Development of T-cell Lymphoma Mouse Models, Cell Lines and 3D Cellular Systems
Patient-derived disease models have emerged as critical tools for biological research. Specifically, patient-derived xenograft models (PDXs), generally provide higher fidelity representations of the underlying cancer than cell lines. The Jain T-cell lymphoma research team has expertise in generating PDX models and is continually expanding their repository of PDX models representing diverse subtypes of T-cell lymphoma. Cancer cell lines are the most widely, efficient and scalable systems for validating candidates and defining preclinical efficacy. Organoids are the miniatures of in vivo tissues, and faithfully recapitulate the architectures. Their co-cultures with immune cells can be leveraged to screen immunotherapeutic compounds for personalized medicine. Through an ongoing collaboration with the Cancer Cell Line Factory at the Broad Institute, we are developing cell lines and organoids for rare types of T-cell lymphoma such as hepatosplenic T-cell lymphoma, T-prolymphocytic leukemia, angioimmunoblastic T-cell lymphoma, enteropathy associated T-cell lymphoma among others.
Understanding Tumor Microenvironment (TME) in T-cell lymphomas
The immunological components within tumors, termed the TME, have long been shown to be strongly related to tumor development, recurrence and metastasis. This remains poorly understood in T-cell lymphomas. We aim to utilize massively multiplexed spatial technologies such as CODEX, mass cytometry among others to untangle the complex relationships between tumor cells and TME. We are also leveraging these cutting edge single cell technologies to gain molecular insights into mechanisms of response and resistance underlying active drugs in T-cell lymphomas.
Detection of Minimal Residual Disease (MRD) in T-cell lymphomas
In aggressive lymphomas, MRD has a relevant prognostic power and can represent the platform for immunotherapy (such as CAR-T). In diffuse large B-cell lymphoma (DLBCL), the assessment of MRD in the plasma (where cell-free DNA and exosomes circulate) seems to be more predictive than the bone marrow analysis or peripheral blood mononuclear cells. Finally, NGS technologies are more useful than the classical “patient allele-specific PCR” because they can identify any possible clone emerging during the treatment or follow-up, even if different from that identified at diagnosis, thus predicting relapse. We aim to use tissue and cfDNA based NGS approaches to predict clonal evolution and relapse in patients with T-cell lymphomas pre- and post autologous stem cell consolidation.