Biology subjects

Shasha, C.

Publications and source records attributed to Shasha, C..

4 recordsLinked to original sources

bioRxiv2026

Human CD4 T cells coordinate antitumor immunity through indirect antigen presentation

Whether human tumor-specific CD4 T cells can drive antitumor immunity in cancers lacking tumor-cell MHC class II expression has not been established. In MHC class II-negative melanoma and breast cancer, CD4 T cells with a transcriptional signature of tumor specificity correlated with myeloid and CD8 T cell activation. Using a patient-derived NY-ESO-specific, HLA-DP04-restricted TCR, we tested whether human CD4 T cells could control tumors through indirect antigen presentation. In vitro, CD4 T cells inhibited HLA-DP-negative melanoma growth through recognition of tumor-derived antigen on antigen presenting cells (APC). In humanized mice, CD4 T cell transfer induced regression of HLA-DP-negative tumors, and control required HLA-matched human APCs. CD4 T cell transfer induced endogenous CD8 T cell proliferation and myeloid activation that mirrored immune programs correlated with the presence of these cells in patients. These findings establish that human tumor-specific CD4 T cells coordinate antitumor immunity through APC without direct tumor recognition, supporting their therapeutic relevance in MHC class II-negative cancers.

immunology↗

bioRxiv2025

Modulating AP-1 enables CAR-T cells to establish an intratumoral PD-1+Tcf1+ stem-like reservoir and overcomes resistance to PD-1 axis blockade

PD-1+Tcf1+ stem-like cells are critical mediators of endogenous T cell responses to PD-1/PD-L1 blockade and are maintained by MHC-dependent interactions with professional antigen-presenting cells (APCs). Unlike conventional T cells, CAR-T cells are activated by intact antigen expressed on tumors, not by peptide/MHC expressed on APCs, restricting their activation to the hostile tumor microenvironment (TME) that may impair preservation of this critical stem-like subset. Indeed, in an autochthonous model of ROR1+ lung cancer that we developed, CAR-T cells targeting the tumor-associated antigen ROR1 were uniformly Tcf1+ prior to infusion but rapidly downregulated Tcf1 in vivo and became terminally exhausted, similar to observations in patients, resulting in faster attrition and no enhancement in response to PD-L1 blockade. We hypothesized that overexpression of AP-1 family transcription factors, which can regulate T cell exhaustion, could enable CAR-Ts to maintain this critical PD-1+Tcf1+ subset within tumors independently of APCs and sensitize them to PD-1/PD-L1 blockade. Overexpression of the AP-1 TF c-Jun, but not BATF, improved preservation of PD-1+Tcf1+ CAR-T cells within tumors in a cell-intrinsic manner that correlated with increased persistence deeper within tumors. Notably, c-Jun overexpression alone was insufficient to prevent CAR-T exhaustion in the lung TME, in contrast to prior work in xenograft models, with progressive CAR-T dysfunction correlated with PD-1-dependent downregulation of c-Jun. However, c-Jun overexpression dramatically sensitized CAR-Ts to PD-L1 blockade, which restored c-Jun levels in CAR-Ts, drove log-fold expansion of CAR-Ts within tumors, and induced nearly complete eradication of ROR1+ tumor in highly aggressive models of lung cancer. Altogether, our data show that combination with PD-L1 blockade is necessary to unleash the full potential of c-Jun-overexpressing CAR-T cells in aggressive solid tumors like lung cancer and suggest that strategies to enhance formation of intratumoral PD-1+Tcf1+ reservoirs can overcome CAR-T resistance to PD-1 blockade.

immunology↗

bioRxiv2024

Hallmarks of tumor-experienced T cells are absent in multiple myeloma patients from diagnosis through maintenance therapy

Dysregulation of the bone marrow (BM) niche in multiple myeloma (MM) alters the composition and state of resident immune cells, potentially impeding anti-tumor immunity. One common mechanism of immune inhibition in solid tumors is the induction of exhaustion in tumor-specific T cells. However, the extent of T cell tumor recognition and exhaustion is not well-characterized in MM. As the specific mechanisms of immune evasion are critical for devising effective therapeutic strategies, we deeply profiled the CD8+ T cell compartment of newly-diagnosed MM (NDMM) patients for evidence of tumor reactivity and T cell exhaustion. We applied single-cell multi-omic sequencing and antigen-specific mass cytometry to longitudinal BM and peripheral blood (PB) samples taken from timepoints spanning from diagnosis through induction therapy, autologous stem cell transplant (ASCT), and maintenance therapy. We identified an exhausted-like population that lacked several canonical exhaustion markers, was not significantly enriched in NDMM patients, and consisted of small, nonpersistent clones. We also observed an activated population with increased frequency in the PB of NDMM patients exhibiting phenotypic and clonal features consistent with homeostatic, antigen-nonspecific activation. However, there was no evidence of "tumor-experienced" T cells displaying hallmarks of terminal exhaustion and/or tumor-specific activation/expansion in NDMM patients at any timepoint.

immunology↗

bioRxiv2021

Superscan: Supervised Single-Cell Annotation

Automated cell type annotation of single-cell RNA-seq data has the potential to significantly improve and streamline single cell data analysis, facilitating comparisons and meta-analyses. However, many of the current state-of-the-art techniques suffer from limitations, such as reliance on a single reference dataset or marker gene set, or excessive run times for large datasets. Acquiring high-quality labeled data to use as a reference can be challenging. With CITE-seq, surface protein expression of cells can be directly measured in addition to the RNA expression, facilitating cell type annotation. Here, we compiled and annotated a collection of 16 publicly available CITE-seq datasets. This data was then used as training data to develop Superscan, a supervised machine learning-based prediction model. Using our 16 reference datasets, we benchmarked Superscan and showed that it performs better in terms of both accuracy and speed when compared to other state-of-the-art cell annotation methods. Superscan is pre-trained on a collection of primarily PBMC immune datasets; however, additional data and cell types can be easily added to the training data for further improvement. Finally, we used Superscan to reanalyze a previously published dataset, demonstrating its applicability even when the dataset includes cell types that are missing from the training set.

bioinformatics↗