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Phenotypic and molecular dissection of metaplastic breast cancer and the prognostic implications

McCart Reed AE, Kalaw E, Nones K, Bettington M, Lim M, Bennett J, et al.

J Pathol, 2019 02;247(2):214-227.
PMID: 30350370 DOI: 10.1002/path.5184

Metaplastic breast carcinoma (MBC) is relatively rare but accounts for a significant proportion of global breast cancer mortality. This group is extremely heterogeneous and by definition exhibits metaplastic change to squamous and/or mesenchymal elements, including spindle, squamous, chondroid, osseous, and rhabdomyoid features. Clinically, patients are more likely to present with large primary tumours (higher stage), distant metastases, and overall, have shorter 5-year survival compared to invasive carcinomas of no special type. The current World Health Organisation (WHO) diagnostic classification for this cancer type is based purely on morphology - the biological basis and clinical relevance of its seven sub-categories are currently unclear. By establishing the Asia-Pacific MBC (AP-MBC) Consortium, we amassed a large series of MBCs (n = 347) and analysed the mutation profile of a subset, expression of 14 breast cancer biomarkers, and clinicopathological correlates, contextualising our findings within the WHO guidelines. The most significant indicators of poor prognosis were large tumour size (T3; p = 0.004), loss of cytokeratin expression (lack of staining with pan-cytokeratin AE1/3 antibody; p = 0.007), EGFR overexpression (p = 0.01), and for 'mixed' MBC, the presence of more than three distinct morphological entities (p = 0.007). Conversely, fewer morphological components and EGFR negativity were favourable indicators. Exome sequencing of 30 cases confirmed enrichment of TP53 and PTEN mutations, and intriguingly, concurrent mutations of TP53, PTEN, and PIK3CA. Mutations in neurofibromatosis-1 (NF1) were also overrepresented [16.7% MBCs compared to ∼5% of breast cancers overall; enrichment p = 0.028; mutation significance p = 0.006 (OncodriveFM)], consistent with published case reports implicating germline NF1 mutations in MBC risk. Taken together, we propose a practically minor but clinically significant modification to the guidelines: all WHO_1 mixed-type tumours should have the number of morphologies present recorded, as a mechanism for refining prognosis, and that EGFR and pan-cytokeratin expression are important prognostic markers. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

Page DB, Broeckx G, Jahangir CA, Verbandt S, Gupta RR, Thagaard J, et al.

J Pathol, 2023 Aug;260(5):514-532.
PMID: 37608771 DOI: 10.1002/path.6165

Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector-based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well-described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.
Fulltext Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

Thagaard J, Broeckx G, Page DB, Jahangir CA, Verbandt S, Kos Z, et al.

J Pathol, 2023 Aug;260(5):498-513.
PMID: 37608772 DOI: 10.1002/path.6155

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
Fulltext Image-based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno-oncology Biomarker Working Group on Breast Cancer

Jahangir CA, Page DB, Broeckx G, Gonzalez CA, Burke C, Murphy C, et al.

J Pathol, 2024 Mar;262(3):271-288.
PMID: 38230434 DOI: 10.1002/path.6238

Recent advances in the field of immuno-oncology have brought transformative changes in the management of cancer patients. The immune profile of tumours has been found to have key value in predicting disease prognosis and treatment response in various cancers. Multiplex immunohistochemistry and immunofluorescence have emerged as potent tools for the simultaneous detection of multiple protein biomarkers in a single tissue section, thereby expanding opportunities for molecular and immune profiling while preserving tissue samples. By establishing the phenotype of individual tumour cells when distributed within a mixed cell population, the identification of clinically relevant biomarkers with high-throughput multiplex immunophenotyping of tumour samples has great potential to guide appropriate treatment choices. Moreover, the emergence of novel multi-marker imaging approaches can now provide unprecedented insights into the tumour microenvironment, including the potential interplay between various cell types. However, there are significant challenges to widespread integration of these technologies in daily research and clinical practice. This review addresses the challenges and potential solutions within a structured framework of action from a regulatory and clinical trial perspective. New developments within the field of immunophenotyping using multiplexed tissue imaging platforms and associated digital pathology are also described, with a specific focus on translational implications across different subtypes of cancer. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.