Assessment of Interobserver Agreement Among Gynecologic Pathologists Between Three-Tier Versus Binary Pattern-based Classification Systems for HPV-associated Endocervical Adenocarcinoma.

The three-tier (A vs. B vs. C) pattern-based (Silva) classification system is a strong and fairly reproducible predictor of the risk of lymph node involvement and recurrence of human papillomavirus (HPV)-associated endocervical adenocarcinoma (EA). Recently, a binary pattern-based classification system has been proposed which incorporates the Silva pattern and lymphovascular invasion (LVI) to assign tumors as "low risk" or "high risk" and this may have superior prognostic significance compared with the three-tier system as well as current International Federation of Gynecology and Obstetrics (FIGO) staging of cervix-confined disease. The interobserver reproducibility of this binary system, however, is unknown. Representative slides from 59 HPV-associated EAs (1-3 slides/case) were independently reviewed by 5 gynecologic pathologists who participated in an online training module before the study. In the first review, a pattern was assigned using the three-tier system. On the second review, a "low risk" or "high risk" designation was assigned and the presence or absence of LVI was specifically documented. Interobserver agreement was assessed using Fleiss' kappa. The binary system showed improved interobserver agreement (kappa=0.634) compared with the three-tier system (kappa=0.564), with a higher proportion of cases having agreement between at least 4/5 reviewers (86% vs. 73%). Nineteen and 8 cases showed improved and worse interobserver agreement using the binary system, respectively; the remainder showed no change. 3/5 reviewers showed no intraobserver discrepancy while the remaining 2 did in a small subset of cases (n=2 and 4, respectively). In this study, a binary pattern-based classification system showed improved interobserver agreement compared with the traditional three-tier system.

Characterization of innate lymphoid cell subsets infiltrating melanoma and epithelial ovarian tumors.

The innate lymphoid cell (ILC) family is composed of heterogeneous innate effector and helper immune cells that preferentially reside in tissues where they promote tissue homeostasis. In cancer, they have been implicated in driving both pro- and anti-tumor responses. This apparent dichotomy highlights the need to better understand differences in the ILC composition and phenotype within different tumor types that could drive seemingly opposite anti-tumor responses. Here, we characterized the frequency and phenotype of various ILC subsets in melanoma metastases and primary epithelial ovarian tumors. We observed high PD-1 expression on ILC subsets isolated from epithelial ovarian tumor samples, while ILC populations in melanoma samples express higher levels of LAG-3. In addition, we found that the frequency of cytotoxic ILCs and NKp46+ILC3 in tumors positively correlates with monocytic cells and conventional type 2 dendritic cells, revealing potentially new interconnected immune cell subsets in the tumor microenvironment. Consequently, these observations may have direct relevance to tumor microenvironment composition and how ILC subset may influence anti-tumor immunity.

FGF21 acts in the brain to drive macronutrient-specific changes in behavioral motivation and brain reward signaling.

Dietary protein restriction induces adaptive changes in food preference, increasing protein consumption over carbohydrates or fat. We investigated whether motivation and reward signaling underpin these preferences. In an operant task, protein-restricted male mice responded more for liquid protein rewards, but not carbohydrate, fat, or sweet rewards compared to non-restricted mice. The protein restriction-induced increase in operant responding for protein was absent in Fgf21-KO mice and mice with neuron-specific deletion of the FGF21 co-receptor beta-Klotho (Klb Cam2ka ) mice. Fiber photometry recording of VTA dopamine neurons revealed that oral delivery of maltodextrin triggered a larger activation as compared to casein in control-fed mice, whereas casein triggered a larger activation in protein-restricted mice. This restriction-induced shift in nutrient-specific VTA dopamine signaling was lost in Fgf21-KO mice. These data strongly suggest that the increased FGF21 during protein restriction acts in the brain to induce a protein-specific appetite by specifically enhancing the reward value of protein-containing foods and the motivation to consume them.

Generation of an Inhibitory NK Cell Subset by TGF-ß1/IL-15 Polarization.

NK cells have been shown to exhibit inflammatory and immunoregulatory functions in a variety of healthy and diseased settings. In the context of chronic viral infection and cancer, distinct NK cell populations that inhibit adaptive immune responses have been observed. To understand how these cells arise and further characterize their immunosuppressive role, we examined in vitro conditions that could polarize human NK cells into an inhibitory subset. TGF-ß1 has been shown to induce regulatory T cells in vitro and in vivo; we therefore investigated if TGF-ß1 could also induce immunosuppressive NK-like cells. First, we found that TGF-ß1/IL-15, but not IL-15 alone, induced CD103+CD49a+ NK-like cells from peripheral blood NK cells, which expressed markers previously associated with inhibitory CD56+ innate lymphoid cells, including high expression of GITR and CD101. Moreover, supernatant from ascites collected from patients with ovarian carcinoma also induced CD103+CD49a+ NK-like cells in vitro in a TGF-ß-dependent manner. Interestingly, TGF-ß1/IL-15-induced CD103+CD56+ NK-like cells suppressed autologous CD4+ T cells in vitro by reducing absolute number, proliferation, and expression of activation marker CD25. Collectively, these findings provide new insight into how NK cells may acquire an inhibitory phenotype in TGF-ß1-rich environments.

Computational pathology: an evolving concept.

The initial enthusiasm about computational pathology (CP) and artificial intelligence (AI) was that they will replace pathologists entirely on the way to fully automated diagnostics. It is becoming clear that currently this is not the immediate model to pursue. On top of the legal and regulatory complexities surrounding its implementation, the majority of tested machine learning (ML)-based predictive algorithms do not display the exquisite performance needed to render them unequivocal, standalone decision makers for matters with direct implications to human health. We are thus moving into a different model of "computer-assisted diagnostics", where AI is there to provide support, rather than replacing, the pathologist. Herein we focus on the practical aspects of CP, from a pathologist perspective. There is a wide range of potential applications where CP can enhance precision of pathology diagnosis, tailor prognostic and predictive information, as well as save time. There are, however, a number of potential limitations for CP that currently hinder their wider adoption in the clinical setting. We address the key necessary steps towards clinical implementation of computational pathology, discuss the significant obstacles that hinders its adoption in the clinical context and summarize some proposed solutions. We conclude that the advancement of CP in the clinic is a promising resource-intensive endeavour that requires broad and inclusive collaborations between academia, industry, and regulatory bodies.

Transforming diagnostics: The implementation of digital pathology in clinical laboratories.

Digital pathology (DP) has emerged as a cutting-edge technology that promises to revolutionise diagnostics in clinical laboratories. This perspective article explores the implementation planning and considerations of DP in a single multicentre institution in Canada, the University Health Network, discussing benefits, challenges, potential implications and considerations for future adopters. We examine the transition from traditional microscopy to digital slide scanning and its impact on pathology practice, patient care and medical research. Furthermore, we address the regulatory, infrastructure and change management considerations for successful integration into clinical laboratories. By highlighting the advantages and addressing concerns, we aim to shed light on the transformative potential of DP and its role in shaping the future of diagnostics.

Targeted RNA Sequencing Highlights a Diverse Genomic and Morphologic Landscape in Low-grade Endometrial Stromal Sarcoma, Including Novel Fusion Genes.

Low-grade endometrial stromal sarcoma (LGESS) represents a morphologically and genetically heterogenous mesenchymal neoplasm. Previous work has shown that approximately half of LGESS are characterized by JAZF1::SUZ12 gene fusions, while a smaller proportion involves rearrangement of other genes. However, a subset of cases has no known genetic abnormalities. To better characterize the genomic landscape of LGESS, we interrogated a cohort with targeted RNA sequencing (RNA-Seq). Cases previously diagnosed as low-grade endometrial stromal neoplasia (n=51) were identified and re-reviewed for morphology and subjected to RNA-Seq, of which 47 were successfully sequenced. The median patient age was 49 years (range: 19 to 85). The most commonly detected fusions were JAZF1::SUZ12 (n=26, 55%) and BRD8::PHF1 (n=3, 6%). In addition to the usual/typical LGESS morphology, some JAZF1::SUZ12 fusion tumors showed other morphologies, including fibrous, smooth muscle, sex-cord differentiation, and myxoid change. Novel translocations were identified in 2 cases: MEAF6::PTGR2 and HCFC1::PHF1 . Ten tumors (21%) had no identifiable fusion, despite a similar morphology and immunophenotype to fusion-positive cases. This suggests that a subset of cases may be attributable to fusion products among genes that are not covered by the assay, or perhaps altogether different molecular mechanisms. In all, these findings confirm that RNA-Seq is a potentially useful ancillary test in the diagnosis of endometrial stromal neoplasms and highlight their diverse morphology.

Clinical outcome and biomarker assessments of a multi-centre phase II trial assessing niraparib with or without dostarlimab in recurrent endometrial carcinoma.

This multi-centre, non-randomized, open-label, phase II trial (NCT03016338), assessed niraparib monotherapy (cohort 1, C1), or niraparib and dostarlimab (cohort 2, C2) in patients with recurrent serous or endometrioid endometrial carcinoma. The primary endpoint was clinical benefit rate (CBR), with ≥5/22 overall considered of interest. Secondary outcomes were safety, objective response rate (ORR), duration of response, progression free survival and overall survival. Translational research was an exploratory outcome. Potential biomarkers were evaluated in archival tissue by immunohistochemistry and next generation sequencing panel. In C1, 25 patients were enrolled, and CBR was 20% (95% CI: 9-39) with median clinical benefit duration of 5.3 months. The ORR was 4% (95% CI: 0-20). In C2, 22 patients were enrolled, and the CBR was 31.8% (95% CI: 16-53) with median clinical benefit duration of 6.8 months. The ORR was 14% (95% CI: 3-35). No new safety signals were detected. No significant association was detected between clinical benefit and IHC markers (PTEN, p53, MMR, PD-L1), or molecular profiling (PTEN, TP53, homologous recombination repair genes). In conclusion, niraparib monotherapy did not meet the efficacy threshold. Niraparib in combination with dostarlimab showed modest activity.

Identification of antigenic epitopes recognized by tumor infiltrating lymphocytes in high grade serous ovarian cancer by multi-omics profiling of the auto-antigen repertoire.

Immunotherapeutic strategies aimed at enhancing tumor cell killing by tumor-specific T cells hold great potential for reducing tumor burden and prolonging survival of cancer patients. Although many potential tumor antigens have been described, identifying relevant targets when designing anti-cancer vaccines or targeted cell therapies remains a challenge. To identify novel, potentially immunogenic candidate tumor antigens, we performed integrated tumor transcriptomic, seromic, and proteomic analyses of high grade serous ovarian cancer (HGSC) patient tumor samples. We identified tumor neo-antigens and over-expressed antigens using whole exome and RNA sequencing and examined these in relation to patient-matched auto-antibody repertoires. Focusing on MHC class I epitopes recognized by CD8+ T cells, HLA-binding epitopes were identified or predicted from the highly expressed, mutated, or auto-antibody target antigen, or MHC-associated peptides (MAPs). Recognition of candidate antigenic peptides was assessed within the tumor-infiltrating T lymphocyte (TIL) population expanded from each patient. Known tumor-associated antigens (TAA) and cancer/testis antigens (CTA) were commonly found in the auto-antibody and MAP repertoires and CD8+ TILs recognizing epitopes from these antigens were detected, although neither expression level nor the presence of auto-antibodies correlated with TIL recognition. Auto-antibodies against tumor-mutated antigens were found in most patients, however, no TIL recognition of the highest predicted affinity neo-epitopes was detected. Using high expression level, auto-antibody recognition, and epitope prediction algorithms, we identified epitopes in 5 novel antigens (MOB1A, SOCS3, TUBB, PRKAR1A, CCDC6) recognized by HGSC patient TILs. Furthermore, selection of epitopes from the MAP repertoire identified 5 additional targets commonly recognized by multiple patient TILs. We find that the repertoire of TIL specificities includes recognition of highly expressed and immunogenic self-antigens that are processed and presented by tumors. These results indicate an ongoing autoimmune response against a range of self-antigens targeted by HGSC TILs.

Genomic characterization of DICER1-associated neoplasms uncovers molecular classes.

DICER1 syndrome is a tumor predisposition syndrome that is associated with up to 30 different neoplastic lesions, usually affecting children and adolescents. Here we identify a group of mesenchymal tumors which is highly associated with DICER1 syndrome, and molecularly distinct from other DICER1-associated tumors. This group of DICER1-associated mesenchymal tumors encompasses multiple well-established clinicopathological tumor entities and can be further divided into three clinically meaningful classes designated "low-grade mesenchymal tumor with DICER1 alteration" (LGMT DICER1), "sarcoma with DICER1 alteration" (SARC DICER1), and primary intracranial sarcoma with DICER1 alteration (PIS DICER1). Our study not only provides a combined approach to classify DICER1-associated neoplasms for improved clinical management but also suggests a role for global hypomethylation and other recurrent molecular events in sarcomatous differentiation in mesenchymal tumors with DICER1 alteration. Our results will facilitate future investigations into prognostication and therapeutic approaches for affected patients.