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Copy number aberrations (CNAs), which are pathogenic copy number variations (CNVs), play an important role in the initiation and progression of cancer. Single-cell DNA-sequencing (scDNAseq) technologies produce data that is ideal for inferring CNAs. In this review, we review eight methods that have been developed for detecting CNAs in scDNAseq data, and categorize them according to the steps of a seven-step pipeline that they employ. Furthermore, we review models and methods for evolutionary analyses of CNAs from scDNAseq data and highlight advances and future research directions for computational methods for CNA detection from scDNAseq data.

Background Colorectal cancer (CRC) cell lines are widely used pre-clinical model systems. Comprehensive insights into their molecular characteristics may improve model selection for biomedical studies. Methods We have performed DNA, RNA and protein profiling of 34 cell lines, including (i) targeted deep sequencing ( n  = 612 genes) to detect single nucleotide variants and insertions/deletions; (ii) high resolution DNA copy number profiling; (iii) gene expression profiling at exon resolution; (iv) small RNA expression profiling by deep sequencing; and (v) protein expression analysis ( n  = 297 proteins) by reverse phase protein microarrays. Results The cell lines were stratified according to the key molecular subtypes of CRC and data were integrated at two or more levels by computational analyses . We confirm that the frequencies and patterns of DNA aberrations are associated with genomic instability phenotypes and that the cell lines recapitulate the genomic profiles of primary carcinomas. Intrinsic expression subgroups are distinct from genomic subtypes, but consistent at the gene-, microRNA- and protein-level and dominated by two distinct clusters; colon-like cell lines characterized by expression of gastro-intestinal differentiation markers and undifferentiated cell lines showing upregulation of epithelial-mesenchymal transition and TGFβ signatures. This sample split was concordant with the gene expression-based consensus molecular subtypes of primary tumors. Approximately ¼ of the genes had consistent regulation at the DNA copy number and gene expression level, while expression of gene-protein pairs in general was strongly correlated. Consistent high-level DNA copy number amplification and outlier gene- and protein- expression was found for several oncogenes in individual cell lines, including MYC and ERBB2 . Conclusions This study expands the view of CRC cell lines as accurate molecular models of primary carcinomas, and we present integrated multi-level molecular data of 34 widely used cell lines in easily accessible formats, providing a resource for preclinical studies in CRC .

Meningioma is the most common primary brain tumor and carries a substantial risk of local recurrence. Methylation profiles of meningioma and their clinical implications are not well understood. We hypothesized that aggressive meningiomas have unique DNA methylation patterns that could be used to better stratify patient management. Samples ( n  = 140) were profiled using the Illumina HumanMethylation450BeadChip. Unsupervised modeling on a training set ( n  = 89) identified 2 molecular methylation subgroups of meningioma (MM) with significantly different recurrence-free survival (RFS) times between the groups: a prognostically unfavorable subgroup (MM-UNFAV) and a prognostically favorable subgroup (MM-FAV). This finding was validated in the remaining 51 samples and led to a baseline meningioma methylation classifier (bMMC) defined by 283 CpG loci (283-bMMC). To further optimize a recurrence predictor, probes subsumed within the baseline classifier were subject to additional modeling using a similar training/validation approach, leading to a 64-CpG loci meningioma methylation predictor (64-MMP). After adjustment for relevant clinical variables [WHO grade, mitotic index, Simpson grade, sex, location, and copy number aberrations (CNAs)] multivariable analyses for RFS showed that the baseline methylation classifier was not significant ( p  = 0.0793). The methylation predictor, however, was significantly associated with tumor recurrence ( p  < 0.0001). CNAs were extracted from the 450k intensity profiles. Tumor samples in the MM-UNFAV subgroup showed an overall higher proportion of CNAs compared to the MM-FAV subgroup tumors and the CNAs were complex in nature. CNAs in the MM-UNFAV subgroup included recurrent losses of 1p, 6q, 14q and 18q, and gain of 1q, all of which were previously identified as indicators of poor outcome. In conclusion, our analyses demonstrate robust DNA methylation signatures in meningioma that correlate with CNAs and stratify patients by recurrence risk.

In the “precision oncology” era the characterization of tumor genetic features is a pivotal step in cancer patients’ management. Liquid biopsy approaches, such as analysis of cell-free DNA from plasma, represent a powerful and noninvasive strategy to obtain information about the genomic status of the tumor. Sequencing-based analyses of cell-free DNA, currently performed with second generation sequencers, are extremely powerful but poorly scalable and not always accessible also due to instrumentation costs. Third generation sequencing platforms, such as Nanopore sequencers, aim at overcoming these obstacles but, unfortunately, are not designed for cell-free DNA analysis. Here we present a customized workflow to exploit low-coverage Nanopore sequencing for the detection of copy number variations from plasma of cancer patients. Whole genome molecular karyotypes of 6 lung cancer patients and 4 healthy subjects were successfully produced with as few as 2 million reads, and common lung-related copy number alterations were readily detected. This is the first successful use of Nanopore sequencing for copy number profiling from plasma DNA. In this context, Nanopore represents a reliable alternative to Illumina sequencing, with the advantages of minute instrumentation costs and extremely short analysis time. The availability of protocols for Nanopore-based cell-free DNA analysis will make this analysis finally accessible, exploiting the full potential of liquid biopsy both for research and clinical purposes.

Bulk DNA sequencing of multiple samples from the same tumor is becoming common, yet most methods to infer copy-number aberrations (CNAs) from this data analyze individual samples independently. We introduce HATCHet2, an algorithm to identify haplotype- and clone-specific CNAs simultaneously from multiple bulk samples. HATCHet2 extends the earlier HATCHet method by improving identification of focal CNAs and introducing a novel statistic, the minor haplotype B-allele frequency (mhBAF), that enables identification of mirrored-subclonal CNAs. We demonstrate HATCHet2’s improved accuracy using simulations and a single-cell sequencing dataset. HATCHet2 analysis of 10 prostate cancer patients reveals previously unreported mirrored-subclonal CNAs affecting cancer genes.

Urothelial bladder carcinoma (UBC) is characterized by a large number of genetic alterations. DNA from urine is a promising source for liquid biopsy in urological malignancies. We aimed to assess the availability of cell-free DNA (cfDNA) and exosomal DNA (exoDNA) in urine as a source for liquid biopsy in UBC. We included 9 patients who underwent surgery for UBC and performed genomic profiling of tumor samples and matched urinary cfDNA and exoDNA. For mutation analysis, deep sequencing was performed for 9 gene targets and shallow whole genome sequencing (sWGS) was used for the detection of copy number variation (CNV). We analyzed whether genetic alteration in tumor samples was reflected in urinary cfDNA or exoDNA. To measure the similarity between copy number profiles of tumor tissue and urinary DNA, the Pearson’s correlation coefficient was calculated. We found 17 somatic mutations in 6 patients. Of the 17 somatic mutations, 14 and 12 were identified by analysis of cfDNA and exoDNA with AFs of 56.2% and 65.6%, respectively. In CNV analysis using sWGS, although the mean depth was 0.6X, we found amplification of MDM2, ERBB2, CCND1 and CCNE1, and deletion of CDKN2A, PTEN and RB1, all known to be frequently altered in UBC. CNV plots of cfDNA and exoDNA showed a similar pattern with those from the tumor samples. Pearson’s correlation coefficients of tumor vs. cfDNA (0.481) and tumor vs. exoDNA (0.412) were higher than that of tumor vs. normal (0.086). We successfully identified somatic mutations and CNV in UBC using urinary cfDNA and exoDNA. Urinary exoDNA could be another source for liquid biopsy. Also, CNV analysis using sWGS is an alternative strategy for liquid biopsy, providing data from the whole genome at a low cost.

Colorectal cancer commonly metastasizes into multiple liver foci, but intermetastatic heterogeneity remains poorly described. Here, we demonstrate that mutations of RAS/BRAF/TP53 are homogeneous within patients, while DNA copy number aberrations can vary greatly. RAS/BRAF/TP53 co‐mutations conferred a dismal prognosis, and co‐mutations combined with a high burden and heterogeneity of copy number aberrations identified patients with the poorest outcome. Hepatic resection is potentially curative for patients with colorectal liver metastases, but the treatment benefit varies. KRAS/NRAS (RAS)/TP53 co‐mutations are associated with a poor prognosis after resection, but there is large variation in patient outcome within the mutation groups, and genetic testing is currently not used to evaluate benefit from surgery. We have investigated the potential for improved prognostic stratification by combined biomarker analysis with DNA copy number aberrations (CNAs), and taking tumor heterogeneity into account. We determined the mutation status of RAS, BRAFV600, and TP53 in 441 liver lesions from 171 patients treated by partial hepatectomy for metastatic colorectal cancer. CNAs were profiled in 232 tumors from 67 of the patients. Mutations and high‐level amplifications of cancer‐critical genes, the latter including ERBB2 and EGFR, were predominantly homogeneous within patients. RAS/BRAFV600E and TP53 co‐mutations were associated with a poor patient outcome (hazard ratio, HR, 3.9, 95% confidence interval, CI, 1.3–11.1, P = 0.012) in multivariable analyses with clinicopathological variables. The genome‐wide CNA burden and intrapatient intermetastatic CNA heterogeneity varied within the mutation groups, and the CNA burden had prognostic associations in univariable analysis. Combined prognostic analyses of RAS/BRAFV600E/TP53 mutations and CNAs, either as a high CNA burden or high intermetastatic CNA heterogeneity, identified patients with a particularly poor outcome (co‐mutation/high CNA burden: HR 2.7, 95% CI 1.2–5.9, P = 0.013; co‐mutation/high CNA heterogeneity: HR 2.5, 95% CI 1.1–5.6, P = 0.022). In conclusion, DNA copy number profiling identified genomic and prognostic heterogeneity among patients with resectable colorectal liver metastases with co‐mutated RAS/BRAFV600E/TP53.

Both Duchenne muscular dystrophy (DMD; OMIM no. 310200) and spinal muscular atrophy (SMA; OMIM no. 253300/253550/253400/271150) are genetic disorders characterized by progressive muscle degeneration and weakness. Genetic copy number aberrations in the pathogenetic genes DMD and SMN1 lead to alterations in functional proteins, resulting in DMD and SMA, respectively. Multiplex ligation-dependent probe amplification (MLPA) has become a standard method for the detection of common copy number aberrations (CNAs), including DMD and SMN1 deletions, both of which are associated with poor clinical outcomes. However, traditional MLPA assays only accommodate a maximum of 60 MLPA probes per test. To increase the number of targeted sequences in one assay, an MLPA-based next-generation sequencing (NGS) assay has been developed that is based on the standard MLPA procedure, allows high-throughput screening for a large number of fragments and samples by integrating additional indices for detection, and can be analyzed on all Illumina NGS platforms.

Whole‐genome sequencing (WGS) of circulating tumour DNA (ctDNA) is now a clinically important biomarker for predicting therapy response, disease burden and disease progression. However, the translation of ctDNA monitoring into vital preclinical PDX models has not been possible owing to low circulating blood volumes in small rodents. Here, we describe the longitudinal detection and monitoring of ctDNA from minute volumes of blood in PDX mice. We developed a xenograft Tumour Fraction (xTF) metric using shallow WGS of dried blood spots (DBS), and demonstrate its application to quantify disease burden, monitor treatment response and predict disease outcome in a preclinical study of PDX mice. Further, we show how our DBS‐based ctDNA assay can be used to detect gene‐specific copy number changes and examine the copy number landscape over time. Use of sequential DBS ctDNA assays could transform future trial designs in both mice and patients by enabling increased sampling and molecular monitoring. Synopsis A novel approach is developed for longitudinal monitoring of tumour burden in patient‐derived xenograft (PDX) models using dried blood spots from minute volumes of blood. Circulating tumour DNA (ctDNA) can be detected in minute volumes of blood (~ 50 µl) collected as dried blood spots (DBS) from the tail vein in PDX mice. The xenograft Tumour Fraction (xTF) is calculated using species‐specific alignment of reads obtained from shallow whole‐genome sequencing of DBS samples. The xTF metric allows accurate monitoring of disease progression over time. The xTF rate of change during the first 30 days of treatment is predictive of disease outcome in PDX mice. Graphical Abstract A novel approach is developed for longitudinal monitoring of tumour burden in patient‐derived xenograft (PDX) models using dried blood spots from minute volumes of blood.

High-grade gliomas (HGGs), the most common and aggressive primary brain tumors in adults, inevitably recur due to incomplete surgery or resistance to therapy. Intratumoral genomic and cellular heterogeneity of HGGs contributes to therapeutic resistance, recurrence, and poor clinical outcomes. Transcriptomic profiles of HGGs at recurrence have not been investigated in detail. Using targeted sequencing of cancer-related genes and transcriptomics, we identified single nucleotide variations, small insertions and deletions, copy number aberrations (CNAs), as well as gene expression changes and pathway deregulation in 16 pairs of primary and recurrent HGGs. Most of the somatic mutations identified in primary HGGs were not detected after relapse, suggesting a subclone substitution during the tumor progression. We found a novel frameshift insertion in the ZNF384 gene which may contribute to extracellular matrix remodeling. An inverse correlation of focal CNAs in EGFR and PTEN genes was detected. Transcriptomic analysis revealed downregulation of genes involved in messenger RNA splicing, cell cycle, and DNA repair, while genes related to interferon signaling and phosphatidylinositol (PI) metabolism are upregulated in secondary HGGs when compared to primary HGGs. In silico analysis of the tumor microenvironment identified M2 macrophages and immature dendritic cells as enriched in recurrent HGGs, suggesting a prominent immunosuppressive signature. Accumulation of those cells in recurrent HGGs was validated by immunostaining. Our findings point to a substantial transcriptomic deregulation and a pronounced infiltration of immature dendritic cells in recurrent HGG, which may impact the effectiveness of frontline immunotherapies in the GBM management.Key messagesMost of the somatic mutations identified in primary HGGs were not detected after relapse.Focal CNAs in EGFR and PTEN genes are inversely correlated in primary and recurrent HGGs.Transcriptomic changes and distinct immune-related signatures characterize HGG recurrence.Recurrent HGGs are characterized by a prominent infiltration of immature dendritic and M2 macrophages.