Genome-wide analysis of aberrant position and sequence of plasma DNA fragment ends in patients with cancer

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.

Early Detection of Malignant and Premalignant Peripheral Nerve Tumors Using Cell-Free DNA Fragmentomics

PURPOSE

Early detection of neurofibromatosis type 1 (NF1)-associated peripheral nerve sheath tumors (PNST) informs clinical decision-making, enabling early definitive treatment and potentially averting deadly outcomes. In this study, we describe a cell-free DNA (cfDNA) fragmentomic approach that distinguishes nonmalignant, premalignant, and malignant forms of PNST in the cancer predisposition syndrome, NF1.

EXPERIMENTAL DESIGN

cfDNA was isolated from plasma samples of a novel cohort of 101 patients with NF1 and 21 healthy controls and underwent whole-genome sequencing. We investigated diagnosis-specific signatures of copy-number alterations with in silico size selection as well as fragment profiles. Fragmentomics were analyzed using complementary feature types: bin-wise fragment size ratios, end motifs, and fragment non-negative matrix factorization signatures.

RESULTS

The novel cohort of patients with NF1 validated that our previous cfDNA copy-number alteration-based approach identifies malignant PNST (MPNST) but cannot distinguish between benign and premalignant states. Fragmentomic methods were able to differentiate premalignant states including atypical neurofibromas (AN). Fragmentomics also adjudicated AN cases suspicious for MPNST, correctly diagnosing samples noninvasively, which could have informed clinical management.

CONCLUSIONS

Novel cfDNA fragmentomic signatures distinguish AN from benign plexiform neurofibromas and MPNST, enabling more precise clinical diagnosis and management. This study pioneers the early detection of malignant and premalignant PNST in NF1 and provides a blueprint for decentralizing noninvasive cancer surveillance in hereditary cancer predisposition syndromes.

Early circulating tumor DNA changes predict outcomes in head and neck cancer patients under re-radiotherapy

Local recurrence after radiotherapy is common in locally advanced head and neck cancer (HNC) patients. Re-irradiation can improve local disease control, but disease progression remains frequent. Hence, predictive biomarkers are needed to adapt treatment intensity to the patient's individual risk. We quantified circulating tumor DNA (ctDNA) in sequential plasma samples and correlated ctDNA levels with disease outcome. Ninety four longitudinal plasma samples from 16 locally advanced HNC patients and 57 healthy donors were collected at re-radiotherapy baseline, after 5 and 10 radiation fractions, at irradiation end, and at routine follow-up visits. Plasma DNA was subjected to low coverage whole genome sequencing for copy number variation (CNV) profiling to quantify ctDNA burden. CNV-based ctDNA burden was detected in 8/16 patients and 25/94 plasma samples. Ten additional ctDNA-positive samples were identified by tracking patient-specific CNVs found in earlier sequential plasma samples. ctDNA-positivity after 5 and 10 radiation fractions (both: log-rank, p = .050) as well as at the end of irradiation correlated with short progression-free survival (log-rank, p = .006). Moreover, a pronounced decrease of ctDNA toward re-radiotherapy termination was associated with worse treatment outcome (log-rank, p = .005). Dynamic ctDNA tracking in serial plasma beyond re-radiotherapy reflected treatment response and imminent disease progression. In five patients, molecular progression was detected prior to tumor progression based on clinical imaging. Our findings emphasize that quantifying ctDNA during re-radiotherapy may contribute to disease monitoring and personalization of adjuvant treatment, follow-up intervals, and dose prescription.

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.

Systematically Evaluating Cell-Free DNA Fragmentation Patterns for Cancer Diagnosis and Enhanced Cancer Detection via Integrating Multiple Fragmentation Patterns

Cell-free DNA (cfDNA) fragmentation patterns have immense potential for early cancer detection. However, the definition of fragmentation varies, ranging from the entire genome to specific genomic regions. These patterns have not been systematically compared, impeding broader research and practical implementation. Here, 1382 plasma cfDNA sequencing samples from 8 cancer types are collected. Considering that cfDNA within open chromatin regions is more susceptible to fragmentation, 10 fragmentation patterns within open chromatin regions as features and employed machine learning techniques to evaluate their performance are examined. All fragmentation patterns demonstrated discernible classification capabilities, with the end motif showing the highest diagnostic value for cross-validation. Combining cross and independent validation results revealed that fragmentation patterns that incorporated both fragment length and coverage information exhibited robust predictive capacities. Despite their diagnostic potential, the predictive power of these fragmentation patterns is unstable. To address this limitation, an ensemble classifier via integrating all fragmentation patterns is developed, which demonstrated notable improvements in cancer detection and tissue-of-origin determination. Further functional bioinformatics investigations on significant feature intervals in the model revealed its impressive ability to identify critical regulatory regions involved in cancer pathogenesis.

Circulating tumor-associated antigen-specific IFNγ+4-1BB+ CD8+ T cells as peripheral biomarkers of treatment outcomes in patients with pancreatic cancer

CD8+ T cells affect the outcomes of pancreatic ductal adenocarcinoma (PDAC). Using tissue samples at pre-treatment to monitor the immune response is challenging, while blood samples are beneficial in overcoming this limitation. In this study, we measured peripheral antigen-specific CD8+ T cell responses against four different tumor-associated antigens (TAAs) in PDAC using flow cytometry and investigated their relationships with clinical features. We analyzed the optimal timing within the treatment course for effective immune checkpoint inhibition in vitro. We demonstrated that the frequency of TAA-specific IFNγ+4-1BB+ CD8+ T cells was correlated with a fold reduction in CA19-9 before and after neoadjuvant therapy. Moreover, patients with TAA-specific IFNγ+4-1BB+ CD8+ T cells after surgery exhibited a significantly improved disease-free survival. Anti-PD-1 treatment in vitro increased the frequency of TAA-specific IFNγ+4-1BB+ CD8+ T cells before neoadjuvant therapy in patients, suggesting the importance of the timing of anti-PD-1 inhibition during the treatment regimen. Our results indicate that peripheral immunophenotyping, combined with highly sensitive identification of TAA-specific responses in vitro as well as detailed CD8+ T cell subset profiling via ex vivo analysis, may serve as peripheral biomarkers to predict treatment outcomes and therapeutic efficacy of immunotherapy plus neoadjuvant chemotherapy.

Early detection of malignant and pre-malignant peripheral nerve tumors using cell-free DNA fragmentomics

Early detection of neurofibromatosis type 1 (NF1) associated peripheral nerve sheath tumors (PNST) informs clinical decision-making, potentially averting deadly outcomes. Here, we describe a cell-free DNA (cfDNA) fragmentomic approach which distinguishes non-malignant, pre-malignant and malignant forms of NF1 PNST. Using plasma samples from a novel cohort of 101 NF1 patients and 21 healthy controls, we validated that our previous cfDNA copy number alteration (CNA)-based approach identifies malignant peripheral nerve sheath tumor (MPNST) but cannot distinguish among benign and premalignant states. We therefore investigated the ability of fragment-based cfDNA features to differentiate NF1-associated tumors including binned genome-wide fragment length ratios, end motif analysis, and non-negative matrix factorization deconvolution of fragment lengths. Fragmentomic methods were able to differentiate pre-malignant states including atypical neurofibromas (AN). Fragmentomics also adjudicated AN cases suspicious for MPNST, correctly diagnosing samples noninvasively, which could have informed clinical management. Overall, this study pioneers the early detection of malignant and premalignant peripheral nerve sheath tumors in NF1 patients using plasma cfDNA fragmentomics. In addition to screening applications, this novel approach distinguishes atypical neurofibromas from benign plexiform neurofibromas and malignant peripheral nerve sheath tumors, enabling more precise clinical diagnosis and management.

Integration of Cell-Free DNA End Motifs and Fragment Lengths Can Identify Active Genes in Liquid Biopsies

Multiple studies have shown that cell-free DNA (cfDNA) from cancer patients differ in both fragment length and fragment end motif (FEM) from healthy individuals, yet there is a lack of understanding of how the two factors combined are associated with cancer and gene transcription. In this study, we conducted cfDNA fragmentomics evaluations using plasma from lung cancer patients (n = 12) and healthy individuals (n = 7). A personal gene expression profile was established from plasma using H3K36me3 cell-free chromatin immunoprecipitation sequencing (cfChIP-seq). The genes with the highest expression displayed an enrichment of short cfDNA fragments (median = 19.99%, IQR: 16.94-27.13%, p < 0.0001) compared to the genes with low expression. Furthermore, distinct GC-rich FEMs were enriched after cfChIP. Combining the frequency of short cfDNA fragments with the presence of distinct FEMs resulted in an even further enrichment of the most expressed genes (median = 37.85%, IQR: 30.10-39.49%, p < 0.0001). An in vitro size selection of <150 bp cfDNA could isolate cfDNA representing active genes and the size-selection enrichment correlated with the cfChIP-seq enrichment (Spearman r range: 0.499-0.882, p < 0.0001). This study expands the knowledge regarding cfDNA fragmentomics and sheds new light on how gene activity is associated with both cfDNA fragment lengths and distinct FEMs.

Multi-modal cell-free DNA genomic and fragmentomic patterns enhance cancer survival and recurrence analysis

The structure of cell-free DNA (cfDNA) is altered in the blood of patients with cancer. From whole-genome sequencing, we retrieve the cfDNA fragment-end composition using a new software (FrEIA [fragment end integrated analysis]), as well as the cfDNA size and tumor fraction in three independent cohorts (n = 925 cancer from >10 types and 321 control samples). At 95% specificity, we detect 72% cancer samples using at least one cfDNA measure, including 64% early-stage cancer (n = 220). cfDNA detection correlates with a shorter overall (p = 0.0086) and recurrence-free (p = 0.017) survival in patients with resectable esophageal adenocarcinoma. Integrating cfDNA measures with machine learning in an independent test set (n = 396 cancer, 90 controls) achieve a detection accuracy of 82% and area under the receiver operating characteristic curve of 0.96. In conclusion, harnessing the biological features of cfDNA can improve, at no extra cost, the diagnostic performance of liquid biopsies.

Multimodal epigenetic sequencing analysis (MESA) of cell-free DNA for non-invasive colorectal cancer detection

BACKGROUND

Detecting human cancers through cell-free DNA (cfDNA) in blood is a sensitive and non-invasive option. However, capturing multiple forms of epigenetic information remains a technical and financial challenge.

METHODS

To address this, we developed multimodal epigenetic sequencing analysis (MESA), a flexible and sensitive approach to capturing and integrating a diverse range of epigenetic features in cfDNA using a single experimental assay, i.e., non-disruptive bisulfite-free methylation sequencing, such as Enzymatic Methyl-seq. MESA enables simultaneous inference of four epigenetic modalities: cfDNA methylation, nucleosome occupancy, nucleosome fuzziness, and windowed protection score for regions surrounding gene promoters and polyadenylation sites.

RESULTS

When applied to 690 cfDNA samples from 3 colorectal cancer clinical cohorts, MESA's novel modalities, which include nucleosome fuzziness, and genomic features, including polyadenylation sites, improve cancer detection beyond the traditional epigenetic markers of promoter DNA methylation.

CONCLUSIONS

Together, MESA stands as a major advancement in the field by utilizing comprehensive and complementary epigenetic profiles of cfDNA for effective non-invasive cancer detection.

Coding Intrinsic Disorder into DNA Hybridization Probes Enables Discrimination of Single Nucleotide Variants over Wide and Tunable Temperature Ranges

DNA hybridization probes are commonly used tools to discriminate clinically important single nucleotide variants (SNVs) but often work at elevated temperatures with very narrow temperature intervals (ΔT). Herein, we investigated the thermodynamic basis of the narrow ΔT both in silico and experimentally. Our study revealed that the high entropy penalty of classic hybridization probe designs was the key attributor for the narrow ΔT. Guided by this finding, we further introduced an entropy-compensate probe (Sprobe) design by coding intrinsic disorder into a stem-loop hybridization probe. Sprobe expanded ΔT from less than 10 °C to over 30 °C. Moreover, both ΔT and the optimal reaction temperature can be fine-tuned by simply altering the length of the loop domain. Sprobe was clinically validated by analyzing EGFR L858R mutation in 36 pairs of clinical tumor tissue samples collected from lung cancer patients, which revealed 100 % clinical sensitivity and specificity. We anticipate that our study will serve as a general guide for designing thermal robust hybridization probes for clinical diagnostics.

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.

Cell-free DNA approaches for cancer early detection and interception

Rapid advancements in the area of early cancer detection have brought us closer to achieving the goals of finding cancer early enough to treat or cure it, while avoiding harms of overdiagnosis. We evaluate progress in the development of early cancer detection tests in the context of the current principles for cancer screening. We review cell-free DNA (cfDNA)-based approaches using mutations, methylation, or fragmentomes for early cancer detection. Lastly, we discuss the challenges in demonstrating clinical utility of these tests before integration into routine clinical care.

Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology Enterprise

Over the last 75 years, artificial intelligence has evolved from a theoretical concept and novel paradigm describing the role that computers might play in our society to a tool with which we daily engage. In this review, we describe AI in terms of its constituent elements, the synthesis of which we refer to as the AI Silecosystem. Herein, we provide an historical perspective of the evolution of the AI Silecosystem, conceptualized and summarized as a Kuhnian paradigm. This manuscript focuses on the role that the AI Silecosystem plays in oncology and its emerging importance in the care of the community oncology patient. We observe that this important role arises out of a unique alliance between the academic oncology enterprise and community oncology practices. We provide evidence of this alliance by illustrating the practical establishment of the AI Silecosystem at the City of Hope Comprehensive Cancer Center and its team utilization by community oncology providers.

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.