Genomic Heterogeneity and Clonal Evolution in Gastroesophageal Junction Cancer Revealed by Single Cell DNA Sequencing

From single cell to spatial multi-omics: unveiling molecular mechanisms in dynamic and heterogeneous systems

Single-cell multi-omics and spatial technology have been widely applied to biomedical studies and recently to environmental studies. The cell size detected by single-cell omics ranges from ∼2 µm (e.g., Bacillus subtilis) to ∼120 µm (e.g., human oocytes). Simultaneous detection of single-cell multi-omics is available to human and plant tissues while limited to microbial samples. Spatial technology enables mapping the detected biomolecules in situ. The recent advances in Matrix-Assisted Laser Desorption/Ionization-Mass Spectrometry Imaging and Micro/Nanodroplet Processing in One Pot for Trace Samples for the first time allow the application of spatial multi-omics in highly heterogeneous environmental samples composed of plants, fungi, and bacteria. We envision that these technologies will continue to advance our understanding of unique cell types, their developmental trajectory, and the intercellular signaling and interaction within biological samples.

Lineage tracing for multiple lung cancer by spatiotemporal heterogeneity using a multi-omics analysis method integrating genomic, transcriptomic, and immune-related features

Introduction

The distinction between multiple primary lung cancer (MPLC) and intrapulmonary metastasis (IPM) holds clinical significance in staging, therapeutic intervention, and prognosis assessment for multiple lung cancer. Lineage tracing by clinicopathologic features alone remains a clinical challenge; thus, we aimed to develop a multi-omics analysis method delineating spatiotemporal heterogeneity based on tumor genomic profiling.

Methods

Between 2012 and 2022, 11 specimens were collected from two patients diagnosed with multiple lung cancer (LU1 and LU2) with synchronous/metachronous tumors. A novel multi-omics analysis method based on whole-exome sequencing, transcriptome sequencing (RNA-Seq), and tumor neoantigen prediction was developed to define the lineage. Traditional clinicopathologic reviews and an imaging-based algorithm were performed to verify the results.

Results

Seven tissue biopsies were collected from LU1. The multi-omics analysis method demonstrated that three synchronous tumors observed in 2018 (LU1B/C/D) had strong molecular heterogeneity, various RNA expression and immune microenvironment characteristics, and unique neoantigens. These results suggested that LU1B, LU1C, and LU1D were MPLC, consistent with traditional lineage tracing approaches. The high mutational landscape similarity score (75.1%), similar RNA expression features, and considerable shared neoantigens (n = 241) revealed the IPM relationship between LU1F and LU1G which were two samples detected simultaneously in 2021. Although the multi-omics analysis method aligned with the imaging-based algorithm, pathology and clinicopathologic approaches suggested MPLC owing to different histological types of LU1F/G. Moreover, controversial lineage or misclassification of LU2's synchronous/metachronous samples (LU2B/D and LU2C/E) traced by traditional approaches might be corrected by the multi-omics analysis method. Spatiotemporal heterogeneity profiled by the multi-omics analysis method suggested that LU2D possibly had the same lineage as LU2B (similarity score, 12.9%; shared neoantigens, n = 71); gefitinib treatment and EGFR, TP53, and RB1 mutations suggested the possibility that LU2E might result from histology transformation of LU2C despite the lack of LU2C biopsy and its histology. By contrast, histological interpretation was indeterminate for LU2D, and LU2E was defined as a primary or progression lesion of LU2C by histological, clinicopathologic, or imaging-based approaches.

Conclusion

This novel multi-omics analysis method improves the accuracy of lineage tracing by tracking the spatiotemporal heterogeneity of serial samples. Further validation is required for its clinical application in accurate diagnosis, disease management, and improving prognosis.

TTN mutations predict a poor prognosis in patients with thyroid cancer

OBJECTIVE

We aimed to investigate the relationship between titin (TTN) gene mutations and thyroid cancer (THCA) and to explore the feasibility of the TTN gene as a potential prognostic indicator of THCA.

METHODS

From TCGA-THCA cohort, we performed a series of analyses to evaluate the prognostic value and potential mechanism of TTN in THCA. These patients were divided into the mutant-type (MUT) group and the wild-type (WT) group. Differentially expressed genes (DEGs) in the two groups were screened using the 'DESeq2' R package. Functional enrichment analysis was performed, and the protein-protein interaction (PPI) network, transcription factor (TF)-target interaction networks, and competitive endogenous RNA (ceRNA) regulatory networks were established for the DEGs. The TIMER database was applied for immune cell infiltration. Survival analysis and Cox regression analysis were used to analyze the potential prognostic value of the TTN gene.

RESULTS

Differential expression analysis showed that 409 genes were significantly up-regulated and 36 genes were down-regulated. Functional enrichment analysis revealed that TTN gene mutations played a potential role in the development of THCA. Analysis of the immune microenvironment indicated that TTN gene mutations were significantly associated with enrichment of M0 macrophages. Survival analysis showed that the MUT group predicted poorer prognosis than the WT group. Cox regression analysis demonstrated that TTN gene mutations were an independent risk factor for THCA. Nomograms also confirmed the prognostic values of the TTN gene in THCA. Conclusions In summary, our results demonstrated that TTN gene mutations predict poor prognosis in patients with THCA. This is the first study to research TTN gene mutations in THCA and to investigate their prognostic value in THCA.

A Review of Circulating Tumor DNA in the Diagnosis and Monitoring of Esophageal Cancer

Circulating tumor DNA (ctDNA) is a type of cell-free DNA released by tumor cells after necrosis and apoptosis, and it can be actively secreted by tumor cells. Since ctDNA is derived from various tumor sites, it can provide far more comprehensive genomic and epigenomic information than a single-site biopsy. Therefore, ctDNA can overcome tumor heterogeneity, which is the major limitation of a traditional tissue biopsy approach. Noninvasive ctDNA assays allow continuous real-time monitoring of the molecular status of cancers. Recently, ctDNA assays have been widely used in clinical practice, including cancer diagnosis, evaluation of therapeutic efficacy and prognosis, and monitoring of relapse and metastasis. Although ctDNA shows a high diagnostic performance in advanced esophageal cancer, it is far from satisfactory for early diagnosis of esophageal cancer. Monitoring the dynamic changes of ctDNA is beneficial for the evaluation of therapeutic efficacy and prediction of early recurrence in esophageal cancer. It is necessary to establish standards for individualized ctDNA detection in the evaluation of treatment response and surveillance of esophageal cancer and to develop clinical practice guideline for the systemic treatment of patients with "ctDNA recurrence." This review aims to provide an update on the role of ctDNA in the diagnosis and monitoring of esophageal cancer.

A Practical Nomogram for Predicting the Prognosis of Elderly Patients with Gastric Adenocarcinoma After Gastrectomy

Purpose

To establish a pragmatic prognostic nomogram for predicting the survival of elderly patients undergoing gastrectomy for gastric adenocarcinoma.

Patients and Methods

Data of elderly patients undergoing gastrectomy for gastric adenocarcinoma between 2004 and 2015 were obtained from the Surveillance, Epidemiology, and End Results database. Prognostic factors were identified by the Kaplan–Meier method and the Cox proportional hazards model. Based on these factors, we developed a nomogram to predict the overall survival (OS) and gastric cancer-specific survival (GCSS). Concordance index (C-index) and calibration curve are employed to assess the predictive accuracy of the model. Decision curve analysis (DCA) and receiver operating characteristic curve (ROC) analysis are applied to further appraise the clinical utility of the model.

Results

A total of 8401 cases were incorporated into this research. After univariate and multivariate analyses, nine prognostic factors of OS were identified, including age (P < 0.001), race (P < 0.001), marital status (P < 0.001), tumor site (P < 0.001), tumor size (P = 0.024), differentiation (P < 0.001), T stage (P < 0.001), N stage (P < 0.001), and M stage (P < 0.001); ten prognostic factors of GCSS were identified, including age (P < 0.001), race (P < 0.001), tumor site (P < 0.001), tumor size (P = 0.002), differentiation (P < 0.001), T stage (P < 0.001), N stage (P < 0.001), M stage (P < 0.001), radiotherapy (P < 0.001) and chemotherapy (P < 0.001). The C-index of the constructed nomogram for OS was 0.708 (95% CI: 0.701–0.715) while for GCSS was 0.745 (95% CI: 0.737–0.753). The calibration curves of the nomogram predictions and actual observations displayed good agreement for the 3- and 5-year OS and GCSS probabilities. The results of DCA and the area under the curve calculated by ROC analysis showed that the developed model was superior than TNM stage.

Conclusion

The nomogram we established could accurately predict the prognosis of individual elderly patients who underwent gastrectomy for gastric adenocarcinoma.

Application of Single-Cell Multi-Omics in Dissecting Cancer Cell Plasticity and Tumor Heterogeneity

Tumor heterogeneity, a hallmark of cancer, impairs the efficacy of cancer therapy and drives tumor progression. Exploring inter- and intra-tumoral heterogeneity not only provides insights into tumor development and progression, but also guides the design of personalized therapies. Previously, high-throughput sequencing techniques have been used to investigate the heterogeneity of tumor ecosystems. However, they could not provide a high-resolution landscape of cellular components in tumor ecosystem. Recently, advance in single-cell technologies has provided an unprecedented resolution to uncover the intra-tumoral heterogeneity by profiling the transcriptomes, genomes, proteomes and epigenomes of the cellular components and also their spatial distribution, which greatly accelerated the process of basic and translational cancer research. Importantly, it has been demonstrated that some cancer cells are able to transit between different states in order to adapt to the changing tumor microenvironment, which led to increased cellular plasticity and tumor heterogeneity. Understanding the molecular mechanisms driving cancer cell plasticity is critical for developing precision therapies. In this review, we summarize the recent progress in dissecting the cancer cell plasticity and tumor heterogeneity by use of single-cell multi-omics techniques.

Analyzing Modern Biomolecules: The Revolution of Nucleic-Acid Sequencing - Review

Recent developments have revolutionized the study of biomolecules. Among them are molecular markers, amplification and sequencing of nucleic acids. The latter is classified into three generations. The first allows to sequence small DNA fragments. The second one increases throughput, reducing turnaround and pricing, and is therefore more convenient to sequence full genomes and transcriptomes. The third generation is currently pushing technology to its limits, being able to sequence single molecules, without previous amplification, which was previously impossible. Besides, this represents a new revolution, allowing researchers to directly sequence RNA without previous retrotranscription. These technologies are having a significant impact on different areas, such as medicine, agronomy, ecology and biotechnology. Additionally, the study of biomolecules is revealing interesting evolutionary information. That includes deciphering what makes us human, including phenomena like non-coding RNA expansion. All this is redefining the concept of gene and transcript. Basic analyses and applications are now facilitated with new genome editing tools, such as CRISPR. All these developments, in general, and nucleic-acid sequencing, in particular, are opening a new exciting era of biomolecule analyses and applications, including personalized medicine, and diagnosis and prevention of diseases for humans and other animals.