Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies

Geeleher, Paul; Zhang, Zhenyu; Wang, Fan; Gruener, Robert F.; Nath, Aritro; Morrison, Gladys; Bhutra, Steven; Grossman, Robert L.; Huang, R. Stephanie

doi:10.1101/gr.221077.117

Cited by 117 publications

(132 citation statements)

References 51 publications

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“…Building on these results, we validated the correlation between EGFR-AS1 and MIR205HG expression with imputed erlotinib response in lung adenocarcinoma (LUAD) patients from the cancer genome atlas lung adenocarcinoma (TCGA) project (Geeleher et al 2017). The analysis of imputed drug response showed higher expression of both EGFR-AS1 and MIR205HG were associated with sensitivity to erlotinib ( Supplementary Figures 4A -D).…”

Section: Lncrnas Augment Drug Response Predictions From Known Pcgs Bimentioning

confidence: 97%

“…RNAseq (RSEM) expression levels of EGFR-AS1 and MIR205HG, and EGFR point mutation, copy number variation data, and survival data for 877 lung adenocarcinoma (LUAD) samples were retrieved using UCSC Xena platform for cancer genomics (Goldman et al 2018). Imputed erlotinib drug sensitivities for TCGA LUAD were obtained from (Geeleher et al 2017).…”

Section: Tcga Lung Adenocarcinoma (Luad) Analysismentioning

confidence: 99%

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Discovering novel long non-coding RNA predictors of anticancer drug sensitivity beyond protein-coding genes

Nath

Lau

Lee

et al. 2019

Preprint

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Large-scale cancer cell line screens have identified thousands of protein-coding genes (PCGs) as biomarkers of anticancer drug response. However, systematic evaluation of long non-coding RNAs (lncRNAs) as pharmacogenomic biomarkers has so far proven challenging. Here, we study the contribution of lncRNAs as drug response predictors beyond spurious associations driven by correlations with proximal PCGs, tissue-lineage or established biomarkers. We show that, as a whole, the lncRNA transcriptome is equally potent as the PCG transcriptome at predicting response to hundreds of anticancer drugs. Analysis of individual lncRNAs transcripts associated with drug response reveals nearly half of the significant associations are in fact attributable to proximal cis-PCGs. However, adjusting for effects of cis-PCGs revealed significant lncRNAs that augment drug response predictions for most drugs, including those with well-established clinical biomarkers. In addition, we identify lncRNA-specific somatic alterations associated with drug response by adopting a statistical approach to determine lncRNAs carrying somatic mutations that undergo positive selection in cancer cells. Lastly, we experimentally demonstrate that two novel lncRNA, EGFR-AS1 and MIR205HG, are functionally relevant predictors of anti-EGFR drug response.3

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Section: Lncrnas Augment Drug Response Predictions From Known Pcgs Bimentioning

confidence: 97%

Section: Tcga Lung Adenocarcinoma (Luad) Analysismentioning

confidence: 99%

Discovering novel long non-coding RNA predictors of anticancer drug sensitivity beyond protein-coding genes

Nath

Lau

Lee

et al. 2019

Preprint

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“…Besides, we annotate drug classes and the AUROC predictions in both the GDSC and CTRP panels. (ii) Network features (columns [10][11][12]. These include distances between module genes and drug targets, "connectivity" within module genes (i.e.…”

Section: Fundingmentioning

confidence: 99%

“…Provided that the panel of cell lines is large enough, this approach allows for a new type of gene expression analysis where basal expression levels are correlated to drug response phenotypes. A series of recent studies demonstrate the value of this strategy for target identification, biomarker discovery, and elucidation of mechanisms of action (MoA) and resistance [8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Encircling the regions of the pharmacogenomic landscape that determine drug response

Fernández‐Torras

Duran‐Frigola

Aloy

2018

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The integration of large-scale drug sensitivity screens and genome-wide experiments is changing the field of pharmacogenomics, revealing molecular determinants of drug response without the need for a priori, hypothesis-driven assumptions about drug action. In particular, transcriptomic signatures of drug sensitivity may guide drug repositioning, the discovery of synergistic drug combinations and suggest new therapeutic biomarkers. However, the inherent complexity of transcriptomic signatures, with thousands of genes differentially expressed, makes them hard to interpret, giving poor mechanistic insights and hampering translation to the clinics. Here we show how network biology can help simplify transcriptomic drug signatures, filtering out irrelevant genes, accounting for tissue-specific biases and ultimately yielding functionally-coherent, less noisy drug modules. We successfully analyzed 170 drugs tested in 637 cancer cell lines, proving a broad applicability of our approach and evincing an intimate relationship between modules' gene expression levels and drugs' mechanisms of action. Further, we have characterized multiple aspects of our transcriptomic modules. As a result, the drugs included in this study are now annotated well beyond the reductionist (targetcentered) view.

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“…15 Ammad-ud-din et al [13] GLM (PLS, SGL, RF, SVM) EXP, PWY LOOCV ρ = 0.375 2017.08. 28 Geeleher et al [14] Ridge EXP 10-fold CV ρ = 0.48 2017.09. 12 Rahman et al [25] RF EXP 3-fold CV AUC = ∼0.3 2017.11.…”

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confidence: 99%

Quantitative Structure-Mutation-Activity Relationship Tests (QSMART) Model for Protein Kinase Inhibitor Response Prediction

Huang

Yeung

Wang

et al. 2019

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Predicting drug sensitivity profiles from genotypes is a major challenge in personalized medicine. Machine learning and deep neural network methods have shown promise in addressing this challenge, but the "black-box" nature of these methods precludes a mechanistic understanding of how and which genomic and proteomic features contribute to the observed drug sensitivity profiles. Here we provide a combination of statistical and neural network framework that not only estimates drug IC 50 in cancer cell lines with high accuracy (R 2 = 0.861 and RMSE = 0.818) but also identifies features contributing to the accuracy, thereby enhancing explainability. Our framework, termed QSMART, uses a multi-component approach that includes (1) collecting drug fingerprints, cancer cell line's multi-omics features, and drug responses, (2) testing the statistical significance of interaction terms, (3) selecting features by Lasso with Bayesian information criterion, and (4) using neural networks to predict drug response. We evaluate the contribution of each of these components and use a case study to explain the biological relevance of several selected features to protein kinase inhibitor response in non-small cell lung cancer cells. Specifically, we illustrate how interaction terms that capture associations between drugs and mutant kinases quantitatively contribute to the response of two EGFR inhibitors (afatinib and lapatinib) in non-small cell lung cancer cells. Although we have tested QSMART on protein kinase inhibitors, it can be extended across the proteome to investigate the complex relationships connecting genotypes and drug sensitivity profiles. Introduction 1 Protein kinases are a class of signaling proteins, greatly valued as therapeutic targets for 2 their key roles in human diseases, such as cancer [1]. For decades, chemotherapy has 3 served as part of a standard set of cancer treatments; however, the resistance of cancer 4 cells to chemotherapy is still a major clinical challenge [2]. Mutations in protein kinase 5 are known to play important roles not only in drug resistance [3] but also in drug 6 sensitivity [4]. Depending on the structural location, mutations can have varying 7 impacts on drug sensitivity. For example, non-small cell lung cancer (NSCLC) cells 8 December 28, 2019 1/25 harboring either the EGFR T790M or L858R mutation respectively leads to resistance 9 or hypersensitivity to the cancer drug gefitinib [5, 6], while those with EGFR 10 T790M/L858R double mutant are only resistant to gefitinib [7]. As mutations impact 11 the efficacy of different cancer drugs, there is a need to incorporate structural 12 knowledge in drug response prediction methods. 13 To facilitate the understanding of the molecular mechanisms that cause drug 14 sensitivity and drug resistance in cancer cells, the Genomics of Drug Sensitivity in 15 Cancer (GDSC) Project [8] recently screened the drug responses of 266 anti-cancer 16 drugs against ∼1,000 human cancer cell lines and provided the largest publicly available 17 drug response datas...

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Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies

Cited by 117 publications

References 51 publications

Discovering novel long non-coding RNA predictors of anticancer drug sensitivity beyond protein-coding genes

Discovering novel long non-coding RNA predictors of anticancer drug sensitivity beyond protein-coding genes

Encircling the regions of the pharmacogenomic landscape that determine drug response

Quantitative Structure-Mutation-Activity Relationship Tests (QSMART) Model for Protein Kinase Inhibitor Response Prediction

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