Liquid biopsy epigenomic profiling for cancer subtyping

CircRNAs in cancer therapy tolerance

The rapidly expanding field of circular RNA (circ-RNA) research has opened new avenues in cancer diagnostics and treatment, highlighting the role of serum circRNAs as potential biomarkers for assessing tumor therapy resistance. This review comprehensively compiles existing knowledge regarding the biogenesis, function, and clinical relevance of circRNAs, emphasizing their stability, abundance, and cell type-specific expression profiles, which make them ideal candidates for noninvasive early biomarkers in cancer treatment. We explored the roles of circRNAs in oncogenesis and tumor progression and their complex interactions with patient responses to various cancer treatments, such as chemotherapy, radiotherapy, targeted therapy, and immunotherapy. Through the analysis of data from recent studies and clinical trials, we underscore the prognostic significance of serum circRNAs in predicting therapeutic outcomes, their involvement in resistance mechanisms, and their capacity to inform personalized treatment approaches. Additionally, this review addresses the obstacles inherent in circRNA research, including the need for standardized protocols for circRNA extraction and quantification and the elucidation of the clinical significance of circRNAs. Furthermore, our investigation extends to future prospects, including embedding circRNA profiling into regular clinical workflows and pioneering circRNA-based therapeutic approaches. We underscore the transformative potential of serum circRNAs in enhancing cancer diagnosis, improving the accuracy of therapy tolerance predictions, and ultimately fostering the advent of precision oncology.

Landscape of HER2-low breast cancer: Insights from a six-year study on prevalence and clinicopathological characteristics

Human epidermal growth factor receptor 2 (HER2)-low breast cancer has emerged as a subtype of breast cancer, defined by HER2 1+/2+ in immunohistochemistry (IHC) and absence of ERBB2 gene amplification on fluorescence in situ hybridization (FISH). Recent trials showed marked response of HER2-low breast cancer to novel anti-HER2 antibody-drug-conjugates. Data on characteristics of HER2-low breast cancer subtype is limited. Real-world data from the Anatomic Pathology Department of Hotel-Dieu de France, spanning 2017-2023, was retrospectively collected. HER2-positive patients were excluded to compare HER2-low to HER2-zero breast cancer subtypes. Clinicopathological characteristics between the groups were compared using a Chi-Squared test. Out of 1195 patients, we observed 341 (28.5 %) HER2-low breast cancers cases. HER2-positive breast cancer cases (n = 178; 14.9 %) were excluded. There was no significant difference in age and sex between HER2-low and HER2-zero group (p = 0.33 and 0.79, respectively). HER2-low breast cancer was associated with positive estrogen receptor status and positive progesterone receptor status (p < 0.001 and p = 0.01, respectively). Ductal adenocarcinomas were more commonly observed in HER2-low group (p < 0.001). When stratified by hormone (HR) status, 87.4 % of patients had HR-positive status and 12.6 % were HR-negative. Among the HR-negative group, HER2-low tumors tended to show lower proliferation index compared to HER2-zero tumors (25%vs.10 %, p = 0.04). This study showed that HER2-low is distinct from HER2-zero and is common among patients with breast cancer. Clinicopathological features such as histological type differ between HER2-zero and HER2-low breast cancer. Within HR-negative breast cancer, those with low HER2 expression exhibit a less aggressive profile compared to HER2-zero tumors.

Selective epigenetic alterations in RNF43 in pancreatic exocrine cells from high-fat-diet-induced obese mice; implications for pancreatic cancer

OBJECTIVE

Pancreatic cancer (PC) originates and progresses with genetic mutations in various oncogenes and suppressor genes, notably KRAS, CDKN2A, TP53, and SMAD4, prevalent across diverse PC cells. In addition to genetic mutations/deletions, persistent exposure to high-risk factors, including obesity, induces whole-genome scale epigenetic alterations contributing to malignancy. However, the impact of obesity on DNA methylation in the presymptomatic stage, particularly in genes prone to PC mutation, remains uncharacterized.

RESULTS

We analyzed the methylation levels of 197 loci in six genes (KRAS, CDKN2A, TP53, SMAD4, GNAS and RNF43) using Illumina Mouse Methylation BeadChip array (280 K) data from pancreatic exocrine cells obtained from high-fat-diet (HFD) induced obese mice. Results revealed no significant differences in methylation levels in loci between HFD- and normal-fat-diet (NFD)-fed mice, except for RNF43, a negative regulator of Wnt signaling, which showed hypermethylation in three loci. These findings indicate that, in mouse pancreatic exocrine cells, high-fat dietary obesity induced aberrant DNA methylation in RNF43 but not in other frequently mutated PC-related genes.

Proximity-Guaranteed DNA Machine for Accurate Identification of Breast Cancer Extracellular Vesicles

Breast cancer is one of the most diagnosed cancers worldwide. Precise diagnosis and subtyping have important significance for targeted therapy and prognosis prediction of breast cancer. Herein, we design a proximity-guaranteed DNA machine for accurate identification of breast cancer extracellular vesicles (EVs), which is beneficial to explore the subtype features of breast cancer. In our design, two proximity probes are located close on the same EV through specific recognition of coexisting surface biomarkers, thus being ligated with the help of click chemistry. Then, the ligated product initiates the operation of a DNA machine involving catalytic hairpin assembly and clusters of regularly interspaced short palindromic repeats (CRISPR)-Cas12a-mediated trans-cleavage, which finally generates a significant response that enables the identification of EVs expressing both biomarkers. Principle-of-proof studies are performed using EVs derived from the breast cancer cell line BT474 as the models, confirming the high sensitivity and specificity of the DNA machine. When further applied to clinical samples, the DNA machine is shown to be capable of not only distinguishing breast cancer patients with special subtypes but also realizing the tumor staging regarding the disease progression. Therefore, our work may provide new insights into the subtype-based diagnosis of breast cancer as well as identification of more potential therapeutic targets in the future.

Tissue informative cell-free DNA methylation sites in amyotrophic lateral sclerosis

Cell-free DNA (cfDNA) is increasingly recognized as a promising biomarker candidate for disease monitoring. However, its utility in neurodegenerative diseases, like amyotrophic lateral sclerosis (ALS), remains underexplored. Existing biomarker discovery approaches are tailored to a specific disease context or are too expensive to be clinically practical. Here, we address these challenges through a new approach combining advances in molecular and computational technologies. First, we develop statistical tools to select tissue-informative DNA methylation sites relevant to a disease process of interest. We then employ a capture protocol to select these sites and perform targeted methylation sequencing. Multi-modal information about the DNA methylation patterns are then utilized in machine learning algorithms trained to predict disease status and disease progression. We applied our method to two independent cohorts of ALS patients and controls (n=192). Overall, we found that the targeted sites accurately predicted ALS status and replicated between cohorts. Additionally, we identified epigenetic features associated with ALS phenotypes, including disease severity. These findings highlight the potential of cfDNA as a non-invasive biomarker for ALS.

Uncovering dark matter in cancer by identifying epigenetic drivers

The complex relationship between chromatin accessibility, transcriptional regulation, and cancer transitions presents a daunting puzzle. Terekhanova et al. created a pan-cancer epigenetic and transcriptomic atlas at single-cell resolution, yielding important insights into the underlying chromatin architecture of cancer transitions and novel discoveries with the potential to advance precision medicine.

Noninvasive Detection of Neuroendocrine Prostate Cancer through Targeted Cell-free DNA Methylation

Castration-resistant prostate cancer (CRPC) is a heterogeneous disease associated with phenotypic subtypes that drive therapy response and outcome differences. Histologic transformation to castration-resistant neuroendocrine prostate cancer (CRPC-NE) is associated with distinct epigenetic alterations, including changes in DNA methylation. The current diagnosis of CRPC-NE is challenging and relies on metastatic biopsy. We developed a targeted DNA methylation assay to detect CRPC-NE using plasma cell-free DNA (cfDNA). The assay quantifies tumor content and provides a phenotype evidence score that captures diverse CRPC phenotypes, leveraging regions to inform transcriptional state. We tested the design in independent clinical cohorts (n = 222 plasma samples) and qualified it achieving an AUC > 0.93 for detecting pathology-confirmed CRPC-NE (n = 136). Methylation-defined cfDNA tumor content was associated with clinical outcomes in two prospective phase II clinical trials geared towards aggressive variant CRPC and CRPC-NE. These data support the application of targeted DNA methylation for CRPC-NE detection and patient stratification.

SIGNIFICANCE

Neuroendocrine prostate cancer is an aggressive subtype of treatment-resistant prostate cancer. Early detection is important, but the diagnosis currently relies on metastatic biopsy. We describe the development and validation of a plasma cell-free DNA targeted methylation panel that can quantify tumor fraction and identify patients with neuroendocrine prostate cancer noninvasively. This article is featured in Selected Articles from This Issue, p. 384.

Plasma Cell-Free Tumor Methylome as a Biomarker in Solid Tumors: Biology and Applications

DNA methylation is a fundamental mechanism of epigenetic control in cells and its dysregulation is strongly implicated in cancer development. Cancers possess an extensively hypomethylated genome with focal regions of hypermethylation at CPG islands. Due to the highly conserved nature of cancer-specific methylation, its detection in cell-free DNA in plasma using liquid biopsies constitutes an area of interest in biomarker research. The advent of next-generation sequencing and newer computational technologies have allowed for the development of diagnostic and prognostic biomarkers that utilize methylation profiling to diagnose disease and stratify risk. Methylome-based predictive biomarkers can determine the response to anti-cancer therapy. An additional emerging application of these biomarkers is in minimal residual disease monitoring. Several key challenges need to be addressed before cfDNA-based methylation biomarkers become fully integrated into practice. The first relates to the biology and stability of cfDNA. The second concerns the clinical validity and generalizability of methylation-based assays, many of which are cancer type-specific. The third involves their practicability, which is a stumbling block for translating technologies from bench to clinic. Future work on developing pan-cancer assays with their respective validities confirmed using well-designed, prospective clinical trials is crucial in pushing for the greater use of these tools in oncology.

Update on Biomarkers in Renal Cell Carcinoma

Immune checkpoint inhibitors have significantly transformed the treatment paradigm for metastatic renal cell carcinoma (RCC), offering prolonged overall survival and achieving remarkable deep and durable responses. However, given the multiple ICI-containing, standard-of-care regimens approved for RCC, identifying biomarkers that predict therapeutic response and resistance is of critical importance. Although tumor-intrinsic features such as pathological characteristics, genomic alterations, and transcriptional signatures have been extensively investigated, they have yet to provide definitive, robust predictive biomarkers. Current research is exploring host factors through in-depth characterization of the immune system. Additionally, innovative technological approaches are being developed to overcome challenges presented by existing techniques, such as tumor heterogeneity. Promising avenues in biomarker discovery include the study of the microbiome, radiomics, and spatial transcriptomics.