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Genomic profiling is critical for the identification of treatment options for patients with metastatic cancer, but it remains unclear how frequently this procedure should be repeated during the course of the disease. To address this, we analyzed whole-genome sequencing (WGS) data of 250 biopsy pairs, longitudinally collected over the treatment course of 231 adult patients with a representative variety of metastatic solid malignancies. Within the biopsy interval (median, 6.4 months), patients received one or multiple lines of (mostly) standard-of-care (SOC) treatments, with all major treatment modalities being broadly represented. SOC biomarkers and biomarkers for clinical trial enrollment could be identified in 23% and 72% of biopsies, respectively. For SOC genomic biomarkers, we observed full concordance between the first and the second biopsy in 99% of pairs. Of the 219 biomarkers for clinical trial enrollment that were identified in the first biopsies, we recovered 94% in the follow-up biopsies. Furthermore, a second WGS analysis did not identify additional biomarkers for clinical trial enrollment in 91% of patients. More-frequent genomic evolution was observed when considering specific genes targeted by small-molecule inhibitors or hormonal therapies (21% and 22% of cases, respectively). Together, our data demonstrate that there is limited evolution of the actionable genome of treated metastases. A single WGS analysis of a metastatic biopsy is generally sufficient to identify SOC genomic biomarkers and to identify investigational treatment opportunities. Whole-genome sequencing of metastatic biopsies longitudinally sampled during the course of anticancer treatment reveals that the actionable metastatic cancer genome remains relatively stable over time.

Metastatic and locally-advanced neuroendocrine neoplasms (aNEN) form clinically and genetically heterogeneous malignancies, characterized by distinct prognoses based upon primary tumor localization, functionality, grade, proliferation index and diverse outcomes to treatment. Here, we report the mutational landscape of 85 whole-genome sequenced aNEN. This landscape reveals distinct genomic subpopulations of aNEN based on primary localization and differentiation grade; we observe relatively high tumor mutational burdens (TMB) in neuroendocrine carcinoma (average 5.45 somatic mutations per megabase) with TP53 , KRAS , RB1 , CSMD3 , APC , CSMD1 , LRATD2 , TRRAP and MYC as major drivers versus an overall low TMB in neuroendocrine tumors (1.09). Furthermore, we observe distinct drivers which are enriched in somatic aberrations in pancreatic ( MEN1 , ATRX , DAXX , DMD and CREBBP ) and midgut-derived neuroendocrine tumors ( CDKN1B ). Finally, 49% of aNEN patients reveal potential therapeutic targets based upon actionable (and responsive) somatic aberrations within their genome; potentially directing improvements in aNEN treatment strategies. Metastatic and locally-advanced neuroendocrine neoplasms (aNEN) display heterogeneous clinical and genetic characteristics. Here, the authors investigate the mutational landscape of 85 aNEN by whole genome sequencing and identify distinct subpopulations, tumour mutational burden patterns, drivers and actionable somatic alterations.

Metastatic castration-resistant prostate cancer (mCRPC) has a highly complex genomic landscape. With the recent development of novel treatments, accurate stratification strategies are needed. Here we present the whole-genome sequencing (WGS) analysis of fresh-frozen metastatic biopsies from 197 mCRPC patients. Using unsupervised clustering based on genomic features, we define eight distinct genomic clusters. We observe potentially clinically relevant genotypes, including microsatellite instability (MSI), homologous recombination deficiency (HRD) enriched with genomic deletions and BRCA2 aberrations, a tandem duplication genotype associated with CDK12 −/− and a chromothripsis-enriched subgroup. Our data suggests that stratification on WGS characteristics may improve identification of MSI, CDK12 −/− and HRD patients. From WGS and ChIP-seq data, we show the potential relevance of recurrent alterations in non-coding regions identified with WGS and highlight the central role of AR signaling in tumor progression. These data underline the potential value of using WGS to accurately stratify mCRPC patients into clinically actionable subgroups. Detecting genomic abnormalities in metastatic castration-resistant prostate cancer (mCRPC) may impact clinical treatment. Here, the authors present whole-genome sequencing of metastatic biopsies from 197 mCRPC patients, highlighting the landscape of microsatellite stability, homologous repair deficiency, and other genomic subgroups.

Objective Pancreatic ductal adenocarcinoma (PDA) is characterised by stromal desmoplasia and vascular dysfunction, which critically impair drug delivery. This study examines the role of an abundant extracellular matrix component, the megadalton glycosaminoglycan hyaluronan (HA), as a novel therapeutic target in PDA. Methods Using a genetically engineered mouse model of PDA, the authors enzymatically depleted HA by a clinically formulated PEGylated human recombinant PH20 hyaluronidase (PEGPH20) and examined tumour perfusion, vascular permeability and drug delivery. The preclinical utility of PEGPH20 in combination with gemcitabine was assessed by short-term and survival studies. Results PEGPH20 rapidly and sustainably depleted HA, inducing the re-expansion of PDA blood vessels and increasing the intratumoral delivery of two chemotherapeutic agents, doxorubicin and gemcitabine. Moreover, PEGPH20 triggered fenestrations and interendothelial junctional gaps in PDA tumour endothelia and promoted a tumour-specific increase in macromolecular permeability. Finally, combination therapy with PEGPH20 and gemcitabine led to inhibition of PDA tumour growth and prolonged survival over gemcitabine monotherapy, suggesting immediate clinical utility. Conclusions The authors demonstrate that HA impedes the intratumoral vasculature in PDA and propose that its enzymatic depletion be explored as a means to improve drug delivery and response in patients with pancreatic cancer.

Current methods for detection of copy number variants (CNV) and aberrations (CNA) from targeted sequencing data are based on the depth of coverage of captured exons. Accurate CNA determination is complicated by uneven genomic distribution and non-uniform capture efficiency of targeted exons. Here we present CopywriteR, which eludes these problems by exploiting 'off-target' sequence reads. CopywriteR allows for extracting uniformly distributed copy number information, can be used without reference, and can be applied to sequencing data obtained from various techniques including chromatin immunoprecipitation and target enrichment on small gene panels. CopywriteR outperforms existing methods and constitutes a widely applicable alternative to available tools.

In contrast to primary colorectal cancer (CRC) little is known about the genomic landscape of metastasized CRC. Here we present whole genome sequencing data of metastases of 429 CRC patients participating in the pan-cancer CPCT-02 study (NCT01855477). Unsupervised clustering using mutational signature patterns highlights three major patient groups characterized by signatures known from primary CRC, signatures associated with received prior treatments, and metastasis-specific signatures. Compared to primary CRC, we identify additional putative (non-coding) driver genes and increased frequencies in driver gene mutations. In addition, we identify specific genes preferentially affected by microsatellite instability. CRC-specific 1kb-10Mb deletions, enriched for common fragile sites, and LINC00672 mutations are associated with response to treatment in general, whereas FBXW7 mutations predict poor response specifically to EGFR-targeted treatment. In conclusion, the genomic landscape of mCRC shows defined changes compared to primary CRC, is affected by prior treatments and contains features with potential clinical relevance. Molecular landscapes of metastatic colorectal cancers (mCRC) have often been restricted to coding regions or low numbers of patients. Here the authors present a whole-genome landscape of 429 mCRC patients, revealing the mutational impact of prior therapies and potential actionable targets.

Response to androgen receptor signaling inhibitors (ARSI) varies widely in metastatic castration resistant prostate cancer (mCRPC). To improve treatment guidance, biomarkers are needed. We use whole-genomics (WGS; n  = 155) with matching whole-transcriptomics (WTS; n  = 113) from biopsies of ARSI-treated mCRPC patients for unbiased discovery of biomarkers and development of machine learning-based prediction models. Tumor mutational burden ( q  < 0.001), structural variants ( q  < 0.05), tandem duplications ( q  < 0.05) and deletions ( q  < 0.05) are enriched in poor responders, coupled with distinct transcriptomic expression profiles. Validating various classification models predicting treatment duration with ARSI on our internal and external mCRPC cohort reveals two best-performing models, based on the combination of prior treatment information with either the four combined enriched genomic markers or with overall transcriptomic profiles. In conclusion, predictive models combining genomic, transcriptomic, and clinical data can predict response to ARSI in mCRPC patients and, with additional optimization and prospective validation, could improve treatment guidance. Prostate cancer is known to have a variable response to androgen receptor signalling inhibitors. Here, the authors use machine learning to predict response to therapy from genomic, transcriptomic and clinical data.

Tumour mutational burden (TMB) has emerged as a promising biomarker to predict immune checkpoint inhibitors (ICIs) response in advanced solid cancers. However, harmonisation of TMB reporting by targeted gene panels is lacking, especially in metastatic tumour samples. To address this issue, we used data of 2841 whole-genome sequenced metastatic cancer biopsies to perform an in silico analysis of TMB determined by seven gene panels (FD1CDx, MSK-IMPACT™, Caris Molecular Intelligence, Tempus xT, Oncomine Tumour Mutation Load, NeoTYPE Discovery Profile and CANCERPLEX) compared to exome-based TMB as a golden standard. Misclassification rates declined from up to 30% to <1% when the cut-point for high TMB was increased. Receiver operating characteristic analysis demonstrated that, for correct classification, the cut-point for each gene panel may vary more than 20%. In conclusion, we here demonstrate that a major limitation for the use of gene panels is inter-assay variation and the need for dynamic thresholds to compare TMB outcomes.

Circulating tumor DNA (ctDNA) has emerged as a potential new biomarker with diagnostic, predictive, and prognostic applications for various solid tumor types. Before beginning large prospective clinical trials to prove the added value of utilizing ctDNA in clinical practice, it is essential to investigate the effects of various preanalytical conditions on the quality of cell‐free DNA (cfDNA) in general and of ctDNA in particular in order to optimize and standardize these conditions. Whole blood samples were collected from patients with metastatic cancer bearing a known somatic variant. The following preanalytical conditions were investigated: (a) different time intervals to plasma isolation (1, 24, and 96 h) and (b) different preservatives in blood collection tubes (EDTA, CellSave, and BCT). The quality of cfDNA/ctDNA was assessed by DNA quantification, digital polymerase chain reaction (dPCR) for somatic variant detection and a β‐actin fragmentation assay for DNA contamination from lysed leukocytes. In 11 (69%) of our 16 patients, we were able to detect the known somatic variant in ctDNA. We observed a time‐dependent increase in cfDNA concentrations in EDTA tubes, which was positively correlated with an increase in wild‐type copy numbers and large DNA fragments (> 420 bp). Using different preservatives did not affect somatic variant detection ability, but did stabilize cfDNA concentrations over time. Variant allele frequency was affected by fluctuations in cfDNA concentration only in EDTA tubes at 96 h. Both CellSave and BCT tubes ensured optimal ctDNA quality in plasma processed within 96 h after blood collection for downstream somatic variant detection by dPCR. The effects of preanalytical conditions on the quality of circulating tumor DNA (ctDNA) were investigated using blood samples from patients with metastatic cancer. There was a time‐dependent increase in cell‐free DNA (cfDNA) in EDTA tubes, which decreased detection of somatic variant allele frequencies (VAF). Both CellSave and BCT tubes ensure optimal ctDNA quality for up to 96 h.

Background DNA methylation detection in liquid biopsies provides a highly promising and much needed means for real-time monitoring of disease load in advanced cancer patient care. Compared to the often-used somatic mutations, tissue- and cancer-type specific epigenetic marks affect a larger part of the cancer genome and generally have a high penetrance throughout the tumour. Here, we describe the successful application of the recently described MeD-seq assay for genome-wide DNA methylation profiling on cell-free DNA (cfDNA). The compatibility of the MeD-seq assay with different types of blood collection tubes, cfDNA input amounts, cfDNA isolation methods, and vacuum concentration of samples was evaluated using plasma from both metastatic cancer patients and healthy blood donors (HBDs). To investigate the potential value of cfDNA methylation profiling for tumour load monitoring, we profiled paired samples from 8 patients with resectable colorectal liver metastases (CRLM) before and after surgery. Results The MeD-seq assay worked on plasma-derived cfDNA from both EDTA and CellSave blood collection tubes when at least 10 ng of cfDNA was used. From the 3 evaluated cfDNA isolation methods, both the manual QIAamp Circulating Nucleic Acid Kit (Qiagen) and the semi-automated Maxwell® RSC ccfDNA Plasma Kit (Promega) were compatible with MeD-seq analysis, whereas the QiaSymphony DSP Circulating DNA Kit (Qiagen) yielded significantly fewer reads when compared to the QIAamp kit ( p  < 0.001). Vacuum concentration of samples before MeD-seq analysis was possible with samples in AVE buffer (QIAamp) or water, but yielded inconsistent results for samples in EDTA-containing Maxwell buffer. Principal component analysis showed that pre-surgical samples from CRLM patients were very distinct from HBDs, whereas post-surgical samples were more similar. Several described methylation markers for colorectal cancer monitoring in liquid biopsies showed differential methylation between pre-surgical CRLM samples and HBDs in our data, supporting the validity of our approach. Results for MSC, ITGA4, GRIA4, and EYA4 were validated by quantitative methylation specific PCR. Conclusions The MeD-seq assay provides a promising new method for cfDNA methylation profiling. Potential future applications of the assay include marker discovery specifically for liquid biopsy analysis as well as direct use as a disease load monitoring tool in advanced cancer patients.