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Deep sequencing and pathway-focused analysis revealed multigene oncodriver signatures predicting survival outcomes in advanced colorectal cancer.

ABSTRACT: Genomic technologies are reshaping the molecular landscape of colorectal cancer (CRC), revealing that oncogenic driver mutations (APC and TP53) coexist with still underappreciated genetic events. We hypothesized that mutational analysis of CRC-linked genes may provide novel information on the connection between genetically-deregulated pathways and clinical outcomes. We performed next-generation sequencing (NGS) analysis of 16 recurrently mutated genes in CRC exploiting tissue specimens from 98 advanced CRC patients. Multiple correspondence analysis (MCA) was used to identify gene sets characterizing negative and positive outliers (patients in the lowest and highest quartile of progression-free survival, PFS). Variables potentially affecting PFS and overall survival (OS) were tested in univariate and multivariate Cox proportional hazard models. Sensitivity analyses and resampling were used to assess the robustness of genomic predictors. MCA revealed that APC and TP53 mutations were close to the negative outlier group, whereas mutations in other WNT pathway genes were in proximity of the positive outliers. Reasoning that genetic alterations interact epistatically, producing greater or weaker consequences in combination than when individually considered, we tested whether patients whose tumors carried a genetic background characterized by APC and TP53 mutations without coexisting mutations in other WNT genes (AMER1, FBXW7, TCF7L2, CTNNB1, SOX9) had adverse survival outcomes. With this approach, we identified two oncodriver signatures (ODS1 and ODS2) associated with shorter PFS (ODS1 multivariate Cox for PFS: HR 2.16, 95%CI: 1.28-3.64, p?=?0.004; ODS2 multivariate Cox for PFS: HR 2.61, 95%CI: 1.49-4.58, p?=?0.001). Clinically-focused and molecularly-focused sensitivity analyses, resampling, and reclassification of mutations confirmed the stability of ODS1/2. Moreover, ODS1/2 negatively impacted OS. Collectively, our results point to co-occurring driver mutations as an adverse molecular factor in advanced CRC. This relationship depends on a broader genetic context highlighting the importance of genetic interactions.

SUBMITTER: De Nicola F

PROVIDER: S-EPMC6054833 | biostudies-other | 2018 Jul

REPOSITORIES: biostudies-other

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Deep sequencing and pathway-focused analysis revealed multigene oncodriver signatures predicting survival outcomes in advanced colorectal cancer.

De Nicola Francesca F Goeman Frauke F Pallocca Matteo M Sperati Francesca F Pizzuti Laura L Melucci Elisa E Casini Beatrice B Amoreo Carla Azzurra CA Gallo Enzo E Diodoro Maria Grazia MG Buglioni Simonetta S Mazzotta Marco M Vici Patrizia P Sergi Domenico D Di Lauro Luigi L Barba Maddalena M Pescarmona Edoardo E Ciliberto Gennaro G De Maria Ruggero R Fanciulli Maurizio M Maugeri-Saccà Marcello M

Oncogenesis 20180722 7

Genomic technologies are reshaping the molecular landscape of colorectal cancer (CRC), revealing that oncogenic driver mutations (APC and TP53) coexist with still underappreciated genetic events. We hypothesized that mutational analysis of CRC-linked genes may provide novel information on the connection between genetically-deregulated pathways and clinical outcomes. We performed next-generation sequencing (NGS) analysis of 16 recurrently mutated genes in CRC exploiting tissue specimens from 98 a ...[more]

PMID: 30032163

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Project description:BACKGROUND:For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognosticators directly from these widely available images. METHODS AND FINDINGS:We hand-delineated single-tissue regions in 86 CRC tissue slides, yielding more than 100,000 HE image patches, and used these to train a CNN by transfer learning, reaching a nine-class accuracy of >94% in an independent data set of 7,180 images from 25 CRC patients. With this tool, we performed automated tissue decomposition of representative multitissue HE images from 862 HE slides in 500 stage I-IV CRC patients in the The Cancer Genome Atlas (TCGA) cohort, a large international multicenter collection of CRC tissue. Based on the output neuron activations in the CNN, we calculated a "deep stroma score," which was an independent prognostic factor for overall survival (OS) in a multivariable Cox proportional hazard model (hazard ratio [HR] with 95% confidence interval [CI]: 1.99 [1.27-3.12], p = 0.0028), while in the same cohort, manual quantification of stromal areas and a gene expression signature of cancer-associated fibroblasts (CAFs) were only prognostic in specific tumor stages. We validated these findings in an independent cohort of 409 stage I-IV CRC patients from the "Darmkrebs: Chancen der Verhütung durch Screening" (DACHS) study who were recruited between 2003 and 2007 in multiple institutions in Germany. Again, the score was an independent prognostic factor for OS (HR 1.63 [1.14-2.33], p = 0.008), CRC-specific OS (HR 2.29 [1.5-3.48], p = 0.0004), and relapse-free survival (RFS; HR 1.92 [1.34-2.76], p = 0.0004). A prospective validation is required before this biomarker can be implemented in clinical workflows. CONCLUSIONS:In our retrospective study, we show that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images.

Dataset Information

Deep sequencing and pathway-focused analysis revealed multigene oncodriver signatures predicting survival outcomes in advanced colorectal cancer.

Publications

Deep sequencing and pathway-focused analysis revealed multigene oncodriver signatures predicting survival outcomes in advanced colorectal cancer.

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