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Research article
Mohanad Mohammed
University of Kwazulu-Natal
Corresponding Author
ORCiD: https://orcid.org/0000-0001-5336-0286
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Colorectal cancer (CRC) is the third most common cancer among women and men in the USA and recent studies have shown an increasing incidence in less developed regions such as Sub-Saharan Africa (SSA). The KRAS gene is mutated in 40% of the CRC cases and hence the RAS pathway activation has become a major focus of drug targeting efforts. However, nearly 60% of patients with wild-type KRAS fail to respond to RAS-targeted therapies, for example the anti-epithelial growth factor receptor inhibitor (EGFRi) combination therapies. Thus, there is a need to develop more reliable molecular signatures to better predict mutation status. In this study, we develop a hybrid (DNA mutation and RNA expression) signature and assess its predictive properties for the mutation status and survival of CRC patients.
Methods: Publicly-available microarray and RNASeq data from 54 matched formalin-fixed paraffin embedded (FFPE) samples from the Affymetrix GeneChip and RNASeq platforms, were used to obtain differentially expressed genes between mutant and wild-type samples. For classification, the support-vector machines, artificial neural networks, random forests, k-nearest neighbor, naïve Bayes, negative binomial linear discriminant analysis, and the Poisson linear discriminant analysis algorithms were employed.
Results: Compared to the genelist from each of the individual platforms, the hybrid genelist had the highest accuracy, sensitivity, specificity and AUC for mutation status, across all the classifiers, and is prognostic for survival in patients with CRC.
Conclusions: This signature could be useful in clinical practice, especially for colorectal cancer diagnosis and therapy.
Keywords
Colorectal cancer, FFPE, Microarray, RAS pathway signatures, RNASeq
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This is a preprint. It has not completed peer review.
Research article
Mohanad Mohammed
University of Kwazulu-Natal
Corresponding Author
ORCiD: https://orcid.org/0000-0001-5336-0286
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 08 Jan, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Colorectal cancer (CRC) is the third most common cancer among women and men in the USA and recent studies have shown an increasing incidence in less developed regions such as Sub-Saharan Africa (SSA). The KRAS gene is mutated in 40% of the CRC cases and hence the RAS pathway activation has become a major focus of drug targeting efforts. However, nearly 60% of patients with wild-type KRAS fail to respond to RAS-targeted therapies, for example the anti-epithelial growth factor receptor inhibitor (EGFRi) combination therapies. Thus, there is a need to develop more reliable molecular signatures to better predict mutation status. In this study, we develop a hybrid (DNA mutation and RNA expression) signature and assess its predictive properties for the mutation status and survival of CRC patients.
Methods: Publicly-available microarray and RNASeq data from 54 matched formalin-fixed paraffin embedded (FFPE) samples from the Affymetrix GeneChip and RNASeq platforms, were used to obtain differentially expressed genes between mutant and wild-type samples. For classification, the support-vector machines, artificial neural networks, random forests, k-nearest neighbor, naïve Bayes, negative binomial linear discriminant analysis, and the Poisson linear discriminant analysis algorithms were employed.
Results: Compared to the genelist from each of the individual platforms, the hybrid genelist had the highest accuracy, sensitivity, specificity and AUC for mutation status, across all the classifiers, and is prognostic for survival in patients with CRC.
Conclusions: This signature could be useful in clinical practice, especially for colorectal cancer diagnosis and therapy.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
This preprint is available for download as a PDF.
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