Predicting breast cancer response to neoadjuvant chemotherapy based on tumor vascular features in needle biopsies

Terisse A. Brocato, Ursa Brown-Glaberman, Zhihui Wang, Reed G. Selwyn, Colin M. Wilson, Edward F. Wyckoff, Lesley C. Lomo, Jennifer L. Saline, Anupama Hooda-Nehra, Renata Pasqualini, Wadih Arap, C. Jeffrey Brinker, Vittorio Cristini

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

In clinical breast cancer intervention, selection of the optimal treatment protocol based on predictive biomarkers remains an elusive goal. Here, we present a modeling tool to predict the likelihood of breast cancer response to neoadjuvant chemotherapy using patient-specific tumor vasculature biomarkers. A semiautomated analysis was implemented and performed on 3990 histological images from 48 patients, with 10-208 images analyzed for each patient. We applied a histology-based mathematical model to 30 resected primary breast cancer tumors and then evaluated a cohort of 18 patients undergoing neoadjuvant chemotherapy, collecting pre- and posttreatment pathology specimens and MRI data. We found that core biopsy samples can be used with acceptable accuracy to determine histological parameters representative of the whole tissue region. Analysis of model histology parameters obtained from tumor vasculature measurements, specifically diffusion distance divided by the radius of the drug-delivering blood vessel (L/r b) and blood volume fraction (BVF), provides a statistically significant separation of patients obtaining a pathologic complete response (pCR) from those who do not. With this model, it is feasible to evaluate primary breast tumor vasculature biomarkers in a patient-specific manner, thereby allowing a precision approach to breast cancer treatment.

Original languageEnglish (US)
Article numbere126518
JournalJCI insight
Volume4
Issue number8
DOIs
StatePublished - 2019

Keywords

  • Breast cancer
  • Oncology

ASJC Scopus subject areas

  • Medicine(all)

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