Citations

Abstract 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.

Abstract 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.

AbstractThe 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.

Colorectal cancer (CRC) is the third most frequent neoplastic disorder and is a main cause of tumor-related mortality, as many patients progress to stage IV metastatic CRC. Standard care consists of combination chemotherapy (FOLFIRI or FOLFOX). Patients with WT KRAS typing are eligible to receive anti-EGFR therapy combined with chemotherapy. Unfortunately, predicting efficacy of CRC anti-EGFR therapy has remained challenging. Here we uncovered that the EGFR pathway component RasGRP1 acts as a CRC tumor suppressor in the context of aberrant Wnt signaling. We found that RasGRP1 suppresses EGF-driven proliferation of colonic epithelial organoids. Having established that RasGRP1 dosage levels impact biology, we next focused on CRC patients. Mining 5 different data platforms, we establish that RasGRP1 expression levels decrease with CRC progression and predict poor clinical outcome of patients. Last, deletion of 1 or 2 Rasgrp1 alleles made CRC spheroids more susceptible to EGFR inhibition. Retrospective analysis of the CALGB 80203 clinical trial showed that addition of anti-EGFR therapy to chemotherapy significantly improved outcome for CRC patients when tumors expressed low levels of RasGRP1 suppressor. In sum, our data support RasGRP1 as a biomarker in the EGFR pathway that has potential relevance to anti-EGFR therapy for CRC patients.

PURPOSETo determine the predictive and prognostic value of the consensus molecular subtypes (CMSs) of colorectal cancer (CRC) that represent a merging of gene expression–based features largely in primary tumors from six independent classification systems and provide a framework for capturing the intrinsic heterogeneity of CRC in patients enrolled in CALGB/SWOG 80405.PATIENTS AND METHODSCALGB/SWOG 80405 is a phase III trial that compared the addition of bevacizumab or cetuximab to infusional fluorouracil, leucovorin, and oxaliplatin or fluorouracil, leucovorin, and irinotecan as first-line treatment of advanced CRC. We characterized the CMS classification using a novel NanoString gene expression panel on primary CRCs from 581 patients enrolled in this study to assess the prognostic and predictive value of CMSs in these patients.RESULTSThe CMSs are highly prognostic for overall survival (OS; P < .001) and progression-free survival (PFS; P < .001). Furthermore, CMSs were predictive for both OS ( P for interaction < .001) and PFS ( P for interaction = .0032). In the CMS1 cohort, patients treated with bevacizumab had a significantly longer OS than those treated with cetuximab ( P < .001). In the CMS2 cohort, patients treated with cetuximab had a significantly longer OS than patients treated with bevacizumab ( P = .0046).CONCLUSIONThese findings highlight the possible clinical utility of CMSs and suggests that refinement of the CMS classification may provide a path toward identifying patients with metastatic CRC who are most likely to benefit from specific targeted therapy as part of the initial treatment.