Citations

Context.— Artificial intelligence is a transforming technology for anatomic pathology. Involvement within the workforce will foster support for algorithm development and implementation. Objective.— To develop a supportive ecosystem that enables pathologists with variable expertise in artificial intelligence to create algorithms in a development environment with seamless transition to a production environment. Design.— Platform requirements included (1) approachable and intuitive user interface, (2) diverse algorithmic modeling options, (3) support capability for internal and external collaborations, (4) a seamless mechanism for discovery to clinical deployment transition, (5) the ability to meet minimum institutional requirements for information technology (IT) review, and (6) the ability to be scalable over time. The ecosystem required platform education, data science guidance, project management structure, and ongoing leadership. Results.— The development team considered internal development and vended solutions. Because of the extended timeline and resource requirements for internal development, a decision was made to use a vended solution. Vendor proposals were solicited and reviewed by pathologists, IT, and security groups. A vendor was selected and pipelines for development and production were established. Proposals for development were solicited from the pathology department. Eighty-four investigators were selected for the initial cohort, receiving training and access to dedicated subject matter experts. A total of 30 of 31 projects progressed through the model development process of annotating, training, and validation. Based on these projects, 15 abstracts were submitted to national meetings. Conclusions.— Democratizing artificial intelligence by creating an ecosystem to support pathologists with varying levels of expertise can break down entry barriers, reduce overall cost of algorithm development, improve algorithm quality, and enhance the speed of adoption.

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 Germline BRCA2 loss-of function (LOF) variants identified by clinical genetic testing predispose to breast, ovarian, prostate and pancreatic cancer. However, variants of uncertain significance (VUS) (n>4000) limit the clinical use of testing results. Thus, there is an urgent need for functional characterization and clinical classification of all BRCA2 variants. Here we report on comprehensive saturation genome editing-based functional characterization of 97% of all possible single nucleotide variants (SNVs) in the BRCA2 DNA Binding Domain hotspot for pathogenic missense variants that is encoded by exons 15 to 26. The assay was based on deep sequence analysis of surviving endogenously targeted haploid cells. A total of 7013 SNVs were characterized as functionally abnormal (n=955), intermediate/uncertain, or functionally normal (n=5224) based on 95% agreement with ClinVar known pathogenic and benign standards. Results were validated relative to batches of nonsense and synonymous variants and variants evaluated using a homology directed repair (HDR) functional assay. Breast cancer case-control association studies showed that pooled SNVs encoding functionally abnormal missense variants were associated with increased risks of breast and ovarian cancer (odds ratio (OR) 3.89, 95%CI: 2.77-5.51). In addition, 86% of tumors associated with abnormal missense SNVs displayed loss of heterozygosity (LOH), whereas 26% of tumors with normal variants had LOH. The functional data were added to other sources of information in a ClinGen/ACMG/AMP-like model and 700 functionally abnormal SNVs, including 220 missense SNVs, were classified as pathogenic or likely pathogenic, while 4862 functionally normal SNVs, including 3084 missense SNVs, were classified as benign or likely benign. These classified variants can now be used for risk assessment and clinical care of variant carriers and the remaining functional scores can be used directly for clinical classification and interpretation of many additional variants.

Abstract Prostate cancer risk is influenced by rare and common germline variants. We examined the aggregate association of rare germline pathogenic/likely pathogenic/deleterious (P/LP/D) variants in ATM, BRCA2, PALB2, and NBN with a polygenic risk score (PRS) on prostate cancer risk among 1,796 prostate cancer cases (222 metastatic) and 1,424 controls of African ancestry. Relative to P/LP/D non-carriers at average genetic risk (33%–66% of PRS), men with low (0%–33%) and high (66%–100%) PRS had Odds Ratios (ORs) for overall prostate cancer of 2.08 [95% confidence interval (CI) = 0.58–7.49] and 18.06 (95% CI = 4.24–76.84) among P/LP/D carriers and 0.57 (95% CI = 0.46–0.71) and 3.02 (95% CI = 2.53–3.60) among non-carriers, respectively. The OR for metastatic prostate cancer was 2.73 (95% CI = 0.24–30.54) and 28.99 (95% CI = 4.39–191.43) among P/LP/D carriers and 0.54 (95% CI = 0.31–0.95) and 3.22 (95% CI = 2.20–4.73) among non-carriers, for men with low and high PRS, respectively. Lifetime absolute risks of overall prostate cancer increased with PRS (low to high) from 9.8% to 51.5% in P/LP/D carriers and 5.5% to 23.9% in non-carriers. Lifetime absolute risks of metastatic prostate cancer increased with PRS from 1.9% to 18.1% in P/LP/D carriers and 0.3% to 2.2% in non-carriers These findings suggest that assessment of prostate cancer risk for rare variant carriers should include PRS status. Significance: These findings highlight the importance of considering rare and common variants to comprehensively assess prostate cancer risk in men of African ancestry.

AbstractQuantifying tumor‐infiltrating lymphocytes (TILs) in breast cancer tumors is a challenging task for pathologists. With the advent of whole slide imaging that digitizes glass slides, it is possible to apply computational models to quantify TILs for pathologists. Development of computational models requires significant time, expertise, consensus, and investment. To reduce this burden, we are preparing a dataset for developers to validate their models and a proposal to the Medical Device Development Tool (MDDT) program in the Center for Devices and Radiological Health of the U.S. Food and Drug Administration (FDA). If the FDA qualifies the dataset for its submitted context of use, model developers can use it in a regulatory submission within the qualified context of use without additional documentation. Our dataset aims at reducing the regulatory burden placed on developers of models that estimate the density of TILs and will allow head‐to‐head comparison of multiple computational models on the same data. In this paper, we discuss the MDDT preparation and submission process, including the feedback we received from our initial interactions with the FDA and propose how a qualified MDDT validation dataset could be a mechanism for open, fair, and consistent measures of computational model performance. Our experiences will help the community understand what the FDA considers relevant and appropriate (from the perspective of the submitter), at the early stages of the MDDT submission process, for validating stromal TIL density estimation models and other potential computational models. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

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.

Abstract Pathogenic protein-truncating variants of RAD51C, which plays an integral role in promoting DNA damage repair, increase the risk of breast and ovarian cancer. A large number of RAD51C missense variants of uncertain significance (VUS) have been identified, but the effects of the majority of these variants on RAD51C function and cancer predisposition have not been established. Here, analysis of 173 missense variants by a homology-directed repair (HDR) assay in reconstituted RAD51C−/− cells identified 30 nonfunctional (deleterious) variants, including 18 in a hotspot within the ATP-binding region. The deleterious variants conferred sensitivity to cisplatin and olaparib and disrupted formation of RAD51C/XRCC3 and RAD51B/RAD51C/RAD51D/XRCC2 complexes. Computational analysis indicated the deleterious variant effects were consistent with structural effects on ATP-binding to RAD51C. A subset of the variants displayed similar effects on RAD51C activity in reconstituted human RAD51C-depleted cancer cells. Case–control association studies of deleterious variants in women with breast and ovarian cancer and noncancer controls showed associations with moderate breast cancer risk [OR, 3.92; 95% confidence interval (95% CI), 2.18–7.59] and high ovarian cancer risk (OR, 14.8; 95% CI, 7.71–30.36), similar to protein-truncating variants. This functional data supports the clinical classification of inactivating RAD51C missense variants as pathogenic or likely pathogenic, which may improve the clinical management of variant carriers. Significance: Functional analysis of the impact of a large number of missense variants on RAD51C function provides insight into RAD51C activity and information for classification of the cancer relevance of RAD51C variants.

Abstract Purpose To estimate the risk of contralateral breast cancer (CBC) among women in the general population with germline pathogenic variants (PVs) in ATM, BRCA1, BRCA2, CHEK2, and PALB2. Methods Among 15,104 prospectively followed women within the CARRIERS study treated with ipsilateral surgery for invasive breast cancer, a subset of 14,237 women were identified from population-based studies. The risk of CBC was estimated for PV carriers in each gene compared to women without PVs in a multivariate proportional hazard regression analysis accounting for the competing risk of death and adjusting for patient and tumor characteristics. The primary analyses focused on the overall cohort and on women from the general population. Secondary analyses examined associations by race/ethnicity, age at primary breast cancer diagnosis, menopausal status, and tumor estrogen receptor status. Results Germline BRCA1, BRCA2, and CHEK2 PV carriers with breast cancer were at significantly elevated risk (Hazard ratio ≥ 1.9, p< 0.05) of CBC, whereas only the PALB2 PV carriers with ER-negative breast cancer had elevated risks. In contrast, ATM PV carriers did not have significantly increased CBC risks. African American PV carriers had similarly elevated risks of CBC as non-Hispanic White PV carriers. Among premenopausal women, the 15-year cumulative incidence of CBC was >20% for BRCA1, BRCA2 and CHEK2 PV carriers with breast cancer, and for PALB2 PV carriers with ER-negative breast cancer. The 15-year cumulative incidence of CBC among postmenopausal PV carriers was < 20% for PV carriers in any of the 5 genes. Conclusions Women diagnosed with breast cancer and known to carry germline PVs in BRCA1, BRCA2, CHEK2, or PALB2 are at substantially increased risk of CBC and may benefit from enhanced surveillance and risk-reduction strategies. Citation Format: Siddhartha Yadav, Nicholas J. Boddicker, Jie Na, Eric C. Polley, Chunling Hu, Steven N. Hart, Rohan D. Gnanaolivu, Nicole Larson, Carolyn Dunn, Susan Holtegaard, Huaizhi Huang, Lauren R. Teras, Alpa V. Patel, James V. Lacey Jr., Susan Neuhausen, Leslie Bernstein, Elena Martinez, Christopher Haiman, Fei Chen, Kathryn Ruddy, Janet Olson, Esther John, Allison W. Kurian, Dale P. Sandler, Katie M. O’Brien, Jack A. Taylor, Clarice R. Weinberg, Hoda Anton-Culver, Argyrios Ziogas, Gary R. Zirpoli, David E. Goldgar, Katherine L. Nathanson, Susan Domchek, Julie R. Palmer, Jeffrey Weitzel, Peter Kraft, Fergus J. Couch. Population-based Estimates of contralateral Breast Cancer Risk among Carriers of Germline Pathogenic Variants in ATM, BRCA1, BRCA2, CHEK2, and PALB2 [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr GS4-04.

Mutations that predispose to familial pheochromocytoma and paraganglioma include inherited variants in the four genes ( SDHA , SDHB , SDHC and SDHD ) encoding subunits of succinate dehydrogenase (SDH), an enzyme of the mitochondrial tricarboxylic acid cycle and complex II of the electron transport chain. In heterozygous variant carriers, somatic loss of heterozygosity is thought to result in tumorigenic accumulation of succinate and reactive oxygen species. Inexplicably, variants affecting the SDHB subunit predict worse clinical outcomes. Why? Here we consider two hypotheses. First, relative to SDH A, C and D subunits, the small SDHB subunit might be more intrinsically ‘fragile’ to missense mutations because of its relatively large fraction of amino acids contacting prosthetic groups and other SDH subunits. We show evidence that supports this hypothesis. Second, the natural pool of human SDHB variants might, by chance, be biased toward severe truncating variants and missense variants causing more disruptive amino acid substitutions. We tested this hypothesis by creating a database of known SDH variants and predicting their biochemical severities. Our data suggest that natural SDHB variants are more pathogenic. It is unclear if this bias is sufficient to explain clinical data. Other explanations include the possibility that SDH subcomplexes remaining after SDHB loss have unique tumorigenic gain-of-function characteristics, and/or that SDHB may have additional unknown tumor-suppressor functions.