Deciphering clonality in aneuploid breast tumors using SNP array and sequencing data

Identification and functional analysis of NPR2 truncating mutations in two Chinese families with short stature

BACKGROUND

The signaling pathway of C-type natriuretic peptide (CNP) and its receptor (natriuretic peptide receptor 2, NPR2) is implicated in the process of endochondral ossification, which is crucial for the linear growth of long bones. Loss-of-function mutations in the NPR2 gene cause short stature. This study aimed to identify and characterize truncating mutations in NPR2 among Chinese families with short stature.

METHODS

Whole-exome sequencing and Sanger sequencing were conducted to identify potential mutations. Bioinformatic analysis was utilized to assess the pathogenicity of two mutations. The effects of candidate mutation on gene expression, subcellular localization, protein stability, and protein function were further assessed through in vitro assays.

RESULTS

In this study, A novel mutation, c.2629_2630delAG, p.S877Hfs*10 and a previously reported mutation, c.1162 C > T, p.R388* (ClinVar database) in NPR2, were identified in the individuals, and these variants were inherited from the mother and father, respectively. Both mutations were predicted to be deleterious and have a significant impact on protein structure based on bioinformatics analysis. In vitro experiments demonstrated that mutant mRNAs evaded nonsense-mediated mRNA decay (NMD) to produce truncated NPR2 proteins with reduced stability and increased degradation. Furthermore, two truncated NPR2 proteins exhibited impaired localization at the cell membrane and severely reduced ability to stimulate cyclic guanosine monophosphate (cGMP) production in HEK293T cells compared to wild-type (WT) NPR2 (p < 0.05).

CONCLUSION

Our study identified two loss-of-function mutations of the NPR2 gene in two Chinese families and offered new insights on the pathogenesis of short stature caused by NPR2 truncating mutations.

Quantification of within-sample genetic heterogeneity from SNP-array data

Intra-tumour genetic heterogeneity (ITH) fosters drug resistance and is a critical hurdle to clinical treatment. ITH can be well-measured using multi-region sampling but this is costly and challenging to implement. There is therefore a need for tools to estimate ITH in individual samples, using standard genomic data such as SNP-arrays, that could be implemented routinely. We designed two novel scores S and R, respectively based on the Shannon diversity index and Ripley's L statistic of spatial homogeneity, to quantify ITH in single SNP-array samples. We created in-silico and in-vitro mixtures of tumour clones, in which diversity was known for benchmarking purposes. We found significant but highly-variable associations of our scores with diversity in-silico (p < 0.001) and moderate associations in-vitro (p = 0.015 and p = 0.085). Our scores were also correlated to previous ITH estimates from sequencing data but heterogeneity in the fraction of tumour cells present across samples hampered accurate quantification. The prognostic potential of both scores was moderate but significantly predictive of survival in several tumour types (corrected p = 0.03). Our work thus shows how individual SNP-arrays reveal intra-sample clonal diversity with moderate accuracy.

A population genetics perspective on the determinants of intra-tumor heterogeneity

Cancer results from the acquisition of somatic alterations in a microevolutionary process that typically occurs over many years, much of which is occult. Understanding the evolutionary dynamics that are operative at different stages of progression in individual tumors might inform the earlier detection, diagnosis, and treatment of cancer. Although these processes cannot be directly observed, the resultant spatiotemporal patterns of genetic variation amongst tumor cells encode their evolutionary histories. Such intra-tumor heterogeneity is pervasive not only at the genomic level, but also at the transcriptomic, phenotypic, and cellular levels. Given the implications for precision medicine, the accurate quantification of heterogeneity within and between tumors has become a major focus of current research. In this review, we provide a population genetics perspective on the determinants of intra-tumor heterogeneity and approaches to quantify genetic diversity. We summarize evidence for different modes of evolution based on recent cancer genome sequencing studies and discuss emerging evolutionary strategies to therapeutically exploit tumor heterogeneity. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.

Assessing intratumor heterogeneity and tracking longitudinal and spatial clonal evolutionary history by next-generation sequencing

Cancer is a disease driven by evolutionary selection on somatic genetic and epigenetic alterations. Here, we propose Canopy, a method for inferring the evolutionary phylogeny of a tumor using both somatic copy number alterations and single-nucleotide alterations from one or more samples derived from a single patient. Canopy is applied to bulk sequencing datasets of both longitudinal and spatial experimental designs and to a transplantable metastasis model derived from human cancer cell line MDA-MB-231. Canopy successfully identifies cell populations and infers phylogenies that are in concordance with existing knowledge and ground truth. Through simulations, we explore the effects of key parameters on deconvolution accuracy and compare against existing methods. Canopy is an open-source R package available at https://cran.r-project.org/web/packages/Canopy/.

At least two well-spaced samples are needed to genotype a solid tumor

Background

Human cancers are often sequenced to identify mutations. However, cancers are spatially heterogeneous populations with public mutations in all cells and private mutations in some cells. Without empiric knowledge of how mutations are distributed within a solid tumor it is uncertain whether single or multiple samples adequately sample its heterogeneity.

Methods

Using a cohort of 12 human colorectal tumors with well-validated mutations, the abilities to correctly classify public and private mutations were tested (paired t-test) with one sample or two samples obtained from opposite tumor sides.

Results

Two samples were significantly better than a single sample for correctly identifying public (99 % versus 97 %) and private mutations (85 % versus 46 %). Confounding single sample accuracy was that many private mutations appeared “clonal” in individual samples. Two samples detected the most frequent private mutations in 11 of the 12 tumors.

Conclusions

Two spatially-separated samples efficiently distinguish public from private mutations because private mutations common in one specimen are usually less frequent or absent in another sample. The patch-like private mutation topography in most colorectal tumors inherently limits the information in single tumor samples. The correct identification of public and private mutations may aid efforts to target mutations present in all tumor cells.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-016-2202-8) contains supplementary material, which is available to authorized users.

Crambled: A Shiny application to enable intuitive resolution of conflicting cellularity estimates

It is now commonplace to investigate tumour samples using whole-genome sequencing, and some commonly performed tasks are the estimation of cellularity (or sample purity), the genome-wide profiling of copy numbers, and the assessment of sub-clonal behaviours. Several tools are available to undertake these tasks, but often give conflicting results - not least because there is often genuine uncertainty due to a lack of model identifiability.  Presented here is a tool, "Crambled", that allows for an intuitive visual comparison of the conflicting solutions. Crambled is implemented as a Shiny application within R, and is accompanied by example images from two use cases (one tumour sample with matched normal sequencing, and one standalone cell line example) as well as functions to generate the necessary images from any sequencing data set.  Through the use of Crambled, a user may gain insight into why each tool has offered its given solution and combined with a knowledge of the disease being studied can choose between the competing solutions in an informed manner.

ENVE: a novel computational framework characterizes copy-number mutational landscapes in colorectal cancers from African American patients

Reliable detection of somatic copy-number alterations (sCNAs) in tumors using whole-exome sequencing (WES) remains challenging owing to technical (inherent noise) and sample-associated variability in WES data. We present a novel computational framework, ENVE, which models inherent noise in any WES dataset, enabling robust detection of sCNAs across WES platforms. ENVE achieved high concordance with orthogonal sCNA assessments across two colorectal cancer (CRC) WES datasets, and consistently outperformed a best-in-class algorithm, Control-FREEC. We subsequently used ENVE to characterize global sCNA landscapes in African American CRCs, identifying genomic aberrations potentially associated with CRC pathogenesis in this population. ENVE is downloadable at https://github.com/ENVE-Tools/ENVE.

Intra-tumor genetic heterogeneity and mortality in head and neck cancer: analysis of data from the Cancer Genome Atlas

BACKGROUND

Although the involvement of intra-tumor genetic heterogeneity in tumor progression, treatment resistance, and metastasis is established, genetic heterogeneity is seldom examined in clinical trials or practice. Many studies of heterogeneity have had prespecified markers for tumor subpopulations, limiting their generalizability, or have involved massive efforts such as separate analysis of hundreds of individual cells, limiting their clinical use. We recently developed a general measure of intra-tumor genetic heterogeneity based on whole-exome sequencing (WES) of bulk tumor DNA, called mutant-allele tumor heterogeneity (MATH). Here, we examine data collected as part of a large, multi-institutional study to validate this measure and determine whether intra-tumor heterogeneity is itself related to mortality.

METHODS AND FINDINGS

Clinical and WES data were obtained from The Cancer Genome Atlas in October 2013 for 305 patients with head and neck squamous cell carcinoma (HNSCC), from 14 institutions. Initial pathologic diagnoses were between 1992 and 2011 (median, 2008). Median time to death for 131 deceased patients was 14 mo; median follow-up of living patients was 22 mo. Tumor MATH values were calculated from WES results. Despite the multiple head and neck tumor subsites and the variety of treatments, we found in this retrospective analysis a substantial relation of high MATH values to decreased overall survival (Cox proportional hazards analysis: hazard ratio for high/low heterogeneity, 2.2; 95% CI 1.4 to 3.3). This relation of intra-tumor heterogeneity to survival was not due to intra-tumor heterogeneity's associations with other clinical or molecular characteristics, including age, human papillomavirus status, tumor grade and TP53 mutation, and N classification. MATH improved prognostication over that provided by traditional clinical and molecular characteristics, maintained a significant relation to survival in multivariate analyses, and distinguished outcomes among patients having oral-cavity or laryngeal cancers even when standard disease staging was taken into account. Prospective studies, however, will be required before MATH can be used prognostically in clinical trials or practice. Such studies will need to examine homogeneously treated HNSCC at specific head and neck subsites, and determine the influence of cancer therapy on MATH values. Analysis of MATH and outcome in human-papillomavirus-positive oropharyngeal squamous cell carcinoma is particularly needed.

CONCLUSIONS

To our knowledge this study is the first to combine data from hundreds of patients, treated at multiple institutions, to document a relation between intra-tumor heterogeneity and overall survival in any type of cancer. We suggest applying the simply calculated MATH metric of heterogeneity to prospective studies of HNSCC and other tumor types.