Larson, Melissa C.

PURPOSE: Patients' concerns regarding clinical trial (CT) participation include apprehension about side effects, quality of life (QoL), financial burden, and quality of care. METHODS: We prospectively evaluated the experience of patients with multiple myeloma or lymphoma who were treated on CTs (CT group, n = 35) versus patients treated with standard approaches (non-CT group, n = 88) focusing on QoL, financial burden of care, and patients' perception of quality of care over a 1-year period. RESULTS: There were no significant differences in any of the patient-reported outcomes in CT versus non-CT groups. We observed an initial decline in overall QoL in the first 3 months across both groups, driven primarily by physical and functional well-being. QoL gradually improved and was above baseline by month 12. Patients reported highest improvement in the functional well-being subdomain. Patients in both groups reported high satisfaction with the quality of care received, and there were no differences in overall satisfaction, communication with team, or access to care. At baseline, 16%-19% of patients reported financial burden, which increased to a peak of 33% in the CT group and to 49% in the non-CT group over the course of 1 year. There was no significant difference in financial burden in the two groups overall. Most of the patients reported getting all the care that was deemed medically necessary in both groups. However, a significant proportion of patients reported having to make other kinds of financial sacrifices because of their cancer (CT group: 33% of patients at baseline and 21%-40% over 1 year; non-CT group: 19% at baseline and 25%-36% over 1 year). CONCLUSION: Patients treated on CTs reported comparable QoL and quality of care with the non-CT group. A high proportion of patients reported financial burden over time in both groups. Our findings can serve as a guide to educate patients regarding CT participation and highlight the need to address the significant financial burden experienced by patients with cancer.

The poly(ADP-ribose) binding protein CHFR regulates cellular responses to mitotic stress. The deubiquitinase UBC13, which regulates CHFR levels, has been associated with better overall survival in paclitaxel-treated ovarian cancer. Despite the extensive use of taxanes in the treatment of ovarian cancer, little is known about expression of CHFR itself in this disease. In the present study, tissue microarrays containing ovarian carcinoma samples from 417 women who underwent initial surgical debulking were stained with anti-CHFR antibody and scored in a blinded fashion. CHFR levels, expressed as a modified H-score, were examined for association with histology, grade, time to progression (TTP) and overall survival (OS). In addition, patient-derived xenografts from 69 ovarian carcinoma patients were examined for CHFR expression and sensitivity to paclitaxel monotherapy. In clinical ovarian cancer specimens, CHFR expression was positively associated with serous histology (p = 0.0048), higher grade (p = 0.000014) and higher stage (p = 0.016). After correction for stage and debulking, there was no significant association between CHFR staining and overall survival (p = 0.62) or time to progression (p = 0.91) in patients with high grade serous cancers treated with platinum/taxane chemotherapy (N = 249). Likewise, no association between CHFR expression and paclitaxel sensitivity was observed in ovarian cancer PDXs treated with paclitaxel monotherapy. Accordingly, differences in CHFR expression are unlikely to play a major role in paclitaxel sensitivity of high grade serous ovarian cancer.

Abstract Background: Event-free survival at 24 months (EFS24) is an important clinical endpoint in newly diagnosed diffuse large B-cell lymphoma and follicular lymphoma, and patients who achieve EFS24 with frontline immunochemotherapy have minimal loss of lifetime compared to age- and sex-matched general population. However, the prognostic role of EFS24 in mantle cell lymphoma (MCL) has not been well studied, possibly due to the perception of continued relapse pattern and poor survival outcome of MCL historically. A recent study from our group demonstrated evolving frontline therapy pattern from 2002-2009 ("Era 1") to 2010-2015 ("Era 2") which was associated with improved EFS and overall survival (OS) in Era 2. In the current study, we sought to explore the prognostic role of EFS24 in the two eras. Methods: Patients with newly diagnosed MCL from 9/2002 through 6/2015 were identified from Molecular Epidemiology Resource (MER), a prospective cohort study of the University of Iowa/Mayo Clinic Lymphoma SPORE. OS was defined as time from diagnosis (or the EFS24 defining event where applicable) to death from any cause and was analyzed using the Kaplan-Meier method. Expected OS accounting for age and sex was generated in R by using the general US population as the reference group. Observed versus expected OS was summarized by using standardized mortality ratio (SMR) and 95% confidence intervals (CI) of observed to expected deaths. Cumulative incidence of lymphoma-specific death was analyzed using Gray's test, with deaths from all other causes treated as competing events. Results: A total of 343 patients were included, 175 from Era 1 (median follow-up 13.0 years) and 168 from Era 2 (median follow-up 6.9 years). Age, sex and simplified MIPI score were similar between the 2 groups. Patients diagnosed in Era 2 had better OS, with a 5-year OS of 68.4% vs 59.2% (simplified MIPI-adjusted hazard ratio 0.68, 95% CI 0.50-0.93). Patients diagnosed in both eras had inferior OS compared to the general population, with an SMR of 3.26 (95% CI 2.70-3.89, P<0.001) in Era 1 and 2.63 (95% CI 2.03-3.36, P<0.001) in Era 2. Patients diagnosed in Era 1 who achieved EFS24 (n=98) still had inferior OS compared to the general population, with an SMR of 2.23 (95% CI 1.67-2.92, P<0.001) (Figure 1A). In contrast, for patients diagnosed in Era 2 who achieved EFS24 (n=99), no evidence of OS difference was found compared to the general population, with an SMR of 1.31 (95% CI 0.78-2.07, P=0.31) (Figure 1B). The primary cause of death after diagnosis was lymphoma-related for patients diagnosed in both eras. The 5-year rate of lymphoma-related death was 28.8% in Era 1 and 20.5% in Era 2. In patients who were diagnosed in Era 1 and achieved EFS24, the primary cause of death after achieving EFS24 remained to be lymphoma-related, with a 5-year rate of 19.8% compared to 6.2% for lymphoma-unrelated causes (Figure 1C). In contrast, in patients who were diagnosed in Era 2 and achieved EFS24, the rate of lymphoma-related death was no longer higher than that of lymphoma-unrelated death, with a 5-year rate of 2.1% vs 5.5%, respectively (Figure 1D). In a sensitivity analysis restricted to only patients who received standard frontline immunochemotherapy, similar results were obtained. For example, in patients who achieved EFS24, the SMR compared to the general population was 2.89 (95% CI 2.00-4.04, P<0.001) in Era 1 and 0.89 (95% CI 0.43-1.64, P=0.82) in Era 2. The 5-year rates of deaths due to lymphoma-related vs lymphoma-unrelated causes were 20.7% vs 6.2% in Era 1 and 1.3% vs 4.9% in Era 2. Conclusions: MCL survival outcome has improved for patients first diagnosed in the more recent era (2010-2015, compared to 2002-2009), likely due to improved frontline therapy as well as better salvage treatments (such as lenalidomide and BTK inhibitors). In the more recent treatment era, patients who achieved EFS24 had survival approaching the age- and sex-matched general population. In addition, these patients had a low risk of dying from lymphoma and were more likely to die from other causes. Longer follow-up (e.g., in Era 2) and external validation in other series are necessary to confirm the prognostic role of EFS24. With more efficacious salvage treatment options and likely continued improvement of OS in MCL, EFS24 may become an important clinical endpoint in frontline therapy of MCL. Figure 1 Figure 1. Disclosures Wang: TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; Eli Lilly: Membership on an entity's Board of Directors or advisory committees; Novartis: Research Funding; InnoCare: Research Funding; LOXO Oncology: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Research Funding; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding; MorphoSys: Research Funding. Maurer: Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; Nanostring: Research Funding. Link: Novartis, Jannsen: Research Funding; Genentech/Roche: Consultancy, Research Funding; MEI: Consultancy. Farooq: Kite, a Gilead Company: Honoraria. Paludo: Karyopharm: Research Funding. Witzig: Celgene/BMS, Acerta Pharma, Kura Oncology, Acrotech Biopharma, Karyopharm Therapeutics: Research Funding; Karyopharm Therapeutics, Celgene/BMS, Incyte, Epizyme: Consultancy, Membership on an entity's Board of Directors or advisory committees. Habermann: Seagen: Other: Data Monitoring Committee; Tess Therapeutics: Other: Data Monitoring Committee; Incyte: Other: Scientific Advisory Board; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Cerhan: Regeneron Genetics Center: Other: Research Collaboration; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: Research Funding; Genentech: Research Funding. Nowakowski: Celgene, NanoString Technologies, MorphoSys: Research Funding; Celgene, MorphoSys, Genentech, Selvita, Debiopharm Group, Kite/Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees.

Abstract Introduction: Low-grade B-cell lymphomas (LGBCL), aside from follicular lymphoma and chronic lymphocytic leukemia/small lymphocytic lymphoma, account for approximately 10% of B-cell non-Hodgkin lymphomas and consist of several subtypes. While a majority of LGBCL cases have an overall favorable prognosis, we have previously shown that cases who have an event (relapse or progression, transformation, or re-treatment) within 24 months of diagnosis (EFS24) have an inferior overall survival (OS) compared to those achieving EFS24 (Tracy et al., AJH 2019;94:658-66). However, the underlying biological characteristics associated with early failure and aggressive disease across LGBCL subtypes are unknown. In this study, we used matched transcriptomic, genomic, and immune profiling data from LGBCL cases, the largest cohort to date, and asked whether there were unique biological phenotypes across different LGBCL subtypes and whether we could identify signatures associated with aggressive LGBCL. Validation of the prognostic utility of this signature was performed on a previously published, independent cohort of 63 pre-treatment LGBCL cases. Methods: Tumors from 64 newly diagnosed LGBCL patients from the Molecular Epidemiology Resource of the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence were included in this study (SMZL (n = 48), NMZL (n = 6), LPL (n = 5), B-NOS (n = 3), EMZL (n = 2)). RNA sequencing (RNAseq) data from 61 LGBCL tumors and 5 benign CD19+CD27+ memory B samples was subjected to NMF clustering to define groups. Differential expression and pathway analysis were used to identify biological characteristics of each cluster. CIBERSORT was used to identify immune cells in the tumor microenvironment. Whole exome sequencing (WES) was performed on 61 tumor-normal pairs. Singscore was used to assign a single score per patient representing gene expression of the survival-associated transcriptomic signature identified in this study. Results: NMF analysis of RNAseq data identified 5 clusters of patients, denoted LGBCL1-5 (Fig 1A). Patients from the same diagnostic subtype did not exclusively cluster together, with all LGBCL clusters comprised of patients from multiple subtypes (Fig 1B). Exploring the association between patient cluster and outcome, we observed significantly inferior event-free survival (EFS) (HR 2.24; 95% CI 1.01-4.98) and overall survival (OS) (HR 5.59; 95% CI 2.00-15.63) in LGBCL5 patients compared to LGBCL1-4 (Fig 1C). In addition, 80% of the transformation cases in our cohort were classified as LGBCL5 (Fig 1D). Differential expression and pathway analysis showed distinct processes significantly upregulated in each cluster (FDR < 0.05), with LGBCL5 demonstrating enrichment of cell cycle and mitosis pathways. CIBERSORT identified increased immune cell content in LGBCL3 and LGBCL5 compared to other clusters, with high frequencies of mast cells in both (p = 0.0002), increased CD8 T cells in LGBCL3 (p < 0.0001), and increased T follicular helper cells in LGBCL5 (p = 0.004). WES identified previously reported alterations in NOTCH, NFkB, and chromatin remodeling pathways and novel variants in LGBCL, including mutations in HNRNPK, CLTC, HLA-A, HLA-B and HLA-C. Assessment of alterations by cluster showed significant enrichment of TNFAIP3 (OR 5.54; 95% CI 1.20-28.14) and BCL2 alterations (OR 5.49; 95% CI 1.07-32.02) in LGBCL5 cluster. Finally, we identified a cell cycle-related transcriptomic signature of 108 genes upregulated in LGBCL5 and EFS24 failure cases. Cases with high expression of this signature showed significantly inferior EFS (HR 14.25; 95% CI 4.90-41.38) and OS (HR 7.82; 95% CI 2.40-25.44) compared to cases with low expression in our discovery cohort. This observation was reproduced in an independent validation cohort, where patients with high expression of this signature demonstrated significantly inferior EFS (HR 5.70; 95% CI 1.49-21.79) and OS (HR 10.07; 95% CI 2.00-50.61). Conclusions: In this study, we are the first to define mechanisms of pathogenesis in LGBCL with shared transcriptomic, genomic, and immune profiles present across LGBCL subtypes. We then further defined a gene expression signature associated with inferior patient outcome, with application of this signature to an independent validation cohort demonstrating proof of concept and utility of this signature as a prognostic marker in LGBCL patients. Figure 1 Figure 1. Disclosures Maurer: Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Research Funding; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; Nanostring: Research Funding. Paludo: Karyopharm: Research Funding. Habermann: Tess Therapeutics: Other: Data Monitoring Committee; Morphosys: Other: Scientific Advisory Board; Incyte: Other: Scientific Advisory Board; Seagen: Other: Data Monitoring Committee; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Link: MEI: Consultancy; Genentech/Roche: Consultancy, Research Funding; Novartis, Jannsen: Research Funding. Rimsza: NanoString Technologies: Other: Fee-for-service contract. Ansell: Bristol Myers Squibb, ADC Therapeutics, Seattle Genetics, Regeneron, Affimed, AI Therapeutics, Pfizer, Trillium and Takeda: Research Funding. Cerhan: Genentech: Research Funding; Regeneron Genetics Center: Other: Research Collaboration; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: Research Funding. Novak: Celgene/BMS: Research Funding.

Abstract Background: Follicular lymphoma (FL) exhibits significant clinical, cellular, molecular, and genetic heterogeneity. Our understanding of FL biology and molecular classifications of FL, to date, has largely been driven by pathologic classification (Grade 1-3b), genetic classification (m7-FLIPI), or gene expression profiling (IR-1/2; Huet-23), along with limited studies on small cohorts or targeted panels. In order to further understand the biological underpinnings and complexity of FL, large-scale and integrated whole exome sequencing (WES) and RNA sequencing (RNAseq) studies are needed. Using a highly-annotated cohort of 93 FL tumors with matched RNAseq, WES, and CyTOF data, we have explored the transcriptomic signature of purified FL B cells and identified unique molecular subsets that are defined by distinct pathway activation, immune content, and genomic signatures. Methods: Frozen cell suspensions from 93 untreated FL (Grade 1-3b) patients' tumor biopsies, enrolled in the University of Iowa/Mayo Clinic Lymphoma SPORE, were used for the study. DNA was isolated from whole tumor cell suspensions, and RNA was isolated from both whole tumor and B cell-enriched cell suspensions. RNAseq and WES were performed in the Mayo Clinic Genome Analysis Core. RNAseq and WES data were processed using a standard pipeline and novel driver genes were identified using Chasm+ driver analysis. Copy number variants were identified from WES data using GISTIC 2.0. NMF clustering and single sample gene set testing for B cell lineage and tumor microenvironment (TME) signatures were performed in R using the NMF and singscore packages. Results: Unsupervised clustering of RNAseq data identified three distinct expression programs which separated patient B cell samples into 3 groups: Group 1 (G1, n=20), Group 2 (G2, n=24), Group 3 (G3, n=43). While no clinical attributes were defined by any single group, G1 consisted of cases that had less aggressive characteristics (63% Stage I-II, 79% FLIPI 0-1). To identify unique transcriptional pathways driving the three expression programs, we scored gene level contributions to NMF expression programs and employed gene set enrichment analysis. This revealed significant pathway association with type-I IFN signaling (G1), DNA repair and stress response (G2), and epigenetic modulation (G3) as differentiating programs between the 3 groups (FDR q<0.001). VIPER master regulator activity inferencing revealed that these pathways were likely being controlled by differential activity in NF-kB, IRFs, STAT1, BCL6, and FOXO1. Each program significantly enriched for, but were not defined by, portions of specific germinal center programs, such as pre-memory (G1), light-zone-to-dark-zone transition (G2), and a pre-light-zone intermediate (G3). We next assessed the connection between B cell programs and the tumor microenvironment (TME) using available paired CyTOF data on 67 cases, which revealed an active TME in G1, with an abundance of CD8 T cell and NK cell populations, a wide variety of immune content in G2 that consisted mostly of Tfh and myeloid cells, and a poorly populated immune compartment in G3 compared to G1 and G2. Finally, somatic driver mutations and copy number alterations from WES were identified and associated with the three clusters. The three groups distinguished themselves by significant enrichment of copy number alterations (TNFAIP3-loss , 1q23-gain, 1q32-gain) in G2, while 10q-loss and mutations in BCL2 and chromatin modifiers (KMT2D and CREBBP) enriched in G3. G1, overall, had lower alteration burden and had weak associations with any specific alterations, suggesting an alternative mechanism for driving the G1 program. Conclusion: In this study, we have identified three unique FL tumor B cell groups, defined by specific transcriptional programs. Pathways such as inflammation, DNA damage response, and chromatin modification contribute most to the differences between B cell samples and group membership. Additionally, each program associated with specific genetic events as well as TME composition, highlighting potential drivers of these B cell states. This study improves the understanding of the mechanisms driving FL tumors and motivates further investigation into transcriptional consequences of genetic events as well as potential tumor intrinsic factors that may influence the TME. Figure 1 Figure 1. Disclosures Maurer: BMS: Research Funding; Genentech: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding. Rimsza: NanoString Technologies: Other: Fee-for-service contract. Link: MEI: Consultancy; Genentech/Roche: Consultancy, Research Funding; Novartis, Jannsen: Research Funding. Habermann: Tess Therapeutics: Other: Data Monitoring Committee; Seagen: Other: Data Monitoring Committee; Incyte: Other: Scientific Advisory Board; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Ansell: Bristol Myers Squibb, ADC Therapeutics, Seattle Genetics, Regeneron, Affimed, AI Therapeutics, Pfizer, Trillium and Takeda: Research Funding. King: Celgene/BMS: Research Funding. Cerhan: Genentech: Research Funding; Regeneron Genetics Center: Other: Research Collaboration; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: Research Funding. Novak: Celgene/BMS: Research Funding.

Abstract PARP inhibitors (PARPi) have activity in homologous recombination (HR) repair-deficient, high-grade serous ovarian cancers (HGSOC). However, even responsive tumors develop PARPi resistance, highlighting the need to delay or prevent the appearance of PARPi resistance. Here, we showed that the ALK kinase inhibitor ceritinib synergizes with PARPis by inhibiting complex I of the mitochondrial electron transport chain, which increases production of reactive oxygen species (ROS) and subsequent induction of oxidative DNA damage that is repaired in a PARP-dependent manner. In addition, combined treatment with ceritinib and PARPi synergized in HGSOC cell lines irrespective of HR status, and a combination of ceritinib with the PARPi olaparib induced tumor regression more effectively than olaparib alone in HGSOC patient-derived xenograft (PDX) models. Notably, the ceritinib and olaparib combination was most effective in PDX models with preexisting PARPi sensitivity and was well tolerated. These findings unveil suppression of mitochondrial respiration, accumulation of ROS, and subsequent induction of DNA damage as novel effects of ceritinib. They also suggest that the ceritinib and PARPi combination warrants further investigation as a means to enhance PARPi activity in HGSOC, particularly in tumors with preexisting HR defects. Significance: The kinase inhibitor ceritinib synergizes with PARPi to induce tumor regression in ovarian cancer models, suggesting that ceritinib combined with PARPi may be an effective strategy for treating ovarian cancer.

Abstract Background: Vaccinations have been hypothesized to play a role in lymphoma etiology, but there are few studies, mixed results, and limited data on lymphoma subtypes. Herein, we investigate the association of vaccinations with risk of major lymphoma subtypes. Methods: We studied 2,461 lymphoma cases and 2,253 controls enrolled from 2002 to 2014. Participants self-reported history of vaccinations against hepatitis A, hepatitis B, yellow fever, and influenza. Polytomous logistic regression was used to estimate OR and 95% confidence intervals (CI), adjusting for potential confounders. Results: After multivariable adjustment, vaccination against influenza was inversely associated with lymphoma (OR = 0.82; 95% CI, 0.66–1.02), which was stronger for last vaccination 1+ years before enrollment (OR = 0.71; 95% CI, 0.56–0.91) and for >5 influenza vaccinations (OR = 0.56; 95% CI, 0.46–0.68). Ever vaccination against hepatitis A (OR = 0.81; 95% CI, 0.66–1.00) but not hepatitis B (OR = 0.97; 95% CI, 0.81–1.18) was associated with lymphoma risk, although more recent vaccinations were inversely associated with lymphoma risk for both hepatitis A (<6 years before enrollment, OR = 0.56; 95% CI, 0.40–0.77) and hepatitis B (<9 years before enrollment, OR = 0.72; 95% CI, 0.55–0.93). Ever vaccination against yellow fever was inversely associated with risk (OR = 0.73; 95% CI, 0.55–0.96), and this did not vary by time since last vaccination. Although there was no overall statistical evidence for heterogeneity of vaccination history by lymphoma subtype, the only statistically significant inverse associations were observed for influenza and yellow fever vaccinations with diffuse large B-cell and follicular lymphoma. Conclusions: Selected vaccinations were inversely associated with lymphoma risk, with time since last vaccination relevant for some of these vaccines. Impact: Vaccinations against hepatitis A, hepatitis B, yellow fever, and influenza are unlikely to increase lymphoma risk.

AbstractBecause there have been a dvances in frontline treatment for mantle cell lymphoma (MCL) over the last 2 decades, we sought to characterize the changes in frontline treatment patterns and their association with outcomes. Patients with newly diagnosed MCL from September 2002 through June 2015 were enrolled in a prospective cohort study, and clinical characteristics, treatment, and clinical outcomes were compared between patients diagnosed from 2002 to 2009 (Era 1) compared with 2010 to 2015 (Era 2). Patient age, sex, and simplified MCL International Prognostic Index (sMIPI) score were similar between the 2 groups. In patients age 65 years or younger, there was less use of rituximab plus hyperfractionated cyclophosphamide, vincristine, doxorubicin, and dexamethasone (R-Hyper-CVAD) (16.1% vs 8.8%) but more use of rituximab plus maximum-strength cyclophosphamide, doxorubicin, vincristine, and prednisone (R-maxi-CHOP) alternating with rituximab plus high-dose cytarabine (R-HiDAC), also known as the Nordic regimen, and R-CHOP alternating with rituximab plus dexamethasone, high-dose cytarabine, and cisplatin (R-DHAP) (1.1% vs 26.4%) and less use of R-CHOP or R-CHOP-like regimens (64.5% vs 35.2%) but more use of R-bendamustine (0% vs 12.1%) in Era 2 (P < .001). These changes were associated with improved event-free survival (EFS; 5-year EFS, 34.3% vs 50.0%; P = .010) and overall survival (OS; 5-year OS, 68.8% vs 81.6%; P = .017) in Era 2. In patients older than age 65 years, there was less use of R-CHOP or R-CHOP-like therapy (39.0% vs 14.3%) and nonstandard systemic therapy (36.6% vs 13.0%) but more use of R-bendamustine (0% vs 49.4%). These changes were associated with a trend for improved EFS (5-year EFS, 25.4% vs 37.5%; P = .051) in Era 2. The shift from R-CHOP or R-CHOP-like regimens to R-bendamustine was associated with improved EFS (5-year EFS, 25.0% vs 44.6%; P = .008) in Era 2. Results from this prospective cohort study provide critical real-world evidence for improved outcomes with evolving frontline patterns of care in patients with MCL.

Abstract Background: Many loci have been found to be associated with risk of epithelial ovarian cancer (EOC). However, although there is considerable variation in progression-free survival (PFS), no loci have been found to be associated with outcome at genome-wide levels of significance. Methods: We carried out a genome-wide association study (GWAS) of PFS in 2,352 women with EOC who had undergone cytoreductive surgery and standard carboplatin/paclitaxel chemotherapy. Results: We found seven SNPs at 12q24.33 associated with PFS (P < 5 × 10–8), the top SNP being rs10794418 (HR = 1.24; 95% CI, 1.15–1.34; P = 1.47 × 10–8). High expression of a nearby gene, ULK1, is associated with shorter PFS in EOC, and with poor prognosis in other cancers. SNP rs10794418 is also associated with expression of ULK1 in ovarian tumors, with the allele associated with shorter PFS being associated with higher expression, and chromatin interactions were detected between the ULK1 promoter and associated SNPs in serous and endometrioid EOC cell lines. ULK1 knockout ovarian cancer cell lines showed significantly increased sensitivity to carboplatin in vitro. Conclusions: The locus at 12q24.33 represents one of the first genome-wide significant loci for survival for any cancer. ULK1 is a plausible candidate for the target of this association. Impact: This finding provides insight into genetic markers associated with EOC outcome and potential treatment options. See related commentary by Peres and Monteiro, p. 1604