Copy Number Alterations as Novel Biomarkers and Therapeutic Targets in Colorectal Cancer

Redefining copy number variation and single-nucleotide polymorphism counting via novel concepts based on recent PCR enhancements

Human genes have numerous copy number variations (CNVs) and single-nucleotide polymorphisms (SNPs) that control most of the body's core functions. On average, 12-16 % of human genes have CNVs, and a single gene can have a few hundred to several thousand SNPs. Numerous genome-wide association studies (GWAS) have shown that CNVs and SNPs can coexist in certain genomic regions, amplifying their effects on gene expression and regulation and disease susceptibility. Researchers initially categorized CNVs and SNPs into two types: homozygous and heterozygous. However, copy numbers were soon found to have a much wider range, underscoring their significance in certain diseases and microbial interactions. Because of the significant impact of CNVs and SNPs, research groups worldwide have eagerly sought effective methods for detecting both simultaneously. Despite yielding some minor results, these simultaneous counting methods have failed to meet expectations, leaving researchers to measure CNVs and SNPs separately. To overcome these limitations, we developed a novel approach by combining primers designed using the STexS method with matching probes used in the STexS II method. This method successfully detected both CNVs and SNPs in CYP2A6 and CYP2A7 using a single quantitative polymerase chain reaction. Once properly adjusted based on the three core principles, this new method markedly improved the time, cost-effectiveness, and overall accuracy of determining an individual's genetic status. Further testing of 100 human genomic DNA samples enabled calculations of the overall frequency of the [T] and [G] alleles of the CYP2A6 -48T > G SNP within an East Asian population yielded results that were highly congruent with those in a National Institutes of Health (NIH) database. This novel method will redefine genetic profiling and provide a means to successfully predict genetic characteristics and enhance personalized medicine by pinpointing appropriate individualized treatments.

The three YTHDF paralogs and VIRMA are strong cross-histotype tumor driver candidates among m6A core genes

N6-Methyladenosine (m6A) is the most abundant internal modification in mRNAs. Despite accumulating evidence for the profound impact of m6A on cancer biology, there are conflicting reports that alterations in genes encoding the m6A machinery proteins can either promote or suppress cancer, even in the same tumor type. Using data from The Cancer Genome Atlas, we performed a pan-cancer investigation of 15 m6A core factors in nearly 10000 samples from 31 tumor types to reveal underlying cross-tumor patterns. Altered expression, largely driven by copy number variations at the chromosome arm level, results in the most common mode of dysregulation of these factors. YTHDF1, YTHDF2, YTHDF3 and VIRMA are the most frequently altered factors and the only ones to be uniquely altered when tumors are grouped according to the expression pattern of the m6A factors. These genes are also the only ones with coherent, pan-cancer predictive power for progression-free survival. On the contrary, METTL3, the most intensively studied m6A factor as a cancer target, shows much lower levels of alteration and no predictive power for patient survival. Therefore, we propose the non-enzymatic YTHDF and VIRMA genes as preferred subjects to dissect the role of m6A in cancer and as priority cancer targets.

NRXN1 as a Prognostic Biomarker: Linking Copy Number Variation to EMT and Survival in Colon Cancer

The role of biomarkers in cancer treatment varies significantly depending on the cancer stage. Thus, in clinical practice, tailoring biomarkers to meet the specific needs and challenges of each cancer stage can increase the precision of treatment. Because they reflect underlying genetic alterations that influence cancer progression, copy number variation (CNV) biomarkers can play crucial prognostic roles. In our previous study, we identified potential survival-related genes for colorectal cancer (CRC) by analyzing CNV and gene expression data using a machine-learning approach. To further investigate the biological function of NRXN1, we assessed the use of RNA sequencing, phosphokinase assays, real-time quantitative PCR, and Western blot analysis. We found that NRXN1 copy number deletion was significantly associated with poor overall survival (OS) and recurrence-free survival (RFS), even in patients who received adjuvant chemotherapy. Compared with its expression in normal tissues, NRXN1 expression was lower in tumors, suggesting its potential role as a tumor suppressor. NRXN1 knockdown enhanced CRC cell viability and invasion, and transcriptome analysis indicated that the increased invasion was caused by GSK3β-mediated epithelial-mesenchymal transition. These findings highlight NRXN1 copy number deletion as a novel biomarker for predicting recurrence and survival in patients with resected colon cancer.

POFUT1 and PLAGL2 are characteristic markers of mucinous colorectal cancer associated with MUC2 expression

Colorectal mucinous adenocarcinoma (MAC) is one of the most lethal histological types of colorectal cancer, and its mechanism of development is not well understood. In this study, we aimed to clarify the molecular characteristics of MAC via in silico analysis using The Cancer Genome Atlas database. The expression of genes on chromosome 20q (Chr20q) was negatively associated with the expression of MUC2, which is a key molecule that can be used to distinguish between MAC and nonmucinous adenocarcinoma (NMAC). This was consistent with a significant difference in copy number alteration of Chr20q between the two histological types. We further identified 475 differentially expressed genes (DEGs) between MAC and NMAC, and some of the Chr20q genes among the DEGs are considered to be pivotal genes used to define MAC. Both in vitro and in vivo analysis showed that simultaneous knockdown of POFUT1 and PLAGL2, both of which are located on Chr20q, promoted MUC2 expression. Moreover, these genes were highly expressed in NMAC but not in MAC according to the results of immunohistological studies using human samples. In conclusion, POFUT1 and PLAGL2 are considered to be important for defining MAC, and these genes are associated with MUC2 expression.

Delineating the early dissemination mechanisms of acral melanoma by integrating single-cell and spatial transcriptomic analyses

Acral melanoma (AM) is a rare subtype of melanoma characterized by a high incidence of lymph node (LN) metastasis, a critical factor in tumor dissemination and therapeutic decision-making. Here, we employ single-cell and spatial transcriptomic analyses to investigate the dynamic evolution of early AM dissemination. Our findings reveal substantial inter- and intra-tumor heterogeneity in AM, alongside a highly immunosuppressive tumor microenvironment and complex intercellular communication networks, particularly in patients with LN metastasis. Notably, we identify a strong association between MYC+ Melanoma (MYC+MEL) and FGFBP2+NKT cells with LN metastasis. Furthermore, we demonstrate that LN metastasis requires a metabolic shift towards fatty acid oxidation (FAO) induced by MITF in MYC+MEL cells. Etomoxir, a clinically approved FAO inhibitor, can effectively suppress MITF-mediated LN metastasis. This comprehensive dataset enhances our understanding of LN metastasis in AM, and provides insights into the potential therapeutic targeting for the management of early AM dissemination.

The epigenetic modifier lysine methyltransferase 2C is frequently mutated in gastric remnant carcinoma

Gastric remnant carcinoma (GRC), which occurs in the stomach after partial gastrectomy, is a rare and aggressive form of gastric adenocarcinoma (GAC). Comprehensive profiling of genomic mutations in GRC could provide the basis for elucidating the origin and characteristics of this cancer. Herein, whole-exome sequencing (WES) was performed on 36 matched tumor-normal samples from patients with GRC and identified recurrent mutations in epigenetic modifiers, notably KMT2C, ARID1A, NSD1, and KMT2D, in 61.11% of cases. Mutational signature analysis revealed a low frequency of microsatellite instability (MSI) in GRC, which was further identified by MSIsensor, MSI-polymerase chain reaction, and immunohistochemistry analysis. Comparative analysis demonstrated that GRC had a distinct mutation spectrum compared to that of GAC in The Cancer Genome Atlas samples, with a significantly higher mutation rate of KMT2C. Targeted deep sequencing (Target-seq) of an additional 25 paired tumor-normal samples verified the high mutation frequency (48%) of KMT2C in GRC. KMT2C mutations correlated with poor overall survival in both WES and Target-seq cohorts and were independent prognosticators in GRC. In addition, KMT2C mutations were positively correlated with favorable outcomes in immune checkpoint inhibitor-treated pan-cancer patients and associated with higher intratumoral CD3+ , CD8+ tumor-infiltrating lymphocyte counts, and PD-L1 expression in GRC samples (p = 0.018, 0.092, 0.047, 0.010, and 0.034, respectively). Our dataset provides a platform for information and knowledge mining of the genomic characteristics of GRC and helps to frame new therapeutic approaches for this disease.

Transcriptomic Maps of Colorectal Liver Metastasis: Machine Learning of Gene Activation Patterns and Epigenetic Trajectories in Support of Precision Medicine

The molecular mechanisms of the liver metastasis of colorectal cancer (CRLM) remain poorly understood. Here, we applied machine learning and bioinformatics trajectory inference to analyze a gene expression dataset of CRLM. We studied the co-regulation patterns at the gene level, the potential paths of tumor development, their functional context, and their prognostic relevance. Our analysis confirmed the subtyping of five liver metastasis subtypes (LMS). We provide gene-marker signatures for each LMS, and a comprehensive functional characterization that considers both the hallmarks of cancer and the tumor microenvironment. The ordering of CRLMs along a pseudotime-tree revealed a continuous shift in expression programs, suggesting a developmental relationship between the subtypes. Notably, trajectory inference and personalized analysis discovered a range of epigenetic states that shape and guide metastasis progression. By constructing prognostic maps that divided the expression landscape into regions associated with favorable and unfavorable prognoses, we derived a prognostic expression score. This was associated with critical processes such as epithelial-mesenchymal transition, treatment resistance, and immune evasion. These factors were associated with responses to neoadjuvant treatment and the formation of an immuno-suppressive, mesenchymal state. Our machine learning-based molecular profiling provides an in-depth characterization of CRLM heterogeneity with possible implications for treatment and personalized diagnostics.

Prognostic role of detailed colorectal location and tumor molecular features: analyses of 13,101 colorectal cancer patients including 2994 early-onset cases

BACKGROUND

The pathogenic effect of colorectal tumor molecular features may be influenced by several factors, including those related to microbiota, inflammation, metabolism, and epigenetics, which may change along colorectal segments. We hypothesized that the prognostic association of colon cancer location might differ by tumor molecular characteristics.

METHODS

Utilizing a consortium dataset of 13,101 colorectal cancer cases, including 2994 early-onset cases, we conducted survival analyses of detailed tumor location stratified by statuses of microsatellite instability (MSI), CpG island methylator phenotype (CIMP), and KRAS and BRAF oncogenic mutation.

RESULTS

There was a statistically significant trend for better colon cancer-specific survival in relation to tumor location from the cecum to sigmoid colon (Ptrend = 0.002), excluding the rectum. The prognostic association of colon location differed by MSI status (Pinteraction = 0.001). Non-MSI-high tumors exhibited the cecum-to-sigmoid trend for better colon cancer-specific survival [Ptrend < 0.001; multivariable hazard ratio (HR) for the sigmoid colon (vs. cecum), 0.80; 95% confidence interval (CI) 0.70-0.92], whereas MSI-high tumors demonstrated a suggestive cecum-to-sigmoid trend for worse survival (Ptrend = 0.020; the corresponding HR, 2.13; 95% CI 1.15-3.92). The prognostic association of colon tumor location also differed by CIMP status (Pinteraction = 0.003) but not significantly by age, stage, or other features. Furthermore, MSI-high status was a favorable prognostic indicator in all stages.

CONCLUSIONS

Both detailed colonic location and tumor molecular features need to be accounted for colon cancer prognostication to advance precision medicine. Our study indicates the important role of large-scale studies to robustly examine detailed colonic subsites in molecular oncology research.

Copy Number Variations as Determinants of Colorectal Tumor Progression in Liquid Biopsies

Over the years, increasing evidence has shown that copy number variations (CNVs) play an important role in the pathogenesis and prognosis of Colorectal Cancer (CRC). Colorectal adenomas are highly prevalent lesions, but only 5% of these adenomas ever progress to carcinoma. This review summarizes the different CNVs associated with adenoma-carcinoma CRC progression and with CRC staging. Characterization of CNVs in circulating free-RNA and in blood-derived exosomes augers well with the potential of using such assays for patient management and early detection of metastasis. To overcome the limitations related to tissue biopsies and tumor heterogeneity, using CNVs to characterize tumor-derived materials in biofluids provides less invasive sampling methods and a sample that collectively represents multiple tumor sites in heterogeneous samples. Liquid biopsies provide a source of circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), tumor-derived exosomes (TDE), circulating free RNA, and non-coding RNA. This review provides an overview of the current diagnostic and predictive models from liquid biopsies.