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Comprehensive genomic sequencing detects important genetic differences between right-sided and left-sided colorectal cancer.

ABSTRACT: Objectives:Anti-epidermal growth factor receptor (EGFR) therapy has been found to be more effective against left-sided colorectal cancer (LCRC) than right-sided colorectal cancer (RCRC). We hypothesized that RCRC is more likely to harbor genetic alterations associated with resistance to anti-EGFR therapy and tested this using comprehensive genomic sequencing. Materials and methods:A total of 201 patients with either primary RCRC or LCRC were analyzed. We investigated tumors for genetic alterations using a 415-gene panel, which included alterations associated with resistance to anti-EGFR therapy: TK receptors (ERBB2, MET, EGFR, FGFR1, and PDGFRA), RAS pathway (KRAS, NRAS, HRAS, BRAF, and MAPK2K1), and PI3K pathway (PTEN and PIK3CA). Patients whose tumors had no alterations in these 12 genes, theoretically considered to respond to anti-EGFR therapy, were defined as "all wild-type", while remaining patients were defined as "mutant-type". Results:Fifty-six patients (28%) and 145 patients (72%) had RCRC and LCRC, respectively. Regarding genetic alterations associated with anti-EGFR therapy, only 6 of 56 patients (11%) with RCRC were "all wild-type" compared with 41 of 145 patients (28%) with LCRC (P = 0.009). Among the 49 patients who received anti-EGFR therapy, RCRC showed significantly worse progression-free survival (PFS) than LCRC (P = 0.022), and "mutant-type" RCRC showed significantly worse PFS compared with "all wild-type" LCRC (P = 0.004). Conclusions:RCRC is more likely to harbor genetic alterations associated with resistance to anti-EGFR therapy compared with LCRC. Furthermore, our data shows primary tumor sidedness is a surrogate for the non-random distribution of genetic alterations in CRC.

SUBMITTER: Shimada Y

PROVIDER: S-EPMC5706819 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Comprehensive genomic sequencing detects important genetic differences between right-sided and left-sided colorectal cancer.

Shimada Yoshifumi Y Kameyama Hitoshi H Nagahashi Masayuki M Ichikawa Hiroshi H Muneoka Yusuke Y Yagi Ryoma R Tajima Yosuke Y Okamura Takuma T Nakano Masato M Sakata Jun J Kobayashi Takashi T Nogami Hitoshi H Maruyama Satoshi S Takii Yasumasa Y Hayashida Tetsu T Takaishi Hiromasa H Kitagawa Yuko Y Oki Eiji E Konishi Tsuyoshi T Ishida Fumio F Kudo Shin-Ei SE Ring Jennifer E JE Protopopov Alexei A Lyle Stephen S Ling Yiwei Y Okuda Shujiro S Ishikawa Takashi T Akazawa Kohei K Takabe Kazuaki K Wakai Toshifumi T

Oncotarget 20170824 55

<h4>Objectives</h4>Anti-epidermal growth factor receptor (EGFR) therapy has been found to be more effective against left-sided colorectal cancer (LCRC) than right-sided colorectal cancer (RCRC). We hypothesized that RCRC is more likely to harbor genetic alterations associated with resistance to anti-EGFR therapy and tested this using comprehensive genomic sequencing.<h4>Materials and methods</h4>A total of 201 patients with either primary RCRC or LCRC were analyzed. We investigated tumors for ge ...[more]

PMID: 29212173

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Project description:BackgroundColorectal cancer (CRC) is a heterogeneous disease, with subtypes that have different clinical behaviours and subsequent prognoses. There is a growing body of evidence suggesting that right-sided colorectal cancer (RCC) and left-sided colorectal cancer (LCC) also differ in treatment success and patient outcomes. Biomarkers that differentiate between RCC and LCC are not well-established. Here, we apply random forest (RF) machine learning methods to identify genomic or microbial biomarkers that differentiate RCC and LCC.MethodsRNA-seq expression data for 58,677 coding and non-coding human genes and count data for 28,557 human unmapped reads were obtained from 308 patient CRC tumour samples. We created three RF models for datasets of human genes-only, microbes-only, and genes-and-microbes combined. We used a permutation test to identify features of significant importance. Finally, we used differential expression (DE) and paired Wilcoxon-rank sum tests to associate features with a particular side.ResultsRF model accuracy scores were 90%, 70%, and 87% with area under curve (AUC) of 0.9, 0.76, and 0.89 for the human genomic, microbial, and combined feature sets, respectively. 15 features were identified as significant in the model of genes-only, 54 microbes in the model of microbes-only, and 28 genes and 18 microbes in the model with genes-and-microbes combined. PRAC1 expression was the most important feature for differentiating RCC and LCC in the genes-only model, with HOXB13, SPAG16, HOXC4, and RNLS also playing a role. Ruminococcus gnavus and Clostridium acetireducens were the most important in the microbial-only model. MYOM3, HOXC4, Coprococcus eutactus, PRAC1, lncRNA AC012531.25, Ruminococcus gnavus, RNLS, HOXC6, SPAG16 and Fusobacterium nucleatum were most important in the combined model.ConclusionsMany of the identified genes and microbes among all models have previously established associations with CRC. However, the ability of RF models to account for inter-feature relationships within the underlying decision trees may yield a more sensitive and biologically interconnected set of genomic and microbial biomarkers.

Dataset Information

Comprehensive genomic sequencing detects important genetic differences between right-sided and left-sided colorectal cancer.

Publications

Comprehensive genomic sequencing detects important genetic differences between right-sided and left-sided colorectal cancer.

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