دورية أكاديمية
Computational pathology of pre-treatment biopsies identifies lymphocyte density as a predictor of response to neoadjuvant chemotherapy in breast cancer
| العنوان: | Computational pathology of pre-treatment biopsies identifies lymphocyte density as a predictor of response to neoadjuvant chemotherapy in breast cancer |
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| المؤلفون: | Ali, HR, Dariush, A, Provenzano, E, Bardwell, H, Abraham, JE, Iddawela, M, Vallier, A-L, Hiller, L, Dunn, JA, Bowden, SJ, Hickish, T, McAdam, K, Houston, S, Irwin, MJ, Pharoah, PDP, Brenton, JD, Walton, NA, Earl, HM, Caldas, C |
| بيانات النشر: | BioMed Central |
| سنة النشر: | 2016 |
| المجموعة: | Imperial College London: Spiral |
| مصطلحات موضوعية: | Science & Technology, Life Sciences & Biomedicine, Oncology, Breast cancer, Computational pathology, Neoadjuvant, Lymphocytes, Treatment resistance, Immunology, TUMOR-INFILTRATING LYMPHOCYTES, ANTHRACYCLINE CHEMOTHERAPY, PROTEIN EXPRESSION, AUTOMATED-ANALYSIS, SYSTEMS PATHOLOGY, NEO-TANGO, TRIAL, ASSOCIATION, PROGNOSIS, MPDL3280A, Adult, Aged, Antineoplastic Combined Chemotherapy Protocols, Biomarkers, Tumor, Biopsy, Breast Neoplasms, Chemotherapy, Adjuvant, Epirubicin, Female |
| الوصف: | 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, ... |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 1465-5411 |
| العلاقة: | Breast Cancer Research; http://hdl.handle.net/10044/1/51078Test; https://dx.doi.org/10.1186/s13058-016-0682-8Test |
| DOI: | 10.1186/s13058-016-0682-8 |
| الإتاحة: | https://doi.org/10.1186/s13058-016-0682-8Test http://hdl.handle.net/10044/1/51078Test |
| حقوق: | © 2016 Ali et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0Test/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http:// creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
| رقم الانضمام: | edsbas.2856B096 |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 14655411 |
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| DOI: | 10.1186/s13058-016-0682-8 |