γδ T cells in human colon adenocarcinomas comprise mainly Vδ1, Vδ2, and Vδ3 cells with distinct phenotype and function.

Γδ T cell infiltration into tumours usually correlates with improved patient outcome, but both tumour-promoting and tumoricidal effects of γδ T cells have been documented. Human γδ T cells can be divided into functionally distinct subsets based on T cell receptor (TCR) Vδ usage. Still, the contribution of these different subsets to tumour immunity remains elusive. Here, we provide a detailed γδ T cell profiling in colon tumours, using mass and flow cytometry, mRNA quantification, and TCR sequencing. δ chain usage in both the macroscopically unaffected colon mucosa and tumours varied considerably between patients, with substantial fractions of Vδ1, Vδ2, and non-Vδ1 Vδ2 cells. Sequencing of the Vδ complementarity-determining region 3 showed that almost all non-Vδ1 Vδ2 cells used Vδ3 and that tumour-infiltrating γδ clonotypes were unique for every patient. Non-Vδ1Vδ2 cells from colon tumours expressed several activation markers but few NK cell receptors and exhaustion markers. In addition, mRNA analyses showed that non-Vδ1 Vδ2 cells expressed several genes for proteins with tumour-promoting functions, such as neutrophil-recruiting chemokines, Galectin 3, and transforming growth factor-beta induced. In summary, our results show a large variation in γδ T cell subsets between individual tumours, and that Vδ3 cells make up a substantial proportion of γδ T cells in colon tumours. We suggest that individual γδ T cell composition in colon tumours may contribute to the balance between favourable and adverse immune responses, and thereby also patient outcome.

Identification of genomic binding sites and direct target genes for the transcription factor DDIT3/CHOP.

DDIT3 is a tightly regulated basic leucine zipper (bZIP) transcription factor and key regulator in cellular stress responses. It is involved in a variety of pathological conditions and may cause cell cycle block and apoptosis. It is also implicated in differentiation of some specialized cell types and as an oncogene in several types of cancer. DDIT3 was originally believed to act as a dominant-negative inhibitor by forming heterodimers with other bZIP transcription factors, preventing their DNA binding and transactivating functions. DDIT3 has, however, been reported to bind DNA and regulate target genes. Here, we employed ChIP sequencing combined with microarray-based expression analysis to identify direct binding motifs and target genes of DDIT3. The results reveal DDIT3 binding to motifs similar to other bZIP transcription factors, known to form heterodimers with DDIT3. Binding to a class III satellite DNA repeat sequence was also detected. DDIT3 acted as a DNA-binding transcription factor and bound mainly to the promotor region of regulated genes. ChIP sequencing analysis of histone H3K27 methylation and acetylation showed a strong overlap between H3K27-acetylated marks and DDIT3 binding. These results support a role for DDIT3 as a transcriptional regulator of H3K27ac-marked genes in transcriptionally active chromatin.

Treatment Monitoring of a Patient with Synchronous Metastatic Angiosarcoma and Breast Cancer Using ctDNA.

Angiosarcoma is a rare and aggressive type of soft-tissue sarcoma with high propensity to metastasize. For patients with metastatic angiosarcoma, prognosis is dismal and treatment options are limited. To improve the outcomes, identifying patients with poor treatment response at an earlier stage is imperative, enabling alternative therapy. Consequently, there is a need for improved methods and biomarkers for treatment monitoring. Quantification of circulating tumor-DNA (ctDNA) is a promising approach for patient-specific monitoring of treatment response. In this case report, we demonstrate that quantification of ctDNA using SiMSen-Seq was successfully utilized to monitor a patient with metastatic angiosarcoma. By quantifying ctDNA levels using 25 patient-specific mutations in blood plasma throughout surgery and palliative chemotherapy, we predicted the outcome and monitored the clinical response to treatment. This was accomplished despite the additional complexity of the patient having a synchronous breast cancer. The levels of ctDNA showed a superior correlation to the clinical outcome compared with the radiological evaluations. Our data propose a promising approach for personalized biomarker analysis to monitor treatment in angiosarcomas, with potential applicability to other cancers and for patients with synchronous malignancies.

UMIErrorCorrect and UMIAnalyzer: Software for Consensus Read Generation, Error Correction, and Visualization Using Unique Molecular Identifiers.

BACKGROUND: Targeted sequencing using unique molecular identifiers (UMIs) enables detection of rare variant alleles in challenging applications, such as cell-free DNA analysis from liquid biopsies. Standard bioinformatics pipelines for data processing and variant calling are not adapted for deep-sequencing data containing UMIs, are inflexible, and require multistep workflows or dedicated computing resources. METHODS: We developed a bioinformatics pipeline using Python and an R package for data analysis and visualization. To validate our pipeline, we analyzed cell-free DNA reference material with known mutant allele frequencies (0%, 0.125%, 0.25%, and 1%) and public data sets. RESULTS: We developed UMIErrorCorrect, a bioinformatics pipeline for analyzing sequencing data containing UMIs. UMIErrorCorrect only requires fastq files as inputs and performs alignment, UMI clustering, error correction, and variant calling. We also provide UMIAnalyzer, a graphical user interface, for data mining, visualization, variant interpretation, and report generation. UMIAnalyzer allows the user to adjust analysis parameters and study their effect on variant calling. We demonstrated the flexibility of UMIErrorCorrect by analyzing data from 4 different targeted sequencing protocols. We also show its ability to detect different mutant allele frequencies in standardized cell-free DNA reference material. UMIErrorCorrect outperformed existing pipelines for targeted UMI sequencing data in terms of variant detection sensitivity. CONCLUSIONS: UMIErrorCorrect and UMIAnalyzer are comprehensive and customizable bioinformatics tools that can be applied to any type of library preparation protocol and enrichment chemistry using UMIs. Access to simple, generic, and open-source bioinformatics tools will facilitate the implementation of UMI-based sequencing approaches in basic research and clinical applications.

A comprehensive survey of integron-associated genes present in metagenomes.

BACKGROUND: Integrons are genomic elements that mediate horizontal gene transfer by inserting and removing genetic material using site-specific recombination. Integrons are commonly found in bacterial genomes, where they maintain a large and diverse set of genes that plays an important role in adaptation and evolution. Previous studies have started to characterize the wide range of biological functions present in integrons. However, the efforts have so far mainly been limited to genomes from cultivable bacteria and amplicons generated by PCR, thus targeting only a small part of the total integron diversity. Metagenomic data, generated by direct sequencing of environmental and clinical samples, provides a more holistic and unbiased analysis of integron-associated genes. However, the fragmented nature of metagenomic data has previously made such analysis highly challenging. RESULTS: Here, we present a systematic survey of integron-associated genes in metagenomic data. The analysis was based on a newly developed computational method where integron-associated genes were identified by detecting their associated recombination sites. By processing contiguous sequences assembled from more than 10 terabases of metagenomic data, we were able to identify 13,397 unique integron-associated genes. Metagenomes from marine microbial communities had the highest occurrence of integron-associated genes with levels more than 100-fold higher than in the human microbiome. The identified genes had a large functional diversity spanning over several functional classes. Genes associated with defense mechanisms and mobility facilitators were most overrepresented and more than five times as common in integrons compared to other bacterial genes. As many as two thirds of the genes were found to encode proteins of unknown function. Less than 1% of the genes were associated with antibiotic resistance, of which several were novel, previously undescribed, resistance gene variants. CONCLUSIONS: Our results highlight the large functional diversity maintained by integrons present in unculturable bacteria and significantly expands the number of described integron-associated genes.

ECOdrug: a database connecting drugs and conservation of their targets across species.

Pharmaceuticals are designed to interact with specific molecular targets in humans and these targets generally have orthologs in other species. This provides opportunities for the drug discovery community to use alternative model species for drug development. It also means, however, there is potential for mode of action related effects in non-target wildlife species as many pharmaceuticals reach the environment through patient use and manufacturing wastes. Acquiring insight in drug target ortholog predictions across species and taxonomic groups has proven difficult because of the lack of an optimal strategy and because necessary information is spread across multiple and diverse sources and platforms. We introduce a new research platform tool, ECOdrug, that reliably connects drugs to their protein targets across divergent species. It harmonizes ortholog predictions from multiple sources via a simple user interface underpinning critical applications for a wide range of studies in pharmacology, ecotoxicology and comparative evolutionary biology. ECOdrug can be used to identify species with drug targets and identify drugs that interact with those targets. As such, it can be applied to support intelligent targeted drug safety testing by ensuring appropriate and relevant species are selected in ecological risk assessments. ECOdrug is freely accessible and available at: http://www.ecodrug.org.

Sewage from Airplanes Exhibits High Abundance and Diversity of Antibiotic Resistance Genes.

Airplane sanitary facilities are shared by an international audience. We hypothesized the corresponding sewage to be an extraordinary source of antibiotic-resistant bacteria (ARB) and resistance genes (ARG) in terms of diversity and quantity. Accordingly, we analyzed ARG and ARB in airplane-borne sewage using complementary approaches: metagenomics, quantitative polymerase chain reaction (qPCR), and cultivation. For the purpose of comparison, we also quantified ARG and ARB in the inlets of municipal treatment plants with and without connection to airports. As expected, airplane sewage contained an extraordinarily rich set of mobile ARG, and the relative abundances of genes were mostly increased compared to typical raw sewage of municipal origin. Moreover, combined resistance against third-generation cephalosporins, fluorochinolones, and aminoglycosides was unusually common (28.9%) among Escherichia coli isolated from airplane sewage. This percentage exceeds the one reported for German clinical isolates by a factor of 8. Our findings suggest that airplane-borne sewage can effectively contribute to the fast and global spread of antibiotic resistance.

HirBin: high-resolution identification of differentially abundant functions in metagenomes.

BACKGROUND: Gene-centric analysis of metagenomics data provides information about the biochemical functions present in a microbiome under a certain condition. The ability to identify significant differences in functions between metagenomes is dependent on accurate classification and quantification of the sequence reads (binning). However, biological effects acting on specific functions may be overlooked if the classes are too general. METHODS: Here we introduce High-Resolution Binning (HirBin), a new method for gene-centric analysis of metagenomes. HirBin combines supervised annotation with unsupervised clustering to bin sequence reads at a higher resolution. The supervised annotation is performed by matching sequence fragments to genes using well-established protein domains, such as TIGRFAM, PFAM or COGs, followed by unsupervised clustering where each functional domain is further divided into sub-bins based on sequence similarity. Finally, differential abundance of the sub-bins is statistically assessed. RESULTS: We show that HirBin is able to identify biological effects that are only present at more specific functional levels. Furthermore we show that changes affecting more specific functional levels are often diluted at the more general level and therefore overlooked when analyzed using standard binning approaches. CONCLUSIONS: HirBin improves the resolution of the gene-centric analysis of metagenomes and facilitates the biological interpretation of the results. HirBin is implemented as a Python package and is freely available for download at http://bioinformatics.math.chalmers.se/hirbin .

Statistical evaluation of methods for identification of differentially abundant genes in comparative metagenomics.

BACKGROUND: Metagenomics is the study of microbial communities by sequencing of genetic material directly from environmental or clinical samples. The genes present in the metagenomes are quantified by annotating and counting the generated DNA fragments. Identification of differentially abundant genes between metagenomes can provide important information about differences in community structure, diversity and biological function. Metagenomic data is however high-dimensional, contain high levels of biological and technical noise and have typically few biological replicates. The statistical analysis is therefore challenging and many approaches have been suggested to date. RESULTS: In this article we perform a comprehensive evaluation of 14 methods for identification of differentially abundant genes between metagenomes. The methods are compared based on the power to detect differentially abundant genes and their ability to correctly estimate the type I error rate and the false discovery rate. We show that sample size, effect size, and gene abundance greatly affect the performance of all methods. Several of the methods also show non-optimal model assumptions and biased false discovery rate estimates, which can result in too large numbers of false positives. We also demonstrate that the performance of several of the methods differs substantially between metagenomic data sequenced by different technologies. CONCLUSIONS: Two methods, primarily designed for the analysis of RNA sequencing data (edgeR and DESeq2) together with a generalized linear model based on an overdispersed Poisson distribution were found to have best overall performance. The results presented in this study may serve as a guide for selecting suitable statistical methods for identification of differentially abundant genes in metagenomes.

Improved production of a heterologous amylase in Saccharomyces cerevisiae by inverse metabolic engineering.

The increasing demand for industrial enzymes and biopharmaceutical proteins relies on robust production hosts with high protein yield and productivity. Being one of the best-studied model organisms and capable of performing posttranslational modifications, the yeast Saccharomyces cerevisiae is widely used as a cell factory for recombinant protein production. However, many recombinant proteins are produced at only 1% (or less) of the theoretical capacity due to the complexity of the secretory pathway, which has not been fully exploited. In this study, we applied the concept of inverse metabolic engineering to identify novel targets for improving protein secretion. Screening that combined UV-random mutagenesis and selection for growth on starch was performed to find mutant strains producing heterologous amylase 5-fold above the level produced by the reference strain. Genomic mutations that could be associated with higher amylase secretion were identified through whole-genome sequencing. Several single-point mutations, including an S196I point mutation in the VTA1 gene coding for a protein involved in vacuolar sorting, were evaluated by introducing these to the starting strain. By applying this modification alone, the amylase secretion could be improved by 35%. As a complement to the identification of genomic variants, transcriptome analysis was also performed in order to understand on a global level the transcriptional changes associated with the improved amylase production caused by UV mutagenesis.

Management of the endoplasmic reticulum stress by activation of the heat shock response in yeast.

In yeast Saccharomyces cerevisiae, accumulation of misfolded proteins in the endoplasmic reticulum (ER) causes ER stress and activates the unfolded protein response (UPR), which is mediated by Hac1p. The heat shock response (HSR) mediated by Hsf1p, mainly regulates cytosolic processes and protects the cell from stresses. Here, we find that a constitutive activation of the HSR could increase ER stress resistance in both wild-type and UPR-deficient cells. Activation of HSR decreased UPR activation in the WT (as shown by the decreased HAC1 mRNA splicing). We analyzed the genome-wide transcriptional response in order to propose regulatory mechanisms that govern the interplay between UPR and HSR and followed up for the hypotheses by experiments in vivo and in vitro. Interestingly, we found that the regulation of ER stress response via HSR is (1) only partially dependent on over-expression of Kar2p (ER resident chaperone induced by ER stress); (2) does not involve the increase in protein turnover via the proteasome activity; (3) is related to the oxidative stress response. From the transcription data, we also propose that HSR enhances ER stress resistance mainly through facilitation of protein folding and secretion. We also find that HSR coordinates multiple stress-response pathways, including the repression of the overall transcription and translation.

Principles of digital sequencing using unique molecular identifiers.

Massively parallel sequencing technologies have long been used in both basic research and clinical routine. The recent introduction of digital sequencing has made previously challenging applications possible by significantly improving sensitivity and specificity to now allow detection of rare sequence variants, even at single molecule level. Digital sequencing utilizes unique molecular identifiers (UMIs) to minimize sequencing-induced errors and quantification biases. Here, we discuss the principles of UMIs and how they are used in digital sequencing. We outline the properties of different UMI types and the consequences of various UMI approaches in relation to experimental protocols and bioinformatics. Finally, we describe how digital sequencing can be applied in specific research fields, focusing on cancer management where it can be used in screening of asymptomatic individuals, diagnosis, treatment prediction, prognostication, monitoring treatment efficacy and early detection of treatment resistance as well as relapse.

Digital RNA sequencing using unique molecular identifiers enables ultrasensitive RNA mutation analysis.

Mutation analysis is typically performed at the DNA level since most technical approaches are developed for DNA analysis. However, some applications, like transcriptional mutagenesis, RNA editing and gene expression analysis, require RNA analysis. Here, we combine reverse transcription and digital DNA sequencing to enable low error digital RNA sequencing. We evaluate yield, reproducibility, dynamic range and error correction rate for seven different reverse transcription conditions using multiplexed assays. The yield, reproducibility and error rate vary substantially between the specific conditions, where the yield differs 9.9-fold between the best and worst performing condition. Next, we show that error rates similar to DNA sequencing can be achieved for RNA using appropriate reverse transcription conditions, enabling detection of mutant allele frequencies <0.1% at RNA level. We also detect mutations at both DNA and RNA levels in tumor tissue using a breast cancer panel. Finally, we demonstrate that digital RNA sequencing can be applied to liquid biopsies, analyzing cell-free gene transcripts. In conclusion, we demonstrate that digital RNA sequencing is suitable for ultrasensitive RNA mutation analysis, enabling several basic research and clinical applications.

Ultrasensitive sequencing of STR markers utilizing unique molecular identifiers and the SiMSen-Seq method.

Massively parallel sequencing (MPS) is increasingly applied in forensic short tandem repeat (STR) analysis. The presence of stutter artefacts and other PCR or sequencing errors in the MPS-STR data partly limits the detection of low DNA amounts, e.g., in complex mixtures. Unique molecular identifiers (UMIs) have been applied in several scientific fields to reduce noise in sequencing. UMIs consist of a stretch of random nucleotides, a unique barcode for each starting DNA molecule, that is incorporated in the DNA template using either ligation or PCR. The barcode is used to generate consensus reads, thus removing errors. The SiMSen-Seq (Simple, multiplexed, PCR-based barcoding of DNA for sensitive mutation detection using sequencing) method relies on PCR-based introduction of UMIs and includes a sophisticated hairpin design to reduce unspecific primer binding as well as PCR protocol adjustments to further optimize the reaction. In this study, SiMSen-Seq is applied to develop a proof-of-concept seven STR multiplex for MPS library preparation and an associated bioinformatics pipeline. Additionally, machine learning (ML) models were evaluated to further improve UMI allele calling. Overall, the seven STR multiplex resulted in complete detection and concordant alleles for 47 single-source samples at 1 ng input DNA as well as for low-template samples at 62.5 pg input DNA. For twelve challenging mixtures with minor contributions of 10 pg to 150 pg and ratios of 1-15% relative to the major donor, 99.2% of the expected alleles were detected by applying the UMIs in combination with an ML filter. The main impact of UMIs was a substantially lowered number of artefacts as well as reduced stutter ratios, which were generally below 5% of the parental allele. In conclusion, UMI-based STR sequencing opens new means for improved analysis of challenging crime scene samples including complex mixtures.

A novel method for cross-species gene expression analysis.

BACKGROUND: Analysis of gene expression from different species is a powerful way to identify evolutionarily conserved transcriptional responses. However, due to evolutionary events such as gene duplication, there is no one-to-one correspondence between genes from different species which makes comparison of their expression profiles complex. RESULTS: In this paper we describe a new method for cross-species meta-analysis of gene expression. The method takes the homology structure between compared species into account and can therefore compare expression data from genes with any number of orthologs and paralogs. A simulation study shows that the proposed method results in a substantial increase in statistical power compared to previously suggested procedures. As a proof of concept, we analyzed microarray data from heat stress experiments performed in eight species and identified several well-known evolutionarily conserved transcriptional responses. The method was also applied to gene expression profiles from five studies of estrogen exposed fish and both known and potentially novel responses were identified. CONCLUSIONS: The method described in this paper will further increase the potential and reliability of meta-analysis of gene expression profiles from evolutionarily distant species. The method has been implemented in R and is freely available at http://bioinformatics.math.chalmers.se/Xspecies/.

Anaerobic α-amylase production and secretion with fumarate as the final electron acceptor in Saccharomyces cerevisiae.

In this study, we focus on production of heterologous α-amylase in the yeast Saccharomyces cerevisiae under anaerobic conditions. We compare the metabolic fluxes and transcriptional regulation under aerobic and anaerobic conditions, with the objective of identifying the final electron acceptor for protein folding under anaerobic conditions. We find that yeast produces more amylase under anaerobic conditions than under aerobic conditions, and we propose a model for electron transfer under anaerobic conditions. According to our model, during protein folding the electrons from the endoplasmic reticulum are transferred to fumarate as the final electron acceptor. This model is supported by findings that the addition of fumarate under anaerobic (but not aerobic) conditions improves cell growth, specifically in the α-amylase-producing strain, in which it is not used as a carbon source. Our results provide a model for the molecular mechanism of anaerobic protein secretion using fumarate as the final electron acceptor, which may allow for further engineering of yeast for improved protein secretion under anaerobic growth conditions.

Heat shock response improves heterologous protein secretion in Saccharomyces cerevisiae.

The yeast Saccharomyces cerevisiae is a widely used platform for the production of heterologous proteins of medical or industrial interest. However, heterologous protein productivity is often low due to limitations of the host strain. Heat shock response (HSR) is an inducible, global, cellular stress response, which facilitates the cell recovery from many forms of stress, e.g., heat stress. In S. cerevisiae, HSR is regulated mainly by the transcription factor heat shock factor (Hsf1p) and many of its targets are genes coding for molecular chaperones that promote protein folding and prevent the accumulation of mis-folded or aggregated proteins. In this work, we over-expressed a mutant HSF1 gene HSF1-R206S which can constitutively activate HSR, so the heat shock response was induced at different levels, and we studied the impact of HSR on heterologous protein secretion. We found that moderate and high level over-expression of HSF1-R206S increased heterologous α-amylase yield 25 and 70 % when glucose was fully consumed, and 37 and 62 % at the end of the ethanol phase, respectively. Moderate and high level over-expression also improved endogenous invertase yield 118 and 94 %, respectively. However, human insulin precursor was only improved slightly and this only by high level over-expression of HSF1-R206S, supporting our previous findings that the production of this protein in S. cerevisiae is not limited by secretion. Our results provide an effective strategy to improve protein secretion and demonstrated an approach that can induce ER and cytosolic chaperones simultaneously.

FUS-DDIT3 Fusion Oncoprotein Expression Affects JAK-STAT Signaling in Myxoid Liposarcoma.

Myxoid liposarcoma is one of the most common sarcoma entities characterized by FET fusion oncogenes. Despite a generally favorable prognosis of myxoid liposarcoma, chemotherapy resistance remains a clinical problem. This cancer stem cell property is associated with JAK-STAT signaling, but the link to the myxoid-liposarcoma-specific FET fusion oncogene FUS-DDIT3 is not known. Here, we show that ectopic expression of FUS-DDIT3 resulted in elevated levels of STAT3 and phosphorylated STAT3. RNA sequencing identified 126 genes that were regulated by both FUS-DDIT3 expression and JAK1/2 inhibition using ruxolitinib. Sixty-six of these genes were connected in a protein interaction network. Fifty-three and 29 of these genes were confirmed as FUS-DDIT3 and STAT3 targets, respectively, using public chromatin immunoprecipitation sequencing data sets. Enriched gene sets among the 126 regulated genes included processes related to cytokine signaling, adipocytokine signaling, and chromatin remodeling. We validated CD44 as a target gene of JAK1/2 inhibition and as a potential cancer stem cell marker in myxoid liposarcoma. Finally, we showed that FUS-DDIT3 interacted with phosphorylated STAT3 in association with subunits of the SWI/SNF chromatin remodeling complex and PRC2 repressive complex. Our data show that the function of FUS-DDIT3 is closely connected to JAK-STAT signaling. Detailed deciphering of molecular mechanisms behind tumor progression opens up new avenues for targeted therapies in sarcomas and leukemia characterized by FET fusion oncogenes.

FET fusion oncoproteins interact with BRD4 and SWI/SNF chromatin remodelling complex subtypes in sarcoma.

FET fusion oncoproteins containing one of the FET (FUS, EWSR1, TAF15) family proteins juxtaposed to alternative transcription-factor partners are characteristic of more than 20 types of sarcoma and leukaemia. FET oncoproteins bind to the SWI/SNF chromatin remodelling complex, which exists in three subtypes: cBAF, PBAF and GBAF/ncBAF. We used comprehensive biochemical analysis to characterize the interactions between FET oncoproteins, SWI/SNF complexes and the transcriptional coactivator BRD4. Here, we report that FET oncoproteins bind all three main SWI/SNF subtypes cBAF, PBAF and GBAF, and that FET oncoproteins interact indirectly with BRD4 via their shared interaction partner SWI/SNF. Furthermore, chromatin immunoprecipitation sequencing and proteomic analysis showed that FET oncoproteins, SWI/SNF components and BRD4 co-localize on chromatin and interact with mediator and RNA Polymerase II. Our results provide a possible molecular mechanism for the FET-fusion-induced oncogenic transcriptional profiles and may lead to novel therapies targeting aberrant SWI/SNF complexes and/or BRD4 in FET-fusion-caused malignancies.

Monitoring Circulating Tumor DNA During Surgical Treatment in Patients with Gastrointestinal Stromal Tumors.

The majority of patients diagnosed with advanced gastrointestinal stromal tumors (GISTs) are successfully treated with a combination of surgery and tyrosine kinase inhibitors (TKIs). However, it remains challenging to monitor treatment efficacy and identify relapse early. Here, we utilized a sequencing strategy based on molecular barcodes and developed a GIST-specific panel to monitor tumor-specific and TKI resistance mutations in cell-free DNA and applied the approach to patients undergoing surgical treatment. Thirty-two patients with GISTs were included, and 161 blood plasma samples were collected and analyzed at routine visits before and after surgery and at the beginning, during, and after surgery. Patients were included regardless of their risk category. Our GIST-specific sequencing approach allowed detection of tumor-specific mutations and TKI resistance mutations with mutant allele frequency < 0.1%. Circulating tumor DNA (ctDNA) was detected in at least one timepoint in nine of 32 patients, ranging from 0.04% to 93% in mutant allele frequency. High-risk patients were more often ctDNA positive than other risk groups (P < 0.05). Patients with detectable ctDNA also displayed higher tumor cell proliferation rates (P < 0.01) and larger tumor sizes (P < 0.01). All patients who were ctDNA positive during surgery became negative after surgery. Finally, in two patients who progressed on TKI treatment, we detected multiple resistance mutations. Our data show that ctDNA may become a clinically useful biomarker in monitoring treatment efficacy in patients with high-risk GISTs and can assist in treatment decision making.