SuperDendrix algorithm integrates genetic dependencies and genomic alterations across pathways and cancer types

Park, Tae Yoon; Leiserson, Mark D.M.; Klau, Gunnar W.; Raphael, Benjamin J.

doi:10.1016/j.xgen.2022.100099

Cited by 3 publications

(2 citation statements)

References 124 publications

(201 reference statements)

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“…To test this concept, we developed a method named functional pathway inference analysis (FPIA), which uses gene dependency data (cell viability data as a function of genome-wide gene targeting) to reveal genes whose inhibition produces similar cell survival phenotypes across cell lines. This is distinct from previously published methods where the aim was to identify genetic or other perturbations correlated to gene dependencies ( Park et al , 2022 ). To evaluate the performance of FPIA, we chose to examine the Class 1A phosphoinositide 3-kinase (PI3K)-AKT signalling pathway as this is one of the most frequently dysregulated pathways in cancer ( Bachman et al , 2004 ; Boehm and Golub, 2015 ; Meyers et al , 2017 ; Tsherniak et al , 2017 ; Vanhaesebroeck et al , 2010 ; Yu et al , 2016 ).…”

Section: Introductionmentioning

confidence: 90%

Systematic identification of biochemical networks in cancer cells by functional pathway inference analysis

Badshah

Cutillas

2022

Bioinformatics

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Motivation Pathway inference methods are key for annotating the genome, for providing insights into the mechanisms of biochemical processes and allow the discovery of signalling members and potential new drug targets. Here, we tested the hypothesis that genes with similar impact on cell viability across multiple cell lines belong to a common pathway, thus providing a conceptual basis for a pathway inference method based on correlated anti-proliferative gene properties. Methods To test this concept, we used recently available large scale RNAi screens to develop a method, termed Functional Pathway Inference Analysis (FPIA), to systemically identify correlated gene dependencies. Results To assess FPIA, we initially focused on PI3K/AKT/MTOR signalling, a prototypic oncogenic pathway for which we have good sense of ground truth. Dependencies for AKT1, MTOR and PDPK1 were among the most correlated with those for PIK3CA (encoding PI3Kα), as returned by FPIA, whereas negative regulators of PI3K/AKT/MTOR signalling, such as PTEN were anti-correlated. Following FPIA, MTOR, PIK3CA and PIK3CB produced significantly greater correlations for genes in the PI3K-Akt pathway versus other pathways. Application of FPIA to two additional pathways (p53 and MAPK) returned expected associations (e.g., MDM2 and TP53BP1 for p53 and MAPK1 and BRAF for MEK1). Over-representation analysis of FPIA-returned genes enriched the respective pathway, and FPIA restricted to specific tumour lineages uncovered cell type-specific networks. Overall, our study demonstrates the ability of FPIA to identify members of pro-survival biochemical pathways in cancer cells. Availability FPIA is implemented in a new R package named ‘cordial’ freely available from https://github.com/CutillasLab/cordial. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Introductionmentioning

confidence: 90%

Systematic identification of biochemical networks in cancer cells by functional pathway inference analysis

Badshah

Cutillas

2022

Bioinformatics

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“…Importantly, although dependency relationships such as mutual exclusivity and co-occurrence are often described as properties of individual driver mutations, the typical practice is to analyze these dependencies at the gene level, treating all observed nonsynonymous single-nucleotide mutations in a gene identically [52, 72, 41, 43, 15, 10, 42, 68, 36, 16, 35,2,45]. (Some methods also analyze larger alterations such as copy number aberrations (CNAs) or DNA methylation changes [59, 41, 10], but we restrict our attention to single nucleotide somatic mutations, which are the vast majority of somatic mutations analyzed by existing methods.) There are three major reasons why mutual exclusivity and co-occurrence analysis is typically performed at the gene level.…”

Section: Introductionmentioning

confidence: 99%

A latent variable model for evaluating mutual exclusivity and co-occurrence between driver mutations in cancer

Shuaibi,

Chitra,

Raphael

2024

Preprint

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A key challenge in cancer genomics is understanding the functional relationships and dependencies between combinations of somatic mutations that drive cancer development. Such driver mutations frequently exhibit patterns of mutual exclusivity or co-occurrence across tumors, and many methods have been developed to identify such dependency patterns from bulk DNA sequencing data of a cohort of patients. However, while mutual exclusivity and co-occurrence are described as properties of driver mutations, existing methods do not explicitly disentangle functional, driver mutations from neutral, passenger mutations. In particular, nearly all existing methods evaluate mutual exclusivity or co-occurrence at the gene level, marking a gene as mutated if any mutation - driver or passenger - is present. Since some genes have a large number of passenger mutations, existing methods either restrict their analyses to a small subset of suspected driver genes - limiting their ability to identify novel dependencies - or make spurious inferences of mutual exclusivity and co-occurrence involving genes with many passenger mutations. We introduce DIALECT, an algorithm to identify dependencies between pairs of driver mutations from somatic mutation counts. We derive a latent variable mixture model for drivers and passengers that combines existing probabilistic models of passenger mutation rates with a latent variable describing the unknown status of a mutation as a driver or passenger. We use an expectation maximization (EM) algorithm to estimate the parameters of our model, including the rates of mutually exclusivity and co-occurrence between drivers. We demonstrate that DIALECT more accurately infers mutual exclusivity and co-occurrence between driver mutations compared to existing methods on both simulated mutation data and somatic mutation data from 5 cancer types in The Cancer Genome Atlas (TCGA).

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The permissive binding theory of cancer

Weisman

2023

Front. Oncol.

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The later stages of cancer, including the invasion and colonization of new tissues, are actively mysterious compared to earlier stages like primary tumor formation. While we lack many details about both, we do have an apparently successful explanatory framework for the earlier stages: one in which genetic mutations hold ultimate causal and explanatory power. By contrast, on both empirical and conceptual grounds, it is not currently clear that mutations alone can explain the later stages of cancer. Can a different type of molecular change do better? Here, I introduce the “permissive binding theory” of cancer, which proposes that novel protein binding interactions are the key causal and explanatory entity in invasion and metastasis. It posits that binding is more abundant at baseline than we observe because it is restricted in normal physiology; that any large perturbation to physiological state revives this baseline abundance, unleashing many new binding interactions; and that a subset of these cause the cellular functions at the heart of oncogenesis, especially invasion and metastasis. Significant physiological perturbations occur in cancer cells in very early stages, and generally become more extreme with progression, providing interactions that continually fuel invasion and metastasis. The theory is compatible with, but not limited to, causal roles for the diverse molecular changes observed in cancer (e.g. gene expression or epigenetic changes), as these generally act causally upstream of proteins, and so may exert their effects by changing the protein binding interactions that occur in the cell. This admits the possibility that molecular changes that appear quite different may actually converge in creating the same few protein complexes, simplifying our picture of invasion and metastasis. If correct, the theory offers a concrete therapeutic strategy: targeting the key novel complexes. The theory is straightforwardly testable by large-scale identification of protein interactions in different cancers.

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SuperDendrix algorithm integrates genetic dependencies and genomic alterations across pathways and cancer types

Cited by 3 publications

References 124 publications

Systematic identification of biochemical networks in cancer cells by functional pathway inference analysis

Systematic identification of biochemical networks in cancer cells by functional pathway inference analysis

A latent variable model for evaluating mutual exclusivity and co-occurrence between driver mutations in cancer

The permissive binding theory of cancer

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