Multimodal analysis of genome-wide methylation, copy number aberrations, and end motif signatures enhances detection of early-stage breast cancer

DNA methylation in breast cancer: early detection and biomarker discovery through current and emerging approaches

Breast cancer remains one of the most common cancers in women worldwide. Early detection is critical for improving patient outcomes, yet current screening methods have limitations. Therefore, there is a pressing need for more sensitive and specific approaches to detect breast cancer in its earliest stages. Liquid biopsy has emerged as a promising non-invasive method for early cancer detection and management. DNA methylation, an epigenetic alteration that often precedes genetic changes, has been observed in precancerous or early cancer stages, making it a valuable biomarker. This review explores the role of DNA methylation in breast cancer and its potential for developing blood-based tests. We discuss advancements in DNA methylation detection methods, recent discoveries of potential DNA methylation biomarkers from both single-omics and multi-omics integration studies, and the role of machine learning in enhancing diagnostic accuracy. Challenges and future directions are also addressed. Although challenges remain, advances in multi-omics integration and machine learning continue to enhance the clinical potential of methylation-based biomarkers. Ongoing research is crucial to further refine these approaches and improve early detection and patient outcomes.

Artificial intelligence utilization in cancer screening program across ASEAN: a scoping review

BACKGROUND

Cancer remains a significant health challenge in the ASEAN region, highlighting the need for effective screening programs. However, approaches, target demographics, and intervals vary across ASEAN member states, necessitating a comprehensive understanding of these variations to assess program effectiveness. Additionally, while artificial intelligence (AI) holds promise as a tool for cancer screening, its utilization in the ASEAN region is unexplored.

PURPOSE

This study aims to identify and evaluate different cancer screening programs across ASEAN, with a focus on assessing the integration and impact of AI in these programs.

METHODS

A scoping review was conducted using PRISMA-ScR guidelines to provide a comprehensive overview of cancer screening programs and AI usage across ASEAN. Data were collected from government health ministries, official guidelines, literature databases, and relevant documents. The use of AI in cancer screening reviews involved searches through PubMed, Scopus, and Google Scholar with the inclusion criteria of only included studies that utilized data from the ASEAN region from January 2019 to May 2024.

RESULTS

The findings reveal diverse cancer screening approaches in ASEAN. Countries like Myanmar, Laos, Cambodia, Vietnam, Brunei, Philippines, Indonesia and Timor-Leste primarily adopt opportunistic screening, while Singapore, Malaysia, and Thailand focus on organized programs. Cervical cancer screening is widespread, using both opportunistic and organized methods. Fourteen studies were included in the scoping review, covering breast (5 studies), cervical (2 studies), colon (4 studies), hepatic (1 study), lung (1 study), and oral (1 study) cancers. Studies revealed that different stages of AI integration for cancer screening: prospective clinical evaluation (50%), silent trial (36%) and exploratory model development (14%), with promising results in enhancing cancer screening accuracy and efficiency.

CONCLUSION

Cancer screening programs in the ASEAN region require more organized approaches targeting appropriate age groups at regular intervals to meet the WHO's 2030 screening targets. Efforts to integrate AI in Singapore, Malaysia, Vietnam, Thailand, and Indonesia show promise in optimizing screening processes, reducing costs, and improving early detection. AI technology integration enhances cancer identification accuracy during screening, improving early detection and cancer management across the ASEAN region.

Prospective validation study: a non-invasive circulating tumor DNA-based assay for simultaneous early detection of multiple cancers in asymptomatic adults

BACKGROUND

Non-invasive multi-cancer early detection (MCED) tests have shown promise in enhancing early cancer detection. However, their clinical utility across diverse populations remains underexplored, limiting their routine implementation. This study aims to validate the clinical utility of a multimodal non-invasive circulating tumor DNA (ctDNA)-based MCED test, SPOT-MAS (Screening for the Presence Of Tumor by DNA Methylation And Size).

METHODS

We conducted a multicenter prospective study, K-DETEK (ClinicalTrials.gov identifier: NCT05227261), involving 9057 asymptomatic individuals aged 40 years or older across 75 major hospitals and one research institute in Vietnam. Participants were followed for 12 months.

RESULTS

Of the 9024 eligible participants, 43 (0.48%) tested positive for ctDNA. Among these, 17 were confirmed with malignant lesions in various primary organs through standard-of-care (SOC) imaging and biopsy, with 9 cases matching our tissue of origin (TOO) predictions. This resulted in a positive predictive value of 39.53% (95%CI 26.37-54.42) and a TOO accuracy of 52.94% (95%CI 30.96-73.83). Among the 8981 participants (99.52%) who tested negative, 8974 were confirmed cancer-free during a 12-month period after testing, yielding a negative predictive value of 99.92% (95% CI 99.84-99.96). The test demonstrated an overall sensitivity of 70.83% (95%CI 50.83-85.09) and a specificity of 99.71% (95% CI 99.58-99.80) for detecting various cancer types, including those without SOC screening options.

CONCLUSIONS

This study presents a prospective validation of a multi-cancer early detection (MCED) test conducted in a lower middle-income country, demonstrating the potential of SPOT-MAS for early cancer detection. Our findings indicate that MCED tests could be valuable additions to national cancer screening programs, particularly in regions where such initiatives are currently limited.

TRIAL REGISTRATION

ClinicalTrials.gov ID: NCT05227261. Date of registration: 07/02/2022.

Epigenetic modifications of cfDNA in liquid biopsy for the cancer care continuum

This review provides a comprehensive overview of the latest advancements in the clinical utility of liquid biopsy, with a particular focus on epigenetic approaches aimed at overcoming challenges in cancer diagnosis and treatment. It begins by elucidating key epigenetic terms, including methylomics, fragmentomics, and nucleosomics. The review progresses to discuss methods for analyzing circulating cell-free DNA (cfDNA) and highlights recent studies showcasing the clinical relevance of epigenetic modifications in areas such as diagnosis, drug treatment response, minimal residual disease (MRD) detection, and prognosis prediction. While acknowledging hurdles like the complexity of interpreting epigenetic data and the absence of standardization, the review charts a path forward. It advocates for the integration of multi-omic data through machine learning algorithms to refine predictive models and stresses the importance of collaboration among clinicians, researchers, and data scientists. Such cooperative efforts are essential to fully leverage the potential of epigenetic features in clinical practice.

Cell-free epigenomes enhanced fragmentomics-based model for early detection of lung cancer

BACKGROUND

Lung cancer is a leading cause of cancer mortality, highlighting the need for innovative non-invasive early detection methods. Although cell-free DNA (cfDNA) analysis shows promise, its sensitivity in early-stage lung cancer patients remains a challenge. This study aimed to integrate insights from epigenetic modifications and fragmentomic features of cfDNA using machine learning to develop a more accurate lung cancer detection model.

METHODS

To address this issue, a multi-centre prospective cohort study was conducted, with participants harbouring suspicious malignant lung nodules and healthy volunteers recruited from two clinical centres. Plasma cfDNA was analysed for its epigenetic and fragmentomic profiles using chromatin immunoprecipitation sequencing, reduced representation bisulphite sequencing and low-pass whole-genome sequencing. Machine learning algorithms were then employed to integrate the multi-omics data, aiding in the development of a precise lung cancer detection model.

RESULTS

Cancer-related changes in cfDNA fragmentomics were significantly enriched in specific genes marked by cell-free epigenomes. A total of 609 genes were identified, and the corresponding cfDNA fragmentomic features were utilised to construct the ensemble model. This model achieved a sensitivity of 90.4% and a specificity of 83.1%, with an AUC of 0.94 in the independent validation set. Notably, the model demonstrated exceptional sensitivity for stage I lung cancer cases, achieving 95.1%. It also showed remarkable performance in detecting minimally invasive adenocarcinoma, with a sensitivity of 96.2%, highlighting its potential for early detection in clinical settings.

CONCLUSIONS

With feature selection guided by multiple epigenetic sequencing approaches, the cfDNA fragmentomics-based machine learning model demonstrated outstanding performance in the independent validation cohort. These findings highlight its potential as an effective non-invasive strategy for the early detection of lung cancer.

KEYPOINTS

Our study elucidated the regulatory relationships between epigenetic modifications and their effects on fragmentomic features. Identifying epigenetically regulated genes provided a critical foundation for developing the cfDNA fragmentomics-based machine learning model. The model demonstrated exceptional clinical performance, highlighting its substantial potential for translational application in clinical practice.

Artificial intelligence and machine learning in cell-free-DNA-based diagnostics

The discovery of circulating fetal and tumor cell-free DNA (cfDNA) molecules in plasma has opened up tremendous opportunities in noninvasive diagnostics such as the detection of fetal chromosomal aneuploidies and cancers and in posttransplantation monitoring. The advent of high-throughput sequencing technologies makes it possible to scrutinize the characteristics of cfDNA molecules, opening up the fields of cfDNA genetics, epigenetics, transcriptomics, and fragmentomics, providing a plethora of biomarkers. Machine learning (ML) and/or artificial intelligence (AI) technologies that are known for their ability to integrate high-dimensional features have recently been applied to the field of liquid biopsy. In this review, we highlight various AI and ML approaches in cfDNA-based diagnostics. We first introduce the biology of cell-free DNA and basic concepts of ML and AI technologies. We then discuss selected examples of ML- or AI-based applications in noninvasive prenatal testing and cancer liquid biopsy. These applications include the deduction of fetal DNA fraction, plasma DNA tissue mapping, and cancer detection and localization. Finally, we offer perspectives on the future direction of using ML and AI technologies to leverage cfDNA fragmentation patterns in terms of methylomic and transcriptional investigations.

A consultation and work-up diagnosis protocol for a multicancer early detection test: a case series study

The emergence of multicancer early detection (MCED) tests holds promise for improving early cancer detection and public health outcomes. However, positive MCED test results require confirmation through recommended cancer diagnostic imaging modalities. To address these challenges, we have developed a consultation and work-up protocol for definitive diagnostic results post MCED testing, named SPOT-MAS. Developed through circulating tumor DNA (ctDNA) analysis and in line with professional guidelines and advisory board consensus, this protocol standardizes information to aid general practitioners in accessing, interpreting and managing SPOT-MAS results. Clinical effectiveness is demonstrated through a series of identified cancer cases. Our research indicates that the protocol could empower healthcare professionals to confidently interpret circulating tumor DNA test results for 5 common types of cancer, thereby facilitating the clinical integration of MCED tests.

Tissue of origin detection for cancer tumor using low-depth cfDNA samples through combination of tumor-specific methylation atlas and genome-wide methylation density in graph convolutional neural networks

BACKGROUND

Cell free DNA (cfDNA)-based assays hold great potential in detecting early cancer signals yet determining the tissue-of-origin (TOO) for cancer signals remains a challenging task. Here, we investigated the contribution of a methylation atlas to TOO detection in low depth cfDNA samples.

METHODS

We constructed a tumor-specific methylation atlas (TSMA) using whole-genome bisulfite sequencing (WGBS) data from five types of tumor tissues (breast, colorectal, gastric, liver and lung cancer) and paired white blood cells (WBC). TSMA was used with a non-negative least square matrix factorization (NNLS) deconvolution algorithm to identify the abundance of tumor tissue types in a WGBS sample. We showed that TSMA worked well with tumor tissue but struggled with cfDNA samples due to the overwhelming amount of WBC-derived DNA. To construct a model for TOO, we adopted the multi-modal strategy and used as inputs the combination of deconvolution scores from TSMA with other features of cfDNA.

RESULTS

Our final model comprised of a graph convolutional neural network using deconvolution scores and genome-wide methylation density features, which achieved an accuracy of 69% in a held-out validation dataset of 239 low-depth cfDNA samples.

CONCLUSIONS

In conclusion, we have demonstrated that our TSMA in combination with other cfDNA features can improve TOO detection in low-depth cfDNA samples.

A comprehensive analysis of minimally differentially methylated regions common to pediatric and adult solid tumors

Cancer is the second most common cause of death in children aged 1-14 years in the United States, with 11,000 new cases and 1200 deaths annually. Pediatric cancers typically have lower mutational burden compared to adult-onset cancers, however, the epigenomes in pediatric cancer are highly altered, with widespread DNA methylation changes. The rarity of pediatric cancers poses a significant challenge to developing cancer-type specific biomarkers for diagnosis, prognosis, or treatment monitoring. In the current study, we explored the potential of a DNA methylation profile common across various pediatric cancers. To do this, we conducted whole genome bisulfite sequencing (WGBS) on 31 recurrent pediatric tumor tissues, 13 normal tissues, and 20 plasma cell-free (cf)DNA samples, representing 11 different pediatric cancer types. We defined minimal focal regions that were differentially methylated across samples in the multiple cancer types which we termed minimally differentially methylated regions (mDMRs). These methylation changes were also observed in 506 pediatric and 5691 adult cancer samples accessed from publicly available databases, and in 44 pediatric cancer samples we analyzed using a targeted hybridization probe capture assay. Finally, we found that these methylation changes were detectable in cfDNA and could serve as potential cfDNA methylation biomarkers for early detection or minimal residual disease.

Current Challenges of Methylation-Based Liquid Biopsies in Cancer Diagnostics

In current clinical practice, effective cancer testing and screening paradigms are limited to specific types of cancer, exhibiting varying efficiency, acceptance, and adherence. Cell-free DNA (cfDNA) methylation profiling holds promise in providing information about the presence of malignity regardless of its type and location while leveraging blood-based liquid biopsies as a method to obtain analytical samples. However, technical difficulties, costs and challenges resulting from biological variations, tumor heterogeneity, and exogenous factors persist. This method exploits the mechanisms behind cfDNA release but faces issues like fragmentation, low concentrations, and high background noise. This review explores cfDNA methylation's origins, means of detection, and profiling for cancer diagnostics. The critical evaluation of currently available multi-cancer early detection methods (MCEDs) as well as tests targeting single genes, emphasizing their potential and limits to refine strategies for early cancer detection, are explained. The current methodology limitations, workflows, comparisons of clinically approved liquid biopsy-based methylation tests for cancer, their utilization in companion diagnostics as well as the biological limitations of the epigenetics approach are discussed, aiming to help healthcare providers as well as researchers to orient themselves in this increasingly complex and evolving field of diagnostics.

Obesity-associated epigenetic alterations and the obesity-breast cancer axis

Both breast cancer and obesity can regulate epigenetic changes or be regulated by epigenetic changes. Due to the well-established link between obesity and an increased risk of developing breast cancer, understanding how obesity-mediated epigenetic changes affect breast cancer pathogenesis is critical. Researchers have described how obesity and breast cancer modulate the epigenome individually and synergistically. In this review, the epigenetic alterations that occur in obesity, including DNA methylation, histone, and chromatin modification, accelerated epigenetic age, carcinogenesis, metastasis, and tumor microenvironment modulation, are discussed. Delineating the relationship between obesity and epigenetic regulation is vital to furthering our understanding of breast cancer pathogenesis.

Circulating Tumor DNA Is a Variant of Liquid Biopsy with Predictive and Prognostic Clinical Value in Breast Cancer Patients

This paper introduces the reader to the field of liquid biopsies and cell-free nucleic acids, focusing on circulating tumor DNA (ctDNA) in breast cancer (BC). BC is the most common type of cancer in women, and progress with regard to treatment has been made in recent years. Despite this, there remain a number of unresolved issues in the treatment of BC; in particular, early detection and diagnosis, reliable markers of response to treatment and for the prediction of recurrence and metastasis, especially for unfavorable subtypes, are needed. It is also important to identify biomarkers for the assessment of drug resistance and for disease monitoring. Our work is devoted to ctDNA, which may be such a marker. Here, we describe its main characteristics and potential applications in clinical oncology. This review considers the results of studies devoted to the analysis of the prognostic and predictive roles of various methods for the determination of ctDNA in BC patients. Currently known epigenetic changes in ctDNA with clinical significance are reviewed. The possibility of using ctDNA as a predictive and prognostic marker for monitoring BC and predicting the recurrence and metastasis of cancer is also discussed, which may become an important part of a precision approach to the treatment of BC.

The utility of liquid biopsy in clinical genetic diagnosis of cancer and monogenic mosaic disorders

Liquid biopsy for minimally invasive diagnosis and monitoring of cancer patients is progressing toward routine clinical practice. With the implementation of highly sensitive next-generation sequencing (NGS) based assays for the analysis of cfDNA, however, consideration of the utility of liquid biopsy for clinical genetic testing is critical. While the focus of liquid biopsy for cancer diagnosis is the detection of circulating tumor DNA (ctDNA) as a fraction of total cell-free DNA (cfDNA), cfDNA analysis reveals both somatic mosaic tumor and germline variants and clonal hematopoiesis. Here we outline advantages and limitations of mosaic and germline variant detection as well as the impact of clonal hematopoiesis on liquid biopsy in cancer diagnosis. We also evaluate the potential of cfDNA analysis for the molecular diagnosis of monogenic mosaic disorders.

Multimodal analysis of methylomics and fragmentomics in plasma cell-free DNA for multi-cancer early detection and localization

Despite their promise, circulating tumor DNA (ctDNA)-based assays for multi-cancer early detection face challenges in test performance, due mostly to the limited abundance of ctDNA and its inherent variability. To address these challenges, published assays to date demanded a very high-depth sequencing, resulting in an elevated price of test. Herein, we developed a multimodal assay called SPOT-MAS (screening for the presence of tumor by methylation and size) to simultaneously profile methylomics, fragmentomics, copy number, and end motifs in a single workflow using targeted and shallow genome-wide sequencing (~0.55×) of cell-free DNA. We applied SPOT-MAS to 738 non-metastatic patients with breast, colorectal, gastric, lung, and liver cancer, and 1550 healthy controls. We then employed machine learning to extract multiple cancer and tissue-specific signatures for detecting and locating cancer. SPOT-MAS successfully detected the five cancer types with a sensitivity of 72.4% at 97.0% specificity. The sensitivities for detecting early-stage cancers were 73.9% and 62.3% for stages I and II, respectively, increasing to 88.3% for non-metastatic stage IIIA. For tumor-of-origin, our assay achieved an accuracy of 0.7. Our study demonstrates comparable performance to other ctDNA-based assays while requiring significantly lower sequencing depth, making it economically feasible for population-wide screening.