bioRxivCancer Biology
Chemoresistome Mapping in Individual Breast Cancer Patients Unravels Diversity in Dynamic Transcriptional Adaptation
M. Dadiani, G. Friedlander, G. Perry, N. Balint-Lahat, S. Gilad, D. Morzaev-Sulzbach, N. Bossel Ben-Moshe, A. Pavlovski, E. Domany, A. Shenoy, I. Barshack, T. Geiger, B. Kaufman, E. N. Gal-Yam
Abstract
Emerging evidence reinforce the role of non-genetic adaptive resistance to chemotherapy, that involves rewiring of transcriptional programs in surviving tumors. We combined longitudinal transcriptomics with temporal pattern analysis to dissect patient-specific emergence of resistance in breast cancer. Matched triplets of tumor biopsies (pre-treatment, post-treatment and adjacent normal) were collected from breast cancer patients who received neo-adjuvant chemotherapy. Full transcriptome was analyzed by longitudinal pattern classification to follow patient-specific expression modulations. We found that dynamics of gene expression dictates resistance-related modulations. The results unraveled important principles in emergence of adaptive resistance: 1. Genes with resistance patterns are already dysregulated in the primary tumor, supporting a primed drug-tolerant state. 2. In each patient, multiple resistance-related genes are rewired but converge into few dysregulated modules. 3. Rewiring of diverse genes and pathway dysregulation vary among individuals who receive the same treatments. Patient-specific chemoresistome maps disclosed tumors acquired resistance and exposed their vulnerabilities. Mapping the complexity of dysregulated pathways in individual patients revealed important insights on adaptive resistance mechanisms. To survive the toxic drug effect, tumor cells either sustain a drug-tolerant state or intensify it, specifically bypassing the drugs interference. Depicting an individual road map to resistance can offer personalized therapeutic strategies.
- organism: human
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
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