845P - A machine learning based clinical platform for cancer subtyping and data integration in hematological malignancies

Background

Next Generation sequencing (NGS) has greatly advanced precision oncology. The growing amount and complexity of NGS data, along with other clinical data such as drug responses and measurable residual disease (MRD), present a big challenge for data integration and interpretation. Machine learning, especially deep neural networks, offer a promising approach for efficiently analyzing intricate relationships within large datasets, with the potential of improving clinical outcomes.

Methods

We performed targeted RNA sequencing in routine diagnostics and sequenced 1849 cases with hematological neoplasms, primarily pediatric B-cell precursor acute lymphoblastic leukemia (BCP-ALL). We used featureCounts in megSAP pipeline and Uniform Manifold Approximation and Projection (UMAP) to analyze the gene expression data, and Bokeh to create the web interface for data integration and visualization. Scikit-learn and PyTorch were used to develop shallow machine learning models and deep learning neural networks (NN), respectively.

Results

Our platform integrates various genetic and clinical data based on UMAP analysis of gene expression patterns. It improves the point-of-care decision making and facilitates the discovery of new patterns such as subpopulations with different genetic and clinical features. The platform is supported by machine learning algorithms for cancer subtyping. Six basic machine learning algorithms were coupled with feature selection methods and the best F1 score achieves 98%. We also built a biologically informed deep NN that can accurately predict BCP-ALL subtypes (F1=97%) with a good interpretability, which helps to identify crucial genetic aberrations associated with disease subtypes.

Conclusions

Our machine learning based platform can not only provide support for clinical decision-making but also bring novel translational insights for hematological malignancies.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Bundesministerium für Bildung und Forschung (BMBF).

Disclosure

All authors have declared no conflicts of interest.