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The application of deep learning on whole-slide histological images (WSIs) can reveal insights for clinical and basic tumor science investigations. Finding quantitative imaging biomarkers from WSIs directly for the prediction of disease-free survival (DFS) in stage I–III melanoma patients is crucial to optimize patient management. In this study, we designed a deep learning-based model with the aim of learning prognostic biomarkers from WSIs to predict 1-year DFS in cutaneous melanoma patients. First, WSIs referred to a cohort of 43 patients (31 DF cases, 12 non-DF cases) from the Clinical Proteomic Tumor Analysis Consortium Cutaneous Melanoma (CPTAC-CM) public database were firstly annotated by our expert pathologists and then automatically split into crops, which were later employed to train and validate the proposed model using a fivefold cross-validation scheme for 5 rounds. Then, the model was further validated on WSIs related to an independent test, i.e. a validation cohort of 11 melanoma patients (8 DF cases, 3 non-DF cases), whose data were collected from Istituto Tumori ‘Giovanni Paolo II’ in Bari, Italy. The quantitative imaging biomarkers extracted by the proposed model showed prognostic power, achieving a median AUC value of 69.5% and a median accuracy of 72.7% on the public cohort of patients. These results remained comparable on the validation cohort of patients with an AUC value of 66.7% and an accuracy value of 72.7%, respectively. This work is contributing to the recently undertaken investigation on how treat features extracted from raw WSIs to fulfil prognostic tasks involving melanoma patients. The promising results make this study as a valuable basis for future research investigation on wider cohorts of patients referred to our Institute.

Standard chemotherapy for soft tissue sarcomas has shown limited efficacy. Here, we sought to evaluate whether β-adrenergic receptor (β-AR) signalling contributed to the progression of sarcomas and therapy resistance. To assess the translational potential of β-adrenergic receptors, we performed immunohistochemical detection of β1-AR, β2-AR and β3-AR in leiomyosarcoma, liposarcoma and angiosarcoma tissue specimens, reporting the results scored for the intensity. By using established and patient-derived sarcoma cells, we demonstrated the antitumour potential of the pharmacological targeting of β-ARs with the nonselective β-blocker propranolol in such sarcomas. Of note, pharmacological β-AR inhibition synergized with doxorubicin in inhibiting the cell viability of liposarcoma and leiomyosarcoma cells and increased the response to docetaxel in angiosarcoma- and solitary fibrous tumour (SFT)-patient-derived cells. Notably, the SFT patient was treated with the combination of propranolol and docetaxel, reporting prolonged disease control. Mechanistically, we found that propranolol reduced the activity of the multidrug resistance efflux pump P-gp, thereby increasing the intracellular doxorubicin concentration and antitumour activity. In addition, propranolol attenuated the Akt-dependent survival signal induced by doxorubicin and strongly reduced the activation of the NF-kB/COX-2 pathway, increasing cell sensitivity to docetaxel. Overall, our study highlighted the therapeutic potential of propranolol, alone or in rational combination therapies, for sarcoma treatment.

Background Clinical drawback in checkpoint inhibitors immunotherapy (ICI) of metastatic melanoma (MM) is monitoring clinical benefit. Soluble forms of PD1(sPD1) and PD-L1(sPD-L1) and extracellular vesicles (EVs) expressing PD1 and PD-L1 have recently emerged as predictive biomarkers of response. As factors released in the blood, EVs and soluble forms could be relevant in monitoring treatment efficacy and adaptive resistance to ICI. Methods We used pre-therapy plasma samples of 110 MM patients and longitudinal samples of 46 patients. Elisa assay and flow cytometry (FCM) were used to measure sPD-L1 and sPD1 concentrations and the percentage of PD1 + EVs and PD-L1 + EVs, released from tumor and immune cells in patients subsets. Transwell assays were conducted to investigate the impact of EVs of each patient subset on MM cells invasion and interaction between tumor cells and macrophages or dendritic cells. Viability assays were performed to assess EVs effect on MM cells and organoids sensitivity to anti-PD1. FCM was used to investigate immunosuppressive markers in EVs and immune cells. Results The concentrations of sPD1 and sPD-L1 in pre-treatment and longitudinal samples did not correlate with anti-PD1 response, instead only tumor-derived PD1 + EVs decreased in long responders while increased during disease progression in responders. Notably, we observed reduction of T cell derived EVs expressing LAG3 + and PD1 + in long responders and their increase in responders experiencing progression. By investigating the impact of EVs on disease progression, we found that those isolated from non-responders and from patients with progression disease accelerated tumor cells invasiveness and migration towards macrophages, while EVs of long responders reduced the metastatic potential of MM cells and neo-angiogenesis. Additionally, the EVs of non-responders and of progression disease patients subset reduced the sensitivity of MM cells and organoids of responder to anti-PD1 and the recruitment of dendritic cells, while the EVs of progression disease subset skewed macrophages to express higher level of PDL-1. Conclusion Collectively, we suggest that the detection of tumor-derived PD1 + EVs may represent a useful tool for monitoring the response to anti-PD1 and a role for EVs shed by tumor and immune cells in promoting tumor progression and immune dysfunction. Graphical Abstract

Background The diagnosis of check-point inhibitor-related pneumonitis (CIP) relies on radiological and clinical patterns which are not specific and can mimic other conditions (cancer progression, infectious diseases or interstitial pneumonitis). Cell pattern analysis of bronchoalveolar lavage (BAL) is well-known to support the diagnosis of interstitial lung disease; nevertheless, this analysis is somewhat performed and not required by immune-toxicity management guidelines for CIP. Methods We performed BAL analysis in 5 metastatic melanoma (MM) patients who developed CIP among 112 patients treated with checkpoint inhibitors. We also correlated the BAL features with the computed tomography (CT) scan patterns and with various peripheral blood parameters to better define the profile of this patient population. Results BAL flow cytometer and cytopathology analyses showed typical and homogeneous features with increased lymphoid population, prevalent CD8 + T cells and inversion of the CD4/CD8 ratio. Moreover, the extent of activated CD3 + HLA-DR + T cells was related to the grading of adverse events. Blood leucocytosis, hypoxemia, normal values for procalcitonin and lactate dehydrogenase were also found together with a cryptogenic organizing pneumonia-like radiologic pattern. In all our patients, CIP was associated with partial or complete response. Conclusions Identification of a specific BAL cellular pattern allows clinicians to place this investigation in the appropriate position of CIP diagnosis and management to avoid misdiagnosis or considering this condition as progressive disease and delaying proper treatment.

Desmoplastic melanoma (DM) is a rare subtype of melanoma characterized by high immunogenicity which makes it particularly suitable for immune checkpoint inhibitors (ICIs) treatment. We report the case of a 53-year-old man with metastatic DM successfully treated with the combination of anti-CTLA-4 and anti-PD-1 antibodies, who developed serious immune-related adverse events (irAEs). The primary tumor was characterized by absent PD-L1 expression and no-brisk lymphocytes infiltration. NGS showed absence of BRAF mutation, a high tumor mutational burden, and an UV-induced DNA damage signature. Metastatic lesions regressed rapidly after few cycles of ICIs until complete response, however the patient developed serious irAEs including hypothyroidism, adrenal deficiency, and acute interstitial nephritis which led to the definitive suspension of treatment. Currently, the patient has normal renal functionality and no disease relapse after 26 months from starting immunotherapy, and after 9 months from its definitive suspension. Efficacy and toxicity are two sides of the same coin of high sensitivity to ICIs in DM. For this reason, these patients should be closely monitored during ICIs therapy to promptly identify serious side effects and to correctly manage them.

The management of cancer patients follows a Diagnostic Therapeutic and Care Pathway (PDTA) approach, aimed at achieving the optimal balance between care and quality of life. To support this process, precision medicine and innovative technologies [e.g., next-generation sequencing (NGS)] allow rapid identification of genetic-molecular alterations useful for the design of PDTA-approved therapies. If the standard approach proves inadequate, the Molecular Tumor Board (MTB), a group comprising specialists from diverse disciplines, can step in to evaluate a broader molecular profile, proposing potential therapies beyond evidence levels I-II or considering enrolment in clinical trials. Our aim is to analyze the role of the MTB in the entire management of patients in our institute and its impact on the strategy of personalized medicine, particularly when all approved treatments have failed. In alignment with European and national guidelines, a panel of clinicians and preclinical specialists from our institution was defined as the MTB core team. We designed and approved a procedure for the operation of this multidisciplinary group, which is the only one operating in the Puglia region. In 29 months (2021-2023), we discussed and analyzed 93 patients. A total of 44% presented pathogenic alterations, of which 40.4% were potentially actionable. Only 11 patients were proposed for enrollment in clinical trials, treatment with off-label drugs, or AIFA (the Italian pharmaceutical agency for drugs)-5% funding. Our process indicators, time to analysis, and number of patient cases discussed are in line with the median data of other European institutions. Such findings underscore both the importance and usefulness of the integration of an MTB process into the care of oncology patients.

[This corrects the article DOI: 10.3389/fmed.2024.1432628.].

Background Risk stratification and treatment benefit prediction models are urgent to improve negative sentinel lymph node (SLN-) melanoma patient selection, thus avoiding costly and toxic treatments in patients at low risk of recurrence. To this end, the application of artificial intelligence (AI) could help clinicians to better calculate the recurrence risk and choose whether to perform adjuvant therapy. Methods We made use of AI to predict recurrence-free status (RFS) within 2-years from diagnosis in 94 SLN- melanoma patients. In detail, we detected quantitative imaging information from H&E slides of a cohort of 71 SLN- melanoma patients, who registered at Istituto Tumori “Giovanni Paolo II” in Bari, Italy (investigational cohort, IC). For each slide, two expert pathologists firstly annotated two Regions of Interest (ROIs) containing tumor cells alone (TUMOR ROI) or with infiltrating cells (TUMOR + INF ROI). In correspondence of the two kinds of ROIs, two AI-based models were developed to extract information directly from the tiles in which each ROI was automatically divided. This information was then used to predict RFS. Performances of the models were computed according to a 5-fold cross validation scheme. We further validated the prediction power of the two models on an independent external validation cohort of 23 SLN- melanoma patients (validation cohort, VC). Results The TUMOR ROIs have revealed more informative than the TUMOR + INF ROIs. An Area Under the Curve (AUC) value of 79.1% and 62.3%, a sensitivity value of 81.2% and 76.9%, a specificity value of 70.0% and 43.3%, an accuracy value of 73.2% and 53.4%, were achieved on the TUMOR and TUMOR + INF ROIs extracted for the IC cohort, respectively. An AUC value of 76.5% and 65.2%, a sensitivity value of 66.7% and 41.6%, a specificity value of 70.0% and 55.9%, an accuracy value of 70.0% and 56.5%, were achieved on the TUMOR and TUMOR + INF ROIs extracted for the VC cohort, respectively. Conclusions Our approach represents a first effort to develop a non-invasive prognostic method to better define the recurrence risk and improve the management of SLN- melanoma patients.

Background BRAF/MEK inhibitors have improved the outcome in metastatic melanoma (MM) patients harboring a BRAF mutation, but no biomarker predictive of response has been identified. Methods We conducted a retrospective analysis on 264 MM patients that had received first-line targeted therapy with BRAF/MEK inhibitors. Next-generation sequencing (NGS) was performed on tissue biopsies, and samples with > 30% tumor cellularity were included in the study. The impact of BRAF variant allele frequency (BRAF-VAF) on clinical treatment outcomes was analyzed. Results BRAF-VAF was dichotomized using two approaches. (1) The “surv_cutpoint” function identified two different cut-off for progression-free survival (PFS: 44.05%) and overall survival (OS:45.1%). Patients with BRAF-VAF > 44.05% showed a significantly lower PFS (median PFS: 10 months, 95% CI: 7–13 months), compared to patients with BRAF-VAF < 44.05% (median PFS: 13 months, 95% CI: 12–21 months). Moreover, patients with higher VAF (> 45.1%) experienced a lower OS (median OS: 26 months, 95% CI: 19–38 months), compared with patients with VAF < 45.1% (median OS: 29 months, 95% CI: 29–51 months). (2) The ROC analysis significantly predicted PFS but not OS. BRAF-VAF normalized with neoplastic cellularity (nVAF) showed a strong association with both PFS, and OS compared to BRAF-VAF alone. nVAF also emerged as an independent predictor for PFS in the multivariate analysis (HR: 3.88, 95% CI: 1.84–8.20), with a higher nVAF score associated with a 3.88-fold increased risk of progression. Conclusions Our study demonstrated the role of the BRAF-VAF as predictor of response in MM patients treated with BRAF/MEK inhibitors. Moreover, VAF normalization predicts PFS better than BRAF-VAF alone. Highlights The role of BRAF-VAF in predicting response to BRAF/MEK inhibitors therapy in melanoma has not been elucidated yet. In 264 metastatic melanoma patients treated with first-line targeted therapy, high BRAF-VAF values correlated with worse clinical outcomes. This evidence is further strengthened when BRAF-VAF was normalized using neoplastic cellularity (nVAF). BRAF-VAF can be used as predictor of clinical outcomes in metastatic melanoma patients treated with first-line targeted therapy.

Advanced colorectal cancer (CRC) is highly metastatic and often results in peritoneal dissemination. The extracellular vesicles (EVs) released by cancer cells in the microenvironment are important mediators of tumor metastasis. We investigated the contribution of EV-mediated interaction between peritoneal mesothelial cells (MCs) and CRC cells in generating a pro-metastatic environment in the peritoneal cavity. Peritoneal MCs isolated from peritoneal lavage fluids displayed high CD44 expression, substantial mesothelial-to-mesenchymal transition (MMT) and released EVs that both directed tumor invasion and caused reprogramming of secretory profiles by increasing TGF-β1 and uPA/uPAR expression and MMP-2/9 activation in tumor cells. Notably, the EVs released by tumor cells induced apoptosis by activating caspase-3, peritoneal MC senescence, and MMT, thereby augmenting the tumor-promoting potential of these cells in the peritoneal cavity. By using pantoprazole, we reduced the biogenesis of EVs and their pro-tumor functions. In conclusion, our findings provided evidence of underlying mechanisms of CRC dissemination driven by the interaction of peritoneal MCs and tumor cells via the EVs released in the peritoneal cavity, which may have important implications for the clinical management of patients.