Jean-Philippe Villemin

Institut de Recherche en Cancérologie de Montpellier

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William Ritchie

Royal Alexandra Hospital

Claudio Lorenzi

Institut de Génétique Humaine

Sylvain Barrière

Institut de Génétique Humaine

Jacques Colinge

Institut Regional du Cancer de Montpellier

Alban Mancheron

Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier

Laureline Dejardin Bretones

Université de Montpellier

Luc Bauchet

Institute for Neurosciences of Montpellier

Didier Auboeuf

Centre National de la Recherche Scientifique

Reini F. Luco

Université de Montpellier

Marie-Sarah Cabrillac

Institut de Génétique Humaine

Cooperative Institutions

Inserm

217

Centre National de la Recherche Scientifique

140

Université Paris Cité

Université de Montpellier

Assistance Publique – Hôpitaux de Paris

Université Claude Bernard Lyon 1

Centre de Recherche en Cancérologie de Lyon

Centre Léon Bérard

Sorbonne Université

Centre Hospitalier Universitaire de Montpellier

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Additional file 2 of iMOKA: k-mer based software to analyze large collections of sequencing data

Figshare (2020)

Claudio Lorenzi Sylvain Barrière Jean-Philippe Villemin Laureline Dejardin Bretones Alban Mancheron

Additional file 2. TCGA_OV_aggregated.json - iMOKA results for the dataset TCGA_OV.

10.6084/m9.figshare.13087998

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Real-time imaging of cotranscriptional splicing reveals a kinetic model that reduces noise: implications for alternative splicing regulation

The Journal of Cell Biology (2011)

Ute Schmidt Eugénia Basyuk Marie-Cécile Robert Minoru Yoshida Jean-Philippe Villemin

Splicing is a key process that expands the coding capacity of genomes. Its kinetics remain poorly characterized, and the distribution of splicing time caused by the stochasticity of single splicing events is expected to affect regulation efficiency. We conducted a small-scale survey on 40 introns in human cells and observed that most were spliced cotranscriptionally. Consequently, we constructed a reporter system that splices cotranscriptionally and can be monitored in live cells and in real time through the use of MS2–GFP. All small nuclear ribonucleoproteins (snRNPs) are loaded on nascent pre-mRNAs, and spliceostatin A inhibits splicing but not snRNP recruitment. Intron removal occurs in minutes and is best described by a model where several successive steps are rate limiting. Each pre-mRNA molecule is predicted to require a similar time to splice, reducing kinetic noise and improving the regulation of alternative splicing. This model is relevant to other kinetically controlled processes acting on few molecules.

snRNP

Exonic splicing enhancer

Small nuclear ribonucleoprotein

Spliceosome

Splicing factor

Minigene

10.1083/jcb.201009012

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Citations (112)

Additional file 4 of iMOKA: k-mer based software to analyze large collections of sequencing data

Figshare (2020)

Claudio Lorenzi Sylvain Barrière Jean-Philippe Villemin Laureline Dejardin Bretones Alban Mancheron

Additional file 4. DLBCL_aggregated.json - iMOKA results for the dataset DLBCL.

10.6084/m9.figshare.13088004

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Additional file 2 of A cell-to-patient machine learning transfer approach uncovers novel basal-like breast cancer prognostic markers amongst alternative splice variants

Figshare (2021)

Jean-Philippe Villemin Claudio Lorenzi Marie-Sarah Cabrillac Andrew J. Oldfield William Ritchie

Additional file 2: Table S1. GEO accession numbers for all the datasets analysed.

splice

Basal (medicine)

File transfer

10.6084/m9.figshare.14404447

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A cell-to-patient machine learning transfer approach uncovers novel basal-like breast cancer prognostic markers amongst alternative splice variants

BMC Biology (2021)

Jean-Philippe Villemin Claudio Lorenzi Marie-Sarah Cabrillac Andrew J. Oldfield William Ritchie

Breast cancer is amongst the 10 first causes of death in women worldwide. Around 20% of patients are misdiagnosed leading to early metastasis, resistance to treatment and relapse. Many clinical and gene expression profiles have been successfully used to classify breast tumours into 5 major types with different prognosis and sensitivity to specific treatments. Unfortunately, these profiles have failed to subclassify breast tumours into more subtypes to improve diagnostics and survival rate. Alternative splicing is emerging as a new source of highly specific biomarkers to classify tumours in different grades. Taking advantage of extensive public transcriptomics datasets in breast cancer cell lines (CCLE) and breast cancer tumours (TCGA), we have addressed the capacity of alternative splice variants to subclassify highly aggressive breast cancers.

Basal (medicine)

Triple-negative breast cancer

10.1186/s12915-021-01002-7

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Citations (19)

Additional file 1 of iMOKA: k-mer based software to analyze large collections of sequencing data

Figshare (2020)

Claudio Lorenzi Sylvain Barrière Jean-Philippe Villemin Laureline Dejardin Bretones Alban Mancheron

Additional file 1. TCGA_BC_aggregated.json - iMOKA results for the dataset TCGA_BC.

File format

10.6084/m9.figshare.13087995

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Splicing switch of an epigenetic regulator by RNA helicases promotes tumor-cell invasiveness

Nature Structural & Molecular Biology (2012)

Étienne Dardenne Sandra Pierredon Keltouma Driouch Lise Gratadou Magali Lacroix‐Triki

Spliceosome

Splicing factor

10.1038/nsmb.2390

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Citations (123)

Differential predictive value of resident memory CD8⁺T cell subpopulations in non-small-cell lung cancer patients treated by immunotherapy

bioRxiv (Cold Spring Harbor Laboratory) (2024)

Léa Paolini Thi Tran Stéphanie Corgnac Jean-Philippe Villemin Marie Wislez

Abstract A high density of resident memory T cells (T RM ) in tumors correlates with improved clinical outcomes in immunotherapy-treated patients. However, in preclinical models, only some subpopulations of T RM are associated with cancer vaccine efficacy. We identified two main T RM subpopulations in tumor-infiltrating lymphocytes derived from non-small cell lung cancer (NSCLC) patients: one co-expressing CD103 and CD49a (DP), and the other expressing only CD49a (MP); both exhibiting additional T RM surface markers like CD69. DP T RM exhibited greater functionality compared to MP T RM . Analysis of T-cell receptor (TCR) repertoire and of the stemness marker TCF-1 revealed shared TCRs between populations, with the MP subset appearing more progenitor-like phenotype. In two NSCLC patient cohorts, only DP T RM predicted PD-1 blockade response. Multivariate analysis, including various biomarkers (CD8, TCF1 + CD8 + T cells, and PD-L1) associated with responses to anti-PD(L)1, showed that only intra-tumoral infiltration by DP T RM remained significant. This study highlights the non-equivalence of T RM populations and emphasizes the importance of distinguishing between them to better define their role in antitumor immunity and as a biomarker of response to immunotherapy.

Value (mathematics)

10.1101/2024.03.07.583820

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Additional file 7 of iMOKA: k-mer based software to analyze large collections of sequencing data

Figshare (2020)

Claudio Lorenzi Sylvain Barrière Jean-Philippe Villemin Laureline Dejardin Bretones Alban Mancheron

Additional file 7. Supplementary Figures S1-S7.

10.6084/m9.figshare.13088013

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Issue Information

Human Mutation (2016)

Madhuri Hegde Susanne Arnold Christophe Béroud Emilia Niemiec Pascal Borry

10.1002/humu.22896

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