High-level gain of mesenchymal-epithelial transition factor (MET) copy number using next-generation sequencing as a predictive biomarker for MET inhibitor efficacy

Advanced HCC with amplified mesenchymal epithelial transition factor receptor responds well to savolitinib: a case report

Objective

Current treatment agents for HCC are mostly immune checkpoint inhibitors (ICIs) plus bevacizumab and multitarget tyrosine kinase inhibitors (TKIs); however, their limited overall response rate and shorter median progression-free survival (PFS) discourage their frequent use. The development of Mesenchymal Epithelial Transition Factor receptor (MET) Tyrosine Kinase Inhibitors (MET-TKI) has transformed the treatment pattern in MET-altered solid tumors and improved their prognosis. However, the benefits of MET-TKIs in MET-amplified hepatocellular carcinoma (HCC) remain unclear.

Methods

Here, we present a case of advanced HCC amplified with MET treated with savolitinib, a MET-TKI, after progression from first-line treatment with bevacizumab plus sintilimab.

Results

The patient achieved a partial response (PR) to savolitinib in the second line setting. The progression-free survival (PFS) of first-line of bevacizumab plus sintilimab and sequential second-line treatment with MET-TKI, savolitinib, are 3 and over 8 months, respectively. furthermore, the patient still had continuous PR status with manageable toxicities.

Conclusions

The present case report provides first-hand evidence that savolitinib may be beneficial for patients with advanced MET-amplified HCC and offers a promising treatment option.

Exploration of prognostic genes and risk signature in breast cancer patients based on RNA binding proteins associated with ferroptosis

Background: Breast cancer (BRCA) is a life-threatening malignancy in women with an unsatisfactory prognosis. The purpose of this study was to explore the prognostic biomarkers and a risk signature based on ferroptosis-related RNA-binding proteins (FR-RBPs). Methods: FR-RBPs were identified using Spearman correlation analysis. Differentially expressed genes (DEGs) were identified by the "limma" R package. The univariate Cox and multivariate Cox analyses were executed to determine the prognostic genes. The risk signature was constructed and verified with the training set, testing set, and validation set. Mutation analysis, immune checkpoint expression analysis in high- and low-risk groups, and correlation between risk signature and chemotherapeutic agents were conducted using the "maftools" package, "ggplot2" package, and the CellMiner database respectively. The Human Protein Atlas (HPA) database was employed to confirm protein expression trends of prognostic genes in BRCA and normal tissues. The expression of prognostic genes in cell lines was verified by Real-time quantitative polymerase chain reaction (RT-qPCR). Kaplan-meier (KM) plotter database analysis was applied to predict the correlation between the expression levels of signature genes and survival statuses. Results: Five prognostic genes (GSPT2, RNASE1, TIPARP, TSEN54, and SAMD4A) to construct an FR-RBPs-related risk signature were identified and the risk signature was validated by the International Cancer Genome Consortium (ICGC) cohort. Univariate and multivariate Cox regression analysis demonstrated the risk score was a robust independent prognostic factor in overall survival prediction. The Tumor Mutational Burden (TMB) analysis implied that the high- and low-risk groups responded differently to immunotherapy. Drug sensitivity analysis suggested that the risk signature may serve as a chemosensitivity predictor. The results of GSEA suggested that five prognostic genes might be related to DNA replication and the immune-related pathways. RT-qPCR results demonstrated that the expression trends of prognostic genes in cell lines were consistent with the results from public databases. KM plotter database analysis suggested that high expression levels of GSPT2, RNASE1, and SAMD4A contributed to poor prognoses. Conclusion: In conclusion, this study identified the FR-RBPs-related prognostic genes and developed an FR-RBPs-related risk signature for the prognosis of BRCA, which will be of great significance in developing new therapeutic targets and prognostic molecular biomarkers for BRCA.

Detection of MET amplification by droplet digital PCR in peripheral blood samples of non-small cell lung cancer

PURPOSE

Mesenchymal-epithelial transition (MET) amplification is one of the mechanisms accounting for the resistance of epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) in lung cancer patients, as well as the poor prognosis. Fluorescence in situ hybridization (FISH) is the most widely used method for MET amplification detection. However, it is inapplicable when tissue samples were unavailable. Herein, we assessed the value of droplet digital PCR (ddPCR) in MET copy number gain (CNG) detection in non-small cell lung cancer (NSCLC) patients treated with EGFR-TKIs.

MATERIALS AND METHODS

A total of 103 cancer tissues and the paired peripheral blood samples from NSCLC patients were collected for MET CNG detection using ddPCR. In parallel, MET amplification in tissue samples was verified by FISH. Also, the relationships between MET CNG and EGFR T790M, as well as the EGFR-TKI resistance were also evaluated using Chi-square or Fisher's exact tests.

RESULT

The concordance rate of ddPCR and FISH in detecting MET CNG in tissue samples was 100% (102/102), and it was 94.17% (97/103) for ddPCR method in detecting the MET CNG among peripheral blood and tissue samples. No statistical difference was observed between MET amplification and EGFR T790M (p = 0.65), while MET amplification rate was significantly increased in patients with resistance to third generations of EGFR-TKIs as compared with patients with resistance to first/second EGFR-TKIs (p < 0.05).

CONCLUSIONS

ddPCR is an alternative method to detect MET CNG in both tissues and peripheral blood samples, which is of worthy in clinical promotion.

Relevance of Circulating Tumor Cells as Predictive Markers for Cancer Incidence and Relapse

Shedding of cancer cells from the primary site or undetectable bone marrow region into the circulatory system, resulting in clinically overt metastasis or dissemination, is the hallmark of unfavorable invasive cancers. The shed cells remain in circulation until they extravasate to form a secondary metastatic lesion or undergo anoikis. The circulating tumor cells (CTCs) found as single cells or clusters carry a plethora of information, are acknowledged as potential biomarkers for predicting cancer prognosis and cancer progression, and are supposed to play key roles in determining tailored therapies for advanced diseases. With the advent of novel technologies that allow the precise isolation of CTCs, more and more clinical trials are focusing on the prognostic and predictive potential of CTCs. In this review, we summarize the role of CTCs as a predictive marker for cancer incidence, relapse, and response to therapy.

Comprehensive data analysis of genomics, epigenomics, and transcriptomics to identify specific biomolecular markers for prostate adenocarcinoma

Background

Multiomics data analysis based on high-throughput sequencing technology has become a hotspot in tumor investigation. The present study aimed to explore prognostic biomarkers via investigating DNA copy number variation (CNV) and methylation variation (MET) data in prostate cancer.

Methods

We obtained the messenger RNA (mRNA) expression, CNV, and methylated data of prostate adenocarcinoma (PRAD) samples via The Cancer Genome Atlas (TCGA)-PRAD cohort. We calculated and assessed the associations between CNV and RNA sequencing (RNA-seq), and between MET and RNA-seq via Pearson correlation coefficients. We then used the "iCluster" package to perform multigroup cluster analysis with CNVcor gene CNV data, METcor gene methylation data, and CNVcor and METcor gene mRNA data. The univariate Cox analysis was used to screen significant hub genes, and multivariate Cox analysis was used to construct risk a model. The nomogram was constructed based on "rms" package, and the immune infiltrating patterns were compared between high- and low-risk groups.

Results

A total of 477 PRAD samples with complete CNV, methylation, mRNA, and matched clinical information were included in our study. A list of 10,073 CNVcor genes and 9841 METcor genes were confirmed with a significance level of P<0.01. We found that CNVcor is more likely to appear on chromosome (chr)8, chr17, and chr10, while METcor is more likely to appear on chr1, chr19, and chr17. Based on the core genes, we finally classified the samples into 4 subtypes, incorporating iC1 (iCluster) (92 samples), iC2 (79 samples), iC3 (165 samples), and iC4 (141 samples). Furthermore, we constructed the prognostic model for PRAD based on the 5 genes (IER3, AOX1, PRKCDBP, UBD, and FBLN5). Nomograms incorporating risk score and other clinical variables were further constructed, and these nomograms exhibited superior predictive ability. We further compared the differential immune infiltrating patterns in 2 risk groups and found significantly low levels of infiltrating cluster of differentiation (CD)8⁺ T cells in high-risk samples.

Conclusions

Our study integrated the multi-omics data to elucidate the molecular features of PRAD and pivotal genes for predicting prognosis.