XLSX file - 243KB, This table summarizes all significant genetic features that differentiate sensitive versus insensitive cell lines for TNKSi/MEKi combination. The first sheet lists all cell lines for each group. The second sheet lists all genetic features that are enriched in the sensitive group, using a False Discovery Rate (FDR) p-value lower than 0.25.

<p>PDF file - 54KB, Chemical inhibitors used in the study.</p>

<p>XLSX file - 234KB, This table shows the features of all 138 cancer cell lines tested for the TNKSi/MEKi combination in the unbiased combination screen. Cell line name, lineage, RAS mutation status and synergy score for the TNKSi/MEKi combination are represented. Cell lines are ranked according to their synergy score.</p>

PDF file - 660KB, Supplementary Figure S1. All RAS mutant cell lines have a higher sensitivity to the TNKSi/MEKi combination. Supplementary Figure S2. Validation of TNKSi/MEKi combination in KRAS mutant cancer cells. Supplementary Figure S3. Combinations with MEK inhibitor in the SW480 cell line. Supplementary Figure S4. TNKSi/MEKi combination leads to enhanced inhibition of AKT signaling activity. Supplementary Figure S5. TNKSi potentiates MEKi by releasing a feedback loop on FGFR2 signaling. Supplementary Figure S6. Consequences of combined inhibition of TNKS and MEK on FGFR2 and AKT signaling pathways in KRAS mutant cell lines.

<p>PDF file - 68KB, Sensitivity of KRAS mutant cancer cells to TNKSi/MEKi combination. Synergistic cell lines are in red (cut-off of synergy score of 2).</p>

<p>PDF file - 81KB, List of identified synergistic cell lines for TNKSi/MEKi combination in the large-scale combination screen and their KRAS status. KRAS mutants are in red.</p>

Abstract Background: Large-scale genomics studies (e.g. AACR Project GENIE, TCGA, TopMed) have sequenced thousands of patients in an attempt to understand disease associated genomic variables and their clinical correlates. Existing online platforms (e.g. cBioPortal) enable simple gene-based queries, but do not allow more complex modeling to understand disease pathogenesis, risk and outcome. There is an urgent need to build an interactive, modular and scalable platform that enables users to perform multivariate machine learning on existing genomic data. Results: We have built a platform, PrismML, that enables a user to interactively query a dataset, and to run a multitude of machine learning tools, from simple statistical tests for differential analysis to multivariate modeling to predict clinical response, or mortality-risk. Since machine learning models are computationally intensive, we have used the power of cloud computing to make the analyses faster and scalable. Key feature of our platform are: (1) availability of extensive statistical and machine learning methods; (2) implementation of best practices for machine learning, e.g. cross-validation; (3) graphical querying of results to understand the interplay among features. Users can choose to analyze existing data/studies, or upload their own data. Examples of possible queries: “identify genomic features that distinguish metastasis from primary tumors, either in a single cancer or pan-cancer”, or, “build a machine learning model to predict survival within ER- breast cancer patients”. In addition, there is also an acute need to integrate the knowledge extracted from the multitude of data types. To this end, we have integrated multiple data types into gene-scores, and have incorporated known biological/functional information by integrating gene-scores into pathway-scores. Summary: PrismML is an interactive and flexible platform to bring the power of machine learning and statistical modeling to the genomics community. This is an active area of development with multiple ongoing features, such as integrating multiple datasets to increase statistical power in rare diseases, and to enable subsetting large diseases to identify prognostic features. Citation Format: Anupama Reddy, Daisy Flemming, Sara Selitsky, Ana Brandusa Pavel, Gabriela Alexe, Gyan Bhanot. PrismML: A machine learning platform to query genotype-phenotype patterns in large genomics studies [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 858.

Elucidation of the mutational landscape of human cancer has progressed rapidly and been accompanied by the development of therapeutics targeting mutant oncogenes. However, a comprehensive mapping of cancer dependencies has lagged behind and the discovery of therapeutic targets for counteracting tumor suppressor gene loss is needed. To identify vulnerabilities relevant to specific cancer subtypes, we conducted a large-scale RNAi screen in which viability effects of mRNA knockdown were assessed for 7,837 genes using an average of 20 shRNAs per gene in 398 cancer cell lines. We describe findings of this screen, outlining the classes of cancer dependency genes and their relationships to genetic, expression, and lineage features. In addition, we describe robust gene-interaction networks recapitulating both protein complexes and functional cooperation among complexes and pathways. This dataset along with a web portal is provided to the community to assist in the discovery and translation of new therapeutic approaches for cancer.

Tankyrases (TNKS) play roles in Wnt signaling, telomere homeostasis, and mitosis, offering attractive targets for anticancer treatment. Using unbiased combination screening in a large panel of cancer cell lines, we have identified a strong synergy between TNKS and MEK inhibitors (MEKi) in KRAS-mutant cancer cells. Our study uncovers a novel function of TNKS in the relief of a feedback loop induced byMEK inhibition on FGFR2 signaling pathway.Moreover, dual inhibition of TNKS andMEK leads tomore robust apoptosis and antitumor activity both in vitro and in vivo than effects observed by previously reportedMEKi combinations. Altogether, our results show how a novel combination of TNKS and MEK inhibitors can be highly effective in targeting KRAS-mutant cancers by suppressing a newly discovered resistance mechanism. Cancer Res; 74(12); 1–12. 2014 AACR.

Tankyrases (TNKS) play roles in Wnt signaling, telomere homeostasis, and mitosis, offering attractive targets for anticancer treatment. Using unbiased combination screening in a large panel of cancer cell lines, we have identified a strong synergy between TNKS and MEK inhibitors (MEKi) in KRAS-mutant cancer cells. Our study uncovers a novel function of TNKS in the relief of a feedback loop induced by MEK inhibition on FGFR2 signaling pathway. Moreover, dual inhibition of TNKS and MEK leads to more robust apoptosis and antitumor activity both in vitro and in vivo than effects observed by previously reported MEKi combinations. Altogether, our results show how a novel combination of TNKS and MEK inhibitors can be highly effective in targeting KRAS-mutant cancers by suppressing a newly discovered resistance mechanism.

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA Tankyrases (TNKS) play roles in Wnt signaling, telomere homeostasis and mitosis, and are therefore considered as attractive targets for anti-cancer treatment. Using unbiased combination screens in a large panel of cancer cell lines, we have identified a strong synergy between TNKS and MEK inhibitors in KRAS mutant cancer cells. Our study uncovers a novel function of TNKS in the relief of a feedback loop induced by MEK inhibition on FGFR2 signaling pathway. Moreover, dual inhibition of TNKS and MEK leads to more robust apoptosis and anti-tumor activity both in vitro and in vivo than effects observed by previously reported MEK inhibitor combinations. Altogether, our data provides a strong rationale for combined targeting of TNKS and MEK in KRAS mutant cancers. Citation Format: Wenlin Shao, Marie Schoumacher, Kristen Hurov, Joseph Lehar, Yan Yan-Neale, Yuji Mishina, Dmitriy Sonkin, Joshua Korn, Daisy Flemming, Michael Jones, Brandon Antonakos, Vessilina Cooke, Mark Stump, Nika Danial, William Sellers. Inhibiting TNKS sensitizes KRAS mutant cancer cells to MEK inhibitors by suppressing FGFR2 feedback signaling. [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 LB-107. doi:10.1158/1538-7445.AM2014-LB-107