TargetMine 2022: a new vision into drug target analysis

Machine learning hypothesis-generation for patient stratification and target discovery in rare disease: our experience with Open Science in ALS

Introduction

Advances in machine learning (ML) methodologies, combined with multidisciplinary collaborations across biological and physical sciences, has the potential to propel drug discovery and development. Open Science fosters this collaboration by releasing datasets and methods into the public space; however, further education and widespread acceptance and adoption of Open Science approaches are necessary to tackle the plethora of known disease states.

Motivation

In addition to providing much needed insights into potential therapeutic protein targets, we also aim to demonstrate that small patient datasets have the potential to provide insights that usually require many samples (>5,000). There are many such datasets available and novel advancements in ML can provide valuable insights from these patient datasets.

Problem statement

Using a public dataset made available by patient advocacy group AnswerALS and a multidisciplinary Open Science approach with a systems biology augmented ML technology, we aim to validate previously reported drug targets in ALS and provide novel insights about ALS subpopulations and potential drug targets using a unique combination of ML methods and graph theory.

Methodology

We use NetraAI to generate hypotheses about specific patient subpopulations, which were then refined and validated through a combination of ML techniques, systems biology methods, and expert input.

Results

We extracted 8 target classes, each comprising of several genes that shed light into ALS pathophysiology and represent new avenues for treatment. These target classes are broadly categorized as inflammation, epigenetic, heat shock, neuromuscular junction, autophagy, apoptosis, axonal transport, and excitotoxicity. These findings are not mutually exclusive, and instead represent a systematic view of ALS pathophysiology. Based on these findings, we suggest that simultaneous targeting of ALS has the potential to mitigate ALS progression, with the plausibility of maintaining and sustaining an improved quality of life (QoL) for ALS patients. Even further, we identified subpopulations based on disease onset.

Conclusion

In the spirit of Open Science, this work aims to bridge the knowledge gap in ALS pathophysiology to aid in diagnostic, prognostic, and therapeutic strategies and pave the way for the development of personalized treatments tailored to the individual's needs.

DKK3 expression is correlated with poorer prognosis in head and neck squamous cell carcinoma: A bioinformatics study based on the TCGA database

OBJECTIVE

We previously reported that dickkopf WNT signaling pathway inhibitor 3 (DKK3) expression is correlated with poorer prognosis in head and neck squamous cell carcinoma (HNSCC). Here we investigated DKK3 expression by using The Cancer Genome Atlas (TCGA) public database and bioinformatic analyses.

METHODS

We used the RNA sequence data and divided the tumor samples into "DKK3-high" and "DKK3-low" groups according to median DKK3 expression. The correlations between DKK3 expression and the clinical data were investigated. Differentially expressed genes (DEGs) were detected using DESEq2 and analyzed by ShinyGO 0.77. A gene set enrichment analysis (GSEA) was also performed using GSEA software. The DEGs were also analyzed with TargetMine to establish the protein-protein interaction (PPI) network.

RESULTS

DKK3 expression was significantly increased in cancer samples, and a high DKK3 expression was significantly associated with shorter overall survival. We identified 854 DEGs, including 284 up-regulated and 570 down-regulated. Functional enrichment analyses revealed several Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways associated with extracellular matrix remodeling. The PPI network identified COL8A1, AGTR1, FN1, P4HA3, PDGFRB, and CEP126 as the key genes.

CONCLUSIONS

These results suggested the cancer-promoting ability of DKK3, the expression of which is a promising prognostic marker and therapeutic target for HNSCC.