bioRxiv Science⌕ Search

Biology subjects

Brazda, P.

Publications and source records attributed to Brazda, P..

2 recordsLinked to original sources

Longitudinal single-cell transcriptomics reveals distinct patterns of recurrence in acute myeloid leukemia

The heterogeneity and evolution of AML blasts can render therapeutic interventions ineffective in a yet poorly understood patient-specific manner. To gain insight into the clonal heterogeneity of diagnosis (Dx) and relapse (Re) pairs, we employed whole-exome sequencing and single-cell RNA-seq to longitudinally profile two t(8;21) (AML1-ETO = RUNX1-RUNX1T1), and four FLT3-ITD AML cases. The single cell RNA data underpinned the tumor heterogeneity amongst patient blasts. The Dx-Re transcriptomes of high risk FLT3-ITD pairs formed a continuum from extensively changed in the absence of significantly mutational changes in AML-associated genes to rather similar Dx-Re pair of an intermediate risk FLT3-ITD. In one high risk FLT3-ITD pair, a pathway switched from an AP-1 regulated network in Dx to mTOR signaling in Re. The distinct AML1-ETO pairs comprise clusters that share genes related to hematopoietic stem cell maintenance and cell migration suggesting that the Re leukemic stem cell-like (LSC-like) cells probably evolved from the Dx LSC-like cells. In summary, our study revealed a continuum from drastic transcriptional changes to extensive similarities between respective Dx-Re pairs that are poorly explained by the well-established model of clonal evolution. Our results suggest alternative and currently unappreciated and unexplored mechanisms leading to therapeutic resistance and AML recurrence.

cancer biology↗

Behavioral-transcriptomic landscape of engineered T cells targeting human cancer organoids

Cellular immunotherapies are rapidly gaining clinical importance, yet predictive platforms for modeling their mode of action are lacking. Here, we developed a dynamic immuno-organoid 3D imaging-transcriptomics platform; BEHAV3D, to unravel the behavioral and underlying molecular mechanisms of solid tumor targeting. Applied to an emerging cancer metabolome-sensing immunotherapy: TEGs, we first demonstrate targeting of multiple breast cancer subtypes. Live-tracking of over 120,000 TEGs revealed a diverse behavioral landscape and identified a super engager cluster with serial killing capability. Inference of single-cell behavior with transcriptomics identified the gene signature of super engager killer TEGs, which contained 27 genes with no previously described T cell function. Furthermore, guided by a dynamic type 1 interferon (IFN-I) signaling module induced by high TEG-sensitive organoids, we show that IFN-I can prime resistant organoids for TEG-mediated killing. Thus, BEHAV3D characterizes behavioral-phenotypic heterogeneity of cellular immunotherapies and holds promise for improving solid tumor-targeting in a patient-specific manner.

cancer biology↗