Individualized Prediction of Drug Response and Rational Combination Therapy in NSCLC Using Artificial
Elizabeth A Coker1,2,3, Adam Stewart4,5, Bugra Ozer1,3
1Department of Data Science, The Institute of Cancer Research, London, United Kingdom.
Abstract:
We hypothesize that the study of acute protein perturbation in signal transduction by targeted anticancer drugs can predict drug sensitivity of these agents used as single agents and rational combination therapy. We assayed dynamic changes in 52 phosphoproteins caused by an acute exposure (1 hour) to clinically relevant concentrations of seven targeted anticancer drugs in 35 non-small cell lung cancer (NSCLC) cell lines and 16 samples of NSCLC cells isolated from pleural effusions. We studied drug sensitivities across 35 cell lines and synergy of combinations of all drugs in six cell lines (252 combinations). We developed orthogonal machine-learning approaches to predict drug response and rational combination therapy. Our methods predicted the most and least sensitive quartiles of drug sensitivity with an AUC of 0.79 and 0.78, respectively, whereas predictions based on mutations in three genes commonly known to predict response to the drug studied, for example, EGFR, PIK3CA, and KRAS, did not predict sensitivity (AUC of 0.5 across all quartiles). The machine-learning predictions of combinations that were compared with experimentally generated data showed a bias to the highest quartile of Bliss synergy scores (P = 0.0243). We confirmed feasibility of running such assays on 16 patient samples of freshly isolated NSCLC cells from pleural effusions. We have provided proof of concept for novel methods of using acute ex vivo exposure of cancer cells to targeted anticancer drugs to predict response as single agents or combinations. These approaches could complement current approaches using gene mutations/amplifications/rearrangements as biomarkers and demonstrate the utility of proteomics data to inform treatment selection in the clinic.
Insights
Acute protein changes from targeted anticancer drugs predict non-small cell lung cancer (NSCLC) sensitivity. Proteomics data offers a novel approach to guide personalized NSCLC treatment selection.
Area of Science:
- Oncology
- Pharmacology
- Proteomics
Background:
- Targeted anticancer drugs are crucial for non-small cell lung cancer (NSCLC) treatment.
- Predicting drug sensitivity and combination therapy effectiveness remains a challenge.
- Current biomarkers (gene mutations) have limitations in predicting treatment response.
Purpose of the Study:
- To investigate if acute protein perturbation by targeted anticancer drugs can predict drug sensitivity in NSCLC.
- To develop machine-learning models for predicting single-agent and combination therapy response.
- To assess the feasibility of using ex vivo patient samples for drug response assays.
Main Methods:
- Assayed dynamic changes in 52 phosphoproteins after 1-hour drug exposure in 35 NSCLC cell lines.
- Evaluated drug sensitivities and synergy of 252 drug combinations.
- Developed orthogonal machine-learning approaches for response prediction.
- Validated predictions against experimental data and compared with gene mutation-based predictions.
Main Results:
- Machine-learning models accurately predicted drug sensitivity quartiles (AUC 0.79-0.78), outperforming gene mutation-based predictions (AUC 0.5).
- Model predictions for drug combinations showed a significant bias towards high synergy scores (P = 0.0243).
- Demonstrated feasibility of performing these proteomic assays on patient-derived NSCLC cells from pleural effusions.
Conclusions:
- Acute proteomic profiling can predict targeted drug sensitivity and combination therapy response in NSCLC.
- Proteomics-based predictions offer a promising complement to traditional genetic biomarkers.
- This approach provides a proof of concept for using ex vivo drug exposure to inform clinical treatment selection.
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