Chemotherapy resistance remains a formidable challenge to the treatment of high-grade serous ovarian cancer (HGSOC). The drug-tolerant cells may originate from a small population of inherently resistant cancer stem cells (CSCs) in primary tumors. In contrast, sufficient evidence suggests that drug tolerance can also be transiently acquired by nonstem cancer cells. Regardless of the route, key regulators of this plastic process are poorly understood. Here, we utilized multiomics, tumor microarrays, and epigenetic modulation to demonstrate that SOX9 is a key chemo-induced driver of chemoresistance in HGSOC. Epigenetic upregulation of SOX9 was sufficient to induce chemoresistance in multiple HGSOC lines. Moreover, this upregulation induced the formation of a stem-like subpopulation and significant chemoresistance in vivo. Mechanistically, SOX9 increased transcriptional divergence, reprogramming the transcriptional state of naive cells into a stem-like state. Supporting this, we identified a rare cluster of SOX9-expressing cells in primary tumors that were highly enriched for CSCs and chemoresistance-associated stress gene modules. Notably, single-cell analysis showed that chemo treatment results in rapid population-level induction of SOX9 that enriches for a stem-like transcriptional state. Altogether, these findings implicate SOX9 as a critical regulator of early steps of transcriptional reprogramming that lead to chemoresistance through a CSC-like state in HGSOC.

CONTEXT.—:Robotic-assisted navigation bronchoscopy (R-ANB) is used to target peripheral pulmonary nodules that are difficult to biopsy using conventional approaches. Frozen sections are requested to confirm that these lesions have been localized and/or to diagnose neoplasms that can be immediately resected. OBJECTIVE.—:To estimate diagnostic concordance between frozen section diagnosis (FSD) and formalin-fixed tissue diagnosis (FFTD) in biopsies obtained with R-ANB, calculate the sensitivity and specificity of FSD and FFTD for a diagnosis of malignancy, and evaluate whether the residual tissue that can be fixed in formalin after frozen section still has sufficient material for molecular studies. DATA SOURCES.—:The results of consecutive FSD rendered on biopsies performed with R-ANB during a 30-month period were used to calculate the metrics listed above. FFTD and/or the diagnoses rendered on computed tomography-guided core biopsy subsequently performed in patients with negative R-ANB and/or lung resections in patients with malignancies were used as true-positive results. The overall concordance between FSD and FFTD in 226 lesions from 203 patients was 72%. Frozen section diagnosed 76 of 123 malignancies with 100% specificity and 68% sensitivity. Adequate material was available in 92% of biopsies where next-generation sequencing and other molecular studies were requested. CONCLUSIONS.—:Intraoperative consultations are helpful to diagnose a variety of lung lesions and help surgeons confirm that targets have been accurately reached by R-ANB. Malignancies can be diagnosed with 100% specificity but only 68% sensitivity. The performance of frozen section did not interfere with the subsequent analysis of tissue with molecular studies in most cases.

The transcriptional scaffolds C-terminal Binding Proteins (CtBP) 1 and 2 are overexpressed and act as oncogenic dependencies in multiple cancers but importantly encode a chemically targetable dehydrogenase domain. CtBP promotes survival of high grade serous ovarian carcinoma (HGSOC) cells by repressing expression of Death Receptors (DR) 4 and 5, which activate caspase 8-dependent apoptosis. We have previously developed a series of substrate competitive CtBP dehydrogenase inhibitors active in multiple cell and preclinical solid tumor models. In the current study, we validated CtBP1 and 2 overexpression in a longitudinal series of primary and metastatic/recurrent HGSOC cases. Our lead CtBP dehydrogenase inhibitor, JW-98, induced apoptosis and exhibited variable single agent IC50 values in HGSOC cell lines. Importantly, depletion of nicotinamide adenine dinucleotide (NAD) species using the NAD synthesis inhibitor GMX1778 strikingly sensitized HGSOC cells to JW-98 treatment. Mechanistically, the JW-98/GMX1778 combination effectively abrogated CtBP dimerization that requires stoichiometric levels of intracellular NAD and is required for CtBP’s oncogenic transcriptional activities. Highlighting translational potential in late-stage HGSOC, combined JW-98/GMX1778 treatment of platinum-resistant OVCAR3 HGSOC mouse xenografts abrogated tumor growth without observable toxicity. Combined inhibition of CtBP and NAD synthesis represents a novel therapeutic strategy that could improve outcomes in chemoresistant HGSOC.

The 2021 WHO classification of thoracic tumours recommends grading pleural mesothelioma to aid prognostication. Robustness of grading and morphological characterisation is key to its clinical utility, though validation of this grading system has largely been conducted by expert thoracic pathologists. We conducted a survey inviting pathologists across a range of practices and expertise to grade digitised images of 50 epithelioid pleural mesotheliomas that had been graded by an expert in thoracic pathology. We included slides that were considered potentially problematic such as small biopsies, focal necrosis, and rare subtypes that may affect grading (small cell and deciduoid features). Using the Sectra Uniview web viewer, participants were asked to score atypia, mitotic count, and necrosis and choose from a list of cytological and architectural features. Seventy-four pathologists anonymously participated. There was 90% agreement of consensus scores with expert opinion using the WHO 2-tier grade and 72% for the 3-tier nuclear grade but only 70% for nuclear atypia, 56% for mitoses, and 84% for necrosis. Both 3-tier nuclear grade and WHO 2-tier grading systems were significantly associated with survival. Our study affirms the overall robustness and utility of grading for pleural mesothelioma, reveals variances, and suggests the need for dedicated training.

Tumor cellularity (TC) in lung adenocarcinoma slides submitted for molecular testing is important in identifying actionable mutations, but lack of best practice guidelines results in high interobserver variability in TC assessments. An artificial intelligence (AI)-based pipeline developed to assess TC in hematoxylin and eosin (H&E) whole slide images (WSIs) and in tumor areas (TAs) within WSIs includes a new model (CaBeSt-Net) trained to mask cancer cells, benign epithelial cells, stroma in H&E WSIs using immunohistochemistry-restained slides, and a model to detect all cell nuclei. High masking accuracy (>91%) by CaBeSt-Net computed using 1024 H&E regions of interest and intraclass correlation coefficient >0.97 assessing TC assessments reliability by one pathologist and AI in 20 test regions of interest supported the pipeline's applicability to TC assessment in 50 study H&E WSIs. Using the pipeline, TCs assessed in TAs and WSIs were compared with those by three pathologists. Reliabilities of these ratings by the pathologists supported by the pipeline were good (intraclass correlation coefficient >0.82, P < 0.0001). The consistency of sample categorizations as inadequate or adequate (TC ≤ 20% cut point) for molecular testing among the pathologists assessing TCs without AI support was moderate in TAs (κ = 0.410, P < 0.0001) and slight in WSIs (κ = 0.132, nonsignificant). With AI support, the consistency was substantial in both WSIs (κ = 0.602, P < 0.0001) and TAs (κ = 0.704, P < 0.0001). By visualizing cancer and measuring TC in the sample, this novel AI-based pipeline assists pathologists in selecting samples for molecular testing.

8540 Background: The liver is a frequent site of metastasis and carries a poor prognosis in patients with non-small cell (NSCLC) and small cell lung cancer (SCLC). Patients with liver metastases (LM) derive limited benefit from immune checkpoint inhibitors (ICI), due to hepatic myeloid derived suppressor cell (MDSC) mediated T cell elimination. Here, we used imaging mass cytometry (IMC) to perform single cell, highly multiplexed, analysis of LM and primary lung tumors to investigate how vascular endothelial growth factor (VEGF) influences T cell depletion within the tumor immune microenvironment (TIME) of LM. Methods: We comprehensively characterizedthe TIME in LM and primary lung tumors in 21 patients with NSCLC or SCLC using IMC. A panel of 40 antibodies was assembled to interrogate immune subsets and VEGF pathway markers. Each antibody was conjugated to a unique metal isotope. After validation, the antibody cocktail was used to stain the biopsies. Tissue images were segmented using Mesmer, and hierarchical clustering was applied to single-cell expression data to identify phenotypes. Similar clustering of cell neighbor profiles was applied to obtain spatial motifs. Phenotypic and motif frequencies, together with functional expression across phenotypes, were compiled from all samples and compared across conditions. Results: Initial visualization of the raw, unsegmented data revealed higher infiltration of CD8 + and CD4 + T cells in LM compared to the lung TIME. After segmentation, marker expression heatmaps uncovered complex cell-cell interaction ecosystems. The liver samples were enriched with M2 macrophages (CD163 + ), MDSC (CD11b + ), and proliferative endothelial cells (CD105 + ) whereas the lung samples were enriched in tumor cells (TTF1 + for NSCLC and INSM1 + /synaptophysin + for SCLC) and T cells (CD4 + , CD8 + ). Spatial neighborhood profiling of NSCLC liver tissues identified 12 neighborhood types, showing a general trend of mutual exclusivity between MDSCs and CD4 + /CD8 + T cells across neighborhoods. Notably, CD8 + T cells in MDSC-enriched neighborhoods exhibited consistently higher FAS expression, a key apoptotic marker. Heterogenous FAS and VEGF signaling across neighborhoods suggested a mixed immune-suppressive and vascularized response in the liver TIME, supporting VEGF’s role in mediating MDSC-driven hepatic CD8 + T cell depletion in patients with LM. Conclusions: Our findings highlight significant differences in the TIME between LM and primary lung tumors, with LM demonstrating a more immunosuppressive and VEGF-enriched milieu. The spatial association of MDSCs with CD8 + T cells, along with elevated FAS expression and VEGF signaling suggests a mechanistic role for VEGF in driving immune evasion within liver tumors. These results underscore the potential of VEGF blockade as a therapeutic strategy to overcome T cell suppression and improve ICI efficacy in patients with lung cancer metastatic to liver (NCT05588388, PI Sankar).

Background:Ovarian cancer treatment includes cytoreductive surgery, platinum-based chemotherapy, and often poly (ADP-ribose) polymerase (PARP) inhibitors. Homologous recombination (HR)-deficiency is a well-established predictor of therapy sensitivity. However, over 50% of HR-proficient tumors also exhibit sensitivity to standard-of-care treatments. Currently, there are no biomarkers to identify which HR-proficient tumors will be sensitive to standard-of-care therapy. Replication stress may serve as a key determinant of response. Methods:We evaluated phospho-RPA2-T21 (pRPA2) foci via immunofluorescence as a potential biomarker of replication stress in formalin-fixed, paraffin-embedded tumor samples collected at diagnosis from patients treated with platinum chemotherapy (discovery cohort: n = 31, validation cohort: n = 244) or PARP inhibitors (n = 87). Recurrent tumors (n = 37) were also analyzed. pRPA2 scores were calculated using automated imaging analysis. Samples were defined as pRPA2-High if > 16% of cells had ≥ 2 pRPA2 foci. Results:In the discovery cohort, HR-proficient, pRPA2-High tumors demonstrated significantly higher rates of pathologic complete response to platinum chemotherapy than HR-proficient, pRPA2-Low tumors. In the validation cohort, patients with HR-proficient, pRPA2-High tumors had significantly longer survival after platinum treatment than those with HR-proficient, pRPA2-Low tumors. Additionally, the pRPA2 assay effectively predicted survival outcomes in patients treated with PARP inhibitors and in recurrent tumor samples. Conclusion:Our study underscores the importance of considering replication stress markers alongside HR status in therapeutic planning. Our work suggest that this assay could be used throughout a patient's treatment course to expand the number of patients receiving effective therapy while reducing unnecessary toxicity.

Effective targeting of cancer-associated fibroblasts (CAFs) is hindered by the lack of specific biomarkers and a poor understanding of the mechanisms by which different populations of CAFs contribute to cancer progression. While the role of TGFβ in CAFs is well-studied, less attention has been focused on a structurally and functionally similar protein, Activin A (encoded by INHBA). Here, we identified INHBA(+) CAFs as key players in tumor promotion and immunosuppression. Spatiotemporal analyses of patient-matched primary, metastatic, and recurrent ovarian carcinomas revealed that aggressive metastatic tumors enriched in INHBA(+) CAFs were also enriched in regulatory T cells (Tregs). In ovarian cancer mouse models, intraperitoneal injection of the Activin A neutralizing antibody attenuated tumor progression and infiltration with pro-tumorigenic subsets of myofibroblasts and macrophages. Downregulation of INHBA in human ovarian CAFs inhibited pro-tumorigenic CAF functions. Co-culture of human ovarian CAFs and T cells revealed the dependence of Treg differentiation on direct contact with INHBA(+) CAFs. Mechanistically, INHBA/recombinant Activin A in CAFs induced the autocrine expression of PD-L1 through SMAD2-dependent signaling, which promoted Treg differentiation. Collectively, our study identified an INHBA(+) subset of immunomodulatory pro-tumoral CAFs as a potential therapeutic target in advanced ovarian cancers which typically show a poor response to immunotherapy.

INTRODUCTION:The Ion Endoluminal Platform (ION) (IEPI Intuitive, Sunnyvale, CA), a minimally invasive robotic-assisted bronchoscopy platform, was recently US Food and Drug Administration approved for the performance of fine needle aspirations (FNAs) and biopsies of peripheral lung lesions. Rapid on-site intraoperative diagnosis (IOD) of FNAs and/or frozen section of biopsies help surgeons confirm adequate sampling of the targeted lesion and allow definitive treatment in selected cases. MATERIALS AND METHODS:We retrospectively reviewed our experience with all FNAs of lung lesions sampled by interventional pulmonologists and thoracic surgeons using Ion from September 2020 to December 2022. IOD rendered during adequacy assessment were compared with final cytology diagnoses (Cyto-FD) and the ultimate final diagnoses (U-FD). The U-FD was based on the sum of all clinical, imaging, cytologic, and histologic diagnoses of the lung lesion which the clinical team used to treat the patient. RESULTS:The IOD and Cyto-FD were concordant in 62% of the 423 lesions that underwent intraoperative evaluation, yielding a sensitivity of 67% and a specificity of 99% for malignancy. The Cyto-FD and U-FD were concordant in 51% of the lesions with a sensitivity and specificity for malignancy of 66% and 100%, respectively. CONCLUSIONS:IODs rendered during Ion were highly accurate but only moderately sensitive for a diagnosis of malignancy.

This brief history of urinary cytology reporting highlights several major contributions to cytology from ancient to current times. Discussion of The Paris System for Reporting Urinary Cytology (TPS) includes a summary of differences between the first (TPS 1.0; 2016) and the recently updated (TPS 2.0; 2022) systems. The need for and advantages of the ongoing transition from unstructured to standardized/automated structured reporting in cytology are also addressed.

High-grade serous ovarian carcinoma (HGSOC), the deadliest form of ovarian cancer, is typically diagnosed after it has metastasized and often relapses after standard-of-care platinum-based chemotherapy, likely due to advanced tumor stage, heterogeneity, and immune evasion and tumor-promoting signaling from the tumor microenvironment. To understand how spatial heterogeneity contributes to HGSOC progression and early relapse, we profiled an HGSOC tissue microarray of patient-matched longitudinal samples from 42 patients. We found spatial patterns associated with early relapse, including changes in T cell localization, malformed tertiary lymphoid structure (TLS)–like aggregates, and increased podoplanin-positive cancer-associated fibroblasts (CAFs). Using spatial features to compartmentalize the tissue, we found that plasma cells distribute in two different compartments associated with TLS-like aggregates and CAFs, and these distinct microenvironments may account for the conflicting reports about the role of plasma cells in HGSOC prognosis.

Background and Objective Histologic assessment of the immune infiltrate in H&E slides is vital in diagnosing and managing inflammatory bowel diseases, but these assessments are subjective and time-consuming even for those with expertise. The development of deep learning models to aid in these assessments has been limited by the paucity of image data with reliably annotated immune cells available for training. Methods To address these challenges, we developed a pipeline that automates the neutrophil and lymphocyte labeling in ROIs from digital H&E slides. The data included ROIs extracted from 19 digitized H&E slides and the same slides restained with immunohistochemistry. Our pipeline first delineates each nucleus in H&E ROIs. Using the colorimetric features of the immunohistochemical stains (red: neutrophils, green: lymphocytes) in the immunohistochemistry ROIs, each cell was labeled as a neutrophil, a lymphocyte, or another cell. The labels were then transferred to the corresponding H&E ROIs by image registration, and the ROI registration accuracy was assessed by the median target registration error resulting in a labeled dataset. The newly formed dataset (NeuLy-IHC) comprising 519 ROIs with 235,256 labeled cells(74,339 lymphocytes, 16,326 neutrophils and 144,591 other cells) was used to train theHoVer-Net(NeuLy) model. The performance of HoVer-Net(NeuLy) measured by DICE coefficient (segmentation accuracy) and F1-scores (classification accuracy), was compared to those achieved by HoVer-Net(MoNuSAC) and SMILE(MoNuSAC) publicly available models trained on cancer-containing ROIs from the MoNuSAC dataset with manual cell labeling and pathologists’ annotations. Results The 1.0 μm median target registration error of ROIs observed was low demonstrating robust transferring of cellular labels from immunohistochemistry ROIs to H&E ROIs. In the test set comprising 76 NeuLy-IHC and 78 MoNuSAC ROIs, the HoVer-Net(NeuLy) achieved a DICE coefficient of 0.861 and F1-sores of 0.827, 0.838, and 0.828, for neutrophils, lymphocytes, and other cells, respectively, outperforming the HoVer-Net(MoNuSAC)'s and SMILE(MoNuSAC)’s DICE coefficient and F1 scores for each cell category. Conclusions We attribute the improved performance of HoVer-Net(NeuLy) to the larger number of immune cells in the NeuLy-IHC dataset (in total 5x more, including 21x more neutrophils) than in the MoNuSAC dataset. Despite being trained on data from inflammatory bowel disease specimens, our model maintained robust performance when tested on previously unseen data derived from cancer specimens. The NeuLy-IHC set provides opportunities for training accurate models to quantify the inflammatory infiltrate in digital histologic slides.

Context.— Mesothelioma is an uncommon tumor that can be difficult to diagnose. Objective.— To provide updated, practical guidelines for the pathologic diagnosis of mesothelioma. Data Sources.— Pathologists involved in the International Mesothelioma Interest Group and others with expertise in mesothelioma contributed to this update. Reference material includes peer-reviewed publications and textbooks. Conclusions.— There was consensus opinion regarding guidelines for (1) histomorphologic diagnosis of mesothelial tumors, including distinction of epithelioid, biphasic, and sarcomatoid mesothelioma; recognition of morphologic variants and patterns; and recognition of common morphologic pitfalls; (2) molecular pathogenesis of mesothelioma; (3) application of immunohistochemical markers to establish mesothelial lineage and distinguish mesothelioma from common morphologic differentials; (4) application of ancillary studies to distinguish benign from malignant mesothelial proliferations, including BAP1 and MTAP immunostains; novel immunomarkers such as Merlin and p53; fluorescence in situ hybridization (FISH) for homozygous deletion of CDKN2A; and novel molecular assays; (5) practical recommendations for routine reporting of mesothelioma, including grading epithelioid mesothelioma and other prognostic parameters; (6) diagnosis of mesothelioma in situ; (7) cytologic diagnosis of mesothelioma, including use of immunostains and molecular assays; and (8) features of nonmalignant peritoneal mesothelial lesions.

Manual screening of Ziehl-Neelsen (ZN)-stained slides that are negative or contain rare acid-fast mycobacteria (AFB) is labor-intensive and requires repetitive refocusing to visualize AFB candidates under the microscope. Whole slide image (WSI) scanners have enabled implementation of AI to classify digital ZN-stained slides as AFB + or AFB-. By default, these scanners acquire a single-layer WSI. However, some scanners can acquire a multilayer WSI with a z-stack and an extended focus image layer embedded. We developed a parameterized WSI classification pipeline to assess whether multilayer imaging improves ZN-stained slide classification accuracy. A CNN built into the pipeline classified tiles in each image layer to form an AFB probability score heatmap. Features extracted from the heatmap were then entered into a WSI classifier. 46 AFB + and 88 AFB-single-layer WSIs were used for the classifier training. 15 AFB + (with rare microorganisms) and 5 AFB-multilayer WSIs comprised the test set. Parameters in the pipeline included: (a) a WSI representation: z-stack of image layers, middle image layer (a single image layer equivalent) or an extended focus image layer, (b) 4 methods of aggregating AFB probability scores across the z-stack, (c) 3 classifiers, (d) 3 AFB probability thresholds, and (e) 9 feature vector types extracted from the aggregated AFB probability heatmaps. Balanced accuracy (BACC) was used to measure the pipeline performance for all parameter combinations. Analysis of Covariance (ANCOVA) was used to statistically evaluate the effect of each parameter on the BACC. After adjusting for other factors, a significant effect of the WSI representation (p-value < 1.99E-76), classifier type (p-value < 1.73E-21), and AFB threshold (p-value = 0.003) was observed on the BACC. The feature type had no significant effect (p-value = 0.459) on the BACC. WSIs represented by the middle layer, extended focus layer and the z-stack followed by the weighted averaging of AFB probability scores were classified with the average BACC of 58.80%, 68.64%, and 77.28%, respectively. The multilayer WSIs represented by the z-stack with the weighted averaging of AFB probability scores were classified by a Random Forest classifier with the average BACC of 83.32%. Low classification accuracy of WSIs represented by the middle layer suggests that they contain fewer features permitting identification of AFB than the multilayer WSIs. Our results indicate that single-layer acquisition can introduce a bias (sampling error) into the WSI. This bias can be mitigated by the multilayer or the extended focus acquisitions. (c) 2023 Elsevier B.V. All rights reserved.