Biology subjects

Rampersaud, S.

Publications and source records attributed to Rampersaud, S..

3 recordsLinked to original sources

bioRxiv2025

Effective imaging and treatment of Acute Myeloid Leukemia with radiotheranostics targeting the activated conformation of integrin-Beta2

There remains an unmet clinical need for improved treatment strategies in Acute Myeloid Leukemia (AML). Although radiopharmaceutical therapies targeting non-cancer-selective antigens have shown promise in AML, their clinical utility is often limited by prolonged bone marrow suppression. Using a unique proteomics-based strategy, we recently identified the active conformation of integrin-{beta}2 (aITGB2) as a novel, tumor-selective target for AML. Importantly, this conformational epitope is expressed widely on AML cells but minimally on normal marrow progenitors/healthy tissues. Here we first confirmed widespread aITGB2 expression on AML tumors that was largely independent of tumor genotype or prior therapeutic regimen. We developed diagnostic and therapeutic radiopharmaceuticals targeting aITGB2 utilizing a conformation-specific antibody (clone 7065). PET/CT imaging with 89Zr and 134Ce-labeled 7065 in AML models revealed high target-mediated uptake, greater than that compared to standard of care [18F]-FDG. PET/CT imaging with [89Zr]DFO*-7065 showed reduced binding to normal bone marrow and immune cells in humanized immune system mice compared to [89Zr]DFO*-anti-CD33. For therapy, we developed [225Ac]Macropa-PEG4-7065 using an optimized chelator-linker combination. Treatment with [225Ac]Macropa-PEG4-7065 in Nomo-1 and PDX AML disseminated models delayed tumor growth and improved overall survival compared to controls, including [225Ac]DOTA-anti-CD33, a clinical stage-radioimmunotherapy under evaluation in AML. Relapsed tumors demonstrated persistent aITGB2 expression, supporting continued development of fractionated dosing schemes, and proteomics analysis indicated activation of TCA cycle and carbon metabolism pathways, consistent with therapy-induced stress responses. These findings highlight [89Zr]DFO*-7065 and [225Ac]Macropa-7065 as a promising aITGB2-targeted theranostic pair with potential for imaging and treatment in future clinical translation. One Sentence SummaryThis study demonstrates promising preclinical efficacy of aITGB2-targeted radiotheranostics for selective imaging and therapy in AML.

cancer biology↗

bioRxiv2024

Targeting high-risk multiple myeloma genotypes with optimized anti-CD70 CAR-T cells

Despite the success of BCMA-targeting CAR-Ts in multiple myeloma, patients with high-risk cytogenetic features still relapse most quickly and are in urgent need of additional therapeutic options. Here, we identify CD70, widely recognized as a favorable immunotherapy target in other cancers, as a specifically upregulated cell surface antigen in high risk myeloma tumors. We use a structure-guided design to define a CD27-based anti-CD70 CAR-T design that outperforms all tested scFv-based CARs, leading to >80-fold improved CAR-T expansion in vivo. Epigenetic analysis via machine learning predicts key transcription factors and transcriptional networks driving CD70 upregulation in high risk myeloma. Dual-targeting CAR-Ts against either CD70 or BCMA demonstrate a potential strategy to avoid antigen escape-mediated resistance. Together, these findings support the promise of targeting CD70 with optimized CAR-Ts in myeloma as well as future clinical translation of this approach. One sentence summaryStructure-optimized CD27-based CAR-T cells targeting CD70 are a promising therapeutic option for high-risk multiple myeloma patients who are most likely to relapse on current BCMA-targeting cellular therapies.

cancer biology↗

bioRxiv2022

Starfysh reveals heterogeneous spatial dynamics in the breast tumor microenvironment

Spatially-resolved gene expression profiling provides valuable insight into tissue organization and cell-cell crosstalk; however, spatial transcriptomics (ST) lacks single-cell resolution. Current ST analysis methods require single-cell RNA sequencing data as a reference for a rigorous interpretation of cell states and do not utilize associated histology images. Significant sample variation further complicates the integration of ST datasets, which is essential for identifying commonalities across tissues or altered cellular wiring in disease. Here, we present Starfysh, the first comprehensive computational toolbox for joint modeling of ST and histology data, dissection of refined cell states, and systematic integration of multiple ST datasets from complex tissues. Starfysh uses an auxiliary deep generative model that incorporates archetypal analysis and any known cell state markers to avoid the need for a single-cell-resolution reference in characterizing known or novel tissue-specific cell states. Additionally, Starfysh improves the characterization of spatial dynamics in complex tissues by leveraging histology images and enables the comparison of niches as spatial "hubs" across tissues. Integrative analysis of primary estrogen receptor-positive (ER+) breast cancer, triple-negative breast cancer (TNBC), and metaplastic breast cancer (MBC) tumors using Starfysh led to the identification of heterogeneous patient- and disease-specific hubs as well as a shared stromal hub with varying spatial orientation. Our results show the ability to delineate the spatial co-evolution of tumor and immune cell states and their crosstalk underlying intratumoral heterogeneity in TNBC and revealed metabolic reprogramming shaping immunosuppressive hubs in aggressive MBC. Starfysh is publicly available (https://github.com/azizilab/starfysh).

bioinformatics↗