The Genomic Landscape of <i>SMARCA4</i> Alterations and Associations with Outcomes in Patients with Lung Cancer

Schoenfeld, Adam J.; Bandlamudi, Chaitanya; Lavery, Jessica A.; Montecalvo, Joseph; Namakydoust, Azadeh; Rizvi, Hira; Egger, Jacklynn V.; Concepcion, Carla P.; Paul, Sonal; Arcila, Maria E.; Daneshbod, Yahya; Chang, Jason C.; Sauter, Jennifer L.; Beras, Amanda; Ladanyi, Marc; Jacks, Tyler; Rudin, Charles M.; Taylor, Barry S.; Donoghue, Mark T.A.; Heller, Glenn; Hellmann, Matthew D.; Rekhtman, Natasha; Riely, Gregory J.

doi:10.1158/1078-0432.ccr-20-1825

Cited by 170 publications

(186 citation statements)

References 32 publications

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“…Moreover, KRAS , KEAP1 , STK11 , SMARCA4 , and NF1 form a subgraph. It is known that KRAS , KEAP1 , STK11 , and SMARCA4 co‐occur in nonsmall cell lung cancers [45] and our algorithm suggests that KRAS and KEAP1 are early events in those tumors.…”

Section: Discussionmentioning

confidence: 91%

Oncogenetic network estimation with disjunctive Bayesian networks

et al. 2021

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Motivation: Cancer is the process of accumulating genetic alterations that confer selective advantages to tumor cells. The order in which aberrations occur is not arbitrary, and inferring the order of events is challenging due to the lack of longitudinal samples from tumors. Moreover, a network model of oncogenesis should capture biological facts such as distinct progression trajectories of cancer subtypes and patterns of mutual exclusivity of alterations in the same pathways. In this paper, we present the disjunctive Bayesian network (DBN), a novel oncogenetic model with a phylogenetic interpretation. DBN is expressive enough to capture cancer subtypes' trajectories and mutually exclusive relations between alterations from unstratified data. Results: In cases where the number of studied alterations is small (<30), we provide an efficient dynamic programming implementation of an exact structure learning method that finds a best DBN in the superexponential search space of networks. In rare cases that the number of alterations is large, we provided an efficient genetic algorithm in our software package, OncoBN. Through numerous synthetic and real data experiments, we show OncoBN's ability in inferring ground truth networks and recovering biologically meaningful progression networks. Availability: OncoBN is implemented in R and is available at https://github.com/phillipnicol/OncoBN.

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Section: Discussionmentioning

confidence: 91%

Oncogenetic network estimation with disjunctive Bayesian networks

et al. 2021

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“…Treatment with ICI was associated with improved outcomes in patients with SMARCA4 -mutant NSCLC and mutations in STK11 and KEAP1 had the strongest association with SMARCA4 -mutant tumors. 41 We thus assessed the interaction between SMARCA4 status and treatments in SKmut aNSCLC patients and no significant result was found (interaction P = .900). Similar analysis was performed between NRF2 status and treatments, and the interaction was also not significant (interaction P = .611).…”

Section: Resultsmentioning

confidence: 97%

Atezolizumab prolongs overall survival over docetaxel in advanced non-small-cell lung cancer patients harboring STK11 or KEAP1 mutation

et al. 2021

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Somatic mutations of STK11 or KEAP1 are associated with poor clinical outcomes for advanced non-small-cell lung cancer (aNSCLC) patients receiving immune checkpoint inhibitors (ICIs), chemotherapy, or targeted therapy. Which treatment regimens work better for STK11 or KEAP1 mutated (SKmut) aNSCLC patients is unknown. In this study, the efficacy of atezolizumab versus docetaxel in SKmut aNSCLC was compared. A total of 157 SKmut aNSCLC patients were identified from POPLAR and OAK trials, who were tested by blood-based FoundationOne next-generation sequencing assay. Detailed clinical data and genetic alterations were collected. Two independent cohorts were used for biomarker validation (n = 30 and 20, respectively). Median overall survival was 7.3 months (95% confidence interval [CI], 4.8 to 9.9) in the atezolizumab group versus 5.8 months (95% CI, 4.4 to 7.2) in the docetaxel group (adjusted hazard ratio [HR] for death, 0.70; 95% CI, 0.49 to 0.99; P = .042). Among atezolizumab-treated patients, objective response rate, disease control rate, and durable clinical benefit were higher when blood tumor mutation burden (bTMB) and PD-L1 being higher (biomarker 1, n = 61) or with FAT3 mutation-positive tumors (biomarker 2, n = 83) than otherwise. The interactions for survival between these two biomarkers and treatments were significant, which were further validated in two independent cohorts. In SKmut patients with aNSCLC, atezolizumab was associated with significantly longer overall survival in comparison to docetaxel. Having FAT3 mutation or high TMB and PD-L1 expression potentially predict favorable response in SKmut patients receiving atezolizumab.

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“…Moreover, KRAS , KEAP1 , STK11 , SMARCA4 , and NF1 form a subgraph. It is known that KRAS , KEAP1 , STK11 and SMARCA4 cooccur in non-small cell lung cancers (Schoenfeld et al, 2020) and our algorithm suggests that KRAS and KEAP1 are early events in those tumors.…”

Section: Discussionmentioning

confidence: 87%

Oncogenetic Network Estimation with Disjunctive Bayesian Networks

Nicol

Coombes

Deaver³

et al. 2020

Preprint

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Cancer is the process of accumulating genetic alterations that confer selective advantages to tumor cells. The order in which aberrations occur is not arbitrary, and inferring the order of events is a challenging problem due to the lack of longitudinal samples from tumors. Moreover, a network model of oncogenesis should capture biological facts such as distinct progression trajectories of cancer subtypes and patterns of mutual exclusivity of alterations in the same pathways. In this paper, we present the Disjunctive Bayesian Network (DBN), a novel cancer progression model. Unlike previous models of oncogenesis, DBN naturally captures mutually exclusive alterations. Besides, DBN is flexible enough to represent progression trajectories of cancer subtypes, therefore allowing one to learn the progression network from unstratified data, i.e., mixed samples from multiple subtypes. We provide a scalable genetic algorithm to learn the structure of DBN from cross-sectional cancer data. To test our model, we simulate synthetic data from known progression networks and show that our algorithm infers the ground truth network with high accuracy. Finally, we apply our model to copy number data for colon cancer and mutation data for bladder cancer and observe that the recovered progression network matches known biological facts.

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The Genomic Landscape of SMARCA4 Alterations and Associations with Outcomes in Patients with Lung Cancer

Cited by 170 publications

References 32 publications

Oncogenetic network estimation with disjunctive Bayesian networks

Oncogenetic network estimation with disjunctive Bayesian networks

Atezolizumab prolongs overall survival over docetaxel in advanced non-small-cell lung cancer patients harboring STK11 or KEAP1 mutation

Oncogenetic Network Estimation with Disjunctive Bayesian Networks

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