Biology subjects

Shapira-Frommer, R.

Publications and source records attributed to Shapira-Frommer, R..

3 recordsLinked to original sources

bioRxiv2026

Spatially enforced engagement of PD-1⁺ T cells with AXL⁺ cells reprograms the tumor microenvironment and overcomes anti-PD-1 resistance

Background: AXL overexpression promotes tumor-cell plasticity, local immune suppression, and resistance to PD-1 blockade. Conventional anti-AXL and anti-PD-1 antibodies combinations act independently and do not ensure physical proximity between PD-1+ T cells and AXL+ cells within the immunosuppressive tumor microenvironment. Methods: To address this limitation, we developed a non-tandem, tetravalent IgG bispecific antibody designed to simultaneously engage PD-1 and AXL (BiTE-AXL/PD-1). Its antitumor activity was evaluated using in vitro functional assays and humanized and immunocompetent head and neck squamous cell carcinoma (HNSCC) models and compared with the combination of monospecific anti-AXL and anti-PD-1 antibodies. Spatial multiplex imaging was used to characterize treatment-associated changes in the tumor microenvironment of an anti-PD-1-resistant murine model. Results: BiTE-AXL/PD-1 enhanced T-cell activation, promoted AXL-dependent tumor-T-cell conjugation, and increased tumor-cell killing in vitro, outperforming the combination of the two monospecific antibodies. In humanized and immunocompetent HNSCC models, BiTE treatment significantly delayed tumor growth compared with the antibody combination and was well tolerated, with no overt toxicity. Spatial multiplex imaging revealed a shift from a fibroblast-dominated microenvironment characterized by exhausted CD8+ T cells toward dendritic-cell- and CD8+ T-cell-enriched niches associated with antitumor immune activity. Conclusion: BiTE-AXL/PD-1 promotes physical proximity between PD-1+ T cells and AXL+ cells, enhances antitumor immune responses, and remodels the immunosuppressive tumor microenvironment. These findings support PD-1/AXL bispecific targeting as a promising therapeutic strategy to overcome resistance to PD-1 blockade in HNSCC.

cancer biology↗

bioRxiv2025

Decoy Antibodies Block Extracellular HSP70, Prevent Self Signaling and Inhibit Melanoma Cell Survival

Melanoma cells actively secrete melanosomes-large, extracellular vesicles (EVs) enriched with oncogenic factors that reprogram the tumor microenvironment, enhance self-signaling, and promote tumor growth. Despite their abundance and immunogenic potential, humoral responses to melanoma-derived melanosomes remain unexplored. Here, we identify a novel immune surveillance mechanism in which melanosome-elicited decoy antibodies target melanoma-derived melanosomes by binding to the extracellular form of heat shock protein 70 (HSP70), a chaperone broadly implicated in cancer cell survival and stress adaptation. Anti-HSP70 decoy antibodies potently and effector-independently inhibit growth and survival of both murine and human melanoma cells and suppress key transcriptional programs involved in proliferation, cytoskeletal dynamics, and metabolism. In a preclinical B16 melanoma model, prophylactic administration of decoy monoclonal antibodies Mel322-34 and Mel321-35 conferred significant survival benefits of 27% and 48%, respectively. Strikingly, anti-HSP70 antibodies were enriched in the sera of melanoma patients achieving complete responses to immune checkpoint blockade, in contrast to non-responders with progressive disease. Collectively, our findings uncover a novel EV-antibody axis as a promising avenue to block cancer-promoting signaling pathways. Decoy autoantibodies targeting the extracellular form of HSP70 advance the understanding of tumor-intrinsic vulnerability and promote biomarker-driven immunotherapy in melanoma.

immunology↗

bioRxiv2022

A novel deep learning pipeline for cell typing and phenotypic marker quantification in multiplex imaging

BackgroundMultiplex immunofluorescence (mIF) can provide invaluable insights into spatial biology and the complexities of the immune tumor microenvironment (iTME). However, existing analysis approaches are both laborious and highly user-dependent. In order to overcome these limitations we developed a novel, end-to-end deep learning (DL) pipeline for rapid and accurate analysis of both tumor-microarray (TMA) and whole slide mIF images. MethodsOur pipeline consists of two DL models: a multi-classifier for classifying multi-channel cell images into 12 different cell types, and a binary classifier for determining the positivity of a given marker in single-channel images. The DL multi-classifier was trained on 7,000 tiles labeled with cell annotations from a publicly available CODEX dataset, consisting of 140 tissue cores from 35 colorectal cancer (CRC) patients. For the binary classifier training, the multi-channel tiles were further split into [~]100,000 single-channel tiles, for which the ground truth was inferred from the known expression of these markers in each cell-type. This DL binary classifier was then utilized to quantify the positivity of various cell state (phenotypic) markers. In addition, the binary classifier was exploited as a cell-typing tool, by predicting the positivity of individual lineage cell markers. The performance of our DL models was evaluated on 1,800 annotations from 14 test tissue cores. The models were further evaluated on a new 6-plex melanoma cohort, stained with PhenoImager(R), and were compared to the performance of clustering, manual thresholding or machine learning-based cell-typing methods applied on the same test sets. ResultsOur DL multi-classifier achieved highly accurate results, outperforming all of the tested cell-typing methods, including clustering, manual-thresholding and ML-based approaches, in both CODEX CRC and PhenoImager melanoma cohorts (accuracy of 91% and 87%, respectively), with F1-scores above 80% in the vast majority of cell types. Our DL binary classifier, which was trained solely on the lineage markers of the CRC dataset, also outperformed existing methods, demonstrating excellent F1-scores (>80%) for determining the positivity of unseen phenotypic and lineage markers across the two tumor types and imaging modalities. Notably, as little as 20 annotations were required in order to boost the performance on an unseen dataset to above 85% accuracy and 80% F1-scores. As a result, the DL binary classifier could successfully be used as a cell-typing model, in a manner that is transferable between experimental approaches. ConclusionsWe present a novel state-of-the-art DL-based framework for multiplex imaging analysis, that enables accurate cell typing and phenotypic marker quantification, which is robust across markers, tumor indications, and imaging modalities.

immunology↗