Cheang, Maggie C.U.

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.

Breast cancer is a heterogenous disease with variability in tumor cells and in the surrounding tumor microenvironment (TME). Understanding the molecular diversity in breast cancer is critical for improving prediction of therapeutic response and prognostication. High-plex spatial profiling of tumors enables characterization of heterogeneity in the breast TME, which can holistically illuminate the biology of tumor growth, dissemination and, ultimately, response to therapy. The GeoMx Digital Spatial Profiler (DSP) enables researchers to spatially resolve and quantify proteins and RNA transcripts from tissue sections. The platform is compatible with both formalin-fixed paraffin-embedded and frozen tissues. RNA profiling was developed at the whole transcriptome level for human and mouse samples and protein profiling of 100-plex for human samples. Tissue can be optically segmented for analysis of regions of interest or cell populations to study biology-directed tissue characterization. The GeoMx Breast Cancer Consortium (GBCC) is composed of breast cancer researchers who are developing innovative approaches for spatial profiling to accelerate biomarker discovery. Here, the GBCC presents best practices for GeoMx profiling to promote the collection of high-quality data, optimization of data analysis and integration of datasets to advance collaboration and meta-analyses. Although the capabilities of the platform are presented in the context of breast cancer research, they can be generalized to a variety of other tumor types that are characterized by high heterogeneity.

Abstract Background: The Preoperative Endocrine Prognostic Index (PEPI) scores the independent prognostic effects of tumor pathologic staging and expression levels of ER and the “proliferation” marker Ki67 in the surgical sample to predict long term outcomes after completion of neoadjuvant endocrine treatment (Ellis et al JNCI 100:1380, 2008). A limitation of the PEPI is that the prognostic information becomes available only after 4 months of treatment. We therefore evaluated the value of an early assessment of the Ki67 level in a tumor biopsy sample taken two to four weeks after initiating treatment in two neoadjuvant endocrine therapy trials for the purposes of the early identification of non- respondersMethods: A Ki67 cut point of greater than 10% for poor outcome in ER+ breast cancer was derived by comparing the PAM50 intrinsic subtype profile using a qRT-PCR assay with Ki67 data in a 700+ sample data set. A baseline level of 10% or less correlated most closely with a PAM50-based definition of LumA breast cancer and above 10% LumB breast cancer. We subsequently applied the 10% cut point to the baseline and early on-treatment Ki67 data in two trials, POL (Olson et al JACS 208:906, 2009) and IMPACT (Smith et al JCO: 23, 5108, 2005).Results: At baseline the dichotomized Ki67 definition was not significantly predictive for surgical Ki67 level, PEPI score or RFS in this modest size sample set. In contrast, in a result that emphasizes the enhaced prognostic properties of the on-treatment Ki67 approach, the one month POL sample Ki67 values (62 patients) predicted a higher level of Ki67 in the surgical samples at four months after treatment initiation (P=.01), a poorer PEPI score (P=0.01), a smaller number of patients in the PEPI risk point zero group (P=0.08) and worse relapse free survival (P=0.003). The IMPACT data (153 patients) confirmed that a two week Ki67 >10% predicted higher Ki67 in the surgical specimen (P=0.001), a poorer PEPI score (P=0.001), smaller numbers of patients in the PEPI 0 risk point group (P= 0.004) and worse relapse free survival (P=0.008).Ki67 and OutcomePOL 4W Ki67% PEPI 0RFS (events)10%>1/19 (5%)5/21 (23%)10%≤10/36 (28%)1/41 (2.4%)P ValueP=0.08 (Fisher)P=0.003 (log rank)IMPACT 2W Ki67% PEPI 0RFS (events)10%>0/32 (0%)9/35 (26%)10%≤21/101 (21%)13/118 (11%)P ValueP=0.004 (Fisher)P=0.008 (log rank) Conclusions: A tumor Ki67 assessment taken a short time (2 to 4 week window) after the initiation of neoadjuvant AI identifies patients with poor outcome ER+ disease. Amendment 6 of the neoadjuvant endocrine therapy protocol ACOSOG Z1031 will triage patients with an “on treatment” Ki67 value above 10% to chemotherapy in order to assess the pathological response rate to cytotoxic therapy in this important tumor subset.Supported by R01 CA095614, Avon PFP award 3P50 CA68438-07S2, U01 CA114722, ACOSOG U10 CA 76001, Breakthrough Cancer UK and AstraZenica (IMPACT trial). Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 78.