Biology subjects

Woo, B. J.

Publications and source records attributed to Woo, B. J..

3 recordsLinked to original sources

bioRxiv2025

Targeting Osteosarcoma heterogeneity to improve therapeutic response

Intra tumor heterogeneity complicates cancer therapy by providing tumors with the ability to alter their phenotypes and become more therapy resistant. Here, we tested the hypothesis that identifying and modulating expression of key state-specific transcription factors could be used as a strategy for driving cells to a more therapy-sensitive state. Recent single-cell studies have explored the inter and intra tumoral heterogeneity of osteosarcoma and identified gene pathways enriched in specific cell states. For example, metastatic tumors are characterized by an expression of genes in the TNF-, PI3K, TGF{beta} and mTOR pathways. We identified similar profiles in osteosarcoma patient-derived xenograft-derived cell lines and potential transcription factor drivers of these states. We then used perturb-seq to downregulate expression of key transcription factors and evaluated the effect of these modulations on single cell RNA profiles and drug responses. Knockdown of NFE2L3 or NR0B1 increased the proportion of cells sensitive to targeted therapy. This approach, which could potentially be applied to other cancers, could be used as a strategy to increase the response to targeted therapies by increasing the proportion of cells in a drug-sensitive state. HighlightsO_LIDistinct transcriptomic states were identified in osteosarcoma cell lines using single-cell RNA sequencing. C_LIO_LILineage tracing identified states with differential sensitivity to therapy. C_LIO_LIUsing perturb-seq, we identified transcription factors that drive cells towards a more sensitive state. C_LIO_LIThe transcription factor NFE2L3 was identified as targets capable of reprogramming cells to a sensitive state. C_LI

genomics↗

bioRxiv2024

Systematic annotation of orphan RNAs reveals blood-accessible molecular barcodes of cancer identity and cancer-emergent oncogenic drivers

From extrachromosomal DNA to neo-peptides, the broad reprogramming of the cancer genome leads to the emergence of molecules that are specific to the cancer state. We recently described orphan non-coding RNAs (oncRNAs) as a class of cancer-specific small RNAs with the potential to play functional roles in breast cancer progression1. Here, we report a systematic and comprehensive search to identify, annotate, and characterize cancer-emergent oncRNAs across 32 tumor types. We also leverage large-scale in vivo genetic screens in xenografted mice to functionally identify driver oncRNAs in multiple tumor types. We have not only discovered a large repertoire of oncRNAs, but also found that their presence and absence represent a digital molecular barcode that faithfully captures the types and subtypes of cancer. Importantly, we discovered that this molecular barcode is partially accessible from the cell-free space as some oncRNAs are secreted by cancer cells. In a large retrospective study across 192 breast cancer patients, we showed that oncRNAs can be reliably detected in the blood and that changes in the cell-free oncRNA burden captures both short-term and long-term clinical outcomes upon completion of a neoadjuvant chemotherapy regimen. Together, our findings establish oncRNAs as an emergent class of cancer-specific non-coding RNAs with potential roles in tumor progression and clinical utility in liquid biopsies and disease monitoring.

cancer biology↗

bioRxiv2023

Integrative identification of non-coding regulatory regions driving metastatic prostate cancer

Large-scale sequencing efforts of thousands of tumor samples have been undertaken to understand the mutational landscape of the coding genome. However, the vast majority of germline and somatic variants occur within non-coding portions of the genome. These genomic regions do not directly encode for specific proteins, but can play key roles in cancer progression, for example by driving aberrant gene expression control. Here, we designed an integrative computational and experimental framework to identify recurrently mutated non-coding regulatory regions that drive tumor progression. Application of this approach to whole-genome sequencing (WGS) data from a large cohort of metastatic castration-resistant prostate cancer (mCRPC) revealed a large set of recurrently mutated regions. We used (i) in silico prioritization of functional non-coding mutations, (ii) massively parallel reporter assays, and (iii) in vivo CRISPR-interference (CRISPRi) screens in xenografted mice to systematically identify and validate driver regulatory regions that drive mCRPC. We discovered that one of these enhancer regions, GH22I030351, acts on a bidirectional promoter to simultaneously modulate expression of U2-associated splicing factor SF3A1 and chromosomal protein CCDC157. We found that both SF3A1 and CCDC157 are promoters of tumor growth in xenograft models of prostate cancer. We nominated a number of transcription factors, including SOX6, to be responsible for higher expression of SF3A1 and CCDC157. Collectively, we have established and confirmed an integrative computational and experimental approach that enables the systematic detection of non-coding regulatory regions that drive the progression of human cancers.

cancer biology↗