Biology subjects

Letouze, E.

Publications and source records attributed to Letouze, E..

7 recordsLinked to original sources

bioRxiv2026

Routine FFPE sections support clinically compatible single-nucleus transcriptomics across six human cancer types

Tumor cellular composition--including malignant cell states, immune populations, and stromal populations--is increasingly recognized as a determinant of therapeutic response and resistance to anti-cancer agents, yet comprehensive cellular profiling remains largely confined to research settings. Here, we present a clinically compatible sample-to-report workflow for tumor composition profiling from routine formalin-fixed paraffin-embedded (FFPE) clinical specimens. By combining low-input single-nucleus RNA sequencing with foundation model- based automated cell annotation, this workflow enables prospective sample-by-sample analysis without dedicated research material or cohort-based processing. Across 116 clinical specimens representing six cancer types, we generated reproducible measurements of cellular composition and cell-type-specific gene expression, demonstrated high technical reproducibility, and showed concordance with pathological assessment of immune infiltration. The workflow was similarly applicable to archival FFPE material and ultra-low-input biopsy specimens. Together, these findings establish a practical framework for routine single-cell profiling from standard pathology specimens and open the perspective of prospective evaluation of cellular composition as a clinical biomarker in precision oncology.

cancer biology↗

bioRxiv2026

Expression profile of CASSIOPEIA patients refines prognostic value of MRD negativity in multiple myeloma

Long-term follow-up of the CASSIOPEIA trial (NCT02541383) demonstrated superior progression-free survival (PFS) with daratumumab, both in combination with bortezomib, thalidomide, and dexamethasone during induction and consolidation, and during maintenance therapy, in transplant- eligible patients newly diagnosed with multiple myeloma (MM). However, outcomes among CASSIOPEIA patients remain heterogeneous across treatment groups. Measurable residual disease (MRD) is a strong indicator of the depth and duration of therapeutic response and is independently associated with both PFS and overall survival (OS), but it does not fully capture the biological diversity of MM. We performed a risk prediction analysis based on transcriptomic subgroups in CASSIOPEIA patients. A subset of 628 patients was characterized using RNA sequencing and consensus clustering identified five transcriptomic subtypes of MM. Long-term follow-up allowed the definition of three transcriptomic risk categories, with estimated 72-month PFS rates of 70%, 51%, and 27% for low, intermediate, and high-risk groups, respectively, among patients who received daratumumab in at least one treatment phase. In these patients, MRD negativity rates after consolidation and six months later were significantly higher in the low and high-risk groups compared with the intermediate-risk group. In the high-risk group, MRD status was not associated with PFS or OS. This suggests that, although daratumumab administered during both the induction/consolidation and maintenance phases improves the clinical outcomes of patients with activation of NSD2 or overexpressing members of the MAF family, highly aggressive minor clones may rapidly expand. These findings emphasize the need for novel therapeutic strategies in this high-risk population.

cancer biology↗

bioRxiv2026

Single-cell trajectories in metastatic urothelial carcinoma reveal tumor-immune reprogramming and macrophage-driven resistance to PD-(L)1 blockade

Immune checkpoint inhibitors (ICI) improved outcomes in metastatic urothelial carcinoma (mUC), but primary and acquired resistance remain poorly understood. We performed single-nuclei RNA sequencing on sequential metastatic biopsies from ICI-treated mUC patients. Tumor cells showed transcriptomic heterogeneity within individual lesions, basal cells being associated with increased immune infiltration and response. Myeloid and lymphoid compartments exhibited features of immune dysfunction in non-responders. Longitudinal analyses revealed convergent adaptive resistance mechanisms, dominated by polarization toward pro-tumoral macrophage states, but also including downregulation of the antigen presentation machinery in tumor cells, increased checkpoint expression with loss of cytotoxicity in T cells. Individual trajectories point to distinct evolutionary routes under ICI pressure. Across pivotal ICI trials, bulk expression of the M2-like macrophage marker HES1 predicted ICI resistance. Our study provides the first single-cell longitudinal atlas of ICI-treated mUC, revealing macrophage reprogramming as a dominant driver of resistance, establishing a framework for individualized immunotherapy strategies.

cancer biology↗

bioRxiv2024

Primary cilia promote EMT-induced triple-negative breast tumor heterogeneity and resistance to therapy

Tumor heterogeneity and plasticity, driven by Epithelial-Mesenchymal Transition (EMT), enable cancer therapeutic resistance. We previously showed that EMT promotes primary cilia formation, which enables stemness and tumorigenesis in triple-negative breast cancer (TNBC). Here, we establish a role for primary cilia in human TNBC chemotherapeutic resistance. We developed patient-derived organoids, and showed that these recapitulated the cellular heterogeneity of TNBC biopsies. Notably, one of the identified cell states bore a quasi-mesenchymal phenotype, primary cilia, and stemness signatures. We treated our TNBC organoids with chemotherapeutics and observed partial killing. The surviving cells with organoid-reconstituting capacity showed selective enrichment for the quasi-mesenchymal ciliated cell subpopulation. Genomic analyses argue that this enrichment reflects a combination of pre-existing cells and ones that arose through drug-induced cellular plasticity. We developed a family of small-molecule inhibitors of ciliogenesis and show that these, or genetic ablation of primary cilia, suppress chemoresistance. We conclude that primary cilia help TNBC to evade chemotherapy. SignificanceCancer cells that activate EMT to acquire a quasi-mesenchymal state form primary cilia to evade chemotherapy in human triple-negative breast cancer. Pharmacological inhibition of primary ciliogenesis counteracts EMT-induced chemoresistance.

cancer biology↗

bioRxiv2022

Discovering cryptic splice mutations in cancers via a deep neural network framework

Somatic mutations can disrupt splicing regulatory elements and have dramatic effects on cancer genes, yet the functional consequences of mutations located in extended splice regions is difficult to predict. Here, we use a deep neural network (SpliceAI) to characterize the landscape of splice-altering mutations in cancer. In our in-house liver cancer series, SpliceAI uncovers many cryptic splice mutations, located outside essential splice sites, that validate at a high rate in matched RNA-seq data. We then extend the analysis to a large pan-cancer cohort of 18,115 tumors, revealing >100,000 cryptic splice mutations. Taking into account these mutations increases the power of driver gene discovery, revealing >100 new candidate driver genes. It also reveals new driver mutations in known cancer genes, doubling the frequency of splice alterations in tumor suppressor genes. Mutational signature analysis reveals the mutational processes that give rise to splice mutations in each cancer type, with an enrichment of signatures related to clock-like processes and DNA repair deficiency. Altogether, this work sheds light on the causes and impact of cryptic splice mutations in cancer, and highlights the power of deep learning approaches to better annotate the functional consequences of mutations in oncology.

bioinformatics↗

bioRxiv2021

H3K27me3 is a determinant of chemotolerance in triple-negative breast cancer

Triple-negative breast cancer is associated with the worst prognosis and the highest risk of recurrence among all breast cancer subtypes1. Residual disease, formed by cancer cells persistent to chemotherapy, remains one of the major clinical challenges towards full cure2,3. There is now consensus that non-genetic processes contribute to chemoresistance in various tumor types, notably through the initial emergence of a reversible chemotolerant state4-6. Understanding non-genetic tumor evolution stands now as a prerequisite for the design of relevant combinatorial approaches to delay recurrence. Here we show that the repressive histone mark H3K27me3 is a determinant of cell fate under chemotherapy exposure, monitoring epigenomes, transcriptomes and lineage with single-cell resolution. We identify a reservoir of persister basal cells with EMT markers and activated TGF-{beta} pathway leading to multiple chemoresistance phenotypes. We demonstrate that, in unchallenged cells, H3K27 methylation is a lock to the expression program of persister cells. Promoters are primed with both H3K4me3 and H3K27me3, and removing H3K27me3 is sufficient for their transcriptional activation. Leveraging lineage barcoding, we show that depleting H3K27me3 alters tumor cell fate under chemotherapy insult - a wider variety of tumor cells tolerate chemotherapy. Our results highlight how chromatin landscapes shape the potential of unchallenged cancer cells to respond to therapeutic stress.

cancer biology↗

bioRxiv2019

MS.liverK: an R package for transcriptome-based computation of molecular subtypes and functional signatures in liver cancer

SummaryLiver cancer is a highly heterogeneous disease in terms of etiology, tissue and cellular morphology, tumor molecular characteristics, microenvironment composition and prognosis. Several studies, based on tumor gene-expression profiling (GEP) data, have dissected the molecular heterogeneity of liver cancer. They resulted in various tools, either delineating homogeneous tumor subtypes or calculating molecular scores of prognostic or biological functions. Here, we present MS.liverK, an easy-to-use R package providing a comprehensive implementation of these tools, for research use. Availability and implementationThe MS.liverK R package is available from GitHub (https://github.com/cit-bioinfo/MS.liverK).

bioinformatics↗