Biology subjects

Stransky, N.

Publications and source records attributed to Stransky, N..

9 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↗

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↗

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↗

bioRxiv2025

Cross-species analysis identifies genotype-driven vulnerabilities in lung adenocarcinoma

While three major genetic alteration subsets, characterized by mutations in STK11, KRAS, and EGFR, are seminal in driving tumorigenesis in LUAD, their distinct effects on tumor cells and the tumor microenvironment are not fully understood. Here, we map critical oncogenic subset-specific vulnerabilities by identifying conserved cell-type-specific reprogrammings between human and mouse LUAD. Through harmonized scRNA-seq analysis of 57 human and 18 mouse specimens, we unveil that genetic alterations impose genotype-specific immune imprints on the tumor microenvironment: KRAS is associated with a transitional immune state, whereas STK11 and EGFR mutations define discrete and contrasting immune phenotypes. We find that STK11-mutant tumors exhibit complement and interferon-rich immune microenvironments while EGFR-mutant tumors harbor a naive T cell-rich phenotype accompanied by a global HLA downregulation, stress-responsive alveolar macrophages marked by MARCO. In the epithelial compartments, cross-species analysis reveals metabolic dependencies, including OGDH in EGFR- and PDE4D in STK11-mutant cells. Statement of SignificanceThis study delineates oncogenotype-specific molecular and immune imprints in lung adenocarcinoma, revealing STK11-, KRAS-, and EGFR-mutated tumors as transcriptionally and immunologically discrete ecosystems. By mapping epithelial vulnerabilities alongside immune imprints, we unveil genotype-imposed dependencies that inform the rational development of precision therapies with direct translational relevance.

cancer biology↗

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↗

bioRxiv2023

A longitudinal single-cell therapeutic atlas of anti-tumour necrosis factor treatment in inflammatory bowel disease

Precision medicine in immune-mediated inflammatory diseases (IMIDs) requires an understanding of how cellular networks change following therapy. We describe a therapeutic atlas for Crohns disease (CD) and ulcerative colitis (UC) following anti-tumour necrosis factor (TNF) therapy. We generated ~1 million single-cell transcriptomes, organised into 109 cell states, from 216 gut biopsies from 38 patients and three controls, revealing disease- and therapy-specific differences. A systems-biology analysis identified distinct spatially-resolved cellular microenvironments: granuloma signatures in CD and interferon (IFN)-response signatures localising to T-cell aggregates and epithelial damage in CD and UC. Longitudinal comparisons demonstrated that disease progression in non-responders associated with myeloid and stromal cell perturbations in CD and increased multi-cellular IFN signalling in UC. IFN signalling was also observed in rheumatoid arthritis (RA) synovium with a lymphoid pathotype. Our therapeutic atlas informs drug positioning across IMIDs, and suggests a rationale for the use of janus kinase (JAK) inhibition following anti-TNF resistance.

immunology↗

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↗

bioRxiv2022

Evaluation of PRKACA as a Therapeutic Target for Fibrolamellar Carcinoma

Background & AimsFibrolamellar carcinoma (FLC) is a rare, difficult-to-treat liver cancer primarily affecting pediatric and adolescent patients, and for which precision medicine approaches have historically not been possible. The DNAJB1-PRKACA gene fusion was identified as a driver of FLC pathogenesis. We aimed to assess whether FLC tumors maintain dependency on this gene fusion and determine if PRKACA is a viable therapeutic target. MethodsFLC patient-derived xenograft (PDX) shRNA cell lines were implanted subcutaneously into female NOD-SCID mice and tumors were allowed to develop prior to randomization to doxycycline (to induce knockdown) or control groups. Tumor development was assessed every 2 days. To assess the effect of treatment with novel selective PRKACA small molecule kinase inhibitors, BLU0588 and BLU2864, FLC PDX tumor cells were implanted subcutaneously into NOD-SCID mice and tumors allowed to develop. Mice were randomized to treatment (BLU0588 and BLU2864, orally, once daily) or control groups and tumor size determined as above. ResultsKnockdown of DNAJB1-PRKACA reversed a FLC-specific gene signature and reduced PDX tumor growth in mice compared to the control group. Furthermore, FLC PDX tumor growth was significantly reduced with BLU0588 and BLU2864 treatment versus control (P = 0.003 and P = 0.0005, respectively). ConclusionsWe demonstrated, using an inducible knockdown and small molecule approaches, that FLC PDX tumors were dependent upon DNAJB1-PRKACA fusion activity. In addition, this study serves as a proof-of-concept that PRKACA is a viable therapeutic target for FLC and warrants further investigation.

cancer biology↗

bioRxiv2017

STAR-Fusion: Fast and Accurate Fusion Transcript Detection from RNA-Seq

MotivationFusion genes created by genomic rearrangements can be potent drivers of tumorigenesis. However, accurate identification of functionally fusion genes from genomic sequencing requires whole genome sequencing, since exonic sequencing alone is often insufficient. Transcriptome sequencing provides a direct, highly effective alternative for capturing molecular evidence of expressed fusions in the precision medicine pipeline, but current methods tend to be inefficient or insufficiently accurate, lacking in sensitivity or predicting large numbers of false positives. Here, we describe STAR-Fusion, a method that is both fast and accurate in identifying fusion transcripts from RNA-Seq data.\n\nResultsWe benchmarked STAR-Fusions fusion detection accuracy using both simulated and genuine Illumina paired-end RNA-Seq data, and show that it has superior performance compared to popular alternative fusion detection methods.\n\nAvailability and implementationSTAR-Fusion is implemented in Perl, freely available as open source software at http://star-fusion.github.io, and supported on Linux.\n\nContactbhaas@broadinstitute.org

bioinformatics↗