Emmanuelle Leteurtre

Summary: Differential lncRNA expression has been correlated to clinical characteristics of colorectal cancers (CRC), which are the second leading cause of cancer-related deaths. LncRNA UCA1 plays a role in epigenetic gene regulation in diverse cancers. We studied CRC cell properties in CRISPR/Cas9 HT29-derived models and, interestingly, UCA1-depleted HT29 cells presented an increased stem-cell phenotype. We show that loss of UCA1 affected SWI/SNF chromatin remodeling complexes, which are previously shown to be involved in maintaining stem-cell properties. Not only was UCA1 permissive for induced SWI/SNF subunit SMARCA2 (BRM) expression upon chemo-drug treatment, but it also affected subunit compositions of SWI/SNF complexes by direct interaction of UCA1 with both ATP helicase BRM and BRG1. UCA1 is known to stimulate proliferation and decrease apoptosis, and we here show that it can restrain cells from a stem cell phenotype. The dual action of UCA1 revealed in this study highlights the complex actions of lncRNAs in cancer.

Determining lymphoma subtypes is a crucial step for better patient treatment targeting to potentially increase their survival chances. In this context, the existing gold standard diagnosis method, which relies on gene expression technology, is highly expensive and time-consuming, making it less accessibility. Although alternative diagnosis methods based on IHC (immunohistochemistry) technologies exist (recommended by the WHO), they still suffer from similar limitations and are less accurate. Whole Slide Image (WSI) analysis using deep learning models has shown promising potential for cancer diagnosis, that could offer cost-effective and faster alternatives to existing methods. In this work, we propose a vision transformer-based framework for distinguishing DLBCL (Diffuse Large B-Cell Lymphoma) cancer subtypes from high-resolution WSIs. To this end, we introduce a multi-modal architecture to train a classifier model from various WSI modalities. We then leverage this model through a knowledge distillation process to efficiently guide the learning of a mono-modal classifier. Our experimental study conducted on a lymphoma dataset of 157 patients shows the promising performance of our mono-modal classification model, outperforming six recent state-of-the-art methods. In addition, the power-law curve, estimated on our experimental data, suggests that with more training data from a reasonable number of additional patients, our model could achieve competitive diagnosis accuracy with IHC technologies. Furthermore, the efficiency of our framework is confirmed through an additional experimental study on an external breast cancer dataset (BCI dataset).

Despite overall good prognosis associated to thyroid cancer (TC), poorly differentiated carcinomas (PDTC) and anaplastic carcinomas (ATC, one of the most lethal human malignancies) represent major clinical challenges. We have shown that the presence of active T172-phosphorylated CDK4 predicts sensitivity to CDK4/6 inhibitory drugs (CDK4/6i) including palbociclib. Here, CDK4 phosphorylation was detected in all well-differentiated TC (n=29), 19/20 PDTC, 16/23 ATC, and 18/21 TC cell lines including 11 ATC-derived ones. The cell lines lacking CDK4 phosphorylation were insensitive to CDK4/6i. RNA-sequencing and immunohistochemistry revealed that tumors and cell lines without phosphorylated CDK4 presented very high p16 ^CDKN2A levels that were associated with proliferative activity. No RB1 mutations were found in 5 of these 7 tumors. p16/KI67 immunohistochemistry and a previously developed 11-gene signature identified the likely insensitive tumors lacking CDK4 phosphorylation. In cell lines, palbociclib synergized with dabrafenib/trametinib, completely and irreversibly arresting proliferation. The combined drugs prevented resistance mechanisms induced by palbociclib, most notably Cyclin E1-CDK2 activation and a paradoxical stabilization of phosphorylated CDK4 complexes. Our study supports the evaluation of CDK4/6i for ATC/PDTC treatment, including in combination with MEK/BRAF inhibitors.

Background: Pancreatic adenocarcinoma (PDAC) is a deadly cancer with an extremely poor prognosis. MUC4 membrane-bound mucin is neoexpressed in early pancreatic neoplastic lesions and is associated with PDAC progression and chemoresistance. In cancers, microRNAs (miRNAs, small noncoding RNAs) are crucial regulators of carcinogenesis, chemotherapy response and even metastatic processes. In this study, we aimed at identifying and characterizing miRNAs activated downstream of MUC4-associated signaling in pancreatic adenocarcinoma. MiRnome analysis comparing MUC4-KD versus Mock cancer cells showed that MUC4 inhibition impaired miR-210-3p expression. Therefore, we aimed to better understand the miR-210-3p biological roles. Methods: miR-210-3p expression level was analyzed by RT-qPCR in PDAC-derived cell lines (PANC89 Mock and MUC4-KD, PANC-1 and MiaPACA-2), as well as in mice and patients tissues. The MUC4-miR-210-3p regulation was investigated using luciferase reporter construct and chromatin immunoprecipitation experiments. Stable cell lines expressing miR-210-3p or anti-miR-210-3p were established using CRISPR/Cas9 technology or lentiviral transduction. We evaluated the biological activity of miR-210-3p in vitro by measuring cell proliferation and migration and in vivo using a model of subcutaneous xenograft. Results: miR-210-3p expression is correlated with MUC4 expression in PDAC-derived cells and human samples, and in pancreatic PanIN lesions of Pdx1-Cre; LstopL-KrasG12D mice. MUC4 enhances miR-210-3p expression levels via alteration of the NF-κB signaling pathway. Chromatin immunoprecipitation experiments showed p50 NF-κB subunit binding on miR-210-3p promoter regions. We established a reciprocal regulation since miR-210-3p repressed MUC4 expression via its 3′-UTR. MiR-210-3p transient transfection of PANC89, PANC-1 and MiaPACA-2 cells led to a decrease in cell proliferation and migration. These biological effects were validated in cells overexpressing or knocked-down for miR-210-3p. Finally, we showed that miR-210-3p inhibits pancreatic tumor growth and proliferation in vivo. Conclusion: We identified a MUC4-miR-210-3p negative feedback loop in early-onset PDAC, but also revealed new functions of miR-210-3p in both in vitro and in vivo proliferation and migration of pancreatic cancer cells, suggesting a complex balance between MUC4 pro-oncogenic roles and miR-210-3p anti-tumoral effects.

The utility of Circumferential Resection Margin (CRM) status in predicting prognosis in oesophageal cancer remains controversial, with two different definitions of a positive CRM, one from the College of American Pathologists (CAP) (tumour at margin) and the other from the Royal College of Pathologists (RCP) (tumour within 1 mm)^1-3. This study aimed to analyze the validity of these definitions in oesophageal tumours and explore the optimal cutoff value for CRM to predict survival. Patients who underwent curative radical oesophageal resection for locally advanced (>pT2) adenocarcinoma or squamous cell carcinoma of the oesophagus were selected from 2007 to 2016. Patients with positive longitudinal resection margins were excluded. CRM was histologically reassessed using an ocular micrometer. Overall survival (OS) and disease-free survival (DFS) were estimated with uni and multivariate analyses. From 860 resected patients, 283 fulfilling the inclusion criteria were selected. CRM was measured as follows: CRM=0mm (n=48), 01mm (n=112). In univariate (figure 1) and multivariate analysis R1 resection, according to both definitions was significantly associated with poor OS (CAP: HR=2.26,p<0.001; RCP: HR=1.42,p=0.035). However only CAP definition accurately predicted DFS (CAP: HR=2.25,p<0.001; RCP: HR=1.28,p=0.094). When comparing the 3 CRM groups and taking 0 < CRM ≤1mm as reference, only CRM=0 predicted OS and DFS (p<0.001). A CRM cutoff at 0.1 mm was the best to predict OS and differed according to histology. Among existing definitions of CRM, CAP definition was more accurate to predict prognosis and recurrence. New cutoffs are promising.