Combined HER3-EGFR score in triple-negative breast cancer provides prognostic and predictive significance superior to individual biomarkers

© 2020, The Author(s). Epidermal growth factor receptor (EGFR) and human epidermal growth factor receptor 3 (HER3) have been investigated as triple-negative breast cancer (TNBC) biomarkers. Reduced EGFR levels can be compensated by increases in HER3; thus, assaying EGFR and HER3 together may improve prognostic value. In a multi-institutional cohort of 510 TNBC patients, we analyzed the impact of HER3, EGFR, or combined HER3-EGFR protein expression in pre-treatment samples on breast cancer-specific and distant metastasis-free survival (BCSS and DMFS, respectively). A subset of 60 TNBC samples were RNA-sequenced using massive parallel sequencing. The combined HER3-EGFR score outperformed individual HER3 and EGFR scores, with high HER3-EGFR score independently predicting worse BCSS (Hazard Ratio [HR] = 2.30, p = 0.006) and DMFS (HR = 1.78, p = 0.041, respectively). TNBCs with high HER3-EGFR scores exhibited significantly suppressed ATM signaling and differential expression of a network predicted to be controlled by low TXN activity, resulting in activation of EGFR, PARP1, and caspases and inhibition of p53 and NFκB. Nuclear PARP1 protein levels were higher in HER3-EGFR-high TNBCs based on immunohistochemistry (p = 0.036). Assessing HER3 and EGFR protein expression in combination may identify which adjuvant chemotherapy-treated TNBC patients have a higher risk of treatment resistance and may benefit from a dual HER3-EGFR inhibitor and a PARP1 inhibitor

A whole slide image-based machine learning approach to predict ductal carcinoma in situ (DCIS) recurrence risk

© 2019 The Author(s). Background: Breast ductal carcinoma in situ (DCIS) represent approximately 20% of screen-detected breast cancers. The overall risk for DCIS patients treated with breast-conserving surgery stems almost exclusively from local recurrence. Although a mastectomy or adjuvant radiation can reduce recurrence risk, there are significant concerns regarding patient over-/under-treatment. Current clinicopathological markers are insufficient to accurately assess the recurrence risk. To address this issue, we developed a novel machine learning (ML) pipeline to predict risk of ipsilateral recurrence using digitized whole slide images (WSI) and clinicopathologic long-term outcome data from a retrospectively collected cohort of DCIS patients (n = 344) treated with lumpectomy at Nottingham University Hospital, UK. Methods: The cohort was split case-wise into training (n = 159, 31 with 10-year recurrence) and validation (n = 185, 26 with 10-year recurrence) sets. The sections from primary tumors were stained with H&E, then digitized and analyzed by the pipeline. In the first step, a classifier trained manually by pathologists was applied to digital slides to annotate the areas of stroma, normal/benign ducts, cancer ducts, dense lymphocyte region, and blood vessels. In the second step, a recurrence risk classifier was trained on eight select architectural and spatial organization tissue features from the annotated areas to predict recurrence risk. Results: The recurrence classifier significantly predicted the 10-year recurrence risk in the training [hazard ratio (HR) = 11.6; 95% confidence interval (CI) 5.3-25.3, accuracy (Acc) = 0.87, sensitivity (Sn) = 0.71, and specificity (Sp) = 0.91] and independent validation [HR = 6.39 (95% CI 3.0-13.8), p < 0.0001;Acc = 0.85, Sn = 0.5, Sp = 0.91] cohorts. Despite the limitations of our cohorts, and in some cases inferior sensitivity performance, our tool showed superior accuracy, specificity, positive predictive value, concordance, and hazard ratios relative to tested clinicopathological variables in predicting recurrences (p < 0.0001). Furthermore, it significantly identified patients that might benefit from additional therapy (validation cohort p = 0.0006). Conclusions: Our machine learning-based model fills an unmet clinical need for accurately predicting the recurrence risk for lumpectomy-treated DCIS patients

Prognostic Role of Androgen Receptor in Triple Negative Breast Cancer: A Multi-Institutional Study

Background: Androgen Receptor (AR) has emerged as a potential therapeutic target for AR-positive triple-negative breast cancer (TNBC). However, conflicting reports regarding AR’s prognostic role in TNBC are putting its usefulness in question. Some studies conclude that AR positivity indicates a good prognosis in TNBC whereas others suggest the opposite, and some show that AR status has no significant bearing on the patients’ prognosis. Methods: We evaluated the prognostic value of AR in resected primary tumors from TNBC patients from six international cohorts {US (n=420), UK (n=239), Norway (n=104), Ireland (n=222), Nigeria (n=180), and India (n=242); total n=1407}. All TNBC samples were stained with the same anti-AR antibody using the same immunohistochemistry protocol, and samples with ≥1% of AR-positive nuclei were deemed AR-positive TNBCs. Results: AR status shows population-specific patterns of association with patients’ overall survival after controlling for age, grade, population, and chemotherapy. We found AR-positive status to be a marker of good prognosis in US and Nigerian cohorts, a marker of poor prognosis in Norway, Ireland and Indian cohorts, and neutral in UK cohort. Conclusion: AR status, on its own, is not a reliable prognostic marker. More research to investigate molecular subtype composition among the different cohorts is warranted

Redlining−associated methylation in breast tumors: the impact of contemporary structural racism on the tumor epigenome

PurposePlace-based measures of structural racism have been associated with breast cancer mortality, which may be driven, in part, by epigenetic perturbations. We examined the association between contemporary redlining, a measure of structural racism at the neighborhood level, and DNA methylation in breast tumor tissue.MethodsWe identified 80 Black and White women diagnosed and treated for a first-primary breast cancer at Emory University Hospitals (2008–2017). Contemporary redlining was derived for census tracts using the Home Mortgage Disclosure Act database. Linear regression models were used to calculate the association between contemporary redlining and methylation in breast tumor tissue. We also examined epigenetic age acceleration for two different metrics, regressing β values for each cytosine-phosphate-guanine dinucleotide (CpG) site on redlining while adjusting for covariates. We employed multivariable Cox-proportional hazards models and 95% confidence intervals (CI) to estimate the association between aberrant methylation and mortality.ResultsContemporary redlining was associated with 5 CpG sites after adjustment for multiple comparisons (FDR&lt;0.10). All genes were implicated in breast carcinogenesis, including genes related to inflammation, immune function and stress response (ANGPT1, PRG4 and PRG4). Further exploration of the top 25 CpG sites, identified interaction of 2 sites (MRPS28 and cg11092048) by ER status and 1 site (GDP1) was associated with all-cause mortality. Contemporary redlining was associated with epigenetic age acceleration by the Hannum metric (β=5.35; CI 95%=0.30,10.4) and showed positive but non-significant correlation with the other clock.ConclusionWe identified novel associations between neighborhood contemporary redlining and the breast tumor DNA methylome, suggesting that racist policies leading to inequitable social and environmental exposures, may impact the breast tumor epigenome. Additional research on the potential implications for prognosis is needed

Pathology Case Study: Cervical Adenopathy

This is a case study presented by the University of Pittsburgh Department of Pathology, which describes a 78-year-old male who presented with a 6 month history of cervical adenopathy. Visitors are given patient history, microscopic description, differential diagnosis, and immunohistochemistry, including images, and are given the opportunity to diagnose the patient. A &quot;Final Diagnosis&quot; section provides a discussion of the findings as well as references. This is an excellent resource for students in the health sciences to familiarize themselves with using patient history and laboratory results to diagnose disease. It is also a helpful site for educators to use to introduce or test student learning in hematopathology

TTF-1 Expression in Rectal Adenocarcinoma: A Case Report and Review of the Literature

Thyroid transcription factor 1 (TTF-1) is considered a highly sensitive and specific marker for primary lung adenocarcinoma. However, in recent years retrospective studies of tumor samples have confirmed that, although rare, TTF-1 can also be expressed in colorectal adenocarcinoma. There are a few case reports of patients with TTF-1-positive colon adenocarcinoma in the medical literature but none of TTF-1-positive rectal adenocarcinoma. Here, we present a case of rectal adenocarcinoma with lung metastasis found to be TTF-1 positive on immunohistochemistry. A review and discussion of the available literature is also included

Cytologic predictors of malignancy in bile duct brushings: a multi-reviewer analysis of 60 cases

Diagnosing malignancy in bile duct brushings is highly challenging. Seven reviewers of variable backgrounds and levels of participation in bile duct brushing sign out blindly reviewed 60 specimens (30 malignant with histologic confirmation and 30 benign (15 stented) with resection or >= 18 months of uneventful follow-up), testing the utility of 14 malignant characteristics. Eleven characteristics were statistically significantly associated with malignancy including 3-dimensional clusters (63% in malignant vs 3% in benign, odds ratio 50, P=0.0003), pleomorphism (62 vs 3, odds ratio 48, P=0.0004), 2-cell population (60% vs 3, odds ratio 44, P=0.0005), chromatin pattern (hypo/hyperchromasia) changes (70% vs 7%, odds ratio 33, P= 3 malignant characteristics, while 23 (77%) benign brushings had none. Of 20 brushings with >= 4 characteristics, 1(5%) proved benign and showed detachment atypia, a close malignant mimicker in brushings. Identification of 3 characteristics maximized the combined sensitivity (70%), specificity (97%) and accuracy (83%), but sensitivity dropped as number of characteristics increased. Identification of 3/11 characteristics (3-dimensional clusters, pleomorphism, high nuclear-to-cytoplasmic ratio, nuclear irregularity, hypercellularity, discohesion, chromatin changes, vacuoles, prominent nucleoli, molding and 2-cell population) improves pathologists' overall performance greatly