Identifying Modules of Cooperating Cancer Drivers

Klein, Michael; Cannataro, Vincent L.; Townsend, Jeffrey P.; Newman, Scott; Stern, David F.; Zhao, Hongyu

doi:10.1101/2020.06.29.168229

Cited by 3 publications

(7 citation statements)

References 99 publications

(93 reference statements)

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“…For instance, lung adenocarcinoma tumor mutations in LRP1B have been associated with chronic obstruction pul-monary disease in patients [29], and have also been suggested to be useful pre-dictors of response to immune checkpoint inhibitors [30]. Moreover, our analyses of epistatic interactions corroborates previous studies that suggest that LRP1B cooperates with TP53 to induce strong selection for high-effect driver mutations of KRAS [5]. An intermediate mutation of LRP1B might compose a valley in the cancer fitness landscape that needs to be crossed to obtain a substantial advantage in selection with a final mutation in KRAS [31].…”

Section: Discussionsupporting

confidence: 84%

“…Despite their lower overall effect size, LRP1B mutations in lung adenocarcinoma have been associated with chronic obstruction pulmonary disease [20], and suggested as predictors of response to immune checkpoint inhibitors [21]. Additionally, LRP1B appears to cooperate with TP53 to induce a large selection for mutant KRAS as a driver of lung adenocarcinoma [3].…”

Section: Discussionmentioning

confidence: 99%

“…Empirically, patterns of mutual exclusivity can be interpreted as a consequence of antagonistic epistasis, and patterns of comutation can be interpreted as a consequence of synergistic epistasis. Other approaches have been applied to identify sets of variants that are sufficient to cause cancer [3]. However, to reveal the evolutionary genetic trajectories of cancer that could be intercepted by precision therapeutic approaches to delay or deny cancer morbidity and mortality due to metastasis, quantification of higher order epistatic effects are needed [4].…”

Section: Introductionmentioning

confidence: 99%

“…Mathematical models in oncology can help to understand the mechanisms of oncogenesis, cancer progression, and optimal treatment [2]. Computational models have revealed extensive pairwise epistasis among germline variants and among gene knockdowns [3, 4], and approaches have been applied to identify sets of variants that are sufficient to cause cancer [5]. Among somatic mutations, patterns of mutual exclusivity have been interpreted as a consequence of antagonistic epistasis, and patterns of co-mutation have been interpreted as a consequence of synergistic epistasis.…”

Section: Introductionmentioning

confidence: 99%

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Pairwise and higher-order epistatic effects among somatic cancer mutations across oncogenesis

Alfaro-Murillo

Townsend

2022

Preprint

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Cancer occurs as a consequence of multiple somatic mutations that lead to uncontrolled cell growth. Mutual exclusivity and co-occurrence of mutations imply---but do not prove---that they can exert synergistic or antagonistic epistatic effects on oncogenesis. Knowledge of these interactions, and the consequent trajectories of mutation and selection that lead to cancer has been a longstanding goal within the cancer research community. Recent research has revealed mutation rates and scaled selection coefficients for specific recurrent variants across many cancer types. However, estimation of pairwise and higher-order effects---essential to estimation of the trajectory of likely cancer genotoypes---has been a challenge. Therefore, we have developed a continuous-time Markov chain model that enables the estimation of mutation origination and fixation (flux), dependent on somatic cancer genotype. Coupling the continuous-time Markov chain model with a deconvolution approach provides estimates of underlying rates of mutation and selection across the trajectory of oncogenesis. We demonstrate computation of fluxes and selection coefficients in a somatic evolutionary model for the four most frequently variant driver genes (TP53, LRP1B, KRAS and STK11) from 585 cases of lung adenocarcinoma. Our analysis reveals multiple antagonistic epistatic effects that reduce the possible routes of oncogenesis, and inform cancer research regarding viable trajectories of somatic evolution whose progression could be forestalled by precision medicine. Synergistic epistatic effects are also identified, most notably in the somatic genotype TP53+LRP1B for mutations in the KRAS gene, and in somatic genotypes containing KRAS or TP53 mutations for mutations in the STK11 gene. Large positive fluxes of KRAS variants were driven by large selection coefficients, whereas the flux toward LRP1B mutations was substantially aided by a large mutation rate for this gene. The approach enables inference of the most likely routes of site-specific variant evolution and estimation of the strength of selection operating on each step along the route, a key component of what we need to know to develop and implement personalized cancer therapies.

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Section: Discussionsupporting

confidence: 84%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pairwise and higher-order epistatic effects among somatic cancer mutations across oncogenesis

Alfaro-Murillo

Townsend

2022

Preprint

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“…NFE2L2 is found mutated in tandem with other specific driver combinations not only in LUSC, but also in head-and-neck, bladder, and esophageal cancers. Therefore, Nrf2 pathway inhibition may interact epistatically with other driving variants to inhibit cancer growth [43]. Therapeutic options providing strong inhibition are highly desirable as NFE2L2-mutant lung cancers have historically had a poor prognosis.…”

Section: Discussionmentioning

confidence: 99%

APOBEC mutagenesis and selection for NFE2L2 contribute to the origin of lung squamous-cell carcinoma

et al. 2022

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BiGPICC: a graph-based approach to identifying carcinogenic gene combinations from mutation data

Oles

Dash

Anandakrishnan

2023

Preprint

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Genome data from cancer patients encapsulates explicit and implicit relationships between the presence of a gene mutation and cancer occurrence in a patient. Different types of cancer in human are thought to be caused by combinations of two to nine gene mutations. Identifying these combinations through traditional exhaustive search requires the amount of computation that scales exponentially with the combination size and in most cases is intractable for even the cutting-edge supercomputers. We propose a parameter-free heuristic approach that leverages the intrinsic topology of gene-patient mutations to identify carcinogenic combinations. The biological relevance of the identified combinations is measured by using them to predict the presence of tumor in previously unseen samples. The resulting classifiers for 16 cancer types perform on par with exhaustive search results, and score the average of 80.1% sensitivity and 91.6% specificity for the best choice of hit range per cancer type. Our approach is able to find higher-hit carcinogenic combinations targeting which would take years of computations using exhaustive search.

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Identifying Modules of Cooperating Cancer Drivers

Cited by 3 publications

References 99 publications

Pairwise and higher-order epistatic effects among somatic cancer mutations across oncogenesis

Pairwise and higher-order epistatic effects among somatic cancer mutations across oncogenesis

APOBEC mutagenesis and selection for NFE2L2 contribute to the origin of lung squamous-cell carcinoma

BiGPICC: a graph-based approach to identifying carcinogenic gene combinations from mutation data

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