Biology subjects

Day, C.-P.

Publications and source records attributed to Day, C.-P..

At least 19 recordsLinked to original sources

bioRxiv2026

Transient Splicing-Associated Isoform Remodeling Defines Early MAPKi Resistance Adaptation in Melanoma

A long-standing question in targeted therapy resistance is how tumor cells rapidly enter drug-tolerant states before stable resistance emerges. In BRAF-mutant melanoma, early adaptation to MAPK inhibitor (MAPKi) therapy is accompanied by profound phenotypic plasticity, yet the molecular mechanisms that enable this transition remain poorly understood. Here, we show that MAPKi adaptation is characterized by a transient transcript isoform remodeling that is largely invisible to conventional gene-expression analysis. We integrated and analyzed multiple different datasets, including bulk and single-cell RNA-seq datasets from melanoma cell lines, murine models, patient-derived xenografts and patient tumors, spanning no-treatment, on-treatment/minimal residual disease, and acquired-resistant states. We find that differential transcript usage peaks during the adaptive on-treatment phase and then is markedly attenuated when resistance was established. These dynamics were conserved across experimental systems and supported by independent exon-usage analyses. Notably, most genes undergoing differential transcript usage showed no corresponding change in total gene expression, and genes with isoform-level changes more strongly highlighted MAPKi resistance-associated pathways, including PI3K/AKT/mTOR and receptor tyrosine kinase signaling. Further, classifiers trained on cell-line transcript-usage features identified the adaptive state more accurately than gene-expression-based models in independent PDX and patient datasets. Interestingly, MAPKi-associated isoform changes recapitulated melanoblast-associated developmental splicing programs and were linked to a few key splicing regulators. Together, these findings identify an underappreciated regulatory layer connecting drug tolerance, developmental plasticity, and acquired resistance. Beyond the current context, our results suggest that comprehensively tracking isoform and splicing alterations may potentially advance our understanding of the emergence of treatment resistance in additional indications and treatment regimes.

cancer biology↗

bioRxiv2026

STITCH links cellular morphology and gene expression in spatial transcriptomics

In situ spatial (ISS) sequencing can uncover co-variation between cellular morphology and gene expression in vivo. However, a principled and interpretable mathematical representation of morphology has not yet been applied in this context. In particular, current deep learning-based representations of cell images confound a cell's shape with its size. We present an interpretable representation of cellular boundary contours, based on tangent principal component analysis (TPCA) in a Kendall shape manifold, that captures size-independent contour shape features. This approach successfully recovers shape-perturbing genes in an RNAi screen than a previous metric geometry-based approach. We build on TPCA to develop KSTITCH (Kendall Shape-TranscriptomIc Correlation and Harmonization), an approach to reveal covariation between cell morphology with gene expression in ISS datasets. In a Xenium dataset, KSTITCH recovers known morphology-transcriptomic relationships in keratinocytes, macrophages and endothelial cells. Across samples in a melanoma CosMx dataset, KSTITCH reproducibly associates elongated and triangular fibroblasts with proximity to malignant cells and myofibroblast-like transcriptional program. Finally, KSTITCH independently recovers a known link between mesenchymal-like malignant cell states and increased cell area in two melanoma cohorts. KSTITCH can thus yield interpretable morphology-transcriptome relationships across cell types, patients, and spatial transcriptomics platforms. KSTITCH is available at https://github.com/vishakagopalan/kstitch .

bioinformatics↗

bioRxiv2026

Histopathology-inferred spatial transcriptomics characterizes the tumor microenvironment in 1,500 head and neck tumors and predicts clinical outcomes

Head and neck squamous cell carcinoma (HNSC) is a prevalent malignancy associated with poor prognosis despite recent therapeutic advances. We hypothesized that a comprehensive understanding of the spatial heterogeneity and organization of the tumor microenvironment (TME) can substantially improve risk stratification and prediction of treatment response in HNSC. As spatial transcriptomics (ST) remains labor-intensive and costly, we developed HEiST (H&E-Inferred Spatial Transcriptomics), a deep learning framework that predicts spatially resolved gene expression profiles directly from routine hematoxylin and eosin (H&E)-stained histology slides. After rigorous validation across two independent external ST cohorts, we applied HEiST to infer spatial transcriptomes across 1,500 HNSC patient tumors spanning two publicly available datasets and two newly generated cohorts, one treated with concurrent chemoradiotherapy (CCRT) and one with immunotherapy. This large-scale analysis uncovered reproducible spatial clusters characterizing the HNSC TME, defining two distinct prognostic Spatiotypes, Immune-Exhausted and Immune-Activated, with significantly distinct survival outcomes. Critically, spatial cluster composition accurately predicts HPV status and yields treatment response predictors for both CCRT/radiotherapy and immunotherapy that outperform costly gene-expression and direct image-based approaches. Notably, the ST cluster-based predictor of immunotherapy response markedly surpasses the performance of commonly used FDA-approved biomarkers, including CPS, TPS, and their combination. To the best of our knowledge, this represents the first virtual spatial profiling effort and the most comprehensive large-scale spatial TME analysis in HNSC to date. HEiST thus introduces a scalable, low-cost, and spatially grounded biomarker discovery for precision oncology in HNSC.

cancer biology↗

bioRxiv2025

Connecting spatial regions to clinical phenotypes by transferring knowledge from bulk patient data

Spatially resolved transcriptomics (SRT) technology has enabled a new level of knowledge about tumors. Many critical tumor properties, such as invasiveness and growth, depend on both specific transcriptomic changes in tumor cells and the tumor microenvironment. However, computational methods to study clinical phenotypes of spatial regions, such as hazard and drug response, have not yet been developed. Since clinical phenotypes are measured at the patient level and not at the level of spatial regions, such a method would require transferring knowledge from the patient level domain to the spot level domain. To overcome this challenge, we developed SpacePhenotyper. Our approach uses algebraic spectral techniques to transfer the predictive relationship between gene expression and clinical phenotypes from bulk gene expression data to SRT data. Our approach captures a gene expression pattern that is predictive of the phenotype of interest in the form of a vector, the "Eigen-Patient," which is then used to quantify the phenotype in spatial spots. After extensively validating SpacePhenotyper on simulated and real data, we utilize it to study how the spatial heterogeneity of breast cancer tumors influences residual cancer burden after treatment. By assigning relative quantities of clinical phenotypes to spatial locations, SpacePhenotyper has proven a powerful tool for the identification and interpretation of transcriptional changes over spatial regions and of spatially-regulated patterns of cellular states. SpacePhenotyper is implemented in Python. The source code and data sets used for and generated during this study are available at https://github.com/ncbi/SpacePhenotyper

cancer biology↗

bioRxiv2025

Long-read sequencing of single cell-derived melanoma subclones reveals divergent and parallel genomic and epigenomic evolutionary trajectories

Tumor evolution is driven by various mutational processes, ranging from single-nucleotide vari- ants (SNVs) to large structural variants (SVs) to dynamic shifts in DNA methylation. Current short-read sequencing methods struggle to accurately capture the full spectrum of these genomic and epigenomic alter- ations due to inherent technical limitations. To overcome that, here we introduce an approach for long-read sequencing of single-cell derived subclones, and use it to profile 23 subclones of a mouse melanoma cell line, characterized with distinct growth phenotypes and treatment responses. We develop a computational frame- work for harmonization and joint analysis of different variant types in the evolutionary context. Uniquely, our framework enables detection of recurrent amplifications of putative driver genes, generated by indepen- dent SVs across different lineages, suggesting parallel evolution. In addition, our approach revealed gradual and lineage-specific methylation changes associated with aggressive clonal phenotypes. We also show our set of phylogeny-constrained variant calls along with openly released sequencing data can be a valuable resource for the development of new computational methods.

genomics↗

bioRxiv2025

Pan-cancer prediction of tumor immune activation and response to immune checkpoint blockade from tumor transcriptomics and histopathology

Accurately predicting which patients will respond to immune checkpoint blockade (ICB) remains a major challenge. Here, we present TIME_ACT, an unsupervised 66-gene transcriptomic signature of tumor immune activation derived from TCGA (The Cancer Genome Atlas) melanoma data. First, we demonstrate that TIME_ACT scores accurately identify tumors with activated immune microenvironments across different cancer types. Further, analysis of spatial features reveals that tumor microenvironment regions with dense lymphocyte infiltration near tumor cells have high TIME_ACT scores, successfully marking localized immune activation. Second, across 25 transcriptomic ICB cohorts encompassing nine cancer types, TIME_ACT achieves a mean AUC of 0.76 and a mean odds ratio of 5.77, significantly outperforming 30 established transcriptomic signatures and prediction methods for ICB response, including a recently developed foundation model for immunotherapy response prediction. Third, we show that TIME_ACT scores can be accurately inferred from routine tumor histopathology slides and that slide-inferred TIME_ACT scores predict ICB response across nine new independent patient cohorts spanning eight cancer types, achieving a mean AUC of 0.72 and a mean odds ratio of 4.99. These findings establish TIME_ACT as a robust, pan-cancer biomarker that enables accurate, low-cost, and clinically scalable prediction of ICB response from routine histopathology.

cancer biology↗

bioRxiv2025

CRISPR-Based Gene Dependency Screens reveal Mechanism of BRAF Inhibitor Resistance in Anaplastic Thyroid Cancer

Anaplastic thyroid cancer (ATC) is the most aggressive form of thyroid cancer. Despite recent advances in treating BRAFV600E-driven ATC, therapy resistance remains a significant challenge, often resulting in disease progression and death. Leveraging a focused CRISPR/KO screen in parallel with a CRISPR/activation screen, both tailored on response to BRAFV600E inhibitor treatment, we identified TAZ (encoded by the WWTR1 gene) deficiency as synthetically lethal with BRAF inhibitor in ATC. TAZ is overexpressed in ATC compared to well-differentiated thyroid tumors. We demonstrate that TAZ-deficient ATC cells display heightened sensitivity to BRAF inhibitors both in vitro and in vivo. Using gene essentiality score across a large panel of cancer cell lines, we found that BRAFV600E-driven cancers are highly sensitive to TAZ loss, unlike their counterparts with wild-type BRAF and non-BRAFV600E. Mechanistically, we demonstrate that dabrafenib triggers the Unfolded Protein Response (UPR) under ER stress and suppresses protein synthesis. TAZ loss represses the UPR, reverses the inhibition of protein synthesis, and triggers increased cell death by ferroptosis in dabrafenib-treated ATC. Collectively, our findings unveil TAZ as a new target to overcome resistance to BRAF inhibitors in undifferentiated thyroid cancer.

cancer biology↗

bioRxiv2025

Single-cell-guided identification of logic-gated antigen combinations for designing effective and safe CAR therapy

Chimeric antigen receptor (CAR) T-cell therapy has revolutionized the treatment of hematological malignancies. However, its application in solid tumors remains limited because single targets are unlikely to suffice due to tumor antigen heterogeneity and off-tumor toxicities. To overcome these obstacles, we developed LogiCAR designer, a computational approach that utilizes single-cell transcriptomics data from patient tumors to systematically identify the cancer-specific antigen circuits with logic gates ("AND," "OR," and "NOT") that target the majority of cancer cells in a tumor while sparing normal cells and tissues as much as possible. LogiCAR designer efficiently scales to higher-order antigen combinations involving up to five genes. Applied to a large-scale dataset encompassing approximately 2 million cells (including > 620k tumor cells) from 342 clinical patient samples across all major breast cancer subtypes, LogiCAR designer identified antigen circuits with enhanced tumor-targeting efficacy and improved safety profiles compared to both previously reported circuits and single-target therapies in clinical trials. However, even these optimized shared circuits still proved insufficient for some patients. We hence systematically studied LogiCAR designers ability to identify highly effective CAR circuits that are individualized to each patient. Remarkably, such personalized CAR circuits provide estimated tumor-targeting efficacy tantamount to complete response in 76% of patients and partial response for all patients. Taken together, this analysis is the first systematic quantification of the efficacy and safety of all possible CAR circuits, showing that: (a) the quality of existing solutions leaves much to be desired; (b) the ability of shared circuits optimized across many patients is moderate, and finally, (c) individually tailored circuits offer significantly higher tumor-targeting efficacies for patients. LogiCAR designer offers a rigorous, data-driven way to facilitate the rational design of safe and effective CAR-based immunotherapies for cancer. Statement of Significance Development of a computational approach that efficiently identifies logic-gated CAR target combinations, called circuits, from single-cell transcriptomics, addressing a critical unmet clinical need. Application to the largest ensemble of breast cancer datasets to date, comprising [~]2 million cells (> 620k tumor cells) from 17 clinical cohorts, to identify CAR circuits predicted to be effective. Comprehensive safety profiling of candidate circuits spanning major tissues at both RNA and protein levels. Logic-gated CAR circuits generated by our pipeline address tumor heterogeneity and achieve efficacy and safety scores that surpass clinical trial and previously computationally identified circuits. Individualized rational CAR design offers a transformative approach to deliver precision-engineered CAR therapies with unprecedented efficacy.

bioinformatics↗

bioRxiv2025

Clinico-genomic features predict distinct metastatic phenotypes in cutaneous melanoma

Metastasis drives mortality and morbidity in cancer. While some patients develop broad metastatic disease across multiple organs, others exhibit organ-specific spread. To identify mechanisms underlying metastatic organotropism, we analyzed clinico-genomic data from over 7,000 patients with metastatic cutaneous melanoma in three independent cohorts (one primary discovery and two validation cohorts including a nationwide electronic health record-derived deidentified database), leveraging machine learning approaches to clinical data. We found that female sex and increased tumor mutational burden associate with decreased metastatic potential, while older age associates with increased lung and adrenal metastases. Using unsupervised analyses, patients clustered into five metastatic patterns: a "highly metastatic" cluster characterized by involvement of many organs, a "low metastatic" cluster characterized by few metastatic sites (mostly lymph node metastases), and three additional clusters each characterized by metastasis to specific sites (brain, lung, liver). Mutations in B2M and PTEN associated with increased overall metastatic potential. PTEN mutations were also associated with brain metastases but were enriched only in the "highly metastatic" cluster and not the brain-specific cluster. Mutations in GNAQ or GNA11 (GNA) associated with increased liver metastasis. To validate this association, we tested and demonstrated liver tropism in two GNA-mutant genetically engineered cutaneous melanoma mouse models of metastasis. Overall, our study elucidates distinct phenotypes of metastasis in patients with melanoma and identifies novel clinical and genomic associations that illuminate the drivers of clinical metastatic organotropism.

cancer biology↗

bioRxiv2024

BRAIN-METASTATIC MELANOMA MODELS UNCOVER IMMUNE AND MOLECULAR CORRELATES OF RESPONSE TO IMMUNOTHERAPY

Despite the promising results of immune checkpoint blockade (ICB) therapy, outcomes for patients with brain metastasis (BrM) remain poor. Identifying resistance mechanisms has been hindered by limited access to patient samples and relevant preclinical models. Here, we developed two mouse melanoma BrM models that recapitulate the disparate responses to ICB seen in patients. We demonstrate that these models capture the cellular and molecular complexity of human disease and reveal key factors shaping the tumor microenvironment and influencing ICB response. BR1-responsive tumor cells express inflammatory programs that polarize microglia into reactive states, eliciting robust T cell recruitment. In contrast, BR3-resistant melanoma cells are enriched in neurological programs and exploit tolerance mechanisms to maintain microglia homeostasis and limit T cell infiltration. In humans, BR1 and BR3 expression signatures correlate positively or negatively with T cell infiltration and BrM patient outcomes, respectively. Our study provides clinically relevant models and uncovers mechanistic insights into BrM ICB responses, offering potential biomarkers and therapeutic targets to improve therapy efficacy.

immunology↗

bioRxiv2024

Preclinical evaluation of pharmacological inhibition of SIRT1 on the growth of tumoral and metastatic granulosa cells

BackgroundClinical management of patients with recurrent ovarian granulosa cell tumor (GCT) remains poor. Sirtuin-1 (SIRT1), a deacetylase enzyme involved in the regulation of tumor growth and metastasis, may represent a therapeutic target due to the availability of selective pharmacological inhibitors with minimal toxicity. MethodsWe assessed the possible overexpression of SIRT1 during tumorigenesis by Western blot and immunohistochemistry. We tested the effects of SIRT1 inhibition by EX-527 on growth, proliferation, death, migration and gene expression by RNA sequencing and RT-qPCR in vitro on three GCT cell lines (AT29, KGN, COV434). Tumor growth in response to EX-527 treatment was examined in nude mice carrying subcutaneous GCT cell grafts using an electronic caliper and in GCT of AT83 mice by 3D ultrasound imaging system. ResultsSIRT1 abundance increased during tumorigenesis. In vitro treatment with EX-527 efficiently reduced cell growth, either by inducing apoptosis or by inhibiting proliferation. EX-527 induced alterations in pathways driven by mTOR, Myc and E2F, and in pathways controlling cell metabolism and oxidative stress. The administration of this treatment for 4 weeks efficiently reduced tumor progression in vivo. ConclusionsOur study reveals a new therapeutic potential of SIRT1 targeting as a treatment option for patients with recurrent GCT.

cancer biology↗

bioRxiv2024

Stochastic modelling of single-cell gene expression adaptation reveals non-genomic contribution to evolution of tumor subclones

Cancer progression is an evolutionary process driven by the selection of cells adapted to gain growth advantage. We present the first formal study on the adaptation of gene expression in subclonal evolution. We model evolutionary changes in gene expression as stochastic Ornstein-Uhlenbeck processes, jointly leveraging the evolutionary history of subclones and single-cell expression data. Applying our model to sublines derived from single cells of a mouse melanoma revealed that sublines with distinct phenotypes are underlined by different patterns of gene expression adaptation, indicating non-genetic mechanisms of cancer evolution. Interestingly, sublines previously observed to be resistant to anti-CTLA-4 treatment showed adaptive expression of genes related to invasion and non-canonical Wnt signaling, whereas sublines that responded to treatment showed adaptive expression of genes related to proliferation and canonical Wnt signaling. Our results suggest that clonal phenotypes emerge as the result of specific adaptivity patterns of gene expression.

cancer biology↗

bioRxiv2023

Melanoma clonal subline analysis uncovers heterogeneity-driven immunotherapy resistance mechanisms

Intratumoral heterogeneity (ITH) can promote cancer progression and treatment failure, but the complexity of the regulatory programs and contextual factors involved complicates its study. To understand the specific contribution of ITH to immune checkpoint blockade (ICB) response, we generated single cell-derived clonal sublines from an ICB-sensitive and genetically and phenotypically heterogeneous mouse melanoma model, M4. Genomic and single cell transcriptomic analyses uncovered the diversity of the sublines and evidenced their plasticity. Moreover, a wide range of tumor growth kinetics were observed in vivo, in part associated with mutational profiles and dependent on T cell-response. Further inquiry into melanoma differentiation states and tumor microenvironment (TME) subtypes of untreated tumors from the clonal sublines demonstrated correlations between highly inflamed and differentiated phenotypes with the response to anti-CTLA-4 treatment. Our results demonstrate that M4 sublines generate intratumoral heterogeneity at both levels of intrinsic differentiation status and extrinsic TME profiles, thereby impacting tumor evolution during therapeutic treatment. These clonal sublines proved to be a valuable resource to study the complex determinants of response to ICB, and specifically the role of melanoma plasticity in immune evasion mechanisms.

cancer biology↗

bioRxiv2023

Exploring tumor-normal cross-talk with TranNet: role of the environment in tumor progression

There is a growing awareness that tumor-adjacent normal tissues used as control samples in cancer studies do not represent fully healthy tissues. Instead, they are intermediates between healthy tissues and tumors. The factors that contribute to the deviation of such control samples from healthy state include exposure to the tumor-promoting factors, tumor-related immune response, and other aspects of tumor microenvironment. Characterizing the relation between gene expression of tumor-adjacent control samples and tumors is fundamental for understanding roles of microenvironment in tumor initiation and progression, as well as for identification of diagnostic and prognostic biomarkers for cancers. To address the demand, we developed and validated TranNet, a computational approach that utilizes gene expression in matched control and tumor samples to study the relation between their gene expression profiles. TranNet infers a sparse weighted bipartite graph from gene expression profiles of matched control samples to tumors. The results allow us to identify predictors (potential regulators) of this transition. To our knowledge, TranNet is the first computational method to infer such regulation. We applied TranNet to the data of several cancer types and their matched control samples from The Cancer Genome Atlas (TCGA). Many predictors identified by TranNet are genes associated with regulation by the tumor microenvironment as they are enriched in G-protein coupled receptor signaling, cell-to-cell communication, immune processes, and cell adhesion. Correspondingly, targets of inferred predictors are enriched in pathways related to tissue remodelling (including the epithelial-mesenchymal Transition (EMT)), immune response, and cell proliferation. This implies that the predictors are markers and potential stromal facilitators of tumor progression. Our results provide new insights for the relationships between tumor adjacent control sample, tumor and the tumor environment. Moreover, the set of predictors identified by TranNet will provide a valuable resource for future investigations. The TranNet method was implemented in python, source codes and the data sets used for and generated during this study are available at the Github site https://github.com/ncbi/TranNet.

systems biology↗

bioRxiv2022

Comprehensive single-cell transcriptomic analysis of embryonic melanoblasts uncovers lineage-specific mechanisms of melanoma metastasis and therapy resistance

Melanoma plasticity, driven by phenotype state switching, underlies clinically relevant traits such as metastasis and therapy resistance. As melanoma progression is thought to recapitulate aspects of neural crest cell (NCC) development, understanding embryonic melanocyte specification and lineage fate decisions of closely related NCCs may illuminate the pathways co-opted during disease evolution. Here, we use a mouse model to isolate and sequence Dopachrome tautomerase (Dct) expressing NCCs, the precursors of melanocytes, at two key developmental stages. We classify these lineages and devise a Developmental Gene Module (DGM) scoring system to interrogate lineage state switching in melanoma samples. In bulk transcriptomes, activation of DGMs representing embryonic Schwann Cell Precursors (SCPs)--multipotent stem cells--in patient tumors predicts poor response to immune checkpoint inhibitors (ICI). Co-activation of SCP and Mesenchymal-like (Mes.) modules further correlates with resistance to MAPK inhibitors. Notably, single-cell analyses reveal that melanoma cells can simultaneously express multiple DGMs, forming "hybrid" states. Cells in a hybrid Neural/SCP state are enriched in early metastasis and ICI-resistant tumors and are insensitive to inflammatory stimuli. We demonstrate that targeting Hdac2, a histone deacetylase associated with this Neural/SCP hybrid state, promotes a mesenchymal-like state switch, remodels the tumor microenvironment, and sensitizes melanoma cells to TNF and tumors to ICI therapy. Our methodology thus reveals dynamic patterns of lineage state switching correlated with melanoma tumor evolution to drive insight into new therapeutic targets. TeaserNewly identified melanoblast cell states are reawakened in metastasizing and therapy-resistant melanomas.

cancer biology↗

bioRxiv2021

Broad misappropriation of developmental splicing profile by cancer in multiple organs

Oncogenesis mimics key aspects of embryonic development. However, the underlying molecular determinants are not completely understood. Leveraging temporal transcriptomic data during development in multiple human organs, we demonstrate that the embryonic positive (EP) alternative splicing events, specifically active during human organogenesis, are broadly reactivated in the organ-specific tumor. EP events are associated with key oncogenic processes and their reactivation predicts proliferation rates in cancer cell lines as well as patient survival. EP exons are significantly enriched for nitrosylation and transmembrane domains coordinately regulating splicing in multiple genes involved in intracellular transport and N-linked glycosylation respectively, known critical players in cancer. We infer critical splicing factors (CSF) potentially regulating these EP events and show that CSFs exhibit copy number amplifications in cancer and are upregulated specifically in malignant cells in the tumor microenvironment. Mutational inactivation of CSFs results in decreased EP splicing, further supporting their causal role. Multiple complementary analyses point to MYC and FOXM1 as potential transcriptional regulators of CSFs in brain and liver, which can be potentially targeted using FDA approved drugs. Our study provides the first comprehensive demonstration of a splicing-mediated link between development and cancer, and suggest novel targets including splicing events, splicing factors, and transcription factors.

cancer biology↗

bioRxiv2021

Sex biases in cancer and autoimmune disease incidence are correlated across human tissues

Cancer occurs more frequently in men while autoimmune diseases (AIDs) occur more frequently in women. To explore whether these sex biases have a common basis, we collected 167 AID incidence studies from many countries for tissues that have both a cancer type and an AID that arise from that tissue. Analyzing a total of 182 country-specific, tissue-matched cancer-AID incidence rate sex bias data pairs, we find that, indeed, the sex biases observed in the incidence of AIDs and cancers that occur in the same tissue are positively correlated across human tissues. The common key factor whose levels across human tissues are most strongly associated with these incidence rate sex biases is the sex bias in the expression of the 37 genes encoded in the mitochondrial genome.

cancer biology↗

bioRxiv2021

A novel PD-L1-targeted shark VNAR single domain-based CAR-T strategy for treating breast cancer and liver cancer

Chimeric antigen receptor (CAR)-T cell therapy shows great potency against hematological malignancies, whereas it remains difficult to treat solid tumors mainly due to lack of appropriate antigenic targets and immunosuppressive tumor microenvironment (TME). Checkpoint molecule PD-L1 is widely overexpressed on multiple tumor types, and the PD-1/PD-L1 interaction is a key mediator of immunosuppression in TME. Here, we isolated anti-PD-L1 single domain antibodies from a newly constructed semi-synthetic nurse shark VNAR phage library. We found that one VNAR, B2, showed cross-reactivity to human, mouse, and canine PD-L1 antigens, and it partially blocked the interaction of human PD-1 to PD-L1. Furthermore, CAR (B2) T cells specifically lysed human breast cancer and liver cancer cells by targeting constitutive and inducible expression of PD-L1, and also hindered tumor metastasis. Importantly, the combination of CAR (B2) T cells with CAR-T cells targeting liver cancer-specific antigen GPC3 regress liver tumors in mice. We concluded that PD-L1-targeted shark VNAR single domain-based CAR-T therapy is a novel strategy to treat breast cancer and liver cancer. This provides a rationale for potential use of CAR (B2) T cells as a monotherapy or combination with a tumor-specific therapy in clinical studies.

cancer biology↗