Efficacy of Combined Therapy with Amantadine, Oseltamivir, and Ribavirin In Vivo against Susceptible and Amantadine-Resistant Influenza A Viruses

The limited efficacy of existing antiviral therapies for influenza – coupled with widespread baseline antiviral resistance – highlights the urgent need for more effective therapy. We describe a triple combination antiviral drug (TCAD) regimen composed of amantadine, oseltamivir, and ribavirin that is highly efficacious at reducing mortality and weight loss in mouse models of influenza infection. TCAD therapy was superior to dual and single drug regimens in mice infected with drug-susceptible, low pathogenic A/H5N1 (A/Duck/MN/1525/81) and amantadine-resistant 2009 A/H1N1 influenza (A/California/04/09). Treatment with TCAD afforded >90% survival in mice infected with both viruses, whereas treatment with dual and single drug regimens resulted in 0% to 60% survival. Importantly, amantadine had no activity as monotherapy against the amantadine-resistant virus, but demonstrated dose-dependent protection in combination with oseltamivir and ribavirin, indicative that amantadine's activity had been restored in the context of TCAD therapy. Furthermore, TCAD therapy provided survival benefit when treatment was delayed until 72 hours post-infection, whereas oseltamivir monotherapy was not protective after 24 hours post-infection. These findings demonstrate in vivo efficacy of TCAD therapy and confirm previous reports of the synergy and broad spectrum activity of TCAD therapy against susceptible and resistant influenza strains in vitro

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases

Identification of multiple risk loci and regulatory mechanisms influencing susceptibility to multiple myeloma

Genome-wide association studies (GWAS) have transformed our understanding of susceptibility to multiple myeloma (MM), but much of the heritability remains unexplained. We report a new GWAS, a meta-analysis with previous GWAS and a replication series, totalling 9974 MM cases and 247,556 controls of European ancestry. Collectively, these data provide evidence for six new MM risk loci, bringing the total number to 23. Integration of information from gene expression, epigenetic profiling and in situ Hi-C data for the 23 risk loci implicate disruption of developmental transcriptional regulators as a basis of MM susceptibility, compatible with altered B-cell differentiation as a key mechanism. Dysregulation of autophagy/apoptosis and cell cycle signalling feature as recurrently perturbed pathways. Our findings provide further insight into the biological basis of MM

Author Correction: Identification of multiple risk loci and regulatory mechanisms influencing susceptibility to multiple myeloma

Correction to: Nature Communications; https://doi.org/10.1038/s41467-018-04989-w, published online 13 September 2018