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.

Predictive significance of FGFR4 p.G388R polymorphism in metastatic colorectal cancer patients receiving trifluridine/tipiracil (TAS-102) treatment.

BACKGROUND: TAS-102 (Lonsurf®) is an oral fluoropyrimidine consisting of a combination of trifluridine (a thymidine analog) and tipiracil (a thymidine phosphorylation inhibitor). The drug is effective in metastatic colorectal cancer (mCRC) patients refractory to fluorouracil, irinotecan and oxaliplatin. This study is a real-world analysis, investigating the interplay of genotype/phenotype in relation to TAS-102 sensitivity. METHODS: Forty-seven consecutive mCRC patients were treated with TAS-102 at the National Cancer Institute of Naples from March 2019 to March 2021, at a dosage of 35 mg/m2, twice a day, in cycles of 28 days (from day 1 to 5 and from day 8 to 12). Clinical-pathological parameters were described. Activity was evaluated with RECIST criteria (v1.1) and toxicity with NCI-CTC (v5.0). Survival was depicted through the Kaplan-Meyer curves. Genetic features of patients were evaluated with Next Generation Sequencing (NGS) through the Illumina NovaSeq 6000 platform and TruSigt™Oncology 500 kit. RESULTS: Median age of patients was 65 years (range: 46-77). Forty-one patients had 2 or more metastatic sites and 38 patients underwent to more than 2 previous lines of therapies. ECOG (Eastern Cooperative Oncology Group) Performance Status (PS) was 2 in 19 patients. The median number of TAS-102 cycles was 4 (range: 2-12). The most frequent toxic event was neutropenia (G3/G4 in 16 patients). There were no severe (> 3) non-haematological toxicities or treatment-related deaths. Twenty-six patients experienced progressive disease (PD), 21 stable disease (SD). Three patients with long-lasting disease control (DC: complete, partial responses or stable disease) shared an FGFR4 (p.Gly388Arg) mutation. Patients experiencing DC had more frequently a low tumour growth rate (P = 0.0306) and an FGFR4 p.G388R variant (P < 0.0001). The FGFR4 Arg388 genotype was associated with better survival (median: 6.4 months) compared to the Gly388 genotype (median: 4 months); the HR was 0.25 (95% CI 0.12- 0.51; P = 0.0001 at Log-Rank test). CONCLUSIONS: This phenotype/genotype investigation suggests that the FGFR4 p.G388R variant may serve as a new marker for identifying patients who are responsive to TAS-102. A mechanistic hypothesis is proposed to interpret these findings.

Circulating vitamin D level before initiating chemotherapy impacts on the time-to-outcome in metastatic colorectal cancer patients: systematic review and meta-analysis.

BACKGROUND: Vitamin D (VD) is implicated in various health conditions, including colorectal cancer (CRC). To investigate potential relationships between pre-chemotherapy VD levels and the time-to-outcome in metastatic CRC patients, we conducted a systematic review and meta-analysis. METHODS: Following the PRISMA 2020 guidelines, we performed thorough searches in PubMed/MEDLINE and Scopus/ELSEVIER databases (covering the years 2002 to 2022). Inclusion criteria mandated studies to report on individuals aged 18 years and above with histologically confirmed stage IV CRC. Additionally, studies needed to provide data on VD levels before chemotherapy, along with hazard ratios (HR) and 95% confidence intervals (CIs) for overall survival (OS) and/or progression-free survival (PFS). Five articles were identified with the aim of establishing a combined risk estimate for death and progression based on pre-chemotherapy VD levels. Heterogeneity among studies and publication bias were evaluated using Tau2, I2 statistics, and a Funnel plot. RESULTS: Although no significant heterogeneity was observed in time-to-outcome among the selected studies, variations in technical assessments and serum VD concentration measurements were noted. The pooled analysis, involving 1712 patients for OS and 1264 patients for PFS, revealed a 47% increased risk of death (HR: 1.47, 95% CI: 1.21-1.79) and a 38% increased risk of progression (HR: 1.38, 95% CI: 1.13-1.70) for patients with lower VD levels, as indicated by fixed-effects models. CONCLUSIONS: Our results emphasize the adverse effects of low VD concentration on the time-to-outcome in metastatic CRC patients. This underscores the importance of investigating VD supplementation as an innovative approach in this clinical setting to enhance patient outcomes.

Intrahepatic cholangiocarcinoma biomarkers: Towards early detection and personalized pharmacological treatments.

Cholangiocarcinoma (CCA) is a rare malignancy originating from the biliary tree and is anatomically categorized as intrahepatic (iCCA), perihilar, and extrahepatic or distal. iCCA, the second most prevalent hepatobiliary cancer following hepatocellular carcinoma (HCC), constitutes 5-20 % of all liver malignancies, with an increasing incidence. The challenging nature of iCCA, combined with nonspecific symptoms, often leads to late diagnoses, resulting in unfavorable outcomes. The advanced phase of this neoplasm is difficult to treat with dismal results. Early diagnosis could significantly reduce mortality attributed to iCCA but remains an elusive goal. The identification of biomarkers specific to iCCA and their translation into clinical practice could facilitate diagnosis, monitor therapy response, and potentially reveal novel interventions and personalized medicine. In this review, we present the current landscape of biomarkers in each of these contexts. In addition to CA19.9, a widely recognized biomarker for iCCA, others such as A1BG, CYFRA 21-1, FAM19A5, MMP-7, RBAK, SSP411, TuM2-PK, WFA, etc., as well as circulating tumor DNA, RNA, cells, and exosomes, are under investigation. Advancing our knowledge and monitoring of biomarkers may enable us to improve diagnosis, prognostication, and apply treatments dynamically and in a more personalized manner.

Machine learning and radiomics analysis by computed tomography in colorectal liver metastases patients for RAS mutational status prediction.

PURPOSE: To assess the efficacy of machine learning and radiomics analysis by computed tomography (CT) in presurgical setting, to predict RAS mutational status in colorectal liver metastases. METHODS: Patient selection in a retrospective study was carried out from January 2018 to May 2021 considering the following inclusion criteria: patients subjected to surgical resection for liver metastases; proven pathological liver metastases; patients subjected to enhanced CT examination in the presurgical setting with a good quality of images; and RAS assessment as standard reference. A total of 851 radiomics features were extracted using the PyRadiomics Python package from the Slicer 3D image computing platform after slice-by-slice segmentation on CT portal phase by two expert radiologists of each individual liver metastasis performed first independently by the individual reader and then in consensus. Balancing technique was performed, and inter- and intraclass correlation coefficients were calculated to assess the between-observer and within-observer reproducibility of features. Receiver operating characteristics (ROC) analysis with the calculation of area under the ROC curve (AUC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV) and accuracy (ACC) were assessed for each parameter. Linear and non-logistic regression model (LRM and NLRM) and different machine learning-based classifiers were considered. Moreover, features selection was performed before and after a normalized procedure using two different methods (3-sigma and z-score). RESULTS: Seventy-seven liver metastases in 28 patients with a mean age of 60 years (range 40-80 years) were analyzed. The best predictors, at univariate analysis for both normalized procedures, were original_shape_Maximum2DDiameter and wavelet_HLL_glcm_InverseVariance that reached an accuracy of 80%, an AUC ≥ 0.75, a sensitivity ≥ 80% and a specificity ≥ 70% (p value < < 0.01). However, a multivariate analysis significantly increased the accuracy in RAS prediction when a linear regression model (LRM) was used. The best performance was obtained using a LRM combining linearly 12 robust features after a z-score normalization procedure: AUC of 0.953, accuracy 98%, sensitivity 96%, specificity of 100%, PPV 100% and NPV 96% (p value < < 0.01). No statistically significant increase was obtained considering the tested machine learning both without normalization and with normalization methods. CONCLUSIONS: Normalized approach in CT radiomics analysis allows to predict RAS mutational status in colorectal liver metastases patients.

Machine learning-based radiomics analysis in predicting RAS mutational status using magnetic resonance imaging.

PURPOSE: To assess the efficacy of radiomics features, obtained by magnetic resonance imaging (MRI) with hepatospecific contrast agent, in pre-surgical setting, to predict RAS mutational status in liver metastases. METHODS: Patients with MRI in pre-surgical setting were enrolled in a retrospective study. Manual segmentation was made by means 3D Slicer image computing, and 851 radiomics features were extracted as median values using the PyRadiomics Python package. The features were extracted considering the agreement with the Imaging Biomarker Standardization Initiative (IBSI). Balancing was performed through synthesis of samples for the underrepresented classes using the self-adaptive synthetic oversampling (SASYNO) approach. Inter- and intraclass correlation coefficients (ICC) were calculated to assess the between-observer and within-observer reproducibility of all radiomics characteristics. For continuous variables, nonparametric Wilcoxon-Mann-Whitney test was utilized. Benjamini and Hochberg's false discovery rate (FDR) adjustment for multiple testing was used. Receiver operating characteristics (ROC) analysis with the calculation of area under the ROC curve (AUC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV) and accuracy (ACC) were assessed for each parameter. Linear and non-logistic regression model (LRM and NLRM) and different machine learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) and support vector machine (SVM) were considered. Moreover, features selection were performed before and after a normalized procedure using two different methods (3-sigma and z-score). McNemar test was used to assess differences statistically significant between dichotomic tables. All statistical procedures were done using MATLAB R2021b Statistics and Machine Toolbox (MathWorks, Natick, MA, USA). RESULTS: Seven normalized radiomics features, extracted from arterial phase, 11 normalized radiomics features, from portal phase, 12 normalized radiomics features from hepatobiliary phase and 12 normalized features from T2-W SPACE sequence were robust predictors of RAS mutational status. The multivariate analysis increased significantly the accuracy in RAS prediction when a LRM was used, combining 12 robust normalized features extracted by VIBE hepatobiliary phase reaching an accuracy of 99%, a sensitivity 97%, a specificity of 100%, a PPV of 100% and a NPV of 98%. No statistically significant increase was obtained, considering the tested classifiers DT, KNN and SVM, both without normalization and with normalization methods. CONCLUSIONS: Normalized approach in MRI radiomics analysis allows to predict RAS mutational status.

Plexiform Fibromyxoma in the Stomach: Immunohistochemical Profile and Comprehensive Genetic Characterization.

Plexiform fibromyxoma (PF), also referred to as plexiform angiomyxoid myofibroblast tumor, is an exceedingly rare mesenchymal neoplasm primarily affecting the stomach. Herein, we present a case of PF diagnosed in a 71-year-old male with a history of lung cancer, initially suspected to have a gastrointestinal stromal tumor (GIST) of the stomach, who subsequently underwent subtotal gastrectomy. The histopathological and molecular features of the tumor, including mutations in ABL1, CCND1, CSF1R, FGFR4, KDR, and MALAT1-GLI1 fusion, are elucidated and discussed in the context of diagnostic, prognostic, and therapeutic considerations.

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.

Beyond Body Size: Adiponectin as a Key Player in Obesity-Driven Cancers.

Obesity, a complex and multifactorial disease influenced by genetic, environmental, and psychological factors, has reached epidemic proportions globally, posing a significant health challenge. In addition to its established association with cardiovascular disease and type II diabetes, obesity has been implicated as a risk factor for various cancers. However, the precise biological mechanisms linking obesity and cancer remain largely understood. Adipose tissue, an active endocrine organ, produces numerous hormones and bioactive molecules known as adipokines, which play a crucial role in metabolism, immune responses, and systemic inflammation. Notably, adiponectin (APN), the principal adipocyte secretory protein, exhibits reduced expression levels in obesity. In this scoping review, we explore and discuss the role of APN in influencing cancer in common malignancies, including lung, breast, colorectal, prostate, gastric, and endometrial cancers. Our review aims to emphasize the critical significance of investigating this field, as it holds great potential for the development of innovative treatment strategies that specifically target obesity-related malignancies. Furthermore, the implementation of more rigorous and comprehensive prevention and treatment policies for obesity is imperative in order to effectively mitigate the risk of associated diseases, such as cancer.

Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment.

OBJECTIVE: The aim of this study was the evaluation radiomics analysis efficacy performed using computed tomography (CT) and magnetic resonance imaging in the prediction of colorectal liver metastases patterns linked to patient prognosis: tumor growth front; grade; tumor budding; mucinous type. Moreover, the prediction of liver recurrence was also evaluated. METHODS: The retrospective study included an internal and validation dataset; the first was composed by 119 liver metastases from 49 patients while the second consisted to 28 patients with single lesion. Radiomic features were extracted using PyRadiomics. Univariate and multivariate approaches including machine learning algorithms were employed. RESULTS: The best predictor to identify tumor growth was the Wavelet_HLH_glcm_MaximumProbability with an accuracy of 84% and to detect recurrence the best predictor was wavelet_HLH_ngtdm_Complexity with an accuracy of 90%, both extracted by T1-weigthed arterial phase sequence. The best predictor to detect tumor budding was the wavelet_LLH_glcm_Imc1 with an accuracy of 88% and to identify mucinous type was wavelet_LLH_glcm_JointEntropy with an accuracy of 92%, both calculated on T2-weigthed sequence. An increase statistically significant of accuracy (90%) was obtained using a linear weighted combination of 15 predictors extracted by T2-weigthed images to detect tumor front growth. An increase statistically significant of accuracy at 93% was obtained using a linear weighted combination of 11 predictors by the T1-weigthed arterial phase sequence to classify tumor budding. An increase statistically significant of accuracy at 97% was obtained using a linear weighted combination of 16 predictors extracted on CT to detect recurrence. An increase statistically significant of accuracy was obtained in the tumor budding identification considering a K-nearest neighbors and the 11 significant features extracted T1-weigthed arterial phase sequence. CONCLUSIONS: The results confirmed the Radiomics capacity to recognize clinical and histopathological prognostic features that should influence the choice of treatments in colorectal liver metastases patients to obtain a more personalized therapy.

Radiomics textural features by MR imaging to assess clinical outcomes following liver resection in colorectal liver metastases.

PURPOSE: To assess the efficacy of radiomics features obtained by T2-weighted sequences to predict clinical outcomes following liver resection in colorectal liver metastases patients. METHODS: This retrospective analysis was approved by the local Ethical Committee board and radiological databases were interrogated, from January 2018 to May 2021, to select patients with liver metastases with pathological proof and MRI study in pre-surgical setting. The cohort of patients included a training set and an external validation set. The internal training set included 51 patients with 61 years of median age and 121 liver metastases. The validation cohort consisted a total of 30 patients with single lesion with 60 years of median age. For each volume of interest, 851 radiomics features were extracted as median values using PyRadiomics. Nonparametric test, intraclass correlation, receiver operating characteristic (ROC) analysis, linear regression modelling and pattern recognition methods (support vector machine (SVM), k-nearest neighbours (KNN), artificial neural network (NNET) and decision tree (DT)) were considered. RESULTS: The best predictor to discriminate expansive versus infiltrative front of tumour growth was obtained by wavelet_LHL_gldm_DependenceNonUniformityNormalized with an accuracy of 82%; to discriminate high grade versus low grade or absent was the wavelet_LLH_glcm_Imc1 with accuracy of 88%; to differentiate the mucinous type of tumour was the wavelet_LLH_glcm_JointEntropy with accuracy of 92% while to identify tumour recurrence was the wavelet_LLL_glcm_Correlation with accuracy of 85%. Linear regression model increased the performance obtained with respect to the univariate analysis exclusively in the discrimination of expansive versus infiltrative front of tumour growth reaching an accuracy of 90%, a sensitivity of 95% and a specificity of 80%. Considering significant texture metrics tested with pattern recognition approaches, the best performance was reached by the KNN in the discrimination of the tumour budding considering the four textural predictors obtaining an accuracy of 93%, a sensitivity of 81% and a specificity of 97%. CONCLUSIONS: Ours results confirmed the capacity of radiomics to identify as biomarkers, several prognostic features that could affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach.

Radiomics and machine learning analysis based on magnetic resonance imaging in the assessment of liver mucinous colorectal metastases.

PURPOSE: The purpose of this study is to evaluate the Radiomics and Machine Learning Analysis based on MRI in the assessment of Liver Mucinous Colorectal Metastases.Query METHODS: The cohort of patients included a training set (121 cases) and an external validation set (30 cases) with colorectal liver metastases with pathological proof and MRI study enrolled in this approved study retrospectively. About 851 radiomics features were extracted as median values by means of the PyRadiomics tool on volume on interest segmented manually by two expert radiologists. Univariate analysis, linear regression modelling and pattern recognition methods were used as statistical and classification procedures. RESULTS: The best results at univariate analysis were reached by the wavelet_LLH_glcm_JointEntropy extracted by T2W SPACE sequence with accuracy of 92%. Linear regression model increased the performance obtained respect to the univariate analysis. The best results were obtained by a linear regression model of 15 significant features extracted by the T2W SPACE sequence with accuracy of 94%, a sensitivity of 92% and a specificity of 95%. The best classifier among the tested pattern recognition approaches was k-nearest neighbours (KNN); however, KNN achieved lower precision than the best linear regression model. CONCLUSIONS: Radiomics metrics allow the mucinous subtype lesion characterization, in order to obtain a more personalized approach. We demonstrated that the best performance was obtained by T2-W extracted textural metrics.

Evolution of Treatment in Advanced Cholangiocarcinoma: Old and New towards Precision Oncology.

Cholangiocarcinoma (CCA) is a malignant neoplasm arising in the epithelium of the biliary tract. It represents the second most common primary liver cancer in the world, after hepatocellular carcinoma, and it constitutes 10-15% of hepatobiliary neoplasms and 3% of all gastrointestinal tumors. As in other types of cancers, recent studies have revealed genetic alterations underlying the establishment and progression of CCA. The most frequently involved genes are APC, ARID1A, AXIN1, BAP1, EGFR, FGFRs, IDH1/2, RAS, SMAD4, and TP53. Actionable targets include alterations of FGFRs, IDH1/2, BRAF, NTRK, and HER2. "Precision oncology" is emerging as a promising approach for CCA, and it is possible to inhibit the altered function of these genes with molecularly oriented drugs (pemigatinib, ivosidenib, vemurafenib, larotrectinib, and trastuzumab). In this review, we provide an overview of new biologic drugs (their structures, mechanisms of action, and toxicities) to treat metastatic CCA, providing readers with panoramic information on the trajectory from "old" chemotherapies to "new" target-oriented drugs.

Is immunotherapy in the future of therapeutic management of sarcomas?

Sarcomas are rare, ubiquitous and heterogeneous tumors usually treated with surgery, chemotherapy, target therapy, and radiotherapy. However, 25-50% of patients experience local relapses and/or distant metastases after chemotherapy with an overall survival about 12-18 months. Recently, immuno-therapy has revolutionized the cancer treatments with initial indications for non-small cell lung cancer (NSCLC) and melanoma (immune-checkpoint inhibitors).Here, we provide a narrative review on the topic as well as a critical description of the currently available trials on immunotherapy treatments in patients with sarcoma. Given the promising results obtained with anti-PD-1 monoclonal antibodies (pembrolizumab and nivolumab) and CAR-T cells, we strongly believe that these new immunotherapeutic approaches, along with an innovative characterization of tumor genetics, will provide an exciting opportunity to ameliorate the therapeutic management of sarcomas.

Intrahepatic cholangiocarcinoma and its differential diagnosis at MRI: how radiologist should assess MR features.

BACKGROUND: Intrahepatic cholangiocarcinoma (ICC) is the second most common type of primary hepatic malignancy. Aim of this work is to analyse the features of ICC and its differential diagnosis at MRI, assessing two categories intraparenchymal and peribiliary lesions. METHODS: The study population included 88 patients with histological diagnosis of ICCs: 61 with mass-forming type, 23 with periductal-infiltrating tumours and 4 with intraductal-growing type. As a control study groups, we identified: 86 consecutive patients with liver colorectal intrahepatic metastases (mCRC) (groups A); 35 consecutive patients with peribiliary metastases (groups B); 62 consecutive patients (groups C) with hepatocellular carcinoma (HCC); 18 consecutive patients (groups D) with combined hepatocellular cholangiocarcinoma (cHCC-CCA); and 26 consecutive patients (groups E) with hepatic hemangioma. For all lesions, magnetic resonance (MR) features were assessed according to Liver Imaging Reporting and Data System (LI-RADS) version 2018. The liver-specific gadolinium ethoxybenzyl dimeglumine-EOB (Primovist, Bayer Schering Pharma, Germany), was employed. Chi-square test was employed to analyse differences in percentage values of categorical variable, while the nonparametric Kruskal-Wallis test was used to test for statistically significant differences between the median values of the continuous variables. However, false discovery rate adjustment according to Benjamin and Hochberg for multiple testing was considered. RESULTS: T1- and T2-weighted signal intensity (SI), restricted diffusion, transitional phase (TP) and hepatobiliary phase (HP) aspects allowed the differentiation between study group (mass-forming ICCs) and each other control group (A, C, D, E) with statistical significance, while arterial phase (AP) appearance allowed the differentiation between study group and the control groups C and D with statistical significance and PP appearance allowed the differentiation between study group and the control groups A, C and D with statistical significance. Instead, no MR feature allowed the differentiation between study group (periductal-infiltrating type) and control group B. CONCLUSION: T1 and T2 W SI, restricted diffusion, TP and HP appearance allowed the differentiation between mass-forming ICCs and mimickers with statistical significance, while AP appearance allowed the differentiation between study group and the control groups C and D with statistical significance and PP appearance allowed the differentiation between study group and the control groups A, C and D.

The Role of microRNAs in Development of Colitis-Associated Colorectal Cancer.

Colorectal cancer (CRC) is the third most deadly cancer worldwide, and inflammatory bowel disease (IBD) is one of the critical factors in CRC carcinogenesis. IBD is responsible for an unphysiological and sustained chronic inflammation environment favoring the transformation. MicroRNAs (miRNAs) belong to a class of highly conserved short single-stranded segments (18-25 nucleotides) non-coding RNA and have been extensively discussed in both CRC and IBD. However, the role of miRNAs in the development of colitis-associated CRC (CAC) is less clear. The aim of this review is to summarize the major upregulated (miR-18a, miR-19a, miR-21, miR-31, miR-155 and miR-214) and downregulated (miR-124, miR-193a-3p and miR-139-5p) miRNAs in CAC, and their roles in genes' expression modulation in chronic colonic-inflammation-induced carcinogenesis, including programmed cell-death pathways. These miRNAs dysregulation could be applied for early CAC diagnosis, to predict therapy efficacy and for precision treatment.

Cetuximab, irinotecan and fluorouracile in fiRst-line treatment of immunologically-selected advanced colorectal cancer patients: the CIFRA study protocol.

BACKGROUND: Combination of chemotherapies (fluoropirimidines, oxaliplatin and irinotecan) with biologic drugs (bevacizumab, panitumumab, cetuximab) have improved clinical responses and survival of metastatic colorectal cancer (mCRC). However, patients' selection thorough the identification of predictive factors still represent a challange. Cetuximab (Erbitux®), a chimeric monoclonal antibody binding to the Epidermal Growth Factor Receptor (EGFR), belongs to the Immunoglobulins (Ig) grade 1 subclass able to elicite both in vitro and in vivo the Antibody-Dependent Cell-mediated Cytotoxicity (ADCC). ADCC is the cytotoxic killing of antibody-coated target cells by immunologic effectors. The effector cells express a receptor for the Fc portion of these antibodies (FcγR); genetic polymorphisms of FcγR modify the binding affinity with the Fc of IgG1. Interestingly, the high-affinity FcγRIIIa V/V is associated with increased ADCC in vitro and in vivo. Thus, ADCC could partially account for cetuximab activity. METHODS/DESIGN: CIFRA is a single arm, open-label, phase II study assessing the activity of cetuximab in combination with irinotecan and fluorouracile in FcγRIIIa V/V patients with KRAS, NRAS, BRAF wild type mCRC. The study is designed with a two-stage Simon model based on a hypothetical higher response rate (+ 10%) of FcγRIIIa V/V patients as compared to previous trials (about 60%) assuming ADCC as one of the possible mechanisms of cetuximab action. The test power is 95%, the alpha value of the I-type error is 5%. With these assumptions the sample for passing the first stage is 14 patients with > 6 responses and the final sample is 34 patients with > 18 responses to draw positive conclusions. Secondary objectives include toxicity, responses' duration, progression-free and overall survival. Furthermore, an associated translational study will assess the patients' cetuximab-mediated ADCC and characterize the tumor microenvironment. DISCUSSION: The CIFRA study will determine whether ADCC contributes to cetuximab activity in mCRC patients selected on an innovative immunological screening. Data from the translational study will support results' interpretation as well as provide new insights in host-tumor interactions and cetuximab activity. TRIAL REGISTRATION: The CIFRA trial (version 0.0, June 21, 2018) has been registered into the NIH-US National Library of Medicine, ClinicalTrials.gov database with the identifier number ( NCT03874062 ).

Hyaluronic Acid Nanohydrogel Loaded With Quercetin Alone or in Combination to a Macrolide Derivative of Rapamycin RAD001 (Everolimus) as a New Treatment for Hormone-Responsive Human Breast Cancer.

The aim of this study is based on the evaluation of anticancer, anti-inflammatory activities, and cellular uptake of hyaluronic acid nanohydrogel of quercetin tested alone and in combination to a macrolide derivative of rapamycin RAD001 (everolimus) on hormone-responsive breast cancer cell line MCF-7. Biological investigations were focused on the receptor mediated cellular internalization of the nanohydrogel and its abilities to reduce secretion of several cytokines (IL-8, IL-6, IL-19), VEGF, and metalloproteases (MMP-2, MMP-9) under pro-inflammatory conditions. Nanohydrogel show a CD44 dependent endocytosis with evident time dependent cytoplasmatic accumulation with abilities to reduce secretion of all cytokines of ∼60% compared to untreated cells. Combination of formulated quercetin and everolimus leads to a synergistic cytotoxic effects with a Combination Index of 0.38. These results highlights the importance of synergistic effect of the hyaluronic acid nanohydrogel of quercetin with everolimus in the regulation of human breast cancer cell proliferation and emphasize the antitumor and anti-inflammatory properties of the nanocarrier. J. Cell. Physiol. 232: 2063-2074, 2017. © 2016 Wiley Periodicals, Inc.