented, where the larger one plotting according to gene symbols, and the smaller one plotting using dots. Red-color and green-color are used to indicate significantly up-regulated (FC{greater than or equal to}0.5 & q-value{less than or equal to}0.01) and down-regulated genes (FC{less than or equal to} -0.5 & q-value{less than or equal to}0.01), respectively. Supplementary Figure 13. Enriched pathways (q-value {less than or equal to}0.1) for S1.MES subtype vs. other molecular subtypes. Green and red colors are used to represent significantly down- and up-regulated pathways, respectively. Supplementary Figure 14. Enriched pathways (q-value {less than or equal to}0.1) for S2.IMM subtype vs. other molecular subtypes. Green and red colors are used to represent significantly down- and up-regulated pathways, respectively. Supplementary Figure 15. Enriched pathways (q-value {less than or equal to}0.1) for S3.PRO subtype vs. other molecular subtypes. Green and red colors are used to represent significantly down- and up-regulated pathways, respectively. Supplementary Figure 16. Enriched pathways (q-value {less than or equal to}0.1) for S4.DIF subtype vs. other molecular subtypes. Green and red colors are used to represent significantly down- and up-regulated pathways, respectively. Supplementary Figure 17. Enriched pathways (q-value {less than or equal to}0.1) for S5.ANM subtype vs. other molecular subtypes. Green and red colors are used to represent significantly down- and up-regulated pathways, respectively. Supplementary Figure 18. Expression scatterplot between PD-L1 and TAP1 genes; the figure was generated using cBioPortal (http://www.cbioportal.org/) according to TCGA HGSOC data. Supplementary Figure 19. Distribution boxplots of four genomics scores with respect to five de novo subtypes for TCGA HGSOC cases.

Purpose: Recent progress in understanding the molecular biology of epithelial ovarian cancer has not yet translated into individualized treatment for these women or improvements in their disease outcome. Gene expression has been utilized to identify distinct molecular subtypes, but there have been no reports investigating whether or not molecular subtyping is predictive of response to bevacizumab in ovarian cancer.Experimental Design: DASL gene expression arrays were performed on FFPE tissue from patients enrolled on the ICON7 trial. Patients were stratified into four TCGA molecular subtypes. Associations between molecular subtype and the efficacy of randomly assigned therapy with bevacizumab were assessed.Results: Molecular subtypes were assigned as follows: 122 immunoreactive (34%), 96 proliferative (27%), 73 differentiated (20%), and 68 mesenchymal (19%). In univariate analysis patients with tumors of proliferative subtype obtained the greatest benefit from bevacizumab with a median PFS improvement of 10.1 months [HR, 0.55 (95% CI, 0.34–0.90), P = 0.016]. For the mesenchymal subtype, bevacizumab conferred a nonsignificant improvement in PFS of 8.2 months [HR 0.78 (95% CI, 0.44–1.40), P = 0.41]. Bevacizumab conferred modest improvements in PFS for patients with immunoreactive subtype (3.8 months; P = 0.08) or differentiated subtype (3.7 months; P = 0.61). Multivariate analysis demonstrated significant PFS improvement in proliferative subtype patients only [HR, 0.45 (95% CI, 0.27–0.74), P = 0.0015].Conclusions: Ovarian carcinoma molecular subtypes with the poorest survival (proliferative and mesenchymal) derive a comparably greater benefit from treatment that includes bevacizumab. Validation of our findings in an independent cohort could enable the use of bevacizumab for those patients most likely to benefit, thereby reducing side effects and healthcare cost. Clin Cancer Res; 23(14); 3794–801. ©2017 AACR.

Appendix 1: Additional Methods for Subtype Assignment Figure S1. Consistency check between published supervised classification vs. novel unsupervised clustering in TCGA Figure S2. Subclasses were computed by applying a consensus NMF clustering method which confirmed the presence of four stable clusters. Consensus matrices and sample correlation matrices are shown for k=2 to k=8 using the 1500 probe sets based on genes described by TCGA. Figure S3. Kaplan Meier analysis of progression free and overall survival for bevacizumab vs. standard treatment. Panel A: total study population. Panel B: patients at high risk for progression (suboptimally cytoreduced stage III and all stage IV patients) Supplemental Table 1. Results of progression-free survival restricted mean

Purpose: APOBEC3 DNA cytosine deaminase family members normally defend against viruses and transposons. However, deregulated APOBEC3 activity causes mutations in cancer. Because of broad expression profiles and varying mixtures of normal and cancer cells in tumors, including immune cell infiltration, it is difficult to determine where different APOBEC3s are expressed. Here, we ask whether correlations exist between APOBEC3 expression and T-cell infiltration in high-grade serous ovarian cancer (HGSOC), and assess whether these correlations have prognostic value.Experimental Design: Transcripts for APOBEC3G, APOBEC3B, and the T-cell markers, CD3D, CD4, CD8A, GZMB, PRF1, and RNF128 were quantified by RT-qPCR for a cohort of 354 HGSOC patients. Expression values were correlated with each other and clinical parameters. Two additional cohorts were used to extend HGSOC clinical results. Immunoimaging was used to colocalize APOBEC3G and the T-cell marker CD3. TCGA data extended expression analyses to additional cancer types.Results: A surprising positive correlation was found for expression of APOBEC3G and several T cell genes in HGSOC. Immunohistochemistry and immunofluorescent imaging showed protein colocalization in tumor-infiltrating T lymphocytes. High APOBEC3G expression correlated with improved outcomes in multiple HGSOC cohorts. TCGA data analyses revealed that expression of APOBEC3D and APOBEC3H also correlates with CD3D across multiple cancer types.Conclusions: Our results identify APOBEC3G as a new candidate biomarker for tumor-infiltrating T lymphocytes and favorable prognoses for HGSOC. Our data also highlight the complexity of the tumor environment with respect to differential APOBEC family gene expression in both tumor and surrounding normal cell types. Clin Cancer Res; 22(18); 4746–55. ©2016 AACR.

Background: Epithelial ovarian cancer (EOC) is the fifth leading cause of cancer-related death in females and leading gynecologic cause of cancer-related death. Despite the identification of a number of serum biomarkers, methods to identify early-stage disease and predict prognosis remain scarce. We have evaluated two biologically connected serum biomarkers, serum leukocyte protease inhibitor (SLPI) and progranulin (PGRN).Methods: Two-hundred frozen plasma samples were acquired from the Mayo Clinic Biospecimen Repository for Ovarian Cancer Research. Samples were obtained from 50 patients with benign conditions, 50 with American Joint Committee on Cancer (AJCC) stage I and II EOC, and 100 with AJCC stage III and IV EOC. Samples were obtained before surgical resection of a mass and were analyzed for absolute levels of SLPI and PGRN using ELISA assays. Receiver-operator characteristic curves were generated for SLPI and PGRN. Median follow-up was 48 months.Results: Absolute levels of SLPI were significantly elevated in patients with EOC compared with benign disease and predicted the presence of EOC (AUC of 0.812; P = 0.04); SLPI remained elevated in the subset of patients with normal CA-125. PGRN levels were not significantly increased in patients with early-stage or late-stage EOC as a whole, but an increase in PGRN levels was associated with decreased overall survival in advanced EOC.Conclusions: SLPI levels are elevated in EOC, and SLPI shows promise as a diagnostic biomarker for patients with both elevated and normal CA-125 levels. An increase in PGRN is associated with decreased overall survival.Impact: SLPI is elevated in EOC and warrants investigation in a screening study in women at risk for EOC. Cancer Epidemiol Biomarkers Prev; 22(10); 1730–5. ©2013 AACR.

PDF - 93K, Receiver-operator curve for SLPI in early stage EOC versus control patients (A) and advanced stage EOC versus control patients (B).

Supplementary Methods, Tables, and Figures

Purpose: Ovarian cancer has a high recurrence and mortality rate. A barrier to improved outcomes includes a lack of accurate models for preclinical testing of novel therapeutics.Experimental Design: Clinically relevant, patient-derived tumorgraft models were generated from sequential patients and the first 168 engrafted models are described. Fresh ovarian, primary peritoneal, and fallopian tube carcinomas were collected at the time of debulking surgery and injected intraperitoneally into severe combined immunodeficient mice.Results: Tumorgrafts demonstrated a 74% engraftment rate with microscopic fidelity of primary tumor characteristics. Low-passage tumorgrafts also showed comparable genomic aberrations with the corresponding primary tumor and exhibit gene set enrichment of multiple ovarian cancer molecular subtypes, similar to patient tumors. Importantly, each of these tumorgraft models is annotated with clinical data and for those that have been tested, response to platinum chemotherapy correlates with the source patient.Conclusions: Presented herein is the largest known living tumor bank of patient-derived, ovarian tumorgraft models that can be applied to the development of personalized cancer treatment. Clin Cancer Res; 20(5); 1288–97. ©2014 AACR.

PDF - 118K, Scatter plot comparing CA-125 concentrations and SLPI concentrations in control, early EOC, and advanced EOC patients. The dashed line shows the institutional upper limit of normal for CA-125 (35 units/ml).

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Purpose:Clear cell ovarian carcinoma (CCOC) is an aggressive disease that often demonstrates resistance to standard chemotherapies. Approximately 25% of patients with CCOC show a strong APOBEC mutation signature. Here, we determine which APOBEC3 enzymes are expressed in CCOC, establish clinical correlates, and identify a new biomarker for detection and intervention.Experimental Designs:APOBEC3 expression was analyzed by IHC and qRT-PCR in a pilot set of CCOC specimens (n = 9 tumors). The IHC analysis of APOBEC3B was extended to a larger cohort to identify clinical correlates (n = 48). Dose-response experiments with platinum-based drugs in CCOC cell lines and carboplatin treatment of patient-derived xenografts (PDXs) were done to address mechanistic linkages.Results:One DNA deaminase, APOBEC3B, is overexpressed in a formidable subset of CCOC tumors and is low or absent in normal ovarian and fallopian tube epithelial tissues. High APOBEC3B expression associates with improved progression-free survival (P = 0.026) and moderately with overall survival (P = 0.057). Cell-based studies link APOBEC3B activity and subsequent uracil processing to sensitivity to cisplatin and carboplatin. PDX studies extend this mechanistic relationship to CCOC tissues.Conclusions:These studies demonstrate that APOBEC3B is overexpressed in a subset of CCOC and, contrary to initial expectations, associated with improved (not worse) clinical outcomes. A likely molecular explanation is that APOBEC3B-induced DNA damage sensitizes cells to additional genotoxic stress by cisplatin. Thus, APOBEC3B is a molecular determinant and a candidate predictive biomarker of the therapeutic response to platinum-based chemotherapy. These findings may have broader translational relevance, as APOBEC3B is overexpressed in many different cancer types.

Additional materials.

Purpose: Here we assess whether molecular subtyping identifies biological features of tumors that correlate with survival and surgical outcomes of high-grade serous ovarian cancer (HGSOC).Experimental Design: Consensus clustering of pooled mRNA expression data from over 2,000 HGSOC cases was used to define molecular subtypes of HGSOCs. This de novo classification scheme was then applied to 381 Mayo Clinic HGSOC patients with detailed survival and surgical outcome information.Results: Five molecular subtypes of HGSOC were identified. In the pooled dataset, three subtypes were largely concordant with prior studies describing proliferative, mesenchymal, and immunoreactive tumors (concordance > 70%), and the group of tumors previously described as differentiated type was segregated into two new types, one of which (anti-mesenchymal) had downregulation of genes that were typically upregulated in the mesenchymal subtype. Molecular subtypes were significantly associated with overall survival (P < 0.001) and with rate of optimal surgical debulking (≤1 cm, P = 1.9E−4) in the pooled dataset. Among stage III-C or IV Mayo Clinic patients, molecular subtypes were also significantly associated with overall survival (P = 0.001), as well as rate of complete surgical debulking (no residual disease; 16% in mesenchymal tumors compared with >28% in other subtypes; P = 0.02).Conclusions: HGSOC tumors may be categorized into five molecular subtypes that associate with overall survival and the extent of residual disease following debulking surgery. Because mesenchymal tumors may have features that were associated with less favorable surgical outcome, molecular subtyping may have future utility in guiding neoadjuvant treatment decisions for women with HGSOC. Clin Cancer Res; 23(15); 4077–85. ©2017 AACR.

Table S1 has RTqPCR primers and probe information. Table S2 has clinical information for the Dutch cohort. Table S3 has a summary of TCGA samples used in our analyses. Figure S1 has RTqPCR assay validation data. Figure S2 shows clinical correlations of APOBEC3G expression in independent cohorts.

PDF file - 481K, Table S1: Cell line information Table S2: Quantitative PCR primer and probe sequences. Table S3: Non-malignant tissues tested Table S4: Early stage serous ovarian tumors used in sequence analyses Table S5: Additional ovarian tumor specimens analyzed Table S6: Microarray analysis of APOBEC3 and select control gene probe sets in ovarian TCGA data Figure S1: Polynucleotide cytosine deaminase expression in ovarian cell lines. Figure S2: Endogenous DNA deaminase activity in APOBEC3B high and low cell lines. Figure S3: Endogenous APOBEC3B activity on alternate dinucleotide substrates. Figure S4: Polynucleotide cytosine deaminase expression in ovarian primary samples. Figure S5: Polynucleotide cytosine deaminase expression in ovarian TCGA samples by RNAseq.