Multi-Omics Analysis of the Therapeutic Value of MAL2 Based on Data Mining in Human Cancers

Recent studies have reported that T-cell differentiation protein 2 (MAL2) is an important regulator in cancers. Here, we downloaded data from multiple databases to analyze MAL2 expression and function in pan-cancers, especially in ovarian cancer (OC). Gene Expression Profiling Interactive Analysis (GEPIA) databases was used to examine MAL2 expression in 13 types of cancer. Kaplan–Meier plotter database was used to analyze the overall survival rate of MAL2 in pan-cancers. The Catalog of Somatic Mutations in Cancer (COSMIC), cBioPortal, and UCSC databases were used to examine MAL2 mutation in human cancers. Metascape, STRING, and GeneMANIA websites were used to explore MAL2 function in OC. Furthermore, ggplot2 package and ROC package were performed to analyze hub gene expression and undertake receiver operating characteristic (ROC) analysis. Drug sensitivity of MAL2 in OC was examined by the GSCALite database. In order to verify the results from databases above, real-time quantitative polymerase chain reaction (qRT-PCR) and western blotting were conducted to detect the expression of MAL2 in OC cells. CRISPR/Cas9 system was used to knockout the MAL2 gene in the OC cell lines HO8910 and OVCAR3, using specific guide RNA targeting the exons of MAL2. Then, we performed proliferation, colony formation, migration, and invasion assays to investigate the impact of MAL2 in OC cell lines in vivo and in vitro. Epithelial-mesenchymal transition (EMT)-associated biomarkers were significantly altered in vitro via western blotting and qRT-PCR. Taken together, we observed that MAL2 was remarkably dysregulated in multiple cancers and was related to patient overall survival (OS), mutation, and drug sensitivity. Furthermore, experimental results showed that MAL2 deletion negatively regulated the proliferation, migration, invasion, and EMT of OC, indicating that MAL2 is a novel oncogene that can activate EMT, significantly promote both the proliferation and migration of OC in vitro and in vivo, and provide new clues for treatment strategies.

Matching anticancer compounds and tumor cell lines by neural networks with ranking loss

ABSTRACT Computational drug sensitivity models have the potential to improve therapeutic outcomes by identifying targeted drug components that are likely to achieve the highest efficacy for a cancer cell line at hand at a therapeutic dose. State of the art drug sensitivity models use regression techniques to predict the inhibitory concentration of a drug for a tumor cell line. This regression objective is not directly aligned with either of these principal goals of drug sensitivity models: We argue that drug sensitivity modeling should be seen as a ranking problem with an optimization criterion that quantifies a drug’s inhibitory capacity for the cancer cell line at hand relative to its toxicity for healthy cells. We derive an extension to the well-established drug sensitivity regression model PaccMann that employs a ranking loss and focuses on the ratio of inhibitory concentration and therapeutic dosage range. We find that the ranking extension significantly enhances the model’s capability to identify the most effective anticancer drugs for unseen tumor cell profiles based in on in-vitro data.

Tumor collection/processing under physioxia uncovers highly relevant signaling networks and drug sensitivity

Tumor tissue collection and processing under physioxia allow highly relevant detection of signaling networks and drug sensitivity.

Comprehensive Analysis of Co-Mutations Identifies Cooperating Mechanisms of Tumorigenesis

Somatic mutations are one of the most important factors in tumorigenesis and are the focus of most cancer-sequencing efforts. The co-occurrence of multiple mutations in one tumor has gained increasing attention as a means of identifying cooperating mutations or pathways that contribute to cancer. Using multi-omics, phenotypical, and clinical data from 29,559 cancer subjects and 1747 cancer cell lines covering 78 distinct cancer types, we show that co-mutations are associated with prognosis, drug sensitivity, and disparities in sex, age, and race. Some co-mutation combinations displayed stronger effects than their corresponding single mutations. For example, co-mutation TP53:KRAS in pancreatic adenocarcinoma is significantly associated with disease specific survival (hazard ratio = 2.87, adjusted p-value = 0.0003) and its prognostic predictive power is greater than either TP53 or KRAS as individually mutated genes. Functional analyses revealed that co-mutations with higher prognostic values have higher potential impact and cause greater dysregulation of gene expression. Furthermore, many of the prognostically significant co-mutations caused gains or losses of binding sequences of RNA binding proteins or micro RNAs with known cancer associations. Thus, detailed analyses of co-mutations can identify mechanisms that cooperate in tumorigenesis.

Identification of a Four-Gene-Based SERM Signature for Prognostic and Drug Sensitivity Prediction in Gastric Cancer

BackgroundGastric cancer (GC) is a highly molecular heterogeneous tumor with poor prognosis. Epithelial-mesenchymal transition (EMT) process and cancer stem cells (CSCs) are reported to share common signaling pathways and cause poor prognosis in GC. Considering about the close relationship between these two processes, we aimed to establish a gene signature based on both processes to achieve better prognostic prediction in GC.MethodsThe gene signature was constructed by univariate Cox and the least absolute shrinkage and selection operator (LASSO) Cox regression analyses by using The Cancer Genome Atlas (TCGA) GC cohort. We performed enrichment analyses to explore the potential mechanisms of the gene signature. Kaplan-Meier analysis and time-dependent receiver operating characteristic (ROC) curves were implemented to assess its prognostic value in TCGA cohort. The prognostic value of gene signature on overall survival (OS), disease-free survival (DFS), and drug sensitivity was validated in different cohorts. Quantitative reverse transcription polymerase chain reaction (RT-qPCR) validation of the prognostic value of gene signature for OS and DFS prediction was performed in the Fudan cohort.ResultsA prognostic signature including SERPINE1, EDIL3, RGS4, and MATN3 (SERM signature) was constructed to predict OS, DFS, and drug sensitivity in GC. Enrichment analyses illustrated that the gene signature has tight connection with the CSC and EMT processes in GC. Patients were divided into two groups based on the risk score obtained from the formula. The Kaplan-Meier analyses indicated high-risk group yielded significantly poor prognosis compared with low-risk group. Pearson’s correlation analysis indicated that the risk score was positively correlated with carboplatin and 5-fluorouracil IC50 of GC cell lines. Multivariate Cox regression analyses showed that the gene signature was an independent prognostic factor for predicting GC patients’ OS, DFS, and susceptibility to adjuvant chemotherapy.ConclusionsOur SERM prognostic signature is of great value for OS, DFS, and drug sensitivity prediction in GC, which may give guidance to the development of targeted therapy for CSC- and EMT-related gene in the future.

CAMSAP1 Mutation Correlates With Improved Prognosis in Small Cell Lung Cancer Patients Treated With Platinum-Based Chemotherapy

Platinum-based chemotherapy is the first-line treatment for small cell lung cancer (SCLC). However, due to patients developing a resistance to the drug, most experience relapse and their cancer can become untreatable. A large number of recent studies have found that platinum drug sensitivity of various cancers is affected by specific gene mutations, and so with this study, we attempted to find an effective genetic biomarker in SCLC patients that indicates their sensitivity to platinum-based drugs. To do this, we first analyzed whole exome sequencing (WES) and clinical data from two cohorts to find gene mutations related to the prognosis and to the platinum drug sensitivity of SCLC patients. The cohorts used were the Zhujiang cohort (N = 138) and the cohort reported by George et al. (N = 101). We then carried out gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) to investigate possible molecular mechanisms through which these gene mutations affect patient prognosis and platinum drug sensitivity. We found that for SCLC patients, CAMSAP1 mutation can activate anti-tumor immunity, mediate tumor cell apoptosis, inhibit epithelial-mesenchymal transition (EMT), improve prognosis, and improve platinum drug sensitivity, suggesting that CAMSAP1 mutation may be a potential biomarker indicating platinum drug sensitivity and patient prognosis in SCLC.

Deep learning transcriptomic model for prediction of pan-drug chemotherapeutic sensitivity

The emergence of precision oncology approaches has begun to inform clinical decision-making in diagnostic, prognostic, and treatment contexts. High-throughput technology has enabled machine learning algorithms to use the molecular characteristics of tumors to generate personalized therapies. However, precision oncology studies have yet to develop a predictive biomarker incorporating pan-cancer gene expression profiles to stratify tumors into similar drug sensitivity profiles. Here we show that a neural network with ten hidden layers accurately classifies pancancer cell lines into two distinct chemotherapeutic response groups based on a pan-drug dataset with 89.0% accuracy (AUC = 0.904). Using unsupervised clustering algorithms, we found a cohort of cell line gene expression data from the Genomics of Drug Sensitivity in Cancer could be clustered into two response groups with significant differences in pan-drug chemotherapeutic sensitivity. After applying the Boruta feature selection algorithm to this dataset, a deep learning model was developed to predict chemotherapeutic response groups. The model’s high classification efficacy validates our hypothesis that cell lines with similar gene expression profiles present similar pan-drug chemotherapeutic sensitivity. This finding provides evidence for the potential use of similar combinatorial biomarkers to select potent candidate drugs that maximize therapeutic response and minimize the cytotoxic burden. Future investigations should aim to recursively subcluster cell lines within the response clusters defined in this study to provide a higher resolution of potential patient response to chemotherapeutics.

LIM-Domain-Binding Protein 1 Mediates Cell Proliferation and Drug Resistance in Colorectal Cancer

Objective: It has been shown that LIM-domain-binding protein 1 (LDB1) is involved in the tumorigenesis of several cancers, but its function in colorectal cancer (CRC) has not been fully explained. This study is aimed to investigate whether LDB1 is involved in regulating the cell growth and drug sensitivity of CRC.Methods: To analyze the protein expression of LDB1 in CRC tissues, western blot was used. KM plotter and UALCAN databases were used to predict the prognosis of CRC patients with low or high LDB1 expression. To do the correlation analysis in CRC tissues, GEPIA database was used. CCK-8 assay and xenograft models were used to evaluate the effects of LDB1 in CRC cell growth. An oxaliplatin-resistant cell line was constructed to evaluate the effect of LDB1 in drug sensitivity of CRC cells.Results: Our current research confirmed that LDB1 was upregulated in CRC tumor tissues, and its elevation predicted a poor prognosis for CRC patients. LDB1 was also found positively correlated with CCNA1, BCL2 and BCLW, but negatively correlated with the pro-apoptotic signals (BID, BAX and BAK). Silence of LDB1 significantly inhibited CRC cell growth in vitro, and CRC cells with low expression of LDB1 had a lower tumorigenesis rate in tumor-bearing nude mice. Our experiments also showed that LDB1 silence enhanced the anti-tumor activity of oxaliplatin in CRC cells. The expression of LDB1 was also found increased in oxaliplatin-resistant CRC cell lines, and silence of LDB1 partly restored the antitumor effect of oxaliplatin in an oxaliplatin-resistant CRC cell line.Conclusion: Our current results revealed the roles of LDB1 in the growth and drug resistance of CRC cells, and may provide the new theoretical support for LDB1 as a potential target for the treatment of CRC in the future.

Mesenchymal tumor organoid models recapitulate rhabdomyosarcoma subtypes

Rhabdomyosarcomas (RMS) are mesenchyme-derived tumors and the most common childhood soft tissue sarcomas. Treatment is intense, with a nevertheless poor prognosis for high-risk patients. Discovery of new therapies would benefit from additional preclinical models. Here we describe the generation of a collection of pediatric RMS tumor organoid (tumoroid) models comprising all major subtypes. For aggressive tumors, tumoroid models can often be established within four to eight weeks, indicating the feasibility of personalized drug screening. Molecular, genetic and histological characterization show that the models closely resemble the original tumors, with genetic stability over extended culture periods of up to six months. Importantly, drug screening reflects established sensitivities and the models can be modified by CRISPR/Cas9 with TP53 knockout in an embryonal RMS model resulting in replicative stress drug sensitivity. Tumors of mesenchymal origin can therefore be used to generate organoid models, relevant for a variety of preclinical and clinical research questions.

Reduced interferon antagonism but similar drug sensitivity in Omicron variant compared to Delta variant SARS-CoV-2 isolates

The SARS-CoV-2 Omicron variant is currently causing a large number of infections in many countries. A number of antiviral agents are approved or in clinical testing for the treatment of COVID-19. Despite the high number of mutations in the Omicron variant, we here show that Omicron isolates display similar sensitivity to eight of the most important anti-SARS-CoV-2 drugs and drug candidates (including remdesivir, molnupiravir, and PF-07321332, the active compound in paxlovid), which is of timely relevance for the treatment of the increasing number of Omicron patients. Most importantly, we also found that the Omicron variant displays a reduced capability of antagonising the host cell interferon response. This provides a potential mechanistic explanation for the clinically observed reduced pathogenicity of Omicron variant viruses compared to Delta variant viruses.