Biology subjects

Reuben, A.

Publications and source records attributed to Reuben, A..

9 recordsLinked to original sources

bioRxiv2026

Distinct Spatial Immune Architectures in Tumor and Tumor-Adjacent Tissues of Early-Stage Non-Small Cell Lung Cancer

Background: Lung cancer remains the leading cause of cancer-related deaths in the United States with over 124,000 estimated deaths for 2026. Previous studies have found that tumor-adjacent lung tissues may provide additional insight into the immune microenvironment of early-stage NSCLC. Methods: Multiplex immunofluorescence (mIF) imaging was performed on 192 tissues from 101 early-stage non-small cell lung cancer patients including 91 matched tumor-adjacent pairs, using three mIF panels. Spatial analyses were performed to identify distinctions between tissues and identify associations with clinical and genomic features. Results: Tumor tissue showed significantly higher densities of T cells, macrophages, B cells, and memory/regulatory populations than adjacent tissue (p<0.001). Tumors exhibited greater spatial heterogeneity, with higher prevalence of spatial patterning (47.7% vs. 31.2%) and more consistently localized organization, whereas adjacent tissue showed stronger individual-cell clustering. Pairwise colocalization (Ripley's L-cross) revealed selective spatial segregation in tumors (including B-cells from memory/regulatory cells and among cytotoxic T-cell subsets) and reduced immune proximity to malignant cells relative to the strong immune-epithelial association in adjacent tissue. Spatial features were linked to genomic features or patient outcomes: tumor neoantigen burden exhibited a positive association with CD3+ T cells in the tumor, while colocalization between CD45RO+CD57+GZMB+ cells and CD57+GZMB+ cells was negatively associated with overall survival outside the tumor. Conclusion: Tumor and adjacent tissues harbor distinct spatial immune architectures, with spatial features displaying associations with genomic features or patient outcomes. These findings highlight immune microenvironment reorganization and the importance of incorporating spatial context from both compartments into risk assessment in NSCLC.

immunology↗

bioRxiv2026

HER2 mutation-derived neoantigens in NSCLC as actionable targets for TCR therapy

HER2 mutations are oncogenic drivers in 1-6% of non-small cell lung cancers (NSCLC), but therapeutic resistance limits the durability of current HER2-targeted treatments. Here, we identify T-cell receptors (TCRs) targeting recurrent HER2 hotspot mutations as a potential immunotherapeutic strategy for HER2-mutant NSCLC. Using neoepitope prediction and antigen-specific T-cell enrichment, we isolated HLA-A*02:01restricted TCRs recognizing HER2 A775insYVMA, S310F, and G776delinsVC mutations, collectively covering approximately 60% of HER2-mutant NSCLC. These TCRs selectively recognized mutant HER2 epitopes without detectable wild-type reactivity and some displayed cross-recognition of related hotspot variants, expanding the spectrum of targetable tumors. The G776delinsVC-specific TCR also exhibited co-receptorindependent activity showcased by its ability to activate CD4+ T cells. Importantly, timelapse single-cell flow cytometry analyses demonstrated that TCR-engineered T cells repeatedly reacquired activated polyfunctional states following serial antigen stimulation, while serial tumor rechallenge assays confirmed sustained cytotoxic activity across multiple rounds of tumor killing. These findings identify recurrent HER2 mutations as shared immunotherapeutic targets and provide a foundation for the development of TCR-based therapies for HER2-mutant NSCLC.

immunology↗

bioRxiv2026

TCR-FramePose: a local-frame representation for decomposing global docking and CDR3 loop geometry in TCR-pMHC recognition

T cell receptor recognition of peptide-MHC depends on sequence, interface chemistry, and three-dimensional geometry, but docking geometry is often summarized at the whole-receptor level, leaving CDR3-local pose difficult to compare across structures. We introduce TCR-FramePose, a local-frame descriptor set that represents each TCR-pMHC complex as three bodies -- whole TCR, CDR3, and CDR3{beta} -- measured relative to a pMHC groove frame. For each body, FramePose decomposes the native pose into reach, offset direction on S2, and orientation on SO(3); for tangent-space analyses, these components are mapped to six coordinates per body and 18 coordinates per complex. Applied to 378 curated {beta}TCR-pMHC crystal structures, FramePose recovers known class-associated receptor-placement differences and additionally resolved whole-TCR and CDR3{beta} orientation shifts that were not captured by crossing angle. The same orientation coordinates identified reverse-polarity and off-axis outliers as distinct modes. In cross-validated association analyses, FramePose added nonredundant BSA- and affinity-associated information beyond conventional descriptors, and the modest affinity gain was concentrated in CDR3 orientation blocks which were least recoverable from conventional descriptors. Biological grouping analyses showed that shared receptor pose over peptide-MHC was organized primarily by germline V-region framework. TCRs recognizing the same peptide-MHC target favors shared FramePose geometries rather than strong receptor-specific divergence, whereas CDR3 sequence did not detectably reposition the rigid-body pose after antigen context and germline framework were fixed. MHC allele and peptide length contributed smaller adjustments, localized mainly to CDR3{beta} and groove-normal orientation axes. Finally, interface analyses showed that affinity tracked interface burial, with CDR3{beta} reach linking FramePose geometry to binding through buried surface area. Within engineered panels, mutation-level effects were panel-specific, with CDR3{beta} remodeling localizing to a recurrent interface region but varying in direction across receptors. These properties enable FramePose to serve as a geometric filter for in silico TCR-pMHC models and as a feature layer for structure-guided TCR engineering. Together, TCR-FramePose provides a nonredundant geometric layer for structure-guided TCR-pMHC analysis, linking germline-scaffolded recognition, CDR3-local pose, and interface organization without replacing sequence, contact, or energetic descriptors.

bioinformatics↗

bioRxiv2024

TimiGP-Response: the pan-cancer immune landscape associated with response to immunotherapy

Accumulating evidence suggests that the tumor immune microenvironment (TIME) significantly influences the response to immunotherapy, yet this complex relationship remains elusive. To address this issue, we developed TimiGP-Response (TIME Illustration based on Gene Pairing designed for immunotherapy Response), a computational framework leveraging single-cell and bulk transcriptomic data, along with response information, to construct cell-cell interaction networks associated with responders and estimate the role of immune cells in treatment response. This framework was showcased in triple-negative breast cancer treated with immune checkpoint inhibitors targeting the PD-1:PD-L1 interaction, and orthogonally validated with imaging mass cytometry. As a result, we identified CD8+ GZMB+ T cells associated with responders and its interaction with regulatory T cells emerged as a potential feature for selecting patients who may benefit from these therapies. Subsequently, we analyzed 3,410 patients with seven cancer types (melanoma, non-small cell lung cancer, renal cell carcinoma, metastatic urothelial carcinoma, hepatocellular carcinoma, breast cancer, and esophageal cancer) treated with various immunotherapies and combination therapies, as well as several chemo- and targeted therapies as controls. Using TimiGP-Response, we depicted the pan-cancer immune landscape associated with immunotherapy response at different resolutions. At the TIME level, CD8 T cells and CD4 memory T cells were associated with responders, while anti-inflammatory (M2) macrophages and mast cells were linked to non-responders across most cancer types and datasets. Given that T cells are the primary targets of these immunotherapies and our TIME analysis highlights their importance in response to treatment, we portrayed the pan-caner landscape on 40 T cell subtypes. Notably, CD8+ and CD4+ GZMK+ effector memory T cells emerged as crucial across all cancer types and treatments, while IL-17-producing CD8+ T cells were top candidates associated with immunotherapy non-responders. In summary, this study provides a computational method to study the association between TIME and response across the pan-cancer immune landscape, offering resources and insights into immune cell interactions and their impact on treatment efficacy.

bioinformatics↗

bioRxiv2023

pan-MHC and cross-Species Prediction of T Cell Receptor-Antigen Binding

Profiling the binding of T cell receptors (TCRs) of T cells to antigenic peptides presented by MHC proteins is one of the most important unsolved problems in modern immunology. Experimental methods to probe TCR-antigen interactions are slow, labor-intensive, costly, and yield moderate throughput. To address this problem, we developed pMTnet-omni, an Artificial Intelligence (AI) system based on hybrid protein sequence and structure information, to predict the pairing of TCRs of {beta} T cells with peptide-MHC complexes (pMHCs). pMTnet-omni is capable of handling peptides presented by both class I and II pMHCs, and capable of handling both human and mouse TCR-pMHC pairs, through information sharing enabled this hybrid design. pMTnet-omni achieves a high overall Area Under the Curve of Receiver Operator Characteristics (AUROC) of 0.888, which surpasses competing tools by a large margin. We showed that pMTnet-omni can distinguish binding affinity of TCRs with similar sequences. Across a range of datasets from various biological contexts, pMTnet-omni characterized the longitudinal evolution and spatial heterogeneity of TCR-pMHC interactions and their functional impact. We successfully developed a biomarker based on pMTnet-omni for predicting immune-related adverse events of immune checkpoint inhibitor (ICI) treatment in a cohort of 57 ICI-treated patients. pMTnet-omni represents a major advance towards developing a clinically usable AI system for TCR-pMHC pairing prediction that can aid the design and implementation of TCR-based immunotherapeutics.

immunology↗

bioRxiv2022

TimiGP: inferring inter-cell functional interactions and clinical values in the tumor immune microenvironment through gene pairs

Determining how immune cells functionally interact in the tumor microenvironment and identifying their biological roles and clinical values are critical for understanding cancer progression and developing new therapeutic strategies. Here we introduce TimiGP, a computational method to infer inter-cell functional interaction networks and annotate the corresponding prognostic effect from bulk gene expression and survival statistics data. When applied to metastatic melanoma, TimiGP overcomes the prognostic bias caused by immune co-infiltration and identifies the prognostic value of immune cells consistent with their anti- or pro-tumor roles. It reveals the functional interaction network in which the interaction X[->]Y indicates a more positive impact of cell X than Y on survival. This network provides immunological insights to facilitate the development of prognostic models, as evidenced by our computational-friendly, biologically interpretable, independently validated models. By leveraging single-cell RNA-seq data for specific immune cell subsets, TimiGP has the flexibility to delineate the tumor microenvironment at different resolutions and is readily applicable to a wide range of cancer types.

bioinformatics↗

bioRxiv2021

The histologic phenotype of lung cancers may be driven by transcriptomic features rather than genomic characteristics

Histology plays an essential role in therapeutic decision-making for lung cancer patients. However, the molecular determinants of lung cancer histology are largely unknown. We conducted whole-exome sequencing(WES) and microarray profiling on 19 micro-dissected tumor regions of different histologic subtypes from 9 patients with lung cancers of mixed histology. A median of 68.9% of point mutations and 83% of copy number aberrations were shared between different histologic components within the same tumors. Furthermore, different histologic components within the tumors demonstrated similar subclonal architecture. On the other hand, transcriptomic profiling revealed shared pathways between the same histologic subtypes from different patients, which was supported by the analyses of the transcriptomic data from 141 cell lines and 343 lung cancers of different histologic subtypes. These data suggest that histology of lung cancers may be determined at the transcriptomic level rather than the genomic level.

genomics↗

bioRxiv2020

Intertumoral Genetic Heterogeneity Generates Distinct Tumor Microenvironments in a Novel Murine Synchronous Melanoma Model

Synchronous metastatic melanoma, clinically defined as multiple lesions diagnosed within 6 months, has a poor prognosis. Despite recent advances in systemic immunotherapy, a majority of patients fail to respond or exhibit lesion-specific responses. While intertumoral heterogeneity has been clinically associated with lesion-specific therapeutic responses, no clear mechanism has been identified, largely due to the scarcity of preclinical models. We developed a novel murine synchronous melanoma model that recapitulates clinical intertumoral heterogeneity. We show that genetic differences between tumors generate distinct tumor immune microenvironments (TIME). These TIMEs can independently upregulate PD-1/PD-L1 expression in response to ongoing anti-tumor immunity and the presence of interferon-gamma. The simultaneous presence of multiple tumors can additionally alter the TIME of each tumor. As such, our model provides a unique approach to investigate the effects of intertumoral heterogeneity on mechanisms of immunotherapy resistance.

cancer biology↗

bioRxiv2020

Genomic and TCR Repertoire Intratumor Heterogeneity of Small-cell Lung Cancer and its Impact on Survival

Small-cell lung cancer (SCLC) is speculated to harbor complex genomic intratumor heterogeneity (ITH) associated with high recurrence rate and suboptimal response to immunotherapy. Here, we revealed a rather homogeneous mutational landscape but extremely suppressed and heterogeneous T cell receptor (TCR) repertoire in SCLCs. Higher mutational burden, lower chromosomal copy number aberration (CNA) burden, less CNA ITH and less TCR ITH were associated with longer overall survival of SCLC patients. Compared to non-small cell lung cancers (NSCLCs), SCLCs had similar predicted neoantigen burden and mutational ITH, but significantly more suppressed and heterogeneous TCR repertoire that may be associated with higher CNA burden and CNA ITH in SCLC. Novel therapeutic strategies targeting CNA could potentially improve the tumor immune microenvironment and response to immunotherapy in SCLC.

genomics↗