Citations

Key Points Using the largest set of patients with newly diagnosed myeloma, we identified 63 mutated driver genes. We identified oncogenic dependencies, particularly relating to primary translocations, indicating a nonrandom accumulation of genetic hits.

Abstract Purpose: Small cell carcinoma of the ovary, hypercalcemic type (SCCOHT) is a rare, aggressive ovarian cancer in young women that is universally driven by loss of the SWI/SNF ATPase subunits SMARCA4 and SMARCA2. A great need exists for effective targeted therapies for SCCOHT. Experimental Design: To identify underlying therapeutic vulnerabilities in SCCOHT, we conducted high-throughput siRNA and drug screens. Complementary proteomics approaches profiled kinases inhibited by ponatinib. Ponatinib was tested for efficacy in two patient-derived xenograft (PDX) models and one cell-line xenograft model of SCCOHT. Results: The receptor tyrosine kinase (RTK) family was enriched in siRNA screen hits, with FGFRs and PDGFRs being overlapping hits between drug and siRNA screens. Of multiple potent drug classes in SCCOHT cell lines, RTK inhibitors were only one of two classes with selectivity in SCCOHT relative to three SWI/SNF wild-type ovarian cancer cell lines. We further identified ponatinib as the most effective clinically approved RTK inhibitor. Reexpression of SMARCA4 was shown to confer a 1.7-fold increase in resistance to ponatinib. Subsequent proteomic assessment of ponatinib target modulation in SCCOHT cell models confirmed inhibition of nine known ponatinib target kinases alongside 77 noncanonical ponatinib targets in SCCOHT. Finally, ponatinib delayed tumor doubling time 4-fold in SCCOHT-1 xenografts while reducing final tumor volumes in SCCOHT PDX models by 58.6% and 42.5%. Conclusions: Ponatinib is an effective agent for SMARCA4-mutant SCCOHT in both in vitro and in vivo preclinical models through its inhibition of multiple kinases. Clinical investigation of this FDA-approved oncology drug in SCCOHT is warranted. Clin Cancer Res; 24(8); 1932–43. ©2018 AACR.

Abstract Background: Chronic inflammation is implicated in pancreatic cancer carcinogenesis. Advanced glycation end-products (AGEs), which are rich in cooked red meat and cigarette smoke, can perpetuate inflammation by binding to the receptor for advanced glycation end products (AGER, also known as RAGE). We hypothesized that genetic variation of the AGEs/AGER pathway affects pancreatic cancer risk by modulating chronic inflammation. Methods: We conducted a two-stage case-control study to examine the association between 96 single nucleotide polymorphisms (SNPs) in 21 genes (GLO1, MSR1, PINK1, KIF17, AGPAT1, AKR1B1, DDOST, CD36, SCARB1, AGER, APP, HMGB1, S100A8, S100B, S100P, S100A12, MMP9, ADAM10, PPARG, NOTCH4, and PDGF) of the AGEs/AGER pathway and risk of pancreatic cancer. The discovery study was conducted in 672 pancreatic cancer cases and 1361 controls ascertained from the Women’s Health Initiative (WHI) Study matched on age, race/ethnicity and study arm. The validation study was conducted in a PANC4 pooled hospital-based case-control study of 1,034 women cases and 989 women controls. A pooled analysis of 1706 cases and 2350 controls was also performed. SNP data were obtained or imputed from the previously performed GWASs. Pancreatic cancer risk was calculated as odds ratios (ORs) and 95% confidence intervals (Cis) using logistic regression models adjusting for age, race/ethnicity, body mass index (BMI), type 2 diabetes, and smoking using an additive genetic model. The potential interaction between individual SNP and red meat intake and cigarette smoking was examined. A false discovery rate (FDR) adjusted q value < 0.20 was considered statistically significant in the pooled analyses. We also conducted logistic-Kernel machine (LKM) test to examine the association between sets of SNP and risk of pancreatic cancer in the pooled dataset. Results: We identified 11 SNPs with raw P values < 0.10 in the discovery stage in the WHI study. However, none of the SNPs was validated in the validation and pooled dataset. The LKM test showed that sets of SNPs in CD36 (P = 9.3E-10), GLO1(P =2.0E-7), and DDOST (P = 2.5E-4) were significantly associated with pancreatic cancer risk. We found an interaction effect between the GLO1 SNP rs6932648 and smoking status (never versus ever) in the WHI study (P for interaction = 0.01) and the pooled dataset (P for interaction = 0.049). In the pooled dataset, the variant T allele of rs6932648 (MAF = 0.12) was associated with increased risk of pancreatic cancer among ever smokers (additive OR = 1.26, 95% CI: 1.02-1.55), but not among never smokers (additive OR = 0.94, 95% CI: 0.78-1.14), compared with the C allele. Summary and Conclusion: We did not observe significant association between any of 96 single SNP and pancreatic cancer risk. However, we found combined set of SNPs in the GLO1, DDOST, and CD36 genes were associated with risk of pancreatic cancer. GLO SNP rs6932648 may modify the association between smoking and risk of pancreatic cancer. GLO1, DDOST, and CD36 are involved in detoxification or clearance of AGEs compounds. Further investigation of AGEs and receptors systems in pancreatic cancer is warranted. Citation Format: Guochong Jia, Kathryn Royse, Hongwei Tang, Donghui Li, Liang Chen, Lesley Tinker, Gloria Petersen, Alison Klein, Peter Richardson, Donna White, Haleh Sangi-Haghapeykar, Hashem B. El-Serag, Li Jiao. Genetic variations in the AGEs/AGER pathway and risk of pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-160. doi:10.1158/1538-7445.AM2017-LB-160

Abstract Introduction: Obesity and diabetes are two major modifiable risk factors for pancreatic cancer. Genetic variation affecting the risk of obesity- and diabetes-related pancreatic cancer have yet to be comprehensively investigated at the genome-wide level. Aims: To identify novel gene-environment interactions (GxE) of obesity/diabetes on pancreatic cancer risk. Methods: We conducted a multi-stage genome-wide gene-diabetes and gene-obesity interaction study using GWAS data of 7095 cases and 10710 controls from Pancreatic Cancer Consortium (PanScan I, PanScan II and PanScan III) and Pancreatic Cancer Case Control Consortium (PanC4). About 870,000 SNPs, having MAF ≥ 0.005 and having been genotyped in at least one study, were analyzed. Missing genotypes were imputed using IMPUTE2 and the Haplotype Reference Consortium (HRC) reference panel. Case-control (CC) and case-only (CO) methods were used for SNP-level GxE analysis. Fixed-, random- and joint-effect models built in “rareGE” R package were used for gene-based GxE analysis. About 20,000 genes that have at least one SNP genotyped were included in the analysis. Age, gender and principal components accounting for subpopulation structure were adjusted in both SNP-level and gene-level analyses. Obesity was defined as body mass index ≥30 mg/m2. Diabetics with <3-year duration were excluded for gene-diabetes interaction analysis. The PanScan I, II, III and PanC4 datasets were analyzed separately followed by meta-analysis of the summary statistics. Fixed-effect and Fisher’s p-value based meta-analysis was applied for SNP-level and gene-level GxE analysis, respectively. Results: No genome-wide significant (P<5 x 10-8 ) interaction at the SNP level was detected in any of the PanScan or PanC4 datasets or in the meta-analysis. A significant interaction of diabetes and FAM63A (family with sequence similarity 63 member A) gene (P<2.5 x 10-6) was observed in the meta-analysis (PFixed= 3.4 x 10-5, PRandom= 1.9 x 10-6, PJoint= 6.8 x 10-7) . This interaction showed nominal significance in all individual dataset analyses. The corresponding P values for this interaction were 4.2 x 10-2, 3.8 x 10-2 and 6.8 x 10-2 in PanScan I, 2.8 x 10-2, 3.9 x 10-2 and 6.5 x 10-2 9.1 x 10-3, 2.2 x 10-4 and 8.8 x 10-6 in PanScan III, and 3.1 x 10-3, 3.4 x 10-3 and 8.8 x 10-3 in PanC4, respectively. Conclusions: In the largest genome-wide GxE analysis for pancreatic cancer to date, we identified a significant diabetes interacting gene in pancreatic cancer, which was consistently replicated in 4 individual GWAS studies. This observation identifies FAM63A as a candidate for future functional studies to understand its role in pancreatic cancer etiology. Further increasing the sample size may unveil additional genetic loci that contribute to the susceptibility to pancreatic cancer via G x E. Citation Format: Hongwei Tang, Lai Jiang, Laufey T. Amundadottir, Harvey A. Risch, Rachael S. Stolzenberg-Solomon, Alison P. Klein, Brian M. Wolpin, Gloria Petersen, Nilanjan Chatterjee, Donghui Li, Peter Kraft, Peng Wei. Genomewide gene-diabetes and gene-obesity interaction scan in 7095 cases and 10710 controls from Pancreatic Cancer Cohort Consortium and Pancreatic Cancer Case Control Consortium [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1295. doi:10.1158/1538-7445.AM2017-1295

Abstract Introduction Pancreatic cancer is a highly lethal malignancy due to late diagnosis and aggressiveness of the tumor. Characterization of high-risk populations for monitoring, intervention and early detection remains challenging. Obesity and diabetes are potentially modifiable contributors to pancreatic cancer. Genetic factors modifying the risk of obesity- and diabetes-induced pancreatic cancer have previously not been fully investigated at the genome-wide level. Hypothesis There are potential genetic factors modifying the associations between obesity/diabetes and pancreatic cancer. Methods Integrating genotype with risk factor data from a GWAS for the Pancreatic Cancer Case Control Consortium, we performed a genome-wide gene-environment interaction (G x E) scan for 457,688 SNPs in a discovery study of 2,028 cases and 2,109 controls using state-of-the-art methods including case-only (CO), case-control (CC), Empirical Hierarchical Bayes (EHB), Empirical-Bayes test (EB) and three 2-step approaches (DG2, EG2, DGEG2). Results We detected a genome-wide significant (P < 1.09 x 10-7 ) interaction of diabetes with rs13061928 (in CNTN4) in CO (P = 8.8 x 10-8 ), which was independent of diabetes in control group (P = 0.08). The minor allele was in a synergism with diabetes on the cancer risk: diabetics carrying GA/AA genotype had a 3.39-fold (95% CI: 2.47-4.65) increased disease risk compared with non-diabetics carrying GG genotype. Consistently, the SNP-diabetes interaction had a high ranking statistic in EHB (No.1) and high p-value ranks for EG2 and DGEG2 methods (No.1, P= 5.44 x 10-5). Conclusions These observations, once validated/confirmed, may help define at-risk subpopulation and initiate targeted intervention and prevention of pancreatic cancer. Genome-wide G x E analysis requires larger sample size than genetic main effects scan; combining the CO method with alternative methods may be the optimal scheme for genome-wide G x E analysis. Citation Format: Hongwei Tang, Eric J. Duell, Harvey A. Risch Risch, Sara H. Olson, H. Bas Bueno-de-Mesquita, Steven Gallinger, Elizabeth A. Holly, Gloria M. Petersen, Paige M. Bracci, Robert R. McWilliams, Mazda Jenab, Elio Riboli, Anne Tjønneland, Marie Christine Boutron-Ruault, Rudolf Kaaks, Dimitrios Trichopoulos, Salvatore Panico, Malin Sund, Petra H. M Peeters, Kay-Tee Khaw, Christopher I Amos, Donghui Li, Peng Wei. Genome-wide gene-diabetes and gene-obesity interaction scan in the pancreatic cancer case control consortium. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2214. doi:10.1158/1538-7445.AM2014-2214

Abstract Background: Genome-wide association studies have identified multiple genetic variants associated with prostate cancer risk which explain a substantial proportion of familial relative risk. These variants can be used to stratify individuals by their risk of prostate cancer. Methods: We genotyped 25 prostate cancer susceptibility loci in 40,414 individuals and derived a polygenic risk score (PRS). We estimated empirical odds ratios (OR) for prostate cancer associated with different risk strata defined by PRS and derived age-specific absolute risks of developing prostate cancer by PRS stratum and family history. Results: The prostate cancer risk for men in the top 1% of the PRS distribution was 30.6 (95% CI, 16.4–57.3) fold compared with men in the bottom 1%, and 4.2 (95% CI, 3.2–5.5) fold compared with the median risk. The absolute risk of prostate cancer by age of 85 years was 65.8% for a man with family history in the top 1% of the PRS distribution, compared with 3.7% for a man in the bottom 1%. The PRS was only weakly correlated with serum PSA level (correlation = 0.09). Conclusions: Risk profiling can identify men at substantially increased or reduced risk of prostate cancer. The effect size, measured by OR per unit PRS, was higher in men at younger ages and in men with family history of prostate cancer. Incorporating additional newly identified loci into a PRS should improve the predictive value of risk profiles. Impact: We demonstrate that the risk profiling based on SNPs can identify men at substantially increased or reduced risk that could have useful implications for targeted prevention and screening programs. Cancer Epidemiol Biomarkers Prev; 24(7); 1121–9. ©2015 AACR.

Key Points High-throughput RNAi screening identified lenalidomide sensitizer genes, including RSK2, RAB, peroxisome, and potassium channel family members. Knockdown or inhibition of RSK2 synergized with lenalidomide to induce myeloma cytotoxicity and downregulation of interferon regulatory factor 4 and MYC.

AbstractTo identify rational therapeutic combinations with cytarabine (Ara-C), we developed a high-throughput, small-interference RNA (siRNA) platform for myeloid leukemia cells. Of 572 kinases individually silenced in combination with Ara-C, silencing of 10 (1.7%) and 8 (1.4%) kinases strongly increased Ara-C activity in TF-1 and THP-1 cells, respectively. The strongest molecular concepts emerged around kinases involved in cell-cycle checkpoints and DNA-damage repair. In confirmatory siRNA assays, inhibition of WEE1 resulted in more potent and universal sensitization across myeloid cell lines than siRNA inhibition of PKMYT1, CHEK1, or ATR. Treatment of 8 acute myeloid leukemia (AML), acute lymphoblastic leukemia (ALL), and chronic myeloid leukemia (CML) cell lines with commercial and the first-in-class clinical WEE1 kinase inhibitor MK1775 confirmed sensitization to Ara-C up to 97-fold. Ex vivo, adding MK1775 substantially reduced viability in 13 of 14 AML, CML, and myelodysplastic syndrome patient samples compared with Ara-C alone. Maximum sensitization occurred at lower to moderate concentrations of both drugs. Induction of apoptosis was increased using a combination of Ara-C and MK1775 compared with using either drug alone. WEE1 is expressed in primary AML, ALL, and CML specimens. Data from this first siRNA-kinome sensitizer screen suggests that inhibiting WEE1 in combination with Ara-C is a rational combination for the treatment of myeloid and lymphoid leukemias.