Title |
Computational pathology of pre-treatment biopsies identifies lymphocyte density as a predictor of response to neoadjuvant chemotherapy in breast cancer
|
---|---|
Published in |
Breast Cancer Research, February 2016
|
DOI | 10.1186/s13058-016-0682-8 |
Pubmed ID | |
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 |
Abstract |
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. 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. 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). 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. ClinicalTrials.gov NCT00070278 ; 03/10/2003. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 2 | 25% |
Belgium | 1 | 13% |
United States | 1 | 13% |
Brazil | 1 | 13% |
Unknown | 3 | 38% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 5 | 63% |
Science communicators (journalists, bloggers, editors) | 2 | 25% |
Scientists | 1 | 13% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 2 | 1% |
Unknown | 164 | 99% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 31 | 19% |
Researcher | 27 | 16% |
Student > Master | 20 | 12% |
Student > Bachelor | 14 | 8% |
Other | 12 | 7% |
Other | 29 | 17% |
Unknown | 33 | 20% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 60 | 36% |
Computer Science | 13 | 8% |
Engineering | 12 | 7% |
Agricultural and Biological Sciences | 11 | 7% |
Biochemistry, Genetics and Molecular Biology | 7 | 4% |
Other | 25 | 15% |
Unknown | 38 | 23% |