Open Access

Introduction: Colorectal 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 results: To 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. Conclusion: Taken 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.

Background: Definite diagnosis and therapeutic management of cholangiocarcinoma (CCA) remains a challenge. The aim of the current study was to investigate feasibility and potential impact on clinical management of targeted sequencing of intraductal biopsies. Methods: Intraductal biopsies with suspicious findings from 16 patients with CCA in later clinical course were analyzed with targeted sequencing including tumor and control benign tissue (n = 55 samples). A CCA-specific sequencing panel containing 41 genes was designed and a dual strand targeted enrichment was applied. Results: Sequencing was successfully performed for all samples. In total, 79 mutations were identified and a mean of 1.7 mutations per tumor sample (range 0–4) as well as 2.3 per biopsy (0–6) were detected and potentially therapeutically relevant genes were identified in 6/16 cases. In 14/18 (78%) biopsies with dysplasia or inconclusive findings at least one mutation was detected. The majority of mutations were found in both surgical specimen and biopsy (68%), while 28% were only present in biopsies in contrast to 4% being only present in the surgical tumor specimen. Conclusion: Targeted sequencing from intraductal biopsies is feasible and potentially improves the diagnostic yield. A profound genetic heterogeneity in biliary dysplasia needs to be considered in clinical management and warrants further investigation. Translational impact: The current study is the first to demonstrate the feasibility of sequencing of intraductal biopsies which holds the potential to impact diagnostic and therapeutical management of patients with biliary dysplasia and neoplasia.

Classical Hodgkin lymphoma (cHL) is one of the most common malignant lymphomas in Western Europe. The nodular sclerosing subtype of cHL (NS cHL) is characterised by a proliferation of fibroblasts in the tumour microenvironment, leading to fibrotic bands surrounding the lymphoma infiltrate. Several studies have described a crosstalk between the tumour cells of cHL, the Hodgkin- and Reed-Sternberg (HRS) cells, and cancerassociated fibroblasts (CAF). However, to date a deep molecular understanding of these fibroblasts is lacking. Aim of the present study therefore was a comprehensive characterisation of these fibroblasts. Moreover, only a few studies describe the interplay of HRS cells and CAF. The paracrine communication and direct interaction of these two cellular fractions have been investigated within this study. Finally, the influence of a few HRS cells within a lymph node orchestrate the mere alteration of its architecture and morphology. Gene expression and methylation profiles of fibroblasts isolated from primary lymph node suspensions revealed persistent differences between fibroblasts obtained from NS cHL and lymphadenitis. NS cHL derived fibroblasts exhibit a myofibroblastic - inflammatory phenotype characterised by MYOCD, CNN1 and IL-6 expression. TIMP3, an inhibitor of matrix metalloproteinases, was strongly upregulated in NS cHL fibroblasts, likely contributing to the accumulation of collagen in sclerotic bands of NS cHL. Treatment by luteolin could reverse this fibroblast phenotype and decrease TIMP3 secretion. NS cHL fibroblasts showed enhanced proliferation when they were exposed to soluble factors released from HRS cells. For HRS cells, soluble factors from fibroblasts were not sufficient to protect them from Brentuximab-Vedotin(BV) induced cell death. However, HRS cells adherent to fibroblasts were protected from BV-induced injury. The cHL specific interaction of both cell fractions reveals an initiation of inflammatory key regulators such as IL13 and IL4. Among important adhesion molecules known from literature the blocking of integrin beta 1 solely interrupted the adhesion of HRS cells to CAF. In summary, this study proves the stable reprograming of CAF phenotype and expression derived from NS cHL. It presents a suitable in vitro model for studying the interaction of HRS cells and CAF by paracrine factors and adherence. Most importantly the observations confirm the importance of fibroblasts for HRS cells´ inflammatory niche and cell survival associated with TIMP3 which probably acts as a major factor to the typical accumulation of fibrosis observed in NS cHL.

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.