Using AI-predicted protein structures as a reference to predict loss-of-function activity in tumor suppressor breast cancer genes.

Background: The loss-of-function (LOF) classification of most missense variants in tumor suppressor breast cancer genes BRCA1, BRCA2, PALB2, and RAD51C remains unclassified and confounds clinical actionability. Classifying these variants is challenging due to their rarity, leading clinicians to rely on in silico predictive methods. Protein stability changes are associated with function, making stability predictors valuable. Stability predictions upon missense variant perturbations require high-resolution protein structures. However, the availability of these high-resolution structures is lacking. This study explores using generative AI to predict high-resolution protein structures, which can then be analyzed with in silico protein stability prediction methods to assess LOF activity in ordered regions of the protein. This study also determines the appropriate in silico protein stability and dedicated in silico missense prediction methods in dbNSFP v4.7 database to predict LOF activity in ordered regions of these four genes. Functional classifications from homology recombination DNA repair (HDR) assays and variant classifications from the ClinVar database provide a reliable dataset for evaluating the performance of these in silico prediction methods. Results: Complex AlphaFold2 structures of the BRCA1-C terminal (BRCT) domain and the DNA-binding (DB) domain of BRCA2, analyzed using protein stability tool FoldX predicts LOF activity from missense variants significantly better than experimentally-derived structures in ordered regions. The BRCT domain achieved an Area Under the Curve (AUC)= 0.861 (95 % CI:0.858-0.863) and AUC= 0.842 (95 % CI:0.840-0.845), while the DB domain achieved an AUC= 0.836 (95 % CI:0.8322-0.841), compared to AUC= 0.847 (95 % CI:0.844-0.850) and AUC= 0.835 (95 % CI:0.832-0.837) from the BRCT domain, and AUC= 0.830 (95 % CI:0.821-0.8320) from the DB domain from experimentally-derived structures. Protein stability does not predict LOF activity from missense variants better than dedicated in silico missense predictors. Overall, we find that AlphaMissense ranks highly, with an average AUC= 0.890 (95 % CI 0.886-0.895) from ordered regions across these four cancer genes, compared to all other in silico missense predictors present in the dbNSFP database. Conclusions: The study reveals that generative AI protein predicted structures can outperform experimentally-derived structures in evaluating LOF activity from predicted protein stability in ordered regions of genes BRCA1, BRCA2, PALB2 and RAD51C. The study also highlights the predictive performance of AlphaMissense as the premier in silico missense prediction method to predict LOF activity from missense variants in these four tumor suppressor breast cancer genes. The code for this study can be downloaded for free on GitHub (https://github.com/rohandavidg/CarePred).

Association of Gene Variant Type and Location with Breast Cancer Risk in the General Population.

Importance: Pathogenic variants (PVs) in ATM, BRCA1, BRCA2, CHEK2 , and PALB2 are associated with increased breast cancer risk. However, it is unknown whether breast cancer risk differs by PV type or location in carriers ascertained from the general population. Objective: To evaluate breast cancer risks associated with PV type and location in ATM, BRCA1, BRCA2, CHEK2 , and PALB2 . Design: Age adjusted case-control association analysis for all participants, subsets of PV carriers, and women with no breast cancer family history in population-based and clinical testing cohorts. Setting: Twelve US population-based studies within the Cancer Risk Estimates Related to Susceptibility (CARRIERS) Consortium, and breast cancer cases from the UK-Biobank and an Ambry Genetics clinical testing cohort. Participants: 32,247 women with and 32,544 age-matched women without a breast cancer diagnosis from CARRIERS; 237 and 1351 women with BRCA2 PVs and breast cancer from the UKBB and Ambry Genetics, respectively. Exposures: PVs in ATM, BRCA1, BRCA2, CHEK2, and PALB2. Main Outcomes and Measures: PVs were grouped by type and location within genes and assessed for risks of breast cancer (odds ratios (OR), 95% confidence intervals (CI), and p-values) using logistic regression. Mean ages at diagnosis were compared using linear regression. Results: Compared to women carrying BRCA2 exon 11 protein truncating variants (PTVs) in the CARRIERS population-based study, women with BRCA2 ex13-27 PTVs (OR=2.7, 95%CI 1.1-7.9) and ex1-10 PTVs (OR=1.6, 95%CI 0.8-3.5) had higher breast cancer risks, lower rates of ER-negative breast cancer (ex13-27 OR=0.5, 95%CI 0.2-0.9; ex1-10 OR=0.5, 95%CI 0.1-1.0), and earlier age of breast cancer diagnosis (ex13-27 5.5 years, p<0.001; ex1-10 2.4 years, p=0.17). These associations with ER-negative breast cancer and age replicated in a high-risk clinical cohort and the population-based UK Biobank cohort. No differences in risk or age at diagnosis by gene region were observed for PTVs in other predisposition genes. Conclusions and Relevance: Population-based and clinical high-risk cohorts establish that PTVs in exon 11 of BRCA2 are associated with reduced risk of breast cancer, later age at diagnosis, and greater risk of ER-negative disease. These differential risks may improve individualized risk prediction and clinical management for women carrying BRCA2 PTVs. Key Points: Question: Does ATM , BRCA1 , BRCA2 , CHEK2 and PALB2 pathogenic variant type and location influence breast cancer risk in population-based studies? Findings: Breast cancer risk and estrogen receptor status differ based on the type and location of pathogenic variants in BRCA2 . Women carrying protein truncating variants in exon 11 have a lower breast cancer risk in the population-based cohorts, older age at diagnosis and higher rates of estrogen receptor negative breast cancer than women with exon 1-10 or exon 13-27 truncation variants in population-based and clinical testing cohorts. Meaning: Incorporating pathogenic variant type and location in cancer risk models may improve individualized risk prediction.

Analysis of more than 400,000 women provides case-control evidence for BRCA1 and BRCA2 variant classification.

Clinical genetic testing identifies variants causal for hereditary cancer, information that is used for risk assessment and clinical management. Unfortunately, some variants identified are of uncertain clinical significance (VUS), complicating patient management. Case-control data is one evidence type used to classify VUS, and previous findings indicate that case-control likelihood ratios (LRs) outperform odds ratios for variant classification. As an initiative of the Evidence-based Network for the Interpretation of Germline Mutant Alleles (ENIGMA) Analytical Working Group we analyzed germline sequencing data of BRCA1 and BRCA2 from 96,691 female breast cancer cases and 303,925 unaffected controls from three studies: the BRIDGES study of the Breast Cancer Association Consortium, the Cancer Risk Estimates Related to Susceptibility consortium, and the UK Biobank. We observed 11,227 BRCA1 and BRCA2 variants, with 6,921 being coding, covering 23.4% of BRCA1 and BRCA2 VUS in ClinVar and 19.2% of ClinVar curated (likely) benign or pathogenic variants. Case-control LR evidence was highly consistent with ClinVar assertions for (likely) benign or pathogenic variants; exhibiting 99.1% sensitivity and 95.4% specificity for BRCA1 and 92.2% sensitivity and 86.6% specificity for BRCA2. This approach provides case-control evidence for 785 unclassified variants, that can serve as a valuable element for clinical classification.

Harnessing the Power of Generative Artificial Intelligence in Pathology Education.

CONTEXT.­: Generative artificial intelligence (AI) technologies are rapidly transforming numerous fields, including pathology, and hold significant potential to revolutionize educational approaches. OBJECTIVE.­: To explore the application of generative AI, particularly large language models and multimodal tools, for enhancing pathology education. We describe their potential to create personalized learning experiences, streamline content development, expand access to educational resources, and support both learners and educators throughout the training and practice continuum. DATA SOURCES.­: We draw on insights from existing literature on AI in education and the collective expertise of the coauthors within this rapidly evolving field. Case studies highlight practical applications of large language models, demonstrating both the potential benefits and unique challenges associated with implementing these technologies in pathology education. CONCLUSIONS.­: Generative AI presents a powerful tool kit for enriching pathology education, offering opportunities for greater engagement, accessibility, and personalization. Careful consideration of ethical implications, potential risks, and appropriate mitigation strategies is essential for the responsible and effective integration of these technologies. Future success lies in fostering collaborative development between AI experts and medical educators, prioritizing ongoing human oversight and transparency to ensure that generative AI augments, rather than supplants, the vital role of educators in pathology training and practice.

Susceptibility gene mutations in germline and tumors of patients with HER2-negative advanced breast cancer.

Germline mutations in BRCA1 and BRCA2 (gBRCA1/2) are required for a PARP inhibitor therapy in patients with HER2-negative (HER2-) advanced breast cancer (aBC). However, little is known about the prognostic impact of gBRCA1/2 mutations in aBC patients treated with chemotherapy. This study aimed to investigate the frequencies and prognosis of germline and somatic BRCA1/2 mutations in HER2- aBC patients receiving the first chemotherapy in the advanced setting. Patients receiving their first chemotherapy for HER2- aBC were retrospectively selected from the prospective PRAEGNANT registry (NCT02338167). Genotyping of 26 cancer predisposition genes was performed with germline DNA of 471 patients and somatic tumor DNA of 94 patients. Mutation frequencies, progression-free and overall survival (PFS, OS) according to germline mutation status were assessed. gBRCA1/2 mutations were present in 23 patients (4.9%), and 33 patients (7.0%) had mutations in other cancer risk genes. Patients with a gBRCA1/2 mutation had a better OS compared to non-mutation carriers (HR: 0.38; 95%CI: 0.17-0.86). PFS comparison was not statistically significant. Mutations in other risk genes did not affect prognosis. Two somatic BRCA2 mutations were found in 94 patients without gBRCA1/2 mutations. Most frequently somatic mutated genes were TP53 (44.7%), CDH1 (10.6%) and PTEN (6.4%). In conclusion, aBC patients with gBRCA1/2 mutations had a more favorable prognosis under chemotherapy compared to non-mutation carriers. The mutation frequency of ~5% with gBRCA1/2 mutations together with improved outcome indicates that germline genotyping of all metastatic patients for whom a PARP inhibitor therapy is indicated should be considered.

Bridging the Clinical-Computational Transparency Gap in Digital Pathology.

CONTEXT.­: Computational pathology combines clinical pathology with computational analysis, aiming to enhance diagnostic capabilities and improve clinical productivity. However, communication barriers between pathologists and developers often hinder the full realization of this potential. OBJECTIVE.­: To propose a standardized framework that improves mutual understanding of clinical objectives and computational methodologies. The goal is to enhance the development and application of computer-aided diagnostic (CAD) tools. DESIGN.­: The article suggests pivotal roles for pathologists and computer scientists in the CAD development process. It calls for increased understanding of computational terminologies, processes, and limitations among pathologists. Similarly, it argues that computer scientists should better comprehend the true use cases of the developed algorithms to avoid clinically meaningless metrics. RESULTS.­: CAD tools improve pathology practice significantly. Some tools have even received US Food and Drug Administration approval. However, improved understanding of machine learning models among pathologists is essential to prevent misuse and misinterpretation. There is also a need for a more accurate representation of the algorithms' performance compared to that of pathologists. CONCLUSIONS.­: A comprehensive understanding of computational and clinical paradigms is crucial for overcoming the translational gap in computational pathology. This mutual comprehension will improve patient care through more accurate and efficient disease diagnosis.

Democratizing Artificial Intelligence in Anatomic Pathology.

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. 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.

Polygenic Risk Score Modifies Prostate Cancer Risk of Pathogenic Variants in Men of African Ancestry.

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.

Organizational preparedness for the use of large language models in pathology informatics.

In this paper, we consider the current and potential role of the latest generation of Large Language Models (LLMs) in medical informatics, particularly within the realms of clinical and anatomic pathology. We aim to provide a thorough understanding of the considerations that arise when employing LLMs in healthcare settings, such as determining appropriate use cases and evaluating the advantages and limitations of these models. Furthermore, this paper will consider the infrastructural and organizational requirements necessary for the successful implementation and utilization of LLMs in healthcare environments. We will discuss the importance of addressing education, security, bias, and privacy concerns associated with LLMs in clinical informatics, as well as the need for a robust framework to overcome regulatory, compliance, and legal challenges.

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.

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.

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

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.

Succinate dehydrogenase variants in paraganglioma: why are B subunit variants 'bad'?

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.

Functional and Clinical Characterization of Variants of Uncertain Significance Identifies a Hotspot for Inactivating Missense Variants in RAD51C.

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.

Cybersecurity and Information Assurance for the Clinical Laboratory.

BACKGROUND: Network-connected medical devices have rapidly proliferated in the wake of recent global catalysts, leaving clinical laboratories and healthcare organizations vulnerable to malicious actors seeking to ransom sensitive healthcare information. As organizations become increasingly dependent on integrated systems and data-driven patient care operations, a sudden cyberattack and the associated downtime can have a devastating impact on patient care and the institution as a whole. Cybersecurity, information security, and information assurance principles are, therefore, vital for clinical laboratories to fully prepare for what has now become inevitable, future cyberattacks. CONTENT: This review aims to provide a basic understanding of cybersecurity, information security, and information assurance principles as they relate to healthcare and the clinical laboratories. Common cybersecurity risks and threats are defined in addition to current proactive and reactive cybersecurity controls. Information assurance strategies are reviewed, including traditional castle-and-moat and zero-trust security models. Finally, ways in which clinical laboratories can prepare for an eventual cyberattack with extended downtime are discussed. SUMMARY: The future of healthcare is intimately tied to technology, interoperability, and data to deliver the highest quality of patient care. Understanding cybersecurity and information assurance is just the first preparative step for clinical laboratories as they ensure the protection of patient data and the continuity of their operations.

Contralateral Breast Cancer Risk Among Carriers of Germline Pathogenic Variants in ATM, BRCA1, BRCA2, CHEK2, and PALB2.

PURPOSE: To estimate the risk of contralateral breast cancer (CBC) among women with germline pathogenic variants (PVs) in ATM, BRCA1, BRCA2, CHEK2, and PALB2. METHODS: The study population included 15,104 prospectively followed women within the CARRIERS study treated with ipsilateral surgery for invasive breast cancer. The risk of CBC was estimated for PV carriers in each gene compared with 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 (ER) status. RESULTS: Germline BRCA1, BRCA2, and CHEK2 PV carriers with breast cancer were at significantly elevated risk (hazard ratio > 1.9) of CBC, whereas only the PALB2 PV carriers with ER-negative breast cancer had elevated risks (hazard ratio, 2.9). By 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 10-year cumulative incidence of CBC was estimated to be 33% for BRCA1, 27% for BRCA2, and 13% for CHEK2 PV carriers with breast cancer and 35% for PALB2 PV carriers with ER-negative breast cancer. The 10-year cumulative incidence of CBC among postmenopausal PV carriers was 12% for BRCA1, 9% for BRCA2, and 4% for CHEK2. CONCLUSION: 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.

Saturation genome editing-based functional evaluation and clinical classification of BRCA2 single nucleotide variants.

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 risk of breast 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. Summary: Germline BRCA2 loss-of function (LOF) variants identified by clinical genetic testing predispose to several types of cancer. However, variants of uncertain significance (VUS) limit the clinical use of testing results. Thus, there is an urgent need for functional characterization and clinical classification of all BRCA2 variants to facilitate current and future clinical management of individuals with these variants. Here we show the results from a saturation genome editing (SGE) and functional analysis of all possible single nucleotide variants (SNVs) from exons 15 to 26 that encode the BRCA2 DNA Binding Domain hotspot for pathogenic missense variants. The assay was based on deep sequence analysis of surviving endogenously targeted human haploid HAP1 cells. The assay was calibrated relative to ClinVar known pathogenic and benign missense standards and 95% prevalence thresholds for functionally abnormal and normal variants were identified. Thresholds were validated based on nonsense and synonymous variants. SNVs encoding functionally abnormal missense variants were associated with increased risks of breast and ovarian cancer. The functional assay results were integrated into a ClinGen/ACMG/AMP-like model for clinical classification of the majority of BRCA2 SNVs as pathogenic/likely pathogenic or benign/likely benign. The classified variants can be used for improved clinical management of variant carriers.

Genetic Risk of Second Primary Cancer in Breast Cancer Survivors: The Multiethnic Cohort Study.

Women who have had breast cancer in the past are at increased risk of developing a second primary cancer (SPC), including second primary breast cancer (SPBC) or a second primary non-breast cancer (SPNBC). In the Multiethnic Cohort (MEC) Study, we conducted a prospective cohort analysis in 3,223 female breast cancer survivors from five racial/ethnic populations (White, African American, Japanese American, Latino, and Native Hawaiian) to assess the association of rare pathogenic variants (PV) in 37 known cancer predisposition genes with risk of SPC. A total of 719 (22.3%) women developed SPC, of which, 323 (10.0%) were SPBC. Germline PVs in BRCA1 (HR, 2.28; 95% CI, 1.11-4.65) and ERCC2 (HR, 3.51; 95% CI, 1.29-9.54) were significantly enriched in women with SPC. In the subtype analysis for SPBC, a significant association of ERCC2 PVs (HR, 5.09; 95% CI, 1.58-16.4) and a suggestive association of BRCA2 PVs (HR, 2.24; 95% CI, 0.91-5.55) were observed. There was also a higher risk of SPNBC in carriers of BRCA1 PVs (HR, 2.98; 95% CI, 1.21-7.36). These results provide evidence that germline PVs in BRCA1, BRCA2, and ERCC2 contribute to the development of SPC in breast cancer survivors. These findings also suggest that compromised DNA repair mechanisms could be a predisposition factor for SPC in patients with breast cancer, supporting the need for closer monitoring of SPC in women carrying PVs in these genes. SIGNIFICANCE: This multiethnic study links germline pathogenic variants in BRCA1, BRCA2, and ERCC2 to the development of second primary cancer in breast cancer survivors, providing biological insights and biomarkers to guide patient monitoring.

An integrative model for the comprehensive classification of BRCA1 and BRCA2 variants of uncertain clinical significance.

Loss-of-function variants in the BRCA1 and BRCA2 susceptibility genes predispose carriers to breast and/or ovarian cancer. The use of germline testing panels containing these genes has grown dramatically, but the interpretation of the results has been complicated by the identification of many sequence variants of undefined cancer relevance, termed "Variants of Uncertain Significance (VUS)." We have developed functional assays and a statistical model called VarCall for classifying BRCA1 and BRCA2 VUS. Here we describe a multifactorial extension of VarCall, called VarCall XT, that allows for co-analysis of multiple forms of genetic evidence. We evaluated the accuracy of models defined by the combinations of functional, in silico protein predictors, and family data for VUS classification. VarCall XT classified variants of known pathogenicity status with high sensitivity and specificity, with the functional assays contributing the greatest predictive power. This approach could be used to identify more patients that would benefit from personalized cancer risk assessment and management.

Classification of BRCA2 Variants of Uncertain Significance (VUS) Using an ACMG/AMP Model Incorporating a Homology-Directed Repair (HDR) Functional Assay.

PURPOSE: The identification of variants of uncertain significance (VUS) in the BRCA1 and BRCA2 genes by hereditary cancer testing poses great challenges for the clinical management of variant carriers. The ACMG/AMP (American College of Medical Genetics and Genomics/Association for Molecular Pathology) variant classification framework, which incorporates multiple sources of evidence, has the potential to establish the clinical relevance of many VUS. We sought to classify the clinical relevance of 133 single-nucleotide substitution variants encoding missense variants in the DNA-binding domain (DBD) of BRCA2 by incorporating results from a validated functional assay into an ACMG/AMP-variant classification model from a hereditary cancer-testing laboratory. EXPERIMENTAL DESIGN: The 133 selected VUS were evaluated using a validated homology-directed double-strand DNA break repair (HDR) functional assay. Results were combined with clinical and genetic data from variant carriers in a rules-based variant classification model for BRCA2. RESULTS: Of 133 missense variants, 44 were designated as non-functional and 89 were designated as functional in the HDR assay. When combined with genetic and clinical information from a single diagnostic laboratory in an ACMG/AMP-variant classification framework, 66 variants previously classified by the diagnostic laboratory were correctly classified, and 62 of 67 VUS (92.5%) were reclassified as likely pathogenic (n = 22) or likely benign (n = 40). In total, 44 variants were classified as pathogenic/likely pathogenic, 84 as benign/likely benign, and 5 remained as VUS. CONCLUSIONS: Incorporation of HDR functional analysis into an ACMG/AMP framework model substantially improves BRCA2 VUS re-classification and provides an important tool for determining the clinical relevance of individual BRCA2 VUS.

Mapping molecular subtype specific alterations in breast cancer brain metastases identifies clinically relevant vulnerabilities.

The molecular events and transcriptional plasticity driving brain metastasis in clinically relevant breast tumor subtypes has not been determined. Here we comprehensively dissect genomic, transcriptomic and clinical data in patient-matched longitudinal tumor samples, and unravel distinct transcriptional programs enriched in brain metastasis. We report on subtype specific hub genes and functional processes, central to disease-affected networks in brain metastasis. Importantly, in luminal brain metastases we identify homologous recombination deficiency operative in transcriptomic and genomic data with recurrent breast mutational signatures A, F and K, associated with mismatch repair defects, TP53 mutations and homologous recombination deficiency (HRD) respectively. Utilizing PARP inhibition in patient-derived brain metastatic tumor explants we functionally validate HRD as a key vulnerability. Here, we demonstrate a functionally relevant HRD evident at genomic and transcriptomic levels pointing to genomic instability in breast cancer brain metastasis which is of potential translational significance.