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NMF-guided feature selection and genetic algorithm-driven framework for tumor mutational burden classification in bladder cancer using multi-omics data

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Abstract

Accurately classifying bladder cancer patients based on Tumor Mutational Burden (TMB) is of paramount significance for prognosis and treatment decisions. To achieve that, we present a novel approach leveraging multi-omics data to differentiate between low and high TMB classes. The model combines feature selection and predictive modeling to unveil robust biomarkers associated with TMB classification. The Genetic Algorithm is employed to perform feature selection across DNA methylation, copy number alteration, and RNA-seq datasets. This process effectively reduces the dimensionality of the input data while retaining the most informative attributes. Subsequently, these selected features are projected into a latent space using non-negative matrix factorization, capturing the underlying patterns within the multi-omics data. Convolutional neural network among other machine learning machines to predict the class of TMB. The model introduces a promising classification power, showcasing the potential of these multi-omics biomarkers in accurately distinguishing between low and high TMB classes. The survival analysis reveals a substantial disparity between the cohorts classified as low-TMB and high-TMB. We propose a robust framework for TMB classification in bladder cancer that integrates multi-omics data, advanced machine learning techniques, and survival analysis to collectively pave the way for improved prognostic insights and personalized therapeutic interventions.

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Data availability

The dataset analysed during the current study is available in the cBioPortal repository at https://www.cbioportal.org/study/summary?id=blca_tcga_pan_can_atlas_2018.

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Funding

This research was funded by the Scientific Research and Innovation Support Fund/Ministry of Higher Education and Scientific Research/Jordan, Grant number (ICT/1/16/2022). The recipients of this fund are Abedalrhman Alkhateeb and Hazem Qattous.

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Authors and Affiliations

  1. King Hussein School of Computing Sciences, Princess Sumaya University for Technology, Al-Jubaiha, Amman, Jordan

    Ibrahim Abed Al-Ghafer, Noor AlAfeshat, Lujain Alshomali, Hazem Qattous & Mohammad Azzeh

  2. Department of Urology, Detroit Medical Center, Detroit, MI, USA

    Shaheen Alanee

  3. Department of Computer Science, Lakehead University, Thunder Bay, ON, Canada

    Abedalrhman Alkhateeb

Authors
  1. Ibrahim Abed Al-Ghafer
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  2. Noor AlAfeshat
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  3. Lujain Alshomali
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  4. Shaheen Alanee
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  5. Hazem Qattous
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  6. Mohammad Azzeh
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  7. Abedalrhman Alkhateeb
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Contributions

Conceptualization, AA, SA, and MA; Data curation, IA, and NA; Formal analysis, IA, LA, HQ, and AA; Funding acquisition, AA; Investigation, AA, LA, and SA; Methodology, IA, NA, AA, and MA; Project administration, AA, MA.

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Correspondence to Abedalrhman Alkhateeb.

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Al-Ghafer, I.A., AlAfeshat, N., Alshomali, L. et al. NMF-guided feature selection and genetic algorithm-driven framework for tumor mutational burden classification in bladder cancer using multi-omics data. Netw Model Anal Health Inform Bioinforma 13, 26 (2024). https://doi.org/10.1007/s13721-024-00460-7

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  • DOI: https://doi.org/10.1007/s13721-024-00460-7

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