In Silico Evaluation of SARS-CoV-2 K417N, L452R, and E484K Detection Assays Against Omicron Variants

The study's objective was to assess whether the performance of the DIAGNOVITAL SARS-CoV-2 Mutation Detection Assays is affected by Omicron mutations. In silico evaluation of 67,717 Variant of Concern, Variant of Interest sequences and 6,612 sequences of the Omicron variants involving BA1., BA2., BA3 sub-lineages downloaded from the GISAID database by 17 December 2021, were performed. The sequences were aligned according to the reference genome MN908947.3 using MAFFT multiple sequence alignment software version 7. Our findings showed that among 6,612 Omicron, 41 Spike gene mutations with a frequency of ≥70% were identified. Some of the Omicron mutations (R408S, N440K, G446S, Q493S, Q498R) could affect the diagnostic performance of K417N, L452R, and E484K assays against the Omicron sub-lineages. However, L452R and K417N mutation tests allow differentiation of the Delta and Omicron variants mutation profile. The COVID-19 pandemic lasted longer than expected, and the rapid modification of diagnostic kits seems necessary to combat the pandemic.

The Prevalence of Protein S Variant in Patients Diagnosed with COVID-19

BACKGROUND

The rapid spread of the new severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) which causes the coronavirus disease 2019 (COVID-19) facilitated the emergence of numerous variants. The present study aimed to investigate the prevalence and change of important "S" protein variants such as N501Y, DelH69/ 70, and E484K in SARS CoV-2 PCR positive patients diagnosed with COVID-19 who have referred to a private hospital within the period that mutations were detected during COVID-19 pandemic.

METHODS

One hundred and eighty-seven patients who have been referred due to the suspicion of COVID-19 between December 2020 and April 2021 and in whom SARS-CoV-2 was detected positive in the PCR test were enrolled into the study. These patients were randomly selected among 285 patients detected in these months, among those with the most accurate graphics and data. The RNA material extracted from the nasopharyngeal swab samples taken from the patients was analyzed and specifically N501Y, del69-70, and E484K mutations were investigated through the Real-Time PCR method.

RESULTS

The mean age of the patients was 37.5 ± 14.1 years. Mutations were detected in 84 (44.9%) samples in total (N501Y + DelH69/70 by 81%, DelH69/70 by 7.1%, E484K by 7.1% and N501Y + E484K by 4.8%). There was no sample detected with the N501Y mutation. The mutation rate between December - February was detected between 1% and 8%, and the mutation rate increased to 39% to 44% in April and March. While DelH69/70 mutation was detected in December 2020 only, it was observed that N501Y + DelH69/70 variants became dominant as of February 2021, and E484K and N501Y + E484K variants started to appear in March and April. It was observed that the variant rates included DelH69/70 (p < 0.001), N501Y (p < 0.001), N501Y + DelH69/70 (p < 0.001), and N501Y + E484K (p = 0.01) mutations increased significantly according to the months. The E484K mu-tation was significantly higher in males (p = 0.037). There was no differences between mutation rates between the age groups.

CONCLUSIONS

Our findings indicate that the appearance of important SARS-CoV-2 variants gradually increases, that rates of mutation increase up to 40% within several months, that the N501Y + DelH69/70 variant gradually becomes to be dominant, and that different variations appear along with mutations.

Variant analysis of SARS-CoV-2 strains with phylogenetic analysis and the Coronavirus Antiviral and Resistance Database

Aims: This study determined SARS-CoV-2 variations by phylogenetic and virtual phenotyping analyses. Materials & methods: Strains isolated from 143 COVID-19 cases in Turkey in April 2021 were assessed. Illumina NexteraXT library preparation kits were processed for next-generation ]sequencing. Phylogenetic (neighbor-joining method) and virtual phenotyping analyses (Coronavirus Antiviral and Resistance Database [CoV-RDB] by Stanford University) were used for variant analysis. Results: B.1.1.7-1/2 (n = 103, 72%), B.1.351 (n = 5, 3%) and B.1.525 (n = 1, 1%) were identified among 109 SARS-CoV-2 variations by phylogenetic analysis and B.1.1.7 (n = 95, 66%), B.1.351 (n = 5, 4%), B.1.617 (n = 4, 3%), B.1.525 (n = 2, 1.4%), B.1.526-1 (n = 1, 0.6%) and missense mutations (n = 15, 10%) were reported by CoV-RDB. The two methods were 85% compatible and B.1.1.7 (alpha) was the most frequent SARS-CoV-2 variation in Turkey in April 2021. Conclusion: The Stanford CoV-RDB analysis method appears useful for SARS-CoV-2 lineage surveillance.

Predictive Gene Signature for Pyrazolopyrimidine Derivative c-Src Inhibitor 10a Sensitivity in Melanoma Cells

Melanoma is a highly aggressive cancer with poor prognosis. Although more than 80% of melanomas harbor an activating mutation in genes within the MAPK pathway, which are mutually exclusive, usefulness of therapies targeting MAPK pathway are impeded by innate and/or acquired resistance in most patients. In this study, using melanoma cells, we report the efficacy of a recently developed pyrazolo[3,4-d]pyrimidine derived c-Src inhibitor 10a and identify a molecular signature which is predictive of 10a chemosensitivity. We show that the expression of TMED7, PLOD2, XRCC5, and NSUN5 are candidate biomarkers for 10a sensitivity. Although an undifferentiated/mesenchymal/invasive status of melanoma cells is associated with resistance to 10a, we show here for the first time that melanoma cells can be sensitized to 10a via treatment with valproic acid, a histone deacetylase inhibitor.

A Stemness and EMT Based Gene Expression Signature Identifies Phenotypic Plasticity and is A Predictive but Not Prognostic Biomarker for Breast Cancer

Aims: Molecular heterogeneity of breast cancer results in variation in morphology, metastatic potential and response to therapy. We previously showed that breast cancer cell line sub-groups obtained by a clustering approach using highly variable genes overlapped almost completely with sub-groups generated by a drug cytotoxicity-profile based approach. Two distinct cell populations thus identified were CSC(cancer stem cell)-like and non-CSC-like. In this study we asked whether an mRNA based gene signature identifying these two cell types would explain variation in stemness, EMT, drug sensitivity, and prognosis in silico and in vitro. Main methods:In silico analyses were performed using publicly available cell line and patient tumor datasets. In vitro analyses of phenotypic plasticity and drug responsiveness were obtained using human breast cancer cell lines. Key findings: We find a novel gene list (CNCL) that can generate both categorical and continuous variables corresponding to the stemness/EMT (epithelial to mesenchymal transition) state of tumors. We are presenting a novel robust gene signature that unites previous observations related either to EMT or stemness in breast cancer. We show in silico, that this signature perfectly predicts behavior of tumor cells tested in vitro, and can reflect tumor plasticity. We thus demonstrate for the first time, that breast cancer subtypes are sensitive to either Lapatinib or Midostaurin. The same gene list is not capable of predicting prognosis in most cohorts, except for one that includes patients receiving neo-adjuvant taxene therapy. Significance: CNCL is a robust gene list that can identify both stemness and the EMT state of cell lines and tumors. It can be used to trace tumor cells during the course of phenotypic changes they undergo, that result in altered responses to therapeutic agents. The fact that such a list cannot be used to identify prognosis in most patient cohorts suggests that presence of factors other than stemness and EMT affect mortality.

A Combined ULBP2 and SEMA5A Expression Signature as a Prognostic and Predictive Biomarker for Colon Cancer

Background: Prognostic biomarkers for cancer have the power to change the course of disease if they add value beyond known prognostic factors, if they can help shape treatment protocols, and if they are reliable. The aim of this study was to identify such biomarkers for colon cancer and to understand the molecular mechanisms leading to prognostic stratifications based on these biomarkers. Methods and Findings: We used an in house R based script (SSAT) for the in silico discovery of stage-independent prognostic biomarkers using two cohorts, GSE17536 and GSE17537, that include 177 and 55 colon cancer patients, respectively. This identified 2 genes, ULBP2 and SEMA5A, which when used jointly, could distinguish patients with distinct prognosis. We validated our findings using a third cohort of 48 patients ex vivo. We find that in all cohorts, a combined ULBP2/SEMA5A classification (SU-GIB) can stratify distinct prognostic sub-groups with hazard ratios that range from 2.4 to 4.5 (p≤0.01) when overall- or cancer-specific survival is used as an end-measure, independent of confounding prognostic parameters. In addition, our preliminary analyses suggest SU-GIB is comparable to Oncotype DX colon(®) in predicting recurrence in two different cohorts (HR: 1.5-2; p≤0.02). SU-GIB has potential as a companion diagnostic for several drugs including the PI3K/mTOR inhibitor BEZ235, which are suitable for the treatment of patients within the bad prognosis group. We show that tumors from patients with worse prognosis have low EGFR autophosphorylation rates, but high caspase 7 activity, and show upregulation of pro-inflammatory cytokines that relate to a relatively mesenchymal phenotype. Conclusions: We describe two novel genes that can be used to prognosticate colon cancer and suggest approaches by which such tumors can be treated. We also describe molecular characteristics of tumors stratified by the SU-GIB signature.

Phenotype-based variation as a biomarker of sensitivity to molecularly targeted therapy in melanoma

Transcriptomic phenotypes defined for melanoma have been reported to correlate with sensitivity to various drugs. In this study, we aimed to define a minimal signature that could be used to distinguish melanoma sub-types in vitro, and to determine suitable drugs by which these sub-types can be targeted. By using primary melanoma cell lines, as well as commercially available melanoma cell lines, we find that the evaluation of MLANA and INHBA expression is as capable as one based on a combined analysis performed with genes for stemness, EMT and invasion/proliferation, in identifying melanoma subtypes that differ in their sensitivity to molecularly targeted drugs. Using this approach, we find that 75% of melanoma cell lines can be treated with either the MEK inhibitor AZD6244 or the HSP90 inhibitor 17AAG.

Cancer-testis gene expression is associated with the methylenetetrahydrofolate reductase 677 C>T polymorphism in non-small cell lung carcinoma

Background

Tumor-specific, coordinate expression of cancer-testis (CT) genes, mapping to the X chromosome, is observed in more than 60% of non-small cell lung cancer (NSCLC) patients. Although CT gene expression has been unequivocally related to DNA demethylation of promoter regions, the underlying mechanism leading to loss of promoter methylation remains elusive. Polymorphisms of enzymes within the 1-carbon pathway have been shown to affect S-adenosyl methionine (SAM) production, which is the sole methyl donor in the cell. Allelic variants of several enzymes within this pathway have been associated with altered SAM levels either directly, or indirectly as reflected by altered levels of SAH and Homocysteine levels, and altered levels of DNA methylation. We, therefore, asked whether the five most commonly occurring polymorphisms in four of the enzymes in the 1-carbon pathway associated with CT gene expression status in patients with NSCLC.

Methods

Fifty patients among a cohort of 763 with NSCLC were selected based on CT gene expression status and typed for five polymorphisms in four genes known to affect SAM generation by allele specific q-PCR and RFLP.

Results

We identified a significant association between CT gene expression and the MTHFR 677 CC genotype, as well as the C allele of the SNP, in this cohort of patients. Multivariate analysis revealed that the genotype and allele strongly associate with CT gene expression, independent of potential confounders.

Conclusions

Although CT gene expression is associated with DNA demethylation, in NSCLC, our data suggests this is unlikely to be the result of decreased MTHFR function.