<i>KRAS</i>mutations impact clinical outcome in metastatic non-small cell lung cancer

Eklund, Ella Ä.; Wiel, Clotilde; Fagman, Henrik; Akyürek, Levent M.; Raghavan, Sukanya; Nyman, Jan; Hallqvist, Andreas; Sayin, Volkan I.

doi:10.1101/2021.11.27.21266822

Cited by 1 publication

(1 citation statement)

References 51 publications

(83 reference statements)

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“…We observe that TP53 mutation status is associated with resistance to three drugs, concordant with earlier observations showing that TP53 mutant are more resistant to chemotherapy [14]. Resistance to Gemcitabine and Vincristine is also associated with KRAS and PI3KCA mutations, known for their proliferative potential [18, 10]. Interestingly, mutations in MYC and MAPK8IP2 are associated with sensitivity to these three drugs.…”

Section: Resultssupporting

confidence: 90%

Percolate: an exponential family JIVE model to design DNA-based predictors of drug response

Mourragui

Loog

Nee³

et al. 2022

Preprint

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Motivation: Anti-cancer drugs may elicit resistance or sensitivity through mechanisms which involve several genomic layers. Nevertheless, we have demonstrated that gene expression contains most of the predictive capacity compared to the remaining omic data types. Unfortunately, this comes at a price: gene expression biomarkers are often hard to interpret and show poor robustness. Results: To capture the best of both worlds, i.e. the accuracy of gene expression and the robustness of other genomic levels, such as mutations, copy-number or methylation, we developed Percolate, a computational approach which extracts the joint signal between gene expression and the other omic data types. We developed an out-of-sample extension of Percolate which allows predictions on unseen samples without the necessity to recompute the joint signal on all data. We employed Percolate to extract the joint signal between gene expression and either mutations, copy-number or methylation, and used the out-of sample extension to perform response prediction on unseen samples. We showed that the joint signal recapitulates, and sometimes exceeds, the predictive performance achieved with each data type individually. Importantly, molecular signatures created by Percolate do not require gene expression to be evaluated, rendering them suitable to clinical applications where only one data type is available.

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Section: Resultssupporting

confidence: 90%