BackgroundMany high-throughput experiments compare two phenotypes such as disease vs. healthy, with the goal of understanding the underlying biological phenomena characterizing the given phenotype. Because of the importance of this type of analysis, more than 70 pathway analysis methods have been proposed so far. These can be categorized into two main categories: non-topology-based (non-TB) and topology-based (TB). Although some review papers discuss this topic from different aspects, there is no systematic, large-scale assessment of such methods. Furthermore, the majority of the pathway analysis approaches rely on the assumption of uniformity of p values under the null hypothesis, which is often not true.ResultsThis article presents the most comprehensive comparative study on pathway analysis methods available to date. We compare the actual performance of 13 widely used pathway analysis methods in over 1085 analyses. These comparisons were performed using 2601 samples from 75 human disease data sets and 121 samples from 11 knockout mouse data sets. In addition, we investigate the extent to which each method is biased under the null hypothesis. Together, these data and results constitute a reliable benchmark against which future pathway analysis methods could and should be tested.ConclusionOverall, the result shows that no method is perfect. In general, TB methods appear to perform better than non-TB methods. This is somewhat expected since the TB methods take into consideration the structure of the pathway which is meant to describe the underlying phenomena. We also discover that most, if not all, listed approaches are biased and can produce skewed results under the null.Electronic supplementary materialThe online version of this article (10.1186/s13059-019-1790-4) contains supplementary material, which is available to authorized users.

Background. Current management efforts of COVID-19 include: early diagnosis, use of antivirals and immune modulation. After the initial viral phase of the illness, identification of the patients developing cytokine storm syndrome is critical. Treatment of this hyper-inflammation in these patients using existing, approved therapies with proven safety profiles could address the immediate need to reduce the rising mortality. Methods. Using data from an A549 cell line, primary human bronchial epithelial (NBHE), as well as from COVID-19-infected lung, we compare the changes in the gene expression, pathways and mechanisms between SARS-CoV2, influenza A, and respiratory syncytial virus. Results. We identified FDA-approved drugs that could be repurposed to help COVID-19 patients with severe symptoms related to hyper-inflammation. An important finding is that drugs in the same class will not achieve similar effects. For instance methylprednisolone and prednisolone were predicted to be effective in reverting many of the changes triggered by COVID-19, while other closely related steroids, such as prednisone or dexamethasone, were not. An independent clinical study evaluated 213 subjects, 81 (38%) and 132 (62%) in pre-and post-methylprednisolone groups, respectively. The composite end point was composed of escalation to intensive care units, need for mechanical ventilation, and death. The composite endpoint occurred at a significantly lower rate in post-methylprednisolone group compared to pre-methylprednisolone group (34.9% vs. 54.3%, p=0.005). Conclusion. Clinical results confirmed the efficacy of the in silico prediction that indicated methyl- prednisolone could improve outcomes in severe COVID-19. These findings are important for any future pandemic regardless of the virus.

Motivation COVID-19 has several distinct clinical phases: a viral replication phase, an inflammatory phase, and in some patients, a hyper-inflammatory phase. High mortality is associated with patients developing cytokine storm syndrome. Treatment of hyper-inflammation in these patients using existing, approved therapies with proven safety profiles could address the immediate need to reduce mortality. Results We analyzed the changes in the gene expression, pathways and putative mechanisms induced by SARS-CoV2 in NHBE, and A549 cells, as well as COVID-19 lung vs. their respective controls. We used these changes to identify FDA approved drugs that could be repurposed to help COVID-19 patients with severe symptoms related to hyper-inflammation. We identified methylprednisolone (MP) as a potential leading therapy. The results were then confirmed in five independent validation data sets including Vero E6 cells, lung and intestinal organoids, as well as additional patient lung sample vs. their respective controls. Finally, the efficacy of MP was validated in an independent clinical study. Thirty-day all-cause mortality occurred at a significantly lower rate in the MP-treated group compared to control group (29.6% vs. 16.6%, p = 0.027). Clinical results confirmed the in silico prediction that MP could improve outcomes in severe cases of COVID-19. A low number needed to treat (NNT = 5) suggests MP may be more efficacious than dexamethasone or hydrocortisone. Availability iPathwayGuide is available at https://ipathwayguide.advaitabio.com/ Supplementary information Supplementary data are available at Bioinformatics online.

Following publication of the original paper [1], the authors reported the following update to the competing interests declaration.

Background: Mortality from ovarian cancer remains high due to the lack of methods for early detection. The difficulty lies in the low prevalence of the disease necessitating a significantly high specificity and positive-predictive value (PPV) to avoid unneeded and invasive intervention. Currently, cancer antigen- 125 (CA-125) is the most commonly used biomarker for the early detection of ovarian cancer. In this study we determine the value of combining macrophage migration inhibitory factor (MIF), osteopontin (OPN), and prolactin (PROL) with CA-125 in the detection of ovarian cancer serum samples from healthy controls. Materials and Methods: A total of 432 serum samples were included in this study. 153 samples were from ovarian cancer patients and 279 samples were from age-matched healthy controls. The four proteins were quantified using a fully automated, multi-analyte immunoassay. The serum samples were divided into training and testing datasets and analyzed using four classification models to calculate accuracy, sensitivity, specificity, PPV, negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). Results: The four-protein biomarker panel yielded an average accuracy of 91% compared to 85% using CA-125 alone across four classification models (p = 3.224 × 10−9). Further, in our cohort, the four-protein biomarker panel demonstrated a higher sensitivity (median of 76%), specificity (median of 98%), PPV (median of 91.5%), and NPV (median of 92%), compared to CA-125 alone. The performance of the four-protein biomarker remained better than CA-125 alone even in experiments comparing early stage (Stage I and Stage II) ovarian cancer to healthy controls. Conclusions: Combining MIF, OPN, PROL, and CA-125 can better differentiate ovarian cancer from healthy controls compared to CA-125 alone.