HRD Tumor Classification Using Genomic Feature Selection
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Solution Overview
Problem
Current methods for classifying tumors as homologous recombination deficiency (HRD) positive or negative are inaccurate and inefficient, particularly for cancers like pancreatic, breast, or prostate cancer, due to insufficient feature selection techniques leading to overfitting and inability to determine HRD status effectively.
Innovation Solution
A trained HRD model is used to characterize tumors by selecting a subset of genomic features, including copy number and short variant features, to accurately classify tumors as HRD-positive or HRD-negative, enabling treatment with combination therapies like fluorouracil and platinum-based chemotherapeutics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current feature selection techniques are used to identify HRD status, then the process can be performed, but the classification accuracy is insufficient due to overfitting
Solution Approach 1:
The patent extracts and removes irrelevant or redundant genomic features from the analysis, keeping only the most discriminative features for HRD status classification. This feature extraction process eliminates features that contribute to overfitting while preserving those that provide genuine predictive signal, thereby improving classification accuracy and model reliability simultaneously.
Solution Approach 2:
The patent employs cross-validation techniques where the model is trained on one dataset and tested on another, creating multiple copies of the validation process. This allows the team to assess whether the model truly generalizes or merely overfits to training data, enabling selection of features that provide robust, reproducible classification performance across different patient cohorts.
2Loss of information
If comprehensive genomic features are analyzed, then more information is available, but processing efficiency decreases
Solution Approach 1:
The patent extracts only the most informative genomic features required for HRD classification, removing redundant features from the analysis pipeline. This selective feature extraction maintains the essential genomic information needed for accurate classification while dramatically reducing computational burden and processing time, enabling efficient clinical implementation.
Solution Approach 2:
The patent segments the genomic analysis into distinct feature categories (e.g., copy number features, structural variation features, sequence features) and processes each segment independently using optimized algorithms. This segmentation allows parallel processing of different feature types, maintaining comprehensive genomic information analysis while improving overall processing efficiency through divided computational tasks.
3Measurement precision
If feature selection is performed without proper techniques, then the model can be trained, but it suffers from overfitting and cannot accurately determine HRD status
Solution Approach 1:
The patent performs preliminary feature selection and model validation using established statistical criteria and cross-validation protocols before final model deployment. By pre-establishing robust feature selection criteria and validating the model architecture in advance, the system achieves accurate HRD status determination without requiring overly complex real-time feature selection processes during clinical use.
Solution Approach 2:
The patent implements feedback loops where model performance on validation data continuously informs feature selection decisions. Features that contribute to overfitting are identified through performance degradation in cross-validation and are removed or downweighted, while features that improve generalization are retained. This feedback-driven feature selection achieves high classification accuracy with a streamlined feature set, avoiding unnecessary complexity.
Data Source
AI summary
Described herein are methods, devices, and systems for identifying a subset of a plurality of features, using one or more feature importance metrics, for training and using a homologous repair deficiency (HRD) classification model. Further described are methods, devices, and systems for classifying a tumor of a cancer, such as pancreatic cancer, as likely HRD positive or likely HRD negative, and for calling the tumor as HRD positive or HRD negative. Also described herein are methods of treating a tumor of a cancer, such as pancreatic cancer, based on the classifications. The cancer identified as HRD-positive may be particularly sensitive to a combination therapy comprising fluorouracil and a platinum-based chemotherapeutic agent (e.g., oxaliplatin), for example FOLFIRINOX.