Mapping genetic interactions in cancer: a road to rational combination therapies.

The discovery of synthetic lethal interactions between poly (ADP-ribose) polymerase (PARP) inhibitors and BRCA genes, which are involved in homologous recombination, led to the approval of PARP inhibition as a monotherapy for patients with BRCA1/2-mutated breast or ovarian cancer. Studies following the initial observation of synthetic lethality demonstrated that the reach of PARP inhibitors is well beyond just BRCA1/2 mutants. Insights into the mechanisms of action of anticancer drugs are fundamental for the development of targeted monotherapies or rational combination treatments that will synergize to promote cancer cell death and overcome mechanisms of resistance. The development of targeted therapeutic agents is premised on mapping the physical and functional dependencies of mutated genes in cancer. An important part of this effort is the systematic screening of genetic interactions in a variety of cancer types. Until recently, genetic-interaction screens have relied either on the pairwise perturbations of two genes or on the perturbation of genes of interest combined with inhibition by commonly used anticancer drugs. Here, we summarize recent advances in mapping genetic interactions using targeted, genome-wide, and high-throughput genetic screens, and we discuss the therapeutic insights obtained through such screens. We further focus on factors that should be considered in order to develop a robust analysis pipeline. Finally, we discuss the integration of functional interaction data with orthogonal methods and suggest that such approaches will increase the reach of genetic-interaction screens for the development of rational combination therapies

Inferring tumor-specific cancer dependencies through integrating ex vivo drug response assays and drug-protein profiling

The development of cancer therapies may be improved by the discovery of tumor-specific molecular dependencies. The requisite tools include genetic and chemical perturbations, each with its strengths and limitations. Chemical perturbations can be readily applied to primary cancer samples at large scale, but mechanistic understanding of hits and further pharmaceutical development is often complicated by the fact that a chemical compound has affinities to multiple proteins. To computationally infer specific molecular dependencies of individual cancers from their ex vivo drug sensitivity profiles, we developed a mathematical model that deconvolutes these data using measurements of protein-drug affinity profiles. Through integrating a drug-kinase profiling dataset and several drug response datasets, our method, DepInfeR, correctly identified known protein kinase dependencies, including the EGFR dependence of HER2+ breast cancer cell lines, the FLT3 dependence of acute myeloid leukemia (AML) with FLT3-ITD mutations and the differential dependencies on the B-cell receptor pathway in the two major subtypes of chronic lymphocytic leukemia (CLL). Furthermore, our method uncovered new subgroup-specific dependencies, including a previously unreported dependence of high-risk CLL on Checkpoint kinase 1 (CHEK1). The method also produced a detailed map of the kinase dependencies in a heterogeneous set of 117 CLL samples. The ability to deconvolute polypharmacological phenotypes into underlying causal molecular dependencies should increase the utility of high-throughput drug response assays for functional precision oncology

Synthetic Lethal Interaction between Oncogenic KRAS Dependency and STK33 Suppression in Human Cancer Cells

An alternative to therapeutic targeting of oncogenes is to perform “synthetic lethality” screens for genes that are essential only in the context of specific cancer-causing mutations. We used high-throughput RNA interference (RNAi) to identify synthetic lethal interactions in cancer cells harboring mutant KRAS, the most commonly mutated human oncogene. We find that cells that are dependent on mutant KRAS exhibit sensitivity to suppression of the serine/threonine kinase STK33 irrespective of tissue origin, whereas STK33 is not required by KRAS-independent cells. STK33 promotes cancer cell viability in a kinase activity-dependent manner by regulating the suppression of mitochondrial apoptosis mediated through S6K1-induced inactivation of the death agonist BAD selectively in mutant KRAS-dependent cells. These observations identify STK33 as a target for treatment of mutant KRAS-driven cancers and demonstrate the potential of RNAi screens for discovering functional dependencies created by oncogenic mutations that may enable therapeutic intervention for cancers with “undruggable” genetic alterations.National Institutes of Health (U.S.) (grant R33 CA128625)National Institutes of Health (U.S.) (grant NIH U54 CA112962)National Institutes of Health (U.S.) (grant P01 CA095616)National Institutes of Health (U.S.) (grant P01 CA66996)Starr Cancer ConsortiumDoris Duke Charitable FoundationMPN Research FoundationDeutsche Forschungsgemeinschaft (grant SCHO 1215/1-1)Deutsche Forschungsgemeinschaft (grant FR 2113/1-1)Brain Science FoundationLeukemia & Lymphoma Society of Americ

Deciphering oncogene dependencies and signaling pathway alterations in non small cell lung cancer

Lung cancer is the leading cause of cancer related death worldwide with 1.4 million cases in 2008. Thus, there is an unmet need to identify novel treatment options for lung cancer patients in the clinic and foster the understanding of tumor biology. The Ph.D. thesis presented here focuses on the identification and characterization of novel oncogenes that play a role in the onset of lung tumor development as well as on the characterization of signaling pathway recruitment downstream of oncogenic receptor tyrosine kinases (RTKs). In this regard, we were able to identify two genes encoding for protein kinases being causative for tumor development and furthermore we were able to functionally validate both genes as being the relevant target of the respective inhibitor in-vitro. In detail, we identified: - amplifications of the human version of v-src avian sarcoma (Schmidt-Ruppin A-2) viral oncogene (cSRC) as being causative and predictive for the sensitivity towards the clinical approved Src-Abl inhibitor dasatinib - amplifications of the fibroblast growth factor receptor 1 (FGFR1) as being causative and predictive for the sensitivity towards the FGFR protein family inhibitor PD173074 - frequent amplifications of FGFR1 in squamous cell but no other type of NSCLC cells. Hence, we strongly suggest to treat patients suffering from FGFR1 amplified squamous cell lung tumors with FGFR1 inhibitors. Furthermore, we utilized in-vitro chemical-genomic approaches and genetic engineering to functionally validate both, cSRC and FGFR1, as the relevant targets of the respective inhibitors in amplified cell lines. And finally, we extended our already established high-throughput screening platform to be able to screen up to 1500 compounds as well as combinations of various signaling pathway inhibitors. To this end, we have screened 136 inhibitor combinations on 105 genetically defined cell lines to identify novel treatment options as well as specific signaling pathway recruitments within defined genetic conditions. Thus, amplifications of cSRC as well as FGFR1 lead to responsiveness towards small molecule inhibitors in NSCLC cells harboring amplification of either gene. We show that high throughput cell based screening of inhibitor combinations can be utilized to shed light into the complex recruitment of signaling pathways downstream of RTKs and lead to novel treatment strategies for patients suffering from lung cancer. And finally, we identified FGFR1 amplifications in up to 20% primary squamous cell lung cancer specimens, strongly suggesting to treat these patients with FGFR1 inhibitors

Large-Scale Profiling of Kinase Dependencies in Cancer Cell Lines

One approach to identifying cancer-specific vulnerabilities and therapeutic targets is to profile genetic dependencies in cancer cell lines. Here, we describe data from a series of siRNA screens that identify the kinase genetic dependencies in 117 cancer cell lines from ten cancer types. By integrating the siRNA screen data with molecular profiling data, including exome sequencing data, we show how vulnerabilities/genetic dependencies that are associated with mutations in specific cancer driver genes can be identified. By integrating additional data sets into this analysis, including protein-protein interaction data, we also demonstrate that the genetic dependencies associated with many cancer driver genes form dense connections on functional interaction networks. We demonstrate the utility of this resource by using it to predict the drug sensitivity of genetically or histologically defined subsets of tumor cell lines, including an increased sensitivity of osteosarcoma cell lines to FGFR inhibitors and SMAD4 mutant tumor cells to mitotic inhibitors

Translational Oncogenomics and Human Cancer Interactome Networks

An overview of translational, human oncogenomics, transcriptomics and cancer interactomic networks is presented together with basic concepts and potential, new applications to Oncology and Integrative Cancer Biology. Novel translational oncogenomics research is rapidly expanding through the application of advanced technology, research findings and computational tools/models to both pharmaceutical and clinical problems. A self-contained presentation is adopted that covers both fundamental concepts and the most recent biomedical, as well as clinical, applications. Sample analyses in recent clinical studies have shown that gene expression data can be employed to distinguish between tumor types as well as to predict outcomes. Potentially important applications of such results are individualized human cancer therapies or, in general, ‘personalized medicine’. Several cancer detection techniques are currently under development both in the direction of improved detection sensitivity and increased time resolution of cellular events, with the limits of single molecule detection and picosecond time resolution already reached. The urgency for the complete mapping of a human cancer interactome with the help of such novel, high-efficiency / low-cost and ultra-sensitive techniques is also pointed out

Integrative molecular and functional profiling of ERBB2-amplified breast cancers identifies new genetic dependencies.

Overexpression of the receptor tyrosine kinase ERBB2 (also known as HER2) occurs in around 15% of breast cancers and is driven by amplification of the ERBB2 gene. ERBB2 amplification is a marker of poor prognosis, and although anti-ERBB2-targeted therapies have shown significant clinical benefit, de novo and acquired resistance remains an important problem. Genomic profiling has demonstrated that ERBB2+ve breast cancers are distinguished from ER+ve and 'triple-negative' breast cancers by harbouring not only the ERBB2 amplification on 17q12, but also a number of co-amplified genes on 17q12 and amplification events on other chromosomes. Some of these genes may have important roles in influencing clinical outcome, and could represent genetic dependencies in ERBB2+ve cancers and therefore potential therapeutic targets. Here, we describe an integrated genomic, gene expression and functional analysis to determine whether the genes present within amplicons are critical for the survival of ERBB2+ve breast tumour cells. We show that only a fraction of the ERBB2-amplified breast tumour lines are truly addicted to the ERBB2 oncogene at the mRNA level and display a heterogeneous set of additional genetic dependencies. These include an addiction to the transcription factor gene TFAP2C when it is amplified and overexpressed, suggesting that TFAP2C represents a genetic dependency in some ERBB2+ve breast cancer cell

Integrative Bioinformatics of Functional and Genomic Profiles for Cancer Systems Medicine

Cancer is a leading cause of death worldwide and a major public health burden. The rapid advancements in high-throughput techniques have now made it possible to molecularly characterize large number of patient tumors, and large-scale genomic and functional profiles are routinely being generated. Such datasets hold immense potential to reveal novel genes driving cancer, biomarkers with prognostic value, and also identify promising targets for drug treatment. But the ‘big data’ nature of these highly complex datasets require concurrent development of computational models and data analysis strategies to be able to mine useful knowledge and unlock the potential of the information content that is latent in such datasets. This thesis presents computational and analytical approaches to extract potentially useful information by integrating genomic and functional profiles of cancer cells.Syöpä on maailmanlaajuisesti johtava kuolinsyy sekä suuri kansanterveystaakka. Edistyneen teknologian ansiosta voimme nykyään tutkia syöpäsoluja molekyylitasolla sekä tuottaa valtavia määriä tietoa. Tällaisissa tietomäärissä piilee suuria mahdollisuuksia uusien syöpää aiheuttavien geenien löytämiseen ja lupaavien syöpähoitokohteiden tunnistamiseen. Näiden erittäin monimutkaisten tietomäärien ”Big data” -luonne vaatii kuitenkin myös laskennallisten mallien kehittämistä ja strategioita tiedon analysointiin, jotta voidaan löytää käyttökelpoista tietoa, joka voisi olla hyödyllistä terveydenhoidossa. Tämä väitöskirja esittelee laskennallisia ja analyyttisiä tapoja löytää mahdollisesti hyödyllistä tietoa yhdistämällä erilaisia syöpäsolujen molekulaarisia malleja, kuten niiden genomisia ja toiminnallisia profiileja

CK1ε Is Required for Breast Cancers Dependent on β-Catenin Activity

Background: Aberrant $\beta$-catenin signaling plays a key role in several cancer types, notably colon, liver and breast cancer. However approaches to modulate $\beta$-catenin activity for therapeutic purposes have proven elusive to date. Methodology: To uncover genetic dependencies in breast cancer cells that harbor active $\beta$-catenin signaling, we performed RNAi-based loss-of-function screens in breast cancer cell lines in which we had characterized $\beta$-catenin activity. Here we identify CSNK1E, the gene encoding casein kinase 1 epsilon (CK1$\varepsilon$) as required specifically for the proliferation of breast cancer cells with activated $\beta$-catenin and confirm its role as a positive regulator of $\beta$-catenin-driven transcription. Furthermore, we demonstrate that breast cancer cells that harbor activated $\beta$-catenin activity exhibit enhanced sensitivity to pharmacological blockade of Wnt/$\beta$-catenin signaling. We also find that expression of CK1$\varepsilon$ is able to promote oncogenic transformation of human cells in a $\beta$-catenin-dependent manner. Conclusions/Significance: These studies identify CK1$\varepsilon$ as a critical contributor to activated $\beta$-catenin signaling in cancer and suggest it may provide a potential therapeutic target for cancers that harbor active $\beta$-catenin. More generally, these observations delineate an approach that can be used to identify druggable synthetic lethal interactions with signaling pathways that are frequently activated in cancer but are difficult to target with the currently available small molecule inhibitors