Molecular subtypes of colorectal cancer: An emerging therapeutic opportunity for personalized medicine

Overexpression of TSPAN8 in consensus molecular subtype 3 colorectal cancer

BACKGROUND

Recently, consensus molecular subtypes (CMSs) have been proposed as a robust transcriptome-based classification system for colorectal cancer (CRC). Tetraspanins (TSPANs) are transmembrane proteins. They have been associated with the development of numerous malignancies, including CRC, through their role as "master organizers" for multi-molecular membrane complexes. No previous study has investigated the correlation between TSPANs and CMS classification. Herein, we investigated the expression of TSPANs in patient-derived primary CRC tissues and their CMS classifications.

METHODS

RNA samples were derived from primary CRC tissues (n = 100 patients diagnosed with colorectal adenocarcinoma) and subjected to RNA sequencing for transcriptome-based CMS classification and TSPAN-relevant analyses. Immunohistochemistry (IHC) and immunofluorescence (IF) stains were conducted to observe the protein expression level. To evaluate the relative biological pathways, gene-set enrichment analysis was performed.

RESULTS

Of the highly expressed TSPAN genes in CRC tissues (TSPAN8, TSPAN29, and TSPAN30), TSPAN8 was notably overexpressed in CMS3-classified primary tissues. The overexpression of TSPAN8 protein in CMS3 CRC was also observed by IHC and IF staining. As a result of gene-set enrichment analysis, TSPAN8 may potentially play a role in organizing signaling complexes for kinase-based metabolic deregulation in CMS3 CRC.

CONCLUSIONS

The present study reports the overexpression of TSPAN8 in CMS3 CRC. This study proposes TSPAN8 as a subtype-specific biomarker for CMS3 CRC. This finding provides a foundation for future CMS-based studies of CRC, a complex disease and the second leading cause of cancer mortality worldwide.

Colorectal cancer: a comprehensive review of carcinogenesis, diagnosis, and novel strategies for classified treatments

Colorectal cancer is the third most common and the second deadliest cancer worldwide. To date, colorectal cancer becomes one of the most important challenges of the health system in many countries. Since the clinical symptoms of this cancer appear in the final stages of the disease and there is a significant golden time between the formation of polyps and the onset of cancer, early diagnosis can play a significant role in reducing mortality. Today, in addition to colonoscopy, minimally invasive methods such as liquid biopsy have received much attention. The treatment of this complex disease has been mostly based on traditional treatments including surgery, radiotherapy, and chemotherapy; the high mortality rate indicates a lack of success for current treatment methods. Moreover, disease recurrence is another problem of traditional treatments. Recently, new approaches such as targeted therapy, immunotherapy, and nanomedicine have opened new doors for cancer treatment, some of which have already entered the market, and many methods have shown promising results in clinical trials. The success of immunotherapy in the treatment of refractory disease, the introduction of these methods into neoadjuvant therapy, and the successful results in tumor shrinkage without surgery have made immunotherapy a tough competitor for conventional treatments. It seems that the combination of those methods with such targeted therapies will go through promising changes in the future of colorectal cancer treatment.

Gene Co-Expression and miRNA Regulation: A Path to Early Intervention in Colorectal Cancer

Early diagnosis and intervention are pivotal in reducing colorectal cancer (CRC) incidence and enhancing patient outcomes. In this study, we focused on three genes, AQP8, GUCA2B, and SPIB, which exhibit high co-expression and play crucial roles in suppressing early-stage CRC. Our objective was to identify key miRNAs that can mitigate CRC tumorigenesis and modulate the co-expression network involving these genes. We conducted a comprehensive analysis using large-scale tissue mRNA data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus to validate the co-expression of AQP8, GUCA2B, and SPIB, and to assess their diagnostic and prognostic significance in CRC. mRNA-miRNA interactions were examined using MiRNet and the Encyclopedia of RNA Interactomes. Furthermore, using various molecular techniques, we conducted miRNA inhibitor transfection experiments in HCT116 cells to evaluate their effects on cell growth, migration, and gene/protein expression. Our findings revealed that, compared to normal tissues, AQP8, GUCA2B, and SPIB exhibited high co-expression and were downregulated in CRC, particularly during tumorigenesis. OncoMirs, hsa-miR-182-5p, and hsa-miR-27a-3p, were predicted to regulate these genes. MiRNA inhibition experiments in HCT116 cells demonstrated the inhibitory effects of miR-27a-3p and miR-182-5p on GUCA2B mRNA and protein expression. These miRNAs promoted the proliferation of CRC cells, possibly through their involvement in the GUCA2B-GUCY2C axis, which is known to promote tumor growth. While the expressions of AQP8 and SPIB were barely detectable, their regulatory relationship with hsa-miR-182-5p remained inconclusive. Our study confirms that hsa-miR-27a-3p and hsa-miR-182-5p are oncomiRs in CRC. These miRNAs may contribute to GUCY2C dysregulation by downregulating GUCA2B, which encodes uroguanylin. Consequently, hsa-miR-182-5p and hsa-miR-27a-3p show promise as potential targets for early intervention and treatment in the early stages of CRC.

Evaluation of the Immunohistochemical Scoring System of CDX2 Expression as a Prognostic Biomarker in Colon Cancer

Encoded by the CDX2 homeobox gene, the CDX2 protein assumes the role of a pivotal transcription factor localized within the nucleus of intestinal epithelial cells, orchestrating the delicate equilibrium of intestinal physiology while intricately guiding the precise development and differentiation of epithelial tissue. Emerging research has unveiled that positive immunohistochemical expression of this protein shows that the CDX2 gene exerts a potent suppressive impact on tumor advancement in colorectal cancer, impeding the proliferation and distant dissemination of tumor cells, while the inhibition or suppression of CDX2 frequently correlates with aggressive behavior in colorectal cancer. In this study, we conducted an immunohistochemical assessment of CDX2 expression on a cohort of 43 intraoperatively obtained tumor specimens from patients diagnosed with colon cancer at Colțea Clinical Hospital in Bucharest, between April 2019 and December 2023. Additionally, we shed light on the morphological diversity within colon tumors, uncovering varying differentiation grades within the same tumor, reflecting the variations in CDX2 expression as well as the genetic complexity underlying these tumors. Based on the findings, we developed an innovative immunohistochemical scoring system that addresses the heterogeneous nature of colon tumors. Comprehensive statistical analysis of CDX2 immunohistochemical expression unveiled significant correlations with known histopathological parameters such as tumor differentiation grades (p-value = 0.011) and tumor budding score (p-value = 0.002), providing intriguing insights into the complex involvement of the CDX2 gene in orchestrating tumor progression through modulation of differentiation processes, and highlighting its role in metastatic predisposition. The compelling correlation identified between CDX2 expression and conventional histopathological parameters emphasizes the prognostic significance of the CDX2 biomarker in colon cancer. Moreover, our novel immunohistochemical scoring system reveals a distinct subset of colon tumors exhibiting reserved prognostic outcomes, distinguished by their "mosaic" CDX2 expression pattern.

Subtype-WGME enables whole-genome-wide multi-omics cancer subtyping

We present an innovative strategy for integrating whole-genome-wide multi-omics data, which facilitates adaptive amalgamation by leveraging hidden layer features derived from high-dimensional omics data through a multi-task encoder. Empirical evaluations on eight benchmark cancer datasets substantiated that our proposed framework outstripped the comparative algorithms in cancer subtyping, delivering superior subtyping outcomes. Building upon these subtyping results, we establish a robust pipeline for identifying whole-genome-wide biomarkers, unearthing 195 significant biomarkers. Furthermore, we conduct an exhaustive analysis to assess the importance of each omic and non-coding region features at the whole-genome-wide level during cancer subtyping. Our investigation shows that both omics and non-coding region features substantially impact cancer development and survival prognosis. This study emphasizes the potential and practical implications of integrating genome-wide data in cancer research, demonstrating the potency of comprehensive genomic characterization. Additionally, our findings offer insightful perspectives for multi-omics analysis employing deep learning methodologies.

Binimetinib in combination with nivolumab or nivolumab and ipilimumab in patients with previously treated microsatellite-stable metastatic colorectal cancer with RAS mutations in an open-label phase 1b/2 study

BACKGROUND

In patients with previously treated RAS-mutated microsatellite-stable (MSS) metastatic colorectal cancer (mCRC), a multicenter open-label phase 1b/2 trial was conducted to define the safety and efficacy of the MEK1/MEK2 inhibitor binimetinib in combination with the immune checkpoint inhibitor (ICI) nivolumab (anti-PD-1) or nivolumab and another ICI, ipilimumab (anti-CTLA4).

METHODS

In phase 1b, participants were randomly assigned to Arm 1A (binimetinib 45 mg twice daily [BID] plus nivolumab 480 mg once every 4 weeks [Q4W]) or Arm 1B (binimetinib 45 mg BID plus nivolumab 480 mg Q4W and ipilimumab 1 mg/kg once every 8 weeks [Q8W]) to determine the maximum tolerable dose (MTD) and recommended phase 2 dose (RP2D) of binimetinib. The MTD/RP2D was defined as the highest dosage combination that did not cause medically unacceptable dose-limiting toxicities in more than 35% of treated participants in Cycle 1. During phase 2, participants were randomly assigned to Arm 2A (binimetinib MTD/RP2D plus nivolumab) or Arm 2B (binimetinib MTD/RP2D plus nivolumab and ipilimumab) to assess the safety and clinical activity of these combinations.

RESULTS

In phase 1b, 21 participants were randomized to Arm 1A or Arm 1B; during phase 2, 54 participants were randomized to Arm 2A or Arm 2B. The binimetinib MTD/RP2D was determined to be 45 mg BID. In phase 2, no participants receiving binimetinib plus nivolumab achieved a response. Of the 27 participants receiving binimetinib, nivolumab, and ipilimumab, the overall response rate was 7.4% (90% CI: 1.3, 21.5). Out of 75 participants overall, 74 (98.7%) reported treatment-related adverse events (AEs), of whom 17 (22.7%) reported treatment-related serious AEs.

CONCLUSIONS

The RP2D binimetinib regimen had a safety profile similar to previous binimetinib studies or nivolumab and ipilimumab combination studies. There was a lack of clinical benefit with either drug combination. Therefore, these data do not support further development of binimetinib in combination with nivolumab or nivolumab and ipilimumab in RAS-mutated MSS mCRC.

TRIAL REGISTRATION

NCT03271047 (09/01/2017).

Social network analysis of cell networks improves deep learning for prediction of molecular pathways and key mutations in colorectal cancer

Colorectal cancer (CRC) is a primary global health concern, and identifying the molecular pathways, genetic subtypes, and mutations associated with CRC is crucial for precision medicine. However, traditional measurement techniques such as gene sequencing are costly and time-consuming, while most deep learning methods proposed for this task lack interpretability. This study offers a new approach to enhance the state-of-the-art deep learning methods for molecular pathways and key mutation prediction by incorporating cell network information. We build cell graphs with nuclei as nodes and nuclei connections as edges of the network and leverage Social Network Analysis (SNA) measures to extract abstract, perceivable, and interpretable features that explicitly describe the cell network characteristics in an image. Our approach does not rely on precise nuclei segmentation or feature extraction, is computationally efficient, and is easily scalable. In this study, we utilize the TCGA-CRC-DX dataset, comprising 499 patients and 502 diagnostic slides from primary colorectal tumours, sourced from 36 distinct medical centres in the United States. By incorporating the SNA features alongside deep features in two multiple instance learning frameworks, we demonstrate improved performance for chromosomal instability (CIN), hypermutated tumour (HM), TP53 gene, BRAF gene, and Microsatellite instability (MSI) status prediction tasks (2.4%-4% and 7-8.8% improvement in AUROC and AUPRC on average). Additionally, our method achieves outstanding performance on MSI prediction in an external PAIP dataset (99% AUROC and 98% AUPRC), demonstrating its generalizability. Our findings highlight the discrimination power of SNA features and how they can be beneficial to deep learning models' performance and provide insights into the correlation of cell network profiles with molecular pathways and key mutations.

The utility of liquid biopsy-based methylation biomarkers for colorectal cancer detection

Colorectal cancer (CRC) is one of the most prevalent cancers and the second leading cause of cancer-related deaths in the United States. It is also one of the few cancers with established screening guidelines, however these methods have significant patient burden (e.g., time, invasive). In recent years, the development of liquid biopsy-based screening methods for biomarker detection have emerged as alternatives to traditional screening. Methylation biomarkers are of particular interest, and these markers can be identified and measured on circulating tumor and cell-free DNA. This perspective summarizes the current state of CRC screening and the potential integration of DNA methylation markers into liquid biopsy-based techniques. Finally, I discuss limitations to these methods and strategies for improvement. The continued development and implementation of liquid biopsy-based cancer screening approaches may provide an acceptable alternative to individuals unwilling to be screened by traditional methods.

Exploring the DNA methylome of Korean patients with colorectal cancer consolidates the clinical implications of cancer-associated methylation markers

Aberrant DNA methylation plays a critical role in the development and progression of colorectal cancer (CRC), which has high incidence and mortality rates in Korea. Various CRC-associated methylation markers for cancer diagnosis and prognosis have been developed; however, they have not been validated for Korean patients owing to the lack of comprehensive clinical and methylome data. Here, we obtained reliable methylation profiles for 228 tumor, 103 adjacent normal, and two unmatched normal colon tissues from Korean patients with CRC using an Illumina Infinium EPIC array; the data were corrected for biological and experiment biases. A comparative methylome analysis confirmed the previous findings that hypermethylated positions in the tumor were highly enriched in CpG island and promoter, 5' untranslated, and first exon regions. However, hypomethylated positions were enriched in the open-sea regions considerably distant from CpG islands. After applying a CpG island methylator phenotype (CIMP) to the methylome data of tumor samples to stratify the CRC patients, we consolidated the previously established clinicopathological findings that the tumors with high CIMP signatures were significantly enriched in the right colon. The results showed a higher prevalence of microsatellite instability status and MLH1 methylation in tumors with high CMP signatures than in those with low or non-CIMP signatures. Therefore, our methylome analysis and dataset provide insights into applying CRC-associated methylation markers for Korean patients regarding cancer diagnosis and prognosis. [BMB Reports 2024; 57(3): 161-166].

The Role of Galectin3, Tubulinβ, and Maspin in Promoting Tumor Budding in Colorectal Carcinoma and Their Clinical Implications

Colorectal cancer (CRC) is a leading cause of death worldwide. Despite the advances in surgical and therapeutic management, tumor metastases and poor prognosis are still major problems. Tumor budding is a relevant prognostic factor in CRC, and it can predict tumor metastasis. Galectin3 is responsible for the development and progression of many cancers through the regulation of cell-cell/cell-matrix interactions and tumor cell invasion. Tubulin is a microtubule protein, and maspin is a serine protease inhibitor; both induce tumor cell invasion through the stimulation of epithelial-mesenchymal transition. This study aims to evaluate the relationship between the expression of galecin3, tubulinβ, and maspin in CRC and clinicopathological features, including tumor budding, their prognostic roles, and clinical implications using immunohistochemistry. Galectin3, tubulinβ, and maspin were detected in tumor cells in 95%, 65%, and 87.5% of cases and in stromal cells in 28.8%, 40%, and 0% of cases. High expression of galectin3 and tubulinβ expression either in tumor cells or stroma was significantly associated with aggressive tumor features such as lymph node metastasis, lymphovascular invasion, tumor budding, and advanced tumor stage. The nucleocytoplasmic expression of maspin in tumor cells showed a significant association with deeper tumor invasion, lymph node metastasis, tumor budding, and advanced tumor stage. Significant associations were found between high galectin3 tumor cell expression and nucleocytoplasmic maspin and shorter survival. High expression of galectin3, tubulinβ, and nucleocytoplasmic maspin were significantly associated with aggressive tumor features such as tumor invasion, metastasis, high tumor budding, and short survival in CRC. They could be used as biomarkers for tumor budding and tumor aggressiveness in CRC and may be considered for future target therapy.

Built to Last? Reproducibility and Reusability of Deep Learning Algorithms in Computational Pathology

Recent progress in computational pathology has been driven by deep learning. While code and data availability are essential to reproduce findings from preceding publications, ensuring a deep learning model's reusability is more challenging. For that, the codebase should be well-documented and easy to integrate into existing workflows and models should be robust toward noise and generalizable toward data from different sources. Strikingly, only a few computational pathology algorithms have been reused by other researchers so far, let alone employed in a clinical setting. To assess the current state of reproducibility and reusability of computational pathology algorithms, we evaluated peer-reviewed articles available in PubMed, published between January 2019 and March 2021, in 5 use cases: stain normalization; tissue type segmentation; evaluation of cell-level features; genetic alteration prediction; and inference of grading, staging, and prognostic information. We compiled criteria for data and code availability and statistical result analysis and assessed them in 160 publications. We found that only one-quarter (41 of 160 publications) made code publicly available. Among these 41 studies, three-quarters (30 of 41) analyzed their results statistically, half of them (20 of 41) released their trained model weights, and approximately a third (16 of 41) used an independent cohort for evaluation. Our review is intended for both pathologists interested in deep learning and researchers applying algorithms to computational pathology challenges. We provide a detailed overview of publications with published code in the field, list reusable data handling tools, and provide criteria for reproducibility and reusability.

Preoperative Imaging Evaluation of Endometrial Cancer in FIGO 2023

The staging of endometrial cancer is based on the International Federation of Gynecology and Obstetrics (FIGO) staging system according to the examination of surgical specimens, and has revised in 2023, 14 years after its last revision in 2009. Molecular and histological classification has incorporated to new FIGO system reflecting the biological behavior and prognosis of endometrial cancer. Nonetheless, the basic role of imaging modalities including ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography, as a preoperative assessment of the tumor extension and also the evaluation points in CT and MRI imaging are not changed, other than several point of local tumor extension. In the field of radiology, it has also undergone remarkable advancement through the rapid progress of computational technology. The application of deep learning reconstruction techniques contributes the benefits of shorter acquisition time or higher quality. Radiomics, which extract various quantitative features from the images, is also expected to have the potential for the quantitative prediction of risk factors such as histological types and lymphovascular space invasion, which is newly included in the new FIGO system. This article reviews the preoperative imaging diagnosis in new FIGO system and recent advances in imaging analysis and their clinical contributions in endometrial cancer. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 3.

Three-Year Analysis of the Rectal Cancer Care Trajectory after the COVID-19 Pandemic

The global pandemic period from 2020 to 2022 caused important alterations in oncology care. This study aimed to describe the trends and variations in patient characteristics, comorbidities, and treatment approaches during this time in Romania. We conducted a retrospective database search to identify patients with rectal cancer who underwent surgical intervention between 2020 and 2022 and the year 2019, which served as a pre-pandemic period control. This study included 164 patients, with a yearly increase of approximately 10% in surgical interventions noted from 2020 (1709 interventions) to 2022 (2118 interventions), but an overall 34.4% decrease compared with the pre-pandemic period. Notable shifts were observed in the type of surgeries performed, with laparoscopic procedures doubling from 2020 (25%) to 2022 (47.5%), confirming the decrease in emergency presentations during the last year of the COVID-19 pandemic and a recovery to normality with planned, elective interventions. Elective interventions increased significantly in 2022 (79.7%) compared with the previous years (p = 0.043), with a concurrent rise in neoadjuvant therapy uptake in 2022 (35.6%). However, significant alterations in the TNM staging, from 12.5% stage IV cases in 2020 to 25.4% in 2022 (p = 0.039), indicated an increased diagnosis of advanced stages of rectal cancer as the years progressed. There was a significant difference in albumin levels over the years (p = 0.019) and in the American Society of Anesthesiology (ASA) scores (from 6.2% ASA stage IV in 2020 to 16.9% in 2022), denoting an increase in case complexity (p = 0.043). This study reveals a trend of increasing surgical interventions and the prevalence of more advanced stages of rectal cancer during the pandemic years. Despite the subtle fluctuations in various patient characteristics and treatment approaches, notable shifts were documented in the severity at diagnosis and surgery types, pointing toward more advanced disease presentations and changes in surgical strategies over the period studied. Nevertheless, the trends in ICU admission rates and mortality did not alter significantly during the pandemic period.

Applications of Innovation Technologies for Personalized Cancer Medicine: Stem Cells and Gene-Editing Tools

Personalized medicine is a new approach toward safer and even cheaper treatments with minimal side effects and toxicity. Planning a therapy based on individual properties causes an effective result in a patient's treatment, especially in a complex disease such as cancer. The benefits of personalized medicine include not only early diagnosis with high accuracy but also a more appropriate and effective therapeutic approach based on the unique clinical, genetic, and epigenetic features and biomarker profiles of a specific patient's disease. In order to achieve personalized cancer therapy, understanding cancer biology plays an important role. One of the crucial applications of personalized medicine that has gained consideration more recently due to its capability in developing disease therapy is related to the field of stem cells. We review various applications of pluripotent, somatic, and cancer stem cells in personalized medicine, including targeted cancer therapy, cancer modeling, diagnostics, and drug screening. CRISPR-Cas gene-editing technology is then discussed as a state-of-the-art biotechnological advance with substantial impacts on medical and therapeutic applications. As part of this section, the role of CRISPR-Cas genome editing in recent cancer studies is reviewed as a further example of personalized medicine application.

Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment

Research into the potential benefits of artificial intelligence for comprehending the intricate biology of cancer has grown as a result of the widespread use of deep learning and machine learning in the healthcare sector and the availability of highly specialized cancer datasets. Here, we review new artificial intelligence approaches and how they are being used in oncology. We describe how artificial intelligence might be used in the detection, prognosis, and administration of cancer treatments and introduce the use of the latest large language models such as ChatGPT in oncology clinics. We highlight artificial intelligence applications for omics data types, and we offer perspectives on how the various data types might be combined to create decision-support tools. We also evaluate the present constraints and challenges to applying artificial intelligence in precision oncology. Finally, we discuss how current challenges may be surmounted to make artificial intelligence useful in clinical settings in the future.

Automated exploitation of deep learning for cancer patient stratification across multiple types

MOTIVATION

Recent frameworks based on deep learning have been developed to identify cancer subtypes from high-throughput gene expression profiles. Unfortunately, the performance of deep learning is highly dependent on its neural network architectures which are often hand-crafted with expertise in deep neural networks, meanwhile, the optimization and adjustment of the network are usually costly and time consuming.

RESULTS

To address such limitations, we proposed a fully automated deep neural architecture search model for diagnosing consensus molecular subtypes from gene expression data (DNAS). The proposed model uses ant colony algorithm, one of the heuristic swarm intelligence algorithms, to search and optimize neural network architecture, and it can automatically find the optimal deep learning model architecture for cancer diagnosis in its search space. We validated DNAS on eight colorectal cancer datasets, achieving the average accuracy of 95.48%, the average specificity of 98.07%, and the average sensitivity of 96.24%, respectively. Without the loss of generality, we investigated the general applicability of DNAS further on other cancer types from different platforms including lung cancer and breast cancer, and DNAS achieved an area under the curve of 95% and 96%, respectively. In addition, we conducted gene ontology enrichment and pathological analysis to reveal interesting insights into cancer subtype identification and characterization across multiple cancer types.

AVAILABILITY AND IMPLEMENTATION

The source code and data can be downloaded from https://github.com/userd113/DNAS-main. And the web server of DNAS is publicly accessible at 119.45.145.120:5001.

Unveiling the genetic and epigenetic landscape of colorectal cancer: new insights into pathogenic pathways

Colorectal cancer (CRC) is a complex disease characterized by genetic and epigenetic alterations, playing a crucial role in its development and progression. This review aims to provide insights into the emerging landscape of these alterations in CRC pathogenesis to develop effective diagnostic tools and targeted therapies. Genetic alterations in signaling pathways such as Wnt/β-catenin, and PI3K/Akt/mTOR are pivotal in CRC development. Genetic profiling has identified distinct molecular subtypes, enabling personalized treatment strategies. Epigenetic modifications, including DNA methylation and histone modifications, also contribute to CRC pathogenesis by influencing critical cellular processes through gene silencing or activation. Non-coding RNAs have emerged as essential players in epigenetic regulation and CRC progression. Recent research highlights the interplay between genetic and epigenetic alterations in CRC. Genetic mutations can affect epigenetic modifications, leading to dysregulated gene expression and signaling cascades. Conversely, epigenetic changes can modulate genetic expression, amplifying or dampening the effects of genetic alterations. Advancements in understanding pathogenic pathways have potential clinical applications. Identifying genetic and epigenetic markers as diagnostic and prognostic biomarkers promises more accurate risk assessment and early detection. Challenges remain, including validating biomarkers and developing robust therapeutic strategies through extensive research and clinical trials. The dynamic nature of genetic and epigenetic alterations necessitates a comprehensive understanding of their temporal and spatial patterns during CRC progression. In conclusion, the genetic and epigenetic landscape of CRC is increasingly being unraveled, providing valuable insights into its pathogenesis. Integrating genetic and epigenetic knowledge holds great potential for improving diagnostics, prognostics, and personalized therapies in CRC. Continued research efforts are vital to translate these findings into clinical practice, ultimately improving patient outcomes.

Notch receptor/ligand diversity: contribution to colorectal cancer stem cell heterogeneity

Cancer cell heterogeneity is a key contributor to therapeutic failure and post-treatment recurrence. Targeting cell subpopulations responsible for chemoresistance and recurrence seems to be an attractive approach to improve treatment outcome in cancer patients. However, this remains challenging due to the complexity and incomplete characterization of tumor cell subpopulations. The heterogeneity of cells exhibiting stemness-related features, such as self-renewal and chemoresistance, fuels this complexity. Notch signaling is a known regulator of cancer stem cell (CSC) features in colorectal cancer (CRC), though the effects of its heterogenous signaling on CRC cell stemness are only just emerging. In this review, we discuss how Notch ligand-receptor specificity contributes to regulating stemness, self-renewal, chemoresistance and cancer stem cells heterogeneity in CRC.

Synergistic cytotoxicity of irinotecan combined with polysaccharide-based nanoparticles for colorectal carcinoma

Functional polymeric nanoparticles (NPs) with antitumor potential were combined with the topoisomerase I inhibitor, irinotecan (IRT), to enhance cytotoxicity against colorectal cancers. The negatively charged γ-polyglutamic acid (γ-PGA) or fucoidan (FCD) was complexed with the positively charged chitosan (CS) to encapsulate IRT. The size of the γ-PGA/CS/IRT NPs and FCD/CS/IRT NPs were 146.0 ± 8.0 nm and 230.8 ± 2.5 nm, respectively, with polydispersity index ≤0.3. The cellular uptake ability of FCD/CS-FITC NPs was better than that of γ-PGA/CS-FITC NPs, especially in p-selectin positive HCT116 colorectal cancer cells (4.8 ± 0.8 μg/mL vs 11.4 ± 2.2 μg/mL). The IC50 of FCD/CS/IRT NPs was 2.4 times lower than that of γ-PGA/CS/IRT NPs in HCT116 cells (4.8 ± 0.8 μg/mL vs 11.4 ± 2.2 μg/mL), indicating its superior antitumor potential. The combination of irinotecan and fucoidan-based NPs exhibited a synergistic effect (CI <1), resulting in better anticancer activity of FCD/CS/IRT NPs than irinotecan alone. The apoptosis-related proteins, caspase 3, caspase 9, and poly(ADP-ribose) polymerase (PARP), were prominently increased in FCD/CS/IRT NPs-treated HCT116 cells by 2.3 folds, 3.5 folds, and 6.3 folds, respectively. All results support that fucoidan-based irinotecan-loaded nanoparticles possess the ability to effectively enhance cellular uptake and induce synergistic apoptosis of colorectal cancer cells.

Differential Perspectives by Specialty on Oligometastatic Colorectal Cancer: A Korean Oligometastasis Working Group's Comparative Survey Study

PURPOSE

Despite numerous studies on the optimal treatments for oligometastatic disease (OMD), there is no established interdisciplinary consensus on its diagnosis or classification. This survey-based study aimed to analyze the differential opinions of colorectal surgeons and radiation oncologists regarding the definition and treatment of OMD from the colorectal primary.

MATERIALS AND METHODS

A total of 141 participants were included in this study, consisting of 63 radiation oncologists (44.7%) and 78 colorectal surgeons (55.3%). The survey consisted of 19 questions related to OMD, and the responses were analyzed using the chi-square test to determine statistical differences between the specialties.

RESULTS

The radiation oncologists chose "bone" more frequently compared to the colorectal surgeons (19.2% vs. 36.5%, p=0.022), while colorectal surgeons favored "peritoneal seeding" (26.9% vs. 9.5%, p=0.009). Regarding the number of metastatic tumors, 48.3% of colorectal surgeons responded that "irrelevant, if all metastatic lesions are amendable to local therapy", while only 21.8% of radiation oncologist chose same answer. When asked about molecular diagnosis, most surgeons (74.8%) said it was important, but only 35.8% of radiation oncologists agreed.

CONCLUSION

This study demonstrates that although radiation oncologists and colorectal surgeons agreed on a majority of aspects such as diagnostic imaging, biomarker, systemic therapy, and optimal timing of OMD, they also had quite different perspectives on several aspects of OMD. Understanding these differences is crucial to achieving multidisciplinary consensus on the definition and optimal management of OMD.

Value of ctDNA methylation biomarkers in diagnosis of colorectal tumors

Tweetable abstract DNA methylation alterations have been identified as promising biological markers for early-stage colorectal cancer detection. Here, the authors highlight some recent advances in DNA methylation and its role in the early diagnosis and overall disease course management of colorectal tumors. New insights into DNA methylation biomarkers for colorectal cancer early diagnosis and management are discussed.

Inverse relationship between Fusobacterium nucleatum amount and tumor CD274 (PD-L1) expression in colorectal carcinoma

Objectives

The CD274 (programmed cell death 1 ligand 1, PD-L1)/PDCD1 (programmed cell death 1, PD-1) immune checkpoint axis is known to regulate the antitumor immune response. Evidence also supports an immunosuppressive effect of Fusobacterium nucleatum. We hypothesised that tumor CD274 overexpression might be inversely associated with abundance of F. nucleatum in colorectal carcinoma.

Methods

We assessed tumor CD274 expression by immunohistochemistry and F. nucleatum DNA within tumor tissue by quantitative PCR in 812 cases among 4465 incident rectal and colon cancer cases that had occurred in two prospective cohort studies. Multivariable logistic regression analyses with inverse probability weighting were used to adjust for selection bias because of tissue data availability and potential confounders including microsatellite instability status, CpG island methylator phenotype, LINE-1 methylation level and KRAS, BRAF and PIK3CA mutations.

Results

Fusobacterium nucleatum DNA was detected in tumor tissue in 109 (13%) cases. Tumor CD274 expression level was inversely associated with the amount of F. nucleatum in colorectal cancer tissue (P = 0.0077). For one category-unit increase in three ordinal F. nucleatum categories (negative vs. low vs. high), multivariable-adjusted odds ratios (with 95% confidence interval) of the low, intermediate and high CD274 categories (vs. negative) were 0.78 (0.41-1.51), 0.64 (0.32-1.28) and 0.50 (0.25-0.99), respectively (P trend = 0.032).

Conclusions

Tumor CD274 expression level was inversely associated with the amount of F. nucleatum in colorectal cancer tissue, suggesting that different immunosuppressive mechanisms (i.e. PDCD1 immune checkpoint activation and tumor F. nucleatum enrichment) tend to be used by different tumor subgroups.

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.

Mutation-Attention (MuAt): deep representation learning of somatic mutations for tumour typing and subtyping

BACKGROUND

Cancer genome sequencing enables accurate classification of tumours and tumour subtypes. However, prediction performance is still limited using exome-only sequencing and for tumour types with low somatic mutation burden such as many paediatric tumours. Moreover, the ability to leverage deep representation learning in discovery of tumour entities remains unknown.

METHODS

We introduce here Mutation-Attention (MuAt), a deep neural network to learn representations of simple and complex somatic alterations for prediction of tumour types and subtypes. In contrast to many previous methods, MuAt utilizes the attention mechanism on individual mutations instead of aggregated mutation counts.

RESULTS

We trained MuAt models on 2587 whole cancer genomes (24 tumour types) from the Pan-Cancer Analysis of Whole Genomes (PCAWG) and 7352 cancer exomes (20 types) from the Cancer Genome Atlas (TCGA). MuAt achieved prediction accuracy of 89% for whole genomes and 64% for whole exomes, and a top-5 accuracy of 97% and 90%, respectively. MuAt models were found to be well-calibrated and perform well in three independent whole cancer genome cohorts with 10,361 tumours in total. We show MuAt to be able to learn clinically and biologically relevant tumour entities including acral melanoma, SHH-activated medulloblastoma, SPOP-associated prostate cancer, microsatellite instability, POLE proofreading deficiency, and MUTYH-associated pancreatic endocrine tumours without these tumour subtypes and subgroups being provided as training labels. Finally, scrunity of MuAt attention matrices revealed both ubiquitous and tumour-type specific patterns of simple and complex somatic mutations.

CONCLUSIONS

Integrated representations of somatic alterations learnt by MuAt were able to accurately identify histological tumour types and identify tumour entities, with potential to impact precision cancer medicine.

Recent application of artificial intelligence on histopathologic image-based prediction of gene mutation in solid cancers

PURPOSE

Evaluation of genetic mutations in cancers is important because distinct mutational profiles help determine individualized drug therapy. However, molecular analyses are not routinely performed in all cancers because they are expensive, time-consuming and not universally available. Artificial intelligence (AI) has shown the potential to determine a wide range of genetic mutations on histologic image analysis. Here, we assessed the status of mutation prediction AI models on histologic images by a systematic review.

METHODS

A literature search using the MEDLINE, Embase and Cochrane databases was conducted in August 2021. The articles were shortlisted by titles and abstracts. After a full-text review, publication trends, study characteristic analysis and comparison of performance metrics were performed.

RESULTS

Twenty-four studies were found mostly from developed countries, and their number is increasing. The major targets were gastrointestinal, genitourinary, gynecological, lung and head and neck cancers. Most studies used the Cancer Genome Atlas, with a few using an in-house dataset. The area under the curve of some of the cancer driver gene mutations in particular organs was satisfactory, such as 0.92 of BRAF in thyroid cancers and 0.79 of EGFR in lung cancers, whereas the average of all gene mutations was 0.64, which is still suboptimal.

CONCLUSION

AI has the potential to predict gene mutations on histologic images with appropriate caution. Further validation with larger datasets is still required before AI models can be used in clinical practice to predict gene mutations.

Cell Therapy as Target Therapy against Colon Cancer Stem Cells

Cancer stem cells (CSCs) are a small subpopulation of cells within tumors with properties, such as self-renewal, differentiation, and tumorigenicity. CSCs have been proposed as a plausible therapeutic target as they are responsible for tumor recurrence, metastasis, and conventional therapy resistance. Selectively targeting CSCs is a promising strategy to eliminate the propagation of tumor cells and impair overall tumor development. Recent research shows that several immune cells play a crucial role in regulating tumor cell proliferation by regulating different CSC maintenance or proliferation pathways. There have been great advances in cellular immunotherapy using T cells, natural killer (NK) cells, macrophages, or stem cells for the selective targeting of tumor cells or CSCs in colorectal cancer (CRC). This review summarizes the CRC molecular profiles that may benefit from said therapy and the main vehicles used in cell therapy against CSCs. We also discuss the challenges, limitations, and advantages of combining conventional and/or current targeted treatments in the late stages of CRC.

Homologous recombination deficiency signatures in gastrointestinal and thoracic cancers correlate with platinum therapy duration

There is emerging evidence about the predictive role of homologous recombination deficiency (HRD), but this is less defined in gastrointestinal (GI) and thoracic malignancies. We reviewed whole genome (WGS) and transcriptomic (RNA-Seq) data from advanced GI and thoracic cancers in the Personalized OncoGenomics trial (NCT02155621) to evaluate HRD scores and single base substitution (SBS)3, which is associated with BRCA1/2 mutations and potentially predictive of defective HRD. HRD scores were calculated by sum of loss of heterozygosity, telomeric allelic imbalance, and large-scale state transitions scores. Regression analyses examined the association between HRD and time to progression on platinum (TTPp). We included 223 patients with GI (n = 154) or thoracic (n = 69) malignancies. TTPp was associated with SBS3 (p < 0.01) but not HRD score in patients with GI malignancies, whereas neither was associated with TTPp in thoracic malignancies. Tumors with gBRCA1/2 mutations and a somatic second alteration exhibited high SBS3 and HRD scores, but these signatures were also present in several tumors with germline but no somatic second alterations, suggesting silencing of the wild-type allele or BRCA1/2 haploinsufficiency. Biallelic inactivation of an HR gene, including loss of XRCC2 and BARD1, was identified in BRCA1/2 wild-type HRD tumors and these patients had prolonged response to platinum. Thoracic cases with high HRD score were associated with high RECQL5 expression (p ≤ 0.025), indicating another potential mechanism of HRD. SBS3 was more strongly associated with TTPp in patients with GI malignancies and may be complementary to using HRD and BRCA status in identifying patients who benefit from platinum therapy.

Transcriptomes of the tumor-adjacent normal tissues are more informative than tumors in predicting recurrence in colorectal cancer patients

BACKGROUND

Previous investigations of transcriptomic signatures of cancer patient survival and post-therapy relapse have focused on tumor tissue. In contrast, here we show that in colorectal cancer (CRC) transcriptomes derived from normal tissues adjacent to tumors (NATs) are better predictors of relapse.

RESULTS

Using the transcriptomes of paired tumor and NAT specimens from 80 Korean CRC patients retrospectively determined to be in recurrence or nonrecurrence states, we found that, when comparing recurrent with nonrecurrent samples, NATs exhibit a greater number of differentially expressed genes (DEGs) than tumors. Training two prognostic elastic net-based machine learning models-NAT-based and tumor-based in our Samsung Medical Center (SMC) cohort, we found that NAT-based model performed better in predicting the survival when the model was applied to the tumor-derived transcriptomes of an independent cohort of 450 COAD patients in TCGA. Furthermore, compositions of tumor-infiltrating immune cells in NATs were found to have better prognostic capability than in tumors. We also confirmed through Cox regression analysis that in both SMC-CRC as well as in TCGA-COAD cohorts, a greater proportion of genes exhibited significant hazard ratio when NAT-derived transcriptome was used compared to when tumor-derived transcriptome was used.

CONCLUSIONS

Taken together, our results strongly suggest that NAT-derived transcriptomes and immune cell composition of CRC are better predictors of patient survival and tumor recurrence than the primary tumor.

4-Octyl itaconate inhibits aerobic glycolysis by targeting GAPDH to promote cuproptosis in colorectal cancer

Cuproptosis, a novel copper-induced cell death pathway, is linked to mitochondrial respiration and mediated by protein lipoylation. The discovery of cuproptosis unfolds new areas of investigation, particularly in cancers. The present study aimed to explore the role of cuproptosis in colorectal cancer progression. The genetic alterations of cuproptosis in colon cancer were evaluated using a database. MTT assays, colony formation, and flow cytometry were used to examine the effect of elesclomol-Cu and 4-Octyl itaconate (4-OI) on colorectal cancer cell and oxaliplatin-resistant cell viability. The anti-tumor effect of elesclomol with 4-OI was verified in vivo assay. The results showed that FDX1, SDHB, DLAT, and DLST genes were more highly expressed in normal tissues than those in primary tumor tissues. Patients with high expressions of these genes in tumor tissues had a better prognosis. Using MTT assay and colony formation analysis, elesclomol-Cu pulse treatment showed significant inhibition of cell viability in HCT116, LoVo, and HCT116-R cells. In addition, flow cytometry revealed elesclomol-Cu significantly promoted apoptosis. Tetrathiomolybdate, a copper chelator, markedly inhibited cuproptosis. Subsequently, we found 2-deoxy-D-glucose, a glucose metabolism inhibitor, sensitized cuproptosis. Furthermore, galactose further promoted cuproptosis. Interestingly, 4-OI significantly enhanced cuproptosis which was irrelevant to ROS production, apoptosis, necroptosis, or pyroptosis pathways. Aerobic glycolysis was inhibited by 4-OI through GAPDH, one of the key enzymes of glycolysis, sensitizing cuproptosis. Meanwhile, FDX1 knockdown weakened the ability of 4-OI to promote cuproptosis. In vivo experiments, 4-OI with elesclomol-Cu showed better anti-tumor effects. These results indicated that elesclomol-Cu rapidly halted cell growth in colorectal cancer cells and oxaliplatin-resistant cell line. Importantly, we revealed that 4-OI inhibited aerobic glycolysis by targeting GAPDH to promote cuproptosis.

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.

Deep learning in digital pathology for personalized treatment plans of cancer patients

Over the past decade, many new cancer treatments have been developed and made available to patients. However, in most cases, these treatments only benefit a specific subgroup of patients, making the selection of treatment for a specific patient an essential but challenging task for oncologists. Although some biomarkers were found to associate with treatment response, manual assessment is time-consuming and subjective. With the rapid developments and expanded implementation of artificial intelligence (AI) in digital pathology, many biomarkers can be quantified automatically from histopathology images. This approach allows for a more efficient and objective assessment of biomarkers, aiding oncologists in formulating personalized treatment plans for cancer patients. This review presents an overview and summary of the recent studies on biomarker quantification and treatment response prediction using hematoxylin-eosin (H&E) stained pathology images. These studies have shown that an AI-based digital pathology approach can be practical and will become increasingly important in improving the selection of cancer treatments for patients.

In Vitro and In Silico Study on the Impact of Chlorogenic Acid in Colorectal Cancer Cells: Proliferation, Apoptosis, and Interaction with β-Catenin and LRP6

Colorectal cancer mortality rate and highly altered proteins from the Wnt/β-catenin pathway increase the scientific community's interest in finding alternatives for prevention and treatment. This study aims to determine the biological effect of chlorogenic acid (CGA) on two colorectal cancer cell lines, HT-29 and SW480, and its interactions with β-catenin and LRP6 to elucidate a possible modulatory mechanism on the Wnt/β-catenin pathway. These effects were determined by propidium iodide and DiOC6 for mitochondrial membrane permeability, MitoTracker Red for mitochondrial ROS production, DNA content for cell distribution on cell cycle phases, and molecular docking for protein-ligand interactions and binding affinity. Here, it was found that CGA at 2000 µM significantly affects cell viability and causes DNA fragmentation in SW480 cells rather than in HT-29 cells, but in both cell lines, it induces ROS production. Additionally, CGA has similar affinity and interactions for LRP6 as niclosamide but has a higher affinity for both β-catenin sites than C2 and iCRT14. These results suggest a possible modulatory role of CGA over the Wnt/β-catenin pathway in colorectal cancer.

Survival improvement for patients with metastatic colorectal cancer over twenty years

Over the past two decades of successive clinical trials in metastatic colorectal cancer (CRC), the median overall survival of both control and experimental arms has steadily improved. However, the incremental change in survival for metastatic CRC patients not treated on trial has not yet been quantified. We performed a retrospective review of 1420 patients with de novo metastatic CRC who received their primary treatment at the University of Texas M.D. Anderson Cancer Center (UTMDACC) from 2004 through 2019. Median OS was roughly stable for patients diagnosed between 2004 and 2012 (22.6 months) but since has steadily improved for those diagnosed in 2013 to 2015 (28.8 months), and 2016 to 2019 (32.4 months). Likewise, 5-year survival rate has increased from 15.7% for patients diagnosed from 2004 to 2006 to 26% for those diagnosed from 2013 to 2015. Notably, survival improved for patients with BRAFV600E mutant as well as microsatellite unstable (MSI-H) tumors. Multivariate regression analysis identified surgical resection of liver metastasis (HR = 0.26, 95% CI, 0.19-0.37), use of immunotherapy (HR = 0.44, 95% CI, 0.29-0.67) and use of third line chemotherapy (regorafenib or trifluridine/tipiracil, HR = 0.74, 95% CI, 0.58-0.95), but not year of diagnosis (HR = 0.99, 95% CI, 0.98-1), as associated with better survival, suggesting that increased use of these therapies are the drivers of the observed improvement in survival.

Current Targeted Therapy for Metastatic Colorectal Cancer

Colorectal cancer (CRC) is the third most common type of cancer and the second leading cause of cancer deaths worldwide. Surgery or surgery plus radiotherapy and/or chemotherapy for patients with metastatic CRC (mCRC) were accepted as the main therapeutic strategies until the early 2000s, when targeted drugs, like cetuximab and bevacizumab, were developed. The use of targeted drugs in clinical practice has significantly increased patients' overall survival. To date, the emergence of several types of targeted drugs has opened new possibilities and revealed new prospects for mCRC treatment. Therapeutic strategies are continually being updated to select the most suitable targeted drugs based on the results of clinical trials that are currently underway. This review discusses the up-to date molecular evidence of targeted therapy for mCRC and summarizes the Food and Drug Administration-approved targeted drugs including the results of clinical trials. We also explain their mechanisms of action and how these affect the choice of a suitable targeted therapy.

Identifying essential genes in genome-scale metabolic models of consensus molecular subtypes of colorectal cancer

Identifying essential targets in the genome-scale metabolic networks of cancer cells is a time-consuming process. The present study proposed a fuzzy hierarchical optimization framework for identifying essential genes, metabolites and reactions. On the basis of four objectives, the present study developed a framework for identifying essential targets that lead to cancer cell death and evaluating metabolic flux perturbations in normal cells that have been caused by cancer treatment. Through fuzzy set theory, a multiobjective optimization problem was converted into a trilevel maximizing decision-making (MDM) problem. We applied nested hybrid differential evolution to solve the trilevel MDM problem to identify essential targets in genome-scale metabolic models for five consensus molecular subtypes (CMSs) of colorectal cancer. We used various media to identify essential targets for each CMS and discovered that most targets affected all five CMSs and that some genes were CMS-specific. We obtained experimental data on the lethality of cancer cell lines from the DepMap database to validate the identified essential genes. The results reveal that most of the identified essential genes were compatible with the colorectal cancer cell lines obtained from DepMap and that these genes, with the exception of EBP, LSS, and SLC7A6, could generate a high level of cell death when knocked out. The identified essential genes were mostly involved in cholesterol biosynthesis, nucleotide metabolisms, and the glycerophospholipid biosynthetic pathway. The genes involved in the cholesterol biosynthetic pathway were also revealed to be determinable, if a cholesterol uptake reaction was not induced when the cells were in the culture medium. However, the genes involved in the cholesterol biosynthetic pathway became non-essential if such a reaction was induced. Furthermore, the essential gene CRLS1 was revealed as a medium-independent target for all CMSs.

Developing a taxonomy for colorectal cancer

Analyzing how gene interaction networks are perturbed in individuals can help identify different types of colorectal cancers, paving the way towards personalized care.

A novel chromatin regulator signature predicts the prognosis, clinical features and immunotherapy of colon cancer

Aim: To elucidate the potential function and prognostic value of chromatin regulators (CRs) in colon cancer. Materials & methods: A comprehensive analysis of CR RNA expression data from public databases was conducted. Results: The authors successfully established and validated a 17 CR-based signature using public databases. Ten CRs of the signature were eventually verified at the protein level using the Human Protein Atlas database. Functional enrichment showed that CRs were significantly enriched in cancer-related pathways. This signature was remarkably relevant to immune cell infiltration, immune checkpoints, tumor immune dysfunction and exclusion (TIDE) score and drug sensitivity. Conclusion: The authors identified a novel, reliable prognostic signature for colon cancer. The study provided new insights into the function of CRs and has important clinical implications for immunotherapy for colon cancer.

Genetic and microenvironmental intra-tumor heterogeneity impacts colorectal cancer evolution and metastatic development

Colorectal cancer (CRC) is a highly diverse disease, where different genomic instability pathways shape genetic clonal diversity and tumor microenvironment. Although intra-tumor heterogeneity has been characterized in primary tumors, its origin and consequences in CRC outcome is not fully understood. Therefore, we assessed intra- and inter-tumor heterogeneity of a prospective cohort of 136 CRC samples. We demonstrate that CRC diversity is forged by asynchronous forms of molecular alterations, where mutational and chromosomal instability collectively boost CRC genetic and microenvironment intra-tumor heterogeneity. We were able to depict predictor signatures of cancer-related genes that can foresee heterogeneity levels across the different tumor consensus molecular subtypes (CMS) and primary tumor location. Finally, we show that high genetic and microenvironment heterogeneity are associated with lower metastatic potential, whereas late-emerging copy number variations favor metastasis development and polyclonal seeding. This study provides an exhaustive portrait of the interplay between genetic and microenvironment intra-tumor heterogeneity across CMS subtypes, depicting molecular events with predictive value of CRC progression and metastasis development.

Genetic and epigenetic dependencies in colorectal cancer development

Recent studies have mapped key genetic changes in colorectal cancer (CRC) that impact important pathways contributing to the multistep models for CRC initiation and development. In parallel with genetic changes, normal and cancer tissues harbor epigenetic alterations impacting regulation of critical genes that have been shown to play profound roles in the tumor initiation. Cumulatively, these molecular changes are only loosely associated with heterogenous transcriptional programs, reflecting the heterogeneity in the various CRC molecular subtypes and the paths to CRC development. Studies from mapping molecular alterations in early CRC lesions and use of experimental models suggest that the intricate dependencies of various genetic and epigenetic hits shape the early development of CRC via different pathways and its manifestation into various CRC subtypes. We highlight the dependency of epigenetic and genetic changes in driving CRC development and discuss factors affecting epigenetic alterations over time and, by extension, risk for cancer.

Attention-based multiple instance learning with self-supervision to predict microsatellite instability in colorectal cancer from histology whole-slide images

Microsatellite instability (MSI) is a clinically important characteristic of colorectal cancer. Standard diagnosis of MSI is performed via genetic analyses, however these tests are not always included in routine care. Histopathology whole-slide images (WSIs) are the gold-standard for colorectal cancer diagnosis and are routinely collected. This study develops a model to predict MSI directly from WSIs. Making use of both weakly- and self-supervised deep learning techniques, the proposed model shows improved performance over conventional deep learning models. Additionally, the proposed framework allows for visual interpretation of model decisions. These results are validated in internal and external testing datasets.

Multivariate Risk Analysis of RAS, BRAF and EGFR Mutations Allelic Frequency and Coexistence as Colorectal Cancer Predictive Biomarkers

Simple Summary

The colorectal cancer (CRC) stage and evolution should be described by biomarker profiles. In 60 CRC cases, KRAS, NRAS, BRAF, and EGFR mutations were analyzed by droplet digital PCR (ddPCR). KRAS G12/G13 mutation was present in all patients with variable allelic frequencies. KRAS Q61 mutation was correlated with tumor invasion beyond the subserosa and poor differentiation, both associated with worst prognosis. Tumors with NRAS and BRAF mutations were prevalently localized on the right segment colon. The discovery of the KRAS Q61 role in tumor phenotypes provides the foundation for new therapeutic strategies and perspectives on molecular subtypes classification of CRC.

Abstract

Background: Biomarker profiles should represent a coherent description of the colorectal cancer (CRC) stage and its predicted evolution. Methods: Using droplet digital PCR, we detected the allelic frequencies (AF) of KRAS, NRAS, BRAF, and EGFR mutations from 60 tumors. We employed a pair-wise association approach to estimate the risk involving AF mutations as outcome variables for clinical data and as predicting variables for tumor-staging. We evaluated correlations between mutations of AFs and also between the mutations and histopathology features (tumor staging, inflammation, differentiation, and invasiveness). Results: KRAS G12/G13 mutations were present in all patients. KRAS Q61 was significantly associated with poor differentiation, high desmoplastic reaction, invasiveness (ypT4), and metastasis (ypM1). NRAS and BRAF were associated with the right-side localization of tumors. Diabetic patients had a higher risk to exhibit NRAS G12/G13 mutations. BRAF and NRAS G12/G13 mutations co-existed in tumors with invasiveness limited to the submucosa. Conclusions: The associations we found and the mutational AF we reported may help to understand disease processes and may be considered as potential CCR biomarker candidates. In addition, we propose representative mutation panels associated with specific clinical and histopathological features of CRC, as a unique opportunity to refine the degree of personalization of CRC treatment.

Recent Applications of Artificial Intelligence from Histopathologic Image-Based Prediction of Microsatellite Instability in Solid Cancers: A Systematic Review

Cancers with high microsatellite instability (MSI-H) have a better prognosis and respond well to immunotherapy. However, MSI is not tested in all cancers because of the additional costs and time of diagnosis. Therefore, artificial intelligence (AI)-based models have been recently developed to evaluate MSI from whole slide images (WSIs). Here, we aimed to assess the current state of AI application to predict MSI based on WSIs analysis in MSI-related cancers and suggest a better study design for future studies. Studies were searched in online databases and screened by reference type, and only the full texts of eligible studies were reviewed. The included 14 studies were published between 2018 and 2021, and most of the publications were from developed countries. The commonly used dataset is The Cancer Genome Atlas dataset. Colorectal cancer (CRC) was the most common type of cancer studied, followed by endometrial, gastric, and ovarian cancers. The AI models have shown the potential to predict MSI with the highest AUC of 0.93 in the case of CRC. The relatively limited scale of datasets and lack of external validation were the limitations of most studies. Future studies with larger datasets are required to implicate AI models in routine diagnostic practice for MSI prediction.

Muscarinic Receptors Associated with Cancer

Simple Summary

Recently, cancer research has described the presence of the cholinergic machinery, specifically muscarinic receptors, in a wide variety of cancers due to their activation and signaling pathways associated with tumor progression and metastasis, providing a wide overview of their contribution to different cancer formation and development for new antitumor targets. This review focused on determining the molecular signatures associated with muscarinic receptors in breast and other cancers and the need for pharmacological, molecular, biochemical, technological, and clinical approaches to improve new therapeutic targets.

Abstract

Cancer has been considered the pathology of the century and factors such as the environment may play an important etiological role. The ability of muscarinic agonists to stimulate growth and muscarinic receptor antagonists to inhibit tumor growth has been demonstrated for breast, melanoma, lung, gastric, colon, pancreatic, ovarian, prostate, and brain cancer. This work aimed to study the correlation between epidermal growth factor receptors and cholinergic muscarinic receptors, the survival differences adjusted by the stage clinical factor, and the association between gene expression and immune infiltration level in breast, lung, stomach, colon, liver, prostate, and glioblastoma human cancers. Thus, targeting cholinergic muscarinic receptors appears to be an attractive therapeutic alternative due to the complex signaling pathways involved.

Mechanobiology of Colorectal Cancer

Simple Summary

It is well documented that colorectal cancer (CRC) is the third most common cancer type, responsible for high mortality in developed countries, resulting in a high socio-economic impact. Several biochemical and gene expression pathways explaining the manifestation of this cancer in humans have already been identified. However, explanations for some of the related biophysical mechanisms and their influence on CRC remain elusive. In CRC, biophysics and medical research have already revealed the importance of studying the effects of the stiffness and viscoelasticity of the substrate on cells, as well as the effect of the shear stress of blood and lymphatic vessels on the behavior of cells and tissues. A deeper understanding of the relationship between the biophysical cues and biochemical signals could be advantageous to develop new diagnostic techniques and therapeutic strategies. Being a disease with a high mortality rate, it becomes crucial to dedicate efforts to finding effective, alternative therapeutic strategies.

Abstract

In this review, the mechanobiology of colorectal cancer (CRC) are discussed. Mechanotransduction of CRC is addressed considering the relationship of several biophysical cues and biochemical pathways. Mechanobiology is focused on considering how it may influence epithelial cells in terms of motility, morphometric changes, intravasation, circulation, extravasation, and metastization in CRC development. The roles of the tumor microenvironment, ECM, and stroma are also discussed, taking into account the influence of alterations and surface modifications on mechanical properties and their impact on epithelial cells and CRC progression. The role of cancer-associated fibroblasts and the impact of flow shear stress is addressed in terms of how it affects CRC metastization. Finally, some insights concerning how the knowledge of biophysical mechanisms may contribute to the development of new therapeutic strategies and targeting molecules and how mechanical changes of the microenvironment play a role in CRC disease are presented.

DNA methylation-based diagnostic, prognostic, and predictive biomarkers in colorectal cancer

DNA methylation is an epigenetic mechanism regulating gene expression. Changes in DNA methylation were suggested to be useful biomarkers for diagnosis, and for the determination of prognosis and treatment response. Here, we provide an overview of methylation-based biomarkers in colorectal cancer. First, we start with the two methylation-based diagnostic biomarkers already approved for colorectal cancer, SEPT9 and the combination of NDRG4 and BMP3. Then, we provide a list-based overview of new biomarker candidates depending on the sample source including plasma, stool, urine, and surgically removed tumor tissues. The most often identified markers like SDC2, VIM, APC, MGMT, SFRP1, SFRP2, and NDRG4 have distinct functions previously linked to tumor progression. Although numerous studies have identified tumor-specific methylation changes, most of these alterations were observed in a single study only. The lack of validation in independent samples means low reproducibility and is a major limitation. The genome-wide determination of methylation status (methylome) can provide data to solve these issues. In the third section of the review, methylome studies focusing on different aspects related to CRC, including precancerous lesions, CRC-specific changes, molecular subtypes, aging, and chemotherapy response are summarized. Notably, techniques simultaneously analyzing a large set of regions can also uncover epigenetic regulation of genes which have not yet been associated with tumorigenesis previously. A remaining constraint of studies published to date is the low patient number utilized in these preventing the identification of clinically valuable biomarker candidates. Either future large-scale studies or the integration of already available methylome-level data will be necessary to uncover biomarkers sufficiently robust for clinical application.

Integrating Gemcitabine-Based Therapy With AdipoRon Enhances Growth Inhibition in Human PDAC Cell Lines

Pancreatic ductal adenocarcinoma (PDAC) accounts for 90% of all pancreatic cancers. Albeit its incidence does not score among the highest in cancer, PDAC prognosis is tremendously fatal. As a result of either aggressiveness or metastatic stage at diagnosis, chemotherapy constitutes the only marginally effective therapeutic approach. As gemcitabine (Gem) is still the cornerstone for PDAC management, the low response rate and the onset of resistant mechanisms claim for additional therapeutic strategies. The first synthetic orally active adiponectin receptor agonist AdipoRon (AdipoR) has recently been proposed as an anticancer agent in several tumors, including PDAC. To further address the AdipoR therapeutic potential, herein we investigated its pharmacodynamic interaction with Gem in human PDAC cell lines. Surprisingly, their simultaneous administration revealed a more effective action in contrasting PDAC cell growth and limiting clonogenic potential than single ones. Moreover, the combination AdipoR plus Gem persisted in being effective even in Gem-resistant MIA PaCa-2 cells. While a different ability in braking cell cycle progression between AdipoR and Gem supported their cooperating features in PDAC, mechanistically, PD98059-mediated p44/42 MAPK ablation hindered combination effectiveness. Taken together, our findings propose AdipoR as a suitable partner in Gem-based therapy and recognize the p44/42 MAPK pathway as potentially involved in combination outcomes.

PBX4 functions as a potential novel oncopromoter in colorectal cancer: a comprehensive analysis of the PBX gene family

Pre-B-cell leukaemia (PBX) is a transcription factor family (PBX1, PBX2, PBX3 and PBX4) that regulates important cellular functions and has been identified to be involved in human cancers. This study aimed to explore the expression of PBX genes and their clinical significance in colorectal cancer (CRC). We analysed the differential expression of PBX genes in CRC vs. normal tissue, using the Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/) and ONCOMINE platform (https://www.oncomine.org/). The UALCAN (http://ualcan.path.uab.edu/) interactive OMICS web-server was used to evaluate the epigenetic regulation of PBX genes via their promoter methylation status. We found that only PBX4 was upregulated whereas PBX1 and PBX3 were downregulated (644 tumour vs. 51 normal samples) (P<0.001). The methylation status of PBX4 promoter appeared to be decreased (P=1.4e-07) whereas the methylation status of PBX1 and PBX3 promoters was increased (P=3.8e-04 and P=3.2e-07, respectively) in cancer vs. normal samples. To determine the prognostic value of PBXs, we conducted a Kaplan-Meier survival analysis and multivariable COX regression. We observed that high PBX4 expression was associated with increased risk for a worse overall survival (OS) in the TCGA CRC patient cohort (n=639), (HR 1.46, 95% CI 1.14-1.88, P=0.003) adjusted for age, gender, tumour location and metastases. We conducted in vitro gene expression modulation experiments to investigate the impact of PBX4 overexpression in CRC cell (HCT116) growth. Additionally, we evaluated the RNA expression of epithelial-mesenchymal transition (EMT) and angiogenesis markers. In vitro studies showed that PBX4 overexpression increased CRC cell proliferation (P<0.001) and upregulated the expression of EMT markers VIM, CDH1, CDH2, ZEB1, SNAI1 (P<0.05) and angiomarker VEGFA (P<0.0001). Lastly, through the Cistrome data browser (http://dbtoolkit.cistrome.org/) we investigated putative transcriptional regulators and we performed gene set enrichment analysis in Enrichr server (https://maayanlab.cloud/Enrichr/) to identify related biological processes. Nineteen factors were identified to be putative regulators of PBX4 and gene set enrichment analysis showed that biological processes related to cell cycle and cell proliferation were enriched (GO:0051726: CDK8, JUN, JUND, and IRF1, P=0.001). In conclusion, our study identified PBX4 as a potential novel oncopromoter in CRC and its overexpression was found to be associated with increased risk for worse survival rate.

HOXB9 Overexpression Promotes Colorectal Cancer Progression and Is Associated with Worse Survival in Liver Resection Patients for Colorectal Liver Metastases

As is known, HOXB9 is an important factor affecting disease progression and overall survival (OS) in cancer. However, its role in colorectal cancer (CRC) remains unclear. We aimed to explore the role of HOXB9 in CRC progression and its association with OS in colorectal liver metastases (CRLM). We analysed differential HOXB9 expression in CRC using the Tissue Cancer Genome Atlas database (TCGA). We modulated HOXB9 expression in vitro to assess its impact on cell proliferation and epithelial-mesenchymal transition (EMT). Lastly, we explored the association of HOXB9 protein expression with OS, using an institutional patient cohort (n = 110) who underwent liver resection for CRLM. Furthermore, HOXB9 was upregulated in TCGA-CRC (n = 644) vs. normal tissue (n = 51) and its expression levels were elevated in KRAS mutations (p < 0.0001). In vitro, HOXB9 overexpression increased cell proliferation (p < 0.001) and upregulated the mRNA expression of EMT markers (VIM, CDH2, ZEB1, ZEB2, SNAI1 and SNAI2) while downregulated CDH1, (p < 0.05 for all comparisons). Conversely, HOXB9 silencing disrupted cell growth (p < 0.0001). High HOXB9 expression (HR = 3.82, 95% CI: 1.59-9.2, p = 0.003) was independently associated with worse OS in CRLM-HOXB9-expressing patients after liver resection. In conclusion, HOXB9 may be associated with worse OS in CRLM and may promote CRC progression, whereas HOXB9 silencing may inhibit CRC growth.

Links Between N6-Methyladenosine and Tumor Microenvironments in Colorectal Cancer

N⁶-methyladenosine (m⁶A) is a critical epigenetic modification for tumor malignancies, but its role in regulating the tumor microenvironments (TMEs) has not been fully studied. By integrating multiple data sets and multi-omics data, we comprehensively evaluated the m⁶A “writers,” “erasers,” and “readers” in colorectal cancer and their association with TME characteristics. The m⁶A regulator genes showed specific patterns in co-mutation, copy number variation, and expression. Based on the transcriptomic data of the m⁶A regulators and their correlated genes, two types of subtyping systems, m⁶A_regCluster and m⁶A_sigCluster, were developed. The clusters were distinct in pathways (metabolism/inflammation/extracellular matrix and interaction), immune phenotypes (immune-excluded/immune-inflamed/immune-suppressive), TME cell composition (lack immune and stromal cells/activated immune cells/stromal and immune-suppressive cells), stroma activities, and survival outcomes. We also established an m⁶Ascore associated with molecular subgroups, microsatellite instability, DNA repair status, mutation burdens, and survival and predicted immunotherapy outcomes. In conclusion, our work revealed a close association between m⁶A modification and TME formation. Evaluating m⁶A in cancer has helped us comprehend the TME status, and targeting m⁶A in tumor cells might help modulate the TME and improve tumor therapy and immunotherapy.