Stromal tumor-infiltrating lymphocytes (sTILs) are promising biomarkers for predicting therapeutic outcomes in triple-negative breast cancer (TNBC), with higher sTIL levels correlating with improved chemotherapy response and survival outcomes. Currently, sTILs are manually evaluated by pathologists, which is prone to inter-reader variability. In this study, we have developed an AI-driven TIL segmentation pipeline to process entire diagnostic hematoxylin-and-eosin-stained whole slide images for reproducible scoring (global TILseg scoring) and reliable prognostication. This pipeline was optimized and tested using two independent TNBC patient cohorts (n = 57 in the discovery cohort, n = 43 in the validation cohort) with clinical outcomes and follow-up data. The global scores generated by TILseg showed moderate to high concordance with expert scoring (Spearman R = 0.84-0.89) and improved patient stratification (p-value = 0.0191) as compared to manual scoring (p-value = 0.0663). Additionally, we investigate how the spatial localization of sTILs (spatial TILseg) impact survival outcomes by identifying TILs in selected stromal subsets (0.02-2 mm from the epithelial clusters). Our findings have shown that TILs up to 50 μm from epithelial regions prove to be most prognostic in predicting recurrence-free survival post-neoadjuvant chemotherapy with higher statistical significance than both manual and global TILseg scoring. Further, spatial TILseg scoring was more significantly associated with pathological complete response status in both patient cohorts. In summary, we present an AI-based digital tool for robust sTIL scoring and spatial mapping to enhance its potential as both a diagnostic and prognostic biomarker, particularly in TNBC patients.

Ductal carcinoma in situ (DCIS) is a noninvasive form of breast cancer that accounts for 15% to 25% of all new breast cancer diagnoses. Clinical management approaches for DCIS include surgical excision, radiotherapy, and endocrine therapy (for patients with estrogen receptor (ER)-positive disease). Active surveillance is also being investigated for patients with low-risk disease. Pathologist assessment of nuclear grade as well as ER expression by immunohistochemistry is currently used to inform clinical decision-making. Exploratory protein biomarkers in DCIS include HER2, p16, p53, and Ki-67. Our study evaluated pathologist interreader agreement for the following immunohistochemical stains applied across 40 cases of DCIS: ER, PR, HER2, p16, Ki-67, and p53. We found that pathologist agreement was good to excellent for assessment of ER and PR (progesterone receptor), both with regard to the percent of cells staining as well as staining intensity (ICC/kappa: 0.76-0.96 for ER, 0.69-0.98 for PR). Interreader agreement was also good to excellent for HER2 interpretation (ICC/kappa: 0.78-0.97). For additional exploratory biomarkers, including p16, p53, and Ki-67, interreader agreement ranged from fair to good, likely reflecting a lack of pathologist training and less familiarity with these biomarkers in the context of DCIS. Our findings suggest that investigators should take potential interreader variability into consideration when designing clinical trials and exploratory biomarker studies.

Accurate disease diagnosis is the cornerstone of patient care, directly impacting the quality of health care delivery and patient outcomes. Developing diagnostic competence is a critical goal of pathology residency training. This study uses longitudinal data from trainees during pathology residency to examine the association between years of training and performance in 4 key interpretive phases: detecting critical regions, recognizing their relevance, describing histopathological features, and rendering an accurate diagnosis. Using a study set of 32 digital whole slide images of breast biopsies, 155 pathology residents from 10 US academic medical centers each reviewed 14 cases across multiple years of residency training. Image-viewing behavior, annotation accuracy, and diagnostic decisions were recorded and analyzed. Generalized estimating equations were used to assess the relationship between residency year and metrics capturing the 4 interpretive phases. Year of residency training was not significantly associated with the detection of critical regions or recognizing their importance. However, each year of residency training was associated with 2% less time spent viewing critical regions (P = 0.025) and 1% more attentional coverage of the image space (P = 0.01), suggesting that residents adopt broader scanning strategies over time. Further, each year of residency training was associated with 19% higher odds of writing an accurate annotation (odds ratio = 1.19, P < 0.001) and 18% higher odds of making an accurate diagnosis (odds ratio = 1.18, P < 0.001). Exploratory analyses indicated that accurate feature annotation was the key predictor of diagnostic accuracy, highlighting its importance as a foundational skill in clinical decision-making. Both the ability to describe histopathological features and diagnostic accuracy increase with years of residency training, and the former appears to be a key component of the latter. Competency-based training in pathology should incorporate targeted interventions to improve histopathological feature recognition and description, which appears most likely to improve overall diagnostic performance.

Ovarian metastasis to the breast is extremely rare. The clinical and radiologic presentation of metastasis to the breast is nonspecific and can mimic primary breast cancers. The most common mammographic findings of ovarian metastasis are superficial, circumscribed, high-density masses without architectural distortion. Compared with other malignancies that metastasize to the breast, ovarian cancer can more frequently show microcalcifications. On US, these masses can be hypoechoic or have heterogeneous echogenicity with posterior acoustic enhancement. Less commonly, ovarian metastasis can present similarly to inflammatory breast cancer, demonstrating diffuse skin thickening on mammography and US. Immunohistochemistry is useful in differentiating ovarian metastasis from primary breast lesions. Ovarian and breast markers, including Wilm's tumor, paired box 8, cancer antigen 125, GATA binding protein 3, and gross cystic disease fluid protein 15, are particularly helpful. Overall, metastatic ovarian cancer to the breast provides a diagnostic challenge requiring close radiologic and pathologic correlation to reach the correct diagnosis.

Pathology reports contain complex medical terminology that may be confusing or overwhelming for patients newly diagnosed with breast cancer. We evaluated the effectiveness of patient-centered pathology reports (PCPRs), which translate pathology results into patient-friendly language. Sixty-six participants newly diagnosed with breast cancer were randomized to receive either a PCPR and standard pathology report (intervention arm) or a standard pathology report alone (control arm). Patients were surveyed at initial pathology disclosure and 1 month later to assess breast cancer knowledge and ratings of decisional confidence, conflict, and self-efficacy for treatment decision-making. Knowledge was assessed for four pathology domains independently. Accuracy of breast cancer knowledge across all domains trended higher for the intervention group compared with the control group (66

Abstract Background Ductal carcinoma in situ (DCIS) is a non-obligate precursor to invasive breast cancer (IBC). Studies have indicated differences in DCIS outcome based on race or ethnicity, but molecular differences have not been investigated. Methods We examined the molecular profile of DCIS by self-reported race (SRR) and outcome groups in Black (n = 99) and White (n = 191) women in a large DCIS case-control cohort study with longitudinal follow up. Results Gene expression and pathway analyses suggested that different genes and pathways are involved in diagnosis and ipsilateral breast outcome (DCIS or IBC) after DCIS treatment in White versus Black women. We identified differences in ER and HER2 expression, tumor microenvironment composition, and copy number variations by SRR and outcome groups. Conclusions Our results suggest that different molecular mechanisms drive initiation and subsequent ipsilateral breast events in Black versus White women.

Ductal carcinoma in situ (DCIS) is overtreated, in part because of inability to predict which DCIS cases diagnosed at core needle biopsy (CNB) will be upstaged at excision. This study aimed to determine whether quantitative magnetic resonance imaging (MRI) features can identify DCIS at risk of upstaging to invasive cancer. This prospective observational clinical trial analyzed women with a diagnosis of DCIS on CNB. All the participants underwent preoperative 3T MRI. Quantitative MRI features from routine dynamic contrast-enhanced (DCE) MR images (e.g., peak percent enhancement [PE]) and from advanced high temporal-resolution DCE MR images (e.g., Ktrans) were measured. Clinical, pathologic, and mammographic features were reviewed. Associations with upstaging were summarized using the area under the receiver operating characteristic curve (AUC). Of 58 DCIS lesions at CNB, 15 (26

e12578 Background: Ductal carcinoma in situ (DCIS) is a preinvasive breast cancer typically excised and treated with adjuvant therapy. While there is consensus that this results in overtreatment, there is little agreement on who may avoid radiation or endocrine therapy. Two freely available prediction models, Van Nuys Prognostic Index (VNPI) and Memorial Sloan Kettering Nomogram (MSK-N), are commonly used to identify low-risk DCIS based on standard clinicopathological features. Oncotype DCIS is a newer commercially available tissue-based multigene assay also used to assess risk, but its use is limited due to cost and unclear value over VNPI and MSK-N. We sought to compare these tests’ agreement in determining DCIS ipsilateral breast recurrence (IBR) risk and potential to de-escalate therapy. Methods: In this subanalysis from a prospective single center clinical trial, we analyzed 38 patients with newly diagnosed pure DCIS confirmed at excision. Each risk assessment tool presents risk in a different manner. Oncotype DCIS provides multiple assessments: a raw score (0-100), a DCIS Score Category (low, intermediate, high), and a Refined Score incorporating clinical features (10-year IBR risk). The VNPI calculates a raw score (4-12) and assigns a risk category (low, intermediate, high). MSK-N calculates a 10-year IBR risk. The tests were dichotomized as low vs. not-low risk categories using these thresholds: DCIS Score Category = low, DCIS Refined Score ≤ 10% IBR risk, VNPI risk category = low, MSK-N ≤ 10% IBR risk. Agreement of the 4 models (DCIS Score Category, DCIS Refined Score, VNPI, MSK-N) were compared using Spearman’s rank correlation for continuous data; percent agreement and Cohen’s kappa (k) were used to compare categorized data. Results: There was poor agreement of continuous risk assessments across the 4 models, with only VNPI and DCIS Refined Score showing significant but moderate correlation (r = 0.53, p = 0.001; others ranged r = 0-0.27, p > 0.1). The number of cases identified as low-risk for each assay was DCIS Score Category = 14 (37%), DCIS Refined Score = 3 (8%), VNPI = 1 (3%), and MSK-N = 0. Percent agreement of low vs. not-low risk categorizations between DCIS Refined Score, VNPI, and MSK-N was 92-97%, but each assay classified 3 or fewer cases as low-risk and kappa was not significant (k = 0-0.48, p > .1). DCIS Score Category demonstrated 63-71% agreement with the other 3 assays (k = 0-0.26). Only 1 case was classified as low-risk DCIS on multiple tests. Conclusions: DCIS risk models have poor agreement for determining IBR risk. DCIS Score Category initially identified many low-risk lesions; however, inclusion of clinical features to create a Refined Score decreased this substantially, providing very few additional low-risk cases for de-escalation over VNPI or MSK-N. Additional studies are needed to determine precise IBR rates when these models are used for adjuvant therapy decision-making. Clinical trial information: NCT03495011 .

The evaluation of biopsied solid organ tissue has long relied on visual examination using a microscope. Immunohistochemistry is critical in this process, labeling and detecting cell lineage markers and therapeutic targets. However, while the practice of immunohistochemistry has reshaped diagnostic pathology and facilitated improvements in cancer treatment, it has also been subject to pervasive challenges with respect to standardization and reproducibility. Efforts are ongoing to improve immunohistochemistry, but for some applications, the benefit of such initiatives could be impeded by its reliance on monospecific antibody-protein reagents and limited multiplexing capacity. This perspective surveys the relevant challenges facing traditional immunohistochemistry and describes how mass spectrometry, particularly liquid chromatography-tandem mass spectrometry, could help alleviate problems. In particular, targeted mass spectrometry assays could facilitate measurements of individual proteins or analyte panels, using internal standards for more robust quantification and improved interlaboratory reproducibility. Meanwhile, untargeted mass spectrometry, showcased to date clinically in the form of amyloid typing, is inherently multiplexed, facilitating the detection and crude quantification of 100s to 1000s of proteins in a single analysis. Further, data-independent acquisition has yet to be applied in clinical practice, but offers particular strengths that could appeal to clinical users. Finally, we discuss the guidance that is needed to facilitate broader utilization in clinical environments and achieve standardization.

PURPOSE:To assess implementation of a next-generation sequencing (NGS) assay to detect microsatellite instability (MSI) as a screen for Lynch syndrome (LS) in endometrial cancer (EC), while determining and comparing characteristics of the four molecular subtypes. METHODS:A retrospective review was performed of 408 total patients with newly diagnosed EC: 140 patients who underwent universal screening with NGS and 268 patients who underwent screening via mismatch repair immunohistochemistry (MMR IHC) as part of a historical screening paradigm. In the NGS cohort, incidental POLE and TP53 mutations along with MSI were identified and used to characterize EC into molecular subtypes: POLE-ultramutated, MSI high (MSI-H), TP53-mutated, and no specific molecular profile (NSMP). In historical cohorts, age- and/or family history-directed screening was performed with MMR IHC. Statistical analysis was performed using a t-test for continuous variables and chi-square or Fisher's exact test for categorical variables. RESULTS:In the NGS cohort, 38 subjects (27%) had MSI-H EC, 100 (71%) had microsatellite stable EC, and two (1%) had an indeterminate result. LS was diagnosed in two subjects (1%), and all but five patients completed genetic screening (96%). Molecular subtypes were ascertained: eight had POLE-ultramutated EC, 28 had TP53-mutated EC (20%), and 66 (47%) had NSMP. MSI-H and TP53-mutated EC had worse prognostic features compared with NSMP EC. Comparison with historical cohorts demonstrated a significant increase in follow-up testing after an initial positive genetic screen in the MSI NGS cohort (56% v 89%; P = .001). CONCLUSION:MSI by NGS allowed for simultaneous screening for LS and categorization of EC into molecular subtypes with prognostic and therapeutic implications.

Abstract Purpose: To examine incremental values of estrogen receptor (ER) status, body mass index (BMI), menopausal status, and a multi-gene classifier over commonly used clinical factors (i.e. age, tumor grade, comedonecrosis, surgical margins, and treatment) in predicting risk of ipsilateral recurrence (IR) within five years after DCIS diagnosis. Methods: A derivation cohort consisted of participants in the discovery cohort, a retrospective multicenter cohort study in women undergoing surgical resection for DCIS between 01/01/1998 and 02/29/2016. The validation cohort provided cases meeting the same eligibility criteria as the discovery cohort. This analysis includes 313 participants with RNA-seq data who either developed IR 1-5 years after initial DCIS diagnosis or were free from subsequent breast events at least five years. Cox proportional hazards regression was used to estimate hazard ratios (HRs) of IR in 216 TBCRC participants (76 with IR). We developed the clinical score using clinical predictors (aforementioned clinical factors and ER) and their regression coefficients from the model with the maximum predictive accuracy (e.g. c-index) and the minimum number of predictors, and the summary score integrating the clinical score and multi-gene classifier. Predictive performance of clinical and summary scores was validated in 97 RAHBT patients (20 with IR). Results: In the discovery cohort, ER negativity, but not BMI or menopausal status, was independently associated with a higher IR risk (HR=2.06, 95% CI 1.18-3.58), and adding ER to the clinical factors-based model increased predictive accuracy (c-index 0.68 to 0.70). The clinical score-adjusted HR was 14.96 (95% CI 8.64-25.91) for multi-gene classifier. Summary scores were better in predicting IR risk than clinical scores (c-index 0.82 vs. 0.70). Compared with their low-risk counterparts, the HR was 4.79 (95% CI 2.55-8.98) in the clinical score-defined high-risk group and 29.02 (95% CI 14.23-59.15) in the summary score-defined high-risk group. In the validation cohort, model performance was improved using summary scores as compared to clinical scores (c-index 0.74 vs. 0.58). Conclusion: Combination of clinical factors and multigene classifier provided more accurate risk estimates of IR within five years after excision of DCIS than clinical factors alone. Citation Format: Graham A. Colditz, Ying Liu, Siri H. Strand, Lorraine King, Jeff Marks, Carlo Maley, Robert B. West, E. Shelley Hwang. Using clinical characteristics and molecular markers to predict the risk of subsequent ipsilateral breast events after excision of DCIS [abstract]. In: Proceedings of the AACR Special Conference on Rethinking DCIS: An Opportunity for Prevention?; 2022 Sep 8-11; Philadelphia, PA. Philadelphia (PA): AACR; Can Prev Res 2022;15(12 Suppl_1): Abstract nr A012.

Purpose New federal legislation in the United States grants patients expanded access to their medical records, making it critical that medical records information is understandable to patients. Provision of informational summaries significantly increase patient perceptions of patient-centered care and reduce feelings of uncertainty, yet their use for cancer pathology is limited. Methods Our team developed and piloted patient-centered versions of pathology reports (PCPRs) for four cancer organ sites: prostate, bladder, breast, and colorectal polyp. The objective of this analysis was to identify common barriers and facilitators to support dissemination of PCPRs in care delivery settings. We analyzed quantitative and qualitative data from pilot PCPR implementations, guided by the RE-AIM framework to explore constructs of reach, effectiveness, adoption, implementation, and maintenance. Results We present two case studies of PCPR implementation – breast cancer and colorectal polyps—that showcase diverse workflows for pathology reporting. Cross-pilot learnings emphasize the potential for PCPRs to improve patient satisfaction, knowledge, quality of shared decision-making activities, yet several barriers to dissemination exist. Conclusion While there is promise in expanding patient-centered cancer communication tools, more work is needed to expand the technological capacity for PCPRs and connect PCPRs to opportunities to reduce costs, improve quality, and reduce waste in care delivery systems.

Background. DCIS consists of a molecularly heterogeneous group of premalignant lesions, with variable risk of invasive progression. Understanding biomarkers for invasive progression could help individualize treatment recommendations based upon tumor biology. As part of the NCI Human Tumor Atlas Network (HTAN), we conducted comprehensive genomic analyses on two large DCIS case-control cohorts. Methods. We performed smart3-seq and low-pass whole genome sequencing on two independent, retrospective, longitudinally sampled DCIS case-control cohorts. TBCRC 038 was a multicenter cohort diagnosed with DCIS between 1998 and 2016 at one of the Translational Breast Cancer Research sites; the RAHBT (Resource of Archival Human Breast Tissue) cohort included women identified through the St. Louis Breast Tissue Repository, and the Women’s Health Repository diagnosed between 1997 and 2001. We studied the spectrum of molecular changes present and sought genomic predictors of subsequent ipsilateral breast events (iBEs: DCIS recurrence or invasive progression) in both DCIS epithelium and stroma in formalin fixed paraffin embedded tissue. We generated de novo tumor and stroma-centric subtypes for DCIS that represents fundamental transcriptomic organization. Copy number analysis was performed using low-pass DNA sequencing. Non-negative matrix factorization (NMF) was applied to the RNA expression of all coding genes to identify clusters. A negative-binomial regression model was used to identify differentially expressed genes. Results. We analyzed 677 DCIS samples from 481 patients with 7.1 years median follow-up. In TBCRC samples, we identified three clusters via NMF in TBCRC referred to as ER low, quiescent, and ER high. The ER-low cluster had significantly higher levels of ERBB2 and lower levels of ESR1 compared to quiescent and ER-high clusters. Quiescent cluster lesions were less proliferative and less metabolically active than ER high and ER low subtypes. These findings were replicated in the RAHBT cohort. Focusing on the stromal component of DCIS from laser capture microdissection in RAHBT samples, we identified four distinct DCIS-associated stromal clusters. A “normal-like” stromal cluster with ECM organization and PI3K-AKT signaling; a “collagen-rich” stromal cluster; a “desmoplastic” stromal cluster with high fibroblast and total myeloid abundance, mostly associated with macrophages and myeloid dendritic cells (mDC); and an “immune-dense” stromal cluster. Further, we compared differentially expressed genes in patients with or without subsequent iBEs within 5 years of diagnosis. Hypothesizing that the resulting 812 DE genes (DESeq2) represent multiple routes to subsequent iBEs, we leveraged NMF to identify paths to progression. In both TBCRC and RAHBT cohorts, poor outcome groups exhibited increased ER, MYC signaling, and oxidative phosphorylation, supporting that these pathways are important for DCIS recurrence and progression. Conclusion. Comprehensive genomic profiling in two independent DCIS cohorts with longitudinal outcomes shows distinct DCIS stromal expression patterns and immune cell composition. RNA expression profiles reveal underlying tumor biology that is associated with later iBEs in both cohorts. These studies provide new insight into DCIS biology and will guide the design of diagnostic strategies to prevent invasive progression. Citation Format: Siri H Strand, Belén Rivero-Gutiérrez, Kathleen E Houlahan, Jose A Seoane, Lorraine M King, Tyler Risom, Lunden Simpson, Sujay Vennam, Aziz Khan, Timothy Hardman, Bryan E Harmon, Fergus J Couch, Kristalyn Gallagher, Mark Kilgore, Shi Wei, Angela DeMichele, Tari King, Priscilla F McAuliffe, Julie Nangia, Joanna Lee, Jennifer Tseng, Anna Maria Storniolo, Alastair Thompson, Gaorav Gupta, Robyn Burns, Deborah J Veis, Katherine DeSchryver, Chunfang Zhu, Magdalena Matusiak, Jason Wang, Shirley X Zhu, Jen Tappenden, Daisy Yi Ding, Dadong Zhang, Jingqin Luo, Shu Jiang, Sushama Varma, Cody Straub, Sucheta Srivastava, Christina Curtis, Rob Tibshirani, Robert Michael Angelo, Allison Hall, Kouros Owzar, Kornelia Polyak, Carlo Maley, Jeffrey R Marks, Graham A Colditz, E Shelley Hwang, Robert B West. The Breast PreCancer Atlas DCIS genomic signatures define biology and correlate with clinical outcomes: An analysis of TBCRC 038 and RAHBT cohorts [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr GS4-07.

Errors in anatomic pathology can result in patients receiving inappropriate treatment and poor patient outcomes. Policies and procedures are necessary to decrease error and improve diagnostic concordance. Breast pathology may be more prone to diagnostic errors than other surgical pathology subspecialties due to inherit borderline diagnostic categories such as atypical ductal hyperplasia and low-grade ductal carcinoma in situ. Mandatory secondary review of internal and outside referral cases before treatment is effective in reducing diagnostic errors and improving concordance. Assessment of error through amendment/addendum tracking, implementing an incident reporting system, and multidisciplinary tumor boards can establish procedures to prevent future error.

Ductal carcinoma in situ (DCIS) is the most common precursor of invasive breast cancer (IBC), with variable propensity for progression. We perform multiscale, integrated molecular profiling of DCIS with clinical outcomes by analyzing 774 DCIS samples from 542 patients with 7.3 years median follow-up from the Translational Breast Cancer Research Consortium 038 study and the Resource of Archival Breast Tissue cohorts. We identify 812 genes associated with ipsilateral recurrence within 5 years from treatment and develop a classifier that predicts DCIS or IBC recurrence in both cohorts. Pathways associated with recurrence include proliferation, immune response, and metabolism. Distinct stromal expression patterns and immune cell compositions are identified. Our multiscale approach employed in situ methods to generate a spatially resolved atlas of breast precancers, where complementary modalities can be directly compared and correlated with conventional pathology findings, disease states, and clinical outcome.