Personalized Medicine: AI Algorithms for Drug Sensitivity Prediction Based on Genomic Data

Project Overview

This project focuses on developing machine learning models to predict drug sensitivity in cancer treatment, utilizing genomic data from the Genomics of Drug Sensitivity in Cancer (GDSC) dataset. The goal is to uncover patterns in drug responses based on genetic variations in cancer cell lines to enable personalized medicine. The project applies exploratory data analysis (EDA) to identify important biomarkers and uses predictive models like Random Forest, Support Vector Machines (SVM), and Linear Regression to predict drug responses. The insights aim to assist in precision oncology, helping tailor treatments to the genetic profiles of cancer patients.

Table of Contents

1. Introduction
2. Background and Motivation
3. Objectives
4. Methodology
5. Data Description
6. Exploratory Data Analysis
7. Model Development
8. Model Evaluation
9. Results
10. Conclusion and Future Work
11. Requirements

Introduction

Cancer remains one of the leading causes of death worldwide, with patients exhibiting varied responses to treatment due to genetic diversity. This project leverages AI and machine learning to predict cancer drug sensitivity based on genomic features. By analyzing the GDSC dataset, we aim to identify predictive biomarkers for personalized treatments, which could potentially revolutionize cancer therapy and reduce adverse effects by tailoring drugs to the genetic profiles of individual patients.

Background and Motivation

Cancer is highly heterogeneous, meaning patients with the same cancer type can respond differently to treatment. Traditional "one-size-fits-all" treatments often fail to account for this genetic diversity. The emergence of precision medicine aims to customize treatments based on an individual’s genetic profile, improving efficacy and reducing adverse effects. Machine learning provides a powerful tool to analyze high-dimensional genomic data, revealing hidden patterns that predict drug responses. This project aims to apply machine learning to the GDSC dataset, identifying key genomic markers and using them to predict drug sensitivity.

Objectives

1. Exploratory Data Analysis (EDA): Understand the GDSC dataset’s structure, identify key trends, and uncover relationships between genomic features and drug sensitivity.
2. Feature Selection and Correlation Patterns: Identify biomarkers associated with drug responses by analyzing correlations between genomic features (e.g., mutations, gene expression) and drug efficacy metrics.
3. Develop Predictive Models: Train machine learning models like Random Forest, SVM, and Linear Regression to predict drug sensitivity based on genomic data.
4. Evaluate Model Performance: Assess and compare the performance of various models using metrics such as R2, MAE, and RMSE.
5. Contribute to Precision Medicine: Provide insights into how genetic factors influence drug responses, aiding clinicians in personalizing cancer treatments.

Methodology

1. Data Preprocessing: Clean and preprocess the GDSC dataset by handling missing values, scaling genomic features, and transforming data into a suitable format for machine learning.
2. Exploratory Data Analysis: Perform EDA using visualization techniques like distribution plots, pair plots, heatmaps, and box plots to explore the relationships between genomic features and drug responses.
3. Feature Selection: Based on EDA results, select relevant features (genomic mutations, gene expression levels) that are likely to influence drug response.
4. Model Training: Train predictive models (Random Forest, SVM, Linear Regression) using the processed data, leveraging feature interactions and genomic data to predict drug sensitivity.
5. Model Evaluation: Evaluate models using appropriate performance metrics (R2, MAE, RMSE) and compare their effectiveness in predicting drug sensitivity.

Data Description

The project uses the Genomics of Drug Sensitivity in Cancer (GDSC) dataset, which includes comprehensive data on genomic mutations, gene expression levels, copy number variations, and drug response data for a wide range of cancer cell lines. The drug response is measured in terms of IC50 values (the concentration of a drug required to inhibit cell growth by 50%). The dataset includes information on hundreds of drug treatments and thousands of genomic features, enabling the development of predictive models for drug sensitivity.

Exploratory Data Analysis

The EDA phase helps uncover important patterns and relationships within the data. Key activities during this phase include:

• Distribution Plots: Visualizing the distribution of drug response values across different cancer types and drug treatments.
• Cancer Type Distribution: Exploring how drug responses vary across different types of cancer.
• Correlation Heatmaps: Identifying relationships between genomic features (mutations, gene expression) and drug sensitivity.
• Cancer-Type Specific Boxplots: Understanding the variations in drug sensitivity within specific cancer types.

Model Development

The machine learning models used for drug sensitivity prediction include:

• Random Forest: A robust ensemble learning model that performs well with complex datasets and captures interactions between features.
• Support Vector Machines (SVM): A model effective in high-dimensional spaces, particularly suitable for classification tasks in genomics.
• Linear Regression: A simpler model used for understanding the linear relationships between genomic features and drug responses.

These models are trained using the genomic features and drug response data, and their predictive capabilities are evaluated using various performance metrics.

Model Evaluation

The models are evaluated using metrics such as:

• R² (Coefficient of Determination): Measures the proportion of the variance in the dependent variable explained by the model.
• Mean Absolute Error (MAE): The average of the absolute errors between the predicted and actual drug responses.
• Root Mean Squared Error (RMSE): The square root of the mean of squared errors, providing an indication of the model's predictive accuracy.

These metrics help assess the performance of each model and guide the selection of the most effective model for drug sensitivity prediction.

Results

The results section provides insights into the performance of each machine learning model. Key findings include:

• Random Forest: Achieved the highest predictive accuracy, indicating the importance of feature interactions in understanding drug sensitivity.
• SVM and Linear Regression: Provided useful insights but performed less effectively than Random Forest in capturing complex relationships in the data.

Conclusion and Future Work

This project demonstrates the power of AI in personalized oncology by predicting drug sensitivity based on genomic data. The insights gained can guide oncologists in selecting the most effective therapies based on the genetic profile of individual patients. Future work could involve:

• Expanding the dataset to include more cancer types and drugs.
• Refining models with more advanced techniques such as deep learning.
• Integrating multi-omics data (e.g., proteomics, metabolomics) to enhance prediction accuracy.

Requirements

To run this project, the following packages and libraries are required:

• Python 3.7 or higher
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn
• XGBoost