Many epidemiological studies provide solid evidence for an association of chronic hepatitis C virus (HCV) infection with B-cell non-Hodgkin's lymphoma (B-NHL). However, the most convincing evidence for a causal relationship between HCV infection and lymphoma development is the observation of B-NHL regression after HCV eradication by antiviral therapy (AVT). We conducted a literature search to identify studies that included patients with HCV-associated B-NHL (HCV-NHL) who received AVT, with the intention to treat lymphoma and viral disease at the same time. The primary end point was the correlation of sustained virological response (SVR) under AVT with lymphoma response. Secondary end points were overall lymphoma response rates and HCV-NHL response in correlation with lymphoma subtypes. We included 20 studies that evaluated the efficacy of AVT in HCV-NHL (n = 254 patients). Overall lymphoma response rate through AVT was 73% [95%>confidence interval, (CI) 67-78%]. Throughout studies there was a strong association between SVR and lymphoma response (83% response rate, 95%>CI, 76-88%) compared to a failure in achieving SVR (53% response rate, 95%>CI, 39-67%, P = 0.0002). There was a trend towards favourable response for AVT in HCV-associated marginal zone lymphomas (response rate 81%, 95%>CI, 74-87%) compared to nonmarginal zone origin (response rate 71%, 95%>CI, 61-79%, P = 0.07). In conclusion, in the current meta-analysis, the overall response rate of HCV-NHL under AVT justifies the recommendation for AVT as first-line treatment in patients who do not need immediate conventional treatment. The strong correlation of SVR and lymphoma regression supports the hypothesis of a causal relationship of HCV and lymphomagenesis.

Recently, a transcriptome-based consensus molecular subtype (CMS) classification of colorectal cancer (CRC) has been established, which may ultimately help to individualize CRC therapy. However, the lack of animal models that faithfully recapitulate the different molecular subtypes impedes adequate preclinical testing of stratified therapeutic concepts. Here, we demonstrate that constitutive AKT activation in intestinal epithelial cells markedly enhances tumor invasion and metastasis in Trp53ΔIEC mice (Trp53ΔIECAktE17K) upon challenge with the carcinogen azoxymethane. Gene-expression profiling indicates that Trp53ΔIECAktE17K tumors resemble the human mesenchymal colorectal cancer subtype (CMS4), which is characterized by the poorest survival rate among the four CMSs. Trp53ΔIECAktE17K tumor cells are characterized by Notch3 up-regulation, and treatment of Trp53ΔIECAktE17K mice with a NOTCH3-inhibiting antibody reduces invasion and metastasis. In CRC patients, NOTCH3 expression correlates positively with tumor grading and the presence of lymph node as well as distant metastases and is specifically up-regulated in CMS4 tumors. Therefore, we suggest NOTCH3 as a putative target for advanced CMS4 CRC patients.

IntroductionColorectal cancers (CRCs) deficient in the DNA mismatch repair protein MutL homolog 1 (MLH1) display distinct clinicopathological features and require a different therapeutic approach compared to CRCs with MLH1 proficiency. However, the molecular basis of this fundamental difference remains elusive. Here, we report that MLH1-deficient CRCs exhibit reduced levels of the cytoskeletal scaffolding protein non-erythroid spectrin αII (SPTAN1), and that tumor progression and metastasis of CRCs correlate with SPTAN1 levels.Methods and resultsTo investigate the link between MLH1 and SPTAN1 in cancer progression, a cohort of 189 patients with CRC was analyzed by immunohistochemistry. Compared with the surrounding normal mucosa, SPTAN1 expression was reduced in MLH1-deficient CRCs, whereas MLH1-proficient CRCs showed a significant upregulation of SPTAN1. Overall, we identified a strong correlation between MLH1 status and SPTAN1 expression. When comparing TNM classification and SPTAN1 levels, we found higher SPTAN1 levels in stage I CRCs, while stages II to IV showed a gradual reduction of SPTAN1 expression. In addition, SPTAN1 expression was lower in metastatic compared with non-metastatic CRCs. Knockdown of SPTAN1 in CRC cell lines demonstrated decreased cell viability, impaired cellular mobility and reduced cell-cell contact formation, indicating that SPTAN1 plays an important role in cell growth and cell attachment. The observed weakened cell-cell contact of SPTAN1 knockdown cells might indicate that tumor cells expressing low levels of SPTAN1 detach from their primary tumor and metastasize more easily.ConclusionTaken together, we demonstrate that MLH1 deficiency, low SPTAN1 expression, and tumor progression and metastasis are in close relation. We conclude that SPTAN1 is a candidate molecule explaining the tumor progression and metastasis of MLH1-deficient CRCs. The detailed analysis of SPTAN1 is now mandatory to substantiate its relevance and its potential value as a candidate protein for targeted therapy, and as a predictive marker of cancer aggressiveness.

Objectives To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. Conclusions The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance. Key Points • Quantitative imaging features differ between normal and malignant tissue of the peripheral zone in prostate cancer. • Radiomic feature analysis of clinical routine multiparametric MRI has the potential to improve the stratification of clinically significant versus insignificant prostate cancer lesions in the peripheral zone. • Certain combinations of standard multiparametric MRI reporting and assessment categories with feature subsets and machine learning algorithms reduced the diagnostic performance over standard clinical assessment categories alone.