Abstract 33: Drug sensitivity prediction modeling from genomics, transcriptomics and inferred protein activity
Bibliographic record
Abstract
Abstract Background: Machine learning models that rely on single omics data for drug sensitivity prediction are challenging and frequently fail within precision medicine scenarios. Proteomics, for example, reflects the system biology and the regulatory network better than genomics. However, it is less likely available for many preclinical and in vivo data, mainly due to the cost of data generation. Currently, there are massive amounts of genomics data available, such as CNV, mutation, and RNA-seq signatures. However, these data do not characterize the post-translational modifications in proteins, limiting their utility for biomarker discovery. Objective: The main objective of this study is to overcome the lack of proteomics data gap. Herein, we propose a novel modeling approach to improve the prediction accuracy of drug sensitivity, that is, combining genomics and proteomics signatures. In addition to genomics and transcriptomics data, the model infers proteomic activity from gene expression using VIPER from in vitro data, and this is extendable to in vivo settings. Material and Methods: Using PharmacoGX package developed in our lab, we downloaded, curated, and annotated the genomic and pharmacologic data of the Cancer Cell line Encyclopedia (CCLE), as well as the Cancer Therapeutics Response Portal (CTRPV2) dataset that is a continuation of the CTRP project and the largest pharmacologic screen conducted to date, containing several hundreds of thousands of drug dose-response curves. In this study, we extracted from CCLE dataset the following signatures: RPPA, RNASeq, Mutation, and CNV, then inferred the VIPER protein using RNASeq. Then, we built a model that a) checks the different omics combinations, b) applies random forest with ten-fold cross-validation for sensitivity prediction using CTRPV2, and c) evaluates the significance of each model using the concordance index (CI) package developed in our lab. Results: The proposed model was tested in vitro using CCLE and CTRPv2 common cell lines. We tested the model with drugs having known biomarkers in pharmacogenomics literature. ERBB2 biomarker for lapatinib showed the best CI=0.95 by combining CNV and VIPER models while CI equals 0.77 and 0.9 for each of them, respectively. Moreover, MET biomarker for crizotinib showed best CI=0.89 by integrating RNASeq, Mutation, RPPA, and VIPER, while each model obtained CI=(0.45, 0.5, 0.85, 0.5), respectively. Conclusion: In conclusion, omics integration boosted the drug sensitivity prediction compared to single models. The application of the proposed model in vivo will improve drug development and increase the prediction quality of precision medicine. Citation Format: Hassan Mahmoud, Benjamin Haibe-Kains. Drug sensitivity prediction modeling from genomics, transcriptomics and inferred protein activity [abstract]. In: Proceedings of the AACR Special Conference on Advancing Precision Medicine Drug Development: Incorporation of Real-World Data and Other Novel Strategies; Jan 9-12, 2020; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_1):Abstract nr 33.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".