# Utilizing cohort-level and individual networks to predict best response in patients with metastatic triple negative breast cancer

## TL;DR

This paper shows how analyzing tumor networks can help predict treatment responses in aggressive breast cancer patients.

## Contribution

A new plasma/B-cell related co-expression module was identified as a strong predictor of treatment response in metastatic triple-negative breast cancer.

## Key findings

- The plasma/B-cell co-expression module predicted clinical response and survival in CALGB and METABRIC cohorts.
- Refinements of the module improved diagnostic accuracy in a CLIA-certified setting.
- Patient-specific networks revealed adaptive responses to therapy, enabling dynamic treatment adjustments.

## Abstract

Given the highly aggressive and heterogeneous nature of metastatic triple-negative breast cancer, molecular subtypes have been evaluated for their utility in patient stratification and therapeutic selection. Leveraging both our unique longitudinal multimodal analysis of serial tumor biopsies, as well as existing public reference cohorts, we refined clinically relevant molecular subtypes through de-novo network-based approaches. A plasma/B-cell related co-expression module emerged as a robust predictor of clinical response. Refinements of this module were significantly associated with pathological complete response and survival in the CALGB and METABRIC cohorts, as well as dramatically improving the call rate in a CLIA setting. We explored patient-specific networks to monitor individual adaptive responses to therapy, allowing for dynamic adjustments in treatment strategies. Our work supports the shift from traditional molecular subtyping towards a more integrated view that includes the tumor microenvironment and immune landscape in a network-based context.