Multimodal analysis of ctDNA methylation and fragmentomic profiles enhances detection of nonmetastatic colorectal cancer

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.

Liquid biopsy biomarkers in breast cancer: An overview of systematic reviews

Breast cancer (BC) is the leading type of cancer affecting women globally and remains a significant cause of death. The diagnostic accuracy of liquid biopsy (LB) in the diagnosis of BC has not been well established. This overview synthesizes and critically evaluates the diagnostic test accuracy (DTA) of LB biomarkers in individuals with BC. Of 433 systematic reviews, eleven were included, assessing Fourier transform infrared (FTIR) spectroscopy, circulating tumor cells (CTCs), cell-free DNA (cfDNA), and microRNAs (miRNAs). The overall methodological quality of most of the reviews included was rated as critically low (n = 9, 81.8 %), and the remaining reviews were ranked as low and moderate. Key findings include CTCs with moderate sensitivity (0.50, 95 % confidence interval (CI) 0.48-0.52) and high specificity (0.93, 95 % CI: 0.92-0.95) with moderate certainty; cfDNA assays with high sensitivity (0.71-0.86) and specificity (0.88) with high certainty; FTIR assays with high sensitivity (0.97, 95 % CI: 0.94-0.96) and specificity (0.92, 95 % CI: 0.88-0.95) but low certainty. The miRNAs showed moderate to high sensitivity, while miR-21 had high specificity. Our overview indicates that identified liquid biopsies could serve as valuable tools for the diagnosis of breast cancer.

The emerging role of blood-based biomarkers in early detection of colorectal cancer: A systematic review

BACKGROUND

Colorectal cancer (CRC), the third most commonly diagnosed and second most lethal cancer worldwide, necessitates efficient early detection strategies to improve patient outcomes. This review evaluates the promise of novel blood-based biomarkers for early detection of CRC.

METHODS

A systematic review, registered with PROSPERO (CRD42024513770) and adhering to PRISMA guidelines, was conducted across multiple databases from January 1st, 2020 to December 31st, 2022. The comprehensive search strategy centered on sensitivity, specificity, and AUC-ROC of multiple types of molecular blood biomarkers.

RESULTS

Of total of 142 included articles, 59 were on protein, 58 on RNA, and 21 on DNA. The investigation into DNA biomarkers revealed that cfDNA and ctDNA carry significant potential for early CRC diagnosis. For instance, methylation patterns in genes such as MYO1-G and NDRG4 exhibited high diagnostic accuracies with AUCs reaching up to 0.996. RNA biomarkers like miRNAs and circRNAs also showed promising results, with circ_0011536 achieving AUCs of 0.982. Protein biomarkers, contrasted with established cancer markers, unveiled notable candidates like Irisin and ANXA2, with AUCs surpassing 0.96. The review highlights several individual markers and panels with the potential to improve upon existing CRC screening tests.

CONCLUSIONS

Despite the promise shown by the novel biomarkers, challenges persist, including small sample sizes, potential selection biases, and a lack of comprehensive cost-effectiveness analysis. Future research should focus on large-scale, multicenter, prospective studies across diverse populations. The findings advocate for an integrated biomarker approach, potentially revolutionizing CRC screening and aligning it with clinical realities through rigorous validation.

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.

Alteration of DNA methyltransferases by eribulin elicits broad DNA methylation changes with potential therapeutic implications for triple-negative breast cancer

Background: Triple-negative breast cancer (TNBC) is an aggressive disease with limited treatment options. Eribulin, a chemotherapeutic drug, induces epigenetic changes in cancer cells, suggesting a unique mechanism of action. Materials & methods: MDA-MB 231 cells were treated with eribulin and paclitaxel, and the samples from 53 patients treated with neoadjuvant eribulin were compared with those from 14 patients who received the standard-of-care treatment using immunohistochemistry. Results: Eribulin treatment caused significant DNA methylation changes in drug-tolerant persister TNBC cells, and it also elicited changes in the expression levels of epigenetic modifiers (DNMT1, TET1, DNMT3A/B) in vitro and in primary TNBC tumors. Conclusion: These findings provide new insights into eribulin's mechanism of action and potential biomarkers for predicting TNBC treatment response.

Artificial Intelligence in the Diagnosis of Colorectal Cancer: A Literature Review

BACKGROUND

The aim of this review is to explore the role of artificial intelligence in the diagnosis of colorectal cancer, how it impacts CRC morbidity and mortality, and why its role in clinical medicine is limited.

METHODS

A targeted, non-systematic review of the published literature relating to colorectal cancer diagnosis was performed with PubMed databases that were scouted to help provide a more defined understanding of the recent advances regarding artificial intelligence and their impact on colorectal-related morbidity and mortality. Articles were included if deemed relevant and including information associated with the keywords.

RESULTS

The advancements in artificial intelligence have been significant in facilitating an earlier diagnosis of CRC. In this review, we focused on evaluating genomic biomarkers, the integration of instruments with artificial intelligence, MR and hyperspectral imaging, and the architecture of neural networks. We found that these neural networks seem practical and yield positive results in initial testing. Furthermore, we explored the use of deep-learning-based majority voting methods, such as bag of words and PAHLI, in improving diagnostic accuracy in colorectal cancer detection. Alongside this, the autonomous and expansive learning ability of artificial intelligence, coupled with its ability to extract increasingly complex features from images or videos without human reliance, highlight its impact in the diagnostic sector. Despite this, as most of the research involves a small sample of patients, a diversification of patient data is needed to enhance cohort stratification for a more sensitive and specific neural model. We also examined the successful application of artificial intelligence in predicting microsatellite instability, showcasing its potential in stratifying patients for targeted therapies.

CONCLUSIONS

Since its commencement in colorectal cancer, artificial intelligence has revealed a multitude of functionalities and augmentations in the diagnostic sector of CRC. Given its early implementation, its clinical application remains a fair way away, but with steady research dedicated to improving neural architecture and expanding its applicational range, there is hope that these advanced neural software could directly impact the early diagnosis of CRC. The true promise of artificial intelligence, extending beyond the medical sector, lies in its potential to significantly influence the future landscape of CRC's morbidity and mortality.

Circulating cell-free DNA fragmentation is a stepwise and conserved process linked to apoptosis

BACKGROUND

Circulating cell-free DNA (cfDNA) is a pool of short DNA fragments mainly released from apoptotic hematopoietic cells. Nevertheless, the precise physiological process governing the DNA fragmentation and molecular profile of cfDNA remains obscure. To dissect the DNA fragmentation process, we use a human leukemia cell line HL60 undergoing apoptosis to analyze the size distribution of DNA fragments by shallow whole-genome sequencing (sWGS). Meanwhile, we also scrutinize the size profile of plasma cfDNA in 901 healthy human subjects and 38 dogs, as well as 438 patients with six common cancer types by sWGS.

RESULTS

Distinct size distribution profiles were observed in the HL60 cell pellet and supernatant, suggesting fragmentation is a stepwise process. Meanwhile, C-end preference was seen in both intracellular and extracellular cfDNA fragments. Moreover, the cfDNA profiles are characteristic and conserved across mammals. Compared with healthy subjects, distinct cfDNA profiles with a higher proportion of short fragments and lower C-end preference were found in cancer patients.

CONCLUSIONS

Our study provides new insight into fragmentomics of circulating cfDNA processing, which will be useful for early diagnosis of cancer and surveillance during cancer progression.

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.

Clinical application of liquid biopsy based on circulating tumor DNA in non-small cell lung cancer

Lung cancer is a widely occurring and deadly malignancy, with high prevalence rates in China and across the globe. Specifically, non-small cell lung cancer (NSCLC) represents about 85% of all lung cancer cases. The 5-year disease-free survival rate after surgery for stage IB-IIIB NSCLC patients (disease-free survival, DFS) has notably declined from 73% to 13%. Early detection of abnormal cancer molecules and subsequent personalized treatment plans are the most effective ways to address this problem. Liquid biopsy, surprisingly, enables safe, accurate, non-invasive, and dynamic tracking of disease progression. Among the various modalities, circulating tumor DNA (ctDNA) is the most commonly used liquid biopsy modality. ctDNA serves as a credible "liquid biopsy" diagnostic tool that, to a certain extent, overcomes tumor heterogeneity and harbors genetic mutations in malignancies, thereby providing early information on tumor genetic alterations. Despite considerable academic interest in the clinical significance of ctDNA, consensus on its utility remains lacking. In this review, we assess the role of ctDNA testing in the diagnosis and management of NSCLC as a reference for clinical intervention in this disease. Lastly, we examine future directions to optimize ctDNA for personalized therapy.

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

Introduction

Breast cancer causes the most cancer-related death in women and is the costliest cancer in the US regarding medical service and prescription drug expenses. Breast cancer screening is recommended by health authorities in the US, but current screening efforts are often compromised by high false positive rates. Liquid biopsy based on circulating tumor DNA (ctDNA) has emerged as a potential approach to screen for cancer. However, the detection of breast cancer, particularly in early stages, is challenging due to the low amount of ctDNA and heterogeneity of molecular subtypes.

Methods

Here, we employed a multimodal approach, namely Screen for the Presence of Tumor by DNA Methylation and Size (SPOT-MAS), to simultaneously analyze multiple signatures of cell free DNA (cfDNA) in plasma samples of 239 nonmetastatic breast cancer patients and 278 healthy subjects.

Results

We identified distinct profiles of genome-wide methylation changes (GWM), copy number alterations (CNA), and 4-nucleotide oligomer (4-mer) end motifs (EM) in cfDNA of breast cancer patients. We further used all three signatures to construct a multi-featured machine learning model and showed that the combination model outperformed base models built from individual features, achieving an AUC of 0.91 (95% CI: 0.87-0.95), a sensitivity of 65% at 96% specificity.

Discussion

Our findings showed that a multimodal liquid biopsy assay based on analysis of cfDNA methylation, CNA and EM could enhance the accuracy for the detection of early- stage breast cancer.

Fragment length profiles of cancer mutations enhance detection of circulating tumor DNA in patients with early-stage hepatocellular carcinoma

BACKGROUND

Late detection of hepatocellular carcinoma (HCC) results in an overall 5-year survival rate of less than 16%. Liquid biopsy (LB) assays based on detecting circulating tumor DNA (ctDNA) might provide an opportunity to detect HCC early noninvasively. Increasing evidence indicates that ctDNA detection using mutation-based assays is significantly challenged by the abundance of white blood cell-derived mutations, non-tumor tissue-derived somatic mutations in plasma, and the mutational tumor heterogeneity.

METHODS

Here, we employed concurrent analysis of cancer-related mutations, and their fragment length profiles to differentiate mutations from different sources. To distinguish persons with HCC (PwHCC) from healthy participants, we built a classification model using three fragmentomic features of ctDNA through deep sequencing of thirteen genes associated with HCC.

RESULTS

Our model achieved an area under the curve (AUC) of 0.88, a sensitivity of 89%, and a specificity of 82% in the discovery cohort consisting of 55 PwHCC and 55 healthy participants. In an independent validation cohort of 54 PwHCC and 53 healthy participants, the established model achieved comparable classification performance with an AUC of 0.86 and yielded a sensitivity and specificity of 81%.

CONCLUSIONS

Our study provides a rationale for subsequent clinical evaluation of our assay performance in a large-scale prospective study.

Clinical validation of a ctDNA-Based Assay for Multi-Cancer Detection: An Interim Report from a Vietnamese Longitudinal Prospective Cohort Study of 2795 Participants

The SPOT-MAS assay "Screening for the Presence Of Tumor by Methylation And Size" detects the five most common cancers in Vietnam by evaluating circulating tumor DNA in the blood. Here, we validated its performance in a prospective multi-center clinical trial, K-DETEK. Our analysis of 2795 participants from 14 sites across Vietnam demonstrates its ability to detect cancers in asymptomatic individuals with a positive predictive value of 60%, with 83.3% accuracy in detecting tumor location. We present a case report to support further using SPOT-MAS as a complementary method to achieve early cancer detection and provide the opportunity for early treatment.