Pan-cancer methylome analysis for cancer diagnosis and classification of cancer cell of origin

Decoding the DNA methylome of central nervous system tumors: An emerging modality for integrated diagnosis

The definitive diagnosis and classification of individual cancers are crucial for patient care and cancer research. To achieve a robust diagnosis of central nervous system (CNS) tumors, a genotype-phenotype integrated diagnostic approach was introduced in recent versions of the World Health Organization classification, followed by the incorporation of a genome-wide DNA methylome-based classification. Microarray-based platforms are widely used to obtain DNA methylome data, and the German Cancer Research Center (Deutsches Krebsforschungszentrum [DKFZ]) has a webtool for a DNA methylation-based classifier (DKFZ classifier). Integration of DNA methylome will further enhance the precision of CNS tumor classification, especially in diagnostically challenging cases. However, in the clinical application of DNA methylome-based classification, challenges related to data interpretation persist, in addition to technical caveats, regulations, and limited accessibility. Dimensionality reduction (DMR) can complement integrated diagnosis by visualizing a profile and comparing it with other known samples. Therefore, DNA methylome-based classification is a highly useful research tool for auxiliary analysis in challenging diagnostic and rare disease cases, and for establishing novel tumor concepts. Decoding the DNA methylome, especially by DMR in addition to DKFZ classifier, emphasizes the capability of grasping the fundamental biological principles that provide new perspectives on CNS tumors.

Hierarchical classification-based pan-cancer methylation analysis to classify primary cancer

Hierarchical classification offers a more specific categorization of data and breaks down large classification problems into subproblems, providing improved prediction accuracy and predictive power for undefined categories, while also mitigating the impact of poor-quality data. Despite these advantages, its application in predicting primary cancer is rare. To leverage the similarity of cancers and the specificity of methylation patterns among them, we developed the Cancer Hierarchy Classification Tool (CHCT) using the idea of hierarchical classification, with methylation data from 30 cancer types and 8239 methylome samples downloaded from publicly available databases (The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO)). We used unsupervised clustering to divide the classification subproblems and screened differentially methylated sites using Analysis of variance (ANOVA) test, Tukey-kramer test, and Boruta algorithms to construct models for each classifier module. After validation, CHCT accurately classified 1568 out of 1660 cases in the test set, with an average accuracy of 94.46%. We further curated an independent validation cohort of 677 cancer samples from GEO and assigned a diagnosis using CHCT, which showed high diagnostic potential with generally high accuracies (an average accuracy of 91.40%). Moreover, CHCT demonstrates predictive capability for additional cancer types beyond its original classifier scope as demonstrated in the medulloblastoma and pituitary tumor datasets. In summary, CHCT can hierarchically classify primary cancer by methylation profile, by splitting a large-scale classification of 30 cancer types into ten smaller classification problems. These results indicate that cancer hierarchical classification has the potential to be an accurate and robust cancer classification method.

Recommendations for Cell-Free DNA Assay Validations: A Joint Consensus Recommendation of the Association for Molecular Pathology and College of American Pathologists

Diagnosing, selecting therapy for, and monitoring cancer in patients using a minimally invasive blood test represents a significant advance in precision medicine. Wide variability exists in how circulating tumor DNA (ctDNA) assays are developed, validated, and reported in the literature, which hinders clinical adoption and may negatively impact patient care. Standardization is needed for factors affecting ctDNA assay performance and reporting, including pre-analytical variables, analytical considerations, and elements of laboratory assay reporting. The Association for Molecular Pathology Clinical Practice Committee's Liquid Biopsy Working Group (LBxWG), including organizational representation from the American Society of Clinical Oncology and the College of American Pathologists, has undertaken a full-text data extraction of 1228 ctDNA publications that describe assays performed in patients with lymphoma and solid tumor malignancies. With an emphasis on clinical assay validation, the LBxWG has developed a set of 13 best practice consensus recommendations for validating, reporting, and publishing clinical ctDNA assays. Recommendations include reporting key pre-analytical considerations and assay performance metrics; this analysis demonstrates these elements are inconsistently included in publications. The LBxWG recommendations are intended to assist clinical laboratories with validating and reporting ctDNA assays and to ensure high-quality data are included in publications. It is expected that these recommendations will need to be updated as the body of literature continues to mature.

Current status of molecular diagnostic approaches using liquid biopsy

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive and lethal cancers, and developing an efficient and reliable approach for its early-stage diagnosis is urgently needed. Precancerous lesions of PDAC, such as pancreatic intraepithelial neoplasia (PanIN) and intraductal papillary mucinous neoplasms (IPMN), arise through multiple steps of driver gene alterations in KRAS, TP53, CDKN2A, SMAD4, or GNAS. Hallmark mutations play a role in tumor initiation and progression, and their detection in bodily fluids is crucial for diagnosis. Recently, liquid biopsy has gained attention as an approach to complement pathological diagnosis, and in addition to mutation signatures in cell-free DNA, cell-free RNA, and extracellular vesicles have been investigated as potential diagnostic and prognostic markers. Integrating such molecular information to revise the diagnostic criteria for pancreatic cancer can enable a better understanding of the pathogenesis underlying inter-patient heterogeneity, such as sensitivity to chemotherapy and disease outcomes. This review discusses the current diagnostic approaches and clinical applications of genetic analysis in pancreatic cancer and diagnostic attempts by liquid biopsy and molecular analyses using pancreatic juice, duodenal fluid, and blood samples. Emerging knowledge in the rapidly advancing liquid biopsy field is promising for molecular profiling and diagnosing pancreatic diseases with significant diversity.

Site-specific protein biomarkers in gastric cancer: a comprehensive review of novel biomarkers and clinical applications

INTRODUCTION

Gastric cancer (GC) is the fifth most common cancer and the fourth leading cause of cancer-related death worldwide, thus representing a significant global health burden. Early detection and monitoring of GC are essential to improve patient outcomes. While traditional cancer biomarkers such as carcinoembryonic antigen, carbohydrate antigen (CA) 19-9, and CA 72-4 are widely used, their limited sensitivity and specificity necessitate the exploration of alternative biomarkers.

AREAS COVERED

This review comprehensively analyzes the landscape of GC protein biomarkers identified from 2019 to 2022, with a focus on tissue, blood, urine, saliva, gastric juice, ascites, and exhaled breath as sample sources. We address the potential clinical applications of these biomarkers in early diagnosis, monitoring recurrence, and predicting survival and therapeutic response of GC patients.

EXPERT OPINION

The discovery of novel protein biomarkers holds great promise for improving the clinical management of GC. However, further validation in large, diverse cohorts is needed to establish the clinical utility of these biomarkers. Integrating these biomarkers with existing diagnostic and monitoring approaches will likely lead to improved personalized treatment plans and patient outcomes.

Identification of a novel glycolysis-related prognosis risk signature in triple-negative breast cancer

Introduction

Triple-negative breast cancer (TNBC) is a particularly aggressive cluster of breast cancer characterized by significant molecular heterogeneity. Glycolysis is a metabolic pathway that is significantly associated with cancer progression, metastasis, recurrence and chemoresistance. However, the potential roles of glycolysis-related genes in TNBC remain unclear.

Methods

In the present study, we identified 108 glycolysis-related differentially expressed genes (DEGs) between breast cancer (BRCA) tumor tissues and normal tissues, and we divided patients into two different clusters with significantly distinct molecular characteristics, clinicopathological features, prognosis, immune cell infiltration and mutation burden. We then constructed a 10-gene signature that classified all TNBCs into low- and high-risk groups.

Results

The high-risk group had significantly lower survival than the low-risk group, which implied that the risk score was an independent prognostic indicator for TNBC patients. Consequently, we constructed and validated a prognostic nomogram, which accurately predicted individual overall survival (OS) of TNBC. Moreover, the risk score predicted the drug sensitivity of chemotherapeutic agents and immunotherapy for TNBC patients.

Discussion

The present comprehensive analysis of glycolysis-related DEGs in TNBC provides new methods for prognosis prediction and more effective treatment strategies.

Shifting the Cancer Screening Paradigm: The Rising Potential of Blood-Based Multi-Cancer Early Detection Tests

Cancer remains a leading cause of death worldwide, partly owing to late detection which entails limited and often ineffective therapeutic options. Most cancers lack validated screening procedures, and the ones available disclose several drawbacks, leading to low patient compliance and unnecessary workups, adding up the costs to healthcare systems. Hence, there is a great need for innovative, accurate, and minimally invasive tools for early cancer detection. In recent years, multi-cancer early detection (MCED) tests emerged as a promising screening tool, combining molecular analysis of tumor-related markers present in body fluids with artificial intelligence to simultaneously detect a variety of cancers and further discriminate the underlying cancer type. Herein, we aim to provide a highlight of the variety of strategies currently under development concerning MCED, as well as the major factors which are preventing clinical implementation. Although MCED tests depict great potential for clinical application, large-scale clinical validation studies are still lacking.

Cell-of-Origin Targeted Drug Repurposing for Triple-Negative and Inflammatory Breast Carcinoma with HDAC and HSP90 Inhibitors Combined with Niclosamide

We recently identified a cell-of-origin-specific mRNA signature associated with metastasis and poor outcome in triple-negative carcinoma (TNBC). This TNBC cell-of-origin signature is associated with the over-expression of histone deacetylases and zinc finger protein HDAC1, HDAC7, and ZNF92, respectively. Based on this signature, we discovered that the combination of three drugs (an HDAC inhibitor, an anti-helminthic Niclosamide, and an antibiotic Tanespimycin that inhibits HSP90) synergistically reduces the proliferation of the twelve tested TNBC cell lines. Additionally, we discovered that four out of five inflammatory breast carcinoma cell lines are sensitive to this combination. Significantly, the concentration of the drugs that are used in these experiments are within or below clinically achievable dose, and the synergistic activity only emerged when all three drugs were combined. Our results suggest that HDAC and HSP90 inhibitors combined with the tapeworm drug Niclosamide can achieve remarkably synergistic inhibition of TNBC and IBC. Since Niclosamide, HDAC, and HSP90 inhibitors were approved for clinical use for other cancer types, it may be possible to repurpose their combination for TNBC and IBC.

ZNF92, an unexplored transcription factor with remarkably distinct breast cancer over-expression associated with prognosis and cell-of-origin

Tumor phenotype is shaped both by transforming genomic alterations and the normal cell-of-origin. We identified a cell-of-origin associated prognostic gene expression signature, ET-9, that correlates with remarkably shorter overall and relapse free breast cancer survival, 8.7 and 6.2 years respectively. The genes associated with the ET-9 signature are regulated by histone deacetylase 7 (HDAC7) partly through ZNF92, a previously unexplored transcription factor with a single PubMed citation since its cloning in 1990s. Remarkably, ZNF92 is distinctively over-expressed in breast cancer compared to other tumor types, on a par with the breast cancer specificity of the estrogen receptor. Importantly, ET-9 signature appears to be independent of proliferation, and correlates with outcome in lymph-node positive, HER2+, post-chemotherapy and triple-negative breast cancers. These features distinguish ET-9 from existing breast cancer prognostic signatures that are generally related to proliferation and correlate with outcome in lymph-node negative, ER-positive, HER2-negative breast cancers. Our results suggest that ET-9 could be also utilized as a predictive signature to select patients for HDAC inhibitor treatment.

Cardio-onco-metabolism: metabolic remodelling in cardiovascular disease and cancer

Cardiovascular disease and cancer are the two leading causes of morbidity and mortality in the world. The emerging field of cardio-oncology has revealed that these seemingly disparate disease processes are intertwined, owing to the cardiovascular sequelae of anticancer therapies, shared risk factors that predispose individuals to both cardiovascular disease and cancer, as well the possible potentiation of cancer growth by cardiac dysfunction. As a result, interest has increased in understanding the fundamental biological mechanisms that are central to the relationship between cardiovascular disease and cancer. Metabolism, appropriate regulation of energy, energy substrate utilization, and macromolecular synthesis and breakdown are fundamental processes for cellular and organismal survival. In this Review, we explore the emerging data identifying metabolic dysregulation as an important theme in cardio-oncology. We discuss the growing recognition of metabolic reprogramming in cardiovascular disease and cancer and view the novel area of cardio-oncology through the lens of metabolism.