Biology subjects

Finotello, F.

Publications and source records attributed to Finotello, F..

18 recordsLinked to original sources

bioRxiv2026

Rectangle: robust and scalable multiscale deconvolution informed by single-cell RNA sequencing data

Bulk RNA-seq enables effective profiling of large cohorts and complex experimental designs, but current single-cell-informed deconvolution methods incompletely resolve closely related cell phenotypes, do not scale efficiently to large single-cell datasets, or fail to account for cellular content not represented in the reference. Here, we present Rectangle, an scverse Python framework for single-cell-informed deconvolution of bulk RNA-seq data. Rectangle combines multiscale deconvolution, capturing cellular composition across multiple resolution levels, with explicit modeling of unknown cellular content. In a diverse, cross-method benchmark, Rectangle achieved consistently strong performance across all evaluated metrics, demonstrating high accuracy, high resolution, low spillover, strong scalability and efficiency, and robustness to unknown cellular content. By bridging the resolution of single-cell transcriptomics with the scale and cost-efficiency of bulk RNA-seq, Rectangle enables cell-type and cell-state profiling at scale, supporting population-scale cellular biomarker discovery and tracking of cellular dynamics in settings impractical for comprehensive single-cell sequencing.

bioinformatics↗

bioRxiv2026

TET2 loss promotes premalignant survival and clonal selection in MYC-driven B cell lymphoma

The DNA demethylase ten-eleven translocation enzyme 2 (TET2) is frequently inactivated in hematologic malignancies, yet how its loss shapes oncogene-driven transformation remains unclear. Using a mouse model in which MYC is overexpressed in the B cell lineage, driving aggressive B cell lymphoma, we show that Tet2 loss increases lymphoma penetrance and biases disease toward an IgM immunophenotype. Established lymphomas are broadly similar across genotypes, suggesting that Tet2 loss exerts much of its effects before lymphoma onset. Accordingly, Tet2 loss expands a premalignant IgM B cell subset with reduced apoptotic sensitivity and an increased frequency of BCL2BIMhi cells. Consistently, Tet2 deficient IgM B cells persist better in in vitro cultures, show increased clonogenic survival, and exhibit clonal skewing. These findings support a model in which Tet2 loss heightens MYC-driven lymphoma penetrance by promoting the survival and selection of premalignant B cells under apoptotic stress.

cancer biology↗

bioRxiv2026

Local Tumor Microenvironment Niches Correlate With Survival And Immunotherapy Response In Human Glioblastoma

Background: Glioblastoma (GBM) is an aggressive form of primary brain cancer. Recent efforts to characterize GBM using single-cell or spatial transcriptomics have revealed tremendous intra-tumoral heterogeneity among malignant cells and across different tumor areas. However, most studies have focused on malignant cells, and the spatial and cellular heterogeneity of the tumor microenvironment (TME) remains poorly understood. Consequently, it is unclear how TME compositions and organizations influence clinical outcomes for patients. Results: Spatial transcriptomics on 25 tumors, integrated with single-cell RNA sequencing and histological analyses, enabled the estimation of cellular composition within the TME across 46,000 spots (55-m wide). This analysis identified spatial associations between mesenchymal-like cancer cells and monocyte-derived macrophages and revealed six unique niches (N1-N6) characterized by distinct cellular composition and biological pathway activations. Deconvolution of large-scale bulk RNA-seq datasets using a spatial transcriptomics reference revealed that tumor niche composition significantly associated with patient survival and response to immunotherapy. Specifically, the N1 niche, characterized by the enrichment of mesenchymal-like cancer cells and monocyte-derived macrophages alongside hypoxic signature, was associated with lower overall survival. In contrast, the N5 niche, characterized by the enrichment of oligodendrocyte progenitor-like cancer cells and microglia-derived tumor-associated macrophages, was associated with longer patients survival. Analysis of data from patients treated with immunotherapy identified that niches N1 and N3, characterized by mixed mesenchymal-like and astrocyte-like cells, were associated with poor response to anti-PD-1 immune checkpoint inhibitors. Conclusions: Our results demonstrate that GBM exhibits significant spatial heterogeneity within the TMEs, comprising distinct niche classes, and that the specific niche compositions are associated with overall survival and immunotherapy responsiveness. Our results suggest incorporation of TME niche categories as biomarkers for risk stratification and therapeutic decisions for GBM patients.

cancer biology↗

bioRxiv2024

NovumRNA: accurate prediction of non-canonical tumor antigens from RNA sequencing data

Non-canonical tumor-specific antigens (ncTSAs) can expand the pool of targets for cancer immunotherapy, but require robust and comprehensive computational pipelines for their prediction. Here, we present NovumRNA, a fully-automated Nextflow pipeline for predicting different classes of ncTSAs from patients RNA sequencing data. We extensively validated NovumRNA using publicly-available and newly-generated datasets, demonstrating the robustness of its analytical modules and predictions. NovumRNA analysis of colorectal cancer organoid data revealed comparable ncTSA potential for microsatellite stable and unstable tumors and candidate therapeutic targets for patients with low tumor mutational burden. Finally, our investigation of glioblastoma cell lines demonstrated increased ncTSAs burden upon indisulam treatment, and detection by NovumRNA of therapy-induced ncTSAs, which we could validate experimentally. These findings underscore the potential of NovumRNA for identifying synergistic drugs and novel therapeutic targets for immunotherapy, which could ultimately extend its benefit to a broader patient population.

bioinformatics↗

bioRxiv2024

Benchmarking second-generation methods for cell-type deconvolution of transcriptomic data

BackgroundIn silico cell-type deconvolution from bulk transcriptomics data is a powerful technique to gain insights into the cellular composition of complex tissues. While first-generation methods used precomputed expression signatures covering limited cell types and tissues, second-generation tools use single-cell RNA sequencing data to build custom signatures for deconvoluting arbitrary cell types, tissues, and organisms. This flexibility poses significant challenges in assessing their deconvolution performance. ResultsHere, we comprehensively benchmark second-generation tools, disentangling different sources of variation and bias using a diverse panel of real and simulated data. Our results reveal substantial differences in accuracy, scalability, and robustness across methods, depending on factors such as cell-type similarity, reference composition, and dataset origin. Conclusions.Our study highlights the strengths, limitations, and complementarity of state-of-the-art tools, shedding light on how different data characteristics and confounders impact deconvolution performance. We provide the scientific community with an ecosystem of tools and resources, omnideconv, simplifying the application, benchmarking, and optimization of deconvolution methods.

bioinformatics↗

bioRxiv2024

Toll-like receptor 3 orchestrates a conserved mechanism of heart regeneration

The humans heart responds to tissue damage with persistent fibrotic scarring. Unlike humans, zebrafish can repair cardiac injury and re-grow heart tissue throughout life. Recently, Toll-like receptor 3 (Tlr3) was identified as an important mediator of cardiac regeneration in neonatal mice. However, no functional analysis of tlr3 knock-out mutant zebrafish in respect to cardiac regeneration has yet been performed. We hypothesize that TLR3 signalling plays a central, conserved role in driving cardiac regeneration upon injury. Therefore, we focused on tlr3 mediated cardiac regeneration in zebrafish, ultimately discovering an evolutionary conserved mechanism of heart repair. Using histological, behavioural, and RNA-Sequencing analysis, we uncovered a conserved mechanism of tlr3 mediated cardiac repair after myocardial injury. Upon myocardial cryoinjury subjection, survival is decreased in tlr3-/- fish as compared to wildtype controls. Tlr3-/- zebrafish fail to recruit immune cells to the injured ventricle, resulting in impaired DNA repair and transcriptional reprogramming of cardiomyocytes. Mechanistically, we uncover an evolutionary conserved mechanism of tlr3 activation in fibroblasts promoting monocyte migration towards an injured ventricular area. Our data reveal tlr3 as a novel therapeutic target to promote cardiac regeneration. Every experiment including human participants has been approved by the ethics committee of the Medical University of Innsbruck (Ref. Nr.: 1262/2023). All experiments including the use of laboratory animals have been approved by the federal ministry of education, science, and research of Austria (Ref. Nr.: 2020-0.345.504).

molecular biology↗

bioRxiv2023

Multimodal analysis unveils tumor microenvironment heterogeneity linked to immune activity and evasion

The cellular and molecular heterogeneity of tumors is a major obstacle to cancer immunotherapy. Here, we use a systems biology approach to derive a signature of the main sources of heterogeneity in the tumor microenvironment (TME) from lung cancer transcriptomic data. We demonstrate that this signature, which we called iHet, is conserved in different cancers and associated with antitumor immunity. Through the analysis of single-cell and spatial transcriptomics data, we trace back the cellular origin of the variability that explains the iHet signature. Finally, we demonstrate that iHet has predictive value for cancer immunotherapy, which can be further improved by disentangling three major determinants of anticancer immune responses: activity of immune cells, immune infiltration or exclusion, and cancer-cell foreignness. This work shows how transcriptomics data can be integrated to derive a holistic representation of the phenotypic heterogeneity of the TME, and ultimately to determine its unfolding and fate during immunotherapy with immune checkpoint blockers.

bioinformatics↗

bioRxiv2023

Mathematically mapping the network of cells in the tumor microenvironment

Cell-cell interaction networks are pivotal in cancer development and treatment response. These networks can be inferred from data; however, this process often combines data from multiple patients, and/or creates networks on a cell-types level. It creates a good average overview of cell-cell interaction networks but fails to capture patient heterogeneity and/or masks potentially relevant local network structures. We propose a mathematical model based on random graphs (called RaCInG) to alleviate these issues using prior knowledge on potential cellular interactions and patients bulk RNA-seq data. We have applied RaCInG to extract 444 network features related to the tumor microenvironment, unveiled associations with immune response and subtypes, and identified cancer-type specific differences in inter-cellular signaling. Additionally, we have used RaCInG to explain how immune phenotypes regulated by context-specific intercellular communication affect immunotherapy response. RaCInG is a modular pipeline, and we envision its application for cell-cell interaction reconstruction in different contexts.

systems biology↗

bioRxiv2022

A Community Challenge to Predict Clinical Outcomes After Immune Checkpoint Blockade in Non-Small Cell Lung Cancer

PurposePredictive biomarkers of immune checkpoint inhibitors (ICIs) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. MethodsParticipants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. ResultsA total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The bestperforming models showed improved predictive power over reference variables, including TMB or PD-L1. ConclusionThis DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICIs clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICIs efficacy. Context summaryO_ST_ABSKey objectiveC_ST_ABSNot all patients with non-small cell lung cancer (NSCLC) eligible for immune checkpoint inhibitor (ICIs) respond to treatment, but accurate predictive biomarkers of ICIs clinical outcomes are currently lacking. This crowdsourced initiative enabled the robust assessment of predictive models using data from two randomized clinical trials of first-line ICI in metastatic NSCLC. Knowledge generatedModels submitted indicate that a combination of programmed death ligand 1 (PD-L1), tumor mutational burden (TMB), and immune gene signatures might be able to identify patients more likely to respond to ICIs. TMB and PD-L1 seemed important to predict progression-free survival and overall survival. Mechanisms including apoptosis, T-cell crosstalk, and adaptive immune resistance appeared essential to predict response. Relevance

cancer biology↗

bioRxiv2022

High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is characterized by molecular heterogeneity with diverse immune cell infiltration patterns, which has been linked to both, therapy sensitivity and resistance. However, full understanding of how immune cell phenotypes vary across different patient and tumor subgroups is lacking. Here, we dissect the NSCLC tumor microenvironment at high resolution by integrating 1,212,463 single-cells from 538 samples and 309 patients across 29 datasets, including our own dataset capturing cells with low mRNA content. Based on the cellular composition we stratified patients into immune deserted, B cell, T cell, and myeloid cell subtypes. Using bulk samples with genomic and clinical information, we identified specific cellular components associated with tumor histology and genotypes. Analysis of cells with low mRNA content uncovered distinct subpopulations of tissue-resident neutrophils (TRNs) that acquire new functional properties in the tissue microenvironment, providing evidence for the plasticity of TRNs. TRN-derived gene signature was associated with anti-PD-L1 treatment failure in a large NSCLC cohort. In briefSalcher, Sturm, Horvath et al. integrate single-cell datasets to generate the largest transcriptome atlas in NSCLC, refining patient stratification based on tumor immune phenotypes, and revealing associations of histological subtypes and genotypes with specific cellular composition patterns. Coverage of cells with low mRNA content by single-cell sequencing identifies distinct tissue-resident neutrophil subpopulations, which acquire new properties within the tumor microenvironment. Gene signature from tissue-resident neutrophils is associated with immune checkpoint inhibitor treatment failure. The integrated atlas is publicly available online (https://luca.icbi.at), allowing the dissection of tumor-immune cell interactions in NSCLC. HighlightsO_LIHigh-resolution single-cell atlas of the tumor microenvironment (TME) in NSCLC. C_LIO_LIHistological tumor subtypes and driver genes imprint specific cellular TME patterns. C_LIO_LIscRNA-seq of cells with low transcript count identifies distinct tissue-resident neutrophil (TRN) subpopulations and non-canonical functional properties in the TME niche. C_LIO_LITRN gene signature identifies patients who are refractory to treatment with PD-L1 inhibitors. C_LI O_FIG O_LINKSMALLFIG WIDTH=199 HEIGHT=200 SRC="FIGDIR/small/491204v1_ufig1.gif" ALT="Figure 1"> View larger version (63K): org.highwire.dtl.DTLVardef@1050b6aorg.highwire.dtl.DTLVardef@3088c8org.highwire.dtl.DTLVardef@6408b8org.highwire.dtl.DTLVardef@1788c22_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2022

SimBu: Bias-aware simulation of bulk RNA-seq data with variable cell type composition

MotivationAs complex tissues are typically composed of various cell types, deconvolution tools have been developed to computationally infer their cellular composition from bulk RNA sequencing (RNA-seq) data. To comprehensively assess deconvolution performance, gold-standard datasets are indispensable. Gold-standard, experimental techniques like flow cytometry or immunohistochemistry are resource-intensive and cannot be systematically applied to the numerous cell types and tissues profiled with high-throughput transcriptomics. The simulation of pseudo-bulk data, generated by aggregating single-cell RNA-seq (scRNA-seq) expression profiles in pre-defined proportions, offers a scalable and cost-effective alternative. This makes it feasible to create in silico gold standards that allow fine-grained control of cell-type fractions not conceivable in an experimental setup. However, at present, no simulation software for generating pseudo-bulk RNA-seq data exists. ResultsWe developed SimBu, an R package capable of simulating pseudo-bulk samples based on various simulation scenarios, designed to test specific features of deconvolution methods. A unique feature of SimBu is the modelling of cell-type-specific mRNA bias using experimentally-derived or data-driven scaling factors. Here, we show that SimBu can generate realistic pseudo-bulk data, recapitulating the biological and statistical features of real RNA-seq data. Finally, we illustrate the impact of mRNA bias on the evaluation of deconvolution tools and provide recommendations for the selection of suitable methods for estimating mRNA content. ConclusionSimBu is a user-friendly and flexible tool for simulating realistic pseudo-bulk RNA-seq datasets serving as in silico gold-standard for assessing cell-type deconvolution methods. AvailabilitySimBu is freely available at https://github.com/omnideconv/SimBu as an R package under the GPL-3 license. Contactalex.dietrich@tum.de and markus.list@tum.de Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2021

easier: interpretable predictions of antitumor immune response from bulk RNA-seq data

Immunotherapy with immune checkpoint blockers (ICB) is associated with striking clinical success, but only in a small fraction of patients. Thus, we need computational biomarker-based methods that can anticipate which patients will respond to treatment. Current established biomarkers are imperfect due to their incomplete view of the tumor and its microenvironment. We have recently presented a novel approach that integrates transcriptomics data with biological knowledge to study tumors at a more holistic level. Validated in four different solid cancers, our approach outperformed the state-of-the-art methods to predict response to ICB. Here, we introduce estimate systems immune response (easier), an R/Bioconductor package that applies our approach to quantify biomarkers and assess patients likelihood to respond to immunotherapy, providing just the patients baseline bulk-tumor RNA-sequencing (RNA-seq) data as input.

systems biology↗

bioRxiv2021

Predictive systems biomarkers of response to immune checkpoint inhibitors

Cancer cells can leverage several cell-intrinsic and -extrinsic mechanisms to escape immune system recognition. The inherent complexity of the tumor microenvironment, with its multicellular and dynamic nature, poses great challenges for the extraction of biomarkers of immune response and immunotherapy efficacy. Here, we use RNA-seq data combined with different sources of prior-knowledge to derive system-based signatures of the tumor microenvironment, quantifying immune-cell composition and intra- and inter-cellular communications. We applied multi-task learning to these signatures to predict different hallmarks of immune responses and derive cancer-type-specific models based on interpretable systems biomarkers. By applying our models to independent RNA-seq data from cancer patients treated with PD-1 inhibitors, we demonstrated that our method to Estimate Systems Immune Response (EaSIeR) accurately predicts therapeutic outcome. We anticipate that EaSIeR will be a valuable tool to provide a holistic description of immune responses in complex and dynamic systems such as tumors using available RNA-seq data.

cancer biology↗

bioRxiv2020

The IRE1 Inhibitor KIRA6 Curtails The Inflammatory Trait Of Immunogenic Anticancer Treatments By Targeting HSP60 Independent Of IRE1

Cellular stress evoked by immunogenic anticancer treatments engaging the unfolded protein response (UPR) can elicit inflammation with conflicting therapeutic outcomes. To define cell-autonomous mechanisms coupling the UPR to molecular mediators of inflammation, we profiled the transcriptome of cancer cells responding to immunogenic or weakly immunogenic-treatments. Bioinformatics-driven pathway analysis indicated that immunogenic treatments instigated NF-{kappa}B/AP-1-inflammatory pathways, which were abolished by the IRE1-kinase inhibitor KIRA6. Cell-free fractions of chemotherapy and KIRA6 co-treated cancer cells were deprived of pro-inflammatory/chemoattractant factors and failed to mobilize innate immune cells. Strikingly, these potent KIRA6 anti-inflammatory effects were found to be independent of IRE1. Generation of a KIRA6-clickable photoaffinity probe, mass spectrometry and co-immunoprecipitation analysis identified cytosolic HSP60 as a KIRA6 off-target in the NF-{kappa}B pathway. In sum, our study unravels that inflammation evoked by immunogenic treatments is curtailed by KIRA6 independently of IRE1 and further suggests great caution in interpreting the anti-inflammatory action of IRE1 chemical inhibitors.

cancer biology↗

bioRxiv2020

Scirpy: A Scanpy extension for analyzing single-cell T-cell receptor sequencing data

SummaryAdvances in single-cell technologies have enabled the investigation of T cell phenotypes and repertoires at unprecedented resolution and scale. Bioinformatic methods for the efficient analysis of these large-scale datasets are instrumental for advancing our understanding of adaptive immune responses in cancer, but also in infectious diseases like COVID-19. However, while well-established solutions are accessible for the processing of single-cell transcriptomes, no streamlined pipelines are available for the comprehensive characterization of T cell receptors. Here we propose Scirpy, a scalable Python toolkit that provides simplified access to the analysis and visualization of immune repertoires from single cells and seamless integration with transcriptomic data. Availability and implementationScirpy source code and documentation are available at https://github.com/icbi-lab/scirpy.

bioinformatics↗

bioRxiv2018

Comprehensive evaluation of cell-type quantification methods for immuno-oncology

MotivationThe composition and density of immune cells in the tumor microenvironment profoundly influence tumor progression and success of anti-cancer therapies. Flow cytometry, immunohistochemistry staining, or single-cell sequencing is often unavailable such that we rely on computational methods to estimate the immune-cell composition from bulk RNA-sequencing (RNA-seq) data. Various methods have been proposed recently, yet their capabilities and limitations have not been evaluated systematically. A general guideline leading the research community through cell type deconvolution is missing. ResultsWe developed a systematic approach for benchmarking such computational methods and assessed the accuracy of tools at estimating nine different immune- and stromal cells from bulk RNA-seq samples. We used a single-cell RNA-seq dataset of [~]11,000 cells from the tumor microenvironment to simulate bulk samples of known cell type proportions, and validated the results using independent, publicly available gold-standard estimates. This allowed us to analyze and condense the results of more than a hundred thousand predictions to provide an exhaustive evaluation across seven computational methods over nine cell types and [~]1,800 samples from five simulated and real-world datasets. We demonstrate that computational deconvolution performs at high accuracy for well-defined cell-type signatures and propose how fuzzy cell-type signatures can be improved. We suggest that future efforts should be dedicated to refining cell population definitions and finding reliable signatures. AvailabilityA snakemake pipeline to reproduce the benchmark is available at https://github.com/grst/immune_deconvolution_benchmark. An R package allows the community to perform integrated deconvolution using different methods (https://grst.github.io/immunedeconv). Contactg.sturm@tum.de Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2017

quanTIseq: quantifying immune contexture of human tumors

The immune contexture has a prognostic value in several cancers and the study of its pharmacological modulation could identify drugs acting synergistically with immune checkpoint blockers. However, the quantification of the immune contexture is hampered by the lack of simple and efficient methods. We developed quanTIseq, a deconvolution method that quantifies the densities of ten immune cell types from bulk RNA sequencing data and tissue imaging data. We performed extensive validation using simulated data, flow cytometry data, and immunohistochemistry data from three cancer cohorts.\n\nAnalysis of 8,000 samples showed that the activation of the CXCR3/CXCL9 axis, rather than the mutational load is associated with cytotoxic T cell infiltration. We also show the prognostic value of deconvolution-based immunoscore and T cell/B cell score in several solid cancers. Finally, we used quanTIseq to show how kinase inhibitors modulate the immune contexture, and we suggest that it might have predictive value for immunotherapy.

bioinformatics↗

bioRxiv2017

Targeting the PD-1/PD-L1 pathway potentiates immunoediting to counterbalance neutral evolution in a mouse model of colorectal cancer

The cancer immunoediting hypothesis postulates a dual role of the immune system: protecting the host by eliminating tumor cells, and shaping the tumor by editing the genome. However, to what extent immunoediting is shaping the cancer genome is still a matter of debate. Moreover, the impact of cancer immunotherapy with checkpoint blockers on modulating immunoediting remains largely unexplored. Here we elucidated the impact of evolutionary and immune-related forces on editing the tumor in a mouse model of colorectal cancer (CRC). We first show that MC38 cell line is a valid model for hypermutated and microsatellite-unstable (MSI) CRC. Analyses of longitudinal samples of wild type and immunodeficient RAG1 knockout mice transplanted with MC38 cells revealed that upregulation of checkpoint molecules and infiltration of Tregs are the major tumor escape mechanisms. Strikingly, the impact of neutral evolution on sculpting the tumor outweighed immunoediting. Targeting the PD-1/PD-L1 pathway potentiated immunoediting and rendered tumors more homogeneous in the MC38 model. The immunoediting effects were less pronounced in a nonhypermutated/MSI- model CT26. Our study demonstrates that neutral evolution is the major force that sculpts the tumor, and that checkpoint blockade effectively enforces T cell dependent immunoselective pressure in a hypermutated/MSI model of CRC.

genomics↗