Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes

Leiserson, Mark D.M.; Vandin, Fabio; Wu, Hsin Ta; Dobson, Jason R.; Eldridge, Jonathan V.; Thomas, Jacob; Papoutsaki, Alexandra; Kim, Younhun; Niu, Beifang; McLellan, Michael D.; Lawrence, Michael S.; González-Pérez, Abel; Tamborero, David; Cheng, Yu‐Wei; Ryslik, Gregory A.; López‐Bigas, Núria; Getz, Gad; Li, Ding; Raphael, Benjamin J.

doi:10.1038/ng.3168

Cited by 804 publications

(935 citation statements)

References 68 publications

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“…Alternatively, more generic signatures of dynamic (e.g. transcriptional) output may first be used to identify a mechanistic rationale 19,20,21,22 to which causative genetic or epigenetic events can then be inferred and aligned as predictive features 23,24 . A surprising result of our Challenge, however, suggested only modest improvement to prediction from inclusion of all data in SC1A compared to only genetics in SC1B.…”

Section: Discussionmentioning

confidence: 99%

A cancer pharmacogenomic screen powering crowd-sourced advancement of drug combination prediction

Wang

Guan

et al. 2017

Preprint

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In the last decade advances in genomics, uptake of targeted therapies, and the advent of personalized treatments have fueled a dramatic change in cancer care. However, the effectiveness of most targeted therapies is short lived, as tumors evolve and develop resistance. Combinations of drugs offer the potential to overcome resistance. The space of possible combinations is vast, and significant advances are required to effectively find optimal treatment regimens tailored to a patient's tumor. DREAM and AstraZeneca hosted a Challenge open to the scientific community aimed at computational prediction of synergistic drug combinations and predictive biomarkers associated to these combinations. We released a data set comprising ~11,500 experimentally tested drug combinations, coupled to deep molecular characterization of the respective 85 cancer cell lines. Among 150 submitted approaches, those that incorporated prior knowledge of putative drug targets showed superior performance predicting drug synergy across independent data. Genomic features of best-performing models revealed putative mechanisms of drug synergy for multiple drugs in combination with PI3K/AKT pathway inhibitors.All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

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Section: Discussionmentioning

confidence: 99%

A cancer pharmacogenomic screen powering crowd-sourced advancement of drug combination prediction

Wang

Guan

et al. 2017

Preprint

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“…The lack of chromosome abnormalities in many cancers with cohesin alterations raises the intriguing possibility that cohesin contributes to cancer through its role in transcription (3,6,7). In MCF7 breast cancer cells, cohesin binds throughout the genome in combination with ER, leading to the idea that cohesin is essential for estrogen-dependent transcription (15,18,36,37).…”

Section: Discussionmentioning

confidence: 99%

“…Recently, cancer genome sequencing projects have revealed that genes encoding cohesin subunits are frequently mutated in several different types of cancer, with particularly high frequency in acute myeloid leukemia (AML) (5)(6)(7)(8)(9). Recent mouse models indicate that cohesin contributes to leukemia progression likely through controlling transcription and genome organization, rather than through chromosome separation (10 -12).…”

mentioning

confidence: 99%

HDAC8 Inhibition Blocks SMC3 Deacetylation and Delays Cell Cycle Progression without Affecting Cohesin-dependent Transcription in MCF7 Cancer Cells

Dasgupta

Antony

Braithwaite

et al. 2016

Journal of Biological Chemistry

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Cohesin, a multi-subunit protein complex involved in chromosome organization, is frequently mutated or aberrantly expressed in cancer. Multiple functions of cohesin, including cell division and gene expression, highlight its potential as a novel therapeutic target. The SMC3 subunit of cohesin is acetylated (ac) during S phase to establish cohesion between replicated chromosomes. Following anaphase, ac-SMC3 is deacetylated by HDAC8. Reversal of SMC3 acetylation is imperative for recycling cohesin so that it can be reloaded in interphase for both non-mitotic and mitotic functions. We blocked deacetylation of ac-SMC3 using an HDAC8-specific inhibitor PCI-34051 in MCF7 breast cancer cells, and examined the effects on transcription of cohesin-dependent genes that respond to estrogen. HDAC8 inhibition led to accumulation of ac-SMC3 as expected, but surprisingly, had no influence on the transcription of estrogen-responsive genes that are altered by siRNA targeting of RAD21 or SMC3. Knockdown of RAD21 altered estrogen receptor ␣ (ER) recruitment at SOX4 and IL20, and affected transcription of these genes, while HDAC8 inhibition did not. Rather, inhibition of HDAC8 delayed cell cycle progression, suppressed proliferation and induced apoptosis in a concentration-dependent manner. We conclude that HDAC8 inhibition does not change the estrogen-specific transcriptional role of cohesin in MCF7 cells, but instead, compromises cell cycle progression and cell survival. Our results argue that candidate inhibitors of cohesin function may differ in their effects depending on the cellular genotype and should be thoroughly tested for predicted effects on cohesin's mechanistic roles.The cohesin complex is a chromatin-associated multi-subunit protein comprised of two SMC (structural maintenance of chromosomes, SMC1A and SMC3) 2 and two non-SMC subunits (RAD21, STAG1/2). Cohesin's canonical function establishes cohesion between replicated sister chromatids, thus ensuring their precise segregation at anaphase (1, 2). Research over the last one and a half decades has revealed that cohesin has additional cohesion-independent functions in interphase nuclei, such as chromatin organization and transcription regulation (3, 4).Recently, cancer genome sequencing projects have revealed that genes encoding cohesin subunits are frequently mutated in several different types of cancer, with particularly high frequency in acute myeloid leukemia (AML) (5-9). Recent mouse models indicate that cohesin contributes to leukemia progression likely through controlling transcription and genome organization, rather than through chromosome separation (10 -12). In breast cancer, the cohesin subunit RAD21 is overexpressed and confers poor prognosis (13,14). We previously showed that cohesin mediates transcription of the MYC gene (15-17) and modulates the transcription of estrogen-dependent genes in MCF7 breast cancer cells (18). The emergence of cohesin as an important contributor to cancer has generated interest in the development of therapeutics that compromise ...

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“…The gene can be otherwise ranked low by the frequency‐based method as we have shown in this study (Figures 1a and 2 and Figure S17, Supporting Information). Third, since PPI data can be very useful in distinguishing drivers from passengers,18, 21 we proposed the new metric MIF to model mutational impacts between mutated genes in PPI networks, motivated from the gravity principle 23. Finally, we rank a candidate gene by the maximal MIF score considering all its neighbors, which integrates the mutation data with PPI data effectively.…”

Section: Discussionmentioning

confidence: 99%

“…Unfortunately, due to the long‐tail phenomenon, methods based on mutation frequency are underpowered for uncovering infrequently mutated driver genes. The observation that mutations in a cancer genome tend to converge on a few biological pathways,15 has prompted the development of pathway‐based or network‐based approaches to cancer gene discovery 16, 17, 18, 19. These studies showed that functional networks could be helpful in identifying cancer driver genes.…”

Section: Introductionmentioning

confidence: 99%

MaxMIF: A New Method for Identifying Cancer Driver Genes through Effective Data Integration

Hou

Gao

et al. 2018

Advanced Science

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Identification of a few cancer driver mutation genes from a much larger number of passenger mutation genes in cancer samples remains a highly challenging task. Here, a novel method for distinguishing the driver genes from the passenger genes by effective integration of somatic mutation data and molecular interaction data using a maximal mutational impact function (MaxMIF) is presented. When evaluated on six somatic mutation datasets of Pan‐Cancer and 19 datasets of different cancer types from TCGA, MaxMIF almost always significantly outperforms all the existing state‐of‐the‐art methods in terms of predictive accuracy, sensitivity, and specificity. It recovers about 30% more known cancer genes in 500 top‐ranked candidate genes than the best among the other tools evaluated. MaxMIF is also highly robust to data perturbation. Intriguingly, MaxMIF is able to identify potential cancer driver genes, with strong experimental data support. Therefore, MaxMIF can be very useful for identifying or prioritizing cancer driver genes in the increasing number of available cancer genomic data.

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Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes

Cited by 804 publications

References 68 publications

A cancer pharmacogenomic screen powering crowd-sourced advancement of drug combination prediction

A cancer pharmacogenomic screen powering crowd-sourced advancement of drug combination prediction

HDAC8 Inhibition Blocks SMC3 Deacetylation and Delays Cell Cycle Progression without Affecting Cohesin-dependent Transcription in MCF7 Cancer Cells

MaxMIF: A New Method for Identifying Cancer Driver Genes through Effective Data Integration

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