Circulating cell-free DNA in breast cancer: size profiling, levels, and methylation patterns lead to prognostic and predictive classifiers

Biomarker potential of plasma cell-free DNA for cholangiocarcinoma

Background

To prevent the development of cholangiocarcinoma, an effective screening opisthorchiasis viverrini and/or differential diagnosis of and the cholangiocarcinoma is crucial needed. The level and quality of cfDNA in plasma are being investigated for their potential role as biomarkers in cholangiocarcinoma.

Methods

The study enrolled 43 healthy controls (N), 36 O. viverrini-infected subjects (OV), and 36 cholangiocarcinoma patients (CCA). Plasma cfDNA was quantified by fluorometry (Qubit 4), and qualified analysis, including % tumor fraction, ctDNA, ploidy number, and somatic copy number alteration (SCNA), was conducted using ULP-WGS and analyzed by iChorCNA. The statistical analysis and comparison among the groups were performed.

Results

The results showed that cfDNA could effectively differentiate between N and OV from CCA statistically both by DNA amount and quality. Using a cut-off of >20.94 ng/ml, the sensitivity and specificity of the cfDNA concentration were determined to be 86.11% and 98.73% for the differential diagnosis of cholangiocarcinoma, respectively. The ULP-WGS with iChorCNA indicated the % tumor fraction of cfDNA (P < 0.001) and the values of the ploidy number (P < 0.001) of the cholangiocarcinoma group and the other groups were statistically significant. Moreover, the SCNA of the cholangiocarcinoma group was shown to be significantly high in comparison to that of the healthy control group with an odds ratio of 11.688 (P < 0.001).

Conclusion

The use of cfDNA concentration and ULP-WGS for analyzing DNA quality including % tumor fraction, ctDNA concentration, tumor ploidy, and SCNA are useful for the differential diagnosis of cholangiocarcinoma from opisthorchiasis viverrini and healthy individuals.

BRCA1 & BRCA2 methylation as a prognostic and predictive biomarker in cancer: Implementation in liquid biopsy in the era of precision medicine

BACKGROUND

BReast CAncer gene 1 (BRCA1) and BReast CAncer gene 2 (BRCA2) encode for tumor suppressor proteins which are critical regulators of the Homologous Recombination (HR) pathway, the most precise and important DNA damage response mechanism. Dysfunctional HR proteins cannot repair double-stranded DNA breaks in mammalian cells, a situation called HR deficiency. Since their identification, pathogenic variants and other alterations of BRCA1 and BRCA2 genes have been associated with an increased risk of developing mainly breast and ovarian cancer. Interestingly, HR deficiency is also detected in tumors not carrying BRCA1/2 mutations, a condition termed "BRCAness".

MAIN TEXT

One of the main mechanisms causing the BRCAness phenotype is the methylation of the BRCA1/2 promoters, and this epigenetic modification is associated with carcinogenesis and poor prognosis mainly among patients with breast and ovarian cancer. BRCA1 promoter methylation has been suggested as an emerging biomarker of great predictive significance, especially concerning Poly (ADP-ribose) Polymerase inhibitors (PARP inhibitor-PARPi) responsiveness, along with or beyond BRCA1/2 mutations. However, as its clinical exploitation is still insufficient, the impact of BRCA1/2 promoter methylation status needs to be further evaluated. The current review aims to gather the latest findings about the mechanisms that underline BRCA1/2 function as well as the molecular characteristics of tumors associated with BRCA1/2 defects, by focusing on DNA methylation. Furthermore, we critically analyze their translational meaning and the validity of BRCA methylation biomarkers in predicting treatment response.

CONCLUSIONS

We believe that BRCA1/2 methylation alone or combined with other biomarkers in a clinical setting is expected to change the scenery in prognosis and predicting treatment response in multiple cancer types and is worthy of further attention. The quantitative BRCA1 promoter methylation assessment might predict treatment response in PARPi and analysis of BRCA1/2 methylation in liquid biopsy might define patient subgroups at different time points that may benefit from PARPi. Finally, we suggest a pipeline that could be implemented in liquid biopsy to aid precision pharmacotherapy in BRCA-associated tumors.

Benchmarking UMI-aware and standard variant callers for low frequency ctDNA variant detection

BACKGROUND

Circulating tumour DNA (ctDNA) is a subset of cell free DNA (cfDNA) released by tumour cells into the bloodstream. Circulating tumour DNA has shown great potential as a biomarker to inform treatment in cancer patients. Collecting ctDNA is minimally invasive and reflects the entire genetic makeup of a patient's cancer. ctDNA variants in NGS data can be difficult to distinguish from sequencing and PCR artefacts due to low abundance, particularly in the early stages of cancer. Unique Molecular Identifiers (UMIs) are short sequences ligated to the sequencing library before amplification. These sequences are useful for filtering out low frequency artefacts. The utility of ctDNA as a cancer biomarker depends on accurate detection of cancer variants.

RESULTS

In this study, we benchmarked six variant calling tools, including two UMI-aware callers for their ability to call ctDNA variants. The standard variant callers tested included Mutect2, bcftools, LoFreq and FreeBayes. The UMI-aware variant callers benchmarked were UMI-VarCal and UMIErrorCorrect. We used both datasets with known variants spiked in at low frequencies, and datasets containing ctDNA, and generated synthetic UMI sequences for these datasets. Variant callers displayed different preferences for sensitivity and specificity. Mutect2 showed high sensitivity, while returning more privately called variants than any other caller in data without synthetic UMIs - an indicator of false positive variant discovery. In data encoded with synthetic UMIs, UMI-VarCal detected fewer putative false positive variants than all other callers in synthetic datasets. Mutect2 showed a balance between high sensitivity and specificity in data encoded with synthetic UMIs.

CONCLUSIONS

Our results indicate UMI-aware variant callers have potential to improve sensitivity and specificity in calling low frequency ctDNA variants over standard variant calling tools. There is a growing need for further development of UMI-aware variant calling tools if effective early detection methods for cancer using ctDNA samples are to be realised.

Application of machine learning for high-throughput tumor marker screening

High-throughput sequencing and multiomics technologies have allowed increasing numbers of biomarkers to be mined and used for disease diagnosis, risk stratification, efficacy assessment, and prognosis prediction. However, the large number and complexity of tumor markers make screening them a substantial challenge. Machine learning (ML) offers new and effective ways to solve the screening problem. ML goes beyond mere data processing and is instrumental in recognizing intricate patterns within data. ML also has a crucial role in modeling dynamic changes associated with diseases. Used together, ML techniques have been included in automatic pipelines for tumor marker screening, thereby enhancing the efficiency and accuracy of the screening process. In this review, we discuss the general processes and common ML algorithms, and highlight recent applications of ML in tumor marker screening of genomic, transcriptomic, proteomic, and metabolomic data of patients with various types of cancers. Finally, the challenges and future prospects of the application of ML in tumor therapy are discussed.

Status of breast cancer detection in young women and potential of liquid biopsy

Young onset breast cancer (YOBC) is an increasing demographic with unique biology, limited screening, and poor outcomes. Further, women with postpartum breast cancers (PPBCs), cancers occurring up to 10 years after childbirth, have worse outcomes than other young breast cancer patients matched for tumor stage and subtype. Early-stage detection of YOBC is critical for improving outcomes. However, most young women (under 45) do not meet current age guidelines for routine mammographic screening and are thus an underserved population. Other challenges to early detection in this population include reduced performance of standard of care mammography and reduced awareness. Women often face significant barriers in accessing health care during the postpartum period and disadvantaged communities face compounding barriers due to systemic health care inequities. Blood tests and liquid biopsies targeting early detection may provide an attractive option to help address these challenges. Test development in this area includes understanding of the unique biology involved in YOBC and in particular PPBCs that tend to be more aggressive and deadly. In this review, we will present the status of breast cancer screening and detection in young women, provide a summary of some unique biological features of YOBC, and discuss the potential for blood tests and liquid biopsy platforms to address current shortcomings in timely, equitable detection.

Mitochondrial Fraction of Circulating Cell-Free DNA as an Indicator of Human Pathology

Circulating cell-free DNA (ccfDNA) of mitochondrial origin (ccf-mtDNA) consists of a minor fraction of total ccfDNA in blood or in other biological fluids. Aberrant levels of ccf-mtDNA have been observed in many pathologies. Here, we introduce a simple and effective standardized Taqman probe-based dual-qPCR assay for the simultaneous detection and relative quantification of nuclear and mitochondrial fragments of ccfDNA. Three pathologies of major burden, one malignancy (Breast Cancer, BrCa), one inflammatory (Osteoarthritis, OA) and one metabolic (Type 2 Diabetes, T2D), were studied. Higher levels of ccf-mtDNA were detected both in BrCa and T2D in relation to health, but not in OA. In BrCa, hormonal receptor status was associated with ccf-mtDNA levels. Machine learning analysis of ccf-mtDNA datasets was used to build biosignatures of clinical relevance. (A) a three-feature biosignature discriminating between health and BrCa (AUC: 0.887) and a five-feature biosignature for predicting the overall survival of BrCa patients (Concordance Index: 0.756). (B) a five-feature biosignature stratifying among T2D, prediabetes and health (AUC: 0.772); a five-feature biosignature discriminating between T2D and health (AUC: 0.797); and a four-feature biosignature identifying prediabetes from health (AUC: 0.795). (C) a biosignature including total plasma ccfDNA with very high performance in discriminating OA from health (AUC: 0.934). Aberrant ccf-mtDNA levels could have diagnostic/prognostic potential in BrCa and Diabetes, while the developed multiparameter biosignatures can add value to their clinical management.

Methylation signatures as biomarkers for non-invasive early detection of breast cancer: A systematic review of the literature

BACKGROUND

Early detection of breast cancer would help alleviate the burden of treatment for early-stage breast cancer and help patient prognosis. There is currently no established gene panel that utilizes the potential of DNA methylation as a molecular signature for the early detection of breast cancer. This systematic review aims to identify the optimal methylation biomarkers for a non-invasive liquid biopsy assay and the gaps in knowledge regarding biomarkers for early detection of breast cancer.

METHODS

Following the PRISMA-ScR method, Pubmed and Google Scholar was searched for publications related to methylation biomarkers in breast cancer over a five-year period. Eligible publications were mined for key data fields such as study aims, cohort demographics, types of breast cancer studied, technologies used, and outcomes. Data was analyzed to address the objectives of the review.

RESULTS

Literature search identified 112 studies of which based on eligibility criteria, 13 studies were included. 28 potential methylation gene targets were identified, of which 23 were methylated at the promoter region, 1 was methylated in the body of the gene and 4 were methylated at yet to be identified locations.

CONCLUSIONS

Our evaluation shows that at minimum APC, RASSFI, and FOXA1 genes would be a promising set of genes to start with for the early detection of breast cancer, based on the sensitivity and specificity outlined in the studies. Prospective studies are needed to optimize biomarkers for broader impact in early detection of breast cancer.

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.

Biomarkers and experimental models for cancer immunology investigation

The rapid advancement of tumor immunotherapies poses challenges for the tools used in cancer immunology research, highlighting the need for highly effective biomarkers and reproducible experimental models. Current immunotherapy biomarkers encompass surface protein markers such as PD-L1, genetic features such as microsatellite instability, tumor-infiltrating lymphocytes, and biomarkers in liquid biopsy such as circulating tumor DNAs. Experimental models, ranging from 3D in vitro cultures (spheroids, submerged models, air-liquid interface models, organ-on-a-chips) to advanced 3D bioprinting techniques, have emerged as valuable platforms for cancer immunology investigations and immunotherapy biomarker research. By preserving native immune components or coculturing with exogenous immune cells, these models replicate the tumor microenvironment in vitro. Animal models like syngeneic models, genetically engineered models, and patient-derived xenografts provide opportunities to study in vivo tumor-immune interactions. Humanized animal models further enable the simulation of the human-specific tumor microenvironment. Here, we provide a comprehensive overview of the advantages, limitations, and prospects of different biomarkers and experimental models, specifically focusing on the role of biomarkers in predicting immunotherapy outcomes and the ability of experimental models to replicate the tumor microenvironment. By integrating cutting-edge biomarkers and experimental models, this review serves as a valuable resource for accessing the forefront of cancer immunology investigation.

The Diversity of Liquid Biopsies and Their Potential in Breast Cancer Management

Analyzing blood as a so-called liquid biopsy in breast cancer (BC) patients has the potential to adapt therapy management. Circulating tumor cells (CTCs), extracellular vesicles (EVs), cell-free DNA (cfDNA) and other blood components mirror the tumoral heterogeneity and could support a range of clinical decisions. Multi-cancer early detection tests utilizing blood are advancing but are not part of any clinical routine yet. Liquid biopsy analysis in the course of neoadjuvant therapy has potential for therapy (de)escalation.Minimal residual disease detection via serial cfDNA analysis is currently on its way. The prognostic value of blood analytes in early and metastatic BC is undisputable, but the value of these prognostic biomarkers for clinical management is controversial. An interventional trial confirmed a significant outcome benefit when therapy was changed in case of newly emerging cfDNA mutations under treatment and thus showed the clinical utility of cfDNA analysis for therapy monitoring. The analysis of PIK3CA or ESR1 variants in plasma of metastatic BC patients to prescribe targeted therapy with alpesilib or elacestrant has already arrived in clinical practice with FDA-approved tests available and is recommended by ASCO. The translation of more liquid biopsy applications into clinical practice is still pending due to a lack of knowledge of the analytes' biology, lack of standards and difficulties in proving clinical utility.

Automated machine learning for genome wide association studies

MOTIVATION

Genome-wide association studies (GWAS) present several computational and statistical challenges for their data analysis, including knowledge discovery, interpretability, and translation to clinical practice.

RESULTS

We develop, apply, and comparatively evaluate an automated machine learning (AutoML) approach, customized for genomic data that delivers reliable predictive and diagnostic models, the set of genetic variants that are important for predictions (called a biosignature), and an estimate of the out-of-sample predictive power. This AutoML approach discovers variants with higher predictive performance compared to standard GWAS methods, computes an individual risk prediction score, generalizes to new, unseen data, is shown to better differentiate causal variants from other highly correlated variants, and enhances knowledge discovery and interpretability by reporting multiple equivalent biosignatures.

AVAILABILITY AND IMPLEMENTATION

Code for this study is available at: https://github.com/mensxmachina/autoML-GWAS. JADBio offers a free version at: https://jadbio.com/sign-up/. SNP data can be downloaded from the EGA repository (https://ega-archive.org/). PRS data are found at: https://www.aicrowd.com/challenges/opensnp-height-prediction. Simulation data to study population structure can be found at: https://easygwas.ethz.ch/data/public/dataset/view/1/.

Label-Free Human Disease Characterization through Circulating Cell-Free DNA Analysis Using Raman Spectroscopy

Circulating cell-free DNA (ccfDNA) is a liquid biopsy biomaterial attracting significant attention for the implementation of precision medicine diagnostics. Deeper knowledge related to its structure and biology would enable the development of such applications. In this study, we employed Raman spectroscopy to unravel the biomolecular profile of human ccfDNA in health and disease. We established reference Raman spectra of ccfDNA samples from healthy males and females with different conditions, including cancer and diabetes, extracting information about their chemical composition. Comparative observations showed a distinct spectral pattern in ccfDNA from breast cancer patients taking neoadjuvant therapy. Raman analysis of ccfDNA from healthy, prediabetic, and diabetic males uncovered some differences in their biomolecular fingerprints. We also studied ccfDNA released from human benign and cancer cell lines and compared it to their respective gDNA, confirming it mirrors its cellular origin. Overall, we explored for the first time Raman spectroscopy in the study of ccfDNA and provided spectra of samples from different sources. Our findings introduce Raman spectroscopy as a new approach to implementing liquid biopsy diagnostics worthy of further elaboration.

Liquid biopsies for cancer: From bench to clinic

Over the past two decades, liquid biopsy has been increasingly used as a supplement, or even, a replacement to the traditional biopsy in clinical oncological practice, due to its noninvasive and early detectable properties. The detections can be based on a variety of features extracted from tumor‑derived entities, such as quantitative alterations, genetic changes, and epigenetic aberrations, and so on. So far, the clinical applications of cancer liquid biopsy mainly aimed at two aspects, prediction (early diagnosis, prognosis and recurrent evaluation, therapeutic response monitoring, etc.) and intervention. In spite of the rapid development and great contributions achieved, cancer liquid biopsy is still a field under investigation and deserves more clinical practice. To better open up future work, here we systematically reviewed and compared the latest progress of the most widely recognized circulating components, including circulating tumor cells, cell-free circulating DNA, noncoding RNA, and nucleosomes, from their discovery histories to clinical values. According to the features applied, we particularly divided the contents into two parts, beyond epigenetics and epigenetic-based. The latter was considered as the highlight along with a brief overview of the advances in both experimental and bioinformatic approaches, due to its unique advantages and relatively lack of documentation.

Extensive review on breast cancer its etiology, progression, prognostic markers, and treatment

As the most frequent and vulnerable malignancy among women, breast cancer universally manifests a formidable healthcare challenge. From a biological and molecular perspective, it is a heterogenous disease and is stratified based on the etiological factors driving breast carcinogenesis. Notably, genetic predispositions and epigenetic impacts often constitute the heterogeneity of this disease. Typically, breast cancer is classified intrinsically into histological subtypes in clinical landscapes. These stratifications empower physicians to tailor precise treatments among the spectrum of breast cancer therapeutics. In this pursuit, numerous prognostic algorithms are extensively characterized, drastically changing how breast cancer is portrayed. Therefore, it is a basic requisite to comprehend the multidisciplinary rationales of breast cancer to assist the evolution of novel therapeutic strategies. This review aims at highlighting the molecular and genetic grounds of cancer additionally with therapeutic and phytotherapeutic context. Substantially, it also renders researchers with an insight into the breast cancer cell lines as a model paradigm for breast cancer research interventions.

Cell-Free Methylated PTGER4 and SHOX2 Plasma DNA as a Biomarker for Therapy Monitoring and Prognosis in Advanced Stage NSCLC Patients

Notwithstanding some improvement in the earlier detection of patients with lung cancer, most of them still present with a late-stage disease at the time of diagnosis. Next to the most frequently utilized factors affecting the prognosis of lung cancer patients (stage, performance, and age), the recent application of biomarkers obtained by liquid profiling has gained more acceptance. In our study, we aimed to answer these questions: (i) Is the quantification of free-circulating methylated PTGER4 and SHOX2 plasma DNA a useful method for therapy monitoring, and is this also possible for patients treated with different therapy regimens? (ii) Is this approach possible when blood-drawing tubes, which allow for a delayed processing of blood samples, are utilized? Baseline values for mPTGER4 and mSHOX2 do not allow for clear discrimination between different response groups. In contrast, the combination of the methylation values for both genes shows a clear difference between responders vs. non-responders at the time of re-staging. Furthermore, blood drawing into tubes stabilizing the sample allows researchers more flexibility.

Enhancing clinical potential of liquid biopsy through a multi-omic approach: A systematic review

In the last years, liquid biopsy gained increasing clinical relevance for detecting and monitoring several cancer types, being minimally invasive, highly informative and replicable over time. This revolutionary approach can be complementary and may, in the future, replace tissue biopsy, which is still considered the gold standard for cancer diagnosis. "Classical" tissue biopsy is invasive, often cannot provide sufficient bioptic material for advanced screening, and can provide isolated information about disease evolution and heterogeneity. Recent literature highlighted how liquid biopsy is informative of proteomic, genomic, epigenetic, and metabolic alterations. These biomarkers can be detected and investigated using single-omic and, recently, in combination through multi-omic approaches. This review will provide an overview of the most suitable techniques to thoroughly characterize tumor biomarkers and their potential clinical applications, highlighting the importance of an integrated multi-omic, multi-analyte approach. Personalized medical investigations will soon allow patients to receive predictable prognostic evaluations, early disease diagnosis, and subsequent ad hoc treatments.

Bridging biological cfDNA features and machine learning approaches

Liquid biopsies (LBs), particularly using circulating tumor DNA (ctDNA), are expected to revolutionize precision oncology and blood-based cancer screening. Recent technological improvements, in combination with the ever-growing understanding of cell-free DNA (cfDNA) biology, are enabling the detection of tumor-specific changes with extremely high resolution and new analysis concepts beyond genetic alterations, including methylomics, fragmentomics, and nucleosomics. The interrogation of a large number of markers and the high complexity of data render traditional correlation methods insufficient. In this regard, machine learning (ML) algorithms are increasingly being used to decipher disease- and tissue-specific signals from cfDNA. Here, we review recent insights into biological ctDNA features and how these are incorporated into sophisticated ML applications.

Evaluation of a Liquid Biopsy-Breast Cancer Methylation (LBx-BCM) Cartridge Assay for Predicting Early Disease Progression and Survival: TBCRC 005 Prospective Trial

PURPOSE

We previously demonstrated that high levels of circulating methylated DNA are associated with subsequent disease progression in women with metastatic breast cancer (MBC). In this study, we evaluated the clinical utility of a novel liquid biopsy-breast cancer methylation (LBx-BCM) prototype assay using the GeneXpert cartridge system for early assessment of disease progression in MBC.

EXPERIMENTAL DESIGN

The 9-marker LBx-BCM prototype assay was evaluated in TBCRC 005, a prospective biomarker study, using plasma collected at baseline, week 4, and week 8 from 144 patients with MBC.

RESULTS

At week 4, patients with MBC with high cumulative methylation (CM) had a significantly shorter median PFS (2.88 months vs. 6.60 months, P = 0.001) and OS (14.52 months vs. 22.44 months, P = 0.005) compared with those with low CM. In a multivariable model, high versus low CM was also associated with shorter PFS (HR, 1.90; 95% CI, 1.20-3.01; P = 0.006). Change in CM from baseline to week 4 (OR, 4.60; 95% CI, 1.77-11.93; P = 0.002) and high levels of CM at week 4 (OR, 2.78; 95% CI, 1.29-5.99; P = 0.009) were associated with progressive disease at the time of first restaging. A robust risk model based on week 4 circulating CM levels was developed to predict disease progression as early as 3 months after initiating a new treatment.

CONCLUSIONS

The automated LBx-BCM prototype assay is a promising clinical tool for detecting disease progression a month after initiating treatment in women with MBC undergoing routine care. The next step is to validate its clinical utility for specific treatments.

Cell-Free DNA Fragmentomics: A Promising Biomarker for Diagnosis, Prognosis and Prediction of Response in Breast Cancer

Identifying novel circulating biomarkers predictive of response and informative about the mechanisms of resistance, is the new challenge for breast cancer (BC) management. The integration of omics information will gradually revolutionize the clinical approach. Liquid biopsy is being incorporated into the diagnostic and decision-making process for the treatment of BC, in particular with the analysis of circulating tumor DNA, although with some relevant limitations, including costs. Circulating cell-free DNA (cfDNA) fragmentomics and its integrity index may become a cheaper, noninvasive biomarker that could provide significant additional information for monitoring response to systemic treatments in BC. The purpose of our review is to focus on the available research on cfDNA integrity and its features as a biomarker of diagnosis, prognosis and response to treatments in BC, highlighting new perspectives and critical issues for future applications.

Autotaxin in Breast Cancer: Role, Epigenetic Regulation and Clinical Implications

Autotaxin (ATX), the protein product of Ectonucleotide Pyrophosphatase Phosphodiesterase 2 (ENPP2), is a secreted lysophospholipase D (lysoPLD) responsible for the extracellular production of lysophosphatidic acid (LPA). ATX-LPA pathway signaling participates in several normal biological functions, but it has also been connected to cancer progression, metastasis and inflammatory processes. Significant research has established a role in breast cancer and it has been suggested as a therapeutic target and/or a clinically relevant biomarker. Recently, ENPP2 methylation was described, revealing a potential for clinical exploitation in liquid biopsy. The current review aims to gather the latest findings about aberrant signaling through ATX-LPA in breast cancer and discusses the role of ENPP2 expression and epigenetic modification, giving insights with translational value.

Improving prediction of response to neoadjuvant treatment in patients with breast cancer by combining liquid biopsies with multiparametric MRI: protocol of the LIMA study - a multicentre prospective observational cohort study

INTRODUCTION

The response to neoadjuvant chemotherapy (NAC) in breast cancer has important prognostic implications. Dynamic prediction of tumour regression by NAC may allow for adaption of the treatment plan before completion, or even before the start of treatment. Such predictions may help prevent overtreatment and related toxicity and correct for undertreatment with ineffective regimens. Current imaging methods are not able to fully predict the efficacy of NAC. To successfully improve response prediction, tumour biology and heterogeneity as well as treatment-induced changes have to be considered. In the LIMA study, multiparametric MRI will be combined with liquid biopsies. In addition to conventional clinical and pathological information, these methods may give complementary information at multiple time points during treatment.

AIM

To combine multiparametric MRI and liquid biopsies in patients with breast cancer to predict residual cancer burden (RCB) after NAC, in adjunct to standard clinico-pathological information. Predictions will be made before the start of NAC, approximately halfway during treatment and after completion of NAC.

METHODS

In this multicentre prospective observational study we aim to enrol 100 patients. Multiparametric MRI will be performed prior to NAC, approximately halfway and after completion of NAC. Liquid biopsies will be obtained immediately prior to every cycle of chemotherapy and after completion of NAC. The primary endpoint is RCB in the surgical resection specimen following NAC. Collected data will primarily be analysed using multivariable techniques such as penalised regression techniques.

ETHICS AND DISSEMINATION

Medical Research Ethics Committee Utrecht has approved this study (NL67308.041.19). Informed consent will be obtained from each participant. All data are anonymised before publication. The findings of this study will be submitted to international peer-reviewed journals.

TRIAL REGISTRATION NUMBER

NCT04223492.

New Perspectives on the Importance of Cell-Free DNA Biology

Body fluids are constantly replenished with a population of genetically diverse cell-free DNA (cfDNA) fragments, representing a vast reservoir of information reflecting real-time changes in the host and metagenome. As many body fluids can be collected non-invasively in a one-off and serial fashion, this reservoir can be tapped to develop assays for the diagnosis, prognosis, and monitoring of wide-ranging pathologies, such as solid tumors, fetal genetic abnormalities, rejected organ transplants, infections, and potentially many others. The translation of cfDNA research into useful clinical tests is gaining momentum, with recent progress being driven by rapidly evolving preanalytical and analytical procedures, integrated bioinformatics, and machine learning algorithms. Yet, despite these spectacular advances, cfDNA remains a very challenging analyte due to its immense heterogeneity and fluctuation in vivo. It is increasingly recognized that high-fidelity reconstruction of the information stored in cfDNA, and in turn the development of tests that are fit for clinical roll-out, requires a much deeper understanding of both the physico-chemical features of cfDNA and the biological, physiological, lifestyle, and environmental factors that modulate it. This is a daunting task, but with significant upsides. In this review we showed how expanded knowledge on cfDNA biology and faithful reverse-engineering of cfDNA samples promises to (i) augment the sensitivity and specificity of existing cfDNA assays; (ii) expand the repertoire of disease-specific cfDNA markers, thereby leading to the development of increasingly powerful assays; (iii) reshape personal molecular medicine; and (iv) have an unprecedented impact on genetics research.

Advances of Epigenetic Biomarkers and Epigenome Editing for Early Diagnosis in Breast Cancer

Epigenetic modifications are known to regulate cell phenotype during cancer progression, including breast cancer. Unlike genetic alterations, changes in the epigenome are reversible, thus potentially reversed by epi-drugs. Breast cancer, the most common cause of cancer death worldwide in women, encompasses multiple histopathological and molecular subtypes. Several lines of evidence demonstrated distortion of the epigenetic landscape in breast cancer. Interestingly, mammary cells isolated from breast cancer patients and cultured ex vivo maintained the tumorigenic phenotype and exhibited aberrant epigenetic modifications. Recent studies indicated that the therapeutic efficiency for breast cancer regimens has increased over time, resulting in reduced mortality. Future medical treatment for breast cancer patients, however, will likely depend upon a better understanding of epigenetic modifications. The present review aims to outline different epigenetic mechanisms including DNA methylation, histone modifications, and ncRNAs with their impact on breast cancer, as well as to discuss studies highlighting the central role of epigenetic mechanisms in breast cancer pathogenesis. We propose new research areas that may facilitate locus-specific epigenome editing as breast cancer therapeutics.

Diagnostic and Therapeutic Potential of Circulating-Free DNA and Cell-Free RNA in Cancer Management

Over time, molecular biology and genomics techniques have been developed to speed up the early diagnosis and clinical management of cancer. These therapies are often most effective when administered to the subset of malignancies harboring the target identified by molecular testing. Important advances in applying molecular testing involve circulating-free DNA (cfDNA)- and cell-free RNA (cfRNA)-based liquid biopsies for the diagnosis, prognosis, prediction, and treatment of cancer. Both cfDNA and cfRNA are sensitive and specific biomarkers for cancer detection, which have been clinically proven through multiple randomized and prospective trials. These help in cancer management based on the noninvasive evaluation of size, quantity, and point mutations, as well as copy number alterations at the tumor site. Moreover, personalized detection of ctDNA helps in adjuvant therapeutics and predicts the chances of recurrence of cancer and resistance to cancer therapy. Despite the controversial diagnostic values of cfDNA and cfRNA, many clinical trials have been completed, and the Food and Drug Administration has approved many multigene assays to detect genetic alterations in the cfDNA of cancer patients. In this review, we underpin the recent advances in the physiological roles of cfDNA and cfRNA, as well as their roles in cancer detection by highlighting recent clinical trials and their roles as prognostic and predictive markers in cancer management.

Circulating Cell-Free DNA Profiling Predicts the Therapeutic Outcome in Advanced Hepatocellular Carcinoma Patients Treated with Combination Immunotherapy

Simple Summary

Atezolizumab/bevacizumab (Atezo/Bev) combination immunotherapy has become a front-line therapy for unresectable hepatocellular carcinoma (u-HCC), but some patients are initially nonresponders. We investigated the potential of cell-free DNA (cfDNA)/circulating tumor DNA (ctDNA) as biomarkers for predicting the therapeutic outcome of u-HCC patients treated with anti-programmed cell death1-ligand1 (PD-L1)/vascular endothelial growth factor (VEGF) therapy. Patients with high levels of cfDNA showed a significantly lower overall response rate and shorter progression-free survival and overall survival (OS) than those with low levels of cfDNA. Ultradeep sequencing of cfDNA showed that the telomerase reverse transcriptase (TERT) promoter, tumor protein 53 (TP53) and catenin beta 1 (CTNNB1) were the most frequently mutated genes in ctDNA. Lastly, a TERT ctDNA mutation and a high alpha-fetoprotein (AFP) level were independent predictors of shorter OS in u-HCC patients treated with Atezo/Bev therapy and could stratify their prognoses. Collectively, cfDNA/ctDNA profiling may be useful to predict therapeutic outcome in u-HCC patients treated with Atezo/Bev therapy.

Abstract

Combination immunotherapy with anti-programmed cell death1-ligand1 (PD-L1) and anti-vascular endothelial growth factor (VEGF) antibodies has become the standard treatment for patients with unresectable HCC (u-HCC). However, limited patients obtain clinical benefits. Cell-free DNA (cfDNA) in peripheral blood contains circulating tumor DNA (ctDNA) that reflects molecular abnormalities in tumor tissue. We investigated the potential of cfDNA/ctDNA as biomarkers for predicting the therapeutic outcome in u-HCC patients treated with anti-PD-L1/VEGF therapy. We enrolled a multicenter cohort of 85 HCC patients treated with atezolizumab and bevacizumab (Atezo/Bev) between 2020 and 2021. Pretreatment plasma was collected, and cfDNA levels were quantified. Ultradeep sequencing of cfDNA was performed with a custom-made panel for detecting mutations in 25 HCC-related cancer genes. We evaluated the association of cfDNA/ctDNA profiles and clinical outcomes. Patients with high plasma cfDNA levels showed a significantly lower response rate and shorter progression-free survival and overall survival (OS) than those with low cfDNA levels. ctDNA detected in 55% of HCC patients included the telomerase reverse transcriptase (TERT) promoter in 31% of these patients, tumor protein 53 (TP53) in 21%, catenin beta 1 (CTNNB1) in 13% and phosphatase and tensin homolog (PTEN) in 7%. The presence or absence of ctDNA did not predict the efficacy of Atezo/Bev therapy. Twenty-six patients with a TERT mutation had significantly shorter OS than those without. The presence of a TERT mutation and alpha-fetoprotein (AFP) ≥ 400 ng/mL were independent predictors of poor OS according to multivariate Cox proportional hazard analysis and could be used to stratify patients treated with Atezo/Bev therapy based on prognosis. In conclusion, pretreatment cfDNA/ctDNA profiling may be useful for predicting the therapeutic outcome in u-HCC patients treated with anti-PD-L1/VEGF therapy.

Pre-PCR Mutation-Enrichment Methods for Liquid Biopsy Applications

Liquid biopsy is having a remarkable impact on healthcare- and disease-management in the context of personalized medicine. Circulating free DNA (cfDNA) is one of the most instructive liquid-biopsy-based biomarkers and harbors valuable information for diagnostic, predictive, and prognostic purposes. When it comes to cancer, circulating DNA from the tumor (ctDNA) has a wide range of applications, from early cancer detection to the early detection of relapse or drug resistance, and the tracking of the dynamic genomic make-up of tumor cells. However, the detection of ctDNA remains technically challenging, due, in part, to the low frequency of ctDNA among excessive circulating cfDNA originating from normal tissues. During the past three decades, mutation-enrichment methods have emerged to boost sensitivity and enable facile detection of low-level mutations. Although most developed techniques apply mutation enrichment during or following initial PCR, there are a few techniques that allow mutation selection prior to PCR, which provides advantages. Pre-PCR enrichment techniques can be directly applied to genomic DNA and diminish the influence of PCR errors that can take place during amplification. Moreover, they have the capability for high multiplexity and can be followed by established mutation detection and enrichment technologies without changes to their established procedures. The first approaches for pre-PCR enrichment were developed by employing restriction endonucleases directly on genomic DNA in the early 1990s. However, newly developed pre-PCR enrichment methods provide higher sensitivity and versatility. This review describes the available pre-PCR enrichment methods and focuses on the most recently developed techniques (NaME-PrO, UVME, and DEASH/MAESTRO), emphasizing their applications in liquid biopsies.

Functional regulations between genetic alteration-driven genes and drug target genes acting as prognostic biomarkers in breast cancer

Differences in genetic molecular features including mutation, copy number alterations and DNA methylation, can explain interindividual variability in response to anti-cancer drugs in cancer patients. However, identifying genetic alteration-driven genes and characterizing their functional mechanisms in different cancer types are still major challenges for cancer studies. Here, we systematically identified functional regulations between genetic alteration-driven genes and drug target genes and their potential prognostic roles in breast cancer. We identified two mutation and copy number-driven gene pairs (PARP1-ACSL1 and PARP1-SRD5A3), three DNA methylation-driven gene pairs (PRLR-CDKN1C, PRLR-PODXL2 and PRLR-SRD5A3), six gene pairs between mutation-driven genes and drug target genes (SLC19A1-SLC47A2, SLC19A1-SRD5A3, AKR1C3-SLC19A1, ABCB1-SRD5A3, NR3C2-SRD5A3 and AKR1C3-SRD5A3), and four copy number-driven gene pairs (ADIPOR2-SRD5A3, CASP12-SRD5A3, SLC39A11-SRD5A3 and GALNT2-SRD5A3) that all served as prognostic biomarkers of breast cancer. In particular, RARP1 was found to be upregulated by simultaneous copy number amplification and gene mutation. Copy number deletion and downregulated expression of ACSL1 and upregulation of SRD5A3 both were observed in breast cancers. Moreover, copy number deletion of ACSL1 was associated with increased resistance to PARP inhibitors. PARP1-ACSL1 pair significantly correlated with poor overall survival in breast cancer owing to the suppression of the MAPK, mTOR and NF-kB signaling pathways, which induces apoptosis, autophagy and prevents inflammatory processes. Loss of SRD5A3 expression was also associated with increased sensitivity to PARP inhibitors. The PARP1-SRD5A3 pair significantly correlated with poor overall survival in breast cancer through regulating androgen receptors to induce cell proliferation. These results demonstrate that genetic alteration-driven gene pairs might serve as potential biomarkers for the prognosis of breast cancer and facilitate the identification of combination therapeutic targets for breast cancers.

Just Add Data: automated predictive modeling for knowledge discovery and feature selection

Fully automated machine learning (AutoML) for predictive modeling is becoming a reality, giving rise to a whole new field. We present the basic ideas and principles of Just Add Data Bio (JADBio), an AutoML platform applicable to the low-sample, high-dimensional omics data that arise in translational medicine and bioinformatics applications. In addition to predictive and diagnostic models ready for clinical use, JADBio focuses on knowledge discovery by performing feature selection and identifying the corresponding biosignatures, i.e., minimal-size subsets of biomarkers that are jointly predictive of the outcome or phenotype of interest. It also returns a palette of useful information for interpretation, clinical use of the models, and decision making. JADBio is qualitatively and quantitatively compared against Hyper-Parameter Optimization Machine Learning libraries. Results show that in typical omics dataset analysis, JADBio manages to identify signatures comprising of just a handful of features while maintaining competitive predictive performance and accurate out-of-sample performance estimation.

Synergy between the Levels of Methylation of microRNA Gene Sets in Primary Tumors and Metastases of Ovarian Cancer Patients

We studied the correlations between the levels of methylation of a group of 21 microRNA genes in 99 primary tumors and 29 macroscopic peritoneal metastases of ovarian cancer. Analysis of the level of methylation by quantitative methylation-specific PCR showed that co-methylation was detected for 13 pairs of microRNA genes in primary tumors and for 22 pairs in metastases. Pairs of microRNA genes that have shown significant co-methylation can be involved in common processes and pathways of gene regulation and interaction and can have common target genes. The results are highly significant and pairs of microRNA genes can be proposed as new potential markers for the diagnosis and prognosis of ovarian cancer metastasis.

Current and Developing Liquid Biopsy Techniques for Breast Cancer

Breast cancer is the most commonly diagnosed cancer and leading cause of cancer mortality among woman worldwide. The techniques of diagnosis, prognosis, and therapy monitoring of breast cancer are critical. Current diagnostic techniques are mammography and tissue biopsy; however, they have limitations. With the development of novel techniques, such as personalized medicine and genetic profiling, liquid biopsy is emerging as the less invasive tool for diagnosing and monitoring breast cancer. Liquid biopsy is performed by sampling biofluids and extracting tumor components, such as circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), cell-free mRNA (cfRNA) and microRNA (miRNA), proteins, and extracellular vehicles (EVs). In this review, we summarize and focus on the recent discoveries of tumor components and biomarkers applied in liquid biopsy and novel development of detection techniques, such as surface-enhanced Raman spectroscopy (SERS) and microfluidic devices.

The double agents in liquid biopsy: promoter and informant biomarkers of early metastases in breast cancer

Breast cancer continues to be a major global problem with significant mortality associated with advanced stage and metastases at clinical presentation. However, several findings suggest that metastasis is indeed an early occurrence. The standard diagnostic techniques such as invasive core needle biopsy, serological protein marker assays, and non-invasive radiological imaging do not provide information about the presence and molecular profile of small fractions of early metastatic tumor cells which are prematurely dispersed in the circulatory system. These circulating tumor cells (CTCs) diverge from the primary tumors as clusters with a defined secretome comprised of circulating cell-free nucleic acids and small microRNAs (miRNAs). These circulatory biomarkers provide a blueprint of the mutational profile of the tumor burden and tumor associated alterations in the molecular signaling pathways involved in oncogenesis. Amidst the multitude of circulatory biomarkers, miRNAs serve as relatively stable and precise biomarkers in the blood for the early detection of CTCs, and promote step-wise disease progression by executing paracrine signaling that transforms the microenvironment to guide the metastatic CTCs to anchor at a conducive new organ. Random sampling of easily accessible patient blood or its serum/plasma derivatives and other bodily fluids collectively known as liquid biopsy (LB), forms an efficient alternative to tissue biopsies. In this review, we discuss in detail the divergence of early metastases as CTCs and the involvement of miRNAs as detectable blood-based diagnostic biomarkers that warrant a timely screening of cancer, serial monitoring of therapeutic response, and the dynamic molecular adaptations induced by miRNAs on CTCs in guiding primary and second-line systemic therapy.

ENPP2 Promoter Methylation Correlates with Decreased Gene Expression in Breast Cancer: Implementation as a Liquid Biopsy Biomarker

Autotaxin (ATX), encoded by the ctonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2) gene, is a key enzyme in lysophosphatidic acid (LPA) synthesis. We have recently described ENPP2 methylation profiles in health and multiple malignancies and demonstrated correlation to its aberrant expression. Here we focus on breast cancer (BrCa), analyzing in silico publicly available BrCa methylome datasets, to identify differentially methylated CpGs (DMCs) and correlate them with expression. Numerous DMCs were identified between BrCa and healthy breast tissues in the gene body and promoter-associated regions (PA). PA DMCs were upregulated in BrCa tissues in relation to normal, in metastatic BrCa in relation to primary, and in stage I BrCa in relation to normal, and this was correlated to decreased mRNA expression. The first exon DMC was also investigated in circulating cell free DNA (ccfDNA) isolated by BrCa patients; methylation was increased in BrCa in relation to ccfDNA from healthy individuals, confirming in silico results. It also differed between patient groups and was correlated to the presence of multiple metastatic sites. Our data indicate that promoter methylation of ENPP2 arrests its transcription in BrCa and introduce first exon methylation as a putative biomarker for diagnosis and monitoring which can be assessed in liquid biopsy.

Tissue-Specific Methylation Biosignatures for Monitoring Diseases: An In Silico Approach

Tissue-specific gene methylation events are key to the pathogenesis of several diseases and can be utilized for diagnosis and monitoring. Here, we established an in silico pipeline to analyze high-throughput methylome datasets to identify specific methylation fingerprints in three pathological entities of major burden, i.e., breast cancer (BrCa), osteoarthritis (OA) and diabetes mellitus (DM). Differential methylation analysis was conducted to compare tissues/cells related to the pathology and different types of healthy tissues, revealing Differentially Methylated Genes (DMGs). Highly performing and low feature number biosignatures were built with automated machine learning, including: (1) a five-gene biosignature discriminating BrCa tissue from healthy tissues (AUC 0.987 and precision 0.987), (2) three equivalent OA cartilage-specific biosignatures containing four genes each (AUC 0.978 and precision 0.986) and (3) a four-gene pancreatic β-cell-specific biosignature (AUC 0.984 and precision 0.995). Next, the BrCa biosignature was validated using an independent ccfDNA dataset showing an AUC and precision of 1.000, verifying the biosignature's applicability in liquid biopsy. Functional and protein interaction prediction analysis revealed that most DMGs identified are involved in pathways known to be related to the studied diseases or pointed to new ones. Overall, our data-driven approach contributes to the maximum exploitation of high-throughput methylome readings, helping to establish specific disease profiles to be applied in clinical practice and to understand human pathology.

Cell-Free DNA Variables including Gene Mutations in CA15-3 Normal Breast Cancer Reflect Prognosis

Background

Cell-free DNA (cfDNA) has attracted considerable attention in precision medicine. However, few data are available regarding to the prognostic value of cfDNA variables in CA15-3 normal breast cancer (BC) patients. Here, we aimed at investigating the prognostic value of cfDNA variables including gene mutations in CA15-3 normal BC patients.

Methods

A total of 68 BC patients with normal CA15-3 levels were enrolled. cfDNA concentration and integrity were assessed based on qPCR. cfDNA gene mutations were conducted by using next gene sequencing (NGS). The association between cfDNA variables and the prognosis of patients was analyzed.

Results

cfDNA concentration was related to tumor stage (P = 0.002), metastases (P = 0.001), and distant metastases (P < 0.001). The elevated copy number variants (CNV) were found in distant metastasis patients compared with patients without distant metastases (P = 0.008). Nineteen mutant genes were validated in enrolled CA15-3 normal BC patients. Thirty-two patients (47.0%) had single nucleotide variants (SNV), and 13 (19.1%) patients had TP53 mutations (TP53^mut). SNV (P = 0.033) was related to tumor stage, and TP53^mut was related to metastases (P = 0.016) and distant metastases (P = 0.006). In multivariate logistic analysis, cfDNA concentration was associated with metastases (OR = 3.404, 95% CI: 1.074-10.788, P = 0.037) and distant metastases (OR = 13.750, 95% CI: 1.473-128.358, P = 0.021). Cases with high cfDNA levels (>15.6 ng/ml), SNV, and TP53^mut showed worse DFS compared with patients with low cfDNA levels (P < 0.001), without SNV (P = 0.002) and with TP53 wildtype (P < 0.001), respectively. In the multivariate Cox proportional hazard model, cfDNA concentration was an independent predictor of poor survival (HR = 5.786, 95% CI: 1.101-30.407, P = 0.038).

Conclusions

Assessment of cfDNA concentration, CNV, SNV, and TP53^mut could be useful in predicting prognosis for CA15-3 normal BC patients. The cfDNA concentration was an independent predictor prognostic factor in CA15-3 normal BC patients.

Liquid Biopsy in Type 2 Diabetes Mellitus Management: Building Specific Biosignatures via Machine Learning

BACKGROUND

The need for minimally invasive biomarkers for the early diagnosis of type 2 diabetes (T2DM) prior to the clinical onset and monitoring of β-pancreatic cell loss is emerging. Here, we focused on studying circulating cell-free DNA (ccfDNA) as a liquid biopsy biomaterial for accurate diagnosis/monitoring of T2DM.

METHODS

ccfDNA levels were directly quantified in sera from 96 T2DM patients and 71 healthy individuals via fluorometry, and then fragment DNA size profiling was performed by capillary electrophoresis. Following this, ccfDNA methylation levels of five β-cell-related genes were measured via qPCR. Data were analyzed by automated machine learning to build classifying predictive models.

RESULTS

ccfDNA levels were found to be similar between groups but indicative of apoptosis in T2DM. INS (Insulin), IAPP (Islet Amyloid Polypeptide-Amylin), GCK (Glucokinase), and KCNJ11 (Potassium Inwardly Rectifying Channel Subfamily J member 11) levels differed significantly between groups. AutoML analysis delivered biosignatures including GCK, IAPP and KCNJ11 methylation, with the highest ever reported discriminating performance of T2DM from healthy individuals (AUC 0.927).

CONCLUSIONS

Our data unravel the value of ccfDNA as a minimally invasive biomaterial carrying important clinical information for T2DM. Upon prospective clinical evaluation, the built biosignature can be disruptive for T2DM clinical management.

Aberrant Methylation of 20 miRNA Genes Specifically Involved in Various Steps of Ovarian Carcinoma Spread: From Primary Tumors to Peritoneal Macroscopic Metastases

Our work aimed to differentiate 20 aberrantly methylated miRNA genes that participate at different stages of development and metastasis of ovarian carcinoma (OvCa) using methylation-specific qPCR in a representative set of clinical samples: 102 primary tumors without and with metastases (to lymph nodes, peritoneum, or distant organs) and 30 peritoneal macroscopic metastases (PMM). Thirteen miRNA genes (MIR107, MIR124-2, MIR124-3, MIR125B-1, MIR127, MIR129-2, MIR130B, MIR132, MIR193A, MIR339, MIR34B/C, MIR9-1, and MIR9-3) were hypermethylated already at the early stages of OvCa, while hypermethylation of MIR1258, MIR137, MIR203A, and MIR375 was pronounced in metastatic tumors, and MIR148A showed high methylation levels specifically in PMM. We confirmed the significant relationship between methylation and expression levels for 11 out of 12 miRNAs analyzed by qRT-PCR. Moreover, expression levels of six miRNAs were significantly decreased in metastatic tumors in comparison with nonmetastatic ones, and downregulation of miR-203a-3p was the most significant. We revealed an inverse relationship between expression levels of miR-203a-3p and those of ZEB1 and ZEB2 genes, which are EMT drivers. We also identified three miRNA genes (MIR148A, MIR9-1, and MIR193A) that likely regulate EMT–MET reversion in the colonization of PMM. According to the Kaplan–Meier analysis, hypermethylation of several examined miRNA genes was associated with poorer overall survival of OvCa patients, and high methylation levels of MIR130B and MIR9-1 were related to the greatest relative risk of death.

Computational challenges in detection of cancer using cell-free DNA methylation

Cell-free DNA(cfDNA) methylation profiling is considered promising and potentially reliable for liquid biopsy to study progress of diseases and develop reliable and consistent diagnostic and prognostic biomarkers. There are several different mechanisms responsible for the release of cfDNA in blood plasma, and henceforth it can provide information regarding dynamic changes in the human body. Due to the fragmented nature, low concentration of cfDNA, and high background noise, there are several challenges in its analysis for regular use in diagnosis of cancer. Such challenges in the analysis of the methylation profile of cfDNA are further aggravated due to heterogeneity, biomarker sensitivity, platform biases, and batch effects. This review delineates the origin of cfDNA methylation, its profiling, and associated computational problems in analysis for diagnosis. Here we also contemplate upon the multi-marker approach to handle the scenario of cancer heterogeneity and explore the utility of markers for 5hmC based cfDNA methylation pattern. Further, we provide a critical overview of deconvolution and machine learning methods for cfDNA methylation analysis. Our review of current methods reveals the potential for further improvement in analysis strategies for detecting early cancer using cfDNA methylation.

Aberrant Methylation of 21 MicroRNA Genes in Breast Cancer: Sets of Genes Associated with Progression and a System of Markers for Predicting Metastasis

Systemic analysis of the relationship between the levels of methylation of 21 microRNA genes and the parameters of breast cancer progression was performed on a representative sample of 91 paired specimens of breast cancer and histologically normal tissues and a system of markers for prediction of metastasis was proposed. A significant association of hypermethylation of 11 genes with late (III-IV) clinical stages was found, and for 6 genes (MIR124-1, MIR127, MIR34B/C, MIR9-3, MIR1258, and MIR339) this association was highly significant (p≤0.001, FDR=0.01). For MIR9-3 and MIR339, an association with tumor size was demonstrated (p<0.001, FDR=0.01). No association of the levels of methylation of the analyzed microRNA genes with the degree of differentiation were found. An association with lymph node metastasis was established for 9 microRNA genes; the most significant association was shown for 6 genes MIR125B-1, MIR127, MIR9-3, MIR339, MIR124-3, and MIR1258 (p<0.005, FDR=0.05). Based on these 6 genes, a marker system for predicting breast cancer metastasis was developed by ROC analysis. This system is characterized by 87% sensitivity and 77% specificity (AUC=0.894). The proposed system may have clinical application in the personalized treatment of breast cancer patients.

Optimized Marker System for Early Diagnosis of Breast Cancer

Changes in the methylation levels of 21 microRNA genes in 91 breast cancer samples in comparison with paired samples of histologically unchanged tissue were studied by quantitative methylation-specific PCR. For 19 microRNA genes, a significant increase in the methylation level in tumors in comparison with normal tissues was shown (Mann-Whitney test). When considering the data for breast cancer samples only from patients with clinical stages I and II (59samples), 17 genes with a significantly increased level of methylation were identified. Increased methylation level for 11 genes (MIR124-1, MIR124-3, MIR125B-1, MIR127, MIR129-2, MIR132, MIR137, MIR193a, MIR34B/C, MIR375, and MIR9-1) compared to the paired norm was highly significant (p<0.001, FDR=0.01). The ROC analysis was used to optimize a set of markers for diagnosing breast cancer at the early stages consisting of 4 microRNA genes: MIR125B1, MIR127, MIR1258, and MIR132; the system is characterized by 100% specificity, 85% sensitivity, and AUC=0.924. Importantly, 100% specificity eliminates false positive results. Detection of methylation of at least one of the 4 genes of this set is sufficient to classify the patient's sample as breast cancer.

ENPP2 Methylation in Health and Cancer

Autotaxin (ATX) encoded by Ectonucleotide Pyrophosphatase/Phosphodiesterase 2 (ENPP2) is a key enzyme in Lysophosphatidic Acid (LPA) synthesis implicated in cancer. Although its aberrant expression has been reported, ENPP2 methylation profiles in health and malignancy are not described. We examined in silico the methylation of ENPP2 analyzing publicly available methylome datasets, to identify Differentially Methylated CpGs (DMCs) which were then correlated with expression at gene and isoform levels. Significance indication was set to be FDR corrected p-value < 0.05. Healthy tissues presented methylation in all gene body CGs and lower levels in Promoter Associated (PA) regions, whereas in the majority of the tumors examined (HCC, melanoma, CRC, LC and PC) the methylation pattern was reversed. DMCs identified in the promoter were located in sites recognized by multiple transcription factors, suggesting involvement in gene expression. Alterations in methylation were correlated to an aggressive phenotype in cancer cell lines. In prostate and lung adenocarcinomas, increased methylation of PA CGs was correlated to decreased ENPP2 mRNA expression and to poor prognosis parameters. Collectively, our results corroborate that methylation is an active level of ATX expression regulation in cancer. Our study provides an extended description of the methylation status of ENPP2 in health and cancer and points out specific DMCs of value as prognostic biomarkers.

Relationship of the Levels of microRNA Gene Methylation with the Level of Their Expression and Pathomorphological Characteristics of Breast Cancer

We studied the relationship of the levels of microRNA group expression and methylation with clinical and pathomorphological parameters of breast cancer and its immunohistochemical status. Quantitative methylation specific PCR analysis showed a significant (p<0.001) increase in the methylation level of 4 microRNA genes (MIR127, MIR129-2, MIR132, and MIR148A) and a significant (p<0.001) decrease for gene MIR375 relative to paired histologically normal tissue. Real-time PCR analysis revealed a significant (p≤0.001) decrease in the expression of 4 microRNAs (miR-127-5p, miR-129-5p, miR-132-3p, and miR-148a-3p) and a significant (p≤0.001) increase in the expression of miR-375-3p. A significant (rs=-0.6--0.7, p≤0.001) relationship between changes in the expression level of miR-129-5p, miR-132-3p, miR-148a-3p, and miR-375-3p and the levels of methylation of the corresponding genes in breast cancer was showed by using Spearman's rank correlation test. Analysis of the samples with consideration of the pathophysiological characteristics of the tumor revealed two significant markers of tumor progression: MIR129-2/miR-129-5p and MIR375/miR-375-3p. Both factors, the increase in the level of MIR129-2 methylation (p<0.001) and a decrease in the expression level of miR-129-5p (p<0.001), are significantly associated (p<0.001) with stage III/IV and the absence of HER2 expression. For MIR375/miR-375-3p, on the contrary, an association of low methylation level and enhanced expression with increased Ki-67 level (>30%, p<0.05) was revealed. These findings are of interest for understanding the mechanisms of breast cancer development and can provide the basis for the diagnosis and prognosis of the course of this disease. Moreover, the revealed features can be useful for adjusting the course of treatment with consideration of the pathophysiological characteristics of the tumor.

Liquid Biopsy to Detect Minimal Residual Disease: Methodology and Impact

One reason why some patients experience recurrent disease after a curative-intent treatment might be the persistence of residual tumor cells, called minimal residual disease (MRD). MRD cannot be identified by standard radiological exams or clinical evaluation. Tumor-specific alterations found in the blood indirectly diagnose the presence of MRD. Liquid biopsies thus have the potential to detect MRD, allowing, among other things, the detection of circulating tumor DNA (ctDNA), circulating tumor cells (CTC), or tumor-specific microRNA. Although liquid biopsy is increasingly studied, several technical issues still limit its clinical applicability: low sensitivity, poor standardization or reproducibility, and lack of randomized trials demonstrating its clinical benefit. Being able to detect MRD could give clinicians a more comprehensive view of the risk of relapse of their patients and could select patients requiring treatment escalation with the goal of improving cancer survival. In this review, we are discussing the different methodologies used and investigated to detect MRD in solid cancers, their respective potentials and issues, and the clinical impacts that MRD detection will have on the management of cancer patients.

Host methylation predicts SARS-CoV-2 infection and clinical outcome

BACKGROUND

Since the onset of the SARS-CoV-2 pandemic, most clinical testing has focused on RT-PCR1. Host epigenome manipulation post coronavirus infection2-4 suggests that DNA methylation signatures may differentiate patients with SARS-CoV-2 infection from uninfected individuals, and help predict COVID-19 disease severity, even at initial presentation.

METHODS

We customized Illumina's Infinium MethylationEPIC array to enhance immune response detection and profiled peripheral blood samples from 164 COVID-19 patients with longitudinal measurements of disease severity and 296 patient controls.

RESULTS

Epigenome-wide association analysis revealed 13,033 genome-wide significant methylation sites for case-vs-control status. Genes and pathways involved in interferon signaling and viral response were significantly enriched among differentially methylated sites. We observe highly significant associations at genes previously reported in genetic association studies (e.g. IRF7, OAS1). Using machine learning techniques, models built using sparse regression yielded highly predictive findings: cross-validated best fit AUC was 93.6% for case-vs-control status, and 79.1%, 80.8%, and 84.4% for hospitalization, ICU admission, and progression to death, respectively.

CONCLUSIONS

In summary, the strong COVID-19-specific epigenetic signature in peripheral blood driven by key immune-related pathways related to infection status, disease severity, and clinical deterioration provides insights useful for diagnosis and prognosis of patients with viral infections.

Integrative analysis of genome-wide DNA methylation and single-nucleotide polymorphism identified ACSM5 as a suppressor of lumbar ligamentum flavum hypertrophy

BACKGROUND

Hypertrophy of ligamentum flavum (HLF) is a common lumbar degeneration disease (LDD) with typical symptoms of low back pain and limb numbness owing to an abnormal pressure on spinal nerves. Previous studies revealed HLF might be caused by fibrosis, inflammatory, and other bio-pathways. However, a global analysis of HLF is needed severely.

METHODS

A genome-wide DNA methylation and single-nucleotide polymorphism analysis were performed from five LDD patients with HLF and five LDD patients without HLF. Comprehensive integrated analysis was performed using bioinformatics analysis and the validated experiments including Sanger sequencing, methylation-specific PCR, qPCR and ROC analysis. Furthermore, the function of novel genes in ligamentum flavum cells (LFCs) was detected to explore the molecular mechanism in HLF through knock down experiment, overexpression experiment, CCK8 assay, apoptosis assay, and so on.

RESULTS

We identified 69 SNP genes and 735 661 differentially methylated sites that were enriched in extracellular matrix, inflammatory, and cell proliferation. A comprehensive analysis demonstrated key genes in regulating the development of HLF including ACSM5. Furthermore, the hypermethylation of ACSM5 that was mediated by DNMT1 led to downregulation of ACSM5 expression, promoted the proliferation and fibrosis, and inhibited the apoptosis of LFCs.

CONCLUSION

This study revealed that DNMT1/ACSM5 signaling could enhance HLF properties in vitro as a potential therapeutic strategy for HLF.

Circulating Biomarkers in Breast Cancer

Breast cancer management has progressed immensely over the decades, but the disease is still a major source of morbidity and mortality worldwide. Even with enhanced imaging detection and tissue biopsy capabilities, disease can progress on an ineffective treatment before additional information is obtained through standard methods of response evaluation, including the RECIST 1.1 criteria, widely used for assessment of treatment response and benefit from therapy.⁶ Circulating biomarkers have the potential to provide valuable insight into disease progression and response to therapy, and they can serve to identify actionable mutations and tumor characteristics that can direct therapy. These biomarkers can be collected at higher frequencies than imaging or tissue sampling, potentially allowing for more informed management. This review will evaluate the roles of circulating biomarkers in breast cancer, including the serum markers Carcinoembryonic antigen CA15-3, CA27-29, HER2 ECD, and investigatory markers such as GP88; and the components of the liquid biopsy, including circulating tumor cells, cell free DNA/DNA methylation, circulating tumor DNA, and circulating microRNA.

Size distribution of cell-free DNA in oncology

Tumor-specific, circulating cell-free DNA (cfDNA) in liquid biopsy test is a novel promising biomarker in the advancement of cancer management, including early diagnosis, screening, prognosis, identification of actionable targets, and serial tumor monitoring. The specific size pattern of DNA fragments derived from cancer cells is observed to differ from that of cfDNA fragments shed by non-cancer cells. Research into the physiological and biological properties of cfDNA reveals the molecular signature carried by each cfDNA fragments, which can reflect their tissue origins, as well as the mutational profiles with significant genetic alterations. Understanding the fragmentation and size distribution of cfDNA might be a valuable hotspot in liquid biopsy research, with the potential to drive innovation in oncology.

The relevance of liquid biopsy in surgical oncology: The application of perioperative circulating nucleic acid dynamics in improving patient outcomes

BACKGROUND

Liquid biopsy is gaining increasing clinical utility in the management of cancer patients. The main components of a liquid biopsy are circulating nucleic acids, circulating tumour cells and extracellular vesicles such as exosomes. Circulating nucleic acids including cell free DNA (cfDNA) and circulating tumour DNA (ctDNA) in particular have been the focus of recent attention as they have demonstrated excellent potential in cancer screening, provision of prognostic information and in genomic profiling of a tumour without the need for repeated tissue biopsies. The aim of this review was to explore the current evidence in relation to the use of liquid biopsy in the perioperative setting and identify ways in which liquid biopsy may be applied in the future.

METHODS

This narrative review is based on a comprehensive literature search up to the 1st of June 2020 for papers relevant to the application of liquid biopsy in surgical oncology, focusing particularly on the perioperative period.

RESULTS

Recent evidence has demonstrated that perioperative liquid biopsy can accurately stratify patients' risk of recurrence compared to conventional biomarkers. Attention to the perioperative dynamics of liquid biopsy components can potentially provide new understanding of the complex relationship between surgery and cancer outcome. In addition, careful evaluation of liquid biopsy components in the perioperative window may provide important diagnostic and therapeutic information for cancer patients.

CONCLUSION

The rapidly evolving concept of the liquid biopsy has the potential to become the cornerstone for decision making around surveillance and adjuvant therapies the era of personalised medicine.

Detection of Cell Types Contributing to Cancer From Circulating, Cell-Free Methylated DNA

Detection of cellular changes in tissue biopsies has been the basis for cancer diagnostics. However, tissue biopsies are invasive and limited by inaccuracies due to sampling locations, restricted sampling frequency, and poor representation of tissue heterogeneity. Liquid biopsies are emerging as a complementary approach to traditional tissue biopsies to detect dynamic changes in specific cell populations. Cell-free DNA (cfDNA) fragments released into the circulation from dying cells can be traced back to the tissues and cell types they originated from using DNA methylation, an epigenetic regulatory mechanism that is highly cell-type specific. Decoding changes in the cellular origins of cfDNA over time can reveal altered host tissue homeostasis due to local cancer invasion and metastatic spread to distant organs as well as treatment responses. In addition to host-derived cfDNA, changes in cancer cells can be detected from cell-free, circulating tumor DNA (ctDNA) by monitoring DNA mutations carried by cancer cells. Here, we will discuss computational approaches to identify and validate robust biomarkers of changed tissue homeostasis using cell-free, methylated DNA in the circulation. We highlight studies performing genome-wide profiling of cfDNA methylation and those that combine genetic and epigenetic markers to further identify cell-type specific signatures. Finally, we discuss opportunities and current limitations of these approaches for implementation in clinical oncology.