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Leiomyosarcoma (LMS) is a soft tissue tumor with a significant degree of morphologic and molecular heterogeneity. We used integrative molecular profiling to discover and characterize molecular subtypes of LMS. Gene expression profiling was performed on 51 LMS samples. Unsupervised clustering showed three reproducible LMS clusters. Array comparative genomic hybridization (aCGH) was performed on 20 LMS samples and showed that the molecular subtypes defined by gene expression showed distinct genomic changes. Tumors from the 'muscle-enriched' cluster showed significantly increased copy number changes (P=0.04). A majority of the muscle-enriched cases showed loss at 16q24, which contains Fanconi anemia, complementation group A, known to have an important role in DNA repair, and loss at 1p36, which contains PRDM16, of which loss promotes muscle differentiation. Immunohistochemistry (IHC) was performed on LMS tissue microarrays (n=377) for five markers with high levels of messenger RNA in the muscle-enriched cluster (ACTG2, CASQ2, SLMAP, CFL2 and MYLK) and showed significantly correlated expression of the five proteins (all pairwise P

Clinical trials in metabolic dysfunction-associated steatohepatitis (MASH, formerly known as nonalcoholic steatohepatitis) require histologic scoring for assessment of inclusion criteria and endpoints. However, variability in interpretation has impacted clinical trial outcomes. We developed an artificial intelligence-based measurement (AIM) tool for scoring MASH histology (AIM-MASH). AIM-MASH predictions for MASH Clinical Research Network necroinflammation grades and fibrosis stages were reproducible (κ = 1) and aligned with expert pathologist consensus scores (κ = 0.62-0.74). The AIM-MASH versus consensus agreements were comparable to average pathologists for MASH Clinical Research Network scores (82% versus 81%) and fibrosis (97% versus 96%). Continuous scores produced by AIM-MASH for key histological features of MASH correlated with mean pathologist scores and noninvasive biomarkers and strongly predicted progression-free survival in patients with stage 3 (P < 0.0001) and stage 4 (P = 0.03) fibrosis. In a retrospective analysis of the ATLAS trial (NCT03449446), responders receiving study treatment showed a greater continuous change in fibrosis compared with placebo (P = 0.02). Overall, these results suggest that AIM-MASH may assist pathologists in histologic review of MASH clinical trials, reducing inter-rater variability on trial outcomes and offering a more sensitive and reproducible measure of patient responses., (© 2024. The Author(s).)

The gold standard for enrollment and endpoint assessment in metabolic dysfunction-associated steatosis clinical trials is histologic assessment of a liver biopsy performed on glass slides. However, obtaining the evaluations from several expert pathologists on glass is challenging, as shipping the slides around the country or around the world is time-consuming and comes with the hazards of slide breakage. This study demonstrated that pathologic assessment of disease activity in steatohepatitis, performed using digital images on the AISight whole slide image management system, yields results that are comparable to those obtained using glass slides. The accuracy of scoring for steatohepatitis (nonalcoholic fatty liver disease activity score ≥4 with ≥1 for each feature and absence of atypical features suggestive of other liver disease) performed on the system was evaluated against scoring conducted on glass slides. Both methods were assessed for overall percent agreement with a consensus "ground truth" score (defined as the median score of a panel of three pathologists' glass slides). Each case was also read by three different pathologists, once on glass and once digitally with a minimum 2-week washout period between the modalities. It was demonstrated that the average agreement across three pathologists of digital scoring with ground truth was noninferior to the average agreement of glass scoring with ground truth [noninferiority margin: -0.05; difference: -0.001; 95% CI: (-0.027, 0.026); and p < 0.0001]. For each pathologist, there was a similar average agreement of digital and glass reads with glass ground truth (pathologist A, 0.843 and 0.849; pathologist B, 0.633 and 0.605; and pathologist C, 0.755 and 0.780). Here, we demonstrate that the accuracy of digital reads for steatohepatitis using digital images is equivalent to glass reads in the context of a clinical trial for scoring using the Clinical Research Network scoring system., (© 2024 PathAI. The Journal of Pathology: Clinical Research published by The Pathological Society of Great Britain and Ireland and John Wiley & Sons Ltd.)

Ulcerative colitis is a chronic inflammatory bowel disease that is characterized by a relapsing and remitting course. Assessment of disease activity critically informs treatment decisions. In addition to endoscopic remission, histologic remission is emerging as a treatment target and a key factor in the evaluation of disease activity and therapeutic efficacy. However, manual pathologist evaluation is semiquantitative and limited in granularity. Machine learning approaches are increasingly being developed to aid pathologists in accurate and reproducible scoring of histology, enabling precise quantitation of clinically relevant features. Here, we report the development and validation of convolutional neural network models that quantify histologic features pertinent to ulcerative colitis disease activity, directly from hematoxylin and eosin-stained whole slide images. Tissue and cell model predictions were used to generate quantitative human-interpretable features to fully characterize the histology samples. Tissue and cell predictions showed comparable agreement to pathologist annotations, and the extracted slide-level human-interpretable features demonstrated strong correlations with disease severity and pathologist-assigned Nancy histological index scores. Moreover, using a random forest classifier based on 13 human-interpretable features derived from the tissue and cell models, we were able to accurately predict Nancy histological index scores, with a weighted kappa (κ = 0.91) and Spearman correlation (⍴ = 0.89, P < .001) when compared with pathologist consensus Nancy histological index scores. We were also able to predict histologic remission, based on the absence of neutrophil extravasation, with a high accuracy of 0.97. This work demonstrates the potential of computer vision to enable a standardized and robust assessment of ulcerative colitis histopathology for translational research and improved evaluation of disease activity and prognosis., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Nonalcoholic steatohepatitis (NASH) is the most common chronic liver disease globally and a leading cause for liver transplantation in the US. Its pathogenesis remains imprecisely defined. We combined two high-resolution modalities to tissue samples from NASH clinical trials, machine learning (ML)-based quantification of histological features and transcriptomics, to identify genes that are associated with disease progression and clinical events. A histopathology-driven 5-gene expression signature predicted disease progression and clinical events in patients with NASH with F3 (pre-cirrhotic) and F4 (cirrhotic) fibrosis. Notably, the Notch signaling pathway and genes implicated in liver-related diseases were enriched in this expression signature. In a validation cohort where pharmacologic intervention improved disease histology, multiple Notch signaling components were suppressed., Competing Interests: Declaration of interests J.C. is an employee and shareholder of PathAI, Inc. M.P. is a shareholder of PathAI, Inc. Y.G. is a shareholder of Gilead Sciences, Inc. D.Z.P. is an employee of and shareholder of Gilead Sciences, Inc. O.M.C.-Z. is a shareholder of PathAI, Inc. V.M. is an employee and shareholder of PathAI, Inc. G.M.S. is an employee and shareholder of OrsoBio, Inc., and a shareholder of Gilead Sciences, Inc. M.C.M. is an employee and shareholder of PathAI, Inc. M.R. is an employee and shareholder of PathAI, Inc. A.H.B. is a co-founder, employee, and shareholder of PathAI, Inc. R.S.H. is an employee and shareholder of OrsoBio, Inc., and a shareholder of Gilead Sciences, Inc. R.P.M. is an employee and shareholder of OrsoBio, Inc., and a shareholder of Gilead Sciences, Inc. A.T.-W. is an employee and shareholder of PathAI, Inc. I.W. is an employee and shareholder of PathAI, Inc. C.C. is an employee and shareholder of Inipharm, Inc., and shareholder of Gilead Sciences, Inc., (Copyright © 2023. Published by Elsevier Inc.)

Background and Aims: The hepatic venous pressure gradient (HVPG) is the standard for estimating portal pressure but requires expertise for interpretation. We hypothesized that HVPG could be extrapolated from liver histology using a machine learning (ML) algorithm., Approach and Results: Patients with NASH with compensated cirrhosis from a phase 2b trial were included. HVPG and biopsies from baseline and weeks 48 and 96 were reviewed centrally, and biopsies evaluated with a convolutional neural network (PathAI, Boston, MA). Using trichrome-stained biopsies in the training set (n = 130), an ML model was developed to recognize fibrosis patterns associated with HVPG, and the resultant ML HVPG score was validated in a held-out test set (n = 88). Associations between the ML HVPG score with measured HVPG and liver-related events, and performance of the ML HVPG score for clinically significant portal hypertension (CSPH) (HVPG ≥ 10 mm Hg), were determined. The ML-HVPG score was more strongly correlated with HVPG than hepatic collagen by morphometry (ρ = 0.47 vs. ρ = 0.28; P < 0.001). The ML HVPG score differentiated patients with normal (0-5 mm Hg) and elevated (5.5-9.5 mm Hg) HVPG and CSPH (median: 1.51 vs. 1.93 vs. 2.60; all P < 0.05). The areas under receiver operating characteristic curve (AUROCs) (95% CI) of the ML-HVPG score for CSPH were 0.85 (0.80, 0.90) and 0.76 (0.68, 0.85) in the training and test sets, respectively. Discrimination of the ML-HVPG score for CSPH improved with the addition of a ML parameter for nodularity, Enhanced Liver Fibrosis, platelets, aspartate aminotransferase (AST), and bilirubin (AUROC in test set: 0.85; 95% CI: 0.78, 0.92). Although baseline ML-HVPG score was not prognostic, changes were predictive of clinical events (HR: 2.13; 95% CI: 1.26, 3.59) and associated with hemodynamic response and fibrosis improvement., Conclusions: An ML model based on trichrome-stained liver biopsy slides can predict CSPH in patients with NASH with cirrhosis., (© 2021 by the American Association for the Study of Liver Diseases.)

Objectives: To estimate the prevalence of disability among Syrian refugees living in Sultanbeyli district, Istanbul and compare people with and without disabilities in terms of demographic and socio-economic characteristics., Methods: Using the municipality refugee database as the sampling frame, 80 clusters of 50 people (aged 2+ years) were selected using probability proportionate to size sampling of clusters and random selection of households within clusters. Disability assessment included: i) self-reported difficulties in functioning (using the Washington Group Short Set-Enhanced tool and Child Functioning Modules), ii) Rapid Assessment of Musculoskeletal Impairment and iii) screening for symptoms of common mental disorders for children aged 8-17., Results: The overall prevalence of disability was 24.7% (95% CI 22.1-27.4), when including people self-reporting a lot of difficulty/cannot do in at least functional domain (15%, 95% CI 13.1-17.2), moderate/severe MSI (8.7%, 95% CI 7.6-9.9), and/or symptomatic anxiety, depression and PTSD among children 8-17 (21.0%, 95% CI 18.2-23.9). Men with disabilities were significantly less likely to be in paid work compared to their peers without disabilities (aOR 0.3 95% CI 0.2-0.5). Overall 60% of households included at least one person with a disability. Households with at least one person with a disability had a significantly higher dependency ratio, lower proportion of working-age adults in paid work, and were more likely to be female headed and in receipt of social protection schemes (p<0.05)., Conclusion: Disability is common among Syrian refugees in Sultanbeyli. People with disabilities in this setting experience greater vulnerability to poverty and exclusion from work, highlighting an urgent need for inclusive services, programmes and policies that are developed and implemented in partnership with people with disabilities., Competing Interests: The authors have declared that no competing interests exist.

Background and Aims: Manual histological assessment is currently the accepted standard for diagnosing and monitoring disease progression in NASH, but is limited by variability in interpretation and insensitivity to change. Thus, there is a critical need for improved tools to assess liver pathology in order to risk stratify NASH patients and monitor treatment response., Approach and Results: Here, we describe a machine learning (ML)-based approach to liver histology assessment, which accurately characterizes disease severity and heterogeneity, and sensitively quantifies treatment response in NASH. We use samples from three randomized controlled trials to build and then validate deep convolutional neural networks to measure key histological features in NASH, including steatosis, inflammation, hepatocellular ballooning, and fibrosis. The ML-based predictions showed strong correlations with expert pathologists and were prognostic of progression to cirrhosis and liver-related clinical events. We developed a heterogeneity-sensitive metric of fibrosis response, the Deep Learning Treatment Assessment Liver Fibrosis score, which measured antifibrotic treatment effects that went undetected by manual pathological staging and was concordant with histological disease progression., Conclusions: Our ML method has shown reproducibility and sensitivity and was prognostic for disease progression, demonstrating the power of ML to advance our understanding of disease heterogeneity in NASH, risk stratify affected patients, and facilitate the development of therapies., (© 2021 PathAI. Hepatology published by Wiley Periodicals LLC on behalf of American Association for the Study of Liver Diseases.)

Background: Epidemiological data on musculoskeletal impairment (MSI) and related service and assistive product (AP) needs for displaced populations are lacking. This study aimed to estimate the prevalence, aetiology, and specific MSI diagnosis and the need for related services and APs among Syrian refugees living in Sultanbeyli, a district in Istanbul, Turkey., Methods: A population-based survey used probability proportionate to size and compact segment sampling to select 80 clusters ('street') of 50 individuals (aged 2+), for total sample size of approximately 4000 participants. An updated version of the Rapid Assessment of MSI tool (RAM) was used to screen all participants using six questions. Any participant who screened positive underwent a standardised examination by a physiotherapist to assess the presence, aetiology, severity and specific diagnosis of MSI and an assessment of need for related services and APs., Results: The all-age prevalence of MSI was 12.2% (95% CI 10.8-13.7) and this increased significantly with age to 43.8% in people 50 and older. Over half (51%) of MSI was classified as moderate, 30% as mild and 19% as severe. The war in Syria was identified as the direct cause for 8% of people with MSI. The majority (56%) of MSI diagnoses were acquired non-traumatic causes. There was high unmet need for rehabilitation services; for example, 83% of people with MSI could benefit from physiotherapy but were not receiving this service. Overall, 19% of people with MSI had an unmet need for at least one AP. Apart from availability of walking sticks/canes, coverage was low with less than half the people with MSI who needed APs and services had received them. The most common reasons for not seeking services and APs were 'need not felt', lack of service availability and of awareness of services, and financial barriers., Conclusions: MSI is common among the Syrian refugee population living in Sultanbeyli District, particularly older adults, however less than half have been able to access relevant services and APs. These findings can inform the planning of health services for migrant populations, including the essential integration of rehabilitation and APs, and increase access to these vital services.

Computational methods have made substantial progress in improving the accuracy and throughput of pathology workflows for diagnostic, prognostic, and genomic prediction. Still, lack of interpretability remains a significant barrier to clinical integration. We present an approach for predicting clinically-relevant molecular phenotypes from whole-slide histopathology images using human-interpretable image features (HIFs). Our method leverages >1.6 million annotations from board-certified pathologists across >5700 samples to train deep learning models for cell and tissue classification that can exhaustively map whole-slide images at two and four micron-resolution. Cell- and tissue-type model outputs are combined into 607 HIFs that quantify specific and biologically-relevant characteristics across five cancer types. We demonstrate that these HIFs correlate with well-known markers of the tumor microenvironment and can predict diverse molecular signatures (AUROC 0.601-0.864), including expression of four immune checkpoint proteins and homologous recombination deficiency, with performance comparable to 'black-box' methods. Our HIF-based approach provides a comprehensive, quantitative, and interpretable window into the composition and spatial architecture of the tumor microenvironment.

Background and Aims: Advanced fibrosis attributable to NASH is a leading cause of end-stage liver disease., Approach and Results: In this phase 2b trial, 392 patients with bridging fibrosis or compensated cirrhosis (F3-F4) were randomized to receive placebo, selonsertib 18 mg, cilofexor 30 mg, or firsocostat 20 mg, alone or in two-drug combinations, once-daily for 48 weeks. The primary endpoint was a ≥1-stage improvement in fibrosis without worsening of NASH between baseline and 48 weeks based on central pathologist review. Exploratory endpoints included changes in NAFLD Activity Score (NAS), liver histology assessed using a machine learning (ML) approach, liver biochemistry, and noninvasive markers. The majority had cirrhosis (56%) and NAS ≥5 (83%). The primary endpoint was achieved in 11% of placebo-treated patients versus cilofexor/firsocostat (21%; P = 0.17), cilofexor/selonsertib (19%; P = 0.26), firsocostat/selonsertib (15%; P = 0.62), firsocostat (12%; P = 0.94), and cilofexor (12%; P = 0.96). Changes in hepatic collagen by morphometry were not significant, but cilofexor/firsocostat led to a significant decrease in ML NASH CRN fibrosis score (P = 0.040) and a shift in biopsy area from F3-F4 to ≤F2 fibrosis patterns. Compared to placebo, significantly higher proportions of cilofexor/firsocostat patients had a ≥2-point NAS reduction; reductions in steatosis, lobular inflammation, and ballooning; and significant improvements in alanine aminotransferase (ALT), aspartate aminotransferase (AST), bilirubin, bile acids, cytokeratin-18, insulin, estimated glomerular filtration rate, ELF score, and liver stiffness by transient elastography (all P ≤ 0.05). Pruritus occurred in 20%-29% of cilofexor versus 15% of placebo-treated patients., Conclusions: In patients with bridging fibrosis and cirrhosis, 48 weeks of cilofexor/firsocostat was well tolerated, led to improvements in NASH activity, and may have an antifibrotic effect. This combination offers potential for fibrosis regression with longer-term therapy in patients with advanced fibrosis attributable to NASH., (© 2020 by the American Association for the Study of Liver Diseases.)

Stromal tumor-infiltrating lymphocytes (sTILs) are important prognostic and predictive biomarkers in triple-negative (TNBC) and HER2-positive breast cancer. Incorporating sTILs into clinical practice necessitates reproducible assessment. Previously developed standardized scoring guidelines have been widely embraced by the clinical and research communities. We evaluated sources of variability in sTIL assessment by pathologists in three previous sTIL ring studies. We identify common challenges and evaluate impact of discrepancies on outcome estimates in early TNBC using a newly-developed prognostic tool. Discordant sTIL assessment is driven by heterogeneity in lymphocyte distribution. Additional factors include: technical slide-related issues; scoring outside the tumor boundary; tumors with minimal assessable stroma; including lymphocytes associated with other structures; and including other inflammatory cells. Small variations in sTIL assessment modestly alter risk estimation in early TNBC but have the potential to affect treatment selection if cutpoints are employed. Scoring and averaging multiple areas, as well as use of reference images, improve consistency of sTIL evaluation. Moreover, to assist in avoiding the pitfalls identified in this analysis, we developed an educational resource available at www.tilsinbreastcancer.org/pitfalls., Competing Interests: Competing interestsA.E. is on the Roche advisory board and has reported honoraria from Amgen, Novartis and Roche. A.J.L. is a consultant for BMS, Merck, AZ/Medimmune, and Genentech. R.S. reports research funding from Roche, Puma, Merck; advisory board and consultancy for BMS; travel funding from Roche, Merck, and Astra Zeneca. S.G. reports Lab research funding from Lilly, Clinical research funding from Eli Lilly and Novartis and is a Paid advisor to Eli Lilly, Novartis, and G1 Therapeutics. J.v.d.L. is member of the scientific advisory boards of Philips, the Netherlands and ContextVision, Sweden and receives research funding from Philips, the Netherlands and Sectra, Sweden. S.A. reports Research funding to institution from Merck, Genentech, BMS, Novartis, Celgene and Amgen and is an uncompensated consultant /steering committee member for Merck, Genentech and BMS. T.O.N. has consulted for Nanostring and received compensation and has intellectual property rights/ownership interests from Bioclassifier LLC [not related to the subject material under consideration]. S.L. receives research funding to institution from Novartis, Bristol Meyers Squibb, Merck, Roche-Genentech, Puma Biotechnology, Pfizer and Eli Lilly, has acted as consultant (not compensated) to Seattle Genetics, Pfizer, Novartis, BMS, Merck, AstraZeneca and Roche-Genentech and acted as consultant (paid to her institution) to Aduro Biotech. S.R.L. has received travel and educational funding from Roche/Ventana. A.M. is an equity holder in Elucid Bioimaging and in Inspirata Inc., a scientific advisory consultant for Inspirata Inc, has served as a scientific advisory board member for Inspirata Inc, Astrazeneca, Bristol Meyers-Squibb and Merck, has sponsored research agreements with Philips and Inspirata Inc, is involved in a NIH U24 grant with PathCore Inc, and 3 different R01 grants with Inspirata Inc. and his technology has been licensed to Elucid Bioimaging and Inspirata Inc. G.C. is on the advisory boards of Roche, BMS, Pfizer, Seattle Genetics and Ellipsis, and reports personal fees from Roche, BMS, Pfizer, Seattle Genetics, and Ellipsis, outside of the submitted work. J.H. is the director and owner of Vivactiv Ltd. J.H. is the director and owner of Slide Score B.V. F.P.L. reports funding from Astrazeneca, BMS, Roche, MSD, Pfizer, Novartis, Sanofi, Eli Lilly. J.B. reports consultancies from Insight Genetics, BioNTech AG, Biotheranostics, Pfizer, RNA Diagnostics and OncoXchange, research funding from Thermo Fisher Scientific, Genoptix, Agendia, NanoString Technologies, Stratifyer GmbH and Biotheranostics, applied for patents, including Jan 2017: Methods and Devices for Predicting Anthracycline Treatment Efficacy, US utility—15/325,472; EPO—15822898.1; Canada—not yet assigned; Jan 2017: Systems, Devices and Methods for Constructing and Using a Biomarker, US utility—15/328,108; EPO—15824751.0; Canada—not yet assigned; Oct 2016: Histone gene module predicts anthracycline benefit, PCT/CA2016/000247; Dec 2016: 95‐Gene Signature of Residual Risk Following Endocrine Treatment, PCT/CA2016/000304; Dec 2016: Immune Gene Signature Predicts Anthracycline Benefit, PCT/CA2016/000305. M.A.S. reports consulting work for Achilles Therapeutics. C.S. reports receipt of grants/research support from Pfizer, AstraZeneca, BMS and Ventana; receipt of honoraria, consultancy, or SAB Member fees from Pfizer, Novartis, GlaxoSmithKline, MSD, BMS, Celgene, AstraZeneca, Illumina, Sarah Canon Research Institute, Genentech, Roche-Ventana, GRAIL, Medicxi; Advisor for Dynamo Therapeutics; Stock shareholder in Apogen Biotechnologies, Epic Bioscience, GRAIL; Co-Founder & stock options in Achilles Therapeutics. A.H.B. is the co-founder and CEO of PathAI. J.K. is an employee of PathAI. D.D. is on the advisory board for Oncology Analytics, Inc, and a consultant for Novartis. D.L.R. is on the advisory board of Amgen, Astra Zeneca, Cell Signaling Technology, Cepheid, Daiichi Sankyo, GSK, Konica/Minolta, Merck, NanoString, Perkin Elmer, Ventana, Ultivue; receives research support from Astra Zeneca, Cepheid, Navigate BioPharma, NextCure, Lilly, Ultivue; instrument support from Ventana, Akoya/Perkin Elmer, NanoString; paid consultant for Biocept; received travel honoraria from BMS, founder and equity holder for PixelGear and received royalty from Rarecyte. A.T. reports benefits from ICR’s Inventors Scheme associated with patents for one of PARP inhibitors in BRCA1/2 associated cancers, as well as honoraria from Pfizer, Vertex, Prime Oncology, Artios, honoraria and stock in InBioMotion, honoraria and financial support for research from AstraZeneca, Medivation, Myriad Genetics and Merck Serono. This work includes contributions from, and was reviewed by, individuals at the FDA. This work has been approved for publication by the agency, but it does not necessarily reflect official agency policy. Certain commercial materials and equipment are identified in order to adequately specify experimental procedures. In no case does such identification imply recommendation or endorsement by the FDA, nor does it imply that the items identified are necessarily the best available for the purpose. This work includes contributions from, and was reviewed by, individuals who received funding from the National Institutes of Health, the U.S. Department of Veterans Affairs and the Department of Defense. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, the U.S. Department of Veterans Affairs, the Department of Defense, or the United States Government., (© The Author(s) 2020.)

Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics. We detail the benefits of computational TILs assessment, the readiness of TILs scoring for computational assessment, and outline considerations for overcoming key barriers to clinical translation in this arena. Specifically, we discuss: 1. ensuring computational workflows closely capture visual guidelines and standards; 2. challenges and thoughts standards for assessment of algorithms including training, preanalytical, analytical, and clinical validation; 3. perspectives on how to realize the potential of machine learning models and to overcome the perceptual and practical limits of visual scoring., Competing Interests: Competing interestsJ.T. is funded by Visiopharm A/S, Denmark. A.M. is an equity holder in Elucid Bioimaging and in Inspirata Inc. He is also a scientific advisory consultant for Inspirata Inc. In addition he has served as a scientific advisory board member for Inspirata Inc, Astrazeneca, Bristol Meyers-Squibb and Merck. He also has sponsored research agreements with Philips and Inspirata Inc. His technology has been licensed to Elucid Bioimaging and Inspirata Inc. He is also involved in an NIH U24 grant with PathCore Inc, and three different R01 grants with Inspirata Inc. S.R.L. received travel and educational funding from Roche/Ventana. A.J.L. serves as a consultant for BMS, Merck, AZ/Medimmune, and Genentech. He is also provides consulting and advisory work for many other companies not relevant to this work. FPL does consulting for Astrazeneca, BMS, Roche, MSD Pfizer, Novartis, Sanofi, and Lilly. S.Ld.H., A.K., M.K., U.K., and M.B. are employees of Roche. J.M.S.B. is consultant for Insight Genetics, BioNTech AG, Biothernostics, Pfizer, RNA Diagnostics, and OncoXchange. He received funding from Thermo Fisher Scientific, Genoptix, Agendia, NanoString technologies, Stratifyer GmBH, and Biotheranostics. L.F.S.K. is a consultant for Roche and Novartis. J.K.K. and A.H.B. are employees of PathAI. D.L.R. is on the advisory board for Amgen, Astra Xeneca, Cell Signaling Technology, Cepheid, Daiichi Sankyo, GSK, Konica/Minolta, Merck, Nanostring, Perking Elmer, Roche/Ventana, and Ultivue. He has received research support from Astrazeneca, Cepheid, Navigate BioPharma, NextCure, Lilly, Ultivue, Roche/Ventana, Akoya/Perkin Elmer, and Nanostring. He also has financial conflicts of interest with BMS, Biocept, PixelGear, and Rarecyte. S.G. is a consultant for and/or receives funding from Eli Lilly, Novartis, and G1 Therapeutics. J.A.W.M.vdL. is a member of the scientific advisory boards of Philips, the Netherlands and ContextVision, Sweden, and receives research funding from Philips, the Netherlands and Sectra, Sweden. S.A. is a consultant for Merck, Genentech, and BMS, and receives funding from Merck, Genentech, BMS, Novartis, Celgene, and Amgen. T.O.N. has consulted for Nanostring, and has intellectual property rights and ownership interests from Bioclassifier LLC. S.L. receives research funding to her institution from Novartis, Bristol Meyers-Squibb, Merck, Roche-Genentech, Puma Biotechnology, Pfizer and Eli Lilly. She has acted as consultant (not compensated) to Seattle Genetics, Pfizer, Novartis, BMS, Merck, AstraZeneca and Roche-Genentech. She has acted as consultant (paid to her institution) to Aduro Biotech. J.H. is director and owner of Slide Score BV. M.M.S. is a medical advisory board member of OptraScan. R.S. has received research support from Merck, Roche, Puma; and travel/congress support from AstraZeneca, Roche and Merck; and he has served as an advisory board member of BMS and Roche and consults for BMS., (© The Author(s) 2020.)

In this white paper, experts from the Digital Pathology Association (DPA) define terminology and concepts in the emerging field of computational pathology, with a focus on its application to histology images analyzed together with their associated patient data to extract information. This review offers a historical perspective and describes the potential clinical benefits from research and applications in this field, as well as significant obstacles to adoption. Best practices for implementing computational pathology workflows are presented. These include infrastructure considerations, acquisition of training data, quality assessments, as well as regulatory, ethical, and cyber-security concerns. Recommendations are provided for regulators, vendors, and computational pathology practitioners in order to facilitate progress in the field. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland., (© 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.)

The success of targeted or immune therapies is often hampered by the emergence of resistance and/or clinical benefit in only a subset of patients. We hypothesized that combining targeted therapy with immune modulation would show enhanced antitumor responses. Here, we explored the combination potential of erdafitinib, a fibroblast growth factor receptor (FGFR) inhibitor under clinical development, with PD-1 blockade in an autochthonous FGFR2 K660N /p53 mut lung cancer mouse model. Erdafitinib monotherapy treatment resulted in substantial tumor control but no significant survival benefit. Although anti-PD-1 alone was ineffective, the erdafitinib and anti-PD-1 combination induced significant tumor regression and improved survival. For both erdafitinib monotherapy and combination treatments, tumor control was accompanied by tumor-intrinsic, FGFR pathway inhibition, increased T-cell infiltration, decreased regulatory T cells, and downregulation of PD-L1 expression on tumor cells. These effects were not observed in a KRAS G12C -mutant genetically engineered mouse model, which is insensitive to FGFR inhibition, indicating that the immune changes mediated by erdafitinib may be initiated as a consequence of tumor cell killing. A decreased fraction of tumor-associated macrophages also occurred but only in combination-treated tumors. Treatment with erdafitinib decreased T-cell receptor (TCR) clonality, reflecting a broadening of the TCR repertoire induced by tumor cell death, whereas combination with anti-PD-1 led to increased TCR clonality, suggesting a more focused antitumor T-cell response. Our results showed that the combination of erdafitinib and anti-PD-1 drives expansion of T-cell clones and immunologic changes in the tumor microenvironment to support enhanced antitumor immunity and survival., (©2019 American Association for Cancer Research.)

To define transcriptional dependencies of triple-negative breast cancer (TNBC), we identified transcription factors highly and specifically expressed in primary TNBCs and tested their requirement for cell growth in a panel of breast cancer cell lines. We found that EN1 (engrailed 1) is overexpressed in TNBCs and its downregulation preferentially and significantly reduced viability and tumorigenicity in TNBC cell lines. By integrating gene expression changes after EN1 downregulation with EN1 chromatin binding patterns, we identified genes involved in WNT and Hedgehog signaling, neurogenesis, and axonal guidance as direct EN1 transcriptional targets. Quantitative proteomic analyses of EN1-bound chromatin complexes revealed association with transcriptional repressors and coactivators including TLE3, TRIM24, TRIM28, and TRIM33. High expression of EN1 correlated with short overall survival and increased risk of developing brain metastases in patients with TNBC. Thus, EN1 is a prognostic marker and a potential therapeutic target in TNBC. SIGNIFICANCE: These findings show that the EN1 transcription factor regulates neurogenesis-related genes and is associated with brain metastasis in triple-negative breast cancer., (©2019 American Association for Cancer Research.)

Background: Hormone receptor signaling is critical in the progression of breast cancers, although the role of the androgen receptor (AR) remains unclear, particularly for estrogen receptor (ER)-negative tumors. This study assessed AR protein expression as a prognostic marker for breast cancer mortality., Methods: This study included 4147 pre- and postmenopausal women with invasive breast cancer from the Nurses' Health Study (diagnosed 1976-2008) and Nurses' Health Study II (1989-2008) cohorts. AR protein expression was evaluated by immunohistochemistry and scored through pathologist review and as a digitally quantified continuous measure. Hazard ratios (HR) and 95% confidence intervals (CI) of breast cancer mortality were estimated from Cox proportional hazards models, adjusting for patient, tumor, and treatment covariates., Results: Over a median 16.5 years of follow-up, there were 806 deaths due to breast cancer. In the 7 years following diagnosis, AR expression was associated with a 27% reduction in breast cancer mortality overall (multivariable HR = 0.73, 95% CI = 0.58 to 0.91) a 47% reduction for ER+ cancers (HR = 0.53, 95% CI = 0.41 to 0.69), and a 62% increase for ER- cancers (HR = 1.62, 95% CI = 1.18 to 2.22) (P heterogeneity < .001). A log-linear association was observed between AR expression and breast cancer mortality among ER- cancers (HR = 1.14, 95% CI = 1.02 to 1.26 per each 10% increase in AR), although no log-linear association was observed among ER+ cancers., Conclusions: AR expression was associated with improved prognosis in ER+ tumors and worse prognosis in ER- tumors in the first 5-10 years postdiagnosis. These findings support the continued evaluation of AR-targeted therapies for AR+/ER- breast cancers., (© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Tumor proliferation is an important biomarker indicative of the prognosis of breast cancer patients. Assessment of tumor proliferation in a clinical setting is a highly subjective and labor-intensive task. Previous efforts to automate tumor proliferation assessment by image analysis only focused on mitosis detection in predefined tumor regions. However, in a real-world scenario, automatic mitosis detection should be performed in whole-slide images (WSIs) and an automatic method should be able to produce a tumor proliferation score given a WSI as input. To address this, we organized the TUmor Proliferation Assessment Challenge 2016 (TUPAC16) on prediction of tumor proliferation scores from WSIs. The challenge dataset consisted of 500 training and 321 testing breast cancer histopathology WSIs. In order to ensure fair and independent evaluation, only the ground truth for the training dataset was provided to the challenge participants. The first task of the challenge was to predict mitotic scores, i.e., to reproduce the manual method of assessing tumor proliferation by a pathologist. The second task was to predict the gene expression based PAM50 proliferation scores from the WSI. The best performing automatic method for the first task achieved a quadratic-weighted Cohen's kappa score of κ = 0.567, 95% CI [0.464, 0.671] between the predicted scores and the ground truth. For the second task, the predictions of the top method had a Spearman's correlation coefficient of r = 0.617, 95% CI [0.581 0.651] with the ground truth. This was the first comparison study that investigated tumor proliferation assessment from WSIs. The achieved results are promising given the difficulty of the tasks and weakly-labeled nature of the ground truth. However, further research is needed to improve the practical utility of image analysis methods for this task., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Frequent SPOP mutation defines the molecular feature underlying one of seven sub-types of human prostate cancer (PrCa). However, it remains largely elusive how SPOP functions as a tumor suppressor in PrCa. Here, we report that SPOP suppresses stem cell traits of both embryonic stem cells and PrCa cells through promoting Nanog poly-ubiquitination and subsequent degradation. Mechanistically, Nanog, but not other pluripotency-determining factors including Oct4, Sox2, and Klf4, specifically interacts with SPOP via a conservative degron motif. Importantly, cancer-derived mutations in SPOP or at the Nanog-degron (S68Y) disrupt SPOP-mediated destruction of Nanog, leading to elevated cancer stem cell traits and PrCa progression. Notably, we identify the Pin1 oncoprotein as an upstream Nanog regulator that impairs its recognition by SPOP and thereby stabilizes Nanog. Thus, Pin1 inhibitors promote SPOP-mediated destruction of Nanog, which provides the molecular insight and rationale to use Pin1 inhibitor(s) for targeted therapies of PrCa patients with wild-type SPOP., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Purpose: In post-menopausal women, high body mass index (BMI) is an established breast cancer risk factor and is associated with worse breast cancer prognosis. We assessed the associations between BMI and gene expression of both breast tumor and adjacent tissue in estrogen receptor-positive (ER+) and estrogen receptor-negative (ER-) diseases to help elucidate the mechanisms linking obesity with breast cancer biology in 519 post-menopausal women from the Nurses' Health Study (NHS) and NHSII., Methods: Differential gene expression was analyzed separately in ER+ and ER- disease both comparing overweight (BMI ≥ 25 to < 30) or obese (BMI ≥ 30) women to women with normal BMI (BMI < 25), and per 5 kg/m 2 increase in BMI. Analyses controlled for age and year of diagnosis, physical activity, alcohol consumption, and hormone therapy use. Gene set enrichment analyses were performed and validated among a subset of post-menopausal cases in The Cancer Genome Atlas (for tumor) and Polish Breast Cancer Study (for tumor-adjacent)., Results: No gene was differentially expressed by BMI (FDR < 0.05). BMI was significantly associated with increased cellular proliferation pathways, particularly in ER+ tumors, and increased inflammation pathways in ER- tumor and ER- tumor-adjacent tissues (FDR < 0.05). High BMI was associated with upregulation of genes involved in epithelial-mesenchymal transition in ER+ tumor-adjacent tissues., Conclusions: This study provides insights into molecular mechanisms of BMI influencing post-menopausal breast cancer biology. Tumor and tumor-adjacent tissues provide independent information about potential mechanisms.

The breast stromal microenvironment is a pivotal factor in breast cancer development, growth and metastases. Although pathologists often detect morphologic changes in stroma by light microscopy, visual classification of such changes is subjective and non-quantitative, limiting its diagnostic utility. To gain insights into stromal changes associated with breast cancer, we applied automated machine learning techniques to digital images of 2387 hematoxylin and eosin stained tissue sections of benign and malignant image-guided breast biopsies performed to investigate mammographic abnormalities among 882 patients, ages 40-65 years, that were enrolled in the Breast Radiology Evaluation and Study of Tissues (BREAST) Stamp Project. Using deep convolutional neural networks, we trained an algorithm to discriminate between stroma surrounding invasive cancer and stroma from benign biopsies. In test sets (928 whole-slide images from 330 patients), this algorithm could distinguish biopsies diagnosed as invasive cancer from benign biopsies solely based on the stromal characteristics (area under the receiver operator characteristics curve = 0.962). Furthermore, without being trained specifically using ductal carcinoma in situ as an outcome, the algorithm detected tumor-associated stroma in greater amounts and at larger distances from grade 3 versus grade 1 ductal carcinoma in situ. Collectively, these results suggest that algorithms based on deep convolutional neural networks that evaluate only stroma may prove useful to classify breast biopsies and aid in understanding and evaluating the biology of breast lesions.

Sex steroid hormone signaling is critical in the development of breast cancers, although the role of the androgen receptor remains unclear. This study evaluated androgen receptor (AR) expression in normal breast tissue as a potential marker of breast cancer risk. We conducted a nested case-control study of women with benign breast disease (BBD) within the Nurses' Health Studies. Epithelial AR expression was assessed by immunohistochemistry in normal tissue from the BBD biopsy and the percent of positive nuclei was estimated in ordinal categories of 10% for 78 breast cancer cases and 276 controls. Logistic regression models adjusting for the matching factors and BBD lesion type were used to calculate odds ratios (ORs) for the association between AR expression (tertiles: ≤10%, 11-30%, and >30%) and breast cancer risk. AR expression in normal breast tissue was not associated with subsequent breast cancer risk (OR T3vsT1  = 0.9, 95% CI = 0.4-1.8, p trend = 0.68). In comparison with low AR/low ER women, ORs of 0.4 (95% CI = 0.1-1.2) for high AR/high ER women, 1.8 (95% CI = 0.4-7.8) for low AR/high ER women, and 0.7 (95% CI = 0.3-1.6) for high AR/low ER women were observed ( p interaction = 0.21). Ki67 did not modify the association between AR expression and breast cancer risk ( p interaction = 0.75). There was little evidence for an overall association between AR expression in normal breast tissue and breast cancer risk. These findings did not show that the AR association varied by Ki67 expression in normal breast tissue, though there was suggestive heterogeneity by ER expression., Competing Interests: A.H.B. is the co-founder and CEO of PathAI, Inc. The remaining authors declare no competing interests.

The goal of this study is to use computational pathology to help guide the development of human-based prognostic H&E biomarker(s) suitable for research and potential clinical use in lung squamous cell carcinoma (SCC). We started with high-throughput computational image analysis with tissue microarrays (TMAs) to screen for histologic features associated with patient overall survival, and found that features related to stromal inflammation were the most strongly prognostic. Based on this, we developed an H&E stromal inflammation (SI) score. The prognostic value of the SI score was validated by two blinded human observers on two large cohorts from a single institution. The SI score was found to be reproducible on TMAs (Spearman rho = 0.88 between the two observers), and highly prognostic (e.g. hazard ratio = 0.32; 95% confidence interval: 0.19-0.54; p-value = 2.5 × 10 -5 in multivariate analyses), particularly in comparison to established histologic biomarkers. Guided by downstream molecular/biomarker correlation studies starting with TCGA cases, we investigated the hypothesis that epithelial PD-L1 expression modified the prognostic value of SI. Our research demonstrates that computational pathology can be an efficient hypothesis generator for human pathology research, and support the histologic evaluation of SI as a prognostic biomarker in lung SCCs.

Importance: Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency., Objective: Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists' diagnoses in a diagnostic setting., Design, Setting, and Participants: Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC)., Exposures: Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation., Main Outcomes and Measures: The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor., Results: The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P < .001). The top 5 algorithms had a mean AUC that was comparable with the pathologist interpreting the slides in the absence of time constraints (mean AUC, 0.960 [range, 0.923-0.994] for the top 5 algorithms vs 0.966 [95% CI, 0.927-0.998] for the pathologist WOTC)., Conclusions and Relevance: In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting.

Purpose: Ki67 is a proliferation marker commonly assessed by immunohistochemistry in breast cancer, and it has been proposed as a clinical marker for subtype classification, prognosis, and prediction of therapeutic response. However, the clinical utility of Ki67 is limited by the lack of consensus on the optimal cut point for each application., Methods: We assessed Ki67 by immunohistochemistry using Definiens digital image analysis (DIA) in 2653 cases of incident invasive breast cancer diagnosed in the Nurses' Health Study from 1976 to 2006. Ki67 was scored as continuous percentage of positive tumor cells, and dichotomized at various cut points. Multivariable hazard ratios (HR) and 95% confidence intervals (CI) were calculated using Cox regression models for distant recurrence, breast cancer-specific mortality and overall mortality in relation to luminal subtypes defined with various Ki67 cut points, adjusting for breast cancer prognostic factors, clinico-pathologic features and treatment., Results: DIA was highly correlated with manual scoring of Ki67 (Spearman correlation ρ = 0.86). Mean Ki67 score was higher in grade-defined luminal B (12.6%), HER2-enriched (17.9%) and basal-like (20.6%) subtypes compared to luminal A (8.9%). In multivariable-adjusted models, luminal B tumors had higher breast cancer-specific mortality compared to luminal A cancer classified using various cut points for Ki67 positivity including the 14% cut point routinely reported in the literature (HR 1.38, 95% CI 1.11-1.72, p = 0.004). There was no significant difference in clinical outcomes for ER- tumors according to Ki67 positivity defined at various cut points., Conclusions: Assessment of Ki67 in breast tumors by DIA was a robust and quantitative method. Results from this large prospective cohort study provide support for the clinical relevance of using Ki67 at the 14% cut point for luminal subtype classification and breast cancer prognosis.

Studies have suggested that hormone receptor and Ki67 expression in normal breast tissue are associated with subsequent breast cancer risk. We examined the associations of breast cancer risk factors with estrogen receptor (ER), progesterone receptor (PR), insulin-like growth factor-1 receptor (IGF-1R), and Ki67 expression in normal breast tissue. This analysis included 388 women with benign breast disease (ages 17-67 years) in the Nurses' Health Studies. Immunohistochemical staining was performed on tissue microarrays constructed from benign biopsies containing normal breast epithelium and scored as the percentage of epithelial cells that were positively stained. Ordinal logistic regression (outcomes in tertiles), adjusting for age and potential confounders, was performed to estimate odds ratios (OR) and 95% confidence intervals (CI) for the associations with risk factors. Alcohol consumption was positively associated (≥2.5 vs.<0.4 drink/wk: OR = 2.69, 95% CI = 1.26-5.75, p -trend = 0.008) and breastfeeding was inversely associated (≥6 months vs. never: OR = 0.11, 95% CI = 0.04-0.35, p -trend = 0.0003) with ER expression. Height (≥66 vs.<64 inches: OR = 2.50, 95% CI = 1.34-4.67, p -trend = 0.005) and BMI at age 18 (≥22 vs.<20 kg/m 2 : OR = 2.33, 95% CI = 1.18-4.62, p -trend = 0.01) were positively associated with PR expression. Body size at age 5-10 years was inversely associated with Ki67 (Level ≥ 2.5 vs. 1: OR = 0.55, 95% CI = 0.30-1.01, p -trend = 0.03). Premenopausal BMI (≥25 vs.<20 kg/m 2 ) was positively associated with cytoplasmic IGF-1R (OR = 5.06, 95% CI = 1.17-21.8, p -trend = 0.04). Our data suggest that anthropometrics, breastfeeding, and alcohol intake may influence the molecular characteristics of normal breast tissue, elucidating the mechanisms by which these risk factors operate. However, larger studies are required to confirm these results.

Background: Alcohol consumption is an established risk factor for breast cancer and the association generally appears stronger among estrogen receptor (ER)-positive tumors. However, the biological mechanisms underlying this association are not completely understood., Methods: We analyzed messenger RNA (mRNA) microarray data from both invasive breast tumors (N = 602) and tumor-adjacent normal tissues (N = 508) from participants diagnosed with breast cancer in the Nurses' Health Study (NHS) and NHSII. Multivariable linear regression, controlling for other known breast cancer risk factors, was used to identify differentially expressed genes by pre-diagnostic alcohol intake. For pathway analysis, we performed gene set enrichment analysis (GSEA). Differentially expressed genes or enriched pathway-defined gene sets with false discovery rate (FDR) <0.1 identified in tumors were validated in RNA sequencing data of invasive breast tumors (N = 166) from The Cancer Genome Atlas., Results: No individual genes were significantly differentially expressed by alcohol consumption in the NHS/NHSII. However, GSEA identified 33 and 68 pathway-defined gene sets at FDR <0.1 among 471 ER+ and 127 ER- tumors, respectively, all of which were validated. Among ER+ tumors, consuming 10+ grams of alcohol per day (vs. 0) was associated with upregulation in RNA metabolism and transport, cell cycle regulation, and DNA repair, and downregulation in lipid metabolism. Among ER- tumors, in addition to upregulation in RNA processing and cell cycle, alcohol intake was linked to overexpression of genes involved in cytokine signaling, including interferon and transforming growth factor (TGF)-β signaling pathways, and translation and post-translational modifications. Lower lipid metabolism was observed in both ER+ tumors and ER+ tumor-adjacent normal samples. Most of the significantly enriched gene sets identified in ER- tumors showed a similar enrichment pattern among ER- tumor-adjacent normal tissues., Conclusions: Our data suggest that moderate alcohol consumption (i.e. 10+ grams/day, equivalent to one or more drinks/day) is associated with several specific and reproducible biological processes and pathways, which adds potential new insight into alcohol-related breast carcinogenesis.