Development and Validation of a Tumor Mutation Burden-Related Immune Prognostic Signature for Ovarian Cancers

Development of a prognostic model based on lysosome-related genes for ovarian cancer: insights into tumor microenvironment, mutation patterns, and personalized treatment strategies

BACKGROUND

Ovarian cancer (OC) is often associated with an unfavorable prognosis. Given the crucial involvement of lysosomes in tumor advancement, lysosome-related genes (LRGs) hold promise as potential therapeutic targets.

METHODS

To identify differentially expressed lysosome-related genes (DE-LRGs), we performed a matching analysis between differentially expressed genes (DEGs) in OC and the pool of LRGs. Genes with prognostic significance were analyzed using multiple regression analyses to construct a prognostic risk signature. The model's efficacy was validated through survival analysis in various cohorts. We further explored the model's correlation with clinical attributes, tumor microenvironment (TME), mutational patterns, and drug sensitivity. The quantitative real-time polymerase chain reaction (qRT-PCR) validated gene expression in OC cells.

RESULTS

A 10-gene prognostic risk signature was established. Survival analysis confirmed its predictive accuracy across cohorts. The signature served as an independent prognostic element for OC. The high-risk and low-risk groups demonstrated notable disparities in terms of immune infiltration patterns, mutational characteristics, and sensitivity to therapeutic agents. The qRT-PCR results corroborated and validated the findings obtained from the bioinformatic analyses.

CONCLUSIONS

We devised a 10-LRG prognostic model linked to TME, offering insights for tailored OC treatments.

The Prognostic Significance of Tumor-Infiltrating Lymphocytes, PD-L1, BRCA Mutation Status and Tumor Mutational Burden in Early-Stage High-Grade Serous Ovarian Carcinoma-A Study by the Spanish Group for Ovarian Cancer Research (GEICO)

Early stages are under-represented in studies on the molecular and immune features of high-grade serous ovarian carcinoma (HGSOC), and specific studies focused on early-stage HGSOC are required for a better prognostic stratification and to personalize chemotherapy. The aim of this study was to determine the prognostic significance of CD8+ and CD4+ tumor-infiltrating lymphocytes (TILs), tumoral cell PD-L1 expression, BRCA mutational status and tumor mutation burden (TMB) in early-stage HGSOC. A retrospective study was performed on stage I and II HGSOC from the Molecular Reclassification of Early Stages of Ovarian Cancer (RECLAMO) cohort from the Spanish Group of Ovarian Cancer Research (GEICO). Centralized histological typing was performed based on morphological and immunohistochemical features. Intraepithelial (i) and stromal (s) CD8+ and CD4+ T cells and PD-L1 were evaluated on tissue microarrays by immunohistochemistry. BRCA1 and BRCA2 mutation status and TMB were analyzed in tumor DNA using next-generation sequencing. The study included 124 tumors. High iCD8+ (>20 TILs/core), low/intermediate CD4+ (<20 TILs/core) and high CD8+/CD4+ ratio (>35/core) were associated with favorable outcomes. Tumor cell PD-L1 expression (TPS ≥ 1) was present in only 8% of tumors. In total, 11 (16%) and 6 (9%) out of 69 HGSOC tested carried pathogenic or likely pathogenic BRCA1 or BRCA2 mutations, respectively. Median TMB of 40 tumors analyzed was 5.04 mutations/Mb and only 6 tumors had 10 or more mutations/Mb. BRCA status and TMB were not associated with TILs or prognosis. When compared with studies on advanced HGSOC, our results suggested that prognostic variables differed according to stage and that more studies focused on early stages of HGSOC are needed to better stratify these tumors.

Tumor cell plasticity in targeted therapy-induced resistance: mechanisms and new strategies

Despite the success of targeted therapies in cancer treatment, therapy-induced resistance remains a major obstacle to a complete cure. Tumor cells evade treatments and relapse via phenotypic switching driven by intrinsic or induced cell plasticity. Several reversible mechanisms have been proposed to circumvent tumor cell plasticity, including epigenetic modifications, regulation of transcription factors, activation or suppression of key signaling pathways, as well as modification of the tumor environment. Epithelial-to-mesenchymal transition, tumor cell and cancer stem cell formation also serve as roads towards tumor cell plasticity. Corresponding treatment strategies have recently been developed that either target plasticity-related mechanisms or employ combination treatments. In this review, we delineate the formation of tumor cell plasticity and its manipulation of tumor evasion from targeted therapy. We discuss the non-genetic mechanisms of targeted drug-induced tumor cell plasticity in various types of tumors and provide insights into the contribution of tumor cell plasticity to acquired drug resistance. New therapeutic strategies such as inhibition or reversal of tumor cell plasticity are also presented. We also discuss the multitude of clinical trials that are ongoing worldwide with the intention of improving clinical outcomes. These advances provide a direction for developing novel therapeutic strategies and combination therapy regimens that target tumor cell plasticity.

A prognostic model for oral squamous cell carcinoma using 7 genes related to tumor mutational burden

Background

Oral squamous cell carcinoma (OSCC) is a rising problem in global public health. The traditional physical and imageological examinations are invasive and radioactive. There is a need for less harmful new biomarkers. Tumor mutational burden (TMB) is a novel prognostic biomarker for various cancers. We intended to explore the relationship between TMB-related genes and the prognosis of OSCC and to construct a prognostic model.

Methods

TMB-related differential expressed genes (DEGs) were screened by differential analysis and optimized via the univariate Cox and LASSO Cox analyses. Risk Score model was constructed by expression values of screened genes multiplying coefficient of LASSO Cox.

Results

Seven TMB-related DEGs (CTSG, COL6A5, GRIA3, CCL21, ZNF662, TDRD5 and GSDMB) were screened. Patients in high-risk group (Risk Score >  − 0.684511507) had worse prognosis compared to the low-risk group (Risk Score <  − 0.684511507). Survival rates of patients in the high-risk group were lower in the gender, age and degrees of differentiation subgroups compared to the low-risk group.

Conclusions

The Risk Score model constructed by 7 TMB-related genes may be a reliable biomarker for predicting the prognosis of OSCC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12903-022-02193-3.

A risk model of gene signatures for predicting platinum response and survival in ovarian cancer

Background

Ovarian cancer (OC) is the deadliest tumor in the female reproductive tract. And increased resistance to platinum-based chemotherapy represents the major obstacle in the treatment of OC currently. Robust and accurate gene expression models are crucial tools in distinguishing platinum therapy response and evaluating the prognosis of OC patients.

Methods

In this study, 230 samples from The Cancer Genome Atlas (TCGA) OV dataset were subjected to mRNA expression profiling, single nucleotide polymorphism (SNP), and copy number variation (CNV) analysis comprehensively to screen out the differentially expressed genes (DEGs). An SVM classifier and a prognostic model were constructed using the Random Forest algorithm and LASSO Cox regression model respectively via R. The Gene Expression Omnibus (GEO) database was applied as the validation set.

Results

Forty-eight differentially expressed genes (DEGs) were figured out through integrated analysis of gene expression, single nucleotide polymorphism (SNP), and copy number variation (CNV) data. A 10-gene classifier was constructed which could discriminate platinum-sensitive samples precisely with an AUC of 0.971 in the training set and of 0.926 in the GEO dataset (GSE638855). In addition, 8 optimal genes were further selected to construct the prognostic risk model whose predictions were consistent with the actual survival outcomes in the training cohort (p = 9.613e-05) and validated in GSE638855 (p = 0.04862). PNLDC1, SLC5A1, and SYNM were then identified as hub genes that were associated with both platinum response status and prognosis, which was further validated by the Fudan University Shanghai cancer center (FUSCC) cohort.

Conclusion

These findings reveal a specific risk model that could serve as effective biomarkers to identify patients’ platinum response status and predict survival outcomes for OC patients. PNLDC1, SLC5A1, and SYNM are the hub genes that may serve as potential biomarkers in OC treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13048-022-00969-3.

Next Generation Sequencing and Molecular Biomarkers in Ovarian Cancer—An Opportunity for Targeted Therapy

Ovarian cancer is the deadliest of all gynecologic malignancies claiming the lives of nearly 14,000 women in the United States annually. Despite therapeutic advances, the ovarian cancer mortality rate has remained stagnant since the 1980’s. The molecular heterogeneity of ovarian cancers suggest they may be more effectively treated via precision medicine. Current guidelines recommend germline and somatic testing for all new epithelial ovarian cancer diagnoses to assist providers in identifying candidates for targeted therapies. Next generation sequencing (NGS) identifies targetable, driver, and novel mutations used to guide treatment decisions. Performing NGS is standard of care in many other malignancies, but for ovarian cancer the use of NGS in daily practice is still emerging. This review discusses the targetable genetic mutations and role of NGS and molecular biomarker testing in the treatment of ovarian cancer.