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| Status |
Public on May 22, 2024 |
| Title |
Cellular adaptation to cancer therapy along a resistance continuum [scRNA-seq: A375_PC9] |
| Organism |
Homo sapiens |
| Experiment type |
Expression profiling by high throughput sequencing
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| Summary |
Advancements in precision oncology over the past decades have led to new therapeutic interventions, but the efficacy of such treatments is generally limited by an adaptive process that fosters drug resistance. Beyond genetic mutations, recent research has shed light on the role of non-genetic plasticity in transient drug tolerance and the acquisition of stable resistance. However, the dynamics of cell state transitions occurring in the adaptation to cancer therapies remain elusive and require a systems-level longitudinal framework. Here we demonstrate that resistance develops through trajectories of cell state transitions accompanied by a progressive increase in cell fitness, which we denote the 'resistance continuum'. This cellular adaptation involves a step-wise assembly of gene expression programs and epigenetically reinforced cell states underpinned by phenotypic plasticity, adaptation to stress and metabolic reprogramming. Our results support the notion that EMT/stemness programs, often considered as a proxy for phenotypic plasticity, enable adaptation, rather than a full resistance mechanism. Through systematic genetic perturbations, we identify an acquisition of metabolic dependencies, exposing vulnerabilities that can be potentially exploited therapeutically. The concept of the resistance continuum highlights the dynamic nature of cellular adaptation and calls for complementary therapies directed at the mechanisms underlying adaptive cell state transitions.
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| Overall design |
Drug-naïve A375 cells were treated in dose escalation from 30 nM to 4 uM of dabrafenib. Adapted cell populations (dabrafenib resistant) from the doses 30 nM, 60 nM, 125 nM, 250 nM, 500 nM, 1 uM, 2 uM and 4 uM (T003, T006, T0125, T0250, T0500, T1, T2 and T4) plus untreated control (C) were collected for single-cell sequencing using the 10X genomics platform. The same procedure was used to generate PC9 resistant cells to osimertinib. Drug-naïve PC9 cells were treated in dose escalation from 8 nM to 1.2 uM of osimertinib. Adapted cell populations (osimertinib resistant) from the doses 8 nM, 16 nM, 32 nM, 75 nM, 150 nM, 300 nM, 600 nM and 1.2 uM (T0008, T0016, T0032, T0075, T0150, T0300, T0600, T1.2) plus untreated control (C). An additional population of persister cells was generated by acute treatment with 1.2 uM (P1.2) for 9 days. Cells were collected for single-cell sequencing using the 10X genomics platform.
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| Contributor(s) |
Starvaggi França G, Yanai I |
| Citation missing |
Has this study been published? Please login to update or notify GEO. |
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| Submission date |
Nov 14, 2023 |
| Last update date |
May 22, 2024 |
| Contact name |
Itai Yanai |
| Organization name |
NYU Langone Health
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| Street address |
435 East. 30th St.
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| City |
New York |
| State/province |
NY |
| ZIP/Postal code |
10016 |
| Country |
USA |
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| Platforms (1) |
| GPL18573 |
Illumina NextSeq 500 (Homo sapiens) |
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| Samples (6)
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| GSM7898872 |
a375_pool_run1_C_to_T4,sequencing run 1,scRNA-Seq |
| GSM7898873 |
a357_pool_run2_C_to_T4,sequencing run 2,scRNA-Seq |
| GSM7898874 |
a375_pool_run1_multiplex_CMO_C_to_T4,sequencing run 1,scRNA-Seq |
| GSM7898875 |
a375_pool_run2_multiplex_CMO_C_to_T4,sequencing run 2,scRNA-Seq |
| GSM7898876 |
pc9_pool_C_to_T1.2_P1.2,scRNA-Seq |
| GSM7898877 |
pc9_pool_multiplex_CMO_C_to_T1.2_P1.2,scRNA-Seq |
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| This SubSeries is part of SuperSeries: |
| GSE247691 |
Cellular adaptation to cancer therapy along a resistance continuum |
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| Relations |
| BioProject |
PRJNA1040170 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSE247684_a375_all_C_to_T4.csv.gz |
25.7 Mb |
(ftp)(http) |
CSV |
| GSE247684_a375_metadata_g1.tsv.gz |
108.9 Kb |
(ftp)(http) |
TSV |
| GSE247684_pc9_all_C_to_T1.2_P1.2.csv.gz |
27.6 Mb |
(ftp)(http) |
CSV |
| GSE247684_pc9_metadata_g1.tsv.gz |
90.2 Kb |
(ftp)(http) |
TSV |
SRA Run Selector |
| Raw data are available in SRA |
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