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Bulk RNA sequencing of colorectal tumors for molecular subtyping

ABSTRACT: Bulk RNA sequencing of colorectal tumors for molecular subtyping

PROVIDER: PRJEB34752 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	ERR3580200.fastq.gz	Fastqsanger.gz
	ERR3580210.fastq.gz	Fastqsanger.gz
	ERR3580201.fastq.gz	Fastqsanger.gz
	ERR3580211.fastq.gz	Fastqsanger.gz
	ERR3580202.fastq.gz	Fastqsanger.gz

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Bulk RNA sequencing of colorectal tumors for molecular subtyping

Project description:We have performed bulk RNA sequencing on colorectal tumors in order to determine their transcriptome-based molecular subtypes. RNA was isolated from fresh frozen human colorectal tumor specimens with Trizol extraction. RNA library preparation was performed with KAPA stranded mRNA sequencing kit, during which mRNA selection was performed with polyT capture beads. Sequencing was performed on Illumina HiSeq4000 as single-end 51 bp reads. File names include dataset name (KUL3), patient code(SCXXX), sample region (core or border) and sample code (EXTXXX): DATASETNAME_PATIENTCODE_SAMPLEREGION_rSAMPLE_CODE_...fastq.gz

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Project description:Purpose: Molecular subtyping for pancreatic cancer has made substantial progress in recent years, facilitating the optimization of existing therapeutic approaches to improve clinical outcomes in pancreatic cancer. With advances in treatment combinations and choices, it is becoming increasingly important to determine ways to place patients on the best therapies upfront. Although various molecular subtyping systems for pancreatic cancer have been proposed, consensus regarding proposed subtypes, as well as their relative clinical utility, remains largely unknown and presents a natural barrier to wider clinical adoption. Methods: We assess three major subtype classification schemas in the context of results from two clinical trials and by meta-analysis of publicly available expression data to assess statistical criteria of subtype robustness and overall clinical relevance. We then developed a single-sample classifier (SSC) using penalized logistic regression based on the most robust and replicable schema. Results: We demonstrate that a tumor-intrinsic two-subtype schema is most robust, replicable, and clinically relevant. We developed Purity Independent Subtyping of Tumors (PurIST), a SSC with robust and highly replicable performance on a wide range of platforms and sample types. We show that PurIST subtypes have meaningful associations with patient prognosis and have significant implications for treatment response to FOLIFIRNOX. Conclusions: The flexibility and utility of PurIST on low-input samples such as tumor biopsies allows it to be used at the time of diagnosis to facilitate the choice of effective therapies for patients with pancreatic ductal adenocarcinoma and should be considered in the context of future clinical trials.

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