Predicting nodal response to neoadjuvant treatment in breast cancer with core biopsy biomarkers of tumor microenvironment using data mining

  • 0Department of Surgical Oncology, Institute of Oncology Ljubljana, Ljubljana, Slovenia.

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Summary

This summary is machine-generated.

A new model predicts nodal response to neoadjuvant systemic treatment (NAST) in node-positive breast cancer patients. Incorporating tumor microenvironment (TME) factors, this tool aids surgical planning and improves prediction accuracy.

Area Of Science

  • Oncology
  • Breast Cancer Research
  • Tumor Microenvironment

Background

  • Accurate prediction of nodal response to neoadjuvant systemic treatment (NAST) is crucial for staging node-positive (cN+) breast cancer patients.
  • Integrating tumor microenvironment (TME) characteristics can enhance predictive models for treatment response.

Purpose Of The Study

  • To develop a predictive model for nodal response to NAST in cN+ breast cancer patients.
  • The model aims to incorporate TME features for improved axillary surgical staging planning.

Main Methods

  • Retrospective collection of clinical and pathological data from 437 cN+ breast cancer patients.
  • Core biopsy samples were analyzed for stromal content and tumor-infiltrating lymphocytes (TILs).
  • Orange Datamining Toolbox was utilized for model development and validation.

Main Results

  • 34.6% of patients achieved pathological complete response (pCR) in the nodes (ypN0).
  • A prediction model was built using ER, Her-2, grade, stromal content, and TILs.
  • The logistic regression model achieved an AUC of 0.86 and an F1 score of 0.72.

Conclusions

  • A novel clinical tool was developed to predict nodal pCR in cN+ breast cancer patients post-NAST.
  • The model effectively integrates TME biomarkers, achieving a high predictive performance (AUC 0.86).
  • This tool can aid in refining surgical staging strategies for breast cancer patients receiving NAST.