Unknown,Transcriptomics,Genomics,Proteomics

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ScRNAseq of mouse colorectal organoids in FOLFIRI treatment

ABSTRACT: The aim of the experiment is to elucidate changes in tumor heterogeneity upon chemotherapy treatment. The experiment includes non-treated samples, samples right after chemotherapy treatment (FOLFIRI) and organoids recovered from FOLFIRI. It was conducted in two different genetic backgrounds, AKP (Apc KO, KrasG12D and P53 KO) and APS (Apc KO, P53 and Smad4KO). In addition to that, the organoids carry an inducible Cre-ERT2 knock-in allele in the mex3a locus. Upon 4-OH-Tamoxifen induction, Mex3a cells will recombine the tracing allele stop-TdTomato, enabling the tracing of this subpopulation. To understand the fate of the traced cells, samples right after chemotherapy and in the recovery setting were sorted based on their tomato expression. This enables a single cell tracing experiment.

INSTRUMENT(S): Illumina NovaSeq 6000

ORGANISM(S): Mus musculus

SUBMITTER: Camille Stephan-Otto Attolini

PROVIDER: E-MTAB-11145 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:In patients with advanced colorectal cancer, leucovorin, fluorouracil, and irinotecan (FOLFIRI) is considered as one of the reference first-line treatments. However, only about half of treated patients respond to this regimen, and there is no clinically useful marker that predicts response. A major clinical challenge is to identify the subset of patients who could benefit from this chemotherapy. We aimed to identify a gene expression profile in primary colon cancer tissue that could predict chemotherapy response. Patients and Methods:- Tumor colon samples from 21 patients with advanced colorectal cancer were analyzed for gene expression profiling using Human Genome GeneChip arrays U133. At the end of the first-line treatment, the best observed response, according to WHO criteria, was used to define the responders and nonresponders. Discriminatory genes were first selected by the significance analysis of microarrays algorithm and the area under the receiver operating characteristic curve. A predictor classifier was then constructed using support vector machines. Finally, leave-one-out cross validation was used to estimate the performance and the accuracy of the output class prediction rule. Results:- We determined a set of 14 predictor genes of response to FOLFIRI. Nine of nine responders (100% specificity) and 11 of 12 nonresponders (92% sensitivity) were classified correctly, for an overall accuracy of 95%. Conclusion:- After validation in an independent cohort of patients, our gene signature could be used as a decision tool to assist oncologists in selecting colorectal cancer patients who could benefit from FOLFIRI chemotherapy, both in the adjuvant and the first-line metastatic setting. All tissue samples were maintained at −180°C (liquid nitrogen) until RNA extraction and were weighed before homogenization. Tissue samples were then disrupted directly into a lysis buffer using Mixer Mill MM 300 (Qiagen, Valencia, CA). Total RNA was isolated from tissue lysates using the RNeasy Mini Kit (Qiagen), and additional DNAse digestion was performed on all samples during the extraction process (RNase-Free DNase Set Protocol for DNase treatment on RNeasy Mini Spin Columns; Qiagen). After each extraction, a small fraction of the total RNA preparation was taken to determine the quality of the sample and the yield of total RNA. Controls analyses were performed by UV spectroscopy and analysis of total RNA profile using the Agilent RNA 6000 Nano LabChip Kit with the Agilent 2100 Bioanalyser (Agilent Technologies, Palo Alto, CA) to determine RNA purity, quantity, and integrity.

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ScRNAseq of mouse colorectal organoids in FOLFIRI treatment

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