Although the emerging area of targeted anticancer agents holds great promise, cytotoxic chemotherapy remains the primary treatment option for many cancer patients. Identifying patients who likely will or will not benefit from cytotoxic chemotherapy through the use of biomarkers could greatly improve clinical management by better defining appropriate treatment options for patients. None of the molecules experimentally identified to cause chemotherapy resistance in vitro was sufficiently validated in primary tumors and thus clinically applicable,1 underscoring the importance of well-designed, clinical study to identify clinically relevant mechanisms for chemotherapy resistance. In fact, however, such predictors derived to date from high-throughput transcriptional profiling of primary tumors, especially gastrointestinal tract cancers, have not shown satisfactory performance.2, 3, 4, 5 It may be primarily owing to the high rate of false-positive discovery in high-throughput data, in addition to the high degree of genetic variation of individual tumor compared with limited number of samples available for the study.
To provide insight into clinically relevant mechanisms for chemotherapy resistance in gastric cancer, we prospectively collected and analyzed 123 endoscopic biopsy samples before cisplatin and fluorouracil (CF) chemotherapy from patients with extended follow-up, using high-throughput transcriptional profiling and comparative genomic hybridization (CGH) analyses. We could identify functional categories enriched in genes correlated with patient outcome, and develop a genomic predictor that was validated in two independent data sets.
Genes correlated with poor survival after CF therapy
As primary gastric cancer lesions cannot be reliably measured by diagnostic imaging, patient survival, not radiographic response, was used as the primary clinical covariate to which gene expression was correlated to identify a predictor of response to CF therapy. To define a gene expression signature that correlates with overall survival, we used expression array data of 96 pretreatment biopsy samples as the training set to develop a predictor (Supplementary Table 1). Ninety-five out of 96 patients (99%) in the training set cohort died with follow-up for one survivor at 39.4 months. None of the clinicopathological or treatment factors listed in Table 1, including second-line chemotherapy, were significantly correlated with survival time of the patients in the training set.
To identify a transcriptional profile related to clinical benefit from CF therapy, the survival times of patients in the array training set were correlated with the mRNA expression levels measured by microarray. One thousand five hundred and sixty-five genes were significantly correlated with the overall survival of the 96 patients (P-value <0.05). Among them, 917 genes had an HR higher than 1 (poor prognosis signature) and 648 genes had an HR lower than 1 (good prognosis signature). We performed gene ontology analyses on this ‘poor prognosis signature’ using Ingenuity Pathway Analysis (www.ingenuity.com). The role of BRCA1 in DNA damage response (BRCA2, E2F5, FANCE, MSH2, NBN, PLK1, RFC, SMARCA4, SLC19A1), nucleotide excision repair (ERCC2, POLR2C, POLOR2J, RAD23A, RAD23B) and estrogen receptor signaling were highly represented canonical pathways. Many of these poor prognosis signature genes belonging to these three pathways are previously linked to in vitro cisplatin resistance.13, 14, 15 Overexpression of ERCC2 (P=0.007 in our data) is associated with cisplatin resistance in lung cancer cell lines.13 Silencing of hHR23A (P=0.022 in our data) decreases the nuclear DRP1 level and cisplatin resistance in lung adenocarcinoma cells.14 Disruption of the Fanconi anemia–BRCA pathway is reported in cisplatin-sensitive ovarian tumors.15 Thus, this gene ontology analysis supports the clinical relevance of these DNA repair canonical pathways, which were shown to be associated with in vitro cisplatin resistance.
Ingenuity Pathway Analysis functional categories enriched in poor prognosis signature were: protein synthesis, DNA replication/recombination/repair and cancer (Supplementary Table 2). The protein synthesis category includes ribosomal subunit mRNAs (RPL13, RPL18, RPL24, RPL30, RPL38, RPL5, RPL7, RPL7A, RPL8, RPS2, RPS5) and eukaryotic translation initiation factors (EIF1, EIF2B2, EIF2B4, EIF2S1, EIF3B, EIF3C, EIF3D, EIF3E, EIF3F, EIF3H, EIF3I, EIF4A1, EIF4A3, EIF4B, EIF4EBP1, EIF5, EIF5B). This result suggests that the most prominent feature of poor prognosis signature is increased protein synthesis, presumably resulting from activation of oncogenes, such as EGFR, FGFR2 and MYC (Supplementary Table 2). MYC-induced transcriptional activation of protein synthesis-related genes is previously shown by a microarray report that the majority of genes responsive to MYC overexpression are involved in macromolecular synthesis, protein turnover and metabolism, including 30 ribosomal protein genes.16
Infinitesimal perturbation analysis canonical pathways enriched in 648 genes in good prognosis signature were antigen presentation pathway, B-cell development and interleukin-15 production. Enriched functional categories were gastrointestinal disease, inflammatory disease and genetic disorder.
Full article: http://www.nature.com/tpj/journal/vaop/ncurrent/full/tpj201087a.html
As primary gastric cancer lesions cannot be reliably measured by diagnostic imaging, patient survival, not radiographic response, was used as the primary clinical covariate to which gene expression was correlated to identify a predictor of response to CF therapy. To define a gene expression signature that correlates with overall survival, we used expression array data of 96 pretreatment biopsy samples as the training set to develop a predictor (Supplementary Table 1). Ninety-five out of 96 patients (99%) in the training set cohort died with follow-up for one survivor at 39.4 months. None of the clinicopathological or treatment factors listed in Table 1, including second-line chemotherapy, were significantly correlated with survival time of the patients in the training set.
To identify a transcriptional profile related to clinical benefit from CF therapy, the survival times of patients in the array training set were correlated with the mRNA expression levels measured by microarray. One thousand five hundred and sixty-five genes were significantly correlated with the overall survival of the 96 patients (P-value <0.05). Among them, 917 genes had an HR higher than 1 (poor prognosis signature) and 648 genes had an HR lower than 1 (good prognosis signature). We performed gene ontology analyses on this ‘poor prognosis signature’ using Ingenuity Pathway Analysis (www.ingenuity.com). The role of BRCA1 in DNA damage response (BRCA2, E2F5, FANCE, MSH2, NBN, PLK1, RFC, SMARCA4, SLC19A1), nucleotide excision repair (ERCC2, POLR2C, POLOR2J, RAD23A, RAD23B) and estrogen receptor signaling were highly represented canonical pathways. Many of these poor prognosis signature genes belonging to these three pathways are previously linked to in vitro cisplatin resistance.13, 14, 15 Overexpression of ERCC2 (P=0.007 in our data) is associated with cisplatin resistance in lung cancer cell lines.13 Silencing of hHR23A (P=0.022 in our data) decreases the nuclear DRP1 level and cisplatin resistance in lung adenocarcinoma cells.14 Disruption of the Fanconi anemia–BRCA pathway is reported in cisplatin-sensitive ovarian tumors.15 Thus, this gene ontology analysis supports the clinical relevance of these DNA repair canonical pathways, which were shown to be associated with in vitro cisplatin resistance.
Ingenuity Pathway Analysis functional categories enriched in poor prognosis signature were: protein synthesis, DNA replication/recombination/repair and cancer (Supplementary Table 2). The protein synthesis category includes ribosomal subunit mRNAs (RPL13, RPL18, RPL24, RPL30, RPL38, RPL5, RPL7, RPL7A, RPL8, RPS2, RPS5) and eukaryotic translation initiation factors (EIF1, EIF2B2, EIF2B4, EIF2S1, EIF3B, EIF3C, EIF3D, EIF3E, EIF3F, EIF3H, EIF3I, EIF4A1, EIF4A3, EIF4B, EIF4EBP1, EIF5, EIF5B). This result suggests that the most prominent feature of poor prognosis signature is increased protein synthesis, presumably resulting from activation of oncogenes, such as EGFR, FGFR2 and MYC (Supplementary Table 2). MYC-induced transcriptional activation of protein synthesis-related genes is previously shown by a microarray report that the majority of genes responsive to MYC overexpression are involved in macromolecular synthesis, protein turnover and metabolism, including 30 ribosomal protein genes.16
Full article: http://www.nature.com/tpj/journal/vaop/ncurrent/full/tpj201087a.html
