135 research outputs found
Abstract B43: Towards a Cancer Dependency Map
No full textAbstract
This abstract is being presented as a short talk in the scientific program. A full abstract is printed in the Proffered Abstracts section (PR02) of the Conference Proceedings.
Citation Format: Aviad Tsherniak, Francisca Vazquez, Barbara Weir, Philip Montgomery, Glenn Cowley, Stanley Gill, Gregory Kryukov, Sasha Pantel, Will Harrington, Mike Burger, Robin Meyers, Levi Ali, Amy Goodale, Yenarae Lee, Levi Garraway, Jesse Boehm, David Root, Todd Golub, William Hahn. Towards a Cancer Dependency Map. [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 B43.</jats:p
Abstract 1555: Pan-cancer patterns of synthetic lethality: statistical modeling of gene dependency profiles
No full textAbstract
We present a methodology to fit statistical models of cell-viability profiles from RNAi-gene knockdowns. This method allows us to classify genes according to the degree of skewness in their viability distributions. The set of genes with the highest degree of skewness is highly enriched with many known oncogenes and tumor suppressors. We characterize many of these genes, compare them against the results of large sequencing efforts, and use them as inputs to a matrix-decomposition procedure that identifies the most salient cell viabilities shared by different cancer types. We catalog these pan-cancer patterns of synthetic lethality and characterize them by the genomic, transcriptional, and phenotypic features. This analysis provides a rich catalog of the most salient Achilles’ Heels of Pan-Cancer that can be helpful to identify new therapeutic strategies across cancers.
Citation Format: Huwate Yeerna, Ramya Rangan, Andrew Aguirre, William Kim, Francisca Vazquez, Barbara Weir, Mahmoud Ghandi, Aviad Tsherniak, Jesse Boehm, William Hahn, Jill Mesirov, Pablo Tamayo. Pan-cancer patterns of synthetic lethality: statistical modeling of gene dependency profiles [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 1555. doi:10.1158/1538-7445.AM2017-1555</jats:p
S3 Fig -
No full texta) Dependence of the CERES essentiality score of LDLR on the expression of the limiting gene in the ngMCSs involving cholesterol. The slope of the regression line is statistically significant (p = 0.00464, r = 0.097). b) Dependence of the CERES essentiality score of SLC5A3 on the expression of the limiting gene in the ngMCSs involving myo-Inositol. The slope of the regression line is statistically significant (p = 6.67·10–6, r = 0.154). Gene expression data was obtained from CCLE (Ghandi et al., 2019) and CERES scores were obtained from the DepMap platform (Tsherniak et al., 2017). (TIF)</p
Abstract 5559: Using cancer dependency data to discover tumor suppressive and oncogenic functional modules
No full textAbstract
Efforts to define protein complexes and their functional networks are critical for systems-level understanding of the pathways involved in human cancer. Current methods to catalog human protein complexes via physical interaction are often unable to resolve functional differences between complex members or infer relationships governed by sub-stoichiometric interactions. While functional wiring maps in yeast have been generated by measuring epistatic interactions between pairs of genes, efforts to scale this concept in individual human cell lines have been met with challenges and have only been able to characterize limited numbers of genes at a time.
We have developed a scalable approach that can measure functional similarity without the constraints of pairwise genetic interaction experiments. Using data from genome-wide RNAi and CRISPR dropout screens performed in hundreds of cancer cell lines, we leveraged the heterogeneity of gene dependencies across cancer types to measure functional similarity between thousands of genes at once, which in turn allowed us to recreate known inter- and intra-complex functional relationships and to uncover tumor suppressive and oncogenic functional modules in cancer-relevant pathways such as proteolysis, metabolism and transcription.
Applying these approaches to the mammalian SWI/SNF (BAF) chromatin remodeling complex, which is mutated in over 20% of human cancer, revealed three functional modules that arose separately during metazoan evolution, one of which is entirely novel and uncharacterized. We then performed biochemical experiments that fully support three specialized complex configurations, each with distinct size, subunit composition, and function. These data reorganize the BAF complex into previously unrecognized modules that better explain mutational burden in human cancer. Notably, we observe that that all known BAF-driven, highly penetrant rare cancers and neurodevelopmental disorders involve disruption within a single functional module we defined, underscoring the value of evaluating disease genomics through the lens of functional modularity.
Citation Format: Joshua Pan, Robin M. Meyers, Brittany C. Michel, Ann E. Sizemore, Francisca Vazquez, Barbara A. Weir, William C. Hahn, Aviad Tsherniak, Cigall Kadoch. Using cancer dependency data to discover tumor suppressive and oncogenic functional modules [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 5559. doi:10.1158/1538-7445.AM2017-5559</jats:p
Copy-number and gene dependency analysis reveals partial copy loss of wild-type SF3B1 as a novel cancer vulnerability
No full textGenomic instability is a hallmark of human cancer, and results in widespread somatic copy number alterations. We used a genome-scale shRNA viability screen in human cancer cell lines to systematically identify genes that are essential in the context of particular copy-number alterations (copy-number associated gene dependencies). The most enriched class of copy-number associated gene dependencies was CYCLOPS (Copy-number alterations Yielding Cancer Liabilities Owing to Partial losS) genes, and spliceosome components were the most prevalent. One of these, the pre-mRNA splicing factor SF3B1, is also frequently mutated in cancer. We validated SF3B1 as a CYCLOPS gene and found that human cancer cells harboring partial SF3B1 copy-loss lack a reservoir of SF3b complex that protects cells with normal SF3B1 copy number from cell death upon partial SF3B1 suppression. These data provide a catalog of copy-number associated gene dependencies and identify partial copy-loss of wild-type SF3B1 as a novel, non-driver cancer gene dependency.National Cancer Institute (U.S.) (R01 CA188228)National Cancer Institute (U.S.) (U01 CA176058)National Cancer Institute (U.S.) (F30 CA192725
Abstract B17: Identification of Druggable Targets through Functional Multi-Omics in Renal Medullary Carcinoma
No full textAbstract
Renal medullary carcinoma is a rare kidney cancer that is primarily seen in adolescent and young adult African American patients with sickle cell trait. Prognosis is poor and treatment options are limited. We have developed several cell line models that recapitulate the primary and relapsed metastatic samples from a patient who succumbed to this disease. We have confirmed by whole exome sequencing that our models have sickle cell trait and loss of heterozygosity of the SMARCB1 loci, both hallmarks of this disease. By RNA-sequencing, we see a lack of SMARCB1 transcription. We have further shown dependency of our models to SMARCB1 re-expression thus suggesting that this cancer is indeed driven by loss of SMARCB1 at a functional level. We performed pooled CRISPR-Cas9 and RNAi loss of function screens and a small molecule screen focused on druggable cancer targets based on our previous work in parallel to a genome-wide pooled CRISPR-Cas9 loss of function screen. Integrating these complementary and orthogonal methods, we identified a number of targets for further validation. These targets, when combined may provide a rational approach to therapeutic targeting for this rare kidney cancer.
Citation Format: Andrew L. Hong, Yuen-Yi Tseng, Bryan D. Kynnap, Mihir B. Doshi, Gabriel Sandoval, Coyin Oh, Abeer Sayeed, Gill Shubhroz, Alanna J. Church, Paula Keskula, Anson Peng, Paul A. Clemons, Aviad Tsherniak, Francisca Vazquez, Carlos Rodriguez-Galindo, Katherine A. Janeway, Levi A. Garraway, Stuart L. Schreiber, David E. Root, Elizabeth Mullen, Kimberly Stegmaier, Cigall Kadoch, Charles W.M. Roberts, Jesse S. Boehm, William C. Hahn. Identification of Druggable Targets through Functional Multi-Omics in Renal Medullary Carcinoma [abstract]. In: Proceedings of the AACR Precision Medicine Series: Opportunities and Challenges of Exploiting Synthetic Lethality in Cancer; Jan 4-7, 2017; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2017;16(10 Suppl):Abstract nr B17.</jats:p
CRISPR-Cas9 screen reveals a MYCN-amplified neuroblastoma dependency on EZH2
No full textPharmacologically difficult targets, such as MYC transcription factors, represent a major challenge in cancer therapy. For the childhood cancer neuroblastoma, amplification of the oncogene MYCN is associated with high-risk disease and poor prognosis. Here, we deployed genome-scale CRISPR-Cas9 screening of MYCN-amplified neuroblastoma and found a preferential dependency on genes encoding the polycomb repressive complex 2 (PRC2) components EZH2, EED, and SUZ12. Genetic and pharmacological suppression of EZH2 inhibited neuroblastoma growth in vitro and in vivo. Moreover, compared with neuroblastomas without MYCN amplification, MYCN-amplified neuroblastomas expressed higher levels of EZH2. ChIP analysis showed that MYCN binds at the EZH2 promoter, thereby directly driving expression. Transcriptomic and epigenetic analysis, as well as genetic rescue experiments, revealed that EZH2 represses neuronal differentiation in neuroblastoma in a PRC2-dependent manner. Moreover, MYCN-amplified and high-risk primary tumors from patients with neuroblastoma exhibited strong repression of EZH2-regulated genes. Additionally, overexpression of IGFBP3, a direct EZH2 target, suppressed neuroblastoma growth in vitro and in vivo. We further observed strong synergy between histone deacetylase inhibitors and EZH2 inhibitors. Together, these observations demonstrate that MYCN upregulates EZH2, leading to inactivation of a tumor suppressor program in neuroblastoma, and support testing EZH2 inhibitors in patients with MYCN-amplified neuroblastoma
Abstract PR02: Towards a Cancer Dependency Map
No full textAbstract
The mapping of cancer genomes is rapidly approaching completion. The genomic information encoded by individual patients' tumors should, in principle, provide a guide for predicting acquired cancer dependencies. Unfortunately, while the success of precision cancer genomics hinges on the decoding of such dependencies, we lack the ability to predict dependencies for most individual tumors. The challenge stems from the absence of clinical data relating genotypes with dependencies since most cancer mutations are rare and our arsenal of cancer drugs is incomplete.
A comprehensive Cancer Dependency Map comprised of a catalog of genetic and small molecule vulnerabilities across a diverse set of cancers, along with robust statistical models able to predict these vulnerabilities from molecular and genomic features, would provide a roadmap of targets ripe for therapeutic development and would help reveal the mechanisms underlying the emergence of these vulnerabilities.
Here, we report progress in creating a Cancer Dependency Map consisting of the following components: 1) Systematic genetic perturbation (RNAi/CRISPR) of over 600 cancer cell models representing a wide range of human cancers and cell lineages using massively parallel genome scale loss-of-function screens. 2) Computational segregation of on- from off-target effects of RNAi enabling the discovery of outlier dependencies. 3) Predictive modeling to discover biomarkers for each dependency.
Our results demonstrate that our analytical approach (DEMETER) that models both gene and miRNA-based seed sequence effects effectively segregates on- from off-target effects of shRNAs. We discover 768 preferential dependencies whose suppression decreases viability at a level greater than six standard deviations in at least one of 503 cancer models and 105 such dependencies each present in at least 15 models. We find that 95% of the cancer models screened are strongly sensitive to the suppression of at least one of these dependencies, and that many models have common dependencies so that all models harbor at least one six-sigma dependency out of a set of only 76. Using a custom random forest based predictive modeling framework (ATLANTIS), we discover predictive biomarkers for hundreds of dependencies. These include known and novel vulnerabilities specified by somatic oncogenic alterations, overexpression of genes that specify lineage and differentiation, copy-number driven essentiality, and loss of functionally redundant paralogs.
These observations provide a rigorous computational and experimental foundation for the creation of a comprehensive Cancer Dependency Map. Subsampling and projection analyses suggest that over 10,000 genomically characterized cancer cell models will be needed to achieve this important goal.
This abstract is also being presented as Poster B43.
Citation Format: Aviad Tsherniak, Francisca Vazquez, Barbara Weir, Philip Montgomery, Glenn Cowley, Stanley Gill, Gregory Kryukov, Sasha Pantel, Will Harrington, Mike Burger, Robin Meyers, Levi Ali, Amy Goodale, Yenarae Lee, Levi Garraway, Jesse Boehm, David Root, Todd Golub, William Hahn. Towards a Cancer Dependency Map. [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 PR02.</jats:p
Abstract B44: Emerging targets from Cancer Dependency Map v0.1
No full textAbstract
Precision Cancer Medicine requires the identification of vulnerabilities linked to genetic features of tumors. Recent studies utilizing highly annotated small molecule collections to assess dependencies across hundreds of genomically annotated cell lines have demonstrated the potential for such large-scale preclinical “Dependency Map” projects. We have undertaken a complementary approach using genetic perturbation tools (RNAi and CRISPR-Cas9 based loss-of-function viability screens), to systematically catalog preferential genetic dependencies and markers that predict response. These efforts are providing a foundation for the discovery of novel targets poised for early therapeutic discovery projects together with patient populations that may be enriched for responders to such therapies.
Here, we present results from our initial Cancer Dependency Map consisting of RNAi loss-of-function screens across 503 cell lines, including both solid and hematopoietic tumors. We discovered 43 genes whose mutation or copy number creates a cancer dependency (oncogene addiction) including a novel dependency on the small GTPase, GNAI2 in Diffuse large B-cell Lymphoma. We discovered 142 genes in which elevated levels of expression create a dependency (gene addiction), a group of genes highly enriched for master regulator transcription factors such as SOX10, SPDEF, PAX8 and HNF1B. We discovered 474 genes for which hemizygous copy number creates a dependency (CYCLOPS genes), a group of genes highly enriched for members of macromolecular protein complexes including the spliceosome and proteasome. Finally, we discovered 171 genes that become a dependency when a redundant functional paralog is lost in cancer cells (redundant essentials). We demonstrate the mechanistic basis behind one such redundant essential dependency relationship in which promoter methylation of the UBB ubiquitin gene eliminates a compensatory mechanism leading to a novel vulnerability on the suppression of the UBC ubiquitin gene.
These observations begin to provide an initial census, categorization and prioritization of robust cancer dependencies and support the potential impact for expanding early efforts to develop dependency maps of cancer.
Citation Format: Francisca Vazquez, Aviad Tsherniak, Barbara Weir, Phil Montgomery, Glenn Cowley, Stanley Gill, Gregory Kryukov, Sasha Pantel, Will Harrington, Mike Burger, Robin Meyers, Levi Ali, Amy Goodale, Yenarae Lee, Levi Garraway, Jesse Boehm, David Root, Todd Golub, William Hahn. Emerging targets from Cancer Dependency Map v0.1. [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 B44.</jats:p
Targetable vulnerabilities in T- and NK-cell lymphomas identified through preclinical models
No full textT- and NK-cell lymphomas (TCL) are a heterogenous group of lymphoid malignancies with poor prognosis. In contrast to B-cell and myeloid malignancies, there are few preclinical models of TCLs, which has hampered the development of effective therapeutics. Here we establish and characterize preclinical models of TCL. We identify multiple vulnerabilities that are targetable with currently available agents (e.g., inhibitors of JAK2 or IKZF1) and demonstrate proof-of-principle for biomarker-driven therapies using patient-derived xenografts (PDXs). We show that MDM2 and MDMX are targetable vulnerabilities within TP53-wild-type TCLs. ALRN-6924, a stapled peptide that blocks interactions between p53 and both MDM2 and MDMX has potent in vitro activity and superior in vivo activity across 8 different PDX models compared to the standard-of-care agent romidepsin. ALRN-6924 induced a complete remission in a patient with TP53-wild-type angioimmunoblastic T-cell lymphoma, demonstrating the potential for rapid translation of discoveries from subtype-specific preclinical models
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