Transcription factor-nucleosome dynamics from plasma cfDNA identifies ER-driven states in breast cancer

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.

Hypobaric hypoxia modulated structural characteristics of circulating cell-free DNA in high-altitude pulmonary edema

The utility of cell-free (cf) DNA has extended as a surrogate or clinical biomarker for various diseases. However, a more profound and expanded understanding of the diverse cfDNA population and its correlation with physiological phenotypes and environmental factors is imperative for using its full potential. The high-altitude (HA; altitude > 2,500 m above sea level) environment characterized by hypobaric hypoxia offers an observational case-control design to study the differential cfDNA profile in patients with high-altitude pulmonary edema (HAPE) (number of subjects, n = 112) and healthy HA sojourners (n = 111). The present study investigated cfDNA characteristics such as concentration, fragment length size, degree of integrity, and subfractions reflecting mitochondrial-cfDNA copies in the two groups. The total cfDNA level was significantly higher in patients with HAPE, and the level increased with increasing HAPE severity (P = 0.0036). A lower degree of cfDNA integrity of 0.346 in patients with HAPE (P = 0.001) indicated the prevalence of shorter cfDNA fragments in circulation in patients compared with the healthy HA sojourners. A significant correlation of cfDNA characteristics with the peripheral oxygen saturation levels in the patient group demonstrated the translational relevance of cfDNA molecules. The correlation was further supported by multivariate logistic regression and receiver operating characteristic curve. To our knowledge, our study is the first to highlight the association of higher cfDNA concentration, a lower degree of cfDNA integrity, and increased mitochondrial-derived cfDNA population with HAPE disease severity. Further deep profiling of cfDNA fragments, which preserves cell-type specific genetic and epigenetic features, can provide dynamic physiological responses to hypoxia.NEW & NOTEWORTHY This study observed altered cell-free (cf) DNA fragment patterns in patients with high-altitude pulmonary edema and the significant correlation of these patterns with peripheral oxygen saturation levels. This suggests deep profiling of cfDNA fragments in the future may identify genetic and epigenetic mechanisms underlying physiological and pathophysiological responses to hypoxia.

Epigenetic mechanisms of cancer progression and therapy resistance in estrogen-receptor (ER+) breast cancer

Estrogen receptor-positive (ER+) breast cancer is the most frequent breast cancer subtype. Agents targeting the ER signaling pathway have been successful in reducing mortality from breast cancer for decades. However, mechanisms of resistance to these treatments arise, especially in the metastatic setting. Recently, it has been recognized that epigenetic dysregulation is a common feature that facilitates the acquisition of cancer hallmarks across cancer types, including ER+ breast cancer. Alterations in epigenetic regulators and transcription factors coupled with changes to the chromatin landscape have been found to orchestrate breast oncogenesis, metastasis, and the development of a resistant phenotype. Here, we review recent advances in our understanding of how the epigenome dictates breast cancer tumorigenesis and resistance to targeted therapies and discuss potential new therapeutic interventions for overcoming resistance.

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.

Therapeutic resistance to anti-oestrogen therapy in breast cancer

The hormone receptor oestrogen receptor-α (ER) orchestrates physiological mammary gland development, breast carcinogenesis and the progression of breast tumours into lethal, treatment-refractory systemic disease. Selective antagonism of ER signalling has been one of the most successful therapeutic approaches in oncology, benefiting patients as both a cancer preventative measure and a cancer treatment strategy. However, resistance to anti-oestrogen therapy is a major clinical challenge. Over the past decade, we have gained an understanding of how breast cancers evolve under the pressure of anti-oestrogen therapy. This is best depicted by the case of oestrogen-independent mutations in the gene encoding ER (ESR1), which are virtually absent in primary breast cancer but highly prevalent (20-40%) in anti-oestrogen-treated metastatic disease. These and other findings highlight the 'evolvability' of ER+ breast cancer and the need to understand molecular processes by which this evolution occurs. Recent development and approval of next-generation ER antagonists to target ESR1-mutant breast cancer underscores the clinical importance of this evolvability and sets a new paradigm for the treatment of ER+ breast cancers.

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.

Studies of the Mechanism of Nucleosome Dynamics: A Review on Multifactorial Regulation from Computational and Experimental Cases

The nucleosome, which organizes the long coil of genomic DNA in a highly condensed, polymeric way, is thought to be the basic unit of chromosomal structure. As the most important protein–DNA complex, its structural and dynamic features have been successively revealed in recent years. However, its regulatory mechanism, which is modulated by multiple factors, still requires systemic discussion. This study summarizes the regulatory factors of the nucleosome’s dynamic features from the perspective of histone modification, DNA methylation, and the nucleosome-interacting factors (transcription factors and nucleosome-remodeling proteins and cations) and focuses on the research exploring the molecular mechanism through both computational and experimental approaches. The regulatory factors that affect the dynamic features of nucleosomes are also discussed in detail, such as unwrapping, wrapping, sliding, and stacking. Due to the complexity of the high-order topological structures of nucleosomes and the comprehensive effects of regulatory factors, the research on the functional modulation mechanism of nucleosomes has encountered great challenges. The integration of computational and experimental approaches, the construction of physical modes for nucleosomes, and the application of deep learning techniques will provide promising opportunities for further exploration.

Nucleosome Patterns in Circulating Tumor DNA Reveal Transcriptional Regulation of Advanced Prostate Cancer Phenotypes

Advanced prostate cancers comprise distinct phenotypes, but tumor classification remains clinically challenging. Here, we harnessed circulating tumor DNA (ctDNA) to study tumor phenotypes by ascertaining nucleosome positioning patterns associated with transcription regulation. We sequenced plasma ctDNA whole genomes from patient-derived xenografts representing a spectrum of androgen receptor active (ARPC) and neuroendocrine (NEPC) prostate cancers. Nucleosome patterns associated with transcriptional activity were reflected in ctDNA at regions of genes, promoters, histone modifications, transcription factor binding, and accessible chromatin. We identified the activity of key phenotype-defining transcriptional regulators from ctDNA, including AR, ASCL1, HOXB13, HNF4G, and GATA2. To distinguish NEPC and ARPC in patient plasma samples, we developed prediction models that achieved accuracies of 97% for dominant phenotypes and 87% for mixed clinical phenotypes. Although phenotype classification is typically assessed by IHC or transcriptome profiling from tumor biopsies, we demonstrate that ctDNA provides comparable results with diagnostic advantages for precision oncology.

SIGNIFICANCE

This study provides insights into the dynamics of nucleosome positioning and gene regulation associated with cancer phenotypes that can be ascertained from ctDNA. New methods for classification in phenotype mixtures extend the utility of ctDNA beyond assessments of somatic DNA alterations with important implications for molecular classification and precision oncology. This article is highlighted in the In This Issue feature, p. 517.