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Golan, S.

Publications and source records attributed to Golan, S..

3 recordsLinked to original sources
bioRxiv2024

Novel immunotherapy for multiple solid cancers using an Anti-HVEM blocking monoclonal antibody

INTRODUCTIONImmune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, yet their efficacy remains limited. Therefore, there is a clear need for new anti-tumor agents. HVEM (Herpes Virus Entry Mediator) plays a regulatory role in immunity, making it a promising cancer therapeutic target. EXPERIMENTAL DESIGNWe have developed Anti-4CB1, a fully human HVEM-BTLA and HVEM-CD160 blocking mAb and tested its anti-tumor activity in various in-vitro, ex-vivo and in-vivo models, alone or in combination with Anti-PD1. Finally, we analyzed HVEM expression in serum samples and melanoma tumor tissues and its correlation with HVEM and PD1 blockade treatment response. RESULTSIn-vitro assays demonstrated enhanced melanoma cell killing by autologous TILs in the presence of Anti-4CB1. In addition, Anti-4CB1 significantly increased cytotoxicity by up to 233% in a variety of ex-vivo cancer tissue samples of different indications. Notably, Anti-4CB1 demonstrated effectiveness in samples where Anti-PD1 was ineffective. In addition, significant anti-tumor activity at 10 mg/kg in combination with Anti-PD1 (TGI 95% p=0.0001) or at 25 mg/kg as monotherapy (TGI 50% p=0.0096) was demonstrated in a colon carcinoma transgenic mice model. Moreover, significant anti-tumor activity was observed in a mouse ImmunoGraft model (TGI 53% p=0.0102 as monotherapy). Finally, we demonstrated that HVEM expression correlated with response to Anti-HVEM and Anti-PD1 treatments. DISCUSSIONWe describe the development of Anti-4CB1, a fully human anti-HVEM mAb, blocking HVEM-BTLA and HVEM-CD160 human interactions and enhancing lymphocytes cytotoxicity against various cancer cells ex-vivo. In-vivo, Anti-4CB1 showed promising anti-tumor effects, particularly in combination with Anti-PD1, suggesting its therapeutic potential. Finally, HVEM expression may serve as a predictive marker for Anti-HVEM and Anti-PD1 treatments response, offering potential diagnostic utility in patient selection for these immunotherapies. These results support Anti-4CB1 potential as an anti-cancer therapeutic agent. Translational RelevanceThe discovery of immune checkpoint inhibitors (ICIs), marks a significant breakthrough in cancer therapy. However, despite the remarkable success of current ICIs targeting CTLA4 and PD1/PD-L1 pathways, a significant proportion of patients fail to respond or develop resistance. Therefore, the search for novel ICIs is crucial for advancing cancer immunotherapy. Anti-HVEM emerged as a promising candidate, as we demonstrate in ex-vivo and in-vivo models, its ability to block the HVEM-BTLA interaction, resulting in enhanced antitumor immune responses in solid tumors, alone or in combination with existing ICIs, to improve treatment outcomes. Moreover, the correlation between HVEM expression and treatment responses highlight its potential as an attractive target for biomarker-driven therapies. Investigating HVEM as an ICI offers a promising path to expand therapeutic options, personalize treatment approaches, address the evolving challenges in cancer immunotherapy and improve patient outcomes.

cancer biology↗
bioRxiv2024

Generating low-density minimizers

Minimizers is the most popular k-mer selection scheme in algorithms and data structures analyzing high-throughput sequencing (HTS) data. In a minimizers scheme, the smallest k-mer by some predefined order is selected as the representative of a sequence window containing w consecutive k-mers, which results in overlapping windows often selecting the same k-mer. Minimizers that achieve the lowest frequency of selected k-mers over a random DNA sequence, termed the expected density, are desired for improved performance of HTS analyses. Yet, no method to date exists to generate minimizers that achieve minimum expected density. Moreover, for k and w values used by common HTS algorithms and data structures there is a gap between the densities achieved by existing selection schemes and a recent theoretical lower bound. Here, we present GreedyMini, a toolkit of methods to generate minimizers with low expected or particular density, to improve minimizers, to extend minimizers to larger alphabets, k, and w, and to measure the expected density of a given minimizer efficiently. We demonstrate over various combinations of k and w values, including those of popular HTS methods, that GreedyMini can generate DNA minimizers that achieve expected densities very close to the lower bound, and both expected and particular densities much lower compared to existing selection schemes. Additionally, we show that the k-mer rank-retrieval time by GreedyMini is comparable to that of common k-mer hash functions. We expect GreedyMini to improve the performance of many HTS algorithms and data structures and advance the research of k-mer selection schemes.

bioinformatics↗
bioRxiv2024

Anomaly detection for high-content image-based phenotypic cell profiling

High-content image-based phenotypic profiling combines automated microscopy and analysis to identify phenotypic alterations in cell morphology and provide insight into the cells physiological state. Classical representations of the phenotypic profile can not capture the full underlying complexity in cell organization, while recent weakly machine-learning based representation-learning methods are hard to biologically interpret. We used the abundance of control wells to learn the in-distribution of control experiments and use it to formulate a self-supervised reconstruction anomaly-based representation that encodes the intricate morphological inter-feature dependencies while preserving the representation interpretability. The performance of our anomaly-based representations was evaluated for downstream tasks with respect to two classical representations across four public Cell Painting datasets. Anomaly-based representations improved reproducibility, Mechanism of Action classification, and complemented classical representations. Unsupervised explainability of autoencoder-based anomalies identified specific inter-feature dependencies causing anomalies. The general concept of anomaly-based representations can be adapted to other applications in cell biology.

bioinformatics↗