Objective. To develop and validate a deep learning model trained on reticulin-stained whole slide images (MesoRet) to accurately identify transitional features and assist in the histologic subtyping of diffuse mesothelioma. Methods. A total of 115 cases of diffuse mesothelioma were collected from two institutions and reviewed by expert thoracic pathologists. Reticulin-stained whole-slide images were used to train a supervised deep learning model on the Aiforia Create platform to distinguish epithelioid, sarcomatoid, and transitional patterns. Model performance was validated on independent slides and compared with expert pathologists' assessments. Results. MesoRet accurately identified reticulin patterns across mesothelioma histotypes achieving 96.32% precision and 99.06% sensitivity, excluding artifacts and non-tumour tissue. It outperformed pathologists in identifying transitional patterns, reducing diagnostic time and minimising errors. Conclusions. MesoRet provides an accurate and objective approach for detecting reticulin patterns in mesothelioma, supporting histological subtyping and contributing to more consistent diagnoses. Although further validation is required, it represents a promising model to improve diagnostic precision and guide therapeutic decision-making.

BackgroundPrognostic models are crucial for prostate cancer (PCa) treatment decision making at the time of diagnosis, particularly for distinguishing active surveillance (AS) candidates from those requiring curative treatment. While several models exist, their ability to predict metastatic disease—the primary driver of PCa mortality—remains underexplored.MethodsWe analysed the Turin Prostate Cancer Prognostication cohort, which includes 891 unselected PCa patients diagnosed between 2008 and 2013 in Turin, Italy. Three widely used prognostic models—D’Amico, CAPRA, and MSKCC—were updated and compared based on optimism-corrected discrimination and overall prediction error for metastatic PCa (mPCa) within five years of diagnosis, accounting for competing risks. Overall survival was also assessed. Additionally, we investigated whether replacing standard AS eligibility criteria with nomogram-based risk thresholds could better identify patients at low risk of metastasis, maximizing AS uptake while minimising metastatic risk.ResultsThe MSKCC nomogram (optimism-corrected AUCt: 0.81; scaled Brier score: 0.15) outperforming the CAPRA score (AUCt: 0.77; Brier score: 0.11) and the D’Amico classification (AUCt 0.64; Brier score: 0.03) in predicting mPCa. The same ranking was observed for overall mortality prediction. When 95th percentile of MSKCC’s predicted probabilities among patients selected for six different AS protocols was used as a threshold, the proportion of potentially eligible patients increased from 7.8% when UCSF criterion was used to 57.0% without substantially increasing metastatic risk (observed 5-year risk: 1.7%).ConclusionsThe MSKCC nomogram outperformed other models in predicting mPCa and overall mortality. Implementing risk-based AS eligibility thresholds derived from MSKCC could enhance patient selection while facilitating shared decision-making between patients and clinicians.

Lung neuroendocrine tumours (NETs, also known as carcinoids) are rapidly rising in incidence worldwide but have unknown aetiology and limited therapeutic options beyond surgery. The current WHO classification, based on mitotic count and presence or absence of necrosis, divides lung NETs into grade-1 typical, and grade-2 atypical tumours. This dichotomous classification however does not account for recently described molecular entities nor is it sufficient for clinical management. Here we conducted integrative multi‐omic analyses on over 300 lung NETs including whole‐genome sequencing, transcriptome profiling, and DNA methylation arrays, followed by archetype analysis, to identify and characterise molecular groups. We further investigated molecular groups using spatial RNA sequencing and proteomics, and deep learning analysis of whole slide images. The integration of multi-omic data provided definitive proof of the existence of four strikingly different molecular groups that vary in patient characteristics, genomic and transcriptomic profiles, microenvironment, and morphology. Among these, we identified a new molecular group, enriched for highly aggressive supra‐carcinoids that displayed an immune‐rich microenvironment linked to tumour-macrophage crosstalk. We uncovered an undifferentiated cell population within supra-carcinoids and show the transcriptomic similarities between supra-carcinoids and the recently identified atypical small cell lung cancer tumours, further demonstrating their molecular link to high-grade lung neuroendocrine carcinomas. Multi-regional genomic analyses identified distinct evolutionary trajectories, suggesting that molecular groups are determined early in tumourigenesis by genomic events, and that transitions between groups, though infrequent, are possible for supra-carcinoids. Deep learning models accurately identified these groups based on morphology alone, outperforming current histological criteria. Together with the validation of a panel of immunohistochemistry markers, we demonstrated that these molecular groups can be accurately identified based on morphological features, facilitating their future implementation in the clinical setting. Our proposed morpho-molecular classification highlights potential group-specific therapeutic opportunities, with differences in expression to DLL3, EGFR, FGFR and TERT inhibitor targets. Overall, our findings unify previously proposed molecular classifications and refine the lung cancer map by revealing novel tumour phenotypes with potential implications for prognosis and therapeutic management.

Background and Aims The pancreatic tumour microenvironment (TME) is a complex ecosystem where tumour cells, cancer-associated fibroblasts and immune cells interact, often in ways that contribute to tumour growth. The role of interleukin (IL17)A in pancreatic cancer progression is now more defined, and it is known to sustain a pro-tumoural microenvironment and inhibit the immune response. Here, we explore the effect of combining IL17A depletion with a cancer vaccine to enhance anti-tumour immunity.Methods We used genetically engineered mice proficient or deficient in IL17A, and orthotopically injected mice with pancreatic tumour cells depleted or not in IL17A, to examine the vaccine effects on tumour growth and immune responses. Both humoral and cellular immune responses were analysed following vaccination in IL17A-deficient and control mice.Results Mice lacking IL17A-either genetically or through pharmacological depletion-exhibited prolonged survival and smaller tumours, compared to vaccinated controls. Vaccination in IL17A-deficient mice significantly increased the influx of immune cells, including Natural Killer (NK) and effector/memory CD8 T cells, which displayed higher cytotoxic activity. CD8 T-cell depletion in these models notably reduced vaccine efficacy, underscoring the essential role of these cells. NK cell depletion in untreated models further demonstrated NK cells' critical function in controlling tumour growth when IL17A was absent. Overall, IL17A depletion enhanced both antigen-specific humoral and cellular immune responses, indicating a shift towards a more robust and responsive immune environment.Conclusions Our findings reveal that the absence of IL17A in the pancreatic TME reprograms it into a more immune-supportive environment, favouring the recruitment of effector/memory immune cells upon vaccination. This approach paves the way for novel therapeutic combinations in pancreatic cancer, where IL17A depletion may boost both immunotherapy efficacy and anti-tumour responses.

KRAS is the most frequently mutated oncogene in cancer. Its activating mutations are associated with aggressive tumor behavior and resistance to certain therapies, including anti-EGFR treatments in colorectal cancer. In particular, the KRAS G12C mutation, which accounts for approximately 3–4% of colorectal cancers (CRCs) and 12–14% of non-small cell lung cancers (NSCLCs), involves a cysteine substitution at codon 12. This has provided the opportunity to develop selective covalent inhibitors that trap the mutant protein in its inactive state. The first targeted therapies for KRAS G12C-mutant cancers comprise sotorasib and adagrasib, both of which have been authorized for use in patients with previously treated NSCLC and CRC. Nevertheless, despite the evidence of clinical activity for this class of agents, primary and acquired resistance, dose optimization, and toxicity management remain significant open challenges. In this review, we summarize recent advances in KRASG12C tumor biology and pharmacological targeting. We also provide additional insights to guide future efforts to overcome the limitations of the current approaches and implement the treatment of KRASG12C-mutant cancers.

Growth hormone-releasing hormone (GHRH) antagonists exert antitumor functions in different experimental cancers. However, their role in combination with radiotherapy in non-small cell lung cancer (NSCLC) remains unknown. Therefore, we investigated the radiosensitizing effect of GHRH antagonists in NSCLC. A549 and H522 NSCLC cell lines were exposed to ionizing radiation (IR) and GHRH antagonists MIA-602 and MIA-690, either individually or in combination. Cell viability and proliferation were evaluated by MTT, BrdU, flow cytofluorimetry, and clonogenic assays; gene and protein expression, signaling pathways, and apoptosis were analyzed by real-time PCR, Western blot, annexin staining, and caspase-3 assay. GHRH antagonists showed antitumor effects alone and potentiated IR-induced inhibition of cell viability and proliferation. The combination of MIA-690 and IR decreased the expression of GHRH receptor, its oncogenic splice variant 1, and IGF1 mRNA levels. Additionally, cell cycle inhibitors and proapoptotic markers were upregulated, whereas cyclins, oncogenic MYC, and the antiapoptotic protein Bcl-2 were downregulated. Radioresistance was prevented by MIA-690, which also blunted epithelial-mesenchymal transition by enhancing E-cadherin and reducing mesenchymal, oxidative, and proangiogenic effectors. Finally, both MIA-602 and MIA-690 enhanced radiosensitivity in primary human NSCLC cells. These findings highlight the potential of GHRH antagonists as radiosensitizers in NSCLC treatment.

Background: The heritage of occupational and environmental asbestos exposure in Piedmont, Italy, has resulted in an enduring diffuse pleural mesothelioma (DPM) epidemic. Our study aimed to investigate the accuracy of Pleural biopsy (PB) via thoracoscopy (or video-assisted thoracic surgery-VATS) and analyze the diagnostic path of patients who experienced an initial DPM misdiagnosis. Methods: Patients who underwent PB by VATS for suspected DPM from 2004 to 2013 were analyzed. The Registry of Malignant Mesothelioma (RMM) records were examined to cross-check incident cases and identify misdiagnosed DPM. The sensitivity and specificity of the initial PB assessment versus the final classification of cases by RMM were evaluated. Results: Data from 552 patients were analyzed, and DPM was diagnosed in 178 cases (32%). Sensitivity and specificity were 93% and 100%, respectively. The number of false-negative PBs was 14 (2%). Of those, 10 (71%) had an initial diagnosis of chronic pleuritis, 3 (28.5%) were initially classified as mesothelial proliferation, and 1 had reactive mesothelial proliferation. All of them reported a history of asbestos exposure, and the correct diagnosis was reached after a median of 160 days. One- and four-year survival rates were 52% and 10% in DPM PB-positive cases and 50% and 19% in false-negative cases. Conclusions: When a strong clinical suspicion after a negative PB remains, iterative biopsy attempts should be considered, especially if a history of asbestos exposure is reported. In high-volume centers, the DPM misdiagnosis rate remains low, and future advancements in diagnostic technologies could further increase the accuracy and efficacy of histologic diagnosis.

The 5th edition of the WHO classification of endocrine and neuroendocrine tumors represents a significant advancement in the diagnostic approach to adrenocortical carcinoma (ACC), integrating novel molecular insights with established histopathological criteria to enhance diagnostic accuracy and to refine prognostic assessment. This review outlines key histopathological features and diagnostic strategies for ACC, offering a practical framework for evaluation and grading in daily practice. The updated WHO classification reaffirms the central role of histopathology, employing multiparametric scoring systems that assess invasion, architectural and cytological features, mitotic activity, and necrosis. However, these parameters often pose interpretive challenges, and no single algorithm ensures complete sensitivity, specificity, or reproducibility. Therefore, combining diagnostic approaches is advisable, particularly in morphologically ambiguous cases. For tumor grading, the WHO employs a two-tiered system based on a mitotic count cut of 20 per 10 mm2, aiming to improve interinstitutional consistency. Immunohistochemistry remains essential for diagnostic confirmation and prognostic evaluation. Among available markers, SF1 is the most specific for adrenocortical origin, while Ki-67, mismatch repair proteins, p53, and β-catenin are useful for predicting patient outcomes or screening for hereditary predisposition. In this complex diagnostic setting, artificial intelligence holds potential to support ACC diagnostics. However, its application is limited by the rarity of the disease, histological variability, and the scarcity of large, well-annotated datasets necessary for algorithm development.

Targeted therapies have pervasively enhanced clinical protocols and significantly improved survival and quality of life of cancer patients. Mostly grounded on small molecules and antibodies targeting deregulated mechanisms in cancer cells, precision oncology approaches are limited to a few tumor types because of the paucity of clinically actionable targets. Here, we report a comparative analysis of the cation channel transient receptor potential melastatin 8 (TRPM8; also known as transient receptor potential cation channel subfamily M member 8) in lung, breast, colorectal, and prostate cancers. Our findings reveal high levels of channel expression in cores of all four carcinomas, irrespective of reduced expression of its RNA. Importantly, cancer cell lines that represent the various tumor types consistently show that sub‐lethal chemotherapy dosages combined with the TRPM8 agonist D‐3263 have a synergistic lethal effect. In addition, administration of D‐3263 increases the cytotoxicity of 5‐FU/Oxaliplatin in patient‐derived colorectal cancer organoids, depending on the levels of TRPM8. Overall, our study strengthens the candidacy of TRPM8 as a molecular target for precision oncology approaches and paves the way for the design of basket trials for its clinical testing in TRPM8‐high tumors.

In non-papillary follicular cell-derived thyroid carcinomas, prognostic factors are scarce. Intratumoral fibrosis was identified as an adverse factor in papillary and medullary carcinomas, but it has not been investigated in other subtypes. We aimed at exploring the presence of intratumoral fibrosclerosis in a cohort of 132 non-papillary follicular cell-derived thyroid carcinomas (53 follicular and 31 oncocytic carcinomas, including 10 high grade differentiated thyroid carcinomas and 48 poorly differentiated carcinomas) and correlating its presence and extent with clinical and pathological features and survival. For each case, all available hematoxylin and eosin slides were reviewed, and the presence of fibrosclerosis was assessed as the percentage of tumor area and semi-quantitatively scored as absent, mild (≤ 10

Lung neuroendocrine neoplasms (NENs) make up a variegated ensemble of malignancies encompassing typical carcinoid (TC) and atypical carcinoid (AC). These are low to intermediate grade neuroendocrine tumors (NETs), and large cell neuroendocrine carcinoma (LCNEC) and small cell lung carcinoma (SCLC), which are full-fledged high-grade neuroendocrine carcinomas (NECs) showing similar clinical outcomes. Through a peer interaction between oncologist and pathologist, we herein constructed a practical approach based on questioning and answering regarding 8 practical issues aimed to provide shared solutions for clinical decision-making. These issues were itemized as sequential steps guided by clinical reasoning and concerned differential diagnosis, combined subtypes, primary and metastatic tumors, small diagnostic material, predictive biomarkers, tumor staging and, lastly, standardizing terminology. This study takes advantage of the close interaction between oncologists and pathologists as a tool to better delineate the decision-making on lung NENs.

INTRODUCTION:Large cell neuroendocrine carcinoma (LCNEC) of the lung is a high-grade non-small cell carcinoma featuring neuroendocrine morphology and neuroendocrine markers. Despite being credited since decades as an independent tumor entity, it continues to represent a diagnostic and therapeutic challenge probably because makes up a heterogeneous melting pot of genetic and epigenetic alterations closely intertwined with host microenvironment. AREAS COVERED:LCNEC of the lung was untangled according to three converging outlooks: (i) diagnostic criteria and molecular alterations as conveyors of pathogenetic diversification; (ii) microenvironmental changes, including immune system, as culprits behind tumor development; and (iii) available clinical trials, including immune-oncology studies as clinical decision-making drivers. EXPERT OPINION:LCNEC of the lung unveils different developmental trajectories, which may account for troubles in diagnosis and clinical decision-making but, at the same time, offer a rationale for personalized targeted treatments or immunotherapy in patient subsets. Currently, the question is fascinating: are we actually ready for a radical route change in the understanding of LCNEC? The answer is still open, but future studies will need to frame LCNEC in its own overarching context among lung cancers according to a holistic vision, which does not limit us to its perception as being 'just one tumor.'

BACKGROUND:An accurate estimation of progression risk in patients with prostate cancer (PCa) amenable to active surveillance (AS) is still an unmet need. Among available biomarkers, we considered Prolaris cell-cycle progression (CCP) test, "triple hit" phenotype (ERG overexpression, PTEN and prostein expression loss) and elevated expression levels of TMPRSS2-ERG gene fusions. METHODS:We performed a case-control study, enrolling patients that entered the AS programme at our tertiary referral Institution. Men subsequently undergoing radical prostatectomy for progression were considered as "cases", while men still on AS at the end of the follow-up period were labeled as "controls". CCP test, triple hit and TMPRSS2-ERG expression analyses were performed on tumoral tissue retrieved from biopsies at enrollment. Their ability to distinguish "cases" and "controls" was evaluated. According to power analysis, the study required 40 patients. RESULTS:Patients had comparable baseline characteristics. CCP test suggested to continue AS in 75% of controls and to undergo an active treatment in 75% of cases. CCP molecular score (HR 8.5, p = 0.02) was significantly associated with progression in multivariable logistic regression. No significant differences were found in terms of "triple hit" or TMPRSS2:ERG expression. IHC analysis was feasible only in 17 patients due to insufficient material. CONCLUSIONS:CCP test may be a useful tool to estimate the risk of progression in PCa patients and guide the decision between AS and active treatment. Triple hit phenotype or TMPRSS:ERG fusion status was not associated with progression.

ABSTRACTHistological staining plays a crucial role in anatomic pathology for the analysis of biological tissues and the formulation of diagnostic reports. Traditional methods like hematoxylin and eosin (H&E) primarily offer morphological information but lack insight into functional details, such as the expression of biomarkers indicative of cellular activity. To overcome this limitation, we propose a computational approach to synthesize virtual immunohistochemical (IHC) stains from H&E input, transferring imaging features across staining domains. Our approach comprises two stages: (i) a multi‐stage registration framework ensuring precise alignment of cellular and subcellular structures between the source H&E and target IHC stains, and (ii) a deep learning‐based generative model which incorporates functional attributes from the target IHC stain by learning cell‐to‐cell mappings from paired training data. We evaluated our approach of virtual restaining H&E slides to simulate IHC staining for phospho‐histone H3, on inguinal lymph node and bladder tissues. Blind pathologist assessments and quantitative metrics validated the diagnostic quality of the synthetic slides. Notably, mitotic counts derived from synthetic images exhibited a strong correlation with physical staining. Moreover, global and stain‐specific metrics confirmed the high quality of the synthetic IHC images generated by our approach. This methodology represents an important advance in automated functional restaining, achieved through robust registration and a model trained on precisely paired H&E and IHC data to transfer functions cell‐by‐cell. Our approach forms the basis for multiparameter histology analysis and comprehensive cohort staining using only digitized H&E slides.

Lung neuroendocrine neoplasms (NENs) are a heterogeneous group of pulmonary neoplasms showing different morphological patterns and clinical and biological characteristics. The World Health Organisation (WHO) classification of lung NENs has been recently updated as part of the broader attempt to uniform the classification of NENs. This much-needed update has come at a time when insights from seminal molecular characterisation studies revolutionised our understanding of the biological and pathological architecture of lung NENs, paving the way for the development of novel diagnostic techniques, prognostic factors and therapeutic approaches. In this challenging and rapidly evolving landscape, the relevance of the 2021 WHO classification has been recently questioned, particularly in terms of its morphology-orientated approach and its prognostic implications. Here, we provide a state-of-the-art review on the contemporary understanding of pulmonary NEN morphology and the potential contribution of artificial intelligence, the advances in NEN molecular profiling with their impact on the classification system and, finally, the key current and upcoming prognostic factors.

Introduction Morphologic and molecular data for staging of multifocal lung squamous cell carcinomas (LSCCs) are limited. In this study, whole exome sequencing (WES) was used as the gold standard to determine whether multifocal LSCC represented separate primary lung cancers (SPLCs) or intrapulmonary metastases (IPMs). Genomic profiles were compared with the comprehensive morphologic assessment. Methods WES was performed on 20 tumor pairs of multifocal LSCC and matched normal lymph nodes using the Illumina NovaSeq6000 S4-Xp (Illumina, San Diego, CA). WES clonal and subclonal analysis data were compared with histologic assessment by 16 thoracic pathologists. In addition, the immune gene profiling of the study cases was characterized by the HTG EdgeSeq Precision Immuno-Oncology Panel. Results By WES data, 11 cases were classified as SPLC and seven cases as IPM. Two cases were technically suboptimal. Analysis revealed marked genomic and immunogenic heterogeneity, but immune gene expression profiles highly correlated with mutation profiles. Tumors classified as IPM have a large number of shared mutations (ranging from 33.5% to 80.7%). The agreement between individual morphologic assessments for each case and WES was 58.3%. One case was unanimously interpreted morphologically as IPM and was in agreement with WES. In a further 17 cases, the number of pathologists whose morphologic interpretation was in agreement with WES ranged from two (one case) to 15 pathologists (one case) per case. Pathologists showed a fair interobserver agreement in the morphologic staging of multiple LSCCs, with an overall kappa of 0.232. Conclusions Staging of multifocal LSCC based on morphologic assessment is unreliable. Comprehensive genomic analyses should be adopted for the staging of multifocal LSCC.

Background & Aims: The Banff Liver Working Group recently published consensus recommendations for steatosis assessment in donor liver biopsy, but few studies reported their use and no automated deep-learning algorithms based on the proposed criteria have been developed so far. We evaluated Banff recommendations on a large monocentric series of donor liver needle biopsies by comparing pathologists' scores with those generated by convolutional neural networks (CNNs) we specifically developed for automated steatosis assessment. Methods: We retrospectively retrieved 292 allograft liver needle biopsies collected between January 2016 and January 2020 and performed steatosis assessment using a former intra-institution method (pre-Banff method) and the newly introduced Banff recommendations. Scores provided by pathologists and CNN models were then compared, and the degree of agreement was measured with the intraclass correlation coefficient (ICC). Results: Regarding the pre-Banff method, poor agreement was observed between the pathologist and CNN models for small droplet macrovesicular steatosis (ICC: 0.38), large droplet macrovesicular steatosis (ICC: 0.08), and the final combined score (ICC: 0.16) evaluation, but none of these reached statistically significance. Interestingly, significantly improved agreement was observed using the Banff approach: ICC was 0.93 for the low-power score (p <0.001), 0.89 for the high-power score (p <0.001), and 0.93 for the final score (p <0.001). Comparing the pre-Banff method with the Banff approach on the same biopsy, pathologist and CNN model assessment showed a mean (+/- SD) percentage of discrepancy of 26.89 (+/- 22.16) and 1.20 (+/- 5.58), respectively. Conclusions: Our findings support the use of Banff recommendations in daily practice and highlight the need for a granular analysis of their effect on liver transplantation outcomes. (c) 2023 The Author(s). Published by Elsevier B.V. on behalf of European Association for the Study of the Liver. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

IntroductionEwing Sarcoma (EWS) has been reported in seven children with Down syndrome (DS). To date, a detailed assessment of this solid tumour in DS patients is yet to be made.MethodsHere, we characterise a chemo-resistant mediastinal EWS in a 2-year-old DS child, the youngest ever reported case, by exploiting sequencing approaches.ResultsThe tumour showed a neuroectodermal development driven by the EWSR1-FLI1 fusion. The inherited myeloperoxidase deficiency of the patient caused failure of neutrophil-mediated cell death and promoted genomic instability.DiscussionIn this context, the tumour underwent genome-wide near haploidisation resulting in a massive overexpression of pro-inflammatory cytokines. Recruitment of defective neutrophils fostered rapid evolution of this EWS.