Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives.

In the field of cancer genomics, the broad availability of genetic information offered by next-generation sequencing technologies and rapid growth in biomedical publication has led to the advent of the big-data era. Integration of artificial intelligence (AI) approaches such as machine learning, deep learning, and natural language processing (NLP) to tackle the challenges of scalability and high dimensionality of data and to transform big data into clinically actionable knowledge is expanding and becoming the foundation of precision medicine. In this paper, we review the current status and future directions of AI application in cancer genomics within the context of workflows to integrate genomic analysis for precision cancer care. The existing solutions of AI and their limitations in cancer genetic testing and diagnostics such as variant calling and interpretation are critically analyzed. Publicly available tools or algorithms for key NLP technologies in the literature mining for evidence-based clinical recommendations are reviewed and compared. In addition, the present paper highlights the challenges to AI adoption in digital healthcare with regard to data requirements, algorithmic transparency, reproducibility, and real-world assessment, and discusses the importance of preparing patients and physicians for modern digitized healthcare. We believe that AI will remain the main driver to healthcare transformation toward precision medicine, yet the unprecedented challenges posed should be addressed to ensure safety and beneficial impact to healthcare.

betaTrCP- and Rsk1/2-mediated degradation of BimEL inhibits apoptosis.

The BimEL tumor suppressor is a potent proapoptotic BH3-only protein. We found that, in response to survival signals, BimEL was rapidly phosphorylated on three serine residues in a conserved degron, facilitating binding and degradation via the F box protein betaTrCP. Phosphorylation of the BimEL degron was executed by Rsk1/2 and promoted by the Erk1/2-mediated phosphorylation of BimEL on Ser69. Compared to wild-type BimEL, a BimEL phosphorylation mutant unable to bind betaTrCP was stabilized and consequently potent at inducing apoptosis by the intrinsic mitochondrial pathway. Moreover, although non-small cell lung cancer (NSCLC) cells often become resistant to gefitinib (a clinically relevant tyrosine kinase inhibitor that induces apoptosis through BimEL), silencing of either betaTrCP or Rsk1/2 resulted in BimEL-mediated apoptosis of both gefitinib-sensitive and gefitinib-insensitive NSCLC cells. Our findings reveal that betaTrCP promotes cell survival in cooperation with the ERK-RSK pathway by targeting BimEL for degradation.

Skp2, the FoxO1 hunter.

Skp2 is an oncoprotein that mediates the degradation of several negative regulators of the cell cycle to promote cell proliferation. A recent report by Huang and colleagues reveals that Skp2 directs the ubiquitylation and subsequent degradation of FoxO1, a member of the FoxO family of transcription factors. Since FoxO proteins possess tumor suppressor functions, this new finding suggests a new mechanism by which Skp2 may favor tumorigenesis.

A peptidomimetic siRNA transfection reagent for highly effective gene silencing.

RNA interference (RNAi) techniques hold forth great promise for therapeutic silencing of deleterious genes. However, clinical applications of RNAi require the development of safe and efficient methods for intracellular delivery of small interfering RNA (siRNA) oligonucleotides specific to targeted genes. We describe the use of a lipitoid, a cationic oligopeptoid-phospholipid conjugate, for non-viral transfection of synthetic siRNA oligos in cell culture. This peptidomimetic delivery vehicle allows for efficient siRNA transfection in a variety of human cell lines with negligible toxicity and promotes extensive downregulation of the targeted genes at both the protein and the mRNA level. We compare the lipitoid reagent to a standard commercial transfection reagent. The lipitoid is highly efficient even in primary IMR-90 human lung fibroblasts in which other commercial reagents are typically ineffective.

Comparative analysis of algorithms for signal quantitation from oligonucleotide microarrays.

MOTIVATION: Recent years' exponential increase in DNA microarrays experiments has motivated the development of many signal quantitation (SQ) algorithms. These algorithms perform various transformations on the actual measurements aimed to enable researchers to compare readings of different genes quantitatively within one experiment and across separate experiments. However, it is relatively unclear whether there is a 'best' algorithm to quantitate microarray data. The ability to compare and assess such algorithms is crucial for any downstream analysis. In this work, we suggest a methodology for comparing different signal quantitation algorithms for gene expression data. Our aim is to enable researchers to compare the effect of different SQ algorithms on the specific dataset they are dealing with. We combine two kinds of tests to assess the effect of an SQ algorithm in terms of signal to noise ratio. To assess noise, we exploit redundancy within the experimental dataset to test the variability of a given SQ algorithm output. For the effect of the SQ on the signal we evaluate the overabundance of differentially expressed genes using various statistical significance tests. RESULTS: We demonstrate our analysis approach with three SQ algorithms for oligonucleotide microarrays. We compare the results of using the dChip software and the RMAExpress software to the ones obtained by using the standard Affymetrix MAS5 on a dataset containing pairs of repeated hybridizations. Our analysis suggests that dChip is more robust and stable than the MAS5 tools for about 60% of the genes while RMAExpress is able to achieve an even greater improvement in terms of signal to noise, for more than 95% of the genes.