ImPlatelet classifier: Image-converted RNA biomarker profiles enable blood-based cancer diagnostics

ABSTRACT: Abstract: Sequencing technologies have enabled in-depth analysis of liquid biopsies in cancer, offering a minimally invasive sample collection. The most widely used material is blood which, next to circulating tumor cells and circulating tumor DNA, is the source of tumor-educated platelets (TEPs). Methods: We developed imPlatelet method which converts RNA-sequenced platelet data to images, additionally implementing biological knowledge from the Kyoto Encyclopedia of Genes and Genomes Pathway. First, we tested imPlatelet method on a cohort of 401 non-small cell lung cancer patients and 62 sarcoma patients. Next, we applied the developed tool to platelets collected from a new, independent cohort of 28 ovarian cancer patients and 30 non-cancer benign gynaecological conditions. Results: imPlatelet provided excellent discrimination between cancer cases and healthy controls, with accuracy equal to 1 in training, validation and independent datasets. When discriminating between ovarian cancer cases and benign conditions, imPlatelet reached 0.91 balanced accuracy, with sensitivity and specificity equal to 0.95 and 0.88, respectively, in an independent test set. ImPlatelet outperformed current state-of-the-art method thromboSeq in the aspects of balanced classification accuracy, the computational power needed, user experience, and execution time. Conclusions: According to our knowledge, this is the first study implementing an image-based deep learning approach combined with biological knowledge to classify human samples. Our results on classification of ovarian cancer considerably outperform previously published methods and our own alternative attempts of discrimination. We show that a deep learning image-based classifier accurately identifies cancer, despite the limited number of samples and even among non-cancer conditions which affect platelet transcriptome making the diagnosis difficult.

ORGANISM(S): Homo sapiens

PROVIDER: GSE158508 | GEO | 2021/10/06

REPOSITORIES: GEO