Juhl, H. | bioRxiv Science

bioRxiv2024

Tumour specimen cold ischemia time impacts molecular cancer drug target discovery

Tumour tissue collections are used to uncover pathways associated with disease outcomes that can also serve as targets for cancer treatment, ideally by comparing the molecular properties of cancer tissues to matching normal tissues. The quality of such collections determines the value of the data and information generated from their analyses including expression and modifications of nucleic acids and proteins. These biomolecules are dysregulated upon ischemia and decomposed once the living cells start to decay into inanimate matter. Therefore, ischemia time before final tissue preservation is the most important determinant of the quality of a tissue collection. Here we show the impact of ischemia time on tumour and matching adjacent normal tissue samples for mRNAs in 1,664, proteins in 1,818 and phosphoproteins in 1,800 cases (tumour and matching normal samples) of four solid tumour types (CRC, HCC, LUAD and LUSC NSCLC subtypes). In CRC, ischemia times exceeding 15 minutes impacted 12.5% (mRNA), 25% (protein) and 50% (phosphosites) of differentially expressed molecules in tumour versus normal tissues. This hypoxia- and decay-induced dysregulation increased with longer ischemia times and was observed across tumour types. Interestingly, the proteomics analysis revealed that specimen ischemia time above 15 minutes is mostly associated with a dysregulation of proteins in the immune response pathway and less so with metabolic processes. We conclude that ischemia time is a crucial quality parameter for tissue collections used for target discovery and validation in prognostic cancer research.

cancer biology↗

bioRxiv2020

Just Add Data: Automated Predictive Modeling and BioSignature Discovery

Fully automated machine learning, statistical modelling, and artificial intelligence for predictive modeling is becoming a reality, giving rise to the field of Automated Machine Learning (AutoML). AutoML systems promise to democratize data analysis to non-experts, drastically increase productivity, improve replicability of the statistical analysis, facilitate the interpretation of results, and shield against common methodological analysis pitfalls. We present the basic ideas and principles of Just Add Data Bio (JADBIO), an AutoML technology applicable to the low-sample, high-dimensional omics data that arise in translational medicine and bioinformatics applications. In addition to predictive and diagnostic models ready for clinical use, JADBIO also returns the corresponding biosignatures, i.e., minimal-size subsets of biomarkers that are jointly predictive of the outcome of interest. A use-case on thymic epithelial tumors is presented, along with an extensive evaluation on 374 public biological datasets. Results show that long-standing challenges with overfitting and overestimation of complex non-linear machine learning pipelines on high-dimensional, low small sample data can be overcome.

bioinformatics↗