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Triple negative breast cancer (TNBC) is a deadly form of breast cancer due to the development of resistance to chemotherapy affecting over 30% of patients. New therapeutics and companion biomarkers are urgently needed. Recognizing the elevated expression of glucose transporter 1 (GLUT1, encoded by SLC2A1 ) and associated metabolic dependencies in TNBC, we investigated the vulnerability of TNBC cell lines and patient-derived samples to GLUT1 inhibition. We report that genetic or pharmacological inhibition of GLUT1 with BAY-876 impairs the growth of a subset of TNBC cells displaying high glycolytic and lower oxidative phosphorylation (OXPHOS) rates. Pathway enrichment analysis of gene expression data suggests that the functionality of the E2F pathway may reflect to some extent OXPHOS activity. Furthermore, the protein levels of retinoblastoma tumor suppressor (RB1) strongly correlate with the degree of sensitivity to GLUT1 inhibition in TNBC, where RB1-negative cells are insensitive to GLUT1 inhibition. Collectively, our results highlight a strong and targetable RB1-GLUT1 metabolic axis in TNBC and warrant clinical evaluation of GLUT1 inhibition in TNBC patients stratified according to RB1 protein expression levels. Triple negative breast cancer is a deadly form of breast cancer with limited therapeutic options. Here the authors show the efficacy of GLUT1 pharmacological inhibition against a subset of tumors expressing RB1, thereby identifying RB1 protein level as a biomarker of sensitivity to anti-GLUT1 therapy.

Triple-negative breast cancer (TNBC) is a breast cancer subtype that lacks targeted treatment options. The activation of the Notch developmental signaling pathway, which is a feature of TNBC, results in the secretion of proinflammatory cytokines and the recruitment of protumoral macrophages to the tumor microenvironment. While the Notch pathway is an obvious therapeutic target, its activity is ubiquitous, and predictably, anti-Notch therapies are burdened with significant on-target side effects. Previously, we discovered that, under conditions of cellular stress commonly found in the tumor microenvironment, the deubiquitinase USP9x forms a multiprotein complex with the pseudokinase tribbles homolog 3 (TRB3) that together activate the Notch pathway. Herein, we provide preclinical studies that support the potential of therapeutic USP9x inhibition to deactivate Notch. Using a murine TNBC model, we show that USP9x knockdown abrogates Notch activation, reducing the production of the proinflammatory cytokines, C-C motif chemokine ligand 2 (CCL2) and interleukin-1 beta (IL-1β). Concomitant with these molecular changes, a reduction in tumor inflammation, the augmentation of antitumor immune response, and the suppression of tumor growth were observed. The pharmacological inhibition of USP9x using G9, a partially selective, small-molecule USP9x inhibitor, reduced Notch activity, remodeled the tumor immune landscape, and reduced tumor growth without associated toxicity. Proving the role of Notch, the ectopic expression of the activated Notch1 intracellular domain rescued G9-induced effects. This work supports the potential of USP9x inhibition to target Notch in metabolically vulnerable tissues like TNBC, while sparing normal Notch-dependent tissues.

Training the next generation of pathologists is vital for high-quality clinical medicine. Competency-Based Medical Education (CBME) and accumulation of \"Entrustable Professional Activities\" (EPAs) are being implemented worldwide to standardize medical training in all specialties. However, CBME research specific to pathology is lacking. Surveys of training programs routinely using EPAs highlight challenges with administrative burden, completion rates, and feedback. To investigate the impact of EPAs on workload and feedback in a pathology program after 5 years of implementation. A cross-sectional survey was administered to residents in the Diagnostic and Molecular (Anatomical) Pathology program at the University of Toronto, which transitioned to an EPA-based CBME system in 2019. The response rate was 83% (n = 25 of 30). More than half of responding residents submitted at least 4 EPAs per week (52%; n = 13), and most residents (76%; n = 19) estimated spending at least a half hour per week submitting and following up on EPAs. Most residents (92%; n = 23) disagreed with the statement that EPAs provided valuable feedback to their learning; however, all residents (100%; n = 25) agreed verbal feedback at sign-out was one of the most useful assessments of their abilities. Only a minority (36%; n = 9) regularly received verbal feedback alongside an EPA. Respondents overwhelmingly felt that EPAs had a negative effect on the rapport between staff and residents, and cited concerns about staff workloads. This study is the first to quantify the workload associated with CBME in a pathology residency program and raises practical considerations for training programs considering implementation of EPAs.

The extracellular matrix (ECM) collagen undergoes major remodeling during tumorigenesis. However, alterations to the ECM are not widely considered in cancer diagnostics, due to mostly uniform appearance of collagen fibers in white light images of hematoxylin and eosin-stained (H&E) tissue sections. Polarimetric second-harmonic generation (P-SHG) microscopy enables label-free visualization and ultrastructural investigation of non-centrosymmetric molecules, which, when combined with texture analysis, provides multiparameter characterization of tissue collagen. This paper demonstrates whole slide imaging of breast tissue microarrays using high-throughput widefield P-SHG microscopy. The resulting P-SHG parameters are used in classification to differentiate tumor from normal tissue, resulting in 94.2% for both accuracy and F1-score, and 6.3% false discovery rate. Subsequently, the trained classifier is employed to predict tumor tissue with 91.3% accuracy, 90.7% F1-score, and 13.8% false omission rate. As such, we show that widefield P-SHG microscopy reveals collagen ultrastructure over large tissue regions and can be utilized as a sensitive biomarker for cancer diagnostics and prognostics studies.

The emergence of large foundation models (FMs) in histopathology, trained on extensive image datasets using high-performance graphics processing unit (GPU) clusters, has demonstrated significant potential in advancing computational pathology. FMs have potential to overcome the domain gap between training and testing datasets, which creates more translation opportunities. However, the reliance on vast computational resources and large-scale data often limits accessibility and widespread adoption of FMs. To address this limitation, we present HistoLite , a lightweight self-supervised learning framework designed to enable domain-invariant representation learning in histopathology. HistoLite utilizes customizable auto-encoders within a self-supervised learning paradigm that learns generalized and transferable features in an efficient manner. We evaluated the proposed framework using breast Whole Slide Images (WSIs) and benchmarked performance with state-of-the-art FMs for domain generalization. A novel dataset was curated that is of the same tissue slides, scanned by two different scanning platforms, which allows for specific analysis of covariate shifts due to scanner bias. Aspects evaluated include the difference in embeddings across scanners using novel representation shift metrics, including a robustness index, and accuracy, which looks at performance on downstream tasks. The top performing models were UNI, Virchow2 and Prov-GigaPath, likely due to large model sizes and training datasets. In general, most FMs were found to be susceptible to scanner-bias, as shown by differences in embeddings and drop in performance on the held-out scanner. This has significant implications for real-world deployment of FMs in histopathology. HistoLite offered low representation shift in embeddings, the lowest performance drop on out-of-domain data with modest classification accuracy, indicating the smaller model may exhibit a tradeoff between accuracy and generalization.

Resident physicians face a higher rate of burnout and depression than the general population. Few studies have examined burnout and depression in Canadian laboratory medicine residents, and none during the COVID-19 pandemic. To identify the prevalence of burnout and depression, contributing factors, and the impact of COVID-19 in this population. An electronic survey was distributed to Canadian laboratory medicine residents. Burnout was assessed using the Oldenburg Burnout Inventory. Depression was assessed using the Patient Health Questionnaire 9. Seventy-nine responses were collected. The prevalence of burnout was 63% (50 of 79). The prevalence of depression was 47% (37 of 79). Modifiable factors significantly associated with burnout included career dissatisfaction, below average academic performance, lack of time off for illness, stress related to finances, lack of a peer or staff physician mentor, and a high level of fatigue. Modifiable factors significantly associated with depression further included a lack of access to wellness resources, lack of time off for leisure, and fewer hours of sleep. Fifty-five percent (41 of 74) of participants reported direct impacts to their personal circumstances by the COVID-19 pandemic. Burnout and depression are significant issues affecting Canadian laboratory medicine residents. As the COVID-19 pandemic continues, we recommend the institution of flexible work arrangements, protected time off for illness and leisure, ongoing evaluation of career satisfaction, formal and informal wellness programming with trainee input, formal mentorship programming, and a financial literacy curriculum as measures to improve trainee wellness.

Metformin may exert anti-cancer effects through indirect (insulin-mediated) or direct (insulin-independent) mechanisms. We report results of a neoadjuvant “window of opportunity” study of metformin in women with operable breast cancer. Newly diagnosed, untreated, non-diabetic breast cancer patients received metformin 500 mg tid after diagnostic core biopsy until definitive surgery. Clinical (weight, symptoms, and quality of life) and blood [fasting serum insulin, glucose, homeostasis model assessment (HOMA), C-reactive protein (CRP), and leptin] attributes were compared pre- and post-metformin as were terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL) and Ki67 scores (our primary endpoint) in tumor tissue. Thirty-nine patients completed the study. Mean age was 51 years, and metformin was administered for a median of 18 days (range 13–40) up to the evening prior to surgery. 51 % had T1 cancers, 38 % had positive nodes, 85 % had ER and/or PgR positive tumors, and 13 % had HER2 overexpressing or amplified tumors. Mild, self-limiting nausea, diarrhea, anorexia, and abdominal bloating were present in 50, 50, 41, and 32 % of patients, respectively, but no significant decreases were seen on the EORTC30-QLQ function scales. Body mass index (BMI) (−0.5 kg/m 2 , p  < 0.0001), weight (−1.2 kg, p  < 0.0001), and HOMA (−0.21, p  = 0.047) decreased significantly while non-significant decreases were seen in insulin (−4.7 pmol/L, p  = 0.07), leptin (−1.3 ng/mL, p  = 0.15) and CRP (−0.2 mg/L, p  = 0.35). Ki67 staining in invasive tumor tissue decreased (from 36.5 to 33.5 %, p  = 0.016) and TUNEL staining increased (from 0.56 to 1.05, p  = 0.004). Short-term preoperative metformin was well tolerated and resulted in clinical and cellular changes consistent with beneficial anti-cancer effects; evaluation of the clinical relevance of these findings in adequately powered clinical trials using clinical endpoints such as survival is needed.

The Ki-67 proliferation index (PI) guides treatment decisions in breast cancer but suffers from poor inter-rater reproducibility. Although AI tools have been designed for Ki-67 assessment, their impact on pathologists' work remains understudied. 90 international pathologists were recruited to assess the Ki-67 PI of ten breast cancer tissue microarrays with and without AI. Accuracy, agreement, and turnaround time with and without AI were compared. Pathologists’ perspectives on AI were collected. Using AI led to a significant decrease in PI error (2.1% with AI vs. 5.9% without AI, p  < 0.001), better inter-rater agreement (ICC: 0.70 vs. 0.92; Krippendorff’s α: 0.63 vs. 0.89; Fleiss’ Kappa: 0.40 vs. 0.86), and an 11.9% overall median reduction in turnaround time. Most pathologists (84%) found the AI reliable. For Ki-67 assessments, 76% of respondents believed AI enhances accuracy, 82% said it improves consistency, and 83% trust it will improve efficiency. This study highlights AI's potential to standardize Ki-67 scoring, especially between 5 and 30% PI—a range with low PI agreement. This could pave the way for a universally accepted PI score to guide treatment decisions, emphasizing the promising role of AI integration into pathologist workflows.

Deep learning has proven capable of automating key aspects of histopathologic analysis. However, its context-specific nature and continued reliance on large expert-annotated training datasets hinders the development of a critical mass of applications to garner widespread adoption in clinical/research workflows. Here, we present an online collaborative platform that streamlines tissue image annotation to promote the development and sharing of custom computer vision models for PHenotyping And Regional Analysis Of Histology (PHARAOH; https://www.pathologyreports.ai/ ). Specifically, PHARAOH uses a weakly supervised, human-in-the-loop learning framework whereby patch-level image features are leveraged to organize large swaths of tissue into morphologically-uniform clusters for batched annotation by human experts. By providing cluster-level labels on only a handful of cases, we show how custom PHARAOH models can be developed efficiently and used to guide the quantification of cellular features that correlate with molecular, pathologic and patient outcome data. Moreover, by using our PHARAOH pipeline, we showcase how correlation of cohort-level cytoarchitectural features with accompanying biological and outcome data can help systematically devise interpretable morphometric models of disease. Both the custom model design and feature extraction pipelines are amenable to crowdsourcing, positioning PHARAOH to become a fully scalable, systems-level solution for the expansion, generalization and cataloging of computational pathology applications. Faust, Chen, and colleagues present PHARAOH, a collaborative computational pathology platform that allows histologists to quickly develop custom labelled image datasets to train and catalogue a variety of machine learning models for histopathological analysis.

Invasive lobular carcinoma (ILC) of the breast is a very common disease. Despite its prevalence, these tumors are relatively understudied. One reason for this is a relative lack of models for ILC. This challenge was addressed by Brisken and colleagues through development of an intraductal injection‐based xenograft system for the study of ERα + breast cancers, including both ILC and more common invasive ductal carcinoma (IDC; Sflomos et al , 2016). In this issue of EMBO Molecular Medicine, the same group have applied intraductal injection‐based xenografts to identify novel tumor cell‐specific transcriptional signatures in ILC (Sflomos et al , 2021). In doing so they found overexpression of lysyl oxidase‐like 1 (LOXL1) to be both responsible for the frequently seen stiff collagen‐rich extracellular matrix of lobular breast cancer and essential for their robust growth and metastatic dissemination in vivo , thereby identifying a novel therapeutic target. Graphical Abstract Despite its prevalence, invasive lobular carcinoma (ILC) is relatively understudied. In their recent study, Brisken and colleagues apply intraductal injection based xenografts to characterize tumor‐cell specific transcriptional signatures in ILC and identify a novel therapeutic target.