Genomic landscape of advanced endometrial cancer analyzed by targeted next-generation sequencing and the cancer genome atlas (TCGA) dataset

Advances in Molecular and Genetic Technologies and the Problems Related to Their Application in Personalized Medicine

Advances in the global personalized medicine market are directly related to innovations and developments in molecular and genetic technologies. This review focuses on the key trends in the development of these technologies in the healthcare sector. The existing global developments having an impact on the evolution of the personalized medicine market are reviewed. Efficient measures to support the development of molecular and genetic technologies are proposed.

Deciphering cutaneous melanoma prognosis through LDL metabolism: Single-cell transcriptomics analysis via 101 machine learning algorithms

Cutaneous melanoma poses a formidable challenge within the field of oncology, marked by its aggressive nature and capacity for metastasis. Despite extensive research uncovering numerous genetic and molecular contributors to cutaneous melanoma development, there remains a critical knowledge gap concerning the role of lipids, notably low-density lipoprotein (LDL), in this lethal skin cancer. This article endeavours to bridge this knowledge gap by delving into the intricate interplay between LDL metabolism and cutaneous melanoma, shedding light on how lipids influence tumour progression, immune responses and potential therapeutic avenues. Genes associated with LDL metabolism were extracted from the GSEA database. We acquired and analysed single-cell sequencing data (GSE215120) and bulk-RNA sequencing data, including the TCGA data set, GSE19234, GSE22153 and GSE65904. Our analysis unveiled the heterogeneity of LDL across various cell types at the single-cell sequencing level. Additionally, we constructed an LDL-related signature (LRS) using machine learning algorithms, incorporating differentially expressed genes and highly correlated genes. The LRS serves as a valuable tool for assessing the prognosis, immunity and mutation status of patients with cutaneous melanoma. Furthermore, we conducted experiments on A375 and WM-115 cells to validate the function of PPP2R1A, a pivotal gene within the LRS. Our comprehensive approach, combining advanced bioinformatics analyses with an extensive review of current literature, presents compelling evidence regarding the significance of LDL within the cutaneous melanoma microenvironment.

Post-surgical ctDNA-based molecular residual disease detection in patients with stage I uterine malignancies

INTRODUCTION

Among uterine malignancies, endometrial cancer (EC) is the most common cancer of the female reproductive tract. Traditionally, risk stratification in EC is determined by standard clinicopathological risk factors. Although circulating tumor DNA (ctDNA) has emerged as a prognostic biomarker in various malignancies, its clinical validity in EC remains to be established.

METHODS

In this analysis of real-world data, 267 plasma samples from 101 patients with stage I EC were analyzed using a tumor-informed ctDNA assay (Signatera™ bespoke mPCR-NGS). Patients were followed post-surgically and monitored with ctDNA testing for a median of 6.8 months (range: 0.37-19.1).

RESULTS

Patients who tested ctDNA-positive at both their first time point and longitudinally experienced inferior recurrence-free survival (RFS) (HR = 6.2; p = 0.0006 and HR = 15.5; p < 0.0001, respectively), and showed a recurrence rate of 58% and 52%, vs. 6% and 0%, respectively for the ctDNA-negative patients. Most ctDNA-positive patients had high-risk histologies or sarcoma, versus low-risk and high-intermediate risk (H-IR) EC. Furthermore, patients with high-risk histologies who were ctDNA-positive showed shorter RFS compared to those who tested negative (HR = 9.5; p = 0.007), and those who tested positive in the low/H-IR cohort (HR = 0.25; p = 0.04). Post-surgically, detectable ctDNA was highly prognostic of clinical outcome and remained the only significant risk factor for recurrence when adjusted for clinicopathological risk factors, such as histologic risk group, mismatch repair (MMR), and p53 status.

CONCLUSION

Incorporating ctDNA monitoring along with traditional known risk factors may aid in identifying patients with stage I EC who are at highest risk of recurrence, and possibly aid in treatment stratification.

Detection of driver mutations and genomic signatures in endometrial cancers using artificial intelligence algorithms

Analyzed endometrial cancer (EC) genomes have allowed for the identification of molecular signatures, which enable the classification, and sometimes prognostication, of these cancers. Artificial intelligence algorithms have facilitated the partitioning of mutations into driver and passenger based on a variety of parameters, including gene function and frequency of mutation. Here, we undertook an evaluation of EC cancer genomes deposited on the Catalogue of Somatic Mutations in Cancers (COSMIC), with the goal to classify all mutations as either driver or passenger. Our analysis showed that approximately 2.5% of all mutations are driver and cause cellular transformation and immortalization. We also characterized nucleotide level mutation signatures, gross chromosomal re-arrangements, and gene expression profiles. We observed that endometrial cancers show distinct nucleotide substitution and chromosomal re-arrangement signatures compared to other cancers. We also identified high expression levels of the CLDN18 claudin gene, which is involved in growth, survival, metastasis and proliferation. We then used in silico protein structure analysis to examine the effect of certain previously uncharacterized driver mutations on protein structure. We found that certain mutations in CTNNB1 and TP53 increase protein stability, which may contribute to cellular transformation. While our analysis retrieved previously classified mutations and genomic alterations, which is to be expected, this study also identified new signatures. Additionally, we show that artificial intelligence algorithms can be effectively leveraged to accurately predict key drivers of cancer. This analysis will expand our understanding of ECs and improve the molecular toolbox for classification, diagnosis, or potential treatment of these cancers.

HER2 Status Assessment in Endometrial Serous Carcinoma: Comparative Analysis of Two Proposed Testing and Interpretation Algorithms

HER2 status is now routinely assessed in endometrial serous carcinoma (ESC) due to the reported predictive value of HER2 protein overexpression and/or gene amplification. Herein the authors compare 2 proposed testing and interpretation guidelines for HER2 in ESC. Forty-three consecutive cases of ESC that had been dually tested by both HER2 immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH) were interpreted using 2 sets of guidelines. Guideline set 1 (GS1) is the 2018 American Society of Clinical Oncology/College of American Pathologists guidelines for breast cancer. Guideline set 2 (GS2) is a recent proposal that is a slight modification of the enrollment criteria for the clinical trial (NCT01367002) that demonstrated a survival benefit for anti-HER2 therapy in ESC. By IHC, GS1 and GS2, respectively classified 39.5% (17/43) and 28% (12/43) of ESC as HER2-negative, 37.2% (16/43) and 53.4% (23/43) as HER2 equivocal, and 23.2% (10/43) and 18.6% (8/43) as HER2-positive ( P > 0.05 for all). IHC and FISH were highly concordant at the extremes using either set of guidelines, as no cases were found to be IHC3+/FISH-negative or IHC 0-1+/FISH-positive. GS1 and GS2 were comparable regarding the proportion of IHC equivocal cases that were HER2 amplified by FISH (19% vs 23% respectively; [ P = 0.71]). GS1 and GS2 displayed 98% (42/43) concordance regarding the final (IHC and/or FISH-based) classification of tumors as being HER2-positive or negative, and the same 13 cases were ultimately classified as HER2 amplified using either GS1 or GS2. One "discordant" case was classified as HER2-positive using GS2 but HER2-negative using GS1 (HER2 IHC score 2+ using both guidelines, HER2:CEP17 signal ratio of 3, HER2 signal number of 3.4). Six (14%) of the 43 cases (FISH Groups: 2, 3, and 4) would require IHC results to interpret the FISH findings using GS1. Because GS1 requires that the HER2 IHC staining be observed within a homogeneous and contiguous invasive cell population, and this is not a requirement in GS2, GS2 may be better suited for ESC given its frequently heterogeneous staining pattern. Additional studies may be required on the optimal interpretation of problematic dual-probe FISH scenarios in GS2 and the necessity for IHC correlation in such scenarios. Using either set of guidelines, our findings support a reflex testing strategy of restricting FISH testing to cases that are IHC equivocal.

p53 Immunohistochemical Staining and TP53 Gene Mutations in Endometrial Cancer: Does Null Pattern Correlate With Prognosis?

Whether immunohistochemistry (IHC) of p53 accurately reflects the TP53 mutational status of endometrial carcinoma (EC) has not yet been established. This study aimed to clarify the relationship between p53 IHC and TP53 mutations in EC and to examine whether p53 IHC can be a more convenient prognostic marker than TP53 mutation in EC. We performed p53 IHC staining of EC samples obtained via surgery and genetic analyses using next-generation sequencing. p53 IHC results showed that of the 101 cases, 71 (70%) were wild-type (WT), 12 (12%) were overexpression (OE), and 18 (18%) were in the null group. Missense mutations were found in 9 cases (47.4%) in OE, 2 (10.5%) in null, and 8 (42.1%) in the WT group. Truncating mutations were found in 1 case (8.3%) in OE, 6 (50%) in null, and 5 (41.7%) in the WT group. The 5-year progression-free survival was 0% in OE, 74.8% in null, and 79.0% in the WT group. In the prognosis for each type of TP53 mutation, the 5-year progression-free survival was missense (32.2%), truncating (65.6%), and WT (79.7%). These survival comparisons showed that the p53 IHC OE had the poorest prognosis. These results suggest that the p53 IHC OE is an independent poor prognostic factor for EC and can be used as a simple and rapid surrogate marker for TP53 mutations. Contrastingly, the complete absence of p53 IHC-the null staining pattern-may not accurately predict a TP53 mutation in EC, and it is necessary to be more careful in making the diagnosis of "abnormal."

Clinical characterization and genomic landscape of gynecological cancers among patients attending a Chinese hospital

Background

Gynecological cancers are the most lethal malignancies among females, most of which are associated with gene mutations. Few studies have compared the differences in the genomic landscape among various types of gynecological cancers. In this study, we evaluated the diversity of mutations in different gynecological cancers.

Methods

A total of 184 patients with gynecological cancer, including ovarian, cervical, fallopian tube, and endometrial cancer, were included. Next-generation sequencing was performed to detect the mutations and tumor mutational burden (TMB). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were also conducted.

Results

We found that 94.57% of patients had at least one mutation, among which single nucleotide variants, insertions and InDels were in the majority. TP53, PIK3CA, PTEN, KRAS, BRCA1, BRCA2, ARID1A, KMT2C, FGFR2, and FGFR3 were the top 10 most frequently mutated genes. Patients with ovarian cancer tended to have higher frequencies of BRCA1 and BRCA2 mutations, and the frequency of germline BRCA1 mutations (18/24, 75.00%) was higher than that of BRCA2 (11/19, 57.89%). A new mutation hotspot in BRCA2 (I770) was firstly discovered among Chinese patients with gynecological cancer. Patients with TP53, PIK3CA, PTEN, and FGFR3 mutations had significantly higher TMB values than those with wild-type genes. A significant cross was discovered between the enriched KEGG pathways of gynecological and breast cancers. GO enrichment revealed that the mutated genes were crucial for the cell cycle, neuronal apoptosis, and DNA repair.

Conclusion

Various gynecological cancer types share similarities and differences both in clinical characterization and genomic mutations. Taken together with the results of TMB and enriched pathways, this study provided useful information on the molecular mechanism underlying gynecological cancers and the development of targeted drugs and precision medicine.

High RIG-I and EFTUD2 expression predicts poor survival in endometrial cancer

PURPOSE

Endometrial cancer is the most common gynecological malignancy. The helicase RIG-I, a part of the innate immune system, and EFTUD2, a splicing factor which can upregulate RIG-I expression, are shown to influence tumor growth and disease progression in several malignancies. For endometrial cancer, an immunogenic cancer, data about RIG-I and EFTUD2 are still missing. The aim of this study was to examine the expression of RIG-I and EFTUD2 in endometrial cancer.

METHODS

225 specimen of endometrial cancer were immunohistochemically stained for RIG-I and EFTUD2. The results were correlated to clinicopathological data, overall survival (OS) and progression-free survival (PFS).

RESULTS

High RIG-I expression correlated with advanced tumor stages (FIGO: p = 0.027; pT: p = 0.010) and worse survival rates (OS: p = 0.009; PFS: p = 0.022). High EFTUD2 expression correlated to worse survival rates (OS: p = 0.026; PFS: p < 0.001) and was determined to be an independent marker for progression-free survival.

CONCLUSION

Our data suggest that the expression of RIG-I and EFTUD2 correlates with survival data, which makes both a possible therapeutic target in the future.

Comprehensive Approach to Genomic and Immune Profiling: Insights of a Real-World Experience in Gynecological Tumors

Gynecological cancer accounts for an elevated incidence worldwide requiring responsiveness regarding its care. The comprehensive genomic approach agrees with the classification of certain tumor types. We evaluated 49 patients with gynecological tumors undergoing high-throughput sequencing to explore whether identifying alterations in cancer-associated genes could characterize concrete histological subtypes. We performed immune examination and analyzed subsequent clinical impact. We found 220 genomic aberrations mostly distributed as single nucleotide variants (SNV, 77%). Only 3% were classified as variants of strong clinical significance in BRCA1 and BRCA2 of ovarian high-grade serous (HGSC) and uterine endometrioid carcinoma. TP53 and BRCA1 occurred in 72% and 28% of HGSC. Cervical squamous cell carcinoma was entirely HPV-associated and mutations occurred in PIK3CA (60%), as well as in uterine serous carcinoma (80%). Alterations were seen in PTEN (71%) and PIK3CA (60%) of uterine endometrioid carcinoma. Elevated programmed death-ligand 1 (PD-L1) was associated with high TILs. Either PD-L1 augmented in deficient mis-matched repair (MMR) proteins or POLE mutated cases when compared to a proficient MMR state. An 18% received genotype-guided therapy and a 4% immunotherapy. The description of tumor subtypes is plausible through high-throughput sequencing by recognizing clinically relevant alterations. Additional concomitant assessment of immune biomarkers identifies candidates for immunotherapy.

Advances and Trends in Omics Technology Development

The human history has witnessed the rapid development of technologies such as high-throughput sequencing and mass spectrometry that led to the concept of “omics” and methodological advancement in systematically interrogating a cellular system. Yet, the ever-growing types of molecules and regulatory mechanisms being discovered have been persistently transforming our understandings on the cellular machinery. This renders cell omics seemingly, like the universe, expand with no limit and our goal toward the complete harness of the cellular system merely impossible. Therefore, it is imperative to review what has been done and is being done to predict what can be done toward the translation of omics information to disease control with minimal cell perturbation. With a focus on the “four big omics,” i.e., genomics, transcriptomics, proteomics, metabolomics, we delineate hierarchies of these omics together with their epiomics and interactomics, and review technologies developed for interrogation. We predict, among others, redoxomics as an emerging omics layer that views cell decision toward the physiological or pathological state as a fine-tuned redox balance.