Supplementary Glioma Gene Mutations from A Hypermutation Phenotype and Somatic MSH6 Mutations in Recurrent Human Malignant Gliomas after Alkylator Chemotherapy

Supplementary Information from A Hypermutation Phenotype and Somatic MSH6 Mutations in Recurrent Human Malignant Gliomas after Alkylator Chemotherapy

Supplementary Tables 1-5 from Novel Candidate Cancer Genes Identified by a Large-Scale Cross-Species Comparative Oncogenomics Approach

Many gene fusions are reported in tumours and for most their role remains unknown. As fusions are used for diagnostic and prognostic purposes, and are targets for treatment, it is crucial to assess their function in cancer. To systematically investigate the role of fusions in tumour cell fitness, we utilized RNA-sequencing data from 1011 human cancer cell lines to functionally link 8354 fusion events with genomic data, sensitivity to >350 anti-cancer drugs and CRISPR-Cas9 loss-of-fitness effects. Established clinically-relevant fusions were identified. Overall, detection of functional fusions was rare, including those involving cancer driver genes, suggesting that many fusions are dispensable for tumour fitness. Therapeutically actionable fusions involving RAF1, BRD4 and ROS1 were verified in new histologies. In addition, recurrent YAP1-MAML2 fusions were identified as activators of Hippo-pathway signaling in multiple cancer types. Our approach discriminates functional fusions, identifying new drivers of carcinogenesis and fusions that could have clinical implications.

Circulating tumor-derived DNA (ctDNA) can be used to monitor cancer dynamics noninvasively. Patients with small tumors have few copies of ctDNA in plasma, resulting in limited sensitivity to detect low-volume or residual disease. We show that sampling limitations can be overcome and sensitivity for ctDNA detection can be improved by massively parallel sequencing when hundreds to thousands of mutations are identified by tumor genotyping. We describe the INtegration of VAriant Reads (INVAR) analysis pipeline, which combines patient-specific mutation lists with both custom error-suppression methods and signal enrichment based on biological features of ctDNA. In this framework, the sensitivity can be estimated independently for each sample based on the number of informative reads, which is the product of the number of mutations analyzed and the average depth of unique sequencing reads. We applied INVAR to deep sequencing data generated by custom hybrid-capture panels, and showed that when ~10 6 informative reads were obtained INVAR allowed detection of tumor-derived DNA fractions to parts per million (ppm). In serial samples from patients with advanced melanoma on treatment, we detected ctDNA when imaging confirmed tumor volume of ~1cm 3 . In patients with resected early-stage melanoma, ctDNA was detected in 40% of patients who later relapsed, with higher rates of detection when more informative reads were obtained. We further demonstrated that INVAR can be generalized and allows improved detection of ctDNA from whole-exome and low-depth whole-genome sequencing data.

The original version of this Article omitted a declaration from the competing interests statement, which should have included the following: ‘K.P.W. is President of Tempus Lab, Inc., Chicago, IL, USA’. This has now been corrected in both the PDF and HTML versions of the Article.

withheld at the author’s request. Speaker abstracts

Anaplastic meningioma is a rare and aggressive brain tumor characterised by intractable recurrences and dismal outcomes. Here, we present an integrated analysis of the whole genome, transcriptome and methylation profiles of primary and recurrent anaplastic meningioma. A key finding was the delineation of distinct molecular subgroups that were associated with diametrically opposed survival outcomes. Relative to lower grade meningiomas, anaplastic tumors harbored frequent driver mutations in SWI/SNF complex genes, which were confined to the poor prognosis subgroup. Aggressive disease was further characterised by transcriptional evidence of increased PRC2 activity, stemness and epithelial-to-mesenchymal transition. Our analyses discern biologically distinct variants of anaplastic meningioma with prognostic and therapeutic significance.

Patients with seemingly the same tumour can respond very differently to treatment. There are strong, well-established effects of somatic mutations on drug efficacy, but there is atmost anecdotal evidence of a germline component to drug response. Here, we report a systematic survey of how inherited germline variants affect drug susceptibility in cancer cell lines. We develop a joint analysis approach that leverages both germline and somatic variants, before applying it to screening data from 993 cell lines and 265 drugs. Surprisingly, we find that the germline contribution to variation in drug susceptibility can be as large or larger than effects due to somatic mutations. Several of the associations identified have a direct relationship to the drug target. Finally, using 17-AAG response as an example, we show how germline effects in combination with transcriptomic data can be leveraged for improved patient stratification and to identify new markers for drug sensitivity.

Transcriptional dysregulation induced by aberrant transcription factors (TF) is a key feature of cancer, but its global influence on drug sensitivity has not been examined. Here, we infer the transcriptional activity of 127 TFs through analysis of RNA-seq gene expression data newly generated for 448 cancer cell lines, combined with publicly available datasets to survey a total of 1,056 cancer cell lines and 9,250 primary tumors. Predicted TF activities are supported by their agreement with independent shRNA essentiality profiles and homozygous gene deletions, and recapitulate mutant-specific mechanisms of transcriptional dysregulation in cancer. By analyzing cell line responses to 265 compounds, we uncovered numerous TFs whose activity interacts with anticancer drugs. Importantly, combining existing pharmacogenomic markers with TF activities often improves the stratification of cell lines in response to drug treatment. Our results, which can be queried freely at dorothea. opentargets. io, offer a broad foundation for discovering opportunities to refine personalized cancer therapies. Significance: Systematic analysis of transcriptional dysregulation in cancer cell lines and patient tumor specimens offers a publicly searchable foundation to discover new opportunities to refine personalized cancer therapies. (C) 2017 AACR.

Introduction Translating our understanding of genetic alterations in cancer into clinical care remains a major challenge. The discovery of gene fusions such as EML4-ALK in lung cancer and BCR-ABL1 in chronic myeloid leukaemia have already led to changes in clinical care. Advances in next-generation sequencing have accelerated the rate at which novel gene fusions are discovered, but important questions remain about their roles in promoting oncogenic phenotypes and their relevance in drug response. Here, we combine RNA sequencing, CRISPR/Cas9 screens and high-throughput drug sensitivity data in a panel of 1000 human cancer cell lines to examine the occurrence and functional relevance of gene fusions in cancer. Material and methods We performed RNA-sequencing on 1015 human cancer cell lines, representing 42 cancer types. We called fusions using three algorithms: TopHat Fusion, DeFuse and RNA-STAR fusion. Further, we integrate high-throughput drug screening data across >350 compounds, single-nucleotide variants, copy number variation, gene expression data and genome-wide CRISPR/Cas9 dropout screening data to systematically search for gene fusions with functional relevance. Results and discussions We find 8546 distinct gene fusion events across our panel of cell lines. These include well understood gene fusions (e.g. ALK-fusions, BCR-ABL1 and EWSR1-FLI1), as well as novel fusions that involve known cancer driver genes. We are able to recapitulate previously identified gene fusion-drug response associations using an unguided statistical analysis. Furthermore, we developed a systematic unguided approach of using CRISPR/Cas9 gene essentiality data to identify essential gene fusions. This approach recapitulates known essential gene fusions and provides evidence for the oncogenic relevance for previously poorly understood gene fusions. Conclusion In this study, we provide an annotation of gene fusions in 1015 human cancer cell lines. Our systematic analysis of the functional role of gene fusions captures the oncogenic and therapeutic relevance of known gene fusions, and highlights potential therapeutic opportunities of previously uncharacterised gene fusions.

AbstractDespite recent therapeutic advances in the management ofBRAFV600-mutant melanoma, there is still a compelling need for more effective treatments for patients who developedBRAF/NRASwild type disease. Since the activity of single targeted agents is limited by innate and acquired resistance, we performed a high-throughput drug screen using 180 drug combinations to generate over 18,000 viability curves, with the aim of identifying agents that synergise to killBRAF/NRASwild type melanoma cells. From this screen we observed strong synergy between the tyrosine kinase inhibitor nilotinib and MEK inhibitors and validated this combination in an independent cell line collection. We found that AXL expression was associated with synergy to the nilotinib/MEK inhibitor combination, and that both drugs work in concert to suppress pERK. This finding was supported by genome-wide CRISPR screening which revealed that resistance mechanisms converge on regulators of the MAPK pathway. Finally, we validated the synergy of nilotinib/trametinib combinationin vivousing patient-derived xenografts. Our results indicate that a nilotinib/MEK inhibitor combination may represent an effective therapy inBRAF/NRASwild type melanoma patients.

BackgroundCirculating tumour DNA (ctDNA) is released by cancer cells into the bloodstream, which can be analysed via liquid biopsy. Analysis of liquid biopsy samples provides a real-time snapshot of tumour burden. After treatment, ctDNA concentrations can be low, making detection challenging. To study clonal evolution during treatment in patients with melanoma with high sensitivity, we sought to maximise the number of mutations targeted through individualised next-generation sequencing panel design.Methods72 patients with stage III or IV melanoma were recruited to MelResist, a translational, multicentre research study. Serial plasma samples were taken from patients at monthly intervals during treatment (median 6·7 samples per patient). Clinical events were scored according to Response Evaluation Criteria In Solid Tumors (RECIST) (version 1.1) criteria. Exome and targeted sequencing were carried out on tumour samples at baseline and progression for nine patients, whose identified mutations were used to design an individualised targeted sequencing panel.FindingsMultiple mutations per patient were tracked to monitor response to therapy. The percentage change in ctDNA mutant allele fraction after treatment initiation agreed with RECIST response for seven out of eight evaluable patients. ctDNA concentration strongly correlated with lactate dehydrogenase (LDH) concentration, a currently used measure of melanoma tumour burden (r2=0·64, p=9·93 × 10−7). ctDNA dynamics were compared against rising LDH, which showed a median lead-time to biochemical progression (rising LDH) of 70 days (IQR 28–152·3). Targeting multiple mutations could improve sensitivity compared with individual mutations.InterpretationIn this study, we applied an individualised targeted sequencing panel on ctDNA from patients with melanoma. As tumour sequencing becomes more routine, individualised sequencing panel design might become more feasible, facilitating a more sensitive approach for ctDNA analysis than targeting individual loci. Tracking clonal evolution during therapy non-invasively may facilitate personalised treatment decisions with molecularly targeted agents.FundingCancer Research UK, Lewis Family Charitable Trust, Addenbrooke's Charitable Trust, Cambridge Cancer Trials Centre, NIHR Cambridge Biomedical Research Centre and Human Research Tissue Bank.

Drug resistance is an almost inevitable consequence of cancer therapy and ultimately proves fatal for the majority of patients. In many cases, this is the consequence of specific gene mutations that have the potential to be targeted to resensitize the tumor. The ability to uniformly saturate the genome with point mutations without chromosome or nucleotide sequence context bias would open the door to identify all putative drug resistance mutations in cancer models. Here, we describe such a method for elucidating drug resistance mechanisms using genome-wide chemical mutagenesis allied to next-generation sequencing. We show that chemically mutagenizing the genome of cancer cells dramatically increases the number of drug-resistant clones and allows the detection of both known and novel drug resistance mutations. We used an efficient computational process that allows for the rapid identification of involved pathways and druggable targets. Such a priori knowledge would greatly empower serial monitoring strategies for drug resistance in the clinic as well as the development of trials for drug-resistant patients.

Abstract Melanoma represents the common tumor whose incidence has increased the most in the last 30 years and causes more than one death every hour in the US alone. Despite significant advances in targeted and immunotherapies, most patients cannot still be cured. Our aim is to identify new drug combinations that are synergistic in BRAF/NRAS wild type melanoma, a sub-type representing 30% of cases for which targeted therapies are not currently available. We high-throughput screened a collection of 20 BRAF/NRAS wild type melanoma cell lines with 180 drug combinations (60 library drugs used at 5 different concentrations combined with 3 clinically relevant anchor drugs) and generated over 8000 survival curves . We found that 25% of cell lines are highly sensitive to a combination of nilotinib plus trametinib and confirmed this finding with 2 independent assays. We further validated the drug synergy firstly using an independent collection of BRAF/NRAS wild type melanoma cell lines (n=7), then a collection of BRAF/NRAS wild type patient derived xenotransplant cultures (n=3), and finally with a collection of BRAFV600E and NRASQ61 melanoma cell lines (n=12). Further, we generated a gene expression signature of cell lines that display synergy for the nilotinib/trametinib combination, and used it to classify human melanomas from Leeds Melanoma Project (N=171) and TCGA (n=470) cohorts. Tumors classified as “synergistic-like” (27.9 and 36.7%, respectively) are associated to decreased overall and recurrence free survival (P<0.05), suggesting that our combination might be effective in a relevant fraction of aggressive tumors. In order to identify drug resistance mechanisms we deployed a genome-wide CRISPR/Cas9 screen. We found that loss of the tuberous sclerosis complex can confer resistance to nilotinib/trametinib, and validated this mechanism using clonal engineered lines. Since tuberous sclerosis complex genes are mutated in 10% of melanomas, this approach can help to identify patients potentially refractory to the treatment. We also investigated the molecular mechanism of nilotinib/trametinib synergy by analysing the level of several phosphoproteins upon treatment. We discovered that the nilotinib/trametinib combination synergistically reduce the level of P-ERK in synergistic cell lines but not in cell lines resistant to the drug combination, thus pointing out the MAPK pathway dependence of the synergy. This finding provides a putative marker to identify tumors responsive to the treatment. Finally, we tested in vivo the nilotinib/trametinib combination in a patient derived xenotransplant mouse model and showed that the combination is well tolerated and significantly more effective than the 2 drugs alone (P<0.01). These data suggest a strong clinical translation potential for nilotinib/trametinib combination and pave the way to the development of clinical trials for BRAF/NRAS wild type melanoma. Citation Format: Marco Ranzani, Kristel Kemper, Magali Michaut, Oscar Krijgsman, Vivek Iyer, Anneliese Speak, Jeremie Nsengimana, Kim Wong, Vera Grinkevich, Nanne Aben, Martin Del Castillo Velasco-Herrera, Clara Alsinet, Marcela Sjoberg, Mamunur Rashid, Gemma Turner, Fiona Behan, Emmanuelle Supper, Nicola Thompson, Graham Bignell, Ken Dutton-Regester, Antonia Pritchard, Chi Wong, Ultan McDermott, Nicholas K. Hayward, Kosuke Yusa, Julia Newton-Bishop, Lodewyk Wessels, Mathew Garnett, Daniel Peeper, David Adams. New therapies for the treatment of BRAF/NRAS wild type melanoma [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 3717. doi:10.1158/1538-7445.AM2017-3717

Abstract Systematic studies of cancer genomes are providing unprecedented insights into the molecular nature of human cancer. Using this information to guide the development and application of therapies in the clinic is challenging. Here we report how cancer-driving alterations identified in 11,289 tumors from 29 tissues (integrating mutations, copy-number alterations, methylation and gene expression) correlate with response to 265 compounds profiled in 1,001 human cancer cell-lines. We find that cell-lines faithfully recapitulate oncogenic aberrations identified in tumors, and that many of these associate with drug sensitivity or resistance. Logic-based modeling uncovers combinations of aberrations that specifically sensitize to drugs, while machine-learning demonstrates the redundancy of different molecular data types in predicting drug response. Our comprehensive analysis and associated datasets are rich resources to identify novel therapeutic options for selected cancer sub-populations. Citation Format: Francesco Iorio, Theo Knijnenburg, Daniel vis, Graham Bignell, Michael Menden, Lodewyk Wessels, Julio Saez-Rodriguez, Ultan McDermott, Mathew Garnett. A landscape of pharmacogenomic interactions in cancer. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Targeting the Vulnerabilities of Cancer; May 16-19, 2016; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2017;23(1_Suppl):Abstract nr A44.

Despite recent therapeutic advances in the management of BRAF V600 -mutant melanoma, there is still a compelling need for more effective treatments for patients who developed BRAF / NRAS wild type disease. Since the activity of single targeted agents is limited by innate and acquired resistance, we performed a high-throughput drug screen using 180 drug combinations to generate over 18,000 viability curves, with the aim of identifying agents that synergise to kill BRAF / NRAS wild type melanoma cells. From this screen we observed strong synergy between the tyrosine kinase inhibitor nilotinib and MEK inhibitors and validated this combination in an independent cell line collection. We found that AXL expression was associated with synergy to the nilotinib/MEK inhibitor combination, and that both drugs work in concert to suppress pERK. This finding was supported by genome-wide CRISPR screening which revealed that resistance mechanisms converge on regulators of the MAPK pathway. Finally, we validated the synergy of nilotinib/trametinib combination in vivo using patient-derived xenografts. Our results indicate that a nilotinib/MEK inhibitor combination may represent an effective therapy in BRAF / NRAS wild type melanoma patients.

Genomic lesions are not investigated during routine diagnostic workup for multiple myeloma (MM). Cytogenetic studies are performed to assess prognosis but with limited impact on therapeutic decisions. Recently, several recurrently mutated genes have been described, but their clinical value remains to be defined. Therefore, clinical-grade strategies to investigate the genomic landscape of myeloma samples are needed to integrate new and old prognostic markers. We developed a target-enrichment strategy followed by next-generation sequencing (NGS) to streamline simultaneous analysis of gene mutations, copy number changes and immunoglobulin heavy chain (IGH) translocations in MM in a high-throughput manner, and validated it in a panel of cell lines. We identified 548 likely oncogenic mutations in 182 genes. By integrating published data sets of NGS in MM, we retrieved a list of genes with significant relevance to myeloma and found that the mutational spectrum of primary samples and MM cell lines is partially overlapping. Gains and losses of chromosomes, chromosomal segments and gene loci were identified with accuracy comparable to conventional arrays, allowing identification of lesions with known prognostic significance. Furthermore, we identified IGH translocations with high positive and negative predictive value. Our approach could allow the identification of novel biomarkers with clinical relevance in myeloma.