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Computational pathology of pre-treatment biopsies identifies lymphocyte density as a predictor of response to neoadjuvant chemotherapy in breast cancer.
Ali, H Raza; Dariush, Aliakbar; Provenzano, Elena; Bardwell, Helen; Abraham, Jean E; Iddawela, Mahesh; Vallier, Anne-Laure; Hiller, Louise; Dunn, Janet A; Bowden, Sarah J; Hickish, Tamas; McAdam, Karen; Houston, Stephen; Irwin, Mike J; Pharoah, Paul D P; Brenton, James D; Walton, Nicholas A; Earl, Helena M; Caldas, Carlos.
Afiliação
  • Ali HR; Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK. raza.ali@cruk.cam.ac.uk.
  • Dariush A; Department of Pathology, University of Cambridge, Cambridge, UK. raza.ali@cruk.cam.ac.uk.
  • Provenzano E; Institute of Astronomy, University of Cambridge, Cambridge, UK. adariush@ast.cam.ac.uk.
  • Bardwell H; Department of Oncology, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK. elena.provenzano@addenbrookes.nhs.uk.
  • Abraham JE; Department of Histopathology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK. elena.provenzano@addenbrookes.nhs.uk.
  • Iddawela M; Cambridge Experimental Cancer Medicine Centre and NIHR Cambridge Biomedical Research Centre, Cambridge, UK. elena.provenzano@addenbrookes.nhs.uk.
  • Vallier AL; Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK. helen.bardwell@cruk.cam.ac.uk.
  • Hiller L; Department of Oncology, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK. ja344@medschl.cam.ac.uk.
  • Dunn JA; Cambridge Experimental Cancer Medicine Centre and NIHR Cambridge Biomedical Research Centre, Cambridge, UK. ja344@medschl.cam.ac.uk.
  • Bowden SJ; Department of Oncology, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK. mahesh.iddawela@monash.edu.
  • Hickish T; Present address: Department of Anatomy and Developmental Biology, Monash University, Clayton, Victoria, Australia. mahesh.iddawela@monash.edu.
  • McAdam K; Department of Oncology, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK. Anne-Laure.Vallier@addenbrookes.nhs.uk.
  • Houston S; Cambridge Experimental Cancer Medicine Centre and NIHR Cambridge Biomedical Research Centre, Cambridge, UK. Anne-Laure.Vallier@addenbrookes.nhs.uk.
  • Irwin MJ; Warwick Clinical Trials Unit, University of Warwick, Coventry, UK. l.hiller@warwick.ac.uk.
  • Pharoah PD; Warwick Clinical Trials Unit, University of Warwick, Coventry, UK. J.A.Dunn@warwick.ac.uk.
  • Brenton JD; Cancer Research UK Clinical Trials Unit, Institute for Cancer Studies, The University of Birmingham, Edgbaston, Birmingham, UK. s.j.bowden@bham.ac.uk.
  • Walton NA; Royal Bournemouth Hospital and Bournemouth University, Castle Lane East, Bournemouth, UK. tamas.hickish@rbch.nhs.uk.
  • Earl HM; Peterborough and Stamford Hospitals NHS Foundation Trust and Cambridge University Hospital NHS Foundation Trust, Peterborough, UK. karen.mcadam@pbh-tr.nhs.uk.
  • Caldas C; Royal Surrey County Hospital NHS Foundation Trust, Egerton Road, Guildford, UK. shouston@nhs.net.
Breast Cancer Res ; 18(1): 21, 2016 Feb 16.
Article em En | MEDLINE | ID: mdl-26882907
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.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias da Mama / Linfócitos / Quimioterapia Adjuvante / Terapia Neoadjuvante Tipo de estudo: Clinical_trials / Prognostic_studies / Risk_factors_studies Limite: Adult / Aged / Female / Humans / Middle aged Idioma: En Revista: Breast Cancer Res Ano de publicação: 2016 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias da Mama / Linfócitos / Quimioterapia Adjuvante / Terapia Neoadjuvante Tipo de estudo: Clinical_trials / Prognostic_studies / Risk_factors_studies Limite: Adult / Aged / Female / Humans / Middle aged Idioma: En Revista: Breast Cancer Res Ano de publicação: 2016 Tipo de documento: Article