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Machine-learning model based on glycosyltransferase expression predicts multiple cancer types, subtypes and survival probability

ABSTRACT: Whereas current tools for diagnosing cancer focus on identifying markers in limited types of the disease, such as the PAM50 kit known primarily for subtyping breast cancer, our research reveals that 27 different cancers have a unique glycosyltransferase (GT) fingerprint that can be detected using a single test, with 93.7% accuracy in external validation. Four models were built on the expression patterns of 71 GTs that are universally influential in inducing the formation of cancer cells and giving them the impetus to metastasize. The first differentiates cancer tissue from normal tissue, the second distinguishes cancer types, and the third and fourth models differentiate between subtypes of cancer, in particular breast cancer and glioma. Data from the Cancer Genome Atlas database were used for deriving the models, while independent publicly available databases and newly collected patient samples were used for validation. Running these models against external databases showed just how powerful they are, producing results that were highly comparable to those achieved in internal testing. Furthermore, the breast cancer classifier differentiated between breast cancer subtypes with an average of 81% accuracy in external testing, far surpassing the industry-standard PAM50’s accuracy ratio of 43%. Meanwhile, a separate prognostic model designed to predict the probability of survival among glioma patients achieved high accuracy by zooming in on four GT genes that are strongly related to prognosis. Taken together, the models demonstrate the revolutionary potential of focusing on GT genes to simplify cancer diagnostics.

ORGANISM(S): Homo sapiens

PROVIDER: GSE254461 | GEO | 2024/02/03

REPOSITORIES: GEO

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Project description:Background: Neoadjuvant chemotherapy is increasingly being used to preoperatively shrink breast tumours prior to surgery. This approach also provides the opportunity to study the molecular changes associated with treatment and evaluate whether on-treatment sequential samples can improve response and outcome predictions over diagnostic or excision samples alone. Methods: A total of 95 samples from a cohort of 50 neoadjuvant chemotherapy-treated primary breast cancer patients (aged 29-76, 48% ER+, 20% HER2+) enrolled in the NEO trial taken before, at 2 weeks on-treatment, mid-therapy and at resection were sequenced with Ion Ampliseq transcriptome yielding expression values for 12,635 genes. Differential expression analysis was performed across 16 responders and 34 non-responders defined by pathological complete response and over treatment time to identify significantly differentially expressed genes, pathways and markers indicative of response status. Prediction accuracy was compared with estimations of established gene signatures, for this dataset and validated using data from the I-SPY1 trial. Results: AAGAB was identified as a novel on-treatment biomarker for pathological complete response, with an accuracy of 100% in the NEO training dataset and 78% accuracy in the I-SPY1 Trial. AAGAB levels on treatment were also significantly predictive of term survival (p = 0.048, p = 0.031) in the two cohorts. This single gene on-treatment biomarker, had greater predictive accuracy than established prognostic tests, Mammaprint and Pam50 risk of recurrence score, although interesting both of these tests performed better in the on-treatment rather than the accepted pre-treatment setting (accuracy improving consistently by 2-8%). Conclusion: Changes in gene expression measured in sequential samples from breast cancer patients receiving neoadjuvant chemotherapy resulted in the identification of a novel on-treatment biomarker and suggest that established prognostic tests may have greater prediction accuracy on- than before treatment. These results support the potential use and further evaluation of on- treatment testing in breast cancer to improve the accuracy of tumour response prediction.

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Machine-learning model based on glycosyltransferase expression predicts multiple cancer types, subtypes and survival probability

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