DOI: 10.7910/dvn/baljnt
title: Auditing Shortcut Learning and Misclassification in AI-Based Genomic Subtyping of Breast Cancer
title: Hidden Biases in AI-Powered Genomic Subtyping of Breast Cancer

description: Borges, Julian (2025) "Auditing Shortcut Learning and Misclassification in AI-Based Genomic Subtyping of Breast Cancer - Hidden Biases in AI-Powered Genomic Subtyping of Breast Cancer" Capstone Project - Harvard Medical School (HMS) - Post Graduate Medical Education (PGME) - Global Clinical Scholars Research Training (GCSRT).

* Date: April 2025
* Purpose: To investigate whether machine learning models trained on gene expression data for breast cancer subtyping:

Rely on shortcut features (e.g., ER/PR/HER2, age, tumor size)
Misclassify high-risk patients (especially hormone-sensitive tumors)
Expose risks of black-box AI in clinical genomics
Audit how AI models use features to classify molecular subtypes and detect shortcut learning.
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1-Research Title (Working):
“Auditing Shortcut Learning and Misclassification in AI-Based Genomic Subtyping of Breast Cancer” "Hidden Biases in AI-Powered Genomic Subtyping of Breast Cancer"
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Research Framework:
Design: Retrospective modeling study
Population: Breast cancer patients from TCGA-BRCA dataset
Outcome: Subtype misclassification (Luminal A/B, HER2, Basal-like)
Predictors: Gene expression + clinical features (e.g., age, tumor size, batch ID)
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Hybrid Workflow (STATA + Python)
Tool Task
STATA Data cleaning, survival analysis, epidemiologic stratification, 
 regression modeling
Python (Colab) XGBoost training, prediction, SHAP feature attribution, 
 misclassification flag
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Project Objectives:

- Train and validate machine learning models to classify breast cancer molecular subtypes using gene expression data.
- Apply feature attribution techniques (e.g., SHAP) to audit the model's reliance on non-genomic features.
- Analyze misclassification patterns across clinical and genomic subgroups.
- Propose a framework to detect shortcut learning and flag high-risk predictions before they impact clinical care.

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2-Data Acquisition
Primary Dataset:
TCGA-BRCA (The Cancer Genome Atlas – Breast Cancer Cohort)

Hosted on the UCSC Xena Browser:
https://xenabrowser.net/datapages/?cohort=TCGA%20Breast%20Cancer%20(BRCA)

Also available via the Genomic Data Commons (GDC) Portal:
https://portal.gdc.cancer.gov/projects/TCGA-BRCA

Data includes:

Normalized RNA-Seq gene expression
Molecular subtypes (PAM50)
Clinical variables: ER/PR/HER2 status, age, tumor stage, tumor size
Batch metadata (for shortcut learning detection)
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3-Data Cleaning and Integration

Merge expression data with subtype labels.
Merge clinical metadata (age, stage, ER/PR/HER2 status, tumor size).
Exclude samples with missing subtype or critical metadata.
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4-Model Development
Goal:
Build an AI model to predict subtype → extract predictions → analyze errors.

Modeling Plan:
Input: Top 50–100 genes by variance + clinical features

Model types:
Logistic Regression (baseline)
Random Forest / XGBoost (advanced)

Outcome: 4-class classifier (PAM50 subtype)
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5-Misclassification & Shortcut Detection
Analyze Misclassification:
Create prediction matrix: TP, FP, FN, TN

Identify false negatives & positives for each subtype.

Audit Feature Importance:
SHAP values or permutation importance
Are non-genomic variables (e.g., age) ranked higher than gene expression?
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6-Stratified Analysis
Stratify error rates by:

ER/PR status
Luminal A vs. others
Age group, ancestry (if available), tumor size

Statistical tests:

Chi-square for categorical features
T-test or ANOVA for continuous variables
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7-Internal Validation
Bootstrap or cross-validation (k=10)

Evaluate:

Overall accuracy
Subgroup error rates
Calibration (Brier score, reliability curves)
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8-Results & Visualization
SHAP summary plots

Confusion matrix heatmaps
Error rate bar charts across subgroups
Risk flags for shortcut-prone predictions
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9-Documentation & Reproducibility
Store:

Cleaned data (no redistribution)
Code notebooks (.do files or Python/R scripts)
README + data dictionary
Visuals and tables for final report
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breast-cancer-ai-misclassification/
│
├── README.md
├── LICENSE
├── .gitignore
│
├── data/
│ ├── raw/ # Original data from TCGA/Xena (no changes)
│ ├── processed/ # Cleaned & merged data ready for modeling
│ ├── metadata/ # Variable dictionaries, subtype labels, cohort lists
│
├── notebooks/
│ ├── 01_data_cleaning.ipynb # Data cleaning and merging script
│ ├── 02_eda.ipynb # Exploratory data analysis
│ ├── 03_model_training.ipynb # Model fitting and evaluation
│ ├── 04_shap_analysis.ipynb # SHAP or feature attribution workflow
│ ├── 05_subgroup_analysis.ipynb # Stratified misclassification analysis
│
├── scripts/
│ ├── run_model.py # CLI script to run model pipeline
│ ├── run_validation.py # Script for cross-validation 
│ ├── utils.py # Helper functions (e.g., metrics, plotting)
│
├── results/
│ ├── figures/ # Confusion matrices, SHAP plots, histograms
│ ├── tables/ # Model performance, FN/FP breakdowns
│ ├── outputs/ # Exported predictions or risk scores
│
├── reports/
│ ├── capstone_summary.pdf # Final written report
│ ├── slides_deck.pptx # Presentation slides
│ ├── GCSRT_FINAL.docx # Formatted protocol
│
└── environment.yml # Conda environment file (reproducibility)
description: In modern breast cancer care, genomic testing has become a cornerstone of treatment planning. Tumor profiling assays such as Oncotype DX, PAM50, and MammaPrint analyze gene expression to classify molecular subtypes and predict recurrence risk — often determining whether a patient should receive chemotherapy. Simultaneously, artificial intelligence (AI) models are increasingly used to interpret these high-dimensional genomic datasets and automate subtype classification. However, the performance of these models is rarely examined beyond accuracy metrics. What remains underexplored is how and why these models sometimes make errors — particularly whether they rely on shortcut features like age or tumor size rather than true biological signals. This capstone project investigates whether artificial intelligence (AI) models used to classify molecular subtypes of breast cancer based on genomic expression data rely on shortcut features (e.g., age, tumor size, batch ID) rather than true biological signals, bridging this safety gap, with the goal of identifying when genomic-AI models misclassify patients and which individuals are at highest risk of receiving misleading results.

This project further explores which patient subgroups are most at risk of misclassification — particularly in hormone-sensitive tumors — using the TCGA-BRCA dataset.
description: Background & References
Shortcut learning in AI
"Shortcut Learning in Deep Neural Networks"
Geirhos et al.
DOI: 10.1038/s42256-020-00257-z
Web address: www.nature.com/articles/s42256-020-00257-z

SHAP & explainability
"A Unified Approach to Interpreting Model Predictions"
Lundberg & Lee (2017)
DOI: 10.48550/arXiv.1705.07874
Web address: arxiv.org/abs/1705.07874

Risks of AI in health care
"A guide to deep learning in healthcare"
Esteva, A., Robicquet, A., Ramsundar, B. et al. A guide to deep learning in healthcare. Nat Med 25, 24–29 (2019). DOI: 10.1038/s41591-018-0316-z

TCGA survival analysis resource
"An Integrated TCGA Pan-Cancer Clinical Data Resource" (TCGA-CDR)
Liu et al.
DOI: 10.1016/j.cell.2018.02.052
Web address: www.cell.com/fulltext/S0092-8674(18)30229-0
description: Data includes clinical and molecular measurements from patients with primary breast cancer. Includes variables such as age, receptor status, PAM50 subtype (derived from RNAseq), survival time, and vital status.
description: Not applicable – contemporary biomedical data
description: STATA, 16
description: PYTHON, 3.10.12
description: XGBoost, 2.0
description: SHAP, 0.43.0
description: Full documentation of TCGA variables and UCSC Xena download methods can be found at: https://xena.ucsc.edu/public-datasets/
This dataset includes only processed, publicly available, and de-identified information compliant with TCGA usage policy.