John F. DiPersio

The paper defines the RNA modification landscape in multiple myeloma, identifying m6A modifications on lncRNAs and showing METTL3-dependent regulation of NEAT1 affecting MM cell survival.

This paper develops machine learning models to predict donor success in stem cell mobilization using clinical data, enabling early intervention and personalized strategies.

The paper characterizes immune microenvironment dysregulation in multiple myeloma using single-cell data and links it to disease progression and patient outcomes.

This paper develops a streptavidin-drug conjugate platform to efficiently optimize antibody-based conditioning for HSCT, finding that PBD dimer SGD-1882 is most effective for targeting HSCs and AML cells.

This study identifies LINC01432 as a lncRNA that binds to CELF2 and inhibits apoptosis in multiple myeloma, contributing to short progression-free survival to standard therapy.

This study uses linked-read sequencing to phase somatic mutations in multiple myeloma, extending haplotype blocks and revealing phasing patterns in key mutated genes.

The paper designs MHC-independent T cell receptors (miTCRs) to enhance cancer immunotherapy by improving receptor stability and function through structural modeling and iterative sequence modifications.