TP53 mutations and survival in ovarian carcinoma patients receiving first-line chemotherapy plus bevacizumab: Results of the MITO16A/MaNGO OV-2 study.

To date, there are no biomarkers that define a patient subpopulation responsive to bevacizumab (BEV), an effective treatment option for advanced ovarian carcinoma (OC). In the context of the MITO16A/MaNGO OV-2 trial, a Phase IV study of chemotherapy combined with BEV in first-line treatment of advanced OC, we evaluated TP53 mutations by next-generation sequencing and p53 expression by immunohistochemistry (IHC) on 202 and 311 cases, respectively. We further correlated TP53 mutations in terms of type, function, and site, and IHC data with patients' clinicopathological characteristics and survival. TP53 missense mutations of unknown function (named unclassified) represented the majority of variants in our population (44.4%) and were associated with a significantly improved overall survival (OS) both in univariable (hazard ratio [HR] = 0.43, 95% confidence interval [CI] = 0.20-0.92, p = .03) and multivariable analysis (HR = 0.39, 95% CI = 0.18-0.86, p = .02). Concordance between TP53 mutational analysis and IHC was 91%. We observed an HR of 0.70 for OS in patients with p53 IHC overexpression compared to p53 wild-type, which however did not reach statistical significance (p = .31, 95% CI = 0.36-1.38). Our results indicate that the presence of unclassified TP53 mutations has favorable prognostic significance in patients with OC receiving upfront BEV plus chemotherapy. In particular, unclassified missense TP53 mutations characterize a subpopulation of patients with a significant survival advantage, independently of clinicopathological characteristics. Our findings warrant future investigations to confirm the prognostic impact of TP53 mutations in BEV-treated OC patients and deserve to be assessed for their potential predictive role in future randomized clinical studies.

The role of HOTAIR in the modulation of resistance to anticancer therapy.

Leading anti-tumour therapeutic strategies typically involve surgery and radiotherapy for locally advanced (non-metastatic) cancers, while hormone therapy, chemotherapy, and molecular targeted therapy are the current treatment options for metastatic cancer. Despite the initially high sensitivity rate to anticancer therapies, a large number of patients develop resistance, leading to a poor prognosis. The mechanisms related to drug resistance are highly complex, and long non-coding RNAs appear to play a crucial role in these processes. Among these, the lncRNA homeobox transcript antisense intergenic RNA (HOTAIR), widely implicated in cancer initiation and progression, likewise plays a significant role in anticancer drug resistance. It can modulate cell activities such as proliferation, apoptosis, hypoxia, autophagy, as well as epithelial-mesenchymal transition, thereby contributing to the development of resistant tumour cells. In this manuscript, we describe different mechanisms of antitumor drug resistance in which HOTAIR is involved and suggest its potential as a therapeutic predictive biomarker for the management of cancer patients.

A multicentric study of radiomics and artificial intelligence analysis on contrast-enhanced mammography to identify different histotypes of breast cancer.

OBJECTIVE: To evaluate the performance of radiomic analysis on contrast-enhanced mammography images to identify different histotypes of breast cancer mainly in order to predict grading, to identify hormone receptors, to discriminate human epidermal growth factor receptor 2 (HER2) and to identify luminal histotype of the breast cancer. METHODS: From four Italian centers were recruited 180 malignant lesions and 68 benign lesions. However, only the malignant lesions were considered for the analysis. All patients underwent contrast-enhanced mammography in cranium caudal (CC) and medium lateral oblique (MLO) view. Considering histological findings as the ground truth, four outcomes were considered: (1) G1 + G2 vs. G3; (2) HER2 + vs. HER2 - ; (3) HR + vs. HR - ; and (4) non-luminal vs. luminal A or HR + /HER2- and luminal B or HR + /HER2 + . For multivariate analysis feature selection, balancing techniques and patter recognition approaches were considered. RESULTS: The univariate findings showed that the diagnostic performance is low for each outcome, while the results of the multivariate analysis showed that better performances can be obtained. In the HER2 + detection, the best performance (73% of accuracy and AUC = 0.77) was obtained using a linear regression model (LRM) with 12 features extracted by MLO view. In the HR + detection, the best performance (77% of accuracy and AUC = 0.80) was obtained using a LRM with 14 features extracted by MLO view. In grading classification, the best performance was obtained by a decision tree trained with three predictors extracted by MLO view reaching an accuracy of 82% on validation set. In the luminal versus non-luminal histotype classification, the best performance was obtained by a bagged tree trained with 15 predictors extracted by CC view reaching an accuracy of 94% on validation set. CONCLUSIONS: The results suggest that radiomics analysis can be effectively applied to design a tool to support physician decision making in breast cancer classification. In particular, the classification of luminal versus non-luminal histotypes can be performed with high accuracy.

Immune cell infiltration and inflammatory landscape in primary brain tumours.

BACKGROUND: Primary malignant brain tumours are more than one-third of all brain tumours and despite the molecular investigation to identify cancer driver mutations, the current therapeutic options available are challenging due to high intratumour heterogeneity. In addition, an immunosuppressive and inflammatory tumour microenvironment strengthens cancer progression. Therefore, we defined an immune and inflammatory profiling of meningioma and glial tumours to elucidate the role of the immune infiltration in these cancer types. METHODS: Using tissue microarrays of 158 brain tumour samples, we assessed CD3, CD4, CD8, CD20, CD138, Granzyme B (GzmB), 5-Lipoxygenase (5-LOX), Programmed Death-Ligand 1 (PD-L1), O-6-Methylguanine-DNA Methyltransferase (MGMT) and Transglutaminase 2 (TG2) expression by immunohistochemistry (IHC). IHC results were correlated using a Spearman correlation matrix. Transcript expression, correlation, and overall survival (OS) analyses were evaluated using public datasets available on GEPIA2 in Glioblastoma (GBM) and Lower Grade Glioma (LGG) cohorts. RESULTS: Seven out of ten markers showed a significantly different IHC expression in at least one of the evaluated cohorts whereas CD3, CD4 and 5-LOX were differentially expressed between GBMs and astrocytomas. Correlation matrix analysis revealed that 5-LOX and GzmB expression were associated in both meningiomas and GBMs, whereas 5-LOX expression was significantly and positively correlated to TG2 in both meningioma and astrocytoma cohorts. These findings were confirmed with the correlation analysis of TCGA-GBM and LGG datasets. Profiling of mRNA levels indicated a significant increase in CD3 (CD3D, CD3E), and CD138 (SDC1) expression in GBM compared to control tissues. CD4 and 5-LOX (ALOX5) mRNA levels were significantly more expressed in tumour samples than in normal tissues in both GBM and LGG. In GBM cohort, GzmB (GZMB), SDC1 and MGMT gene expression predicted a poor overall survival (OS). Moreover, in LGG cohort, an increased expression of CD3 (CD3D, CD3E, CD3G), CD8 (CD8A), GZMB, CD20 (MS4A1), SDC1, PD-L1, ALOX5, and TG2 (TGM2) genes was associated with worse OS. CONCLUSIONS: Our data have revealed that there is a positive and significant correlation between the expression of 5-LOX and GzmB, both at RNA and protein level. Further evaluation is needed to understand the interplay of 5-LOX and immune infiltration in glioma progression.

Treatments, prognostic factors, and genetic heterogeneity in advanced cholangiocarcinoma: A multicenter real-world study.

BACKGROUND AND AIMS: Cholangiocarcinoma (CCA), a rare and aggressive hepatobiliary malignancy, presents significant clinical management challenges. Despite rising incidence and evolving treatment options, prognosis remains poor, motivating the exploration of real-world data for enhanced understanding and patient care. METHODS: This multicenter study analyzed data from 120 metastatic CCA patients at three institutions from 2016 to 2023. Kaplan-Meier curves assessed overall survival (OS), while univariate and multivariate analyses evaluated links between clinical variables (age, gender, tumor site, metastatic burden, ECOG performance status, response to first-line chemotherapy) and OS. Genetic profiling was conducted selectively. RESULTS: Enrolled patients had a median age of 68.5 years, with intrahepatic tumors predominant in 79 cases (65.8%). Among 85 patients treated with first-line chemotherapy, cisplatin and gemcitabine (41.1%) was the most common regimen. Notably, one-third received no systemic treatment. After a median 14-month follow-up, 81 CCA-related deaths occurred, with a median survival of 13.1 months. Two clinical variables independently predicted survival: response to first-line chemotherapy (disease control vs. no disease control; HR: 0.27; 95% CI: 0.14-0.50; p < 0.0001) and metastatic involvement (>1 site vs. 1 site; HR: 1.99; 95% CI: 1.04-3.80; p = 0.0366). The three most common genetic alterations involved the ARID1A, tp53, and CDKN2A genes. CONCLUSIONS: Advanced CCA displays aggressive clinical behavior, emphasizing the need for treatments beyond chemotherapy. Genetic diversity supports potential personalized therapies. Collaborative research and deeper CCA biology understanding are crucial to enhance patient outcomes in this challenging malignancy.

rs822336 binding to C/EBPß and NFIC modulates induction of PD-L1 expression and predicts anti-PD-1/PD-L1 therapy in advanced NSCLC.

Efficient predictive biomarkers are needed for immune checkpoint inhibitor (ICI)-based immunotherapy in non-small cell lung cancer (NSCLC). Testing the predictive value of single nucleotide polymorphisms (SNPs) in programmed cell death 1 (PD-1) or its ligand 1 (PD-L1) has shown contrasting results. Here, we aim to validate the predictive value of PD-L1 SNPs in advanced NSCLC patients treated with ICIs as well as to define the molecular mechanisms underlying the role of the identified SNP candidate. rs822336 efficiently predicted response to anti-PD-1/PD-L1 immunotherapy in advanced non-oncogene addicted NSCLC patients as compared to rs2282055 and rs4143815. rs822336 mapped to the promoter/enhancer region of PD-L1, differentially affecting the induction of PD-L1 expression in human NSCLC cell lines as well as their susceptibility to HLA class I antigen matched PBMCs incubated with anti-PD-1 monoclonal antibody nivolumab. The induction of PD-L1 expression by rs822336 was mediated by a competitive allele-specificity binding of two identified transcription factors: C/EBPß and NFIC. As a result, silencing of C/EBPß and NFIC differentially regulated the induction of PD-L1 expression in human NSCLC cell lines carrying different rs822336 genotypes. Analysis by binding microarray further validated the competitive allele-specificity binding of C/EBPß and NFIC to PD-L1 promoter/enhancer region based on rs822336 genotype in human NSCLC cell lines. These findings have high clinical relevance since identify rs822336 and induction of PD-L1 expression as novel biomarkers for predicting anti-PD-1/PD-L1-based immunotherapy in advanced NSCLC patients.

Oligo-metastatic neoPlasms from the gastro-intestinal tract: iDentIfiCaTIon of cliNical and molecular drivers: the PREDICTION study.

BACKGROUND: Metastatic disease in tumors originating from the gastrointestinal tract can exhibit varying degrees of tumor burden at presentation. Some patients follow a less aggressive disease course, characterized by a limited number of metastatic sites, referred to as "oligo-metastatic disease" (OMD). The precise biological characteristics that define the oligometastatic behavior remain uncertain. In this study, we present a protocol designed to prospectively identify OMD, with the aim of proposing novel therapeutic approaches and monitoring strategies. METHODS: The PREDICTION study is a monocentric, prospective, observational investigation. Enrolled patients will receive standard treatment, while translational activities will involve analysis of the tumor microenvironment and genomic profiling using immunohistochemistry and next-generation sequencing, respectively. The first primary objective (descriptive) is to determine the prevalence of biological characteristics in OMD derived from gastrointestinal tract neoplasms, including high genetic concordance between primary tumors and metastases, a significant infiltration of T lymphocytes, and the absence of clonal evolution favoring specific driver genes (KRAS and PIK3CA). The second co-primary objective (analytic) is to identify a prognostic score for true OMD, with a primary focus on metastatic colorectal cancer. The score will comprise genetic concordance (> 80%), high T-lymphocyte infiltration, and the absence of clonal evolution favoring driver genes. It is hypothesized that patients with true OMD (score 3+) will have a lower rate of progression/recurrence within one year (20%) compared to those with false OMD (80%). The endpoint of the co-primary objective is the rate of recurrence/progression at one year. Considering a reasonable probability (60%) of the three factors occurring simultaneously in true OMD (score 3+), using a significance level of α = 0.05 and a test power of 90%, the study requires a minimum enrollment of 32 patients. DISCUSSION: Few studies have explored the precise genetic and biological features of OMD thus far. In clinical settings, the diagnosis of OMD is typically made retrospectively, as some patients who undergo intensive treatment for oligometastases develop polymetastatic diseases within a year, while others do not experience disease progression (true OMD). In the coming years, the identification of true OMD will allow us to employ more personalized and comprehensive strategies in cancer treatment. TRIAL REGISTRATION: ClinicalTrials.gov ID NCT05806151.

Biological and clinical impact of membrane EGFR expression in a subgroup of OC patients from the phase IV ovarian cancer MITO-16A/MANGO-OV2A trial.

BACKGROUND: Validated prognostic biomarkers for anti-angiogenic therapy using the anti-VEGF antibody Bevacizumab in ovarian cancer (OC) patients are still an unmet clinical need. The EGFR can contribute to cancer-associated biological mechanisms in OC cells including angiogenesis, but its targeting gave disappointing results with less than 10% of OC patients treated with anti-EGFR compounds showing a positive response, likely due to a non adequate selection and stratification of EGFR-expressing OC patients. METHODS: EGFR membrane expression was evaluated by immunohistochemistry in a cohort of 310 OC patients from the MITO-16A/MANGO-OV2A trial, designed to identify prognostic biomarkers of survival in patients treated with first line standard chemotherapy plus bevacizumab. Statistical analyses assessed the association between EGFR and clinical prognostic factors and survival outcomes. A single sample Gene Set Enrichment-like and Ingenuity Pathway Analyses were applied to the gene expression profile of 195 OC samples from the same cohort. In an OC in vitro model, biological experiments were performed to assess specific EGFR activation. RESULTS: Based on EGFR-membrane expression, three OC subgroups of patients were identified being the subgroup with strong and homogeneous EGFR membrane localization, indicative of possible EGFR out/in signalling activation, an independent negative prognostic factor for overall survival of patients treated with an anti-angiogenic agent. This OC subgroup resulted statistically enriched of tumors of histotypes different than high grade serous lacking angiogenic molecular characteristics. At molecular level, among the EGFR-related molecular traits identified to be activated only in this patients' subgroup the crosstalk between EGFR with other RTKs also emerged. In vitro, we also showed a functional cross-talk between EGFR and AXL RTK; upon AXL silencing, the cells resulted more sensitive to EGFR targeting with erlotinib. CONCLUSIONS: Strong and homogeneous cell membrane localization of EGFR, associated with specific transcriptional traits, can be considered a prognostic biomarker in OC patients and could be useful for a better OC patients' stratification and the identification of alternative therapeutic target/s in a personalized therapeutic approach.

Oligo-Metastatic Cancers: Putative Biomarkers, Emerging Challenges and New Perspectives.

Some cancer patients display a less aggressive form of metastatic disease, characterized by a low tumor burden and involving a smaller number of sites, which is referred to as "oligometastatic disease" (OMD). This review discusses new biomarkers, as well as methodological challenges and perspectives characterizing OMD. Recent studies have revealed that specific microRNA profiles, chromosome patterns, driver gene mutations (ERBB2, PBRM1, SETD2, KRAS, PIK3CA, SMAD4), polymorphisms (TCF7L2), and levels of immune cell infiltration into metastases, depending on the tumor type, are associated with an oligometastatic behavior. This suggests that OMD could be a distinct disease with specific biological and molecular characteristics. Therefore, the heterogeneity of initial tumor burden and inclusion of OMD patients in clinical trials pose a crucial methodological question that requires responses in the near future. Additionally, a solid understanding of the molecular and biological features of OMD will be necessary to support and complete the clinical staging systems, enabling a better distinction of metastatic behavior and tailored treatments.

AXL and MET Tyrosine Kinase Receptors Co-Expression as a Potential Therapeutic Target in Malignant Pleural Mesothelioma.

Malignant pleural mesothelioma (MPM) is a highly lethal malignancy that unfortunately cannot benefit from molecularly targeted therapies. Although previous results showed the pivotal role of various receptor tyrosine kinases (RTKs) in MPM tumorigenesis, the treatment with a single inhibitor targeting one specific RTK has been shown to be ineffective in MPM patients. The main aim of the present study was to investigate the potential role of AXL and MET receptors in MPM and the possible efficacy of treatment with AXL and MET multitarget inhibitors. Immunohistochemical and FISH analyses were performed in a wide series of formalin-fixed paraffin-embedded MPM samples to detect the expression of two receptors and the potential gene amplification. In vitro studies were performed to evaluate putative correlations between the target's expression and the cell sensitivity to AXL-MET multitarget inhibitors. In our series, 10.4% of cases showed a co-expression of AXL and MET, regardless of their ligand expression, and the gene amplification. Furthermore, our in vitro results suggest that the concomitant pharmacological inhibition of AXL and MET may affect the proliferative and aggressiveness of MPM cells. In conclusion, the subset of MPM patients with AXL-MET co-activation could benefit from treatment with specific multitarget inhibitors.

Combination of Spirulina platensis, Ganoderma lucidum and Moringa oleifera Improves Cardiac Functions and Reduces Pro-Inflammatory Biomarkers in Preclinical Models of Short-Term Doxorubicin-Mediated Cardiotoxicity: New Frontiers in Cardioncology?

Anthracyclines are essential adjuvant therapies for a variety of cancers, particularly breast, gastric and esophageal cancers. Whilst prolonging cancer-related survival, these agents can induce drug-related cardiotoxicity. Spirulina, Reishi (Ganoderma lucidum) and Moringa are three nutraceuticals with anti-inflammatory effects that are currently used in cancer patients as complementary and alternative medicines to improve quality of life and fatigue. We hypothesize that the nutraceutical combination of Spirulina, Reishi and Moringa (Singo) could reduce inflammation and cardiotoxicity induced by anthracyclines. Female C57Bl/6 mice were untreated (Sham, n = 6) or treated for 7 days with short-term doxorubicin (DOXO, n = 6) or Singo (Singo, n = 6), or pre-treated with Singo for 3 days and associated with DOXO for remaining 7 days (DOXO−Singo, n = 6). The ejection fraction and radial and longitudinal strain were analyzed through transthoracic echocardiography (Vevo 2100, Fujifilm, Tokyo, Japan). The myocardial expressions of NLRP3, DAMPs (galectin-3 and calgranulin S100) and 13 cytokines were quantified through selective mouse ELISA methods. Myocardial fibrosis, necrosis and hypertrophy were analyzed through immunohistochemistry (IHC). Human cardiomyocytes were exposed to DOXO (200 nM) alone or in combination with Singo (at 10, 25 and 50 µg/mL) for 24 and 48 h. Cell viability and inflammation studies were also performed. In preclinical models, Singo significantly improved ejection fraction and fractional shortening. Reduced expressions of myocardial NLRP3 and NF-kB levels in cardiac tissues were seen in DOXO−Singo mice vs. DOXO (p < 0.05). The myocardial levels of calgranulin S100 and galectin-3 were strongly reduced in DOXO−Singo mice vs. DOXO (p < 0.05). Immunohistochemistry analysis indicates that Singo reduces fibrosis and hypertrophy in the myocardial tissues of mice during exposure to DOXO. In conclusion, in the preclinical model of DOXO-induced cardiotoxicity, Singo is able to improve cardiac function and reduce biomarkers involved in heart failure and fibrosis.

CXCR4-CXCL12-CXCR7 and PD-1/PD-L1 in Pancreatic Cancer: CXCL12 Predicts Survival of Radically Resected Patients.

Pancreatic ductal adenocarcinoma (PDAC) is currently the most deadly cancer. Although characterized by 5-20% of neoplastic cells in the highly fibrotic stroma, immunotherapy is not a valid option in PDAC treatment. As CXCR4-CXCL12 regulates tumor invasion and T-cell access and PD-1/PD-L1 controls immune tolerance, 76 PDACs were evaluated for CXCR4-CXCL12-CXCR7 and PD-1/PD-L1 in the epithelial and stromal component. Neoplastic CXCR4 and CXCL12 discriminated PDACs for recurrence-free survival (RFS), while CXCL12 and CXCR7 discriminated patients for cancer-specific survival (CSS). Interestingly, among patients with radical resection (R0), high tumor CXCR4 clustered patients with worse RFS, high CXCL12 identified poor prognostic patients for both RFS and CSS, while stromal lymphocytic-monocytic PD-L1 associated with improved RFS and CSS. PD-1 was only sporadically expressed (<1%) in focal lymphocyte infiltrate and does not impact prognosis. In multivariate analysis, tumoral CXCL12, perineural invasion, and AJCC lymph node status were independent prognostic factors for RFS; tumoral CXCL12, AJCC Stage, and vascular invasion were independent prognostic factors for CSS. CXCL12's poor prognostic meaning was confirmed in an additional perspective-independent 13 fine-needle aspiration cytology advanced stage-PDACs. Thus, CXCR4-CXCL12 evaluation in PDAC identifies prognostic categories and could orient therapeutic approaches.

BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images.

Breast cancer is the most commonly diagnosed cancer and registers the highest number of deaths for women. Advances in diagnostic activities combined with large-scale screening policies have significantly lowered the mortality rates for breast cancer patients. However, the manual inspection of tissue slides by pathologists is cumbersome, time-consuming and is subject to significant inter- and intra-observer variability. Recently, the advent of whole-slide scanning systems has empowered the rapid digitization of pathology slides and enabled the development of Artificial Intelligence (AI)-assisted digital workflows. However, AI techniques, especially Deep Learning, require a large amount of high-quality annotated data to learn from. Constructing such task-specific datasets poses several challenges, such as data-acquisition level constraints, time-consuming and expensive annotations and anonymization of patient information. In this paper, we introduce the BReAst Carcinoma Subtyping (BRACS) dataset, a large cohort of annotated Hematoxylin and Eosin (H&E)-stained images to advance AI development in the automatic characterization of breast lesions. BRACS contains 547 Whole-Slide Images (WSIs) and 4539 Regions Of Interest (ROIs) extracted from the WSIs. Each WSI and respective ROIs are annotated by the consensus of three board-certified pathologists into different lesion categories. Specifically, BRACS includes three lesion types, i.e., benign, malignant and atypical, which are further subtyped into seven categories. It is, to the best of our knowledge, the largest annotated dataset for breast cancer subtyping both at WSI and ROI levels. Furthermore, by including the understudied atypical lesions, BRACS offers a unique opportunity for leveraging AI to better understand their characteristics. We encourage AI practitioners to develop and evaluate novel algorithms on the BRACS dataset to further breast cancer diagnosis and patient care. Database URL: https://www.bracs.icar.cnr.it/.

Targeting immunometabolism mediated by the IDO1 Pathway: A new mechanism of immune resistance in endometrial cancer.

Immunotherapy is acquiring a primary role in treating endometrial cancer (EC) with a relevant benefit for many patients. Regardless, patients progressing during immunotherapy or those who are resistant represent an unmet need. The mechanisms of immune resistance and escape need to be better investigated. Here, we review the major mechanisms of immune escape activated by the indolamine 2,3-dioxygenase 1 (IDO1) pathway in EC and focus on potential therapeutic strategies based on IDO1 signaling pathway control. IDO1 catalyzes the first rate-limiting step of the so-called "kynurenine (Kyn) pathway", which converts the essential amino acid l-tryptophan into the immunosuppressive metabolite l-kynurenine. Functionally, IDO1 has played a pivotal role in cancer immune escape by catalyzing the initial step of the Kyn pathway. The overexpression of IDO1 is also associated with poor prognosis in EC. These findings can lead to advantages in immunotherapy-based approaches as a rationale for overcoming the immune escape. Indeed, besides immune checkpoints, other mechanisms, including the IDO enzymes, contribute to the EC progression due to the immunosuppression induced by the tumor milieu. On the other hand, the IDO1 enzyme has recently emerged as both a promising therapeutic target and an unfavorable prognostic biomarker. This evidence provides the basis for translational strategies of immune combination, whereas IDO1 expression would serve as a potential prognostic biomarker in metastatic EC.

Analysis of A Disintegrin and Metalloprotease 17 (ADAM17) Expression as a Prognostic Marker in Ovarian Cancer Patients Undergoing First-Line Treatment Plus Bevacizumab.

To find prognostic factors for advanced ovarian cancer patients undergoing first-line therapy with carboplatin, paclitaxel and bevacizumab, we investigated the expression of a disintegrin and metalloprotease 17 (ADAM17) in cancer tissues. ADAM17 has been involved in ovarian cancer development, progression and cell resistance to cisplatin. Tissue microarrays from 309 ovarian cancer patients enrolled in the MITO16A/MANGO-OV2 clinical trial were analyzed by immunohistochemistry for ADAM17 protein expression. Intensity and extent of staining were combined into a semi-quantitative visual grading system (H score) which was related to clinicopathological characteristics of cases and the clinical outcome of patients by univariate and multivariate Cox regression models. ADAM17 immunostaining was detected in most samples, mainly localized in the tumor cells, with variable intensity across the cohort. Kaplan-Meier survival curves, generated according to the best cut-off value for the ADAM17 H score, showed that high ADAM17 expression was associated with worse prognosis for PFS and OS. However, after the application of a shrinkage procedure to adjust for overfitting hazard ratio estimates, the ADAM17 value as prognostic factor was lost. As subgroup analysis suggested that ADAM17 expression could be prognostically relevant in cases with no residual disease at baseline, further studies in this patient category may be worth planning.

Characterization of KRAS Mutational Regression in Oligometastatic Patients.

Background: We previously reported rare regressive genetic trajectories of KRAS pathogenic mutations as a specific hallmark of the genuine oligometastatic status in colorectal cancer (CRC). Methods: Survival and prognostic impact of disease extent in 140 metastatic CRC patients were evaluated through the Kaplan-Meyer curves and the Log-Rank test. KRAS mutations were assessed through the Illumina NovaSeq 6000 platform and TruSight™ Oncology 500 kit. HLA typing was carried out by PCR with sequence-specific oligonucleotides. Lymphocyte densities in tumors were expressed as cells per square millimeter. NKs isolated and CD8+ from NK-depleted PBMCs were characterized through flow cytometry. CD107a externalization was evaluated as NKs/CD8 cytotoxicity toward human colon cancer cells HT29, SW620, HCT116, and LS174T carrying different KRAS mutations. Results: The oligometastatic status was a strong and independent variable for survival (HR: 0.08 vs. polymetastatic disease; 95% CI: 0.02-0.26; p < 0.001). Eighteen oligometastatic patients were selected. Twelve were alive at the last follow-up, and 9 were characterized. Genetic regression of KRAS was observed in 3 patients: patient (PAT)2, PAT5, and PAT8. PAT2 and PAT5 presented the highest levels of GrzB+ lymphocytes in the tumor cores of the metastases (120 ± 11.2 and 132 ± 12.2 cells/mm2, respectively). Six out of 9 patients (67%), including PAT2 and PAT5, expressed HLA-C7. Twopatients (PAT2 and PAT5) presented high CD3+/CD8+-dependent cytotoxicity against HLA-C7+ SW620 cells (p.G12V-mutated cells), which was consistent with their observed mutational regression (p.G12V/p.G13D in primary→p.G13D in metastatic tumor). Conclusions: We provide evidence that CD3+/CD8+ lymphocytes from oligometastatic CRC patients display differential cytotoxicity against human colon cancer cells carrying KRAS mutations. This could provide an interesting basis for monitoring oligometastatic disease and developing future adoptive immunotherapies.

Biological Role of Tumor/Stromal CXCR4-CXCL12-CXCR7 in MITO16A/MaNGO-OV2 Advanced Ovarian Cancer Patients.

This study investigated the prognostic role of the CXCR4-CXCL12-CXCR7 axis in advanced epithelial ovarian cancer (EOC) patients receiving first-line treatment within the MITO16A/MaNGO-OV2 phase-IV trial. CXCR4-CXCL12-CXCR7 expression was evaluated in the epithelial and stromal component of 308 EOC IHC-stained tumor samples. The statistical analysis focused on biomarkers' expression, their association with other variables and prognostic value. Zero-inflated tests, shrinkage, bootstrap procedures, and multivariable models were applied. The majority of EOC (75.0%) expressed CXCR4 and CXCR7, 56.5% expressed the entire CXCR4-CXCL12-CXCR7 axis, while only 4.6% were negative for CXCL12 and its cognate receptors, in regard to the epithelial component. Stromal CXCL12 and CXCR7, expressed in 11.2% and 65.5%, respectively, were associated with the FIGO stage. High CXCL12 in epithelial cancer cells was associated with shorter progression-free and overall survival. However, after adjusting for overfitting due to best cut-off multiplicity testing, the significance was lost. This is a wide-ranging, prospective study in which CXCR4-CXCL12-CXCR7 were systematically evaluated in epithelial and stromal components, in selected stage III-IV EOC. Although CXCL12 was not prognostic, epithelial expression identified high-risk FIGO stage III patients for PFS. These data suggest that it might be worth studying the CXCL12 axis as a therapeutic target to improve treatment efficacy in EOC patients.

Radiomic and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to Detect Breast Malignant Lesions.

Purpose:The purpose of this study was to discriminate between benign and malignant breast lesions through several classifiers using, as predictors, radiomic metrics extracted from CEM and DCE-MRI images. In order to optimize the analysis, balancing and feature selection procedures were performed. Methods: Fifty-four patients with 79 histo-pathologically proven breast lesions (48 malignant lesions and 31 benign lesions) underwent both CEM and DCE-MRI. The lesions were retrospectively analyzed with radiomic and artificial intelligence approaches. Forty-eight textural metrics were extracted, and univariate and multivariate analyses were performed: non-parametric statistical test, receiver operating characteristic (ROC) and machine learning classifiers. Results: Considering the single metrics extracted from CEM, the best predictors were KURTOSIS (area under ROC curve (AUC) = 0.71) and SKEWNESS (AUC = 0.71) calculated on late MLO view. Considering the features calculated from DCE-MRI, the best predictors were RANGE (AUC = 0.72), ENERGY (AUC = 0.72), ENTROPY (AUC = 0.70) and GLN (gray-level nonuniformity) of the gray-level run-length matrix (AUC = 0.72). Considering the analysis with classifiers and an unbalanced dataset, no significant results were obtained. After the balancing and feature selection procedures, higher values of accuracy, specificity and AUC were reached. The best performance was obtained considering 18 robust features among all metrics derived from CEM and DCE-MRI, using a linear discriminant analysis (accuracy of 0.84 and AUC = 0.88). Conclusions: Classifiers, adjusted with adaptive synthetic sampling and feature selection, allowed for increased diagnostic performance of CEM and DCE-MRI in the differentiation between benign and malignant lesions.

Hierarchical graph representations in digital pathology.

Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens highly depend on the phenotype and topological distribution of constituting histological entities. Thus, adequate tissue representations for encoding histological entities is imperative for computer aided cancer patient care. To this end, several approaches have leveraged cell-graphs, capturing the cell-microenvironment, to depict the tissue. These allow for utilizing graph theory and machine learning to map the tissue representation to tissue functionality, and quantify their relationship. Though cellular information is crucial, it is incomplete alone to comprehensively characterize complex tissue structure. We herein treat the tissue as a hierarchical composition of multiple types of histological entities from fine to coarse level, capturing multivariate tissue information at multiple levels. We propose a novel multi-level hierarchical entity-graph representation of tissue specimens to model the hierarchical compositions that encode histological entities as well as their intra- and inter-entity level interactions. Subsequently, a hierarchical graph neural network is proposed to operate on the hierarchical entity-graph and map the tissue structure to tissue functionality. Specifically, for input histology images, we utilize well-defined cells and tissue regions to build HierArchical Cell-to-Tissue (HACT) graph representations, and devise HACT-Net, a message passing graph neural network, to classify the HACT representations. As part of this work, we introduce the BReAst Carcinoma Subtyping (BRACS) dataset, a large cohort of Haematoxylin & Eosin stained breast tumor regions-of-interest, to evaluate and benchmark our proposed methodology against pathologists and state-of-the-art computer-aided diagnostic approaches. Through comparative assessment and ablation studies, our proposed method is demonstrated to yield superior classification results compared to alternative methods as well as individual pathologists. The code, data, and models can be accessed at https://github.com/histocartography/hact-net.

MHC-Optimized Peptide Scaffold for Improved Antigen Presentation and Anti-Tumor Response.

Tumor Associated Antigens (TAAs) may suffer from an immunological tolerance due to expression on normal cells. In order to potentiate their immunogenicity, heteroclitic peptides (htcPep) were designed according to prediction algorithms. In particular, specific modifications were introduced in peptide residues facing to TCR. Moreover, a MHC-optimized scaffold was designed for improved antigen presentation to TCR by H-2Db allele. The efficacy of such htcPep was assessed in C57BL/6 mice injected with syngeneic melanoma B16F10 or lung TC1 tumor cell lines, in combination with metronomic chemotherapy and immune checkpoint inhibitors. The immunogenicity of htcPep was significantly stronger than the corresponding wt peptide and the modification involving both MHC and TCR binding residues scored the strongest. In particular, the H-2Db-specific scaffold significantly potentiated the peptides' immunogenicity and control of tumor growth was comparable to wt peptide in a therapeutic setting. Overall, we demonstrated that modified TAAs show higher immunogenicity compared to wt peptide. In particular, the MHC-optimized scaffold can present different antigen sequences to TCR, retaining the conformational characteristics of the corresponding wt. Cross-reacting CD8+ T cells are elicited and efficiently kill tumor cells presenting the wild-type antigen. This novel approach can be of high clinical relevance in cancer vaccine development.