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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.
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Neoplasias da Mama , Humanos , Feminino , Biomarcadores Tumorais/genética , Prognóstico , Fenótipo , Reino Unido , Microambiente TumoralRESUMO
AIMS: Over 50% of breast cancer cases are "Human epidermal growth factor receptor 2 (HER2) low breast cancer (BC)", characterized by HER2 immunohistochemistry (IHC) scores of 1+ or 2+ alongside no amplification on fluorescence in situ hybridization (FISH) testing. The development of new anti-HER2 antibody-drug conjugates (ADCs) for treating HER2-low breast cancers illustrates the importance of accurately assessing HER2 status, particularly HER2-low breast cancer. In this study we evaluated the performance of a deep-learning (DL) model for the assessment of HER2, including an assessment of the causes of discordances of HER2-Null between a pathologist and the DL model. We specifically focussed on aligning the DL model rules with the ASCO/CAP guidelines, including stained cells' staining intensity and completeness of membrane staining. METHODS AND RESULTS: We trained a DL model on a multicentric cohort of breast cancer cases with HER2-IHC scores (n = 299). The model was validated on two independent multicentric validation cohorts (n = 369 and n = 92), with all cases reviewed by three senior breast pathologists. All cases underwent a thorough review by three senior breast pathologists, with the ground truth determined by a majority consensus on the final HER2 score among the pathologists. In total, 760 breast cancer cases were utilized throughout the training and validation phases of the study. The model's concordance with the ground truth (ICC = 0.77 [0.68-0.83]; Fisher P = 1.32e-10) is higher than the average agreement among the three senior pathologists (ICC = 0.45 [0.17-0.65]; Fisher P = 2e-3). In the two validation cohorts, the DL model identifies 95% [93% - 98%] and 97% [91% - 100%] of HER2-low and HER2-positive tumours, respectively. Discordant results were characterized by morphological features such as extended fibrosis, a high number of tumour-infiltrating lymphocytes, and necrosis, whilst some artefacts such as nonspecific background cytoplasmic stain in the cytoplasm of tumour cells also cause discrepancy. CONCLUSION: Deep learning can support pathologists' interpretation of difficult HER2-low cases. Morphological variables and some specific artefacts can cause discrepant HER2-scores between the pathologist and the DL model.
Assuntos
Biomarcadores Tumorais , Neoplasias da Mama , Aprendizado Profundo , Imuno-Histoquímica , Receptor ErbB-2 , Humanos , Neoplasias da Mama/patologia , Neoplasias da Mama/diagnóstico , Neoplasias da Mama/metabolismo , Neoplasias da Mama/genética , Receptor ErbB-2/metabolismo , Receptor ErbB-2/genética , Feminino , Biomarcadores Tumorais/análise , Biomarcadores Tumorais/metabolismo , Patologistas , Hibridização in Situ Fluorescente , Pessoa de Meia-IdadeRESUMO
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.
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Neoplasias do Colo , Humanos , Biomarcadores , Benchmarking , Linfócitos do Interstício Tumoral , Análise Espacial , Microambiente TumoralRESUMO
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.
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Neoplasias Mamárias Animais , Neoplasias de Mama Triplo Negativas , Humanos , Animais , Linfócitos do Interstício Tumoral , Biomarcadores , Aprendizado de MáquinaRESUMO
INTRODUCTION: Gestational trophoblastic neoplasia (GTN) is a group of malignant neoplasms that arise from abnormal proliferation of trophoblastic tissue. The metastatic spread rate is depended on the histopathological type, with pulmonary metastases being the most common (80%) in patients with metastases. Pneumothorax as a primary manifestation is extremely rare. We hereby discuss a unique case of spontaneous hemi-pneumothorax due to metastatic GTN in a 30-weeks pregnant woman. CASE PRESENTATION: A 25-year-old woman - G2 P0 A1 - was admitted to our maternal intensive care department with atypical respiratory symptoms. A chest x-ray revealed a large right sided pneumothorax. The patient underwent an urgent percutaneous chest tube. Since halting of the suction resulted in residual pneumothorax, a video-assisted thoracoscopic surgery (VATS) with wig resection of a bullous lesion was performed followed by chemical pleurodesis. Histopathological examination identified the lesion as a gestational trophoblastic metastasis with some features of choriocarcinoma. After primary section Caesarea adjuvant chemotherapy (MTX) was instigated with rapid decline of serum HCG values. Six months after surgery she was doing well with no biochemical or radiographic evidence of recurrent metastasis. CONCLUSION: Lung metastases are common in patients with metastatic GTN; however, pneumothorax is an extremely rare complication. We report a case of pneumothorax in a 30-week pregnant woman caused by pulmonary spread of GTN from a previous miscarriage. This case illustrates that in patients with pneumothorax and a history of miscarriage, metastatic GTN should be considered as a possible cause.
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PURPOSE: We studied the long-term outcomes of invasive micropapillary carcinoma (IMPCs) of the breast in relation to stromal tumor infiltrating lymphocytes (sTILs), prognostic biomarkers and clinicopathological features. METHODS: Stage I-III IMPCs treated with upfront surgery at our institution (January 2000 and December 2016) were included. Central pathology review was performed and sTILs (including zonal distribution and hot spot analysis) and tumor-associated plasma cells (TAPC) were evaluated. Expression of P53, BCL2, FOXP3, and WT1, which are variably linked to breast cancer prognosis, was measured by immunohistochemistry using tissue microarrays. Time-to-event endpoints were distant recurrence free interval (DRFI) and breast cancer-specific survival (BCSS). RESULTS: We included 111 patients of whom 59% were pure IMPCs. Standard clinicopathological features were comparable between pure and non-pure IMPCs. Overall, the mean sTILs level was 20% with higher proportion of sTILs present at the invasive front. There were no significant differences between pure- and non-pure IMPCs in sTILs levels, nor in the spatial distribution of the hot spot regions or in the distribution of TAPC. Higher sTILs correlated with worse DRFI (HR = 1.55; p = 0.0172) and BCSS (HR = 2.10; p < 0.001). CONCLUSIONS: Clinicopathological features, geographical distribution of sTILs and TAPC are similar between pure and non-pure IMPCs. Despite a high proportion of grade 3 tumors and lymph node involvement, we observed a low rate of distant recurrences and breast cancer-related death in this cohort of stage I-III IMPCs treated with primary surgery. Caution in interpretation of the observed prognostic correlations is required given the very low number of events, warranting validation in other cohorts.