Projects
My research projects are mainly focused on the utilization and development of bioinformatics technologies and methods to study pediatric cancers, mostly pediatric leukemia.
(i) Characterize the heterogeneity of pediatric leukemia
Because of the low prevalence of cancer in children compared to adults, there is historically a lack of publicly available datasets and specific research pertaining to childhood cancer. Using single-cell RNA sequencing and other next-generation sequencing technologies, we can characterize the heterogeneity inter-subtype and intra-subtype malignant and microenvironment populations.
Leukemia is the most common type of childhood cancer. It encompasses about a quarter of new childhood cancer cases each year (NCI), and is a cancer of the blood and bone marrow. Leukemia occurs when hematopoiesis, or the process by which blood componenets are formed, becomes dysregulated and the bone marrow produces abnormal, undifferentiated (blast) cells.
Mixed-phenotype acute leukemia (MPAL)
In our 2023 publication in Genome Medicine, we characterize the heterogeneity in mixed phenotype acute leukemia, MPAL, which occurs when there are two or more blast populations in the bone marrow, through single-cell RNA sequencing. MPAL is a rare subtype of acute leukemia, and we performed the first pediatric single-cell study of MPAL.
Mumme HL, Raikar SS, Bhasin SS et al. Single-cell RNA sequencing distinctly characterizes the wide heterogeneity in pediatric mixed phenotype acute leukemia. Genome Med 15, 83 (2023). https://doi.org/10.1186/s13073-023-01241-z
Acute myeloid leukemia (AML)
In our 2023 publication in Nature Communications, we again utilize single-cell RNA sequencing to characterize acute myeloid leukemia, AML, which occurs when there are myeloid-like blasts in the bone marrow. AML is a more common subtype of acute leukemia.
Mumme H, Thomas BE, Bhasin SS et al. Single-cell analysis reveals altered tumor microenvironments of relapse- and remission-associated pediatric acute myeloid leukemia. Nat Commun 14, 6209 (2023). https://doi.org/10.1038/s41467-023-41994-0
Pan-Leukemia
Through our labs indvidual acute leukemia subtype studies, we have compiled a large consortium of pediatric acute leukemia single-cell RNA sequencing samples (over eighty). Through single-cell analytical methods, we integrate and perform a pan-leukemia analysis to assess how acute leukemia subtypes are similarity dysregulated compared to normal bone marrow from healthy donors.
(ii) Create tools to increase accessibility of pan-cancer datasets and analytical modules
While single-cell technologies are incredibly useful in understanding cellular population transcriptome patterns, the utilization of these sequencing and analysis methods requires the use of bioinformatics and high-performance computing to analyze these large datasets. We are developing a Pediatric Single-Cell Cancer Atlas, or PedSCAtlas, to facilitate the analysis and exploration of multiple pan-cancer datasets without the requirement of bioinformatics expertise by the user. This increases the accessibility of pediatric datasets, which is incredibly important due to the lack of pediatric-specific studies and datasets due to the lower incidence of cancer in children compared to adults.
The first version of the PedSCAtlas is available online at https://bhasinlab.bmi.emory.edu/PediatricSCAtlas/.
(iii) Use omics technologies to identify novel immunotherapy targets
Chimeric antigen receptor T cell targets in T-ALL
Chimeric antigen receptor T cells, or CAR-T cells, are human dervied effector T-cells engineering to target an epitope on malignant cancer cells. CAR-T immunotherapies have been developed and successful in B-cell acute lymphoblastic leukemia, B-ALL, with CD19 and BCMA as targets. Current trials are undergoing for novel AML targets, such as CD33 and CD123, but there is a lack of feasible CAR-T targets for t-cell acute lymphoblastic leukemia, T-ALL, due to the unique challges of developing a T-cell therapy for T-cell specific disease.
We are utilizing our T-ALL single-cell sequencing data to identify novel CAR-T targets through a single-cell pipeline that tests for efficacy and safety of target genes by utilizing public and local single-cell datasets, of T-ALL and healthy samples.