Biomarker Discovery for Meta-Classification of Melanoma Metastatic Progression Using Transfer Learning.

Melanoma is considered to be the most serious and aggressive type of skin cancer, and metastasis appears to be the most important factor in its prognosis. Herein, we developed a transfer learning-based biomarker discovery model that could aid in the diagnosis and prognosis of this disease. After applying it to the ensemble machine learning model, results revealed that the genes found were consistent with those found using other methodologies previously applied to the same TCGA (The Cancer Genome Atlas) data set. Further novel biomarkers were also found. Our ensemble model achieved an AUC of 0.9861, an accuracy of 91.05, and an F1 score of 90.60 using an independent validation data set. This study was able to identify potential genes for diagnostic classification (C7 and GRIK5) and diagnostic and prognostic biomarkers (S100A7, S100A7, KRT14, KRT17, KRT6B, KRTDAP, SERPINB4, TSHR, PVRL4, WFDC5, IL20RB) in melanoma. The results show the utility of a transfer learning approach for biomarker discovery in melanoma.

Are phenome-wide association studies feasible in a developing country?

Phenome-wide association studies (PheWASs), a powerful approach that examines phenotypes associated with a genetic marker, have been used extensively in highly developed countries. Although there may be a clear need for PheWAS in a developing country such as the Philippines, limitations related to resources and practicality would make conducting them a challenge.

Age-Stratified Infection Probabilities Combined With a Quarantine-Modified Model for COVID-19 Needs Assessments: Model Development Study.

BACKGROUND: Classic compartmental models such as the susceptible-exposed-infectious-removed (SEIR) model all have the weakness of assuming a homogenous population, where everyone has an equal chance of getting infected and dying. Since it was identified in Hubei, China, in December 2019, COVID-19 has rapidly spread around the world and been declared a pandemic. Based on data from Hubei, infection and death distributions vary with age. To control the spread of the disease, various preventive and control measures such as community quarantine and social distancing have been widely used. OBJECTIVE: Our aim is to develop a model where age is a factor, considering the study area's age stratification. Additionally, we want to account for the effects of quarantine on the SEIR model. METHODS: We use the age-stratified COVID-19 infection and death distributions from Hubei, China (more than 44,672 infections as of February 11, 2020) as an estimate or proxy for a study area's infection and mortality probabilities for each age group. We then apply these probabilities to the actual age-stratified population of Quezon City, Philippines, to predict infectious individuals and deaths at peak. Testing with different countries shows the predicted number of infectious individuals skewing with the country's median age and age stratification, as expected. We added a Q parameter to the SEIR model to include the effects of quarantine (Q-SEIR). RESULTS: The projections from the age-stratified probabilities give much lower predicted incidences of infection than the Q-SEIR model. As expected, quarantine tends to delay the peaks for both the exposed and infectious groups, and to "flatten" the curve or lower the predicted values for each compartment. These two estimates were used as a range to inform the local government's planning and response to the COVID-19 threat. CONCLUSIONS: Age stratification combined with a quarantine-modified model has good qualitative agreement with observations on infections and death rates. That younger populations will have lower death rates due to COVID-19 is a fair expectation for a disease where most fatalities are among older adults.

Pathways- and epigenetic-based assessment of relative immune infiltration in various types of solid tumors.

Recent clinical studies document the power of immunotherapy in treating subsets of patients with advanced cancers. In this context and with multiple cancer immunotherapeutics already evaluated in the clinic and a large number in various stages of clinical trials, it is imperative to comprehensively examine genomics data to better comprehend the role of immunity in different cancers in predicting response to therapy and in directing appropriate therapies. The approach we chose is to scrutinize the pathways and epigenetic factors predicted to drive immune infiltration in different cancer types using publicly available TCGA transcriptional and methylation datasets, along with accompanying clinico-pathological data. We observed that the relative activation of T cells and other immune signaling pathways differs across cancer types. For example, pathways related to activation and proliferation of helper and cytotoxic T cells appear to be more highly enriched in kidney, skin, head and neck, and esophageal cancers compared to those of lung, colorectal, and liver or bile duct cancers. The activation of these immune-related pathways positively associated with prognosis in certain cancer types, most notably melanoma, head and neck, and cervical cancers. Integrated methylation and expression data (along with publicly available, ENCODE-generated histone ChIP Seq and DNAse hypersensitivity data) predict that epigenetic regulation is a primary factor driving transcriptional activation of a number of genes crucial to immunity in cancer, including T cell receptor genes (e.g., CD3D, CD3E), CTLA4, and GZMA. However, the extent to which epigenetic factors (primarily methylation at promoter regions) affect transcription of immune-related genes may vary across cancer types. For example, there is a high negative correlation between promoter CpG methylation and CD3D expression in renal and thyroid cancers, but not in brain tumors. The types of analyses we have undertaken provide insights into the relationships between immune modulation and cancer etiology and progression, offering clues into ways of therapeutically manipulating the immune system to promote immune recognition and immunotherapy.

An analysis of the epitope knowledge related to Mycobacteria.

BACKGROUND: Tuberculosis, caused by the bacterium Mycobacterium tuberculosis, remains a leading cause of infectious disease morbidity and mortality, and is responsible for more than 2 million deaths a year. Reports about extremely drug resistant (XDR) strains have further heightened the sense of urgency for the development of novel strategies to prevent and treat TB. Detailed knowledge of the epitopes recognized by immune responses can aid in vaccine and diagnostics development, and provides important tools for basic research. The analysis of epitope data corresponding to M. tuberculosis can also identify gaps in our knowledge, and suggest potential areas for further research and discovery. The Immune Epitope Database (IEDB) is compiled mainly from literature sources, and describes a broad array of source organisms, including M. tuberculosis and other Mycobacterial species. DESCRIPTION: A comprehensive analysis of IEDB data regarding the genus Mycobacteria was performed. The distribution of antibody/B cell and T cell epitopes was analyzed in terms of their associated recognition cell type effector function and chemical properties. The various species, strains and proteins which the epitope were derived, were also examined. Additional variables considered were the host in which the epitopes were defined, the specific TB disease state associated with epitope recognition, and the HLA associated with disease susceptibility and endemic regions were also scrutinized. Finally, based on these results, standardized reference datasets of mycobacterial epitopes were generated. CONCLUSION: All current TB-related epitope data was cataloged for the first time from the published literature. The resulting inventory of more than a thousand different epitopes should prove a useful tool for the broad scientific community. Knowledge gaps specific to TB epitope data were also identified. In summary, few non-peptidic or post-translationally modified epitopes have been defined. Most importantly epitopes have apparently been defined from only 7% of all ORFs, and the top 30 most frequently studied protein antigens contain 65% of the epitopes, leaving the majority of M. tuberculosis genome unexplored. A lack of information related to the specific strains from which epitopes are derived is also evident. Finally, the generation of reference lists of mycobacterial epitopes should also facilitate future vaccine and diagnostic research.

Curation of complex, context-dependent immunological data.

BACKGROUND: The Immune Epitope Database and Analysis Resource (IEDB) is dedicated to capturing, housing and analyzing complex immune epitope related data http://www.immuneepitope.org. DESCRIPTION: To identify and extract relevant data from the scientific literature in an efficient and accurate manner, novel processes were developed for manual and semi-automated annotation. CONCLUSION: Formalized curation strategies enable the processing of a large volume of context-dependent data, which are now available to the scientific community in an accessible and transparent format. The experiences described herein are applicable to other databases housing complex biological data and requiring a high level of curation expertise.

Differential responses of spinal axons to transection: influence of the NG2 proteoglycan.

Spinal cord transections were performed in wild type and NG2 proteoglycan null mice in order to study penetration of regenerating axons into the scar that forms in response to this type of injury. Aside from the presence or absence of NG2, the features of the transection scar did not differ between the two genotypes. In both cases, the rostral and caudal spinal cord stumps were separated by collagenous connective tissue that was continuous with the spinal cord meninges. In wild type mice, oligodendrocyte progenitors, macrophages, and microvascular pericytes contributed to up-regulation of NG2 expression in and around the scar. Substantial amounts of non-cell associated NG2 were also observed in the scar. The abilities of two classes of spinal axons to penetrate the transection scar were examined. Serotonergic efferents and calcitonin gene-related peptide-positive sensory afferents both were observed within the lesion, with calcitonin gene-related peptide-positive axons exhibiting a greater capability to penetrate deeply into the scar tissue. These observations demonstrate inherent differences in the abilities of distinct types of neurons to penetrate the scar. Significantly, growth of serotonergic axons into the transection scar was observed twice as frequently in wild type mice as in NG2 knockout mice, suggesting a stimulatory role for the proteoglycan in regeneration of these fibers. These findings run counter to in vitro evidence implicating NG2 as an inhibitor of nerve regeneration. This work therefore emphasizes the importance of including in vivo models in evaluating the responses of specific types of neurons to spinal cord injury.