Clonal Evolution and Timing of Metastatic Colorectal Cancer

Single-cell exome sequencing reveals polyclonal seeding and TRPS1 mutations in colon cancer metastasis

Liver metastasis remains the primary cause of mortality in patients with colon cancer. Identifying specific driver gene mutations that contribute to metastasis may offer viable therapeutic targets. To explore clonal evolution and genetic heterogeneity within the metastasis, we conducted single-cell exome sequencing on 150 single cells isolated from the primary tumor, liver metastasis, and lymphatic metastasis from a stage IV colon cancer patient. The genetic landscape of the tumor samples revealed that both lymphatic and liver metastases originated from the same region of the primary tumor. Notably, the liver metastasis was derived directly from the primary tumor, bypassing the lymph nodes. Comparative analysis of the sequencing data for individual cell pairs within different tumors demonstrated that the genetic heterogeneity of both liver and lymphatic metastases was also greater than that of the primary tumor. This finding indicates that liver and lymphatic metastases arose from clusters of circulating tumor cell (CTC) of a polyclonal origin, rather than from a single cell from the primary tumor. Single-cell transcriptome analysis suggested that higher EMT score and CNV scores were associated with more polyclonal metastasis. Additionally, a mutation in the TRPS1 (Transcriptional repressor GATA binding 1) gene, TRPS1 R544Q, was enriched in the single cells from the liver metastasis. The mutation significantly increased CRC invasion and migration both in vitro and in vivo through the TRPS1R544Q/ZEB1 axis. Further TRPS1 mutations were detected in additional colon cancer cases, correlating with advanced-stage disease and inferior prognosis. These results reveal polyclonal seeding and TRPS1 mutation as potential mechanisms driving the development of liver metastases in colon cancer.

Chronic arsenic exposure induces malignant transformation of human HaCaT cells through both deterministic and stochastic changes in transcriptome expression

Biological processes are inherently stochastic, i.e., are partially driven by hard to predict random probabilistic processes. Carcinogenesis is driven both by stochastic and deterministic (predictable non-random) changes. However, very few studies systematically examine the contribution of stochastic events leading to cancer development. In differential gene expression studies, the established data analysis paradigms incentivize expression changes that are uniformly different across the experimental versus control groups, introducing preferential inclusion of deterministic changes at the expense of stochastic processes that might also play a crucial role in the process of carcinogenesis. In this study, we applied simple computational techniques to quantify: (i) The impact of chronic arsenic (iAs) exposure as well as passaging time on stochastic gene expression and (ii) Which genes were expressed deterministically and which were expressed stochastically at each of the three stages of cancer development. Using biological coefficient of variation as an empirical measure of stochasticity we demonstrate that chronic iAs exposure consistently suppressed passaging related stochastic gene expression at multiple time points tested, selecting for a homogenous cell population that undergo transformation. Employing multiple balanced removal of outlier data, we show that chronic iAs exposure induced deterministic and stochastic changes in the expression of unique set of genes, that populate largely unique biological pathways. Together, our data unequivocally demonstrate that both deterministic and stochastic changes in transcriptome-wide expression are critical in driving biological processes, pathways and networks towards clonal selection, carcinogenesis, and tumor heterogeneity.

Capsaicin and Cold exposure promote EMT-mediated premetastatic niche formation to facilitate colorectal cancer metastasis

Colorectal cancer (CRC) is a common malignant tumor worldwide. Capsaicin and cold exposure were positively correlated with CRC metastasis. However, the mechanisms of action underlying capsaicin and cold exposure in 1,2-dimethylhyrazine (DMH)-induced CRC remain unknown. Multiple assays were utilized in the present study, including methylene blue, hematoxylin eosin (H&E) and immunohistochemistry (IHC) staining, western blotting and Duolink proximity ligation assay (PLA), in order to assess the influence of capsaicin and cold exposure on CRC rat models induced by DMH. The present study reported that capsaicin and cold exposure treatment significantly increased the size and number of colonic tumors, and the CRC metastasis rate in the capsaicin and cold exposure groups was higher than that in DMH model group.Moreover, it was observed that capsaicin and cold exposure increased mRNA and protein expression levels of LAMC2 and integrin-β1 induced by DMH. Duolink PLA results indicated that cold exposure and capsaicin significantly promoted interaction formation between LAMC2 and ITGB1 in CRC rats induced by DMH. Furthermore, western blot and IHC analysis confirmed that cold exposure and capsaicin inhibited DMH-induced decreases in the expression levels of E-cadherin, and increases in the expression levels of p-FAK, Snails, Fibronectin and N-cadherin. In addition, the serum levels of IL-1β and IL-6 in capsaicin and cold exposure group were higher than those of model group. In conclusion, our study suggests that both capsaicin and cold exposure may contribute to EMT-mediated the formation of premetastatic niche, which may lead to CRC metastasis by activating the early interaction between LAMC2 and integrin-β1.

Patient-derived xenograft model in colorectal cancer basic and translational research

Colorectal cancer (CRC) is one of the most popular malignancies globally, with 930 000 deaths in 2020. The evaluation of CRC-related pathogenesis and the discovery of potential therapeutic targets will be meaningful and helpful for improving CRC treatment. With huge efforts made in past decades, the systematic treatment regimens have been applied to improve the prognosis of CRC patients. However, the sensitivity of CRC to chemotherapy and targeted therapy is different from person to person, which is an important cause of treatment failure. The emergence of patient-derived xenograft (PDX) models shows great potential to alleviate the straits. PDX models possess similar genetic and pathological characteristics as the features of primary tumors. Moreover, PDX has the ability to mimic the tumor microenvironment of the original tumor. Thus, the PDX model is an important tool to screen precise drugs for individualized treatment, seek predictive biomarkers for prognosis supervision, and evaluate the unknown mechanism in basic research. This paper reviews the recent advances in constructed methods and applications of the CRC PDX model, aiming to provide new knowledge for CRC basic research and therapeutics.

Visual Clustering of Transcriptomic Data from Primary and Metastatic Tumors-Dependencies and Novel Pitfalls

Personalized oncology is a rapidly evolving area and offers cancer patients therapy options that are more specific than ever. However, there is still a lack of understanding regarding transcriptomic similarities or differences of metastases and corresponding primary sites. Applying two unsupervised dimension reduction methods (t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP)) on three datasets of metastases (n = 682 samples) with three different data transformations (unprocessed, log10 as well as log10 + 1 transformed values), we visualized potential underlying clusters. Additionally, we analyzed two datasets (n = 616 samples) containing metastases and primary tumors of one entity, to point out potential familiarities. Using these methods, no tight link between the site of resection and cluster formation outcome could be demonstrated, or for datasets consisting of solely metastasis or mixed datasets. Instead, dimension reduction methods and data transformation significantly impacted visual clustering results. Our findings strongly suggest data transformation to be considered as another key element in the interpretation of visual clustering approaches along with initialization and different parameters. Furthermore, the results highlight the need for a more thorough examination of parameters used in the analysis of clusters.

Cancer Evolution in Precision Medicine Era

Recent advances in our understanding of cancer, driven mainly by the emergence of new technologies have highlighted that heterogeneity shapes not only the genetic profile of tumors but also their epigenetic and gene expression profile [...]

Proof-of-Concept Pilot Study on Comprehensive Spatiotemporal Intra-Patient Heterogeneity for Colorectal Cancer With Liver Metastasis

Introduction

The mechanisms underlying high drug resistance and relapse rates after multi-modal treatment in patients with colorectal cancer (CRC) and liver metastasis (LM) remain poorly understood.

Objective

We evaluate the potential translational implications of intra-patient heterogeneity (IPH) comprising primary and matched metastatic intratumor heterogeneity (ITH) coupled with circulating tumor DNA (ctDNA) variability.

Methods

A total of 122 multi-regional tumor and perioperative liquid biopsies from 18 patients were analyzed via targeted next-generation sequencing (NGS).

Results

The proportion of patients with ITH were 53% and 56% in primary CRC and LM respectively, while 35% of patients harbored de novo mutations in LM indicating spatiotemporal tumor evolution and the necessity of multiregional analysis. Among the 56% of patients with alterations in liquid biopsies, de novo mutations in cfDNA were identified in 25% of patients, which were undetectable in both CRC and LM. All 17 patients with driver alterations harbored mutations targetable by molecularly targeted drugs, either approved or currently under evaluation.

Conclusion

Our proof-of-concept prospective study provides initial evidence on potential clinical superiority of IPH and warrants the conduction of precision oncology trials to evaluate the clinical utility of I PH-driven matched therapy.

Prognostic and immunological roles of Fc fragment of IgG binding protein in colorectal cancer

Valuable diagnostic and prognostic biomarkers are urgently needed for colorectal cancer (CRC), which is one of the leading causes of mortality worldwide. Previous studies have reported altered expression of a mucin-like protein Fc fragment of IgG binding protein (FCGBP) in various types of cancer, but its potential diagnostic, prognostic and immunological roles in CRC remain to be determined. Therefore, the aim of current study was to investigate the potential roles of FCGBP in CRC. The present study investigated FCGBP mutations and changes in its expression levels using a combination of microarray and public dataset analyses, as well as immunohistochemistry. The results demonstrated a 10.5% mutation frequency in the FCGBP coding sequence in CRC tissues, and identified decreased FCGBP mRNA or protein expression levels in colorectal adenoma and CRC (compared with those in normal colorectal tissues from healthy control subjects), including pathologically advanced CRC (stage III+IV vs. I+II). Survival analysis using the GEPIA and Kaplan-Meier Plotter databases revealed that low FCGBP expression levels were associated with short overall, disease-free, relapse-free and event-free survival times in patients with CRC. Notably, analysis using the online Tumor IMmune Estimation Resource database revealed a positive correlation between FCGBP expression levels and the extent of infiltrating immune cells, such as B cells and dendritic cells. Consistently, the expression levels of most markers (51/57) for various types of immune cells were significantly correlated with FCGBP expression levels in CRC tissues. These findings suggested that FCGBP may serve as a diagnostic and prognostic biomarker, and that FCGBP may be associated with immune infiltration in CRC.

Recent Advances in Our Knowledge of mCRC Tumor Biology and Genetics: A Focus on Targeted Therapy Development

Metastatic colorectal cancer (mCRC) remains a highly lethal malignancy although considerable progress has resulted from characterizing molecular alterations such as RAS mutation status and extent of microsatellite instability (MSI) to guide optimal use of available therapies. The availability of gene expression profiling, next generation sequencing technologies, proteomics analysis and other technologies provides high resolution information on individual tumors, including metastatic lesions to better define intra-tumor and inter-tumor heterogeneity. Recent literature applying this information to further customize personalized therapies is reviewed. Current biomarker-based stratification used to select optimal therapy that is personalized to the mutation profile of individual tumors is described. Recent literature using whole exome sequencing of metastatic lesions and primary CRC tumors and other advanced technologies to more fully elucidate the tumor biology specific to mCRC sub-types and to develop more precise therapies that improve outcomes is also reviewed.