Bisulfite-independent analysis of CpG island methylation enables genome-scale stratification of single cells.

Conventional DNA bisulfite sequencing has been extended to single cell level, but the coverage consistency is insufficient for parallel comparison. Here we report a novel method for genome-wide CpG island (CGI) methylation sequencing for single cells (scCGI-seq), combining methylation-sensitive restriction enzyme digestion and multiple displacement amplification for selective detection of methylated CGIs. We applied this method to analyzing single cells from two types of hematopoietic cells, K562 and GM12878 and small populations of fibroblasts and induced pluripotent stem cells. The method detected 21 798 CGIs (76% of all CGIs) per cell, and the number of CGIs consistently detected from all 16 profiled single cells was 20 864 (72.7%), with 12 961 promoters covered. This coverage represents a substantial improvement over results obtained using single cell reduced representation bisulfite sequencing, with a 66-fold increase in the fraction of consistently profiled CGIs across individual cells. Single cells of the same type were more similar to each other than to other types, but also displayed epigenetic heterogeneity. The method was further validated by comparing the CpG methylation pattern, methylation profile of CGIs/promoters and repeat regions and 41 classes of known regulatory markers to the ENCODE data. Although not every minor methylation differences between cells are detectable, scCGI-seq provides a solid tool for unsupervised stratification of a heterogeneous cell population.

An Evolutionary Approach for Identifying Driver Mutations in Colorectal Cancer.

The traditional view of cancer as a genetic disease that can successfully be treated with drugs targeting mutant onco-proteins has motivated whole-genome sequencing efforts in many human cancer types. However, only a subset of mutations found within the genomic landscape of cancer is likely to provide a fitness advantage to the cell. Distinguishing such "driver" mutations from innocuous "passenger" events is critical for prioritizing the validation of candidate mutations in disease-relevant models. We design a novel statistical index, called the Hitchhiking Index, which reflects the probability that any observed candidate gene is a passenger alteration, given the frequency of alterations in a cross-sectional cancer sample set, and apply it to a mutational data set in colorectal cancer. Our methodology is based upon a population dynamics model of mutation accumulation and selection in colorectal tissue prior to cancer initiation as well as during tumorigenesis. This methodology can be used to aid in the prioritization of candidate mutations for functional validation and contributes to the process of drug discovery.

Aberration in DNA methylation in B-cell lymphomas has a complex origin and increases with disease severity.

Despite mounting evidence that epigenetic abnormalities play a key role in cancer biology, their contributions to the malignant phenotype remain poorly understood. Here we studied genome-wide DNA methylation in normal B-cell populations and subtypes of B-cell non-Hodgkin lymphoma: follicular lymphoma and diffuse large B-cell lymphomas. These lymphomas display striking and progressive intra-tumor heterogeneity and also inter-patient heterogeneity in their cytosine methylation patterns. Epigenetic heterogeneity is initiated in normal germinal center B-cells, increases markedly with disease aggressiveness, and is associated with unfavorable clinical outcome. Moreover, patterns of abnormal methylation vary depending upon chromosomal regions, gene density and the status of neighboring genes. DNA methylation abnormalities arise via two distinct processes: i) lymphomagenic transcriptional regulators perturb promoter DNA methylation in a target gene-specific manner, and ii) aberrant epigenetic states tend to spread to neighboring promoters in the absence of CTCF insulator binding sites.

Evolution of the cancer genome.

Human tumors result from an evolutionary process operating on somatic cells within tissues, whereby natural selection operates on the phenotypic variability generated by the accumulation of genetic, genomic and epigenetic alterations. This somatic evolution leads to adaptations such as increased proliferative, angiogenic, and invasive phenotypes. In this review we outline how cancer genomes are beginning to be investigated from an evolutionary perspective. We describe recent progress in the cataloging of somatic genetic and genomic alterations, and investigate the contributions of germline as well as epigenetic factors to cancer genome evolution. Finally, we outline the challenges facing researchers who investigate the processes driving the evolution of the cancer genome.

Cross-study validation for the assessment of prediction algorithms.

MOTIVATION: Numerous competing algorithms for prediction in high-dimensional settings have been developed in the statistical and machine-learning literature. Learning algorithms and the prediction models they generate are typically evaluated on the basis of cross-validation error estimates in a few exemplary datasets. However, in most applications, the ultimate goal of prediction modeling is to provide accurate predictions for independent samples obtained in different settings. Cross-validation within exemplary datasets may not adequately reflect performance in the broader application context. METHODS: We develop and implement a systematic approach to 'cross-study validation', to replace or supplement conventional cross-validation when evaluating high-dimensional prediction models in independent datasets. We illustrate it via simulations and in a collection of eight estrogen-receptor positive breast cancer microarray gene-expression datasets, where the objective is predicting distant metastasis-free survival (DMFS). We computed the C-index for all pairwise combinations of training and validation datasets. We evaluate several alternatives for summarizing the pairwise validation statistics, and compare these to conventional cross-validation. RESULTS: Our data-driven simulations and our application to survival prediction with eight breast cancer microarray datasets, suggest that standard cross-validation produces inflated discrimination accuracy for all algorithms considered, when compared to cross-study validation. Furthermore, the ranking of learning algorithms differs, suggesting that algorithms performing best in cross-validation may be suboptimal when evaluated through independent validation. AVAILABILITY: The survHD: Survival in High Dimensions package (http://www.bitbucket.org/lwaldron/survhd) will be made available through Bioconductor.

DNA copy number analysis of metastatic urothelial carcinoma with comparison to primary tumors.

BACKGROUND: To date, there have been no reports characterizing the genome-wide somatic DNA chromosomal copy-number alteration landscape in metastatic urothelial carcinoma. We sought to characterize the DNA copy-number profile in a cohort of metastatic samples and compare them to a cohort of primary urothelial carcinoma samples in order to identify changes that are associated with progression from primary to metastatic disease. METHODS: Using molecular inversion probe array analysis we compared genome-wide chromosomal copy-number alterations between 30 metastatic and 29 primary UC samples. Whole transcriptome RNA-Seq analysis was also performed in primary and matched metastatic samples which was available for 9 patients. RESULTS: Based on a focused analysis of 32 genes in which alterations may be clinically actionable, there were significantly more amplifications/deletions in metastases (8.6% vs 4.5%, p < 0.001). In particular, there was a higher frequency of E2F3 amplification in metastases (30% vs 7%, p = 0.046). Paired primary and metastatic tissue was available for 11 patients and 3 of these had amplifications of potential clinical relevance in metastases that were not in the primary tumor including ERBB2, CDK4, CCND1, E2F3, and AKT1. The transcriptional activity of these amplifications was supported by RNA expression data. CONCLUSIONS: The discordance in alterations between primary and metastatic tissue may be of clinical relevance in the era of genomically directed precision cancer medicine.

Expressed Barcoding Enables High-Resolution Tracking of the Evolution of Drug Tolerance.

For a majority of patients with non-small cell lung cancer with EGFR mutations, treatment with EGFR inhibitors (EGFRi) induces a clinical response. Despite this initial reduction in tumor size, residual disease persists that leads to disease relapse. Elucidating the preexisting biological differences between sensitive cells and surviving drug-tolerant persister cells and deciphering how drug-tolerant cells evolve in response to treatment could help identify strategies to improve the efficacy of EGFRi. In this study, we tracked the origins and clonal evolution of drug-tolerant cells at a high resolution by using an expressed barcoding system coupled with single-cell RNA sequencing. This platform enabled longitudinal profiling of gene expression and drug sensitivity in response to EGFRi across a large number of clones. Drug-tolerant cells had higher expression of key survival pathways such as YAP and EMT at baseline and could also differentially adapt their gene expression following EGFRi treatment compared with sensitive cells. In addition, drug combinations targeting common downstream components (MAPK) or orthogonal factors (chemotherapy) showed greater efficacy than EGFRi alone, which is attributable to broader targeting of the heterogeneous EGFRi-tolerance mechanisms present in tumors. Overall, this approach facilitates thorough examination of clonal evolution in response to therapy that could inform the development of improved diagnostic approaches and treatment strategies for targeting drug-tolerant cells. SIGNIFICANCE: The evolution and heterogeneity of EGFR inhibitor tolerance are identified in a large number of clones at enhanced cellular and temporal resolution using an expressed barcode technology coupled with single-cell RNA sequencing.

A first-in-human phase 1/2 study of FGF401 and combination of FGF401 with spartalizumab in patients with hepatocellular carcinoma or biomarker-selected solid tumors.

BACKGROUND: Deregulation of FGF19-FGFR4 signaling is found in several cancers, including hepatocellular carcinoma (HCC), nominating it for therapeutic targeting. FGF401 is a potent, selective FGFR4 inhibitor with antitumor activity in preclinical models. This study was designed to determine the recommended phase 2 dose (RP2D), characterize PK/PD, and evaluate the safety and efficacy of FGF401 alone and combined with the anti-PD-1 antibody, spartalizumab. METHODS: Patients with HCC or other FGFR4/KLB expressing tumors were enrolled. Dose-escalation was guided by a Bayesian model. Phase 2 dose-expansion enrolled patients with HCC from Asian countries (group1), non-Asian countries (group2), and patients with other solid tumors expressing FGFR4 and KLB (group3). FGF401 and spartalizumab combination was evaluated in patients with HCC. RESULTS: Seventy-four patients were treated in the phase I with single-agent FGF401 at 50 to 150 mg. FGF401 displayed favorable PK characteristics and no food effect when dosed with low-fat meals. The RP2D was established as 120 mg qd. Six of 70 patients experienced grade 3 dose-limiting toxicities: increase in transaminases (n = 4) or blood bilirubin (n = 2). In phase 2, 30 patients in group 1, 36 in group 2, and 20 in group 3 received FGF401. In total, 8 patients experienced objective responses (1 CR, 7 PR; 4 each in phase I and phase II, respectively). Frequent adverse events (AEs) were diarrhea (73.8%), increased AST (47.5%), and ALT (43.8%). Increase in levels of C4, total bile acid, and circulating FGF19, confirmed effective FGFR4 inhibition. Twelve patients received FGF401 plus spartalizumab. RP2D was established as FGF401 120 mg qd and spartalizumab 300 mg Q3W; 2 patients reported PR. CONCLUSIONS: At biologically active doses, FGF401 alone or combined with spartalizumab was safe in patients with FGFR4/KLB-positive tumors including HCC. Preliminary clinical efficacy was observed. Further clinical evaluation of FGF401 using a refined biomarker strategy is warranted. TRIAL REGISTRATION: NCT02325739 .

A differentiation-based phylogeny of cancer subtypes.

Histopathological classification of human tumors relies in part on the degree of differentiation of the tumor sample. To date, there is no objective systematic method to categorize tumor subtypes by maturation. In this paper, we introduce a novel computational algorithm to rank tumor subtypes according to the dissimilarity of their gene expression from that of stem cells and fully differentiated tissue, and thereby construct a phylogenetic tree of cancer. We validate our methodology with expression data of leukemia, breast cancer and liposarcoma subtypes and then apply it to a broader group of sarcomas. This ranking of tumor subtypes resulting from the application of our methodology allows the identification of genes correlated with differentiation and may help to identify novel therapeutic targets. Our algorithm represents the first phylogeny-based tool to analyze the differentiation status of human tumors.

Specific effects of microRNAs on the plant transcriptome.

Most plant microRNAs (miRNAs) have perfect or near-perfect complementarity with their targets. This is consistent with their primary mode of action being cleavage of target mRNAs, similar to that induced by perfectly complementary small interfering RNAs (siRNAs). However, there are natural targets with up to five mismatches. Furthermore, artificial siRNAs can have substantial effects on so-called off-targets, to which they have only limited complementarity. By analyzing the transcriptome of plants overexpressing different miRNAs, we have deduced a set of empirical parameters for target recognition. Compared to artificial siRNAs, authentic plant miRNAs appear to have much higher specificity, which may reflect their coevolution with the remainder of the transcriptome. We also demonstrate that miR172, previously thought to act primarily by translational repression, can efficiently guide mRNA cleavage, although the effects on steady-state levels of target transcripts are obscured by strong feedback regulation. This finding unifies the view of plant miRNA action.

FRANz: reconstruction of wild multi-generation pedigrees.

SUMMARY: We present a software package for pedigree reconstruction in natural populations using co-dominant genomic markers such as microsatellites and single nucleotide polymorphisms (SNPs). If available, the algorithm makes use of prior information such as known relationships (sub-pedigrees) or the age and sex of individuals. Statistical confidence is estimated by Markov Chain Monte Carlo (MCMC) sampling. The accuracy of the algorithm is demonstrated for simulated data as well as an empirical dataset with known pedigree. The parentage inference is robust even in the presence of genotyping errors. AVAILABILITY: The C source code of FRANz can be obtained under the GPL from http://www.bioinf.uni-leipzig.de/Software/FRANz/.

Reconstruction of pedigrees in clonal plant populations.

We present a Bayesian method for the reconstruction of pedigrees in clonal populations using co-dominant genomic markers such as microsatellites and single nucleotide polymorphisms (SNPs). The accuracy of the algorithm is demonstrated for simulated data. We show that the joint estimation of parameters of interest such as the rate of self-fertilization is possible with high accuracy even with marker panels of moderate power. Classical methods can only assign a very limited number of statistically significant parentages in this case and would therefore fail. Statistical confidence is estimated by Markov Chain Monte Carlo (MCMC) sampling. The method is implemented in a fast and easy to use open source software that scales to large datasets with many thousand individuals.

Reliable Analysis of Clinical Tumor-Only Whole-Exome Sequencing Data.

PURPOSE: Allele-specific copy number alteration (CNA) analysis is essential to study the functional impact of single-nucleotide variants (SNVs) and the process of tumorigenesis. However, controversy over whether it can be performed with sufficient accuracy in data without matched normal profiles and a lack of open-source implementations have limited its application in clinical research and diagnosis. METHODS: We benchmark allele-specific CNA analysis performance of whole-exome sequencing (WES) data against gold standard whole-genome SNP6 microarray data and against WES data sets with matched normal samples. We provide a workflow based on the open-source PureCN R/Bioconductor package in conjunction with widely used variant-calling and copy number segmentation algorithms for allele-specific CNA analysis from WES without matched normals. This workflow further classifies SNVs by somatic status and then uses this information to infer somatic mutational signatures and tumor mutational burden (TMB). RESULTS: Application of our workflow to tumor-only WES data produces tumor purity and ploidy estimates that are highly concordant with estimates from SNP6 microarray data and matched normal WES data. The presence of cancer type-specific somatic mutational signatures was inferred with high accuracy. We also demonstrate high concordance of TMB between our tumor-only workflow and matched normal pipelines. CONCLUSION: The proposed workflow provides, to our knowledge, the only open-source option with demonstrated high accuracy for comprehensive allele-specific CNA analysis and SNV classification of tumor-only WES. An implementation of the workflow is available on the Terra Cloud platform of the Broad Institute (Cambridge, MA).

HGNChelper: identification and correction of invalid gene symbols for human and mouse.

Gene symbols are recognizable identifiers for gene names but are unstable and error-prone due to aliasing, manual entry, and unintentional conversion by spreadsheets to date format. Official gene symbol resources such as HUGO Gene Nomenclature Committee (HGNC) for human genes and the Mouse Genome Informatics project (MGI) for mouse genes provide authoritative sources of valid, aliased, and outdated symbols, but lack a programmatic interface and correction of symbols converted by spreadsheets. We present HGNChelper, an R package that identifies known aliases and outdated gene symbols based on the HGNC human and MGI mouse gene symbol databases, in addition to common mislabeling introduced by spreadsheets, and provides corrections where possible. HGNChelper identified invalid gene symbols in the most recent Molecular Signatures Database (MSigDB 7.0) and in platform annotation files of the Gene Expression Omnibus, with prevalence ranging from ~3% in recent platforms to 30-40% in the earliest platforms from 2002-03. HGNChelper is installable from CRAN.

Multiomic Analysis of Subtype Evolution and Heterogeneity in High-Grade Serous Ovarian Carcinoma.

Multiple studies have identified transcriptome subtypes of high-grade serous ovarian carcinoma (HGSOC), but their interpretation and translation are complicated by tumor evolution and polyclonality accompanied by extensive accumulation of somatic aberrations, varying cell type admixtures, and different tissues of origin. In this study, we examined the chronology of HGSOC subtype evolution in the context of these factors using a novel integrative analysis of absolute copy-number analysis and gene expression in The Cancer Genome Atlas complemented by single-cell analysis of six independent tumors. Tumor purity, ploidy, and subclonality were reliably inferred from different genomic platforms, and these characteristics displayed marked differences between subtypes. Genomic lesions associated with HGSOC subtypes tended to be subclonal, implying subtype divergence at later stages of tumor evolution. Subclonality of recurrent HGSOC alterations was evident for proliferative tumors, characterized by extreme genomic instability, absence of immune infiltration, and greater patient age. In contrast, differentiated tumors were characterized by largely intact genome integrity, high immune infiltration, and younger patient age. Single-cell sequencing of 42,000 tumor cells revealed widespread heterogeneity in tumor cell type composition that drove bulk subtypes but demonstrated a lack of intrinsic subtypes among tumor epithelial cells. Our findings prompt the dismissal of discrete transcriptome subtypes for HGSOC and replacement by a more realistic model of continuous tumor development that includes mixtures of subclones, accumulation of somatic aberrations, infiltration of immune and stromal cells in proportions correlated with tumor stage and tissue of origin, and evolution between properties previously associated with discrete subtypes. SIGNIFICANCE: This study infers whether transcriptome-based groupings of tumors differentiate early in carcinogenesis and are, therefore, appropriate targets for therapy and demonstrates that this is not the case for HGSOC.

TGFß-blockade uncovers stromal plasticity in tumors by revealing the existence of a subset of interferon-licensed fibroblasts.

Despite the increasing interest in targeting stromal elements of the tumor microenvironment, we still face tremendous challenges in developing adequate therapeutics to modify the tumor stromal landscape. A major obstacle to this is our poor understanding of the phenotypic and functional heterogeneity of stromal cells in tumors. Herein, we perform an unbiased interrogation of tumor mesenchymal cells, delineating the co-existence of distinct subsets of cancer-associated fibroblasts (CAFs) in the microenvironment of murine carcinomas, each endowed with unique phenotypic features and functions. Furthermore, our study shows that neutralization of TGFß in vivo leads to remodeling of CAF dynamics, greatly reducing the frequency and activity of the myofibroblast subset, while promoting the formation of a fibroblast population characterized by strong response to interferon and heightened immunomodulatory properties. These changes correlate with the development of productive anti-tumor immunity and greater efficacy of PD1 immunotherapy. Along with providing the scientific rationale for the evaluation of TGFß and PD1 co-blockade in the clinical setting, this study also supports the concept of plasticity of the stromal cell landscape in tumors, laying the foundation for future investigations aimed at defining pathways and molecules to program CAF composition for cancer therapy.

Genomic profiling of NETs: a comprehensive analysis of the RADIANT trials.

Neuroendocrine tumors (NETs) have historically been subcategorized according to histologic features and the site of anatomic origin. Here, we characterize the genomic alterations in patients enrolled in 3 phase 3 clinical trials of NET of different anatomic origins and assessed the potential correlation with clinical outcomes. Whole-exome and targeted panel sequencing was used to characterize 225 NET samples collected in the RADIANT series of clinical trials. Genomic profiling of NET was analyzed along with nongenomic biomarker data on tumor grade and circulating chromogranin A (CgA) and neuron specific enolase (NSE) levels from these patients enrolled in clinical trials. Our results highlight recurrent large-scale chromosomal alterations as a common theme among NET. Although the specific pattern of chromosomal alterations differed between tumor subtypes, the evidence for generalized chromosomal instability (CIN) was observed across all primary sites of NET. In pancreatic NET, although the P-value was not significant, higher CIN suggests a trend towards longer survival (HR, 0.55, P=0.077); whereas in the gastrointestinal NET, lower CIN was associated with longer survival (HR, 0.44, P=0.0006). Our multivariate analyses demonstrated that when combined with other clinical data among patients with progressive advanced NETs, chromosomal level alteration adds important prognostic information. Large-scale CIN is a common feature of NET, and specific patterns of chromosomal gain and loss appeared to have independent prognostic value in NET subtypes. However, whether CIN in general has clinical significance in NET requires validation in larger patient cohort and warrants further mechanistic studies.

Author Correction: Characterization of Nigerian breast cancer reveals prevalent homologous recombination deficiency and aggressive molecular features.

The original version of this Article contained an error in the author affiliations. The affiliation of Kevin P. White with Tempus Labs, Inc. Chicago, IL, USA was inadvertently omitted. This has now been corrected in both the PDF and HTML versions of the Article.

Genetic mechanisms of target antigen loss in CAR19 therapy of acute lymphoblastic leukemia.

We identified genetic mutations in CD19 and loss of heterozygosity at the time of CD19- relapse to chimeric antigen receptor (CAR) therapy. The mutations are present in the vast majority of resistant tumor cells and are predicted to lead to a truncated protein with a nonfunctional or absent transmembrane domain and consequently to a loss of surface antigen. This irreversible loss of CD19 advocates for an alternative targeting or combination CAR approach.

Tracking the Evolution of Resistance to ALK Tyrosine Kinase Inhibitors through Longitudinal Analysis of Circulating Tumor DNA.

PURPOSE: ALK rearrangements predict for sensitivity to ALK tyrosine kinase inhibitors (TKIs). However, responses to ALK TKIs are generally short-lived. Serial molecular analysis is an informative strategy for identifying genetic mediators of resistance. Although multiple studies support the clinical benefits of repeat tissue sampling, the clinical utility of longitudinal circulating tumor DNA analysis has not been established in ALK-positive lung cancer. METHODS: Using a 566-gene hybrid-capture next-generation sequencing (NGS) assay, we performed longitudinal analysis of plasma specimens from 22 ALK-positive patients with acquired resistance to ALK TKIs to track the evolution of resistance during treatment. To determine tissue-plasma concordance, we compared plasma findings to results of repeat biopsies. RESULTS: At progression, we detected an ALK fusion in plasma from 19 (86%) of 22 patients, and identified ALK resistance mutations in plasma specimens from 11 (50%) patients. There was 100% agreement between tissue- and plasma-detected ALK fusions. Among 16 cases where contemporaneous plasma and tissue specimens were available, we observed 100% concordance between ALK mutation calls. ALK mutations emerged and disappeared during treatment with sequential ALK TKIs, suggesting that plasma mutation profiles were dependent on the specific TKI administered. ALK G1202R, the most frequent plasma mutation detected after progression on a second-generation TKI, was consistently suppressed during treatment with lorlatinib. CONCLUSIONS: Plasma genotyping by NGS is an effective method for detecting ALK fusions and ALK mutations in patients progressing on ALK TKIs. The correlation between plasma ALK mutations and response to distinct ALK TKIs highlights the potential for plasma analysis to guide selection of ALK-directed therapies.