A framework for clinical cancer subtyping from nucleosome profiling of cell-free DNA

Liquid Biopsy Approaches for Cancer Characterization, Residual Disease Detection, and Therapy Monitoring

Liquid biopsy encompasses a variety of molecular approaches to detect circulating tumor DNA (ctDNA) and has become a powerful tool in the diagnosis and treatment of solid tumors. Current applications include comprehensive genomic profiling for identifying targetable mutations and therapeutic resistance mechanisms, with emerging applications in minimal residual disease detection and treatment response monitoring. Increasingly, the potential for liquid biopsy in guiding treatment decisions is under active investigation through prospective clinical trials using ctDNA-adaptive interventions in patients with early-stage and metastatic cancers. Limitations arise on the basis of the sensitivity and feasibility of individual liquid biopsy assays; nonetheless, emerging technologies set the stage for improving these shortcomings. As the global oncology community continues to ascertain the clinical value of liquid biopsy across the continuum of patient care, this minimally invasive approach heralds a significant advancement in the promise of precision oncology.

Early detection of multiple cancer types using multidimensional cell-free DNA fragmentomics

The multicancer early detection (MCED) test has the potential to enhance current cancer-screening methods. We evaluated a new MCED test that analyzes plasma cell-free DNA using genetic- and fragmentomics-based features from whole-genome sequencing. The present study included an internal validation cohort of 3,021 patients with cancer and 3,370 noncancer controls, and an independent cohort of 677 patients with cancer and 687 noncancer individuals. The results demonstrated an overall sensitivity of 87.4%, specificity of 97.8% and tissue-of-origin prediction accuracy of 82.4% in the independent validation cohort. Preliminary results from a prospective study of 3,724 asymptomatic participants showed a sensitivity of 53.5% (predominantly early stage cancers) and specificity of 98.1%. These findings indicate that the MCED test has strong potential to improve early cancer detection and support clinical decision-making.

Longitudinal Profiling of Circulating Tumor DNA Reveals the Evolutionary Dynamics of Metastatic Prostate Cancer during Serial Therapy

Treatment decisions in metastatic castration-resistant prostate cancer are mostly guided by clinical variables, but efforts to molecularly monitor the disease remain hampered by challenges in acquiring tumor tissue repeatedly. In this study, we simultaneously profiled the genome copy number and exome in longitudinal plasma circulating tumor DNA (ctDNA) acquired before, during, and upon progression to serial treatments with androgen signaling inhibitors and taxane chemotherapy from 60 patients with metastatic castration-resistant prostate cancer (2-10 samples per patient). The genomic data were used to delineate the clonal substructure and evolutionary dynamics of each patient, and an evolutionary dynamic index was developed to measure the longitudinal changes of the tumor subclones. Treatment with androgen signaling inhibitors resulted in greater subclonal selection and population structure changes than taxane treatment. The subclones that emerged in association with serial therapy resistance harbored recurrent aberrations in previously identified and new candidate genes, with particular enrichment in genes related to PI3K-AKT signaling. These findings indicate that the integration of detailed clinical and genomic data can provide a framework for future unbiased genomic applications for ctDNA in the clinic to enable precision medicine. Significance: Profiling of the genomic copy number changes and mutations in circulating tumor DNA collected longitudinally from prostate cancer patients receiving serial life-prolonging therapies elucidates evolutionary dynamics and identifies emerging resistant subclones.

Genomic and fragmentomic landscapes of cell-free DNA for early cancer detection

Genomic analyses of cell-free DNA (cfDNA) in plasma are enabling noninvasive blood-based biomarker approaches to cancer detection and disease monitoring. Current approaches for identification of circulating tumour DNA typically use targeted tumour-specific mutations or methylation analyses. An emerging approach is based on the recognition of altered genome-wide cfDNA fragmentation in patients with cancer. Recent studies have revealed a multitude of characteristics that can affect the compendium of cfDNA fragments across the genome, collectively called the 'cfDNA fragmentome'. These changes result from genomic, epigenomic, transcriptomic and chromatin states of an individual and affect the size, position, coverage, mutation, structural and methylation characteristics of cfDNA. Identifying and monitoring these changes has the potential to improve early detection of cancer, especially using highly sensitive multi-feature machine learning approaches that would be amenable to broad use in populations at increased risk. This Review highlights the rapidly evolving field of genome-wide analyses of cfDNA characteristics, their comparison to existing cfDNA methods, and recent related innovations at the intersection of large-scale sequencing and artificial intelligence. As the breadth of clinical applications of cfDNA fragmentome methods have enormous public health implications for cancer screening and personalized approaches for clinical management of patients with cancer, we outline the challenges and opportunities ahead.

Analysis of the clinical application value of cfDNA methylation and fragmentation in early diagnosis of esophageal cancer

BACKGROUND

This study explores the clinical value of cfDNA methylation and fragmentation for the early diagnosis of esophageal cancer using liquid biopsy.

METHODS

Whole genome bisulfite sequencing and low-pass whole genome sequencing were utilized to detect cfDNA biomarkers, comparing 30 esophageal cancer patients with 10 healthy controls.

RESULTS

Significant differences in cfDNA methylation and fragmentation were observed between cancerous and non-cancerous samples (p < 0.05). A volcano plot identified 822 differentially methylated markers (817 upregulated, 5 downregulated), with SOX17, SOX1, ZNF382, ZNF667-AS1, and TFPI2 highly associated with esophageal cancer. Fragmentation markers (EDM, FSD, FSR, TFBS, CNV) showed 95 % specificity and sensitivity, with EDM demonstrating the best performance.

CONCLUSION

Our study highlights the clinical potential of cfDNA methylation and fragmentation biomarkers for the early diagnosis of esophageal cancer.

Cell-Free DNA: Features and Attributes Shaping the Next Frontier in Liquid Biopsy

Cell-free DNA (cfDNA) is changing the face of liquid biopsy as a minimally invasive tool for disease detection and monitoring, with its main applications in oncology and prenatal testing, and rising roles in transplant patient monitoring. However, the processes of cfDNA biogenesis, fragmentation, and clearance are complex and require further investigation. Evidence suggests that cfDNA production relates to mechanisms of cell death and DNA repair, both of which further influence fragment size and its applicability as a biomarker. An emerging domain, cfDNA fragmentomics is being explored for advancing the field of diagnostics using non-mutational signatures such as fragment size ratios and methylation patterns. Thus, this review examines structural diversity in cfDNA with various fragment sizes. In examining these cfDNA subsets, we discuss their distinct biological origins and potential clinical utility. Development of sequencing methodologies has broadened the application of cfDNA in diagnosing cancers and organ-specific pathologies, as well as directing personalized therapies. This has been achieved by identifying and uncovering different subsets of cfDNA in biofluids using different methodologies and biofluids. Different cfDNA subsets provide important insights regarding genomic and epigenetic features, enhancing the understanding of gene regulation, tissue-specific functions, and disease progression. Advancement of these key areas further asserts increasing clinical relevance for the use of cfDNA as a biomarker. Continued exploration of cfDNA subsets is expected to drive further innovation in liquid biopsy and its integration into routine clinical practice.

Stratified Medicine Pediatrics: Cell-Free DNA and Serial Tumor Sequencing Identifies Subtype-Specific Cancer Evolution and Epigenetic States

SIGNIFICANCE

In tumors of childhood, we identify mutations in epigenetic genes as drivers of relapse, with matched cfDNA sequencing showing significant intratumor genetic heterogeneity and cell-state specific patterns of chromatin accessibility. This highlights the power of cfDNA analysis to identify both genetic and epigenetic drivers of aggressive disease in pediatric cancers.

Preeclampsia risk prediction from prenatal cell-free DNA screening

Preeclampsia is characterized by placental dysfunction and results in significant morbidity, but reliable early prediction remains challenging. We investigated whether clinically obtained prenatal cell-free DNA (cfDNA) screening (PDNAS) using whole-genome sequencing (WGS) data can be leveraged to predict preeclampsia risk early in pregnancy (≤16 weeks). Using 1,854 routinely collected clinical PDNAS samples (median, 12.1 weeks) with low-coverage (0.5×) WGS data, we developed a framework to quantify maternal and fetal tissue signatures using nucleosome accessibility, revealing early placental and endothelial dysfunction. These signatures informed a prediction model for preeclampsia risk, which achieved a validation performance of 0.85 area under the receiver operating characteristic curve (AUC) (81% sensitivity at 80% specificity) for preterm phenotypes several months prior to disease onset in a separate cohort of 831 consecutively collected samples, and subsequently confirmed in an external cohort of 141 samples (AUC 0.84, 79% sensitivity). We demonstrate that assessment of cfDNA nucleosome accessibility from early-pregnancy cfDNA sequence data enables the detection of early placental and endothelial-tissue aberrations and may aid in the determination of preeclampsia risk.

Circulating tumor DNA to monitor treatment response in solid tumors and advance precision oncology

Circulating tumor DNA (ctDNA) has emerged as a dynamic biomarker in cancer, as evidenced by its increasing integration into clinical practice. Carrying tumor specific characteristics, ctDNA can be used to inform treatment selection, monitor response, and identify drug resistance. In this review, we provide a comprehensive, up-to-date summary of ctDNA in monitoring treatment response with a focus on lung, colorectal, and breast cancers, and discuss current challenges and future directions.

Artificial Intelligence in Gynecological Oncology from Diagnosis to Surgery

Background: The field of medicine, both clinical and surgical, has recently been overwhelmed by artificial intelligence technology, which promises countless application scenarios and, above all, implementation in clinical practice and research. Novelties are riding the wave fast, but where do we stand? A small overview in gynecological oncology of future challenges, evidence already investigated, and possible scenarios to be derived was conducted. Methods: Both diagnostic and surgical work in the field of gynecological oncology was conducted, selecting the most interesting articles on the subject. Results: From the narrative review of the literature, it emerged how much further ahead the diagnostic field is at present compared to the surgical one, which appeared to be more limited to ovarian surgery. Most current evidence focuses on the role of different biomarkers in predicting diagnostic, prognostic, and treatment-integrated patterns. Conclusions: Everything we know to date is related to a dynamic photograph that is constantly and rapidly changing as much as AI is becoming inextricably linked to our medical field.

Lessons (to be) learned from liquid biopsies: assessment of circulating cells and cell-free DNA in cancer and pregnancy-acquired microchimerism

Tumors constantly shed cancer cells that are considered the mediators of metastasis via the blood stream. Analysis of circulating cells and circulating cell-free DNA (cfDNA) in liquid biopsies, mostly taken from peripheral blood, have emerged as powerful biomarkers in oncology, as they enable the detection of genomic aberrations. Similarly, liquid biopsies taken from pregnant women serve as prenatal screening test for an abnormal number of chromosomes in the fetus, e.g., via the analysis of microchimeric fetal cells and cfDNA circulating in maternal blood. Liquid biopsies are minimally invasive and, consequently, associated with reduced risks for the patients. However, different challenges arise in oncology and pregnancy-acquired liquid biopsies with regard to the analyte concentration and biological (background) noise among other factors. In this review, we highlight the unique biological properties of circulating tumor cells (CTC), summarize the various techniques that have been developed for the enrichment, detection and analysis of CTCs as well as for analysis of genetic and epigenetic aberrations in cfDNA and highlight the range of possible clinical applications. Lastly, the potential, but also the challenges of liquid biopsies in oncology as well as their translational value for the analysis of pregnancy-acquired microchimerism are discussed.

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

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

Personalized Medicine: Leave no Patient Behind; Report From the 2024 Coffey-Holden Prostate Cancer Academy Meeting

INTRODUCTION

The 11th Annual 2024 Coffey - Holden Prostate Cancer Academy (CHPCA) Meeting, was themed "Personalized Medicine: Leave No Patient Behind," and was held from June 20 to 23, 2024 at the University of California, Los Angeles, Luskin Conference Center, in Los Angeles, CA.

METHODS

The CHPCA Meeting is an academy-styled annual conference organized by the Prostate Cancer Foundation, to focus discussion on the most critical emerging research that have the greatest potential to advance knowledge of prostate cancer biology and treatment. The 2024 CHPCA Meeting was attended by 75 academic investigators and included 37 talks across 8 sessions.

RESULTS

The meeting sessions focused on: novel human, mouse and systems biology research models, novel immunotherapies for prostate cancer, efforts to overcome treatment resistance, the role of metabolism and diet in prostate cancer biology and as a therapeutic target, mechanisms that drive differentiation into neuroendocrine cancer subtypes, the evolving prostate cancer epigenome in disease progression and treatment resistance, and machine learning and advanced computational approaches for precision oncology.

DISCUSSION

This article summarizes the presentations and discussions from the 2024 CHPCA Meeting. We hope that sharing this knowledge will inspire and accelerate research into new discoveries and solutions for prostate cancer.

Exploration of ANKRD27 as an immune-related prognostic factor in pan-cancer and hepatocellular carcinoma

Introduction

Ankyrin repeat domain 27 (ANKRD27) has been found to be associated with certain cancers. However, its clinical potential in pan-cancer remains unclear.

Methods

Public datasets (TCGA and GTEx) were applied to analyze ANKRD27 expression in multiple cancer types and its correlations with immune scores, immune checkpoint genes, and immune modulatory genes. We also examined ANKRD27 expression in hepatocellular carcinoma (HCC) patients using TCGA and GSE14520 datasets. The upregulation of ANKRD27 was verified via qRT-PCR in vitro. Based on TCGA-HCC, external, and GSE14520 cohorts, the associations between ANKRD27 expression and survival outcome were explored via the Kaplan-Meier survival curve. The effects of ANKRD27 reduction on HCC cell growth, movement, and invasion were evaluated by CCK-8, Wound healing, and Transwell assays.

Results

ANKRD27 exhibited aberrant expression in multiple cancers and was correlated with immune traits, including immune infiltration, immune checkpoint genes, and immune modulatory genes. Elevated expression of ANKRD27 was found in TCGA-HCC and GSE14520 cohorts and was confirmed in HCC cell lines. HCC patients with high ANKRD27 expression had poorer prognosis. In vitro, reducing ANKRD27 decreased the capability of proliferation, migration, and invasion in HCC cells. High ANKRD27 expression was associated with sensitivity to certain drugs.

Conclusion

ANKRD27 displays abnormal levels of expression in different cancer types and is linked to immune status in cancer. Furthermore, ANKRD27 may serve as a prognostic predictor for HCC.

Genomic and Epigenomic Analysis of Plasma Cell-Free DNA Identifies Stemness Features Associated with Worse Survival in Lethal Prostate Cancer

PURPOSE

Metastatic castration-resistant prostate cancer (mCRPC) resistant to androgen receptor signaling inhibitors (ARSI) is often lethal. Liquid biopsy biomarkers for this deadly form of disease remain under investigation, and underpinning mechanisms remain ill-understood.

EXPERIMENTAL DESIGN

We applied targeted cell-free DNA (cfDNA) sequencing to 126 patients with mCRPC from three academic cancer centers and separately performed genome-wide cfDNA methylation sequencing on 43 plasma samples collected prior to the initiation of first-line ARSI treatment. To analyze the genome-wide sequencing data, we performed nucleosome positioning and differential methylated region analysis. We additionally analyzed single-cell and bulk RNA sequencing data from 14 and 80 patients with mCRPC, respectively, to develop and validate a stem-like signature, which we inferred from cfDNA.

RESULTS

Targeted cfDNA sequencing detected AR/enhancer alterations prior to first-line ARSIs that correlated with significantly worse progression-free survival (P = 0.01; HR = 2.12) and overall survival (P = 0.02; HR = 2.48). Plasma methylome analysis revealed that AR/enhancer lethal mCRPC patients have significantly higher promoter-level hypomethylation than AR/enhancer wild-type mCRPC patients (P < 0.0001). Moreover, gene ontology and CytoTRACE analysis of nucleosomally more accessible transcription factors in cfDNA revealed enrichment for stemness-associated transcription factors in patients with lethal mCRPC. The resulting stemness signature was then validated in a completely held-out cohort of 80 patients with mCRPC profiled by tumor RNA sequencing.

CONCLUSIONS

We analyzed a total of 220 patients with mCRPC, validated the importance of cell-free AR/enhancer alterations as a prognostic biomarker in lethal mCRPC, and showed that the underlying mechanism for lethality involves reprogramming developmental states toward increased stemness. See related commentary by Nawfal et al., p. 7.

Modulating cell-free DNA biology as the next frontier in liquid biopsies

Technical advances over the past two decades have enabled robust detection of cell-free DNA (cfDNA) in biological samples. Yet, higher clinical sensitivity is required to realize the full potential of liquid biopsies. This opinion article argues that to overcome current limitations, the abundance of informative cfDNA molecules - such as circulating tumor DNA (ctDNA) - collected in a sample needs to increase. To accomplish this, new methods to modulate the biological processes that govern cfDNA production, trafficking, and clearance in the body are needed, informed by a deeper understanding of cfDNA biology. Successful development of such methods could enable a major leap in the performance of liquid biopsies and vastly expand their utility across the spectrum of clinical care.

LBFextract: Unveiling transcription factor dynamics from liquid biopsy data

Motivation

The analysis of circulating cell-free DNA (cfDNA) holds immense promise as a non-invasive diagnostic tool across various human conditions. However, extracting biological insights from cfDNA fragments entails navigating complex and diverse bioinformatics methods, encompassing not only DNA sequence variation, but also epigenetic characteristics like nucleosome footprints, fragment length, and methylation patterns.

Results

We introduce Liquid Biopsy Feature extract (LBFextract), a comprehensive package designed to streamline feature extraction from cfDNA sequencing data, with the aim of enhancing the reproducibility and comparability of liquid biopsy studies. LBFextract facilitates the integration of preprocessing and postprocessing steps through alignment fragment tags and a hook mechanism. It incorporates various methods, including coverage-based and fragment length-based approaches, alongside two novel feature extraction methods: an entropy-based method to infer TF activity from fragmentomics data and a technique to amplify signals from nucleosome dyads. Additionally, it implements a method to extract condition-specific differentially active TFs based on these features for biomarker discovery. We demonstrate the use of LBFextract for the subtype classification of advanced prostate cancer patients using coverage signals at transcription factor binding sites from cfDNA. We show that LBFextract can generate robust and interpretable features that can discriminate between different clinical groups. LBFextract is a versatile and user-friendly package that can facilitate the analysis and interpretation of liquid biopsy data.

Data and Code Availability and Implementation

LBFextract is freely accessible at https://github.com/Isy89/LBF. It is implemented in Python and compatible with Linux and Mac operating systems. Code and data to reproduce these analyses have been uploaded to 10.5281/zenodo.10964406.

Evaluation and integration of cell-free DNA signatures for detection of lung cancer

Cell-free DNA (cfDNA) analysis has shown potential in detecting early-stage lung cancer based on non-genetic features. To distinguish patients with lung cancer from healthy individuals, peripheral blood were collected from 926 lung cancer patients and 611 healthy individuals followed by cfDNA extraction. Low-pass whole genome sequencing and targeted methylation sequencing were conducted and various features of cfDNA were evaluated. With our customized algorithm using the most optimal features, the ensemble stacked model was constructed, called ESim-seq (Early Screening tech with Integrated Model). In the independent validation cohort, the ESim-seq model achieved an area under the curve (AUC) of 0.948 (95 % CI: 0.915-0.981), with a sensitivity of 79.3 % (95 % CI: 71.5-87.0 %) across all stages at a specificity of 96.0 % (95 % CI: 90.6-100.0 %). Specifically, the sensitivity of the ESim-seq model was 76.5 % (95 % CI: 67.3-85.8 %) in stage I patients, 100 % (95 % CI: 100.0-100.0 %) in stage II patients, 100 % (95 % CI: 100.0-100.0 %) in stage III patients and 87.5 % (95 % CI: 64.6%-100.0 %) in stage IV patients in the independent validation cohort. Besides, we constructed LCSC model (Lung Cancer Subtype multiple Classification), which was able to accurately distinguish patients with small cell lung cancer from those with non-small cell lung cancer, achieving an AUC of 0.961 (95 % CI: 0.949-0.957). The present study has established a framework for assessing cfDNA features and demonstrated the benefits of integrating multiple features for early detection of lung cancer.

Cell-free nucleic acid fragmentomics: A non-invasive window into cellular epigenomes

Clinical genomic profiling of cell-free nucleic acids (e.g. cell-free DNA or cfDNA) from blood and other body fluids has ushered in a new era in non-invasive diagnostics and treatment monitoring strategies for health conditions and diseases such as cancer. Genomic analysis of cfDNAs not only identifies disease-associated mutations, but emerging findings suggest that structural, topological, and fragmentation characteristics of cfDNAs reveal crucial information about the location of source tissues, their epigenomes, and other clinically relevant characteristics, leading to the burgeoning field of fragmentomics. The field has seen rapid developments in computational and genomics methodologies for conducting large-scale studies on health conditions and diseases - that have led to fundamental, mechanistic discoveries as well as translational applications. Several recent studies have shown the clinical utilities of the cfDNA fragmentomics technique which has the potential to be effective for early disease diagnosis, determining treatment outcomes, and risk-free continuous patient monitoring in a non-invasive manner. In this article, we outline recent developments in computational genomic methodologies and analysis strategies, as well as the emerging insights from cfNA fragmentomics. We conclude by highlighting the current challenges and opportunities.

Multidimensional Fragmentomics Enables Early and Accurate Detection of Colorectal Cancer

Colorectal cancer is frequently diagnosed in advanced stages, highlighting the need for developing approaches for early detection. Liquid biopsy using cell-free DNA (cfDNA) fragmentomics is a promising approach, but the clinical application is hindered by complexity and cost. This study aimed to develop an integrated model using cfDNA fragmentomics for accurate, cost-effective early-stage colorectal cancer detection. Plasma cfDNA was extracted and sequenced from a training cohort of 360 participants, including 176 patients with colorectal cancer and 184 healthy controls. An ensemble stacked model comprising five machine learning models was employed to distinguish patients with colorectal cancer from healthy controls using five cfDNA fragmentomic features. The model was validated in an independent cohort of 236 participants (117 patients with colorectal cancer and 119 controls) and a prospective cohort of 242 participants (129 patients with colorectal cancer and 113 controls). The ensemble stacked model showed remarkable discriminatory power between patients with colorectal cancer and controls, outperforming all base models and achieving a high area under the receiver operating characteristic curve of 0.986 in the validation cohort. It reached 94.88% sensitivity and 98% specificity for detecting colorectal cancer in the validation cohort, with sensitivity increasing as the cancer progressed. The model also demonstrated consistently high accuracy in within-run and between-run tests and across various conditions in healthy individuals. In the prospective cohort, it achieved 91.47% sensitivity and 95.58% specificity. This integrated model capitalizes on the multiplex nature of cfDNA fragmentomics to achieve high sensitivity and robustness, offering significant promise for early colorectal cancer detection and broad patient benefit.  Significance: The development of a minimally invasive, efficient approach for early colorectal cancer detection using advanced machine learning to analyze cfDNA fragment patterns could expedite diagnosis and improve treatment outcomes for patients. See related commentary by Rolfo and Russo, p. 3128.

Mining nucleic acid "omics" to boost liquid biopsy in cancer

Treatments for cancer patients are becoming increasingly complex, and there is a growing desire from clinicians and patients for biomarkers that can account for this complexity to support informed decisions about clinical care. To achieve precision medicine, the new generation of biomarkers must reflect the spatial and temporal heterogeneity of cancer biology both between patients and within an individual patient. Mining the different layers of 'omics in a multi-modal way from a minimally invasive, easily repeatable, liquid biopsy has increasing potential in a range of clinical applications, and for improving our understanding of treatment response and resistance. Here, we detail the recent developments and methods allowing exploration of genomic, epigenomic, transcriptomic, and fragmentomic layers of 'omics from liquid biopsy, and their integration in a range of applications. We also consider the specific challenges that are posed by the clinical implementation of multi-omic liquid biopsies.

Detecting Small Cell Transformation in Patients with Advanced EGFR Mutant Lung Adenocarcinoma through Epigenomic cfDNA Profiling

PURPOSE

Histologic transformation to small cell lung cancer (SCLC) is a mechanism of treatment resistance in patients with advanced oncogene-driven lung adenocarcinoma (LUAD) that currently requires histologic review for diagnosis. Herein, we sought to develop an epigenomic cell-free DNA (cfDNA)-based approach to noninvasively detect small cell transformation in patients with EGFR mutant (EGFRm) LUAD.

EXPERIMENTAL DESIGN

To characterize the epigenomic landscape of transformed (t)SCLC relative to LUAD and de novo SCLC, we performed chromatin immunoprecipitation sequencing (ChIP-seq) to profile the histone modifications H3K27ac, H3K4me3, and H3K27me3; methylated DNA immunoprecipitation sequencing (MeDIP-seq); assay for transposase-accessible chromatin sequencing; and RNA sequencing on 26 lung cancer patient-derived xenograft (PDX) tumors. We then generated and analyzed H3K27ac ChIP-seq, MeDIP-seq, and whole genome sequencing cfDNA data from 1 mL aliquots of plasma from patients with EGFRm LUAD with or without tSCLC.

RESULTS

Analysis of 126 epigenomic libraries from the lung cancer PDXs revealed widespread epigenomic reprogramming between LUAD and tSCLC, with a large number of differential H3K27ac (n = 24,424), DNA methylation (n = 3,298), and chromatin accessibility (n = 16,352) sites between the two histologies. Tumor-informed analysis of each of these three epigenomic features in cfDNA resulted in accurate noninvasive discrimination between patients with EGFRm LUAD versus tSCLC [area under the receiver operating characteristic curve (AUROC) = 0.82-0.87]. A multianalyte cfDNA-based classifier integrating these three epigenomic features discriminated between EGFRm LUAD versus tSCLC with an AUROC of 0.94.

CONCLUSIONS

These data demonstrate the feasibility of detecting small cell transformation in patients with EGFRm LUAD through epigenomic cfDNA profiling of 1 mL of patient plasma.

Pattern-centric transformation of omics data grounded on discriminative gene associations aids predictive tasks in TCGA while ensuring interpretability

The increasing prevalence of omics data sources is pushing the study of regulatory mechanisms underlying complex diseases such as cancer. However, the vast quantities of molecular features produced and the inherent interplay between them lead to a level of complexity that hampers both descriptive and predictive tasks, requiring custom-built algorithms that can extract relevant information from these sources of data. We propose a transformation that moves data centered on molecules (e.g., transcripts and proteins) to a new data space focused on putative regulatory modules given by statistically relevant co-expression patterns. To this end, the proposed transformation extracts patterns from the data through biclustering and uses them to create new variables with guarantees of interpretability and discriminative power. The transformation is shown to achieve dimensionality reductions of up to 99% and increase predictive performance of various classifiers across multiple omics layers. Results suggest that omics data transformations from gene-centric to pattern-centric data supports both prediction tasks and human interpretation, notably contributing to precision medicine applications.

Cell-free DNA from germline TP53 mutation carriers reflect cancer-like fragmentation patterns

Germline pathogenic TP53 variants predispose individuals to a high lifetime risk of developing multiple cancers and are the hallmark feature of Li-Fraumeni syndrome (LFS). Our group has previously shown that LFS patients harbor shorter plasma cell-free DNA fragmentation; independent of cancer status. To understand the functional underpinning of cfDNA fragmentation in LFS, we conducted a fragmentomic analysis of 199 cfDNA samples from 82 TP53 mutation carriers and 30 healthy TP53-wildtype controls. We find that LFS individuals exhibit an increased prevalence of A/T nucleotides at fragment ends, dysregulated nucleosome positioning at p53 binding sites, and loci-specific changes in chromatin accessibility at development-associated transcription factor binding sites and at cancer-associated open chromatin regions. Machine learning classification resulted in robust differentiation between TP53 mutant versus wildtype cfDNA samples (AUC-ROC = 0.710-1.000) and intra-patient longitudinal analysis of ctDNA fragmentation signal enabled early cancer detection. These results suggest that cfDNA fragmentation may be a useful diagnostic tool in LFS patients and provides an important baseline for cancer early detection.

Chromatin- and nucleosome-associated features in liquid biopsy: implications for cancer biomarker discovery

Cell-free DNA (cfDNA) from the bloodstream has been studied for cancer biomarker discovery, and chromatin-derived epigenetic features have come into the spotlight for their potential to expand clinical applications. Methylation, fragmentation, and nucleosome positioning patterns of cfDNA have previously been shown to reveal epigenomic and inferred transcriptomic information. More recently, histone modifications have emerged as a tool to further identify tumor-specific chromatin variants in plasma. A number of sequencing methods have been developed to analyze these epigenetic markers, offering new insights into tumor biology. Features within cfDNA allow for cancer detection, subtype and tissue of origin classification, and inference of gene expression. These methods provide a window into the complexity of cancer and the dynamic nature of its progression. In this review, we highlight the array of epigenetic features in cfDNA that can be extracted from chromatin- and nucleosome-associated organization and outline potential use cases in cancer management.

Disrupting prostate cancer research: Challenge accepted; report from the 2023 Coffey-Holden Prostate Cancer Academy Meeting

INTRODUCTION

The 2023 Coffey-Holden Prostate Cancer Academy (CHPCA) Meeting, themed "Disrupting Prostate Cancer Research: Challenge Accepted," was convened at the University of California, Los Angeles, Luskin Conference Center, in Los Angeles, CA, from June 22 to 25, 2023.

METHODS

The 2023 marked the 10th Annual CHPCA Meeting, a discussion-oriented scientific think-tank conference convened annually by the Prostate Cancer Foundation, which centers on innovative and emerging research topics deemed pivotal for advancing critical unmet needs in prostate cancer research and clinical care. The 2023 CHPCA Meeting was attended by 81 academic investigators and included 40 talks across 8 sessions.

RESULTS

The central topic areas covered at the meeting included: targeting transcription factor neo-enhancesomes in cancer, AR as a pro-differentiation and oncogenic transcription factor, why few are cured with androgen deprivation therapy and how to change dogma to cure metastatic prostate cancer without castration, reducing prostate cancer morbidity and mortality with genetics, opportunities for radiation to enhance therapeutic benefit in oligometastatic prostate cancer, novel immunotherapeutic approaches, and the new era of artificial intelligence-driven precision medicine.

DISCUSSION

This article provides an overview of the scientific presentations delivered at the 2023 CHPCA Meeting, such that this knowledge can help in facilitating the advancement of prostate cancer research worldwide.

Early detection of uterine corpus endometrial carcinoma utilizing plasma cfDNA fragmentomics

BACKGROUND

Uterine corpus endometrial carcinoma (UCEC) is a prevalent gynecologic malignancy with a favorable prognosis if detected early. However, there is a lack of accurate and reliable early detection tests for UCEC. This study aims to develop a precise and non-invasive diagnostic method for UCEC using circulating cell-free DNA (cfDNA) fragmentomics.

METHODS

Peripheral blood samples were collected from all participants, and cfDNA was extracted for analysis. Low-coverage whole-genome sequencing was performed to obtain cfDNA fragmentomics data. A robust machine learning model was developed using these features to differentiate between UCEC and healthy conditions.

RESULTS

The cfDNA fragmentomics-based model showed high predictive power for UCEC detection in training (n = 133; AUC 0.991) and validation cohorts (n = 89; AUC 0.994). The model manifested a specificity of 95.5% and a sensitivity of 98.5% in the training cohort, and a specificity of 95.5% and a sensitivity of 97.8% in the validation cohort. Physiological variables and preanalytical procedures had no significant impact on the classifier's outcomes. In terms of clinical benefit, our model would identify 99% of Chinese UCEC patients at stage I, compared to 21% under standard care, potentially raising the 5-year survival rate from 84 to 95%.

CONCLUSION

This study presents a novel approach for the early detection of UCEC using cfDNA fragmentomics and machine learning showing promising sensitivity and specificity. Using this model in clinical practice could significantly improve UCEC management and control, enabling early intervention and better patient outcomes. Further optimization and validation of this approach are warranted to establish its clinical utility.

Multi-dimensional cell-free DNA-based liquid biopsy for sensitive early detection of gastric cancer

BACKGROUND

Gastric cancer is the fifth most common cancer type. Most patients are diagnosed at advanced stages with poor prognosis. A non-invasive assay for the detection of early-stage gastric cancer is highly desirable for reducing associated mortality.

METHODS

We collected a prospective study cohort of 110 stage I-II gastric cancer patients and 139 non-cancer individuals. We performed whole-genome sequencing with plasma samples and profiled four types of cell-free DNA (cfDNA) characteristics, fragment size pattern, copy number variation, nucleosome coverage pattern, and single nucleotide substitution. With these differential profiles, we developed an ensemble model to detect gastric cancer signals. Further, we validated the assay in an in-house first validation cohort of 73 gastric cancer patients and 94 non-cancer individuals and an independent second validation cohort of 47 gastric cancer patients and 49 non-cancer individuals. Additionally, we evaluated the assay in a hypothetical 100,000 screening population by Monte Carlo simulation.

RESULTS

Our cfDNA-based assay could distinguish early-stage gastric cancer from non-cancer at an AUROC of 0.962 (95% CI: 0.942-0.982) in the study cohort, 0.972 (95% CI: 0.953-0.992) in the first validation cohort and 0.937 (95% CI: 0.890-0.983) in the second validation cohort. The model reached a specificity of 92.1% (128/139) and a sensitivity of 88.2% (97/110) in the study cohort. In the first validation cohort, 91.5% (86/94) of non-cancer individuals and 91.8% (67/73) of gastric cancer patients were correctly identified. In the second validation cohort, 89.8% (44/49) of non-cancer individuals and 87.2% (41/47) of gastric cancer patients were accurately classified.

CONCLUSIONS

We introduced a liquid biopsy assay using multiple dimensions of cfDNA characteristics that could accurately identify early-stage gastric cancer from non-cancerous conditions. As a cost-effective non-invasive approach, it may provide population-wide benefits for the early detection of gastric cancer.

TRIAL REGISTRATION

This study was registered on ClinicalTrials.gov under the identifier NCT05269056 on March 7, 2022.

Assay Validation of Cell-Free DNA Shallow Whole-Genome Sequencing to Determine Tumor Fraction in Advanced Cancers

Blood-based liquid biopsy is increasingly used in clinical care of patients with cancer, and fraction of tumor-derived DNA in circulation (tumor fraction; TFx) has demonstrated clinical validity across multiple cancer types. To determine TFx, shallow whole-genome sequencing of cell-free DNA (cfDNA) can be performed from a single blood sample, using an established computational pipeline (ichorCNA), without prior knowledge of tumor mutations, in a highly cost-effective manner. We describe assay validation of this approach to facilitate broad clinical application, including evaluation of assay sensitivity, precision, repeatability, reproducibility, pre-analytic factors, and DNA quality/quantity. Sensitivity to detect TFx of 3% (lower limit of detection) was 97.2% to 100% at 1× and 0.1× mean sequencing depth, respectively. Precision was demonstrated on distinct sequencing instruments (HiSeqX and NovaSeq) with no observable differences. The assay achieved prespecified 95% agreement of TFx across replicates of the same specimen (repeatability) and duplicate samples in different batches (reproducibility). Comparison of samples collected in EDTA and Streck tubes from single venipuncture in 23 patients demonstrated that EDTA or Streck tubes were comparable if processed within 8 hours. On the basis of a range of DNA inputs (1 to 50 ng), 20 ng cfDNA is the preferred input, with 5 ng minimum acceptable. Overall, this shallow whole-genome sequencing of cfDNA and ichorCNA approach offers sensitive, precise, and reproducible quantitation of TFx, facilitating assay application in clinical cancer care.

Molecular phenotyping of small cell lung cancer using targeted cfDNA profiling of transcriptional regulatory regions

We report an approach for cancer phenotyping based on targeted sequencing of cell-free DNA (cfDNA) for small cell lung cancer (SCLC). In SCLC, differential activation of transcription factors (TFs), such as ASCL1, NEUROD1, POU2F3, and REST defines molecular subtypes. We designed a targeted capture panel that identifies chromatin organization signatures at 1535 TF binding sites and 13,240 gene transcription start sites and detects exonic mutations in 842 genes. Sequencing of cfDNA from SCLC patient-derived xenograft models captured TF activity and gene expression and revealed individual highly informative loci. Prediction models of ASCL1 and NEUROD1 activity using informative loci achieved areas under the receiver operating characteristic curve (AUCs) from 0.84 to 0.88 in patients with SCLC. As non-SCLC (NSCLC) often transforms to SCLC following targeted therapy, we applied our framework to distinguish NSCLC from SCLC and achieved an AUC of 0.99. Our approach shows promising utility for SCLC subtyping and transformation monitoring, with potential applicability to diverse tumor types.

Nucleosome reorganisation in breast cancer tissues

BACKGROUND

Nucleosome repositioning in cancer is believed to cause many changes in genome organisation and gene expression. Understanding these changes is important to elucidate fundamental aspects of cancer. It is also important for medical diagnostics based on cell-free DNA (cfDNA), which originates from genomic DNA regions protected from digestion by nucleosomes.

RESULTS

We have generated high-resolution nucleosome maps in paired tumour and normal tissues from the same breast cancer patients using MNase-assisted histone H3 ChIP-seq and compared them with the corresponding cfDNA from blood plasma. This analysis has detected single-nucleosome repositioning at key regulatory regions in a patient-specific manner and common cancer-specific patterns across patients. The nucleosomes gained in tumour versus normal tissue were particularly informative of cancer pathways, with ~ 20-fold enrichment at CpG islands, a large fraction of which marked promoters of genes encoding DNA-binding proteins. The tumour tissues were characterised by a 5-10 bp decrease in the average distance between nucleosomes (nucleosome repeat length, NRL), which is qualitatively similar to the differences between pluripotent and differentiated cells. This effect was correlated with gene activity, differential DNA methylation and changes in local occupancy of linker histone variants H1.4 and H1X.

CONCLUSIONS

Our study offers a novel resource of high-resolution nucleosome maps in breast cancer patients and reports for the first time the effect of systematic decrease of NRL in paired tumour versus normal breast tissues from the same patient. Our findings provide a new mechanistic understanding of nucleosome repositioning in tumour tissues that can be valuable for patient diagnostics, stratification and monitoring.

Cell‐free DNA‐associated multi‐feature applications in cancer diagnosis and treatment

Malignant tumours pose significant challenges in terms of high morbidity and mortality rates, primarily due to the lack of large‐scale applicable screening methods and efficient treatment strategies. However, the development of liquid biopsies, particularly circulating cell‐free DNA (cfDNA), offers promising solutions characterised by their non‐invasiveness and cost‐effectiveness, providing comprehensive tumour information on a global scale. The release of cfDNA is predominantly associated with cell death and turnover, while its elimination occurs through nuclease digestion, renal excretion into the urine and uptake by the liver and spleen. Extensive research into the biological properties of cfDNA has led to the identification of novel applications, including non‐invasive cancer screening, cancer subtype classification, tissue‐of‐origin detection and monitoring of treatment efficacy. Additionally, emerging fields such as methylation‐omics, fragment‐omics and nucleosome‐omics show immense potential as tissue‐ and disease‐specific markers. Therefore, this review aims to comprehensively introduce the latest detection techniques of cfDNA, along with detailed information on its characteristics and applications, providing valuable insights for cancer diagnosis and monitoring, which will assist us in purposefully enhancing relevant features for a more comprehensive application in clinical practice.

Circulating tumor DNA validity and potential uses in metastatic breast cancer

Following the first characterization of circulating tumor DNA (ctDNA) in the 1990s, recent advances led to its introduction in the clinics. At present, the European Society Of Medical Oncology (ESMO) recommendations endorse ctDNA testing in routine clinical practice for tumor genotyping to direct molecularly targeted therapies in patients with metastatic cancer. In studies on metastatic breast cancer, ctDNA has been utilized for treatment tailoring, tracking mechanisms of drug resistance, and for predicting disease response before imaging. We review the available evidence regarding ctDNA applications in metastatic breast cancer.

Cell type signatures in cell-free DNA fragmentation profiles reveal disease biology

Circulating cell-free DNA (cfDNA) fragments have characteristics that are specific to the cell types that release them. Current methods for cfDNA deconvolution typically use disease tailored marker selection in a limited number of bulk tissues or cell lines. Here, we utilize single cell transcriptome data as a comprehensive cellular reference set for disease-agnostic cfDNA cell-of-origin analysis. We correlate cfDNA-inferred nucleosome spacing with gene expression to rank the relative contribution of over 490 cell types to plasma cfDNA. In 744 healthy individuals and patients, we uncover cell type signatures in support of emerging disease paradigms in oncology and prenatal care. We train predictive models that can differentiate patients with colorectal cancer (84.7%), early-stage breast cancer (90.1%), multiple myeloma (AUC 95.0%), and preeclampsia (88.3%) from matched controls. Importantly, our approach performs well in ultra-low coverage cfDNA datasets and can be readily transferred to diverse clinical settings for the expansion of liquid biopsy.

Circulating cell-free DNA-based multi-cancer early detection

Patients benefit considerably from early detection of cancer. Existing single-cancer tests have various limitations, which could be effectively addressed by circulating cell-free DNA (cfDNA)-based multi-cancer early detection (MCED). With sensitive detection and accurate localization of multiple cancer types at a very low and fixed false-positive rate (FPR), MCED has great potential to revolutionize early cancer detection. Herein, we review state-of-the-art approaches for cfDNA-based MCED and their limitations and discuss both technical and clinical challenges in the development and application of MCED tests. Given the constant improvements in technology and understanding of cancer biology, we propose that a cfDNA-based targeted sequencing assay that integrates multimodal features should be optimized for MCED.

Plasma cell-free DNA as a sensitive biomarker for multi-cancer detection and immunotherapy outcomes prediction

BACKGROUND

Cell-free DNA (cfDNA) has shown promise in detecting various cancers, but the diagnostic performance of cfDNA end motifs for multiple cancer types requires verification. This study aimed to assess the utility of cfDNA end motifs for multi-cancer detection.

METHODS

This study included 206 participants: 106 individuals with cancer, representing 20 cancer types, and 100 healthy individuals. The participants were divided into training and testing cohorts. All plasma cfDNA samples were profiled by whole-genome sequencing. A random forest model was constructed using cfDNA 4 bp-end-motif profiles to predict cancer in the training cohort, and its performance was evaluated in the testing cohort. Additionally, a separate random forest model was developed to predict immunotherapy responses.

RESULTS

In the training cohort, the model based on 4 bp-end-motif profiles achieved an AUC of 0.962 (95% CI 0.936-0.987). The AUC in the testing cohort was 0.983 (95% CI 0.960-1.000). The model also maintained excellent predictive ability in different tumor sub-cohorts, including lung cancer (AUC 0.918, 95% CI 0.862-0.974), gastrointestinal cancer (AUC 0.966, 95% CI 0.938-0.993), and other cancer cohort (AUC 0.859, 95% CI 0.776-0.942). Moreover, the model utilizing 4 bp-end-motif profiles exhibited sensitivity in identifying responders to immunotherapy (AUC 0.784, 95% CI 0.609-0.960).

CONCLUSION

The model based on 4 bp-end-motif profiles demonstrates superior sensitivity in multi-cancer detection. Detection of 4 bp-end-motif profiles may serve as potential predictive biomarkers for cancer immunotherapy.

cfDNA UniFlow: a unified preprocessing pipeline for cell-free DNA data from liquid biopsies

BACKGROUND

Cell-free DNA (cfDNA), a broadly applicable biomarker commonly sourced from urine or blood, is extensively used for research and diagnostic applications. In various settings, genetic and epigenetic information is derived from cfDNA. However, a unified framework for its processing is lacking, limiting the universal application of innovative analysis strategies and the joining of data sets.

FINDINGS

Here, we describe cfDNA UniFlow, a unified, standardized, and ready-to-use workflow for processing cfDNA samples. The workflow is written in Snakemake and can be scaled from stand-alone computers to cluster environments. It includes methods for processing raw genome sequencing data as well as specialized approaches for correcting sequencing errors, filtering, and quality control. Sophisticated methods for detecting copy number alterations and estimating and correcting GC-related biases are readily incorporated. Furthermore, it includes methods for extracting, normalizing, and visualizing coverage signals around user-defined regions in case-control settings. Ultimately, all results and metrics are aggregated in a unified report, enabling easy access to a wide variety of information for further research and downstream analysis.

CONCLUSIONS

We provide an automated pipeline for processing cell-free DNA sampled from liquid biopsies, including a wide variety of additional functionalities like bias correction and signal extraction. With our focus on scalability and extensibility, we provide a foundation for future cfDNA research and faster clinical applications. The source code and extensive documentation are available on our GitHub repository (https://github.com/kircherlab/cfDNA-UniFlow).

GCparagon: evaluating and correcting GC biases in cell-free DNA at the fragment level

Analyses of cell-free DNA (cfDNA) are increasingly being employed for various diagnostic and research applications. Many technologies aim to increase resolution, e.g. for detecting early-stage cancer or minimal residual disease. However, these efforts may be confounded by inherent base composition biases of cfDNA, specifically the over - and underrepresentation of guanine (G) and cytosine (C) sequences. Currently, there is no universally applicable tool to correct these effects on sequencing read-level data. Here, we present GCparagon, a two-stage algorithm for computing and correcting GC biases in cfDNA samples. In the initial step, length and GC base count parameters are determined. Here, our algorithm minimizes the inclusion of known problematic genomic regions, such as low-mappability regions, in its calculations. In the second step, GCparagon computes weights counterbalancing the distortion of cfDNA attributes (correction matrix). These fragment weights are added to a binary alignment map (BAM) file as alignment tags for individual reads. The GC correction matrix or the tagged BAM file can be used for downstream analyses. Parallel computing allows for a GC bias estimation below 1 min. We demonstrate that GCparagon vastly improves the analysis of regulatory regions, which frequently show specific GC composition patterns and will contribute to standardized cfDNA applications.

Genomic and epigenomic analysis of plasma cell-free DNA identifies stemness features associated with worse survival in AR -altered lethal prostate cancer

Metastatic castration-resistant prostate cancer (mCRPC) resistant to androgen receptor (AR)-targeted agents is often lethal. Unfortunately, biomarkers for this deadly disease remain under investigation, and underpinning mechanisms are ill-understood. Here, we applied deep sequencing to ∼100 mCRPC patients prior to the initiation of first-line AR-targeted therapy, which detected AR /enhancer alterations in over a third of patients, which correlated with lethality. To delve into the mechanism underlying why these patients with cell-free AR /enhancer alterations developed more lethal prostate cancer, we next performed genome-wide cell-free DNA epigenomics. Strikingly, we found that binding sites for transcription factors associated with developmental stemness were nucleosomally more accessible. These results were corroborated using cell-free DNA methylation data, as well as tumor RNA sequencing from a held-out cohort of mCRPC patients. Thus, we validated the importance of AR /enhancer alterations as a prognostic biomarker in lethal mCRPC, and showed that the underlying mechanism for lethality involves reprogramming developmental states toward increased stemness.

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.

Liquid biopsy epigenomic profiling for cancer subtyping

Although circulating tumor DNA (ctDNA) assays are increasingly used to inform clinical decisions in cancer care, they have limited ability to identify the transcriptional programs that govern cancer phenotypes and their dynamic changes during the course of disease. To address these limitations, we developed a method for comprehensive epigenomic profiling of cancer from 1 ml of patient plasma. Using an immunoprecipitation-based approach targeting histone modifications and DNA methylation, we measured 1,268 epigenomic profiles in plasma from 433 individuals with one of 15 cancers. Our assay provided a robust proxy for transcriptional activity, allowing us to infer the expression levels of diagnostic markers and drug targets, measure the activity of therapeutically targetable transcription factors and detect epigenetic mechanisms of resistance. This proof-of-concept study in advanced cancers shows how plasma epigenomic profiling has the potential to unlock clinically actionable information that is currently accessible only via direct tissue sampling.

Cancer signature ensemble integrating cfDNA methylation, copy number, and fragmentation facilitates multi-cancer early detection

Cell-free DNA (cfDNA) sequencing has demonstrated great potential for early cancer detection. However, most large-scale studies have focused only on either targeted methylation sites or whole-genome sequencing, limiting comprehensive analysis that integrates both epigenetic and genetic signatures. In this study, we present a platform that enables simultaneous analysis of whole-genome methylation, copy number, and fragmentomic patterns of cfDNA in a single assay. Using a total of 950 plasma (361 healthy and 589 cancer) and 240 tissue samples, we demonstrate that a multifeature cancer signature ensemble (CSE) classifier integrating all features outperforms single-feature classifiers. At 95.2% specificity, the cancer detection sensitivity with methylation, copy number, and fragmentomic models was 77.2%, 61.4%, and 60.5%, respectively, but sensitivity was significantly increased to 88.9% with the CSE classifier (p value < 0.0001). For tissue of origin, the CSE classifier enhanced the accuracy beyond the methylation classifier, from 74.3% to 76.4%. Overall, this work proves the utility of a signature ensemble integrating epigenetic and genetic information for accurate cancer detection.

Phase I trial of Ganitumab plus Dasatinib to Cotarget the Insulin-Like Growth Factor 1 Receptor and Src Family Kinase YES in Rhabdomyosarcoma

PURPOSE

Antibodies against insulin-like growth factor (IGF) type 1 receptor have shown meaningful but transient tumor responses in patients with rhabdomyosarcoma (RMS). The SRC family member YES has been shown to mediate IGF type 1 receptor (IGF-1R) antibody acquired resistance, and cotargeting IGF-1R and YES resulted in sustained responses in murine RMS models. We conducted a phase I trial of the anti-IGF-1R antibody ganitumab combined with dasatinib, a multi-kinase inhibitor targeting YES, in patients with RMS (NCT03041701).

PATIENTS AND METHODS

Patients with relapsed/refractory alveolar or embryonal RMS and measurable disease were eligible. All patients received ganitumab 18 mg/kg intravenously every 2 weeks. Dasatinib dose was 60 mg/m2/dose (max 100 mg) oral once daily [dose level (DL)1] or 60 mg/m2/dose (max 70 mg) twice daily (DL2). A 3+3 dose escalation design was used, and maximum tolerated dose (MTD) was determined on the basis of cycle 1 dose-limiting toxicities (DLT).

RESULTS

Thirteen eligible patients, median age 18 years (range 8-29) enrolled. Median number of prior systemic therapies was 3; all had received prior radiation. Of 11 toxicity-evaluable patients, 1/6 had a DLT at DL1 (diarrhea) and 2/5 had a DLT at DL2 (pneumonitis, hematuria) confirming DL1 as MTD. Of nine response-evaluable patients, one had a confirmed partial response for four cycles, and one had stable disease for six cycles. Genomic studies from cell-free DNA correlated with disease response.

CONCLUSIONS

The combination of dasatinib 60 mg/m2/dose daily and ganitumab 18 mg/kg every 2 weeks was safe and tolerable. This combination had a disease control rate of 22% at 5 months.

Fragmentomic analysis of circulating tumor DNA-targeted cancer panels

BACKGROUND

The isolation of cell-free DNA (cfDNA) from the bloodstream can be used to detect and analyze somatic alterations in circulating tumor DNA (ctDNA), and multiple cfDNA-targeted sequencing panels are now commercially available for Food and Drug Administration (FDA)-approved biomarker indications to guide treatment. More recently, cfDNA fragmentation patterns have emerged as a tool to infer epigenomic and transcriptomic information. However, most of these analyses used whole-genome sequencing, which is insufficient to identify FDA-approved biomarker indications in a cost-effective manner.

PATIENTS AND METHODS

We used machine learning models of fragmentation patterns at the first coding exon in standard targeted cancer gene cfDNA sequencing panels to distinguish between cancer and non-cancer patients, as well as the specific tumor type and subtype. We assessed this approach in two independent cohorts: a published cohort from GRAIL (breast, lung, and prostate cancers, non-cancer, n = 198) and an institutional cohort from the University of Wisconsin (UW; breast, lung, prostate, bladder cancers, n = 320). Each cohort was split 70%/30% into training and validation sets.

RESULTS

In the UW cohort, training cross-validated accuracy was 82.1%, and accuracy in the independent validation cohort was 86.6% despite a median ctDNA fraction of only 0.06. In the GRAIL cohort, to assess how this approach performs in very low ctDNA fractions, training and independent validation were split based on ctDNA fraction. Training cross-validated accuracy was 80.6%, and accuracy in the independent validation cohort was 76.3%. In the validation cohort where the ctDNA fractions were all <0.05 and as low as 0.0003, the cancer versus non-cancer area under the curve was 0.99.

CONCLUSIONS

To our knowledge, this is the first study to demonstrate that sequencing from targeted cfDNA panels can be utilized to analyze fragmentation patterns to classify cancer types, dramatically expanding the potential capabilities of existing clinically used panels at minimal additional cost.

Genome-wide studies in prostate cancer poised liquid biopsy as a molecular discovery tool

Liquid biopsy is emerging as an intriguing tool in clinical disease detection and monitoring. Compared to a standard tissue biopsy, performing a liquid biopsy incurs minimal invasiveness, captures comprehensive disease representation, and can be more sensitive at an early stage. Recent genome-wide liquid biopsy studies in prostate cancer analyzing plasma samples have provided insights into the genome and epigenome dynamics during disease progression. In-depth genomic sequencing can offer a comprehensive understanding of cancer evolution, enabling more accurate clinical decision-making. Furthermore, exploring beyond the DNA sequence itself provides opportunities to investigate the regulatory mechanisms underlying various disease phenotypes. Here, we summarize these advances and offer prospects for their future application.

Genomic approaches to cancer and minimal residual disease detection using circulating tumor DNA

Liquid biopsies using cell-free circulating tumor DNA (ctDNA) are being used frequently in both research and clinical settings. ctDNA can be used to identify actionable mutations to personalize systemic therapy, detect post-treatment minimal residual disease (MRD), and predict responses to immunotherapy. ctDNA can also be isolated from a range of different biofluids, with the possibility of detecting locoregional MRD with increased sensitivity if sampling more proximally than blood plasma. However, ctDNA detection remains challenging in early-stage and post-treatment MRD settings where ctDNA levels are minuscule giving a high risk for false negative results, which is balanced with the risk of false positive results from clonal hematopoiesis. To address these challenges, researchers have developed ever-more elegant approaches to lower the limit of detection (LOD) of ctDNA assays toward the part-per-million range and boost assay sensitivity and specificity by reducing sources of low-level technical and biological noise, and by harnessing specific genomic and epigenomic features of ctDNA. In this review, we highlight a range of modern assays for ctDNA analysis, including advancements made to improve the signal-to-noise ratio. We further highlight the challenge of detecting ultra-rare tumor-associated variants, overcoming which will improve the sensitivity of post-treatment MRD detection and open a new frontier of personalized adjuvant treatment decision-making.

Liquid biopsy for brain metastases and leptomeningeal disease in patients with breast cancer

A significant subset of patients with metastatic breast cancer develops brain metastasis. As efficacy of systemic therapies has improved and patients live longer with metastatic breast cancer, the incidence of breast cancer brain metastases has increased. Brain metastases pose a clinical challenge in diagnosis, treatment, and monitoring across all breast cancer subtypes, and better tools are needed. Liquid biopsy, which enables minimally invasive sampling of a patient's cancer, has the potential to shed light on intra-cranial tumor biology and to improve patient care by enabling therapy tailoring. Here we review current evidence for the clinical validity of liquid biopsy in patients with breast cancer brain metastases, with a focus on circulating tumor cells and circulating tumor DNA.

Genetic and Genomic Testing for Prostate Cancer: Beyond DNA Repair

Significant progress has been made in genetic and genomic testing for prostate cancer across the disease spectrum. Molecular profiling is increasingly relevant for routine clinical management, fueled in part by advancements in testing technology and integration of biomarkers into clinical trials. In metastatic prostate cancer, defects in DNA damage response genes are now established predictors of benefit to US Food and Drug Administration-approved poly (ADP-ribose) polymerase inhibitors and immune checkpoint inhibitors, and trials are actively investigating these and other targeted treatment strategies in earlier disease states. Excitingly, opportunities for molecularly informed management beyond DNA damage response genes are also maturing. Germline genetic variants (eg, BRCA2 or MSH2/6) and polygenic germline risk scores are being investigated to inform cancer screening and active surveillance in at-risk carriers. RNA expression tests have recently gained traction in localized prostate cancer, enabling patient risk stratification and tailored treatment intensification via radiotherapy and/or androgen deprivation therapy for localized or salvage treatment. Finally, emerging minimally invasive circulating tumor DNA technology promises to enhance biomarker testing in advanced disease pending additional methodological and clinical validation. Collectively, genetic and genomic tests are rapidly becoming indispensable tools for informing the optimal clinical management of prostate cancer.