Biology subjects

Shorer, O.

Publications and source records attributed to Shorer, O..

5 recordsLinked to original sources

bioRxiv2026

Revealing pan-cancer clonal niches of T cells from single-cell RNA sequencing using contrastive learning

T-cell clonal organization is central to anti-tumor immunity, yet its functional characterization typically requires paired single-cell RNA and TCR sequencing (scRNA/TCR-seq). Existing multimodal methods integrate transcriptome and TCR information but are limited when TCR data are unavailable. Here we present CloneTrast, a supervised contrastive-learning framework that uses paired scRNA/TCR-seq during training to learn clonal-functional states from gene expression, while requiring only scRNA-seq at inference. Using paired scRNA/TCR-seq profiles from 2.6 million human T cells across 16 cancer types, CloneTrast identified distinct T-cell clonal niches not explained solely by TCR sequence similarity or conventional cell-state annotations. Among these, an NK-like T-cell niche enriched for tumor-blood shared TCRs was consistently associated with poor immunotherapy response at the pan-cancer level. Analysis of antigen-specific T-cell clones revealed that tumor-resident and blood-borne antigen-specific T cells were preferentially associated with distinct clonal niches. Overall, CloneTrast enables scalable and interpretable inference of clonal organization from single-cell transcriptomes.

bioinformatics↗

bioRxiv2025

scXpand: Pan-cancer detection of T-cell clonal expansion from single-cell RNA sequencing without paired single-cell TCR sequencing

Advances in single-cell sequencing have enabled detailed characterization of T-cell clonal dynamics in cancer. However, analyses aiming to link transcriptional landscape to T-cell clonality remain limited by confounding factors unequally controlled in different studies. To address this challenge, we developed scXpand, the first machine learning framework for pan-cancer detection of T-cell clonal expansion directly from single-cell RNA sequencing (scRNA-seq), without paired T-cell receptor (TCR) sequencing. Trained and tested using our in-house-constructed human pan-cancer database of paired scRNA/TCR-seq profiles from 2.6 million T cells, scXpand demonstrates robust and accurate detection of clonal expansion across tissues and T-cell subtypes. Applied to datasets lacking TCR sequencing, scXpand predictions correspond with known characteristics of the tumor microenvironment. Overall, scXpand is the first framework to detect T-cell clonal expansion across cancers directly from scRNA-seq, enabling broad use on datasets lacking scTCR-seq, while supporting scalable, memory-efficient processing, including pre-trained models with user-friendly documentation for flexible applications.

bioinformatics↗

bioRxiv2024

Single-cell meta-analysis of T cells reveals clonal dynamics of response to checkpoint immunotherapy

Despite the crucial role of T cell clones in the anti-tumor activity, their characterization and association with clinical outcome following immune checkpoint inhibitors (ICI) is lacking. Here we analyzed paired single-cell RNA-sequencing/T-cell receptor sequencing of 767,606 T cells from 460 samples spanning 6 cancer types. We found a robust signature of response based on expanded CD8+ clones that differentiates between responders and non-responders. Analysis of persistent clones showed transcriptional changes that are differentially induced by therapy in the different response groups, suggesting an improved reinvigoration capacity in responding patients. Moreover, a gene trajectory analysis revealed changes in the pseudo-temporal state of de-novo clones that are associated with response to therapy. Lastly, we found that clones shared between tumor and blood are more abundant in non-responders and execute distinct transcriptional programs. Overall, our results highlight differences in clonal transcriptional states that are linked to patient response, offering valuable insights into the mechanisms driving effective anti-tumor immunity. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/614877v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@176f326org.highwire.dtl.DTLVardef@5573baorg.highwire.dtl.DTLVardef@20cf13org.highwire.dtl.DTLVardef@1faf7d8_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

Disrupting CD38-driven T cell dysfunction restores sensitivity to cancer immunotherapy

A central problem in cancer immunotherapy with immune checkpoint blockade (ICB) is the development of resistance, which affects 50% of patients with metastatic melanoma1,2. T cell exhaustion, resulting from chronic antigen exposure in the tumour microenvironment, is a major driver of ICB resistance3. Here, we show that CD38, an ecto-enzyme involved in nicotinamide adenine dinucleotide (NAD+) catabolism, is highly expressed in exhausted CD8+ T cells in melanoma and is associated with ICB resistance. Tumour-derived CD38hiCD8+ T cells are dysfunctional, characterised by impaired proliferative capacity, effector function, and dysregulated mitochondrial bioenergetics. Genetic and pharmacological blockade of CD38 in murine and patient-derived organotypic tumour models (MDOTS/PDOTS) enhanced tumour immunity and overcame ICB resistance. Mechanistically, disrupting CD38 activity in T cells restored cellular NAD+ pools, improved mitochondrial function, increased proliferation, augmented effector function, and restored ICB sensitivity. Taken together, these data demonstrate a role for the CD38-NAD+ axis in promoting T cell exhaustion and ICB resistance, and establish the efficacy of CD38 directed therapeutic strategies to overcome ICB resistance using clinically relevant, patient-derived 3D tumour models.

cancer biology↗

bioRxiv2023

Metabolic predictors of response to immune checkpoint blockade therapy

Metabolism of immune cells in the tumor microenvironment (TME) plays a critical role in cancer patient response to immune checkpoint inhibitors (ICI). Yet, a metabolic characterization of immune cells in the TME of patients treated with ICI is lacking. To bridge this gap we performed a semi-supervised analysis of [~]1700 metabolic genes using single-cell RNA-seq data of >1 million immune cells from [~]230 tumor and blood samples treated with ICI. When clustering cells based on their metabolic gene expression, we found that similar immunological states are found in different metabolic states. Most importantly, we found metabolic states that are significantly associated with patient response. We then built a metabolic predictor based on a dozen gene signature which significantly differentiates between responding and non-responding patients across different cancer types (AUC = 0.8-0.86). Taken together, our results demonstrate the importance of metabolism in predicting patient response to ICI.

bioinformatics↗