A tumor microenvironment-specific gene expression signature predicts chemotherapy resistance in colorectal cancer patients.
Xiaoqiang ZhuXianglong TianLinhua JiXinyu ZhangYingying CaoChaoqin ShenYe HuJason Wing Hon WongJing-Yuan FangJie HongHaoyan ChenPublished in: NPJ precision oncology (2021)
Studies have shown that tumor microenvironment (TME) might affect drug sensitivity and the classification of colorectal cancer (CRC). Using TME-specific gene signature to identify CRC subtypes with distinctive clinical relevance has not yet been tested. A total of 18 "bulk" RNA-seq datasets (total n = 2269) and four single-cell RNA-seq datasets were included in this study. We constructed a "Signature associated with FOLFIRI resistant and Microenvironment" (SFM) that could discriminate both TME and drug sensitivity. Further, SFM subtypes were identified using K-means clustering and verified in three independent cohorts. Nearest template prediction algorithm was used to predict drug response. TME estimation was performed by CIBERSORT and microenvironment cell populations-counter (MCP-counter) methods. We identified six SFM subtypes based on SFM signature that discriminated both TME and drug sensitivity. The SFM subtypes were associated with distinct clinicopathological, molecular and phenotypic characteristics, specific enrichments of gene signatures, signaling pathways, prognosis, gut microbiome patterns, and tumor lymphocytes infiltration. Among them, SFM-C and -F were immune suppressive. SFM-F had higher stromal fraction with epithelial-to-mesenchymal transition phenotype, while SFM-C was characterized as microsatellite instability phenotype which was responsive to immunotherapy. SFM-D, -E, and -F were sensitive to FOLFIRI and FOLFOX, while SFM-A, -B, and -C were responsive to EGFR inhibitors. Finally, SFM subtypes had strong prognostic value in which SFM-E and -F had worse survival than other subtypes. SFM subtypes enable the stratification of CRC with potential chemotherapy response thereby providing more precise therapeutic options for these patients.
Keyphrases
- rna seq
- single cell
- gene expression
- end stage renal disease
- stem cells
- ejection fraction
- high throughput
- newly diagnosed
- machine learning
- prognostic factors
- genome wide
- signaling pathway
- oxidative stress
- small cell lung cancer
- cell proliferation
- copy number
- risk assessment
- transcription factor
- radiation therapy
- pi k akt
- single molecule
- cancer therapy
- adverse drug
- electronic health record
- tyrosine kinase
- epidermal growth factor receptor
- patient reported
- genome wide identification
- drug delivery