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An Interpretable Machine Learning Framework for Non-Small Cell Lung Cancer Drug Response Analysis

Ann Rachel, Pranav M Pawar, Mithun Mukharjee, Raja M, Tojo Mathew

Abstract

Lung cancer is a condition where there is abnormal growth of malignant cells that spread in an uncontrollable fashion in the lungs. Some common treatment strategies are surgery, chemotherapy, and radiation which aren't the best options due to the heterogeneous nature of cancer. In personalized medicine, treatments are tailored according to the individual's genetic information along with lifestyle aspects. In addition, AI-based deep learning methods can analyze large sets of data to find early signs of cancer, types of tumor, and prospects of treatment. The paper focuses on the development of personalized treatment plans using specific patient data focusing primarily on the genetic profile. Multi-Omics data from Genomics of Drug Sensitivity in Cancer have been used to build a predictive model along with machine learning techniques. The value of the target variable, LN-IC50, determines how sensitive or resistive a drug is. An XGBoost regressor is utilized to predict the drug response focusing on molecular and cellular features extracted from cancer datasets. Cross-validation and Randomized Search are performed for hyperparameter tuning to further optimize the model's predictive performance. For explanation purposes, SHAP (SHapley Additive exPlanations) was used. SHAP values measure each feature's impact on an individual prediction. Furthermore, interpreting feature relationships was performed using DeepSeek, a large language model trained to verify the biological validity of the features. Contextual explanations regarding the most important genes or pathways were provided by DeepSeek alongside the top SHAP value constituents, supporting the predictability of the model.

An Interpretable Machine Learning Framework for Non-Small Cell Lung Cancer Drug Response Analysis

Abstract

Lung cancer is a condition where there is abnormal growth of malignant cells that spread in an uncontrollable fashion in the lungs. Some common treatment strategies are surgery, chemotherapy, and radiation which aren't the best options due to the heterogeneous nature of cancer. In personalized medicine, treatments are tailored according to the individual's genetic information along with lifestyle aspects. In addition, AI-based deep learning methods can analyze large sets of data to find early signs of cancer, types of tumor, and prospects of treatment. The paper focuses on the development of personalized treatment plans using specific patient data focusing primarily on the genetic profile. Multi-Omics data from Genomics of Drug Sensitivity in Cancer have been used to build a predictive model along with machine learning techniques. The value of the target variable, LN-IC50, determines how sensitive or resistive a drug is. An XGBoost regressor is utilized to predict the drug response focusing on molecular and cellular features extracted from cancer datasets. Cross-validation and Randomized Search are performed for hyperparameter tuning to further optimize the model's predictive performance. For explanation purposes, SHAP (SHapley Additive exPlanations) was used. SHAP values measure each feature's impact on an individual prediction. Furthermore, interpreting feature relationships was performed using DeepSeek, a large language model trained to verify the biological validity of the features. Contextual explanations regarding the most important genes or pathways were provided by DeepSeek alongside the top SHAP value constituents, supporting the predictability of the model.
Paper Structure (18 sections, 8 figures, 4 tables)

This paper contains 18 sections, 8 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Traditional Methods
  3. Personalized Medicine in Lung Cancer
  4. AI Applications
  5. Key Components of the Proposed Method
  6. Related Works
  7. ML/DL Applications for Personalised Medicine in Cancer
  8. AI Applications of Personalised Medicine in Lung Cancer
  9. Methodology
  10. Model Framework for Drug Sensitivity Prediction
  11. Dataset
  12. Data Preprocessing
  13. Development and Implementation of Model
  14. Explainability
  15. Results and Discussion
  16. ...and 3 more sections

Figures (8)

  • Figure 1: AI/ML for Personalised Cancer Treatment
  • Figure 2: Histogram Showing the Distribution of Log-Transformed IC50 (LN-IC50) Across All Cancer Samples
  • Figure 3: Boxplot representation of LN-IC50 Distribution in LUAD vs LUSC
  • Figure 4: Proposed Predictive Model Architecture for Personalized Lung Cancer Drug Response Treatment
  • Figure 5: Model Flowchart for Drug Response Prediction System
  • ...and 3 more figures