Early changes in tumor-naive cell-free methylomes and fragmentomes predict outcomes in pembrolizumab-treated solid tumors.

Early kinetics of circulating tumor DNA (ctDNA) in plasma predict response to pembrolizumab, but typically requires sequencing of matched tumor tissue or fixed gene panels. We analyzed genome-wide methylation and fragment length profiles using cell-free methylated DNA immunoprecipitation and sequencing (cfMeDIP-seq) in 204 plasma samples from 87 patients before and during treatment with pembrolizumab from a pan-cancer phase II investigator-initiated trial (INSPIRE). We trained a pan-cancer methylation signature using independent methylation array data from The Cancer Genome Atlas to quantify a cancer-specific methylation (CSM) and fragment length score (FLS) for each sample. CSM and FLS are strongly correlated with tumor-informed ctDNA levels. Early kinetics of CSM predict overall survival and progression-free survival, independently of tumor type, PD-L1, and tumor mutation burden. Early kinetics of FLS are associated with overall survival independently of CSM. Our tumor-naïve mutation-agnostic ctDNA approach integrating methylomics and fragmentomics could predict outcomes in patients treated with pembrolizumab.

Sensitive and reproducible cell-free methylome quantification with synthetic spike-in controls.

Cell-free methylated DNA immunoprecipitation sequencing (cfMeDIP-seq) identifies genomic regions with DNA methylation, using a protocol adapted to work with low-input DNA samples and with cell-free DNA (cfDNA). We developed a set of synthetic spike-in DNA controls for cfMeDIP-seq to provide a simple and inexpensive reference for quantitative normalization. We designed 54 DNA fragments with combinations of methylation status (methylated and unmethylated), fragment length (80 bp, 160 bp, 320 bp), G + C content (35%, 50%, 65%), and fraction of CpG dinucleotides within the fragment (1/80 bp, 1/40 bp, 1/20 bp). Using 0.01 ng of spike-in controls enables training a generalized linear model that absolutely quantifies methylated cfDNA in MeDIP-seq experiments. It mitigates batch effects and corrects for biases in enrichment due to known biophysical properties of DNA fragments and other technical biases.

Dynamic sumoylation of promoter-bound general transcription factors facilitates transcription by RNA polymerase II.

Transcription-related proteins are frequently identified as targets of sumoylation, including multiple subunits of the RNA polymerase II (RNAPII) general transcription factors (GTFs). However, it is not known how sumoylation affects GTFs or whether they are sumoylated when they assemble at promoters to facilitate RNAPII recruitment and transcription initiation. To explore how sumoylation can regulate transcription genome-wide, we performed SUMO ChIP-seq in yeast and found, in agreement with others, that most chromatin-associated sumoylated proteins are detected at genes encoding tRNAs and ribosomal proteins (RPGs). However, we also detected 147 robust SUMO peaks at promoters of non-ribosomal protein-coding genes (non-RPGs), indicating that sumoylation also regulates this gene class. Importantly, SUMO peaks at non-RPGs align specifically with binding sites of GTFs, but not other promoter-associated proteins, indicating that it is GTFs specifically that are sumoylated there. Predominantly, non-RPGs with SUMO peaks are among the most highly transcribed, have high levels of TFIIF, and show reduced RNAPII levels when cellular sumoylation is impaired, linking sumoylation with elevated transcription. However, detection of promoter-associated SUMO by ChIP might be limited to sites with high levels of substrate GTFs, and promoter-associated sumoylation at non-RPGs may actually be far more widespread than we detected. Among GTFs, we found that TFIIF is a major target of sumoylation, specifically at lysines 60/61 of its Tfg1 subunit, and elevating Tfg1 sumoylation resulted in decreased interaction of TFIIF with RNAPII. Interestingly, both reducing promoter-associated sumoylation, in a sumoylation-deficient Tfg1-K60/61R mutant strain, and elevating promoter-associated SUMO levels, by constitutively tethering SUMO to Tfg1, resulted in reduced RNAPII occupancy at non-RPGs. This implies that dynamic GTF sumoylation at non-RPG promoters, not simply the presence or absence of SUMO, is important for maintaining elevated transcription. Together, our findings reveal a novel mechanism of regulating the basal transcription machinery through sumoylation of promoter-bound GTFs.

Tumor-Naïve Multimodal Profiling of Circulating Tumor DNA in Head and Neck Squamous Cell Carcinoma.

PURPOSE: Circulating tumor DNA (ctDNA) enables personalized treatment strategies in oncology by providing a noninvasive source of clinical biomarkers. In patients with low ctDNA abundance, tumor-naïve methods are needed to facilitate clinical implementation. Here, using locoregionally confined head and neck squamous cell carcinoma (HNSCC) as an example, we demonstrate tumor-naïve detection of ctDNA by simultaneous profiling of mutations and methylation. EXPERIMENTAL DESIGN: We conducted CAncer Personalized Profiling by deep Sequencing (CAPP-seq) and cell-free Methylated DNA ImmunoPrecipitation and high-throughput sequencing (cfMeDIP-seq) for detection of ctDNA-derived somatic mutations and aberrant methylation, respectively. We analyzed 77 plasma samples from 30 patients with stage I-IVA human papillomavirus-negative HNSCC as well as plasma samples from 20 risk-matched healthy controls. In addition, we analyzed leukocytes from patients and controls. RESULTS: CAPP-seq identified mutations in 20 of 30 patients at frequencies similar to that of The Tumor Genome Atlas (TCGA). Differential methylation analysis of cfMeDIP-seq profiles identified 941 ctDNA-derived hypermethylated regions enriched for CpG islands and HNSCC-specific methylation patterns. Both methods demonstrated an association between ctDNA abundance and shorter fragment lengths. In addition, mutation- and methylation-based ctDNA abundance was highly correlated (r > 0.85). Patients with detectable pretreatment ctDNA by both methods demonstrated significantly worse overall survival (HR = 7.5; P = 0.025) independent of clinical stage, with lack of ctDNA clearance post-treatment strongly correlating with recurrence. We further leveraged cfMeDIP-seq profiles to validate a prognostic signature identified from TCGA samples. CONCLUSIONS: Tumor-naïve detection of ctDNA by multimodal profiling may facilitate biomarker discovery and clinical use in low ctDNA abundance applications.

Preparation of cfMeDIP-seq libraries for methylome profiling of plasma cell-free DNA.

Circulating cell-free DNA (cfDNA) comprises small DNA fragments derived from normal and tumor tissue that are released into the bloodstream. Recently, methylation profiling of cfDNA as a liquid biopsy tool has been gaining prominence due to the presence of tissue-specific markers in cfDNA. We have previously reported cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq) as a sensitive, low-input, cost-efficient and bisulfite-free approach to profiling DNA methylomes of plasma cfDNA. cfMeDIP-seq is an extension of a previously published MeDIP-seq protocol and is adapted to allow for methylome profiling of samples with low input (ranging from 1 to 10 ng) of DNA, which is enabled by the addition of 'filler DNA' before immunoprecipitation. This protocol is not limited to plasma cfDNA; it can also be applied to other samples that are naturally sheared and at low availability (e.g., urinary cfDNA and cerebrospinal fluid cfDNA), and is potentially applicable to other applications beyond cancer detection, including prenatal diagnostics, cardiology and monitoring of immune response. The protocol presented here should enable any standard molecular laboratory to generate cfMeDIP-seq libraries from plasma cfDNA in ~3-4 d.

Cell-free DNA as a post-treatment surveillance strategy: current status.

Circulating tumor DNA (ctDNA) consists of cell-free DNA (cfDNA) fragments that are released from tumor cells into the bloodstream. ctDNA harbors cancer-specific genetic and epigenetic alterations that allow its detection and quantification using a variety of emerging techniques. The promise of convenient non-invasive access to the complex and dynamic molecular features of cancer through peripheral blood has galvanized translational researchers around this topic with compelling routes to clinical implementation, particularly in the post-treatment surveillance setting. Although analysis methods must contend with the small quantities of ctDNA present in most patients, and the relative over-abundance of background cfDNA derived from normal tissues, recent technical innovations have led to dramatic improvements in the sensitivity of ctDNA detection. As a result, ever more studies are investigating the clinical utility of ctDNA for applications in (1) treatment response assessment, (2) identification of emerging resistance mechanisms, (3) minimal residual disease detection, and (4) characterization of clonal heterogeneity and selection. In this review, we describe the detection methods currently used in clinical studies to assess low fractions of ctDNA, as well as their utility in the applications previously described. Finally, we address current limitations that have hampered the clinical implementation of ctDNA analysis for post-treatment surveillance and propose steps that could be made to address them.

Sumoylation controls the timing of Tup1-mediated transcriptional deactivation.

The small ubiquitin-like modifier (SUMO) is implicated in various cellular activities, including transcriptional regulation. We previously showed that the yeast activator Gcn4 becomes sumoylated during activation, facilitating its eventual promoter eviction and transcriptional shut off. Here we show that the corepressor Tup1 is sumoylated, at two specific lysines, under various stress conditions. Mutation of these sites has no effect on Tup1 recruitment or RNAP II promoter occupancy immediately following induction. However, Tup1 levels subsequently decrease, while RNAP II and transcription increase in Tup1 mutant cells. Consistent with this, a Tup1 mutant displaying increased sumoylation led to reduced transcription. We also show that coordinated sumoylation of Gcn4 and Tup1 enhances Gcn4 promoter eviction and that multiple Tup1-interacting proteins become sumoylated after stress. Together, our studies provide evidence that coordinated sumoylation of Gcn4, Tup1 and likely other factors dampens activated transcription by stabilizing Tup1 binding and stimulating Gcn4 and RNAP II removal.