Stern, Marc‐Henri

Abstract Chromosome 5p15.33 harbors several independent association signals which demonstrate antagonistic pleiotropy across cancer types, with causal mechanisms largely unresolved. To identify functional variants and enhancer elements at this locus, we performed statistical fine-mapping followed by massively parallel reporter assays (MPRA) and proliferation based CRISPRi screens. This approach identified eight multi-cancer functional variants (MCFVs) across three GWAS signals. Targeting rs421629 (part of the CLPTM1L signal marked by rs465498) with CRISPRi revealed opposing effects on TERT expression in pancreatic versus lung cancer cells, consistent with the antagonistic pleiotropy observed for this signal. Furthermore, CRISPRi nominated an intronic CLPTM1L variable number tandem repeat (VNTR) as a potent enhancer. Long-read sequencing established VNTR polymorphisms as potential causal variants for the rs465498 signal. We showed that Hippo-pathway transcription factors mediate VNTR enhancer activity in lung and pancreatic cancer cells. Together, these findings indicate that cancer susceptibility at 5p15.33 may be mediated by both SNPs and VNTRs and provide an integrated framework for resolving complex pleiotropic loci.

Abstract Background Mendelian randomization (MR) studies are susceptible to metadata errors (e.g. incorrect specification of the effect allele column) and other analytical issues that can introduce substantial bias into analyses. We developed a quality control (QC) pipeline for the Fatty Acids in Cancer Mendelian Randomization Collaboration (FAMRC) that can be used to identify and correct for such errors. Methods We collated summary association statistics from fatty acid and cancer genome-wide association studies (GWAS) and subjected the collated data to a comprehensive QC pipeline. We identified metadata errors through comparison of study-specific statistics to external reference data sets (the National Human Genome Research Institute-European Bioinformatics Institute GWAS catalogue and 1000 genome super populations) and other analytical issues through comparison of reported to expected genetic effect sizes. Comparisons were based on three sets of genetic variants: (i) GWAS hits for fatty acids, (ii) GWAS hits for cancer and (iii) a 1000 genomes reference set. Results We collated summary data from 6 fatty acid and 54 cancer GWAS. Metadata errors and analytical issues with the potential to introduce substantial bias were identified in seven studies (11.6%). After resolving metadata errors and analytical issues, we created a data set of 219 842 genetic associations with 90 cancer types, generated in analyses of 566 665 cancer cases and 1 622 374 controls. Conclusions In this large MR collaboration, 11.6% of included studies were affected by a substantial metadata error or analytical issue. By increasing the integrity of collated summary data prior to their analysis, our protocol can be used to increase the reliability of downstream MR analyses. Our pipeline is available to other researchers via the CheckSumStats package (https://github.com/MRCIEU/CheckSumStats).

Abstract T-cell prolymphocytic leukemia (T-PLL) is a rare disease with an aggressive clinical course and a median overall survival of less than three years. Although almost 75% of T-PLL patients are reported to harbor translocations causing the activation of the proto-oncogene TCL1A, T-PLL is genetically heterogenous: most T-PLL patients also have mutations or deletions in the ATM gene and the genes involved in the JAK-STAT pathway are mutated in 76% of cases. There is an urgent need for more rational based therapies, but clinical trials are difficult to perform due to the rareness of the disease. Here, we systematically explored the diversity of drug responses in T-PLL patient samples ex vivo using a drug sensitivity and resistance testing (DSRT) system including 306 oncology drugs (approved or investigational). We also aimed to determine any associations between the genetic aberrations and drug sensitivities in T-PLL patients. Primary mononuclear cells were gathered from 30 T-PLL patients for drug testing. Cells were plated in 384-well plates and subjected to the 306 substances using a 10,000-fold concentration range. After 72 hours, cell viabilities were measured, the results were depicted as dose-response curves for each compound, and differential drug sensitivity scores (sDSS), representing leukemia-specific responses, were computed by comparing patient samples to healthy donors. Drug response profiles across patients were clustered and visualized by hierarchical clustering. The subgroups resulting from the clustering were statistically compared using a two-sample t-test to understand which drug classes were driving the grouping. To delineate heterogeneous pathway dependencies, drug sensitivities were correlated with somatic genetic variants and recurrent chromosomal aberrations. Genetic characterization was performed by targeted amplicon sequencing of tumor cells to profile known recurrent genetic variants (STAT5b, IL2RG, JAK1, JAK3, ATM). Information on chromosomal aberrations (TCL1A translocations, ATM deletions) was derived from parallel clinicopathologic databases. Amplicon sequencing revealed that 70% of T-PLL patients (21/30) harbored a mutation in genes involved in the JAK-STAT pathway (JAK1, JAK3, STAT5b or IL2RG). The most prevalent mutation led to an M511I amino acid exchange in the JAK3 protein (26% of patients). Interestingly, the STAT5b mutations (5/30) did not coexist with any of the JAK mutations in our cohort. Based on DSRT analysis, all T-PLL samples were sensitive to the CDK-inhibitor SNS-032 and the anti-cancer antibiotic actinomycin D. Next, we clustered patients using sDSS values for all 306 different substances, and this showed that patient samples could be divided into 3 main groups, based on their drug responses (Figure). According to two-sample t-test, the grouping was driven by the selective sensitivities of Groups II and III to HDAC inhibitors (belinostat, panobinostat, quisinostat, CUDC-101 and vorinostat) and the selective sensitivity of Group III to PI3K/AKT/mTOR inhibitors (AZD-8055, MK-2206, apitolisib, dactolisib, PF-04691502, ZSTK474, and omipalisib), HSP90 inhibitors (BIIB021, luminespib, alvespimycin, and tanespimycin) as well as JAK inhibitors (ruxolitinib, momelotinib, tofacitinib, gandotinib). Group I samples were on the other hand relatively resistant to these classes of drugs. Surprisingly, despite the prevalence of the signature event of activation of TCL1 (the established AKT coactivator) in nearly all cases, only a subset of cases (group III) responded to PI3K/AKT/mTOR inhibitors. Strikingly, the grouping of selective responses to HDAC, JAK, PI3K/mTOR/Akt and HSP90 inhibitors did not link to the presence of JAK/STAT mutations, TCL1A translocations, or ATM deletion status. Ex vivo drug screening of primary T-PLL samples revealed heterogenous selective drug responses in specific drug classes (such as HDAC-, JAK-, HSP90- and PI3K/Akt/mTOR-inhibitors). Surprisingly, the drug response patterns did not correlate with known recurrent genetic aberrations suggesting that sequencing for recurrent genetic biomarkers cannot easily be turned into effective therapeutic strategies in T-PLL, and that further elucidation of the biological pathways driving T-PLL is needed. Figure 1. Mutation status and clustering of HDAC-, PI3K/mTOR/Akt-, HSP90-, and JAK-inhibitor responses in PLL patients based on sDSS values Figure 1. Mutation status and clustering of HDAC-, PI3K/mTOR/Akt-, HSP90-, and JAK-inhibitor responses in PLL patients based on sDSS values Disclosures Koschmieder: Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel reimbursement for scientific conferences, Research Funding; Novartis Foundation: Research Funding; Baxalta/CTI: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Janssen Cilag: Other: Travel reimbursement for scientific conferences ; Pfizer: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel reimbursement for scientific conferences. Wennerberg:Pfizer: Honoraria, Research Funding. Ding:Merek: Research Funding. Mustjoki:Pfizer: Honoraria, Research Funding; Bristol Myers Squibb: Honoraria, Research Funding; Novartis: Honoraria, Research Funding.