Artificial intelligence-based digital scores of stromal tumour-infiltrating lymphocytes and tumour-associated stroma predict disease-specific survival in triple-negative breast cancer.
artificial intelligence
disease-specific survival analysis
histology images
triple-negative breast cancer
tumour microenvironment
Journal
The Journal of pathology
ISSN: 1096-9896
Titre abrégé: J Pathol
Pays: England
ID NLM: 0204634
Informations de publication
Date de publication:
05 2023
05 2023
Historique:
revised:
12
01
2023
received:
10
08
2022
accepted:
23
01
2023
medline:
6
4
2023
pubmed:
28
1
2023
entrez:
27
1
2023
Statut:
ppublish
Résumé
Triple-negative breast cancer (TNBC) is known to have a relatively poor outcome with variable prognoses, raising the need for more informative risk stratification. We investigated a set of digital, artificial intelligence (AI)-based spatial tumour microenvironment (sTME) features and explored their prognostic value in TNBC. After performing tissue classification on digitised haematoxylin and eosin (H&E) slides of TNBC cases, we employed a deep learning-based algorithm to segment tissue regions into tumour, stroma, and lymphocytes in order to compute quantitative features concerning the spatial relationship of tumour with lymphocytes and stroma. The prognostic value of the digital features was explored using survival analysis with Cox proportional hazard models in a cross-validation setting on two independent international multi-centric TNBC cohorts: The Australian Breast Cancer Tissue Bank (AUBC) cohort (n = 318) and The Cancer Genome Atlas Breast Cancer (TCGA) cohort (n = 111). The proposed digital stromal tumour-infiltrating lymphocytes (Digi-sTILs) score and the digital tumour-associated stroma (Digi-TAS) score were found to carry strong prognostic value for disease-specific survival, with the Digi-sTILs and Digi-TAS scores giving C-index values of 0.65 (p = 0.0189) and 0.60 (p = 0.0437), respectively, on the TCGA cohort as a validation set. Combining the Digi-sTILs feature with the patient's positivity status for axillary lymph nodes yielded a C-index of 0.76 on unseen validation cohorts. We surmise that the proposed digital features could potentially be used for better risk stratification and management of TNBC patients. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
32-42Informations de copyright
© 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
Références
Balkenhol MCA, Vreuls W, Wauters CAP, et al. Histological subtypes in triple negative breast cancer are associated with specific information on survival. Ann Diagn Pathol 2020; 46: 151490.
Urru SAM, Gallus S, Bosetti C, et al. Clinical and pathological factors influencing survival in a large cohort of triple-negative breast cancer patients. BMC Cancer 2018; 18: 56.
Irvin WJ, Carey LA. What is triple-negative breast cancer? Eur J Cancer 2008; 44: 2799-2805.
Gluz O, Liedtke C, Gottschalk N, et al. Triple-negative breast cancer - current status and future directions. Ann Oncol 2009; 20: 1913-1927.
Dent R, Trudeau M, Pritchard KI, et al. Triple-negative breast cancer: clinical features and patterns of recurrence. Clin Cancer Res 2007; 13: 4429-4434.
Vikas P, Borcherding N, Zhang W. The clinical promise of immunotherapy in triple-negative breast cancer. Cancer Manag Res 2018; 10: 6823-6833.
Zagami P, Carey LA. Triple negative breast cancer: pitfalls and progress. NPJ Breast Cancer 2022; 8: 95.
Yin L, Duan J-J, Bian X-W, et al. Triple-negative breast cancer molecular subtyping and treatment progress. Breast Cancer Res 2020; 22: 61.
Dieci MV, Miglietta F, Guarneri V. Immune infiltrates in breast cancer: recent updates and clinical implications. Cell 2021; 10: 223.
Bianchini G, Balko JM, Mayer IA, et al. Triple-negative breast cancer: challenges and opportunities of a heterogeneous disease. Nat Rev Clin Oncol 2016; 13: 674-690.
Vangangelt KM, Green AR, Heemskerk IM, et al. The prognostic value of the tumor-stroma ratio is most discriminative in patients with grade III or triple-negative breast cancer. Int J Cancer 2020; 146: 2296-2304.
García-Teijido P, Cabal ML, Fernández IP, et al. Tumor-infiltrating lymphocytes in triple negative breast cancer: the future of immune targeting. Clin Med Insights Oncol 2016; 10: 31-39.
Blackley EF, Loi S. Targeting immune pathways in breast cancer: review of the prognostic utility of TILs in early stage triple negative breast cancer (TNBC). Breast 2019; 48: S44-S48.
Pruneri G, Vingiani A, Bagnardi V, et al. Clinical validity of tumor-infiltrating lymphocytes analysis in patients with triple-negative breast cancer. Ann Oncol 2016; 27: 249-256.
Kos Z, Roblin E, Kim RS, et al. Pitfalls in assessing stromal tumor infiltrating lymphocytes (sTILs) in breast cancer. NPJ Breast Cancer 2020; 6: 17.
Loi S, Drubay D, Adams S, et al. Tumor-infiltrating lymphocytes and prognosis: a pooled individual patient analysis of early-stage triple-negative breast cancers. J Clin Oncol 2019; 37: 559-569.
Salgado R, Denkert C, Demaria S, et al. The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: recommendations by an International TILs Working Group 2014. Ann Oncol 2015; 26: 259-271.
de Kruijf EM, van Nes JG, van de Velde CJ, et al. Tumor-stroma ratio in the primary tumor is a prognostic factor in early breast cancer patients, especially in triple-negative carcinoma patients. Breast Cancer Res Treat 2011; 125: 687-696.
Moorman AM, Vink R, Heijmans HJ, et al. The prognostic value of tumour-stroma ratio in triple-negative breast cancer. Eur J Surg Oncol 2012; 38: 307-313.
Hagenaars SC, Vangangelt KM, Van Pelt GW, et al. Standardization of the tumor-stroma ratio scoring method for breast cancer research. Breast Cancer Res Treat 2022; 193: 545-553.
Ibrahim A, Gamble P, Jaroensri R, et al. Artificial intelligence in digital breast pathology: techniques and applications. Breast 2020; 49: 267-273.
Deng L, Yu D. Deep Learning: Methods and Applications. In Foundations and Trends® in Signal Processing (Vol. 7). now Publishers Inc.: Hanover, MA, 2014; 197-387.
Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal 2017; 42: 60-88.
Graham S, Vu QD, Raza SEA, et al. Hover-net: simultaneous segmentation and classification of nuclei in multi-tissue histology images. Med Image Anal 2019; 58: 101563.
Sirinukunwattana K, Ahmed Raza SE, Tsang Y-W, et al. Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans Med Imaging 2016; 35: 1196-1206.
Awan R, Koohbanani NA, Shaban M, et al. Context-aware learning using transferable features for classification of breast cancer histology images. In International Conference Image Analysis and Recognition (Vol. 1). Springer: Cham, 2018; 788-795.
Ker J, Bai Y, Lee HY, et al. Automated brain histology classification using machine learning. J Clin Neurosci 2019; 66: 239-245.
Balkenhol MC, Bult P, Tellez D, et al. Deep learning and manual assessment show that the absolute mitotic count does not contain prognostic information in triple negative breast cancer. Cell Oncol 2019; 42: 555-569.
Corredor G, Toro P, Lu C, et al. Computational features of tumor-infiltrating lymphocyte architecture of residual disease after chemotherapy on H&E images as prognostic of overall and disease-free survival for triple-negative breast cancer. J Clin Oncol 2021; 39: 584.
Saltz J, Gupta R, Hou L, et al. Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. Cell Rep 2018; 23: 181-193.e7.
Albusayli R, Graham D, Pathmanathan N, et al. Simple non-iterative clustering and CNNs for coarse segmentation of breast cancer whole-slide images. In Medical Imaging 2021: Digital Pathology (Vol. 11603). SPIE, San Diego, CA, 2021; 100-108.
He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE: Las Vegas, NV, 2016; 770-778.
Litjens G. Automated Slide Analysis Platform. [Accessed 19 October 2019]. Available from: https://github.com/computationalpathologygroup/ASAP.
Achanta R, Susstrunk S. Superpixels and polygons using simple non-iterative clustering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE: Honolulu, HI, 2017; 4651-4660.
Steck H, Krishnapuram B, Dehing-Oberije C, et al. On ranking in survival analysis: bounds on the concordance index. In Proceedings of the 20th International Conference on Neural Information Processing Systems. Curran Associates Inc.: Vancouver, 2007; 1209-1216.
Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 1996; 15: 361-387.
Uno H, Cai T, Pencina MJ, et al. On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Stat Med 2011; 30: 1105-1117.
Schröder MS, Culhane AC, Quackenbush J, et al. survcomp: an R/Bioconductor package for performance assessment and comparison of survival models. Bioinformatics 2011; 27: 3206-3208.
Pölsterl S, Navab N, Katouzian A. Fast training of support vector machines for survival analysis. In Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2015. Lecture Notes in Computer Science, Appice A, Rodrigues P, Santos Costa V, et al (eds), Vol 9285. Springer: Cham, 2015; 243-259.
Romano JP, DiCiccio C. Multiple data splitting for testing. Technical Report No. 2019-03. Department of Statistics, Stanford University: Stanford, CA, 2019 Available from: https://purl.stanford.edu/fb041jg0790.
Rakha EA, El-Sayed ME, Green AR, et al. Prognostic markers in triple-negative breast cancer. Cancer 2007; 109: 25-32.
Liedtke C, Hess KR, Karn T, et al. The prognostic impact of age in patients with triple-negative breast cancer. Breast Cancer Res Treat 2013; 138: 591-599.
Niazi MKK, Parwani AV, Gurcan MN. Digital pathology and artificial intelligence. Lancet Oncol 2019; 20: e253-e261.
Van Berckelaer C, Rypens C, van Dam P, et al. Infiltrating stromal immune cells in inflammatory breast cancer are associated with an improved outcome and increased PD-L1 expression. Breast Cancer Res 2019; 21: 28.
Kramer CJH, Vangangelt KMH, van Pelt GW, et al. The prognostic value of tumour-stroma ratio in primary breast cancer with special attention to triple-negative tumours: a review. Breast Cancer Res Treat 2019; 173: 55-64.
Beck AH, Sangoi AR, Leung S, et al. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci Transl Med 2011; 3: 108ra113.
Conklin MW, Keely PJ. Why the stroma matters in breast cancer: insights into breast cancer patient outcomes through the examination of stromal biomarkers. Cell Adh Migr 2012; 6: 249-260.