Transcriptional heterogeneity and regulatory mechanisms in tumor microenvironment

Division Of Basic Sciences - Nci

Investigators

Linked publications, trials & patents

Abstract

(1) Our collaborative work with Kaplan lab on understanding the microenvironment of the pre-metastatic niche from the metastases-free tissues from patients with metastatic Adrenal Cortical Carcinoma is still ongoing. It was reviewed by Cell and rejected. We continue to strength the work by strengthening the evidence to show the prognostic value of signatures we have derived. Our adjacent healthy tissue single cell data-based machine learning model can very broadly predict survival, metastasis, and therapy response and relapse. We will resubmit the work in coming month. Together, this study unveils shared and microenvironment-specific gene expression programs governing metastatic initiation and progression in patients. (2) Another major collaborative work with Goldschmidt lab on understanding the determinants of therapy resistance in melanoma brain metastasis in mouse models, was reviewed and rejected by Cell and the work continues and will be resubmitted. (3) We have developed a deep leaning model to prioritize non-coding polymorphisms based on their putative effect in transcriptional regulatory function, to identify polymorphisms potentially underlying the differences in prostate cancer incidence rates between African and White men. Besides identifying numerous potentially functional SNPs shown to affect FOXA1 and AR binding (in collaboration with David Takeda), our work also offers a novel polygenic risk score for prostate cancer. We are currently writing the manuscript. This work is in advance states of review at Nat Communications. (4) Therapeutic resistance is the leading cause of treatment failure and death from cancer. While resistance can be driven by genetic mutations, mounting evidence also points to an epigenetic basis of resistance. Much of this epigenetic, or non-genetic, resistance has been attributed to drug-resistant transcriptional cell states that are either induced by drug treatment or pre-exist in a fraction of cells selected by treatment. However, the extent to which long-term resistance is manifested in the early inherent cellular response to drugs is poorly understood, and which aspects, if any, of this early response to drug-induced transcriptional response are evolutionarily conserved properties of cells. To address these questions, we integrate datasets of long-term drug resistance and early drug response data across multiple cell lines with drug response and resistance data from bacteria and yeast. Our findings suggest that cancer cell states in both the drug-naive populations as well as in populations shortly after treatment share transcriptional properties with fully established resistant cell populations, and CRISPR-cas9 knockout of transcription factors predicted to regulate the resistant transcriptional programs result in increased drug sensitivity. Furthermore, the resistance states manifested as early drug response are evolutionarily conserved. Finally, we show that early resistant states discriminate responders from non-responders across multiple human cancer trials. This work has ben submitted for review.