Biology subjects

Davis, F. P.

Publications and source records attributed to Davis, F. P..

7 recordsLinked to original sources

bioRxiv2026

Multimodal cell communication networks nominate immunotherapies for RCC subgroups with discrete T cell recruitment or expansion

Renal cell carcinoma (RCC) is amongst the most immune-infiltrated solid tumours, but only a small subset of patients achieves durable response to immune checkpoint blockade therapy. Efforts to characterize the immune microenvironment and molecular regulators responsible for treatment responses have explored numerous facets of disease biology using compartmentalized genomics, transcriptomics, and proteomics datasets, yielding many important yet context and data specific insights. Therefore, to provide a more integrated approach to informing future precision medicine strategies, we combined the complementary strengths of multiple technological platforms to profile multi-regional, spatially annotated surgical biospecimens from 65 RCC patients by single-cell RNA sequencing with paired TCR and BCR repertoire analysis, imaging mass cytometry, suspension mass cytometry, spatial transcriptomics and deconvolved bulk RNA sequencing. With this resource dataset, we explored patient subgroups and precision immunotherapy strategies using an integrated analysis of transcripts and proteins across single cell and spatial modalities. Proximal cell interactions and distinct receptor-ligand pairings identified 7 recurrent cellular communication networks. Robustly mapping reproducible gene signatures across technologies and to a variety of publicly available datasets, we show these highly refined immune subgroups stratify patients with tumour microenvironments associated with prognosis and immunotherapy response. Notably, this reveals that highly infiltrated environments with the potential for immunotherapy response may in fact comprise two distinct communication networks, with differing modes of T cell clonal expansion and immune evasion axes associated with T cell exhaustion or myeloid and NK reprogramming, which could inform targeted combination therapeutic strategies to improve outcomes. Overall, we provide a high-dimensional multi-modal resource dataset that enables cross-platform integration, links stages of T cell clonal expansion with enabling or suppressive RCC immune cell communication networks and nominates rational strategies for combinatorial precision immunotherapy. (Funded by University Health Network, Toronto; REMEDY ClinicalTrials.gov number, NCT04005183.)

cancer biology↗

bioRxiv2025

Caris-ComBat-seq: Directionally Harmonizing Large-Scale RNA-seq Datasets

BackgroundBulk RNA sequencing (RNA-seq) is an essential research and clinical diagnostics tool capable of uncovering biological insights across experimental conditions, sample types and diseases at scale. However, RNA-seq data is sensitive to batch effects introduced by technical variation. Harmonizing expression (or batch-correcting) is critical when analyzing measurements from different RNA-seq platforms. In the context of oncology, efficiently and accurately harmonizing expression at scale is important for harnessing massively large datasets (hundreds of thousands of samples) of tumor molecular profiles from different assay platforms. We aimed to develop a method for harmonizing expression in this challenging context. ResultsHere, we extend the widely used ComBat-seq method, as implemented in the pyComBat tool, to enable three key advances: (i) directionally adjusting counts from one batch towards a reference batch, rather than an average expression profile, (ii) separating the training and adjusting steps so that newly profiled samples not available at the time of initial model training can be harmonized, and (iii) flexibly handling outliers to improve the quality of harmonized counts. The resulting model correctly learns gene-specific differences between assay platforms and can near-instantaneously harmonize individual samples. We validated the use of the Caris-ComBat-seq tool to harmonize RNA-seq measurements on a benchmarking dataset of [~]10,000 TCGA tumor samples. Finally, we demonstrated its strength for very large datasets, by harmonizing RNA-seq data from nearly half a million tumor samples profiled by two different next-generation sequencing assays in Caris Life Sciences clinical laboratory. Source code, tutorials and manuscript data are available at: https://github.com/Caris-Life-Sciences/Caris-ComBat-seq and https://doi.org/10.5281/zenodo.17154014. ConclusionsWe present Caris-ComBat-seq, a new variant of the ComBat-seq algorithm designed to harmonize count-based expression data in the context of high-throughput profiling laboratories. It offers the same ability to ameliorate batch effects and retain biological signal as the original ComBat-seq, with additional operational flexibility and scaling that benefits its high-throughput application.

bioinformatics↗

bioRxiv2023

Tumor-intrinsic LKB1-LIF signaling axis establishes a myeloid niche to promote immune evasion and tumor growth

Tumor mutations can influence the surrounding microenvironment leading to suppression of anti-tumor immune responses and thereby contributing to tumor progression and failure of cancer therapies. Here we use genetically engineered lung cancer mouse models and patient samples to dissect how LKB1 mutations accelerate tumor growth by reshaping the immune microenvironment. Comprehensive immune profiling of LKB1-mutant vs wildtype tumors revealed dramatic changes in myeloid cells, specifically enrichment of Arg1+ interstitial macrophages and SiglecFHi neutrophils. We discovered a novel mechanism whereby autocrine LIF signaling in Lkb1-mutant tumors drives tumorigenesis by reprogramming myeloid cells in the immune microenvironment. Inhibiting LIF signaling in Lkb1-mutant tumors, via gene targeting or with a neutralizing antibody, resulted in a striking reduction in Arg1+ interstitial macrophages and SiglecFHi neutrophils, expansion of antigen specific T cells, and inhibition of tumor progression. Thus, targeting LIF signaling provides a new therapeutic approach to reverse the immunosuppressive microenvironment of LKB1-mutant tumors.

cancer biology↗

bioRxiv2022

Inflammation in the tumor-adjacent lung as a predictor of clinical outcome in lung adenocarcinoma

Early-stage lung adenocarcinoma is typically treated by surgical resection of the tumor. While in the majority of cases surgery can lead to cure, approximately 30% of patients progress. Despite intense efforts to map the genetic landscape of early-stage lung tumors, there has been limited success in discovering accurate biomarkers that can predict clinical outcomes. Meanwhile, the role of the tumor-adjacent tissue in cancer progression has been largely ignored. To test whether tumor-adjacent tissue can be informative of progression-free survival and to probe the underlying molecular pathways involved, we designed a multi-omic study in both tumor and matched tumor-adjacent histologically normal lung tissue from the same patient. Our study includes 143 treatment naive stage I cases with long-term patient follow-up and is, to our knowledge, the largest such study with the longest follow-up. We performed a comprehensive histologic characterization of all tumors, mapped the mutational landscape and probed the transcriptome of both tumor and adjacent normal tissue. We evaluated the predictive power of each data modality and showed that the transcriptome of tumor-adjacent histologically normal lung tissue is the only reliable predictor of clinical outcome. Unbiased discovery of co-expressed gene modules revealed that inflammatory pathways are upregulated in the tumor-adjacent tissue of patients at high risk for disease progression. Furthermore, single-cell transcriptome analysis in the tumor-adjacent lung demonstrated that progression-associated inflammatory signatures were broadly expressed by both immune and non-immune cells including mesothelial cells, alveolar type 2 cells and fibroblasts, CD1 dendritic cells and MAST cells. Collectively, our studies suggest that molecular profiling of tumor-adjacent tissue can identify patients that are at high risk for disease progression.

bioinformatics↗

bioRxiv2018

Nuclear transcriptomes of the seven neuronal cell types that constitute the Drosophila mushroom bodies.

The insect mushroom body (MB) is a conserved brain structure that plays key roles in a diverse array of behaviors. The Drosophila melanogaster MB is the primary invertebrate model of neural circuits related to memory formation and storage, and its development, morphology, wiring, and function has been extensively studied. MBs consist of intrinsic Kenyon Cells that are divided into three major neuron classes ({gamma}, /{beta} and /{beta}) and 7 cell subtypes ({gamma}d, {gamma}m, /{beta}ap, /{beta}m, /{beta}p, /{beta}s and /{beta}c) based on their birth order, morphology, and connectivity. These subtypes play distinct roles in memory processing, however the underlying transcriptional differences are unknown. Here, we used RNA sequencing (RNA-seq) to profile the nuclear transcriptomes of each MB neuronal cell subtypes. We identified 350 MB class- or subtype-specific genes, including the widely used /{beta} class marker Fas2 and the /{beta} class marker trio. Immunostaining corroborates the RNA-seq measurements at the protein level for several cases. Importantly, our data provide a full accounting of the neurotransmitter receptors, transporters, neurotransmitter biosynthetic enzymes, neuropeptides, and neuropeptide receptors expressed within each of these cell types. This high-quality, cell type-level transcriptome catalog for the Drosophila MB provides a valuable resource for the fly neuroscience community.

neuroscience↗

bioRxiv2018

A genetic, genomic, and computational resource for exploring neural circuit function

The anatomy of many neural circuits is being characterized with increasing resolution, but their molecular properties remain mostly unknown. Here, we characterize gene expression patterns in distinct neural cell types of the Drosophila visual system using genetic lines to access individual cell types, the TAPIN-seq method to measure their transcriptomes, and a probabilistic method to interpret these measurements. We used these tools to build a resource of high-resolution transcriptomes for 100 driver lines covering 67 cell types, available at http://www.opticlobe.com. Combining these transcriptomes with recently reported connectomes helps characterize how information is transmitted and processed across a range of scales, from individual synapses to circuit pathways. We describe examples that include identifying neurotransmitters, including cases of co-release, generating functional hypotheses based on receptor expression, as well as identifying strong commonalities between different cell types.\n\nHighlightsO_LITranscriptomes reveal transmitters and receptors expressed in Drosophila visual neurons\nC_LIO_LITandem affinity purification of intact nuclei (TAPIN) enables neuronal genomics\nC_LIO_LITAPIN-seq and genetic drivers establish transcriptomes of 67 Drosophila cell types\nC_LIO_LIProbabilistic modeling simplifies interpretation of large transcriptome catalogs\nC_LI

neuroscience↗

bioRxiv2017

Predictions of Protein-Protein Interactions in Schistosoma Mansoni

BackgroundSchistosoma mansoni invasion of the human host involves a variety of cross-species protein-protein interactions. The pathogen expresses a diverse arsenal of proteins that facilitate the breach of physical and biochemical barriers present in skin, evasion of the immune system, and digestion of human hemoglobin, allowing schistosomes to reside in the host for years. However, only a small number of specific interactions between S. mansoni and human proteins have been identified. We present and apply a protocol that generates testable predictions of S. mansoni-human protein interactions.\n\nMethodsIn this study, we first predict S. mansoni-human protein interactions based on similarity to known protein complexes. Putative interactions were then scored and assessed using several contextual filters, including the use of annotation automatically derived from literature using a simple natural language processing methodology. Our method predicted 7 out of the 10 previously known cross-species interactions.\n\nConclusionsSeveral predictions that warrant experimental follow-up were presented and discussed, including interactions involving potential vaccine candidate antigens, protease inhibition, and immune evasion. The application framework provides an integrated methodology for investigation of host-pathogen interactions and an extensive source of orthogonal data for experimental analysis. We have made the predictions available online for community perusal.\n\nAuthor SummaryThe S. mansoni parasite is the etiological agent of the disease Schistomiasis. However, protein-protein interactions have been experimentally characterized that relate to pathogenesis and establishment of infection. As with many pathogens, the understanding of these interactions is a key component for the development of new vaccines. In this project, we have applied a computational whole-genome comparative approach to aid in the prediction of interactions between S. mansoni and human proteins and to identify important proteins involved in infection. The results of applying this method recapitulate several previously characterized interactions, as well as suggest additional ones as potential therapeutic targets.

bioinformatics↗