Biology subjects

Foll, M.

Publications and source records attributed to Foll, M..

12 recordsLinked to original sources

bioRxiv2026

PACMOS: an R package for Projection And Classification of Multi-Omic Samples

MotivationIntegrated multi-omic analyses have transformed our understanding of cancer biology, giving rise to data-driven molecular classifications that capture disease heterogeneity beyond conventional histopathology. Among these approaches, multi-omic factor analysis (MOFA), a multimodal extension of principal component analysis, has been widely used to identify sources of molecular variation across omic layers and classify samples into molecular groups. However, classifying query samples according to an existing MOFA-based classification remains challenging, as there is no validated computational method for projecting samples into pretrained MOFA latent factor spaces. ResultsWe present PACMOS, an R package that provides a generalizable approach to project query samples into pretrained MOFA latent factor spaces. We validate PACMOS using two cancer datasets with published MOFA-based classifications--lung neuroendocrine neoplasms and pleural mesothelioma--showing that PACMOS preserves the existing MOFA latent factor space while allowing query samples to be classified. Availability and implementationPACMOS is an open-source R package available at https://github.com/IARCbioinfo/PACMOS and archived on Zenodo at https://doi.org/10.5281/zenodo.20933824, along with installation instructions and a vignette. Supplementary informationSupplementary data are available in separate files. Key messagesO_LIPACMOS enables the projection of query cancer samples into pretrained multi-omic latent factor spaces. C_LIO_LIThe package supports both continuous and discrete classifications. C_LIO_LIPACMOS provides a reproducible, per-sample workflow implemented in an R package. C_LIO_LIPACMOS demonstrates robust performance on pleural mesothelioma and lung neuroendocrine tumor datasets. C_LI

bioinformatics↗

bioRxiv2026

A multi-omic, spatial, and whole-slide image dataset of lung neuroendocrine tumours from the lungNENomics cohort

Lung neuroendocrine tumours (lung NETs) are rare neoplasms comprising approximately 2% of lung cancers. Recent studies have identified distinct molecular groups based on transcriptome and methylome data, but genomic and morphological features remain underexplored due to limited whole-genome and imaging data. We have generated the largest multi-omic dataset of lung NETs to date (201 participants, for a total of n = 294 tumours), including RNA sequencing, EPIC 850K methylation arrays, and whole-genome sequencing. This multiomic dataset also include multi-regional whole-genome sequencing for 41 participants, allowing for the quantification of intra-tumoural heterogeneity. We additionally generated spatial proteomics (64 participants), spatial transcriptomics (4 participants) and whole-slide histopathology images for 212 cases. This dataset enables a comprehensive characterization of lung NET molecular groups and the identification of group-specific morphological features using deep learning algorithms. All quality control analyses, processed data, and scripts are provided to ensure reproducibility. This dataset is available as a basis for further molecular and morphological analysis of lung NETs, and for future research on multi-scale integration.

bioinformatics↗

bioRxiv2024

An integrative RNA spliceosomic landscape of pancreatic neuroendocrine tumors unveils novel clinicomolecular associations

Alterations in alternative splicing are emerging as a novel hallmark in cancer biology, offering new insights. However, integrative analyses of splicing are still scarce, particularly in rare cancers such as pancreatic neuroendocrine tumors (PanNETs). These tumors are highly heterogeneous, complicating diagnosis and treatment. This study is the first to comprehensively investigate the RNA splicing landscape in PanNETs, identifying distinct spliceosomic profiles correlated with unique clinical and molecular characteristics. We analyzed RNA-seq data from 174 samples, identifying three distinct spliceosomic groups (SPN1, SPN2, SPN3) with unique clinical and molecular characteristics. SPN1 exhibited intermediate clinical features and specific splicing machinery profile, SPN2 was associated with frequent mutations in MEN1 and DAXX/ATRX genes, and SPN3 showed a prevalence of well-differentiated tumors with distinct splicing patterns. These groups were linked to different clinical outcomes and activated key biological processes like mTOR signaling and hormone secretion pathways. Our findings underscore the significant impact of RNA splicing on PanNET heterogeneity and suggest that detailed splicing profiles could serve as valuable tools for identifying novel biomarkers and therapeutic targets. This study provides crucial insights into PanNET molecular biology and paves the way for personalized therapies based on splicing features.

cancer biology↗

bioRxiv2023

Multi-omic dataset of patient-derived tumor organoids of neuroendocrine neoplasms

BackgroundOrganoids are three-dimensional experimental models that summarize the anatomical and functional structure of an organ. Although a promising experimental model for precision medicine, patient-derived tumor organoids (PDTOs) have currently been developed only for a fraction of tumor types. ResultsWe have generated the first multi-omic dataset (whole-genome sequencing, WGS, and RNA-sequencing, RNA-seq) of PDTOs from the rare and understudied pulmonary neuroendocrine tumors (n = 12; 6 grade 1, 6 grade 2), and provide data from other rare neuroendocrine neoplasms: small intestine (ileal) neuroendocrine tumors (n = 6; 2 grade 1 and 4 grade 2) and large-cell neuroendocrine carcinoma (n = 5; 1 pancreatic and 4 pulmonary). This dataset includes a matched sample from the parental sample (primary tumor or metastasis) for a majority of samples (21/23) and longitudinal sampling of the PDTOs (1 to 2 time-points), for a total of n = 47 RNA-seq and n = 33 WGS. We here provide quality control for each technique, and provide the raw and processed data as well as all scripts for genomic analyses to ensure an optimal re-use of the data. In addition, we report somatic small variant calls and describe how they were generated, in particular how we used WGS somatic calls to train a random-forest classifier to detect variants in tumor-only RNA-seq. ConclusionsThis dataset will be critical to future studies relying on this PDTO biobank, such as drug screens for novel therapies and experiments investigating the mechanisms of carcinogenesis in these understudied diseases.

cancer biology↗

bioRxiv2022

Druggable Growth Dependencies and Tumor Evolution Analysis in Patient-Derived Organoids of Neuroendocrine Cancer

Neuroendocrine neoplasms (NENs) comprise well-differentiated neuroendocrine tumors and poorly-differentiated carcinomas. Treatment options for patients with NENs are limited, in part due to lack of accurate models. To address this need we established the first patient-derived tumor organoids (PDTOs) from pulmonary neuroendocrine tumors and derived PDTOs from an understudied NEN subtype, large cell neuroendocrine carcinoma (LCNEC). PDTOs maintain the gene expression patterns, intra-tumoral heterogeneity, and evolutionary processes of parental tumors. Through drug sensitivity analyses, we uncover therapeutic sensitivities to an inhibitor of NAD salvage biosynthesis and to an inhibitor of BCL-2. Finally, we identify a dependency on EGF in pulmonary neuroendocrine tumor PDTOs. Consistent with these findings, analysis of an independent cohort showed that approximately 50% of pulmonary neuroendocrine tumors expressed EGFR. This study identifies a potentially actionable vulnerability for a subset of NENs, and further highlights the utility of these novel PDTO models for the study of NENs. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=188 HEIGHT=200 SRC="FIGDIR/small/514549v1_ufig1.gif" ALT="Figure 1"> View larger version (57K): org.highwire.dtl.DTLVardef@14bed0borg.highwire.dtl.DTLVardef@1b7791forg.highwire.dtl.DTLVardef@7120a6org.highwire.dtl.DTLVardef@116b21d_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIPDTOs of pulmonary NETs and LCNEC were established C_LIO_LIPDTOs recapitulate intra-tumoral heterogeneity and evolution of parental tumors C_LIO_LIDrug assays reveal therapeutic vulnerabilities and biomarkers C_LIO_LIPulmonary NET PDTOs are dependent on EGF C_LI

cancer biology↗

bioRxiv2022

A molecular phenotypic map of Malignant Pleural Mesothelioma

BackgroundMalignant Pleural Mesothelioma (MPM) is a rare understudied cancer associated with exposure to asbestos. So far, MPM patients have benefited marginally from the genomics medicine revolution due to the limited size or breadth of existing molecular studies. In the context of the MESOMICS project, we have performed the most comprehensive molecular characterization of MPM to date, with the underlying dataset made of the largest whole genome sequencing series yet reported, together with transcriptome sequencing and methylation arrays for 120 MPM patients. ResultsWe first provide comprehensive quality controls for all samples, of both raw and processed data. Due to the difficulty in collecting specimens from such rare tumors, a part of the cohort does not include matched normal material. We provide a detailed analysis of data processing of these tumor-only samples, showing that all somatic alteration calls match very stringent criteria of precision and recall. Finally, integrating our data with previously published multi-omic MPM datasets (n=374 in total), we provide an extensive molecular phenotype map of MPM based on the multi-task theory. The generated map can be interactively explored and interrogated on the UCSC TumorMap portal (https://tumormap.ucsc.edu/?bookmark=746c4bc0e8bc4eb5f280cdd8lc7dcc783955faf2e2b493d0d205b7dle92b98c4). ConclusionsThis new high quality MPM multi-omics dataset, together with the state-of-art bioinformatics and interactive visualization tools we provide, will support the development of precision medicine in MPM that is particularly challenging to implement in rare cancers due to limited molecular studies.

genomics↗

bioRxiv2019

Reconstruction of nine thousand years of agriculture-based diet and impact on human genetic diversity in Asia

Domestication of crops and animals during the Holocene epoch played a critical role in shaping human culture, diet and genetic variation. This domestication process took place across a span of time and space, especially in Asia. We hypothesize that domestication of plants and animals around the world must have influenced the human genome differentially among human populations to a far greater degree than has been appreciated previously. The range of domesticated foods that were available in different regions can be expected to have created regionally distinct nutrient intake profiles and deficiencies. To capture this complexity, we used archaeobotanical evidence to construct two models of dietary nutrient composition over a 9000 year time span in Asia: one based on Larson et al. (2014) and measured through composition of 8 nutrients, and another taking into account a wider range of crops, cooking and lifestyle variation, and the dietary variables glycemic index and carbohydrate content. We hypothesize that the subtle dietary shifts through time and space have also influenced current human genetic variation among Asians. We used statistical methods BayeScEnv, BayeScan and Baypass, to examine the impact of our reconstructed long-term dietary habits on genome-wide genetic variation in 29 current-day Asian populations (Figure S1, Figure 1, Figure 2). Our results show that genetic variation in diet-related pathways is correlated with dietary differences among Asian populations. SNPs in five genes, GHR, LAMA1, SEMA3A, CAST and TCF7L2, involved in the gene ontologies salivary gland morphogenesis and negative regulation of type B pancreatic cell apoptotic process suggest that metabolism may have been primary targets of selection driven by dietary shifts. These shifts may have influenced biological pathways in ways that have a lasting impact on health. We present a case that archaeobotanical evidence can provide valuable insight for understanding how historical human niche construction might have influenced modern human genetic variation.\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=119 SRC=\"FIGDIR/small/747709v1_fig1.gif\" ALT=\"Figure 1\">\nView larger version (26K):\norg.highwire.dtl.DTLVardef@1aa917corg.highwire.dtl.DTLVardef@135990forg.highwire.dtl.DTLVardef@1d3e0d3org.highwire.dtl.DTLVardef@14d5112_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO Organization of the study and analysis pipeline. The two figures in column Archaeological data refer to the two constructed dietary models, with the boxes beside them referring to the dietary variables from each model.\n\nC_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC=\"FIGDIR/small/747709v1_fig2a.gif\" ALT=\"Figure 2\">\nO_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC=\"FIGDIR/small/747709v1_fig2b.gif\" ALT=\"Figure 2\">\nView larger version (52K):\norg.highwire.dtl.DTLVardef@1059950org.highwire.dtl.DTLVardef@94662dorg.highwire.dtl.DTLVardef@c01de2org.highwire.dtl.DTLVardef@ea07cb_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 2.C_FLOATNO Summary of Diet Model 1. a) Levels of nutrients contributed by domestication, based on Diet Model 1, as a sum total of values across all three population groups. b) Average levels of nutrients across the three time intervals (9000 YBP-present) in each region, based on Diet Model 1. Supplementary Tables 1-8 contain the data used to generate these figures.\n\nC_FIG

genetics↗

bioRxiv2019

Detecting selection from linked sites using an F-model

Allele frequencies vary across populations and loci, even in the presence of migration. While most differences may be due to genetic drift, divergent selection will further increase differentiation at some loci. Identifying those is key in studying local adaptation, but remains statistically challenging. A particularly elegant way to describe allele frequency differences among populations connected by migration is the F-model, which measures differences in allele frequencies by population specific FST coefficients. This model readily accounts for multiple evolutionary forces by partitioning FST coefficients into locus and population specific components reflecting selection and drift, respectively. Here we present an extension of this model to linked loci by means of a hidden Markov model (HMM) that characterizes the effect of selection on linked markers through correlations in the locus specific component along the genome. Using extensive simulations we show that our method has up to two-fold the statistical power of previous implementations that assume sites to be independent. We finally evidence selection in the human genome by applying our method to data from the Human Genome Diversity Project (HGDP).

genomics↗

bioRxiv2019

Needlestack: an ultra-sensitive variant caller for multi-sample next generation sequencing data

The emergence of Next-Generation Sequencing (NGS) has revolutionized the way of reaching a genome sequence, with the promise of potentially providing a comprehensive characterization of DNA variations. Nevertheless, detecting somatic mutations is still a difficult problem, in particular when trying to identify low abundance mutations such as subclonal mutations, tumour-derived alterations in body fluids or somatic mutations from histological normal tissue. The main challenge is to precisely distinguish between sequencing artefacts and true mutations, particularly when the latter are so rare they reach similar abundance levels as artefacts. Here, we present needlestack, a highly sensitive variant caller, which directly learns from the data the level of systematic sequencing errors to accurately call mutations. Needlestack is based on the idea that the sequencing error rate can be dynamically estimated from analyzing multiple samples together. We show that the sequencing error rate varies across alterations, illustrating the need to precisely estimate it. We evaluate the performance of needlestack for various types of variations, and we show that needlestack is robust among positions and outperforms existing state-of-the-art method for low abundance mutations. Needlestack, along with its source code is freely available on the GitHub plateform: https://github.com/IARCbioinfo/needlestack.

bioinformatics↗

bioRxiv2018

The fitness consequences of genetic variation in wild populations of mice

Adaptive evolution can occur when genetic change affects traits subject to natural selection. Although selection is a deterministic process, adaptation can be difficult to predict in finite populations because the functional connections between genotype, phenotype, and fitness are complex. Here, we make these connections using a combination of field and laboratory experiments. We conduct a large-scale manipulative field experiment with wild populations of deer mice in distinct habitats to directly estimate natural selection on pigmentation traits and next test whether this selection drives changes in allele frequency at an underlying pigment locus. We find that divergent cryptic phenotypes are repeatedly favoured in each habitat, leaving footprints of selection in the Agouti gene. Next, using transgenic experiments in Mus, we functionally test one of the Agouti mutations associated with survival, a Serine deletion in exon 2, and find that it causes lighter coat colour via changes in its protein binding properties. Finally, we show significant change in the frequency of this mutation in our field experiment. Together, our findings demonstrate how a sequence variant alters phenotype and show the ensuing ecological consequences that drive changes in population allele frequency, thereby revealing the full process of evolution by natural selection.

evolutionary biology↗

bioRxiv2018

Redefining mesothelioma types as a continuum uncovers the immune and vascular systems as key players in the diagnosis and prognosis of this disease

Malignant Pleural Mesothelioma (MPM) is an aggressive disease related to asbestos exposure, which incidence is expected to increase in the future, and with no effective therapeutic options. We have performed unsupervised analyses of publicly available RNAseq data for 297 MPM. We found that the molecular profile and the prognosis of this disease is better explained by a continuous model rather than by the current WHO classification into the epitheloid, biphasic and sarcomatoid histological types. The main source of variation of this continuum was explained by the immune and vascular pathways, with strong differences in the expression of pro-angiogenic genes and immune checkpoint inhibitors across samples. These data may inform future classifications of MPM and may also guide personalised therapeutic approaches for this disease.\n\nSignificanceMalignant Pleural Mesothelioma (MPM) is an aggressive disease with no effective therapeutic options. Unsupervised transcriptomic analyses of 297 MPM unveiled the vascular and the immune systems as key players in the prognosis of this disease, and identified potential therapeutic approaches for this disease targeting these pathways.

cancer biology↗

bioRxiv2017

The evolutionary history of Nebraska deer mice: local adaptation in the face of strong gene flow

The interplay of gene flow, genetic drift, and local selective pressure is a dynamic process that has been well studied from a theoretical perspective over the last century. Wright and Haldane laid the foundation for expectations under an island-continent model, demonstrating that an island-specific beneficial allele may be maintained locally if the selection coefficient is larger than the rate of migration of the ancestral allele from the continent. Subsequent extensions of this model have provided considerably more insight. Yet, connecting theoretical results with empirical data has proven challenging, owing to a lack of information on the relationship between genotype, phenotype, and fitness. Here, we examine the demographic and selective history of deer mice in and around the Nebraska Sand Hills, a system in which variation at the Agouti locus affects cryptic coloration that in turn affects the survival of mice in their local habitat. We first genotyped 250 individuals from eleven sites along a transect spanning the Sand Hills at 660,000 SNPs across the genome. Using these genomic data, we found that deer mice first colonized the Sand Hills following the last glacial period. Subsequent high rates of gene flow have served to homogenize the majority of the genome between populations on and off the Sand Hills, with the exception of the Agouti pigmentation locus. Furthermore, mutations at this locus are strongly associated with the pigment traits that are strongly correlated with local soil coloration and thus responsible for cryptic coloration.

evolutionary biology↗