The Cancer Genome Atlas (TCGA) and analogous projects have yielded invaluable tumor-associated genomic data. Despite several web-based platforms designed to enhance accessibility, certain analyses require prior bioinformatic expertise. To address this need, we developed Gene ENrichment Identifier (GENI, https://www.shaullab.com/geni), which is designed to promptly compute correlations for genes of interest against the entire transcriptome and rank them against well-established biological gene sets. Additionally, it generates comprehensive tables containing genes of interest and their corresponding correlation coefficients, presented in publication-quality graphs. Furthermore, GENI has the capability to analyze multiple genes simultaneously within a given gene set, elucidating their significance within a specific biological context. Overall, GENI\'s user-friendly interface simplifies the biological interpretation and analysis of cancer patient-associated data, advancing the understanding of cancer biology and accelerating scientific discoveries.

Predicting tumor drug response using cancer cell line drug response values for a large number of anti-cancer drugs is a significant challenge in personalized medicine. Predicting patient response to drugs from data obtained from preclinical models is made easier by the availability of different knowledge on cell lines and drugs. This paper proposes the TCLMF method, a predictive model for predicting drug response in tumor samples that was trained on preclinical samples and is based on the logistic matrix factorization approach. The TCLMF model is designed based on gene expression profiles, tissue type information, the chemical structure of drugs and drug sensitivity (IC 50) data from cancer cell lines. We use preclinical data from the Genomics of Drug Sensitivity in Cancer dataset (GDSC) to train the proposed drug response model, which we then use to predict drug sensitivity of samples from the Cancer Genome Atlas (TCGA) dataset. The TCLMF approach focuses on identifying successful features of cell lines and drugs in order to calculate the probability of the tumor samples being sensitive to drugs. The closest cell line neighbours for each tumor sample are calculated using a description of similarity between tumor samples and cell lines in this study. The drug response for a new tumor is then calculated by averaging the low-rank features obtained from its neighboring cell lines. We compare the results of the TCLMF model with the results of the previously proposed methods using two databases and two approaches to test the model\'s performance. In the first approach, 12 drugs with enough known clinical drug response, considered in previous methods, are studied. For 7 drugs out of 12, the TCLMF can significantly distinguish between patients that are resistance to these drugs and the patients that are sensitive to them. These approaches are converted to classification models using a threshold in the second approach, and the results are compared. The results demonstrate that the TCLMF method provides accurate predictions across the results of the other algorithms. Finally, we accurately classify tumor tissue type using the latent vectors obtained from TCLMF\'s logistic matrix factorization process. These findings demonstrate that the TCLMF approach produces effective latent vectors for tumor samples. The source code of the TCLMF method is available in https://github.com/emdadi/TCLMF.

The plasma concentration profile of bleomycin in the distribution phase of patients younger than 65 years is needed to determine the suitable time interval for efficient application of electric pulses during electrochemotherapy. Additionally, bleomycin concentrations in the treated tumors for effective tumor response are not known. In this study, the pharmacokinetic profile of bleomycin in the distribution phase in 12 patients younger than 65 years was determined. In 17 patients, the intratumoral bleomycin concentration was determined before the application of electric pulses. In younger patients, the pharmacokinetics of intravenously injected bleomycin demonstrated a faster plasma clearance rate than that in patients older than 65 years. This outcome might indicate that the lowering of the standard bleomycin dose of 15,000 IU/m2 with intravenous bleomycin injection for electrochemotherapy is not recommended in younger patients. Based on the plasma concentration data gathered, a time interval for electrochemotherapy of 5-15 min after bleomycin injection was determined. The median bleomycin concentration in tumors 8 min after bleomycin injection, at the time of electroporation, was 170 ng/g. Based on collected data, the reduction of the bleomycin dose is not recommended in younger patients; however, a shortened time interval for application of electric pulses in electrochemotherapy to 5-15 min after intravenous bleomycin injection should be considered.

Background and objectives: Over the last two decades, human DNA identification and kinship tests have been conducted mainly through the analysis of short tandem repeats (STRs). However, other types of markers, such as insertion/deletion polymorphisms (InDels), may be required when DNA is highly degraded. In forensic genetics, tumor samples may sometimes be used in some cases of human DNA identification and in paternity tests. Nevertheless, tumor genomic instability related to forensic DNA markers should be considered in forensic analyses since it can compromise genotype attribution. Therefore, it is useful to know what impact tumor transformation may have on the forensic interpretation of the results obtained from the analysis of these polymorphisms. Materials and Methods: The aim of this study was to investigate the genomic instability of InDels and STRs through the analysis of 55 markers in healthy tissue and tumor samples (hepatic, gastric, breast, and colorectal cancer) in 66 patients. The evaluation of genomic instability was performed comparing InDel and STR genotypes of tumor samples with those of their healthy counterparts. Results: With regard to STRs, colorectal cancer was found to be the tumor type affected by the highest number of mutations, whereas in the case of InDels the amount of genetic mutations turned out to be independent of the tumor type. However, the phenomena of genomic instability, such as loss of heterozygosity (LOH) and microsatellite instability (MSI), seem to affect InDels more than STRs hampering genotype attribution. Conclusion: We suggest that the use of STRs rather than InDels could be more suitable in forensic genotyping analyses given that InDels seem to be more affected than STRs by mutation events capable of compromising genotype attribution.

Glioblastoma Multiforme (GBM) is the most common, invasive, and malignant primary brain tumor with a poor prognosis and a median survival of 12-15 months. This study tried to identify the most significant miRNA biomarkers in both tissue and serum samples of GBM. GSE25632 was employed from gene expression omnibus and using WGCNA package, association of miRNA networks and clinical data was explored and brown and green modules identified as the most relevant modules. Independently, Limma package was utilized to identify differentially expressed miRNAs (DEMs) in GSE25632 by cutoff logFC > 2 and P.value < 0.05. By merging the results of Limma and WGCNA, the miRNAs that were in brown and green modules and had mentioned cutoff were selected as hub miRNAs. Performing enrichment analysis, Pathways in cancer, Prostate cancer, Glioma, p53 signaling pathway, and Focal adhesion were identified as the most important signaling pathways. Based on miRNA- target genes, has-mir-330-3p and has-mir-485-5p were identified as core miRNAs. The expression level of core miRNAs was validated by GSE90604, GSE42657, and GSE93850. We evaluated the expression level of common target genes of two detected core genes based on GSE77043, GSE42656, GSE22891, GSE15824, and GSE122498. The ability of detected miRNAs to discriminate GBM from healthy controls was assessed by area under the curve (AUC) using the ROC curve analysis. Based on TCGA database, we tested the prognostic significance of miRNAs using overall survival analysis. We evaluated the expression level of the miRNAs in tissue of 83 GBM patients and also non-tumoral adjacent (as control) tissues. We used serum samples of 34 GBM patients to evaluate the expression levels of the hub miRNAs compare to the controls. Our results showed that has-mir-330-3p and has-mir-485-5p could be potential biomarkers in GBM.

Usually, genes with a higher-than-expected number of somatic mutations in tumor samples are assumed to be cancer related. We identified genes with a fewer-than-expected number of somatic mutations - \"untouchable genes\".
To predict the expected number of somatic mutations, we used a linear regression model with the number of mutations in the gene as an outcome, and gene characteristics, including gene size, nucleotide composition, level of evolutionary conservation, expression level and others, as predictors. Analysis of residuals from the regression model was used to compare the observed and predicted number of mutations.
We have identified 19 genes with a less-than-expected number of loss-off-function (nonsense, frameshift or pathogenic missense) mutations - i.e., untouchable genes. The number of silent or neutral missense mutations in untouchable genes was equal or higher than the expected number. Many mucins, including MUC16, MUC17, MUC6, MUC5AC, MUC5B, and MUC12, are untouchable. We hypothesized that untouchable mucins help tumor cells to avoid immune response by providing a protective coat that prevents direct contact between effector immune cells, e.g., cytotoxic T-cells, and tumor cells. Survival analysis of available TCGA data demonstrated that overall survival of patients with low (below the median) expression of untouchable mucins was better compared to patients with high expression of untouchable mucins. Aside from mucins, we have identified a number of other untouchable genes.
Untouchable genes may be ideal targets for cancer treatment since suppression of untouchable genes is expected to inhibit survival of tumor cells.

In KEYNOTE-010, pembrolizumab versus docetaxel improved overall survival (OS) in patients with programmed death-1 protein (PD)-L1-positive advanced non-small-cell lung cancer (NSCLC). A prespecified exploratory analysis compared outcomes in patients based on PD-L1 expression in archival versus newly collected tumor samples using recently updated survival data.
PD-L1 was assessed centrally by immunohistochemistry (22C3 antibody) in archival or newly collected tumor samples. Patients received pembrolizumab 2 or 10 mg/kg Q3W or docetaxel 75 mg/m2 Q3W for 24 months or until progression/intolerable toxicity/other reason. Response was assessed by RECIST v1.1 every 9 weeks, survival every 2 months. Primary end points were OS and progression-free survival (PFS) in tumor proportion score (TPS) ≥50% and ≥1%; pembrolizumab doses were pooled in this analysis.
At date cut-off of 24 March 2017, median follow-up was 31 months (range 23-41) representing 18 additional months of follow-up from the primary analysis. Pembrolizumab versus docetaxel continued to improve OS in patients with previously treated, PD-L1-expressing advanced NSCLC; hazard ratio (HR) was 0.66 [95% confidence interval (CI): 0.57, 0.77]. Of 1033 patients analyzed, 455(44%) were enrolled based on archival samples and 578 (56%) on newly collected tumor samples. Approximately 40% of archival samples and 45% of newly collected tumor samples were PD-L1 TPS ≥50%. For TPS ≥50%, the OS HRs were 0.64 (95% CI: 0.45, 0.91) and 0.40 (95% CI: 0.28, 0.56) for archival and newly collected samples, respectively. In patients with TPS ≥1%, OS HRs were 0.74 (95% CI: 0.59, 0.93) and 0.59 (95% CI: 0.48, 0.73) for archival and newly collected samples, respectively. In TPS ≥50%, PFS HRs were similar across archival [0.63 (95% CI: 0.45, 0.89)] and newly collected samples [0.53 (95% CI: 0.38, 0.72)]. In patients with TPS ≥1%, PFS HRs were similar across archival [0.82 (95% CI: 0.66, 1.02)] and newly collected samples [0.83 (95% CI: 0.68, 1.02)].
Pembrolizumab continued to improve OS over docetaxel in intention to treat population and in subsets of patients with newly collected and archival samples.
ClinicalTrials.gov: NCT01905657.

Comprehensive understanding of cellular immune subsets involved in regulation of tumor progression is central to the development of cancer immunotherapies. Single cell immunophenotyping has historically been accomplished by flow cytometry (FC) analysis, enabling the analysis of up to 18 markers. Recent advancements in mass cytometry (MC) have facilitated detection of over 50 markers, utilizing high resolving power of mass spectrometry (MS). This study examined an analytical and operational feasibility of MC for an in-depth immunophenotyping analysis of the tumor microenvironment, using the commercial CyTOF™ instrument, and further interrogated challenges in managing the integrity of tumor specimens.
Initial longitudinal studies with frozen peripheral blood mononuclear cells (PBMCs) showed minimal MC inter-assay variability over nine independent runs. In addition, detection of common leukocyte lineage markers using MC and FC detection confirmed that these methodologies are comparable in cell subset identification. An advanced multiparametric MC analysis of 39 total markers enabled a comprehensive evaluation of cell surface marker expression in fresh and cryopreserved tumor samples. This comparative analysis revealed significant reduction of expression levels of multiple markers upon cryopreservation. Most notably myeloid derived suppressor cells (MDSC), defined by co-expression of CD66b+ and CD15+, HLA-DRdim and CD14- phenotype, were undetectable in frozen samples.
These results suggest that optimization and evaluation of cryopreservation protocols is necessary for accurate biomarker discovery in frozen tumor specimens.