EXPANDS: expanding ploidy and allele frequency on nested subpopulations

Exploring the spatiotemporal genetic heterogeneity in metastatic lung adenocarcinoma using a nuclei flow-sorting approach

Variable tumor cellularity can limit sensitivity and precision in comparative genomics because differences in tumor content can result in misclassifying truncal mutations as region-specific private mutations in stroma-rich regions, especially when studying tissue specimens of mediocre tumor cellularity such as lung adenocarcinomas (LUADs). To address this issue, we refined a nuclei flow-sorting approach by sorting nuclei based on ploidy and the LUAD lineage marker thyroid transcription factor 1 and applied this method to investigate genome-wide somatic copy number aberrations (SCNAs) and mutations of 409 cancer genes in 39 tumor populations obtained from 16 primary tumors and 21 matched metastases. This approach increased the mean tumor purity from 54% (range 7-89%) of unsorted material to 92% (range 79-99%) after sorting. Despite this rise in tumor purity, we detected limited genetic heterogeneity between primary tumors and their metastases. In fact, 88% of SCNAs and 80% of mutations were propagated from primary tumors to metastases and low allele frequency mutations accounted for much of the mutational heterogeneity. Even though the presence of SCNAs indicated a history of chromosomal instability (CIN) in all tumors, metastases did not have more SCNAs than primary tumors. Moreover, tumors with biallelic TP53 or ATM mutations had high numbers of SCNAs, yet they were associated with a low interlesional genetic heterogeneity. The results of our study thus provide evidence that most macroevolutionary events occur in primary tumors before metastatic dissemination and advocate for a limited degree of CIN over time and space in this cohort of LUADs. Sampling of primary tumors thus may suffice to detect most mutations and SCNAs. In addition, metastases but not primary tumors had seeded additional metastases in three of four patients; this provides a genomic rational for surgical treatment of such oligometastatic LUADs. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Pan-cancer analysis of intratumor heterogeneity associated with patient prognosis using multidimensional measures

Human cancers accumulate various mutations during development and consist of highly heterogeneous cell populations. This phenomenon is called intratumor heterogeneity (ITH). ITH is known to be involved in tumor growth, progression, invasion, and metastasis, presenting obstacles to accurate diagnoses and effective treatments. Numerous studies have explored the dynamics of ITH, including constructions of phylogenetic trees in cancer samples using multiregional ultradeep sequencing and simulations of evolution using statistical models. Although ITH is associated with prognosis, it is still challenging to use the characteristics of ITH as prognostic factors because of difficulties in quantifying ITH precisely. In this study, we analyzed the relationship between patient prognosis and the distribution of variant allele frequencies (VAFs) in cancer samples (n = 6,064) across 16 cancer types registered in The Cancer Genome Atlas. To measure VAF distributions multidimensionally, we adopted parameters that define the shape of VAF distributions and evaluated the relationships between these parameters and prognosis. In seven cancer types, we found significant relationships between prognosis and VAF distributions. Moreover, we observed that samples with a larger amount of mutations were not necessarily linked to worse prognosis. By evaluating the ITH from multidimensional viewpoints, it will be possible to provide a more accurate prediction of cancer prognosis.

Genetic Abnormalities, Clonal Evolution, and Cancer Stem Cells of Brain Tumors

Brain tumors are highly heterogeneous and have been classified by the World Health Organization in various histological and molecular subtypes. Gliomas have been classified as ranging from low-grade astrocytomas and oligodendrogliomas to high-grade astrocytomas or glioblastomas. These tumors are characterized by a peculiar pattern of genetic alterations. Pediatric high-grade gliomas are histologically indistinguishable from adult glioblastomas, but they are considered distinct from adult glioblastomas because they possess a different spectrum of driver mutations (genes encoding histones H3.3 and H3.1). Medulloblastomas, the most frequent pediatric brain tumors, are considered to be of embryonic derivation and are currently subdivided into distinct subgroups depending on histological features and genetic profiling. There is emerging evidence that brain tumors are maintained by a special neural or glial stem cell-like population that self-renews and gives rise to differentiated progeny. In many instances, the prognosis of the majority of brain tumors remains negative and there is hope that the new acquisition of information on the molecular and cellular bases of these tumors will be translated in the development of new, more active treatments.

Pan-cancer inference of intra-tumor heterogeneity reveals associations with different forms of genomic instability

Genomic instability is a major driver of intra-tumor heterogeneity. However, unstable genomes often exhibit different molecular and clinical phenotypes, which are associated with distinct mutational processes. Here, we algorithmically inferred the clonal phylogenies of ~6,000 human tumors from 32 tumor types to explore how intra-tumor heterogeneity depends on different implementations of genomic instability. We found that extremely unstable tumors associated with DNA repair deficiencies or high chromosomal instability are not the most intrinsically heterogeneous. Conversely, intra-tumor heterogeneity is greatest in tumors exhibiting relatively high numbers of both mutations and copy number alterations, a feature often observed in cancers associated with exogenous mutagens. Independently of the type of instability, tumors with high number of clones invariably evolved through branching phylogenies that could be stratified based on the extent of clonal (early) and subclonal (late) instability. Interestingly, tumors with high number of subclonal mutations frequently exhibited chromosomal instability, TP53 mutations, and APOBEC-related mutational signatures. Vice versa, mutations of chromatin remodeling genes often characterized tumors with few subclonal but multiple clonal mutations. Understanding how intra-tumor heterogeneity depends on genomic instability is critical to identify markers predictive of the tumor complexity and envision therapeutic strategies able to exploit this association.

Cancer is characterized by cells accumulating molecular alterations promoting specific phenotypic features, such as uncontrolled proliferation and survival. Cancer cells sometimes exhibit a high number of such alterations, often driven by defects of the DNA repair pathway or by external mutagens, such as tobacco smoking or UV-radiation. Highly altered cells are termed genomically unstable. A major consequence of genomic instability is that a single tumor is often composed by cells that have accumulated distinct alterations. This diversity is termed intra-tumor heterogeneity and represents a critical clinical challenge. In this study, we examined how different forms of genomic instability are associated with intra-tumor heterogeneity. We inferred intra-tumor heterogeneity in ~6000 human tumors and found that tumors with extreme mutational or chromosomal instability were not the tumors with the highest number of clones. Instead, tumors harboring both mutational and chromosomal alterations were the most diverse. Furthermore, we identified specific genetic fingerprints that are associated with early and/or late genomic instability. These results show that cancer genomic instability does not necessarily lead to high intra-tumor heterogeneity and, importantly, they provide markers to recognize when it does.

The genomic landscape of cutaneous SCC reveals drivers and a novel azathioprine associated mutational signature

Cutaneous squamous cell carcinoma (cSCC) has a high tumour mutational burden (50 mutations per megabase DNA pair). Here, we combine whole-exome analyses from 40 primary cSCC tumours, comprising 20 well-differentiated and 20 moderately/poorly differentiated tumours, with accompanying clinical data from a longitudinal study of immunosuppressed and immunocompetent patients and integrate this analysis with independent gene expression studies. We identify commonly mutated genes, copy number changes and altered pathways and processes. Comparisons with tumour differentiation status suggest events which may drive disease progression. Mutational signature analysis reveals the presence of a novel signature (signature 32), whose incidence correlates with chronic exposure to the immunosuppressive drug azathioprine. Characterisation of a panel of 15 cSCC tumour-derived cell lines reveals that they accurately reflect the mutational signatures and genomic alterations of primary tumours and provide a valuable resource for the validation of tumour drivers and therapeutic targets.

Pan-cancer deconvolution of tumour composition using DNA methylation

The nature and extent of immune cell infiltration into solid tumours are key determinants of therapeutic response. Here, using a DNA methylation-based approach to tumour cell fraction deconvolution, we report the integrated analysis of tumour composition and genomics across a wide spectrum of solid cancers. Initially studying head and neck squamous cell carcinoma, we identify two distinct tumour subgroups: 'immune hot' and 'immune cold', which display differing prognosis, mutation burden, cytokine signalling, cytolytic activity and oncogenic driver events. We demonstrate the existence of such tumour subgroups pan-cancer, link clonal-neoantigen burden to cytotoxic T-lymphocyte infiltration, and show that transcriptional signatures of hot tumours are selectively engaged in immunotherapy responders. We also find that treatment-naive hot tumours are markedly enriched for known immune-resistance genomic alterations, potentially explaining the heterogeneity of immunotherapy response and prognosis seen within this group. Finally, we define a catalogue of mediators of active antitumour immunity, deriving candidate biomarkers and potential targets for precision immunotherapy.

Functional diversity and cooperativity between subclonal populations of pediatric glioblastoma and diffuse intrinsic pontine glioma cells

The failure to develop effective therapies for pediatric glioblastoma (pGBM) and diffuse intrinsic pontine glioma (DIPG) is in part due to their intrinsic heterogeneity. We aimed to quantitatively assess the extent to which this was present in these tumors through subclonal genomic analyses and to determine whether distinct tumor subpopulations may interact to promote tumorigenesis by generating subclonal patient-derived models in vitro and in vivo. Analysis of 142 sequenced tumors revealed multiple tumor subclones, spatially and temporally coexisting in a stable manner as observed by multiple sampling strategies. We isolated genotypically and phenotypically distinct subpopulations that we propose cooperate to enhance tumorigenicity and resistance to therapy. Inactivating mutations in the H4K20 histone methyltransferase KMT5B (SUV420H1), present in <1% of cells, abrogate DNA repair and confer increased invasion and migration on neighboring cells, in vitro and in vivo, through chemokine signaling and modulation of integrins. These data indicate that even rare tumor subpopulations may exert profound effects on tumorigenesis as a whole and may represent a new avenue for therapeutic development. Unraveling the mechanisms of subclonal diversity and communication in pGBM and DIPG will be an important step toward overcoming barriers to effective treatments.

Reliance upon ancestral mutations is maintained in colorectal cancers that heterogeneously evolve during targeted therapies

Attempts at eradicating metastatic cancers with targeted therapies are limited by the emergence of resistant subclones bearing heterogeneous (epi)genetic changes. We used colorectal cancer (CRC) to test the hypothesis that interfering with an ancestral oncogenic event shared by all the malignant cells (such as WNT pathway alterations) could override heterogeneous mechanisms of acquired drug resistance. Here, we report that in CRC-resistant cell populations, phylogenetic analysis uncovers a complex subclonal architecture, indicating parallel evolution of multiple independent cellular lineages. Functional and pharmacological modulation of WNT signalling induces cell death in CRC preclinical models from patients that relapsed during the treatment, regardless of the drug type or resistance mechanisms. Concomitant blockade of WNT and MAPK signalling restrains the emergence of drug-resistant clones. Reliance upon the WNT-APC pathway is preserved throughout the branched genomic drift associated with emergence of treatment relapse, thus offering the possibility of a common therapeutic strategy to overcome secondary drug resistance.

Harnessing Tumor Evolution to Circumvent Resistance

High-throughput sequencing can be used to measure changes in tumor composition across space and time. Specifically, comparisons of pre- and post-treatment samples can reveal the underlying clonal dynamics and resistance mechanisms. Here, we discuss evidence for distinct modes of tumor evolution and their implications for therapeutic strategies. In addition, we consider the utility of spatial tissue sampling schemes, single-cell analysis, and circulating tumor DNA to track tumor evolution and the emergence of resistance, as well as approaches that seek to forestall resistance by targeting tumor evolution. Ultimately, characterization of the (epi)genomic, transcriptomic, and phenotypic changes that occur during tumor progression coupled with computational and mathematical modeling of tumor evolutionary dynamics may inform personalized treatment strategies.

Enhancing knowledge discovery from cancer genomics data with Galaxy

The field of cancer genomics has demonstrated the power of massively parallel sequencing techniques to inform on the genes and specific alterations that drive tumor onset and progression. Although large comprehensive sequence data sets continue to be made increasingly available, data analysis remains an ongoing challenge, particularly for laboratories lacking dedicated resources and bioinformatics expertise. To address this, we have produced a collection of Galaxy tools that represent many popular algorithms for detecting somatic genetic alterations from cancer genome and exome data. We developed new methods for parallelization of these tools within Galaxy to accelerate runtime and have demonstrated their usability and summarized their runtimes on multiple cloud service providers. Some tools represent extensions or refinement of existing toolkits to yield visualizations suited to cohort-wide cancer genomic analysis. For example, we present Oncocircos and Oncoprintplus, which generate data-rich summaries of exome-derived somatic mutation. Workflows that integrate these to achieve data integration and visualizations are demonstrated on a cohort of 96 diffuse large B-cell lymphomas and enabled the discovery of multiple candidate lymphoma-related genes. Our toolkit is available from our GitHub repository as Galaxy tool and dependency definitions and has been deployed using virtualization on multiple platforms including Docker.

Evolution of Barrett's esophagus through space and time at single-crypt and whole-biopsy levels

The low risk of progression of Barrett's esophagus (BE) to esophageal adenocarcinoma can lead to over-diagnosis and over-treatment of BE patients. This may be addressed through a better understanding of the dynamics surrounding BE malignant progression. Although genetic diversity has been characterized as a marker of malignant development, it is still unclear how BE arises and develops. Here we uncover the evolutionary dynamics of BE at crypt and biopsy levels in eight individuals, including four patients that experienced malignant progression. We assay eight individual crypts and the remaining epithelium by SNP array for each of 6-11 biopsies over 2 time points per patient (358 samples in total). Our results indicate that most Barrett's segments are clonal, with similar number and inferred rates of alterations observed for crypts and biopsies. Divergence correlates with geographical location, being higher near the gastro-esophageal junction. Relaxed clock analyses show that genomic instability precedes and is enhanced by genome doubling. These results shed light on the clinically relevant evolutionary dynamics of BE.

Accurity: accurate tumor purity and ploidy inference from tumor-normal WGS data by jointly modelling somatic copy number alterations and heterozygous germline single-nucleotide-variants

Motivation

Tumor purity and ploidy have a substantial impact on next-gen sequence analyses of tumor samples and may alter the biological and clinical interpretation of results. Despite the existence of several computational methods that are dedicated to estimate tumor purity and/or ploidy from The Cancer Genome Atlas (TCGA) tumor-normal whole-genome-sequencing (WGS) data, an accurate, fast and fully-automated method that works in a wide range of sequencing coverage, level of tumor purity and level of intra-tumor heterogeneity, is still missing.

Results

We describe a computational method called Accurity that infers tumor purity, tumor cell ploidy and absolute allelic copy numbers for somatic copy number alterations (SCNAs) from tumor-normal WGS data by jointly modelling SCNAs and heterozygous germline single-nucleotide-variants (HGSNVs). Results from both in silico and real sequencing data demonstrated that Accurity is highly accurate and robust, even in low-purity, high-ploidy and low-coverage settings in which several existing methods perform poorly. Accounting for tumor purity and ploidy, Accurity significantly increased signal/noise gaps between different copy numbers. We are hopeful that Accurity is of clinical use for identifying cancer diagnostic biomarkers.

Availability and implementation

Accurity is implemented in C++/Rust, available at http://www.yfish.org/software/.

Supplementary information

Supplementary data are available at Bioinformatics online.

The ten-year evolutionary trajectory of a highly recurrent paediatric high grade neuroepithelial tumour with MN1:BEND2 fusion

Astroblastomas are rare brain tumours which predominate in children and young adults, and have a controversial claim as a distinct entity, with no established WHO grade. Reports suggest a better outcome than high grade gliomas, though they frequently recur. Recently, they have been described to overlap with a newly-discovered group of tumours described as'high grade neuroepithelial tumour with MN1 alteration' (CNS HGNET-MN1), defined by global methylation patterns and strongly associated with gene fusions targeting MN1. We have studied a unique case of astroblastoma arising in a 6 year-old girl, with multiple recurrences over a period of 10 years, with the pathognomonic MN1:BEND2 fusion. Exome sequencing allowed for a phylogenetic reconstruction of tumour evolution, which when integrated with clinical, pathological and radiological data provide for a detailed understanding of disease progression, with initial treatment driving tumour dissemination along four distinct trajectories. Infiltration of distant sites was associated with a later genome doubling, whilst there was evidence of convergent evolution of different lesions acquiring distinct alterations targeting NF-κB. These data represent an unusual opportunity to understand the evolutionary history of a highly recurrent childhood brain tumour, and provide novel therapeutic targets for astroblastoma/CNS HGNET-MN1.

Integrated molecular analysis of tumor biopsies on sequential CTLA-4 and PD-1 blockade reveals markers of response and resistance

Immune checkpoint blockade produces clinical benefit in many patients. However, better biomarkers of response are still needed, and mechanisms of resistance remain incompletely understood. To address this, we recently studied a cohort of melanoma patients treated with sequential checkpoint blockade against cytotoxic T lymphocyte antigen-4 (CTLA-4) followed by programmed death receptor-1 (PD-1) and identified immune markers of response and resistance. Building on these studies, we performed deep molecular profiling including T cell receptor sequencing and whole-exome sequencing within the same cohort and demonstrated that a more clonal T cell repertoire was predictive of response to PD-1 but not CTLA-4 blockade. Analysis of CNAs identified a higher burden of copy number loss in nonresponders to CTLA-4 and PD-1 blockade and found that it was associated with decreased expression of genes in immune-related pathways. The effect of mutational load and burden of copy number loss on response was nonredundant, suggesting the potential utility of a combinatorial biomarker to optimize patient care with checkpoint blockade therapy.

Cell subpopulation deconvolution reveals breast cancer heterogeneity based on DNA methylation signature

Tumour heterogeneity describes the coexistence of divergent tumour cell clones within tumours, which is often caused by underlying epigenetic changes. DNA methylation is commonly regarded as a significant regulator that differs across cells and tissues. In this study, we comprehensively reviewed research progress on estimating of tumour heterogeneity. Bioinformatics-based analysis of DNA methylation has revealed the evolutionary relationships between breast cancer cell lines and tissues. Further analysis of the DNA methylation profiles in 33 breast cancer-related cell lines identified cell line-specific methylation patterns. Next, we reviewed the computational methods in inferring clonal evolution of tumours from different perspectives and then proposed a deconvolution strategy for modelling cell subclonal populations dynamics in breast cancer tissues based on DNA methylation. Further analysis of simulated cancer tissues and real cell lines revealed that this approach exhibits satisfactory performance and relative stability in estimating the composition and proportions of cellular subpopulations. The application of this strategy to breast cancer individuals of the Cancer Genome Atlas's identified different cellular subpopulations with distinct molecular phenotypes. Moreover, the current and potential future applications of this deconvolution strategy to clinical breast cancer research are discussed, and emphasis was placed on the DNA methylation-based recognition of intra-tumour heterogeneity. The wide use of these methods for estimating heterogeneity to further clinical cohorts will improve our understanding of neoplastic progression and the design of therapeutic interventions for treating breast cancer and other malignancies.

Tumor Heterogeneity Predicts Metastatic Potential in Colorectal Cancer

Purpose: Tumors continuously evolve to maintain growth; secondary mutations facilitate this process, resulting in high tumor heterogeneity. In this study, we compared mutations in paired primary and metastatic colorectal cancer tumor samples to determine whether tumor heterogeneity can predict tumor metastasis.Experimental Design: Somatic variations in 46 pairs of matched primary-liver metastatic tumors and 42 primary tumors without metastasis were analyzed by whole-exome sequencing. Tumor clonality was estimated from single-nucleotide and copy-number variations. The correlation between clinical parameters of patients and clonal heterogeneity in liver metastasis was evaluated.Results: Tumor heterogeneity across colorectal cancer samples was highly variable; however, a high degree of tumor heterogeneity was associated with a worse disease-free survival. Highly heterogeneous primary colorectal cancer was correlated with a higher rate of liver metastasis. Recurrent somatic mutations in APC, TP53, and KRAS were frequently detected in highly heterogeneous colorectal cancer. The variant allele frequency of these mutations was high, while somatic mutations in other genes such as PIK3CA and NOTCH1 were low. The number and distribution of primary colorectal cancer subclones were preserved in metastatic tumors.Conclusions: Heterogeneity of primary colorectal cancer tumors can predict the potential for liver metastasis and thus, clinical outcome of patients. Clin Cancer Res; 23(23); 7209-16. ©2017 AACR.

Quantification of Multiple Tumor Clones Using Gene Array and Sequencing Data

Cancer development is driven by genomic alterations, including copy number aberrations. The detection of copy number aberrations in tumor cells is often complicated by possible contamination of normal stromal cells in tumor samples and intratumor heterogeneity, namely the presence of multiple clones of tumor cells. In order to correctly quantify copy number aberrations, it is critical to successfully de-convolute the complex structure of the genetic information from tumor samples. In this article, we propose a general Bayesian method for estimating copy number aberrations when there are normal cells and potentially more than one tumor clones. Our method provides posterior probabilities for the proportions of tumor clones and normal cells. We incorporate prior information on the distribution of the copy numbers to prioritize biologically more plausible solutions and alleviate possible identifiability issues that have been observed by many researchers. Our model is flexible and can work for both SNP array and next-generation sequencing data. We compare our method to existing ones and illustrate the advantage of our approach in multiple datasets.

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.

Multilevel genomics of colorectal cancers with microsatellite instability-clinical impact of JAK1 mutations and consensus molecular subtype 1

BACKGROUND

Approximately 15% of primary colorectal cancers have DNA mismatch repair deficiency, causing a complex genome with thousands of small mutations-the microsatellite instability (MSI) phenotype. We investigated molecular heterogeneity and tumor immunogenicity in relation to clinical endpoints within this distinct subtype of colorectal cancers.

METHODS

A total of 333 primary MSI+ colorectal tumors from multiple cohorts were analyzed by multilevel genomics and computational modeling-including mutation profiling, clonality modeling, and neoantigen prediction in a subset of the tumors, as well as gene expression profiling for consensus molecular subtypes (CMS) and immune cell infiltration.

RESULTS

Novel, frequent frameshift mutations in four cancer-critical genes were identified by deep exome sequencing, including in CRTC1, BCL9, JAK1, and PTCH1. JAK1 loss-of-function mutations were validated with an overall frequency of 20% in Norwegian and British patients, and mutated tumors had up-regulation of transcriptional signatures associated with resistance to anti-PD-1 treatment. Clonality analyses revealed a high level of intra-tumor heterogeneity; however, this was not associated with disease progression. Among the MSI+ tumors, the total mutation load correlated with the number of predicted neoantigens (P = 4 × 10-5), but not with immune cell infiltration-this was dependent on the CMS class; MSI+ tumors in CMS1 were highly immunogenic compared to MSI+ tumors in CMS2-4. Both JAK1 mutations and CMS1 were favorable prognostic factors (hazard ratios 0.2 [0.05-0.9] and 0.4 [0.2-0.9], respectively, P = 0.03 and 0.02).

CONCLUSIONS

Multilevel genomic analyses of MSI+ colorectal cancer revealed molecular heterogeneity with clinical relevance, including tumor immunogenicity and a favorable patient outcome associated with JAK1 mutations and the transcriptomic subgroup CMS1, emphasizing the potential for prognostic stratification of this clinically important subtype. See related research highlight by Samstein and Chan 10.1186/s13073-017-0438-9.

Spatial heterogeneity in medulloblastoma

Spatial heterogeneity of transcriptional and genetic markers between physically isolated biopsies of a single tumor poses major barriers to the identification of biomarkers and the development of targeted therapies that will be effective against the entire tumor. We analyzed the spatial heterogeneity of multiregional biopsies from 35 patients, using a combination of transcriptomic and genomic profiles. Medulloblastomas (MBs), but not high-grade gliomas (HGGs), demonstrated spatially homogeneous transcriptomes, which allowed for accurate subgrouping of tumors from a single biopsy. Conversely, somatic mutations that affect genes suitable for targeted therapeutics demonstrated high levels of spatial heterogeneity in MB, malignant glioma, and renal cell carcinoma (RCC). Actionable targets found in a single MB biopsy were seldom clonal across the entire tumor, which brings the efficacy of monotherapies against a single target into question. Clinical trials of targeted therapies for MB should first ensure the spatially ubiquitous nature of the target mutation.

The evolution of tumour phylogenetics: principles and practice

Rapid advances in high-throughput sequencing and a growing realization of the importance of evolutionary theory to cancer genomics have led to a proliferation of phylogenetic studies of tumour progression. These studies have yielded not only new insights but also a plethora of experimental approaches, sometimes reaching conflicting or poorly supported conclusions. Here, we consider this body of work in light of the key computational principles underpinning phylogenetic inference, with the goal of providing practical guidance on the design and analysis of scientifically rigorous tumour phylogeny studies. We survey the range of methods and tools available to the researcher, their key applications, and the various unsolved problems, closing with a perspective on the prospects and broader implications of this field.

Pan-cancer analysis of intratumor heterogeneity as a prognostic determinant of survival

As tumors accumulate genetic alterations, an evolutionary process occurs in which genetically distinct subclonal populations of cells co-exist, resulting in intratumor genetic heterogeneity (ITH). The clinical implications of ITH remain poorly defined. Data are limited with respect to whether ITH is an independent determinant of patient survival outcomes, across different cancer types. Here, we report the results of a pan-cancer analysis of over 3300 tumors, showing a varied landscape of ITH across 9 cancer types. While some gene mutations are subclonal, the majority of driver gene mutations are clonal events, present in nearly all cancer cells. Strikingly, high levels of ITH are associated with poorer survival across diverse types of cancer. The adverse impact of high ITH is independent of other clinical, pathologic and molecular factors. High ITH tends to be associated with lower levels of tumor-infiltrating immune cells, but this association is not able to explain the observed survival differences. Together, these data show that ITH is a prognostic marker in multiple cancers. These results illuminate the natural history of cancer evolution, indicating that tumor heterogeneity represents a significant obstacle to cancer control.

Clinical and molecular relevance of mutant-allele tumor heterogeneity in breast cancer

PURPOSE

Intra-tumor heterogeneity (ITH) plays a pivotal role in driving breast cancer progression and therapeutic resistance. We used a mutant-allele tumor heterogeneity (MATH) algorithm to measure ITH and explored its correlation with clinical parameters and multi-omics data.

METHODS

We assessed 916 female breast cancer patients from The Cancer Genome Atlas. We calculated the MATH values from whole-exome sequencing data and further investigated their correlation with clinical characteristics, somatic mutations, somatic copy number alterations (SCNAs), and gene enrichment.

RESULTS

The patients were divided into low, intermediate, and high MATH groups. High T stage, African American race, and triple-negative or basal-like subtype were associated with a higher MATH level (all P < 0.001). In hormone receptor-positive and human epidermal growth factor receptor-negative patients, the high MATH group showed a tendency toward a worse overall survival (P = 0.052); Furthermore, the TP53 mutation rate increased as MATH was elevated (P < 0.001), whereas CDH1 mutations were correlated with a lower level of MATH (P = 0.002). Several focal and arm-level SCNA events were more common in the high MATH group (P < 0.05), including Chr8q24 with only the MYC gene in the "peak" region. Similarly, high MATH was associated with gene set enrichment related to the MYC pathway and proliferation.

CONCLUSION

Our integrative analysis reveals the clinical and genetic relevance of ITH in breast cancer. These results also suggest the origin and natural history of clonal evolution and intra-tumor genetic heterogeneity, which warrant further investigation.

Measuring cancer evolution from the genome

The temporal dynamics of cancer evolution remain elusive, because it is impractical to longitudinally observe cancers unperturbed by treatment. Consequently, our knowledge of how cancers grow largely derives from inferences made from a single point in time - the endpoint in the cancer's evolution, when it is removed from the body and studied in the laboratory. Fortuitously however, the cancer genome, by virtue of ongoing mutations that uniquely mark clonal lineages within the tumour, provides a rich, yet surreptitious, record of cancer development. In this review, we describe how a cancer's genome can be analysed to reveal the temporal history of mutation and selection, and discuss why both selective and neutral evolution feature prominently in carcinogenesis. We argue that selection in cancer can only be properly studied once we have some understanding of what the absence of selection looks like. We review the data describing punctuated evolution in cancer, and reason that punctuated phenotype evolution is consistent with both gradual and punctuated genome evolution. We conclude that, to map and predict evolutionary trajectories during carcinogenesis, it is critical to better understand the relationship between genotype change and phenotype change. Copyright © 2016 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Measuring intratumor heterogeneity by network entropy using RNA-seq data

Intratumor heterogeneity (ITH) is observed at different stages of tumor progression, metastasis and reouccurence, which can be important for clinical applications. We used RNA-sequencing data from tumor samples, and measured the level of ITH in terms of biological network states. To model complex relationships among genes, we used a protein interaction network to consider gene-gene dependency. ITH was measured by using an entropy-based distance metric between two networks, nJSD, with Jensen-Shannon Divergence (JSD). With nJSD, we defined transcriptome-based ITH (tITH). The effectiveness of tITH was extensively tested for the issues related with ITH using real biological data sets. Human cancer cell line data and single-cell sequencing data were investigated to verify our approach. Then, we analyzed TCGA pan-cancer 6,320 patients. Our result was in agreement with widely used genome-based ITH inference methods, while showed better performance at survival analysis. Analysis of mouse clonal evolution data further confirmed that our transcriptome-based ITH was consistent with genetic heterogeneity at different clonal evolution stages. Additionally, we found that cell cycle related pathways have significant contribution to increasing heterogeneity on the network during clonal evolution. We believe that the proposed transcriptome-based ITH is useful to characterize heterogeneity of a tumor sample at RNA level.

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/.

Bulk Genotyping of Biopsies Can Create Spurious Evidence for Hetereogeneity in Mutation Content

When multiple samples are taken from the neoplastic tissues of a single patient, it is natural to compare their mutation content. This is often done by bulk genotyping of whole biopsies, but the chance that a mutation will be detected in bulk genotyping depends on its local frequency in the sample. When the underlying mutation count per cell is equal, homogenous biopsies will have more high-frequency mutations, and thus more detectable mutations, than heterogeneous ones. Using simulations, we show that bulk genotyping of data simulated under a neutral model of somatic evolution generates strong spurious evidence for non-neutrality, because the pattern of tissue growth systematically generates differences in biopsy heterogeneity. Any experiment which compares mutation content across bulk-genotyped biopsies may therefore suggest mutation rate or selection intensity variation even when these forces are absent. We discuss computational and experimental approaches for resolving this problem.

Researchers who take multiple samples from a cancer or pre-cancer tissue and find that some samples show far more mutations than others are likely to conclude that the high-mutation samples reflect cells with an abnormal mutation or growth rate. We considered the common practice of testing a bulk sample for mutations, which finds only mutations that are common within the sample. Our computer simulations show that even when all cells have identical mutation and growth rates, testing bulk samples frequently leads to spurious detection of rate differences. This can lead to false conclusions about the causes and progress of cancer. We discuss possible solutions involving either genetic testing of single cells or the use of computer algorithms to detect rare mutations within a sample.

A comprehensive profile of recurrent glioblastoma

In spite of relentless efforts to devise new treatment strategies, primary glioblastomas invariably recur as aggressive, therapy-resistant relapses and patients rapidly succumb to these tumors. Many therapeutic agents are first tested in clinical trials involving recurrent glioblastomas. Remarkably, however, fundamental knowledge on the biology of recurrent glioblastoma is just slowly emerging. Here, we review current knowledge on recurrent glioblastoma and ask whether and how therapies change intra-tumor heterogeneity, molecular traits and growth pattern of glioblastoma, and to which extent this information can be exploited for therapeutic decision-making. We conclude that the ability to characterize and predict therapy-induced changes in recurrent glioblastoma will determine, whether, one day, glioblastoma can be contained in a state of chronic disease.

A divide-and-conquer strategy in tumor sampling enhances detection of intratumor heterogeneity in routine pathology: A modeling approach in clear cell renal cell carcinoma

Intratumor heterogeneity (ITH) is an inherent process in cancer development which follows for most of the cases a branched pattern of evolution, with different cell clones evolving independently in space and time across different areas of the same tumor. The determination of ITH (in both spatial and temporal domains) is nowadays critical to enhance patient treatment and prognosis. Clear cell renal cell carcinoma (CCRCC) provides a good example of ITH. Sometimes the tumor is too big to be totally analyzed for ITH detection and pathologists decide which parts must be sampled for the analysis. For such a purpose, pathologists follow internationally accepted protocols. In light of the latest findings, however, current sampling protocols seem to be insufficient for detecting ITH with significant reliability. The arrival of new targeted therapies, some of them providing promising alternatives to improve patient survival, pushes the pathologist to obtain a truly representative sampling of tumor diversity in routine practice. How large this sampling must be and how this must be performed are unanswered questions so far.  Here we present a very simple method for tumor sampling that enhances ITH detection without increasing costs. This method follows a divide-and-conquer (DAC) strategy, that is, rather than sampling a small number of large-size tumor-pieces as the routine protocol (RP) advises, we suggest sampling many small-size pieces along the tumor. We performed a computational modeling approach to show that the usefulness of the DAC strategy is twofold: first, we show that DAC outperforms RP with similar laboratory costs, and second, DAC is capable of performing similar to total tumor sampling (TTS) but, very remarkably, at a much lower cost. We thus provide new light to push forward a shift in the paradigm about how pathologists should sample tumors for achieving efficient ITH detection.

A divide-and-conquer strategy in tumor sampling enhances detection of intratumor heterogeneity in routine pathology: A modeling approach in clear cell renal cell carcinoma

Intratumor heterogeneity (ITH) is an inherent process in cancer development which follows for most of the cases a branched pattern of evolution, with different cell clones evolving independently in space and time across different areas of the same tumor. The determination of ITH (in both spatial and temporal domains) is nowadays critical to enhance patient treatment and prognosis. Clear cell renal cell carcinoma (CCRCC) provides a good example of ITH. Sometimes the tumor is too big to be totally analyzed for ITH detection and pathologists decide which parts must be sampled for the analysis. For such a purpose, pathologists follow internationally accepted protocols. In light of the latest findings, however, current sampling protocols seem to be insufficient for detecting ITH with significant reliability. The arrival of new targeted therapies, some of them providing promising alternatives to improve patient survival, pushes the pathologist to obtain a truly representative sampling of tumor diversity in routine practice. How large this sampling must be and how this must be performed are unanswered questions so far.  Here we present a very simple method for tumor sampling that enhances ITH detection without increasing costs. This method follows a divide-and-conquer (DAC) strategy, that is, rather than sampling a small number of large-size tumor-pieces as the routine protocol (RP) advises, we suggest sampling many small-size pieces along the tumor. We performed a computational modeling approach to show that the usefulness of the DAC strategy is twofold: first, we show that DAC outperforms RP with similar laboratory costs, and second, DAC is capable of performing similar to total tumor sampling (TTS) but, very remarkably, at a much lower cost. We thus provide new light to push forward a shift in the paradigm about how pathologists should sample tumors for achieving efficient ITH detection.

Ultra-fast local-haplotype variant calling using paired-end DNA-sequencing data reveals somatic mosaicism in tumor and normal blood samples

Somatic mosaicism refers to the existence of somatic mutations in a fraction of somatic cells in a single biological sample. Its importance has mainly been discussed in theory although experimental work has started to emerge linking somatic mosaicism to disease diagnosis. Through novel statistical modeling of paired-end DNA-sequencing data using blood-derived DNA from healthy donors as well as DNA from tumor samples, we present an ultra-fast computational pipeline, LocHap that searches for multiple single nucleotide variants (SNVs) that are scaffolded by the same reads. We refer to scaffolded SNVs as local haplotypes (LH). When an LH exhibits more than two genotypes, we call it a local haplotype variant (LHV). The presence of LHVs is considered evidence of somatic mosaicism because a genetically homogeneous cell population will not harbor LHVs. Applying LocHap to whole-genome and whole-exome sequence data in DNA from normal blood and tumor samples, we find wide-spread LHVs across the genome. Importantly, we find more LHVs in tumor samples than in normal samples, and more in older adults than in younger ones. We confirm the existence of LHVs and somatic mosaicism by validation studies in normal blood samples. LocHap is publicly available at http://www.compgenome.org/lochap.

Divergent clonal selection dominates medulloblastoma at recurrence

The development of targeted anti-cancer therapies through the study of cancer genomes is intended to increase survival rates and decrease treatment-related toxicity. We treated a transposon-driven, functional genomic mouse model of medulloblastoma with 'humanized' in vivo therapy (microneurosurgical tumour resection followed by multi-fractionated, image-guided radiotherapy). Genetic events in recurrent murine medulloblastoma exhibit a very poor overlap with those in matched murine diagnostic samples (<5%). Whole-genome sequencing of 33 pairs of human diagnostic and post-therapy medulloblastomas demonstrated substantial genetic divergence of the dominant clone after therapy (<12% diagnostic events were retained at recurrence). In both mice and humans, the dominant clone at recurrence arose through clonal selection of a pre-existing minor clone present at diagnosis. Targeted therapy is unlikely to be effective in the absence of the target, therefore our results offer a simple, proximal, and remediable explanation for the failure of prior clinical trials of targeted therapy.

Targeting a Plk1-Controlled Polarity Checkpoint in Therapy-Resistant Glioblastoma-Propagating Cells

The treatment of glioblastoma (GBM) remains challenging in part due to the presence of stem-like tumor-propagating cells that are resistant to standard therapies consisting of radiation and temozolomide. Among the novel and targeted agents under evaluation for the treatment of GBM are BRAF/MAPK inhibitors, but their effects on tumor-propagating cells are unclear. Here, we characterized the behaviors of CD133(+) tumor-propagating cells isolated from primary GBM cell lines. We show that CD133(+) cells exhibited decreased sensitivity to the antiproliferative effects of BRAF/MAPK inhibition compared to CD133(-) cells. Furthermore, CD133(+) cells exhibited an extended G2-M phase and increased polarized asymmetric cell divisions. At the molecular level, we observed that polo-like kinase (PLK) 1 activity was elevated in CD133(+) cells, prompting our investigation of BRAF/PLK1 combination treatment effects in an orthotopic GBM xenograft model. Combined inhibition of BRAF and PLK1 resulted in significantly greater antiproliferative and proapoptotic effects beyond those achieved by monotherapy (P < 0.05). We propose that PLK1 activity controls a polarity checkpoint and compensates for BRAF/MAPK inhibition in CD133(+) cells, suggesting the need for concurrent PLK1 inhibition to improve antitumor activity against a therapy-resistant cell compartment.

Systematic pan-cancer analysis of tumour purity

The tumour microenvironment is the non-cancerous cells present in and around a tumour, including mainly immune cells, but also fibroblasts and cells that comprise supporting blood vessels. These non-cancerous components of the tumour may play an important role in cancer biology. They also have a strong influence on the genomic analysis of tumour samples, and may alter the biological interpretation of results. Here we present a systematic analysis using different measurement modalities of tumour purity in >10,000 samples across 21 cancer types from the Cancer Genome Atlas. Patients are stratified according to clinical features in an attempt to detect clinical differences driven by purity levels. We demonstrate the confounding effect of tumour purity on correlating and clustering tumours with transcriptomics data. Finally, using a differential expression method that accounts for tumour purity, we find an immunotherapy gene signature in several cancer types that is not detected by traditional differential expression analyses.

The importance of the tumour microenvironment has now been realised, however the presence of non-tumour cells in cancer samples can complicate genomic analyses. Here, the authors estimate tumour purity in 10,000 samples from the TCGA dataset and can detect a signature of T cell activation.

Pan-cancer analysis of the extent and consequences of intratumor heterogeneity

Intratumor heterogeneity (ITH) drives neoplastic progression and therapeutic resistance. We used the bioinformatics tools 'expanding ploidy and allele frequency on nested subpopulations' (EXPANDS) and PyClone to detect clones that are present at a ≥10% frequency in 1,165 exome sequences from tumors in The Cancer Genome Atlas. 86% of tumors across 12 cancer types had at least two clones. ITH in the morphology of nuclei was associated with genetic ITH (Spearman's correlation coefficient, ρ = 0.24-0.41; P < 0.001). Mutation of a driver gene that typically appears in smaller clones was a survival risk factor (hazard ratio (HR) = 2.15, 95% confidence interval (CI): 1.71-2.69). The risk of mortality also increased when >2 clones coexisted in the same tumor sample (HR = 1.49, 95% CI: 1.20-1.87). In two independent data sets, copy-number alterations affecting either <25% or >75% of a tumor's genome predicted reduced risk (HR = 0.15, 95% CI: 0.08-0.29). Mortality risk also declined when >4 clones coexisted in the sample, suggesting a trade-off between the costs and benefits of genomic instability. ITH and genomic instability thus have the potential to be useful measures that can universally be applied to all cancers.

Disease evolution and outcomes in familial AML with germline CEBPA mutations

In-depth molecular investigation of familial leukemia has been limited by the rarity of recognized cases. This study examines the genetic events initiating leukemia and details the clinical progression of disease across multiple families harboring germ-line CEBPA mutations. Clinical data were collected from 10 CEBPA-mutated families, representing 24 members with acute myeloid leukemia (AML). Whole-exome (WES) and deep sequencing were performed to genetically profile tumors and define patterns of clonal evolution. Germline CEBPA mutations clustered within the N-terminal and were highly penetrant, with AML presenting at a median age of 24.5 years (range, 1.75-46 years). In all diagnostic tumors tested (n = 18), double CEBPA mutations (CEBPAdm) were detected, with acquired (somatic) mutations preferentially targeting the C-terminal. Somatic CEBPA mutations were unstable throughout the disease course, with different mutations identified at recurrence. Deep sequencing of diagnostic and relapse paired samples confirmed that relapse-associated CEBPA mutations were absent at diagnosis, suggesting recurrence was triggered by novel, independent clones. Integrated WES and deep sequencing subsequently revealed an entirely new complement of mutations at relapse, verifying the presentation of a de novo leukemic episode. The cumulative incidence of relapse in familial AML was 56% at 10 years (n = 11), and 3 patients experienced ≥3 disease episodes over a period of 17 to 20 years. Durable responses to secondary therapies were observed, with prolonged median survival after relapse (8 years) and long-term overall survival (10-year overall survival, 67%). Our data reveal that familial CEBPA-mutated AML exhibits a unique model of disease progression, associated with favorable long-term outcomes.

MAD Bayes for Tumor Heterogeneity - Feature Allocation with Exponential Family Sampling

We propose small-variance asymptotic approximations for inference on tumor heterogeneity (TH) using next-generation sequencing data. Understanding TH is an important and open research problem in biology. The lack of appropriate statistical inference is a critical gap in existing methods that the proposed approach aims to fill. We build on a hierarchical model with an exponential family likelihood and a feature allocation prior. The proposed implementation of posterior inference generalizes similar small-variance approximations proposed by Kulis and Jordan (2012) and Broderick et.al (2012b) for inference with Dirichlet process mixture and Indian buffet process prior models under normal sampling. We show that the new algorithm can successfully recover latent structures of different haplotypes and subclones and is magnitudes faster than available Markov chain Monte Carlo samplers. The latter are practically infeasible for high-dimensional genomics data. The proposed approach is scalable, easy to implement and benefits from the exibility of Bayesian nonparametric models. More importantly, it provides a useful tool for applied scientists to estimate cell subtypes in tumor samples. R code is available on http://www.ma.utexas.edu/users/yxu/.

Glioblastoma stem cells (GSCs) epigenetic plasticity and interconversion between differentiated non-GSCs and GSCs

Cancer stem cells (CSCs) or cancer initiating cells (CICs) maintain self-renewal and multilineage differentiation properties of various tumors, as well as the cellular heterogeneity consisting of several subpopulations within tumors. CSCs display the malignant phenotype, self-renewal ability, altered genomic stability, specific epigenetic signature, and most of the time can be phenotyped by cell surface markers (e.g., CD133, CD24, and CD44). Numerous studies support the concept that non-stem cancer cells (non-CSCs) are sensitive to cancer therapy while CSCs are relatively resistant to treatment. In glioblastoma stem cells (GSCs), there is clonal heterogeneity at the genetic level with distinct tumorigenic potential, and defined GSC marker expression resulting from clonal evolution which is likely to influence disease progression and response to treatment. Another level of complexity in glioblastoma multiforme (GBM) tumors is the dynamic equilibrium between GSCs and differentiated non-GSCs, and the potential for non-GSCs to revert (dedifferentiate) to GSCs due to epigenetic alteration which confers phenotypic plasticity to the tumor cell population. Moreover, exposure of the differentiated GBM cells to therapeutic doses of temozolomide (TMZ) or ionizing radiation (IR) increases the GSC pool both in vitro and in vivo. This review describes various subtypes of GBM, discusses the evolution of CSC models and epigenetic plasticity, as well as interconversion between GSCs and differentiated non-GSCs, and offers strategies to potentially eliminate GSCs.

PhyloWGS: reconstructing subclonal composition and evolution from whole-genome sequencing of tumors

Tumors often contain multiple subpopulations of cancerous cells defined by distinct somatic mutations. We describe a new method, PhyloWGS, which can be applied to whole-genome sequencing data from one or more tumor samples to reconstruct complete genotypes of these subpopulations based on variant allele frequencies (VAFs) of point mutations and population frequencies of structural variations. We introduce a principled phylogenic correction for VAFs in loci affected by copy number alterations and we show that this correction greatly improves subclonal reconstruction compared to existing methods. PhyloWGS is free, open-source software, available at https://github.com/morrislab/phylowgs.

Whole-genome and multisector exome sequencing of primary and post-treatment glioblastoma reveals patterns of tumor evolution

Glioblastoma (GBM) is a prototypical heterogeneous brain tumor refractory to conventional therapy. A small residual population of cells escapes surgery and chemoradiation, resulting in a typically fatal tumor recurrence ∼7 mo after diagnosis. Understanding the molecular architecture of this residual population is critical for the development of successful therapies. We used whole-genome sequencing and whole-exome sequencing of multiple sectors from primary and paired recurrent GBM tumors to reconstruct the genomic profile of residual, therapy resistant tumor initiating cells. We found that genetic alteration of the p53 pathway is a primary molecular event predictive of a high number of subclonal mutations in glioblastoma. The genomic road leading to recurrence is highly idiosyncratic but can be broadly classified into linear recurrences that share extensive genetic similarity with the primary tumor and can be directly traced to one of its specific sectors, and divergent recurrences that share few genetic alterations with the primary tumor and originate from cells that branched off early during tumorigenesis. Our study provides mechanistic insights into how genetic alterations in primary tumors impact the ensuing evolution of tumor cells and the emergence of subclonal heterogeneity.

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.

MixClone: a mixture model for inferring tumor subclonal populations

BACKGROUND

Tumor genomes are often highly heterogeneous, consisting of genomes from multiple subclonal types. Complete characterization of all subclonal types is a fundamental need in tumor genome analysis. With the advancement of next-generation sequencing, computational methods have recently been developed to infer tumor subclonal populations directly from cancer genome sequencing data. Most of these methods are based on sequence information from somatic point mutations, However, the accuracy of these algorithms depends crucially on the quality of the somatic mutations returned by variant calling algorithms, and usually requires a deep coverage to achieve a reasonable level of accuracy.

RESULTS

We describe a novel probabilistic mixture model, MixClone, for inferring the cellular prevalences of subclonal populations directly from whole genome sequencing of paired normal-tumor samples. MixClone integrates sequence information of somatic copy number alterations and allele frequencies within a unified probabilistic framework. We demonstrate the utility of the method using both simulated and real cancer sequencing datasets, and show that it significantly outperforms existing methods for inferring tumor subclonal populations. The MixClone package is written in Python and is publicly available at https://github.com/uci-cbcl/MixClone.

CONCLUSIONS

The probabilistic mixture model proposed here provides a new framework for subclonal analysis based on cancer genome sequencing data. By applying the method to both simulated and real cancer sequencing data, we show that integrating sequence information from both somatic copy number alterations and allele frequencies can significantly improve the accuracy of inferring tumor subclonal populations.

PRC2 is recurrently inactivated through EED or SUZ12 loss in malignant peripheral nerve sheath tumors

Malignant peripheral nerve sheath tumors (MPNSTs) represent a group of highly aggressive soft-tissue sarcomas that may occur sporadically, in association with neurofibromatosis type I (NF1 associated) or after radiotherapy. Using comprehensive genomic approaches, we identified loss-of-function somatic alterations of the Polycomb repressive complex 2 (PRC2) components (EED or SUZ12) in 92% of sporadic, 70% of NF1-associated and 90% of radiotherapy-associated MPNSTs. MPNSTs with PRC2 loss showed complete loss of trimethylation at lysine 27 of histone H3 (H3K27me3) and aberrant transcriptional activation of multiple PRC2-repressed homeobox master regulators and their regulated developmental pathways. Introduction of the lost PRC2 component in a PRC2-deficient MPNST cell line restored H3K27me3 levels and decreased cell growth. Additionally, we identified frequent somatic alterations of CDKN2A (81% of all MPNSTs) and NF1 (72% of non-NF1-associated MPNSTs), both of which significantly co-occur with PRC2 alterations. The highly recurrent and specific inactivation of PRC2 components, NF1 and CDKN2A highlights their critical and potentially cooperative roles in MPNST pathogenesis.

Quantifying tumor heterogeneity in whole-genome and whole-exome sequencing data

MOTIVATION

Most tumor samples are a heterogeneous mixture of cells, including admixture by normal (non-cancerous) cells and subpopulations of cancerous cells with different complements of somatic aberrations. This intra-tumor heterogeneity complicates the analysis of somatic aberrations in DNA sequencing data from tumor samples.

RESULTS

We describe an algorithm called THetA2 that infers the composition of a tumor sample-including not only tumor purity but also the number and content of tumor subpopulations-directly from both whole-genome (WGS) and whole-exome (WXS) high-throughput DNA sequencing data. This algorithm builds on our earlier Tumor Heterogeneity Analysis (THetA) algorithm in several important directions. These include improved ability to analyze highly rearranged genomes using a variety of data types: both WGS sequencing (including low ∼7× coverage) and WXS sequencing. We apply our improved THetA2 algorithm to WGS (including low-pass) and WXS sequence data from 18 samples from The Cancer Genome Atlas (TCGA). We find that the improved algorithm is substantially faster and identifies numerous tumor samples containing subclonal populations in the TCGA data, including in one highly rearranged sample for which other tumor purity estimation algorithms were unable to estimate tumor purity.