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Biomedical subjects

Rawan Shraim

Publications and source records attributed to Rawan Shraim.

2 recordsLinked to original sources

Multimodal analysis of CD38 in T-cell Acute Lymphoblastic Leukemia Identifies Combinatorial Therapeutic Strategies.

Outcomes for pediatric patients with refractory or relapsed T-cell acute lymphoblastic leukemia (T-ALL) are poor, underscoring the need for improved therapeutic strategies. CD38, a type II transmembrane glycoprotein, is a promising target in T-ALL, with clinical trials evaluating CD38-targeting immunotherapies in frontline and relapsed settings. However, the biological role of CD38 in T-ALL has not been systematically defined. We interrogated CD38 biology through multimodal profiling of pediatric T-ALL samples. Bulk RNA sequencing of 1,335 primary tumors revealed that CD38 expression varies across genomic and immunophenotypic subtypes in T-ALL. Flow cytometry of 150 primary samples and CITE-sequencing of 40 cases demonstrated broad surface expression of CD38. A transcription factor CRISPR-screen identified RUNX1, RUNX3, and TP53 as candidate positive regulators of CD38. Metabolomic profiling of cell lines further revealed disruption of the polyamine pathway following CD38 perturbation. Supporting this finding, co-targeting CD38 with difluoromethylornithine (DFMO), a polyamine metabolism disruptor, improved survival in preclinical models. Across transcriptomic datasets, including primary tumors, cell lines, and patient-derived xenograft models, IL32 expression consistently decreased following CD38 loss or negativity, supporting an association between CD38 and inflammatory signaling pathways. Additionally, CD38 and LCK expression were positively correlated across majority of genomic subtypes, implicating SRC kinase signaling. Consistent with this, daratumumab in cell lines increased LCK phosphorylation, and combination therapy with dasatinib improved survival compared to monotherapy. Collectively, these findings define previously unrecognized interactions between CD38 and targetable pathways and genes in T-ALL and identify rational combinatorial strategies to enhance CD38-directed therapies and reduce relapse risk.

Journal Article

ImmunoTar-integrative prioritization of cell surface targets for cancer immunotherapy.

MOTIVATION: Cancer remains a leading cause of mortality globally. Recent improvements in survival have been facilitated by the development of targeted and less toxic immunotherapies, such as chimeric antigen receptor (CAR)-T cells and antibody-drug conjugates (ADCs). These therapies, effective in treating both pediatric and adult patients with solid and hematological malignancies, rely on the identification of cancer-specific surface protein targets. While technologies like RNA sequencing and proteomics exist to survey these targets, identifying optimal targets for immunotherapies remains a challenge in the field. RESULTS: To address this challenge, we developed ImmunoTar, a novel computational tool designed to systematically prioritize candidate immunotherapeutic targets. ImmunoTar integrates user-provided RNA-sequencing or proteomics data with quantitative features from multiple public databases, selected based on predefined criteria, to generate a score representing the gene's suitability as an immunotherapeutic target. We validated ImmunoTar using three distinct cancer datasets, demonstrating its effectiveness in identifying both known and novel targets across various cancer phenotypes. By compiling diverse data into a unified platform, ImmunoTar enables comprehensive evaluation of surface proteins, streamlining target identification and empowering researchers to efficiently allocate resources, thereby accelerating the development of effective cancer immunotherapies. AVAILABILITY AND IMPLEMENTATION: Code and data to run and test ImmunoTar are available at https://github.com/sacanlab/immunotar.

Humans