Bridging biological cfDNA features and machine learning approaches.

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

Blocking STAT3/5 through direct or upstream kinase targeting in leukemic cutaneous T-cell lymphoma.

Leukemic cutaneous T-cell lymphomas (L-CTCL) are lymphoproliferative disorders of skin-homing mature T-cells causing severe symptoms and high mortality through chronic inflammation, tissue destruction, and serious infections. Despite numerous genomic sequencing efforts, recurrent driver mutations have not been identified, but chromosomal losses and gains are frequent and dominant. We integrated genomic landscape analyses with innovative pharmacologic interference studies to identify key vulnerable nodes in L-CTCL. We detected copy number gains of loci containing the STAT3/5 oncogenes in 74% (n = 17/23) of L-CTCL, which correlated with the increased clonal T-cell count in the blood. Dual inhibition of STAT3/5 using small-molecule degraders and multi-kinase blockers abolished L-CTCL cell growth in vitro and ex vivo, whereby PAK kinase inhibition was specifically selective for L-CTCL patient cells carrying STAT3/5 gains. Importantly, the PAK inhibitor FRAx597 demonstrated encouraging anti-leukemic activity in vivo by inhibiting tumor growth and disease dissemination in intradermally xenografted mice. We conclude that STAT3/5 and PAK kinase interaction represents a new therapeutic node to be further explored in L-CTCL.

Vocal Fold Fibroblasts in Reinke's Edema Show Alterations Involved in Extracellular Matrix Production, Cytokine Response and Cell Cycle Control.

The voice disorder Reinke's edema (RE) is a smoking- and voice-abuse associated benign lesion of the vocal folds, defined by an edema of the Reinke's space, accompanied by pathological microvasculature changes and immune cell infiltration. Vocal fold fibroblasts (VFF) are the main cell type of the lamina propria and play a key role in the disease progression. Current therapy is restricted to symptomatic treatment. Hence, there is an urgent need for a better understanding of the molecular causes of the disease. In the present study, we investigated differential expression profiles of RE and control VFF by means of RNA sequencing. In addition, fast gene set enrichment analysis (FGSEA) was performed in order to obtain involved biological processes, mRNA and protein levels of targets of interest were further evaluated. We identified 74 differentially regulated genes in total, 19 of which were upregulated and 55 downregulated. Differential expression analysis and FGSEA revealed upregulated genes and pathways involved in extracellular matrix (ECM) remodeling, inflammation and fibrosis. Downregulated genes and pathways were involved in ECM degradation, cell cycle control and proliferation. The current study addressed for the first time a direct comparison of VFF from RE to control and evaluated immediate functional consequences.

Publisher Correction: Inference of transcription factor binding from cell-free DNA enables tumor subtype prediction and early detection.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Inference of transcription factor binding from cell-free DNA enables tumor subtype prediction and early detection.

Deregulation of transcription factors (TFs) is an important driver of tumorigenesis, but non-invasive assays for assessing transcription factor activity are lacking. Here we develop and validate a minimally invasive method for assessing TF activity based on cell-free DNA sequencing and nucleosome footprint analysis. We analyze whole genome sequencing data for >1,000 cell-free DNA samples from cancer patients and healthy controls using a bioinformatics pipeline developed by us that infers accessibility of TF binding sites from cell-free DNA fragmentation patterns. We observe patient-specific as well as tumor-specific patterns, including accurate prediction of tumor subtypes in prostate cancer, with important clinical implications for the management of patients. Furthermore, we show that cell-free DNA TF profiling is capable of detection of early-stage colorectal carcinomas. Our approach for mapping tumor-specific transcription factor binding in vivo based on blood samples makes a key part of the noncoding genome amenable to clinical analysis.