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Breast Cancer Research
Published in: Breast Cancer Research 1/2016

Open Access 01-12-2016 | Research article

Computational pathology of pre-treatment biopsies identifies lymphocyte density as a predictor of response to neoadjuvant chemotherapy in breast cancer

Authors: H. Raza Ali, Aliakbar Dariush, Elena Provenzano, Helen Bardwell, Jean E. Abraham, Mahesh Iddawela, Anne-Laure Vallier, Louise Hiller, Janet. A. Dunn, Sarah J. Bowden, Tamas Hickish, Karen McAdam, Stephen Houston, Mike J. Irwin, Paul D. P. Pharoah, James D. Brenton, Nicholas A. Walton, Helena M. Earl, Carlos Caldas

Published in: Breast Cancer Research | Issue 1/2016

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Abstract

Background

There is a need to improve prediction of response to chemotherapy in breast cancer in order to improve clinical management and this may be achieved by harnessing computational metrics of tissue pathology. We investigated the association between quantitative image metrics derived from computational analysis of digital pathology slides and response to chemotherapy in women with breast cancer who received neoadjuvant chemotherapy.

Methods

We digitised tissue sections of both diagnostic and surgical samples of breast tumours from 768 patients enrolled in the Neo-tAnGo randomized controlled trial. We subjected digital images to systematic analysis optimised for detection of single cells. Machine-learning methods were used to classify cells as cancer, stromal or lymphocyte and we computed estimates of absolute numbers, relative fractions and cell densities using these data. Pathological complete response (pCR), a histological indicator of chemotherapy response, was the primary endpoint. Fifteen image metrics were tested for their association with pCR using univariate and multivariate logistic regression.

Results

Median lymphocyte density proved most strongly associated with pCR on univariate analysis (OR 4.46, 95 % CI 2.34-8.50, p < 0.0001; observations = 614) and on multivariate analysis (OR 2.42, 95 % CI 1.08-5.40, p = 0.03; observations = 406) after adjustment for clinical factors. Further exploratory analyses revealed that in approximately one quarter of cases there was an increase in lymphocyte density in the tumour removed at surgery compared to diagnostic biopsies. A reduction in lymphocyte density at surgery was strongly associated with pCR (OR 0.28, 95 % CI 0.17-0.47, p < 0.0001; observations = 553).

Conclusions

A data-driven analysis of computational pathology reveals lymphocyte density as an independent predictor of pCR. Paradoxically an increase in lymphocyte density, following exposure to chemotherapy, is associated with a lack of pCR. Computational pathology can provide objective, quantitative and reproducible tissue metrics and represents a viable means of outcome prediction in breast cancer.

Trial registration

ClinicalTrials.gov NCT00070278; 03/10/2003
Appendix
Available only for authorised users
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Metadata
Title
Computational pathology of pre-treatment biopsies identifies lymphocyte density as a predictor of response to neoadjuvant chemotherapy in breast cancer
Authors
H. Raza Ali
Aliakbar Dariush
Elena Provenzano
Helen Bardwell
Jean E. Abraham
Mahesh Iddawela
Anne-Laure Vallier
Louise Hiller
Janet. A. Dunn
Sarah J. Bowden
Tamas Hickish
Karen McAdam
Stephen Houston
Mike J. Irwin
Paul D. P. Pharoah
James D. Brenton
Nicholas A. Walton
Helena M. Earl
Carlos Caldas
Publication date
01-12-2016
Publisher
BioMed Central
Published in
Breast Cancer Research / Issue 1/2016
Electronic ISSN: 1465-542X
DOI
https://doi.org/10.1186/s13058-016-0682-8

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