Liquid-biopsy transcriptomic profiling uncovers molecular mediators of resistance to androgen receptor signaling inhibition in lethal prostate cancer
Zhang, J.; Zimmermann, B.; Galletti, G.; Halabi, S.; Gjyrezi, A.; Yang, Q.; Gupta, S.; Verma, A.; Sboner, A.; Anand, M.; George, D. J.; Gregory, S. G.; Hong, S.; Pascual, V.; Mavragani, C. P.; Antonarakis, E. S.; Nanus, D. M.; Tagawa, S. T.; Elemento, O.; Armstrong, A. J.; Giannakakou, P.
Show abstract
Androgen receptor signaling inhibitors (ARSi) are a mainstay for patients with metastatic castration-resistant prostate cancer (mCRPC). However, patient response is heterogeneous and the molecular underpinnings of ARSi resistance are not well elucidated. Here we performed transcriptome analysis of circulating tumor cells (CTCs) and peripheral blood mononuclear cells (PBMC) in the context of a prospective clinical trial of men with mCRPC treated with abiraterone (Abi) or enzalutamide (Enza). CTC RNA-sequencing identified that RB loss and enhanced E2F signaling along with BRCA loss transcriptional networks were associated with intrinsic ARSi resistance, while an inflammatory response signature was significantly associated with acquired resistance. Transcriptomic analysis of matching PBMCs identified enrichment of inflammasome gene signatures indicative of activated innate immunity at progression, with concurrent downregulation of T and NK cells. Importantly, CTC gene signatures had a significant positive association with circulating immune macroenvironment (CIME) signatures. Taken together, these data demonstrate that liquid biopsy transcriptomics can identify molecular pathways associated with clinical ARSi resistance paving the way for treatment optimization in patients with mCRPC.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Evaluation of homologous recombination repair status in metastatic prostate cancer by next-generation sequencing and functional tissue-based immunofluorescence assays 95%
- Enhancer profiling identifies epigenetic markers of endocrine resistance and reveals therapeutic options for metastatic castration-resistant prostate cancer patients 95%
- Single-Cell Proteomics Defines the Cellular Heterogeneity of Localized Prostate Cancer 93%
Similar papers in this journal
- DUX4 is a common driver of immune evasion and immunotherapy failure in metastatic cancers 95%
- Defining cellular population dynamics at single cell resolution during prostate cancer progression 95%
- The CIC-ERF co-deletion underlies fusion independent activation of ETS family member, ETV1, to drive prostate cancer progression 94%
Similar papers in this journal
- Single-cell ATAC and RNA sequencing reveal pre-existing and persistent subpopulations of cells associated with relapse of prostate cancer 95%
- The genomic landscape of metastatic castration-resistant prostate cancers reveals multiple distinct genotypes with potential clinical impact 94%
- Role of Specialized mSWI/SNF Complexes in Prostate Cancer Lineage Plasticity 94%
Similar papers in this journal
- Localized high-risk prostate cancer harbors an androgen receptor low subpopulation susceptible to HER2 inhibition 94%
- Prostate cancer androgen receptor activity dictates efficacy of Bipolar Androgen Therapy 93%
- Development and validation of multivariate predictors of primary endocrine resistance to tamoxifen and aromatase inhibitors in luminal breast cancer reveal drug-specific differences 93%
Similar papers in this journal
- Circulating tumor DNA analysis in advanced urothelial carcinoma: insights from biological analysis and extended clinical follow-up 94%
- Myeloid cell-associated resistance to PD-1/PD-L1 blockade in urothelial cancer revealed through bulk and single-cell RNA sequencing 94%
- A Subset of Localized Prostate Cancer Displays an Immunogenic Phenotype Associated with Losses of Key Tumor Suppressor Genes 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.