GENI: A web server to identify gene set enrichments in tumor samples.

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.

CRISPR/Cas9-based genome-wide screening for deubiquitinase subfamily identifies USP1 regulating MAST1-driven cisplatin-resistance in cancer cells.

Background: Cisplatin is one of the frontline anticancer agents. However, development of cisplatin-resistance limits the therapeutic efficacy of cisplatin-based treatment. The expression of microtubule-associated serine/threonine kinase 1 (MAST1) is a primary factor driving cisplatin-resistance in cancers by rewiring the MEK pathway. However, the mechanisms responsible for MAST1 regulation in conferring drug resistance is unknown. Methods: We implemented a CRISPR/Cas9-based, genome-wide, dual screening system to identify deubiquitinating enzymes (DUBs) that govern cisplatin resistance and regulate MAST1 protein level. We analyzed K48- and K63-linked polyubiquitination of MAST1 protein and mapped the interacting domain between USP1 and MAST1 by immunoprecipitation assay. The deubiquitinating effect of USP1 on MAST1 protein was validated using rescue experiments, in vitro deubiquitination assay, immunoprecipitation assays, and half-life analysis. Furthermore, USP1-knockout A549 lung cancer cells were generated to validate the deubiquitinating activity of USP1 on MAST1 abundance. The USP1-MAST1 correlation was evaluated using bioinformatics tool and in different human clinical tissues. The potential role of USP1 in regulating MAST1-mediated cisplatin resistance was confirmed using a series of in vitro and in vivo experiments. Finally, the clinical relevance of the USP1-MAST1 axis was validated by application of small-molecule inhibitors in a lung cancer xenograft model in NSG mice. Results: The CRISPR/Cas9-based dual screening system identified USP1 as a novel deubiquitinase that interacts, stabilizes, and extends the half-life of MAST1 by preventing its K48-linked polyubiquitination. The expression analysis across human clinical tissues revealed a positive correlation between USP1 and MAST1. USP1 promotes MAST1-mediated MEK1 activation as an underlying mechanism that contributes to cisplatin-resistance in cancers. Loss of USP1 led to attenuation of MAST1-mediated cisplatin-resistance both in vitro and in vivo. The combined pharmacological inhibition of USP1 and MAST1 using small-molecule inhibitors further abrogated MAST1 level and synergistically enhanced cisplatin efficacy in a mouse xenograft model. Conclusions: Overall, our study highlights the role of USP1 in the development of cisplatin resistance and uncovers the regulatory mechanism of MAST1-mediated cisplatin resistance in cancers. Co-treatment with USP1 and MAST1 inhibitors abrogated tumor growth and synergistically enhanced cisplatin efficacy, suggesting a novel alternative combinatorial therapeutic strategy that could further improve MAST1-based therapy in patients with cisplatin-resistant tumors.

Immune receptor CDR3 chemical features that preserve sequence information are highly efficient in reflecting survival distinctions: A pan-cancer analysis.

Adaptive immune receptor (IR) chemical features have been used as signatures of an immune response for numerous medical conditions, raising the question of whether certain approaches to assessing the IR chemical features are more robust than others? In the cancer setting, a very large dataset of IR complementarity determining region-3 (CDR3) amino acid (AA) sequences has become available via the mining of cancer specimen and blood genomics files for IR recombination reads. The IR CDR3 AA sequences have been evaluated for chemical features, and survival rates have been correlated with distinct chemical features. Two common approaches have been i) to assign a single value to the CDR3, representing a chemical attribute, such as aromaticity; or ii) to reduce the actual CDR3 AA sequence to a chemical sequence motif, which merges similar CDR3 chemistries represented by distinct AA sequences but preserves potential functional aspects of the order of the AAs in the sequence. While a controlled comparison of the two approaches is not possible, the application of the two approaches to the same clinical datasets offers the opportunity to appreciate a trend with regard to the overall potential in distinguishing survival probabilities. We demonstrate that application of the chemical sequence motif approach is more likely to identify survival distinctions within cancer datasets, for both tumor specimen and blood sourced, adaptive IR CDR3 AA sequences.

Clinical drug response prediction from preclinical cancer cell lines by logistic matrix factorization approach.

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.

Bleomycin Concentration in Patients' Plasma and Tumors after Electrochemotherapy. A Study from InspECT Group.

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.

Comparison of the Allelic Alterations between InDel and STR Markers in Tumoral Tissues Used for Forensic Purposes.

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.

MiR-330-3p and miR-485-5p as biomarkers for glioblastoma: An integrated bioinformatics and experimental study.

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.

POFUT1 mRNA expression as an independent prognostic parameter in muscle-invasive bladder cancer.

Muscle-invasive bladder cancer (MIBC) is characterized by high recurrence and rapid progression. Progression is linked to changes in glycan structures and altered levels of glycosyltransferases. The relationship of mRNA expression by glycosyltransferase genes B4GALT1, EXT1, MGAT5B, and POFUT1 to the probability of surviving MIBC after radical cystectomy has not yet been investigated. mRNA expression was analyzed using qRT-PCR in formalin-fixed and paraffin-embedded tumor samples (n = 105; 74% male patients and 26% female patients; median age = 72 years), correlated with histopathological variables, and evaluated by means of multivariable Cox regression analysis regarding to overall survival (OS), cancer-specific survival (CSS), and disease-free survival (DFS). Multivariable Cox regression analysis identified POFUT1 mRNA expression as superior prognostic marker, compared with currently used histological tumor stage methods, for CSS by MIBC patients following radical cystectomy. Thus, the patients with low POFUT1 mRNA were at a 4.9-fold greater risk for cancer-specific death according to the multivariable analysis (p = 0.0001). Low mRNA levels predicted poor survival according to the Kaplan-Meier analysis ((POFUT1:OS p = 0.0014; CSS p = 0.0007; DFS p = 0.0088); (EXT1:OS p = 0.0150; CSS p = 0.0130; DFS p = 0.0286); (B4GALT1:CSS p = 0.0134; DFS p = 0.0493)). A subgroup analysis of patients without lymph node metastasis (pN-; n = 73) indicated that low expression of POFUT1 predicted reduced OS (p = 0.0073), CSS (p = 0.0058,) and DSS (p = 0.0079). Low levels of POFUT1 mRNA are an independent prognostic indicator for OS and CSS in MIBC patients following radical cystectomy. This finding demonstrates the importance of altered glycosylation for the progress of MIBC.

Joint analysis of single-cell and bulk tissue sequencing data to infer intratumor heterogeneity.

Many computational methods have been developed to discern intratumor heterogeneity (ITH) using DNA sequence data from bulk tumor samples. These methods share an assumption that two mutations arise from the same subclone if they have similar mutant allele-frequencies (MAFs), and thus it is difficult or impossible to distinguish two subclones with similar MAFs. Single-cell DNA sequencing (scDNA-seq) data can be very informative for ITH inference. However, due to the difficulty of DNA amplification, scDNA-seq data are often very noisy. A promising new study design is to collect both bulk and single-cell DNA-seq data and jointly analyze them to mitigate the limitations of each data type. To address the analytic challenges of this new study design, we propose a computational method named BaSiC (Bulk tumor and Single Cell), to discern ITH by jointly analyzing DNA-seq data from bulk tumor and single cells. We demonstrate that BaSiC has comparable or better performance than the methods using either data type. We further evaluate BaSiC using bulk tumor and single-cell DNA-seq data from a breast cancer patient and several leukemia patients.

Untouchable genes in the human genome: Identifying ideal targets for cancer treatment.

BACKGROUND: 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". METHODS: 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. RESULTS: 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. CONCLUSIONS: Untouchable genes may be ideal targets for cancer treatment since suppression of untouchable genes is expected to inhibit survival of tumor cells.