Single-cell exome sequencing and monoclonal evolution of a JAK2-negative myeloproliferative neoplasm

Navigating cancer network attractors for tumor-specific therapy

Cells employ highly dynamic signaling networks to drive biological decision processes. Perturbations to these signaling networks may attract cells to new malignant signaling and phenotypic states, termed cancer network attractors, that result in cancer development. As different cancer cells reach these malignant states by accumulating different molecular alterations, uncovering these mechanisms represents a grand challenge in cancer biology. Addressing this challenge will require new systems-based strategies that capture the intrinsic properties of cancer signaling networks and provide deeper understanding of the processes by which genetic lesions perturb these networks and lead to disease phenotypes. Network biology will help circumvent fundamental obstacles in cancer treatment, such as drug resistance and metastasis, empowering personalized and tumor-specific cancer therapies.

Massively parallel sequencing for chromosomal abnormality testing in trophectoderm cells of human blastocysts

Preimplantation genetic diagnosis and screening are widely accepted for chromosomal abnormality identification to avoid transferring embryos with genetic defects. Massively parallel sequencing (MPS) is a rapidly developing approach for genome analysis with increasing application in clinical practice. The purpose of this study was to use MPS for identification of aneuploidies and unbalanced chromosomal rearrangements after blastocyst biopsy. Trophectoderm (TE) samples of 38 blastocysts from 16 in vitro fertilization cycles were subjected to analysis. Low-coverage whole genome sequencing was performed using the Illumina HiSeq2000 platform with a novel algorithm purposely created for chromosomal analysis. The efficiency of this MPS approach was estimated by comparing results obtained by an Affymetrix single-nucleotide polymorphism (SNP) array. Whole genome amplification (WGA) products of TE cells were detected by MPS, with an average of 0.07× depth and 5.5% coverage of the human genome. Twenty-six embryos (68.4%) were detected as euploid, while six embryos (15.8%) contained uniform aneuploidies. Four of these (10.5%) were with solely unbalanced chromosomal rearrangements, whereas the remaining two embryos (5.3%) showed both aneuploidies and unbalanced rearrangements. Almost all these results were confirmed by the SNP array, with the exception of one sample, where different sizes of unbalanced rearrangements were detected, possibly due to chromosomal GC bias in array analysis. Our study demonstrated MPS could be applied to accurately detect embryonic chromosomal abnormality with a flexible and cost-effective strategy and higher potential accuracy.

Clonal architecture of chronic myelomonocytic leukemias

Early clonal dominance may distinguish chronic myelomonocytic leukemia from other chronic myeloid neoplasms with similar gene mutations. Early dominance of TET2-mutated cells in the hematopoietic tissue promotes myeloid differentiation skewing toward the granulomonocytic line.

Single-stranded DNA library preparation for the sequencing of ancient or damaged DNA

This protocol describes a method for converting short single-stranded and double-stranded DNA into libraries compatible with high-throughput sequencing using Illumina technology. This method has primarily been developed to improve sequence retrieval from ancient DNA, but it is also applicable to the sequencing of short or degraded DNA from other sources, and it can also be used for sequencing oligonucleotides. Single-stranded library preparation is performed by ligating a biotinylated adapter oligonucleotide to the 3' ends of heat-denatured DNA. The resulting strands are then immobilized on streptavidin-coated beads and copied with a polymerase. A second adapter is attached by blunt-end ligation, and library preparation is completed by PCR amplification. We estimate that intact DNA strands are recovered in the library with ∼50% efficiency. Libraries can be generated from up to 12 DNA or oligonucleotide samples in parallel within 2 d.

Complex tumor genomes inferred from single circulating tumor cells by array-CGH and next-generation sequencing

Circulating tumor cells (CTC) released into blood from primary cancers and metastases reflect the current status of tumor genotypes, which are prone to changes. Here, we conducted the first comprehensive genomic profiling of CTCs using array-comparative genomic hybridization (CGH) and next-generation sequencing. We used the U.S. Food and Drug Administration-cleared CellSearch system, which detected CTCs in 21 of 37 patients (range, 1-202/7.5 mL sample) with stage IV colorectal carcinoma. In total, we were able to isolate 37 intact CTCs from six patients and identified in those multiple colorectal cancer-associated copy number changes, many of which were also present in the respective primary tumor. We then used massive parallel sequencing of a panel of 68 colorectal cancer-associated genes to compare the mutation spectrum in the primary tumors, metastases, and the corresponding CTCs from two of these patients. Mutations in known driver genes [e.g., adenomatous polyposis coli (APC), KRAS, or PIK3CA] found in the primary tumor and metastasis were also detected in corresponding CTCs. However, we also observed mutations exclusively in CTCs. To address whether these mutations were derived from a small subclone in the primary tumor or represented new variants of metastatic cells, we conducted additional deep sequencing of the primary tumor and metastasis and applied a customized statistical algorithm for analysis. We found that most mutations initially found only in CTCs were also present at subclonal level in the primary tumors and metastases from the same patient. This study paves the way to use CTCs as a liquid biopsy in patients with cancer, providing more effective options to monitor tumor genomes that are prone to change during progression, treatment, and relapse.

Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples

Detection of somatic point substitutions is a key step in characterizing the cancer genome. Mutations in cancer are rare (0.1–100/Mb) and often occur only in a subset of the sequenced cells, either due to contamination by normal cells or due to tumor heterogeneity. Consequently, mutation calling methods need to be both specific, avoiding false positives, and sensitive to detect clonal and sub-clonal mutations. The decreased sensitivity of existing methods for low allelic fraction mutations highlights the pressing need for improved and systematically evaluated mutation detection methods. Here we present MuTect, a method based on a Bayesian classifier designed to detect somatic mutations with very low allele-fractions, requiring only a few supporting reads, followed by a set of carefully tuned filters that ensure high specificity. We also describe novel benchmarking approaches, which use real sequencing data to evaluate the sensitivity and specificity as a function of sequencing depth, base quality and allelic fraction. Compared with other methods, MuTect has higher sensitivity with similar specificity, especially for mutations with allelic fractions as low as 0.1 and below, making MuTect particularly useful for studying cancer subclones and their evolution in standard exome and genome sequencing data.

Advances for studying clonal evolution in cancer

The "clonal evolution" model of cancer emerged and "evolved" amid ongoing advances in technology, especially in recent years during which next generation sequencing instruments have provided ever higher resolution pictures of the genetic changes in cancer cells and heterogeneity in tumors. It has become increasingly clear that clonal evolution is not a single sequential process, but instead frequently involves simultaneous evolution of multiple subclones that co-exist because they are of similar fitness or are spatially separated. Co-evolution of subclones also occurs when they complement each other's survival advantages. Recent studies have also shown that clonal evolution is highly heterogeneous: different individual tumors of the same type may undergo very different paths of clonal evolution. New methodological advancements, including deep digital sequencing of a mixed tumor population, single cell sequencing, and the development of more sophisticated computational tools, will continue to shape and reshape the models of clonal evolution. In turn, these will provide both an improved framework for the understanding of cancer progression and a guide for treatment strategies aimed at the elimination of all, rather than just some, of the cancer cells within a patient.

Heterogeneity of neoplastic stem cells: theoretical, functional, and clinical implications

Accumulating evidence suggests that human cancers develop through a step-wise, but nonlinear process of cellular diversification and evolution. Recent mutational analyses indicate that this process is more complex and diverse than anticipated before whole-genome sequencing methods were readily available. Examples are also emerging now of genetically abnormal clones of cells that have acquired mutations with known oncogenic potential but, nevertheless, may show no manifestations of malignant change for many years. To accommodate these diverse realities, we suggest the term neoplastic refer to clones of cells that have any type of somatic aberrancy associated with an increased propensity to become malignant, and the derivative term neoplastic stem cell be adopted to identify the cells responsible for the long-term maintenance of such clones. Neoplastic clones would thus include those that never evolve further, as well as those that eventually give rise to fully malignant populations, and all stages in between. The term cancer stem cells would then be more appropriately restricted to cells generating subclones that have established malignant properties. More precise molecular understanding of the different stem cell states thus distinguished should contribute to the development of more effective prognostic and therapeutic tools for cancer diagnosis and treatment.

A single cell level based method for copy number variation analysis by low coverage massively parallel sequencing

Copy number variations (CNVs), a common genomic mutation associated with various diseases, are important in research and clinical applications. Whole genome amplification (WGA) and massively parallel sequencing have been applied to single cell CNVs analysis, which provides new insight for the fields of biology and medicine. However, the WGA-induced bias significantly limits sensitivity and specificity for CNVs detection. Addressing these limitations, we developed a practical bioinformatic methodology for CNVs detection at the single cell level using low coverage massively parallel sequencing. This method consists of GC correction for WGA-induced bias removal, binary segmentation algorithm for locating CNVs breakpoints, and dynamic threshold determination for final signals filtering. Afterwards, we evaluated our method with seven test samples using low coverage sequencing (4∼9.5%). Four single-cell samples from peripheral blood, whose karyotypes were confirmed by whole genome sequencing analysis, were acquired. Three other test samples derived from blastocysts whose karyotypes were confirmed by SNP-array analysis were also recruited. The detection results for CNVs of larger than 1 Mb were highly consistent with confirmed results reaching 99.63% sensitivity and 97.71% specificity at base-pair level. Our study demonstrates the potential to overcome WGA-bias and to detect CNVs (>1 Mb) at the single cell level through low coverage massively parallel sequencing. It highlights the potential for CNVs research on single cells or limited DNA samples and may prove as a promising tool for research and clinical applications, such as pre-implantation genetic diagnosis/screening, fetal nucleated red blood cells research and cancer heterogeneity analysis.

Clonal evolution of preleukemic hematopoietic stem cells precedes human acute myeloid leukemia

Given that most bone marrow cells are short-lived, the accumulation of multiple leukemogenic mutations in a single clonal lineage has been difficult to explain. We propose that serial acquisition of mutations occurs in self-renewing hematopoietic stem cells (HSCs). We investigated this model through genomic analysis of HSCs from six patients with de novo acute myeloid leukemia (AML). Using exome sequencing, we identified mutations present in individual AML patients harboring the FLT3-ITD (internal tandem duplication) mutation. We then screened the residual HSCs and detected some of these mutations including mutations in the NPM1, TET2, and SMC1A genes. Finally, through single-cell analysis, we determined that a clonal progression of multiple mutations occurred in the HSCs of some AML patients. These preleukemic HSCs suggest the clonal evolution of AML genomes from founder mutations, revealing a potential mechanism contributing to relapse. Such preleukemic HSCs may constitute a cellular reservoir that should be targeted therapeutically for more durable remissions.

Personal genomes, quantitative dynamic omics and personalized medicine

The rapid technological developments following the Human Genome Project have made possible the availability of personalized genomes. As the focus now shifts from characterizing genomes to making personalized disease associations, in combination with the availability of other omics technologies, the next big push will be not only to obtain a personalized genome, but to quantitatively follow other omics. This will include transcriptomes, proteomes, metabolomes, antibodyomes, and new emerging technologies, enabling the profiling of thousands of molecular components in individuals. Furthermore, omics profiling performed longitudinally can probe the temporal patterns associated with both molecular changes and associated physiological health and disease states. Such data necessitates the development of computational methodology to not only handle and descriptively assess such data, but also construct quantitative biological models. Here we describe the availability of personal genomes and developing omics technologies that can be brought together for personalized implementations and how these novel integrated approaches may effectively provide a precise personalized medicine that focuses on not only characterization and treatment but ultimately the prevention of disease.

Single-neuron sequencing analysis of L1 retrotransposition and somatic mutation in the human brain

A major unanswered question in neuroscience is whether there exists genomic variability between individual neurons of the brain, contributing to functional diversity or to an unexplained burden of neurological disease. To address this question, we developed a method to amplify genomes of single neurons from human brains. Because recent reports suggest frequent LINE-1 (L1) retrotransposition in human brains, we performed genome-wide L1 insertion profiling of 300 single neurons from cerebral cortex and caudate nucleus of three normal individuals, recovering >80% of germline insertions from single neurons. While we find somatic L1 insertions, we estimate <0.6 unique somatic insertions per neuron, and most neurons lack detectable somatic insertions, suggesting that L1 is not a major generator of neuronal diversity in cortex and caudate. We then genotyped single cortical cells to characterize the mosaicism of a somatic AKT3 mutation identified in a child with hemimegalencephaly. Single-neuron sequencing allows systematic assessment of genomic diversity in the human brain.

An evolutionary perspective on anti-tumor immunity

The challenges associated with demonstrating a durable response using molecular-targeted therapies in cancer has sparked a renewed interest in viewing cancer from an evolutionary perspective. Evolutionary processes have three common traits: heterogeneity, dynamics, and a selective fitness landscape. Mutagens randomly alter the genome of host cells creating a population of cells that contain different somatic mutations. This genomic rearrangement perturbs cellular homeostasis through changing how cells interact with their tissue microenvironment. To counterbalance the ability of mutated cells to outcompete for limited resources, control structures are encoded within the cell and within the organ system, such as innate and adaptive immunity, to restore cellular homeostasis. These control structures shape the selective fitness landscape and determine whether a cell that harbors particular somatic mutations is retained or eliminated from a cell population. While next-generation sequencing has revealed the complexity and heterogeneity of oncogenic transformation, understanding the dynamics of oncogenesis and how cancer cells alter the selective fitness landscape remain unclear. In this technology review, we will summarize how recent advances in technology have impacted our understanding of these three attributes of cancer as an evolutionary process. In particular, we will focus on how advances in genome sequencing have enabled quantifying cellular heterogeneity, advances in computational power have enabled explicit testing of postulated intra- and intercellular control structures against the available data using simulation, and advances in proteomics have enabled identifying novel mechanisms of cellular cross-talk that cancer cells use to alter the fitness landscape.

Stochastic profiling of transcriptional regulatory heterogeneities in tissues, tumors and cultured cells

Single-cell variations in gene and protein expression are important during development and disease. Such cell-to-cell heterogeneities can be directly inspected one cell at a time, but global methods are usually not sensitive enough to work with the starting material of a single cell. Here we provide a detailed protocol for stochastic profiling, a method that infers single-cell regulatory heterogeneities by repeatedly sampling small collections of cells selected at random. Repeated stochastic sampling is performed by laser-capture microdissection or limiting dilution, followed by careful exponential cDNA amplification, hybridization to microarrays and statistical analysis. Stochastic profiling surveys the transcriptome for programs that are heterogeneously regulated among cellular subpopulations in their native tissue context. The protocol is readily optimized for specific biological applications and takes about 1 week to complete.

Genome-wide copy number profiling of single cells in S-phase reveals DNA-replication domains

Single-cell genomics is revolutionizing basic genome research and clinical genetic diagnosis. However, none of the current research or clinical methods for single-cell analysis distinguishes between the analysis of a cell in G1-, S- or G2/M-phase of the cell cycle. Here, we demonstrate by means of array comparative genomic hybridization that charting the DNA copy number landscape of a cell in S-phase requires conceptually different approaches to that of a cell in G1- or G2/M-phase. Remarkably, despite single-cell whole-genome amplification artifacts, the log2 intensity ratios of single S-phase cells oscillate according to early and late replication domains, which in turn leads to the detection of significantly more DNA imbalances when compared with a cell in G1- or G2/M-phase. Although these DNA imbalances may, on the one hand, be falsely interpreted as genuine structural aberrations in the S-phase cell’s copy number profile and hence lead to misdiagnosis, on the other hand, the ability to detect replication domains genome wide in one cell has important applications in DNA-replication research. Genome-wide cell-type-specific early and late replicating domains have been identified by analyses of DNA from populations of cells, but cell-to-cell differences in DNA replication may be important in genome stability, disease aetiology and various other cellular processes.

Genome-wide detection of single-nucleotide and copy-number variations of a single human cell

Kindred cells can have different genomes because of dynamic changes in DNA. Single-cell sequencing is needed to characterize these genomic differences but has been hindered by whole-genome amplification bias, resulting in low genome coverage. Here, we report on a new amplification method-multiple annealing and looping-based amplification cycles (MALBAC)-that offers high uniformity across the genome. Sequencing MALBAC-amplified DNA achieves 93% genome coverage ≥1x for a single human cell at 25x mean sequencing depth. We detected digitized copy-number variations (CNVs) of a single cancer cell. By sequencing three kindred cells, we were able to identify individual single-nucleotide variations (SNVs), with no false positives detected. We directly measured the genome-wide mutation rate of a cancer cell line and found that purine-pyrimidine exchanges occurred unusually frequently among the newly acquired SNVs.

High-throughput sequencing for biology and medicine

Advances in genome sequencing have progressed at a rapid pace, with increased throughput accompanied by plunging costs. But these advances go far beyond faster and cheaper. High-throughput sequencing technologies are now routinely being applied to a wide range of important topics in biology and medicine, often allowing researchers to address important biological questions that were not possible before. In this review, we discuss these innovative new approaches-including ever finer analyses of transcriptome dynamics, genome structure and genomic variation-and provide an overview of the new insights into complex biological systems catalyzed by these technologies. We also assess the impact of genotyping, genome sequencing and personal omics profiling on medical applications, including diagnosis and disease monitoring. Finally, we review recent developments in single-cell sequencing, and conclude with a discussion of possible future advances and obstacles for sequencing in biology and health.

Radiation survivors: understanding and exploiting the phenotype following fractionated radiation therapy

Radiation oncology modalities such as intensity-modulated and image-guided radiation therapy can reduce the high dose to normal tissue and deliver a heterogeneous dose to tumors, focusing on areas deemed at highest risk for tumor persistence. Clinical radiation oncology produces daily doses ranging from 1 to 20 Gy, with tissues being exposed to 30 or more daily fractions. Hypothesizing the cells that survive fractionated radiation therapy have a substantially different phenotype than the untreated cells, which might be exploitable for targeting with molecular therapeutics or immunotherapy, three prostate cancer cell lines (PC3, DU145, and LNCaP) and normal endothelial cells were studied to understand the biology of differential effects of multifraction (MF) radiation of 0.5, 1, and/or 2 Gy fraction to 10 Gy total dose, and a single dose of 5 and 10 Gy. The resulting changes in mRNA, miRNA, and phosphoproteome were analyzed. Significant differences were observed in the MF radiation exposures including those from the 0.5 Gy MF that produces little cell killing. As expected, p53 function played a major role in response. Pathways modified by MF include immune response, DNA damage, cell-cycle arrest, TGF-β, survival, and apoptotic signal transduction. The radiation-induced stress response will set forth a unique platform for exploiting the effects of radiation therapy as "focused biology" for cancer treatment in conjunction with molecular targeted or immunologically directed therapy. Given that more normal tissue is treated, albeit to lower doses with these newer techniques, the response of the normal tissue may also influence long-term treatment outcome.

Genome-scale technology driven advances to research into normal and malignant haematopoiesis

Haematopoiesis or blood development has long served as a model system for adult stem cell biology. Moreover, when combined, the various cancers of the blood represent one of the commonest human malignancies. Large numbers of researchers have therefore dedicated their scientific careers to studying haematopoiesis for more than a century. Throughout this period, many new technologies have first been applied towards the study of blood cells, and the research fields of normal and malignant haematopoiesis have also been some of the earliest adopters of genome-scale technologies. This has resulted in significant new insights with implications ranging from basic biological mechanisms to patient diagnosis and prognosis and also produced lessons likely to be relevant for many other areas of biomedical research. This paper discusses the current state of play for a range of genome-scale applications within haemopoiesis research, including gene expression profiling, ChIP-sequencing, genomewide association analysis, and cancer genome sequencing. A concluding outlook section explores likely future areas of progress as well as potential technological and educational bottlenecks.

Circulating tumor cells as prognostic markers in neuroendocrine tumors

PURPOSE

To determine the prognostic significance of circulating tumor cells (CTCs) in patients with neuroendocrine cancer.

PATIENTS AND METHODS

In this single-center prospective study, 176 patients with measurable metastatic neuroendocrine tumors (NETs) were recruited. CTCs were measured using a semiautomated technique based on immunomagnetic separation of epithelial cell adhesion molecule-expressing cells.

RESULTS

Overall, 49% patients had ≥ one CTC, 42% had ≥ two CTCs, and 30% had ≥ five CTCs in 7.5 mL blood. Presence of CTCs was associated with increased burden, increased tumor grade, and elevated serum chromogranin A (CgA). Using a 90-patient training set and 85-patient validation set, we defined a cutoff of < one or ≥ one as the optimal prognostic threshold with respect to progression-free survival (PFS). Applying this threshold, the presence of ≥ one CTC was associated with worse PFS and overall survival (OS; hazard ratios [HRs], 6.6 and 8.0, respectively; both P < .001). In multivariate analysis, CTCs remained significant when other prognostic markers, grade, tumor burden, and CgA were included. Within grades, presence of CTCs was able to define a poor prognostic subgroup. For grade 1, HRs were 5.0 for PFS (P = .017) and 7.2 for OS (P = .023); for grade 2, HRs were 3.5 for PFS (P = .018) and 5.2 for OS (P = .036).

CONCLUSION

CTCs are a promising prognostic marker for patients with NETs and should be assessed in the context of clinical trials with defined tumor subtypes and therapy.

Preimplantation genetic diagnosis: recent triumphs and remaining challenges

Over the last 20 years, preimplantation genetic diagnosis (PGD) has changed from being an experimental procedure to one that is carried out in specialized diagnostic centers worldwide. Genetic awareness and the rapid identification of germline mutations or chromosomal abnormalities enable individuals to know their risk of transmitting a genetic disease before they have children. This has created a demand for PGD from couples who wish to avoid terminations of affected pregnancies. Although PGD is expensive because it requires couples to go through IVF, there is a trend for diagnosis to move towards automation, which will reduce cost and the need for specialized expertise. This will allow diagnosis to be carried out in routine molecular diagnostic laboratories.

Promise of personalized omics to precision medicine

The rapid development of high-throughput technologies and computational frameworks enables the examination of biological systems in unprecedented detail. The ability to study biological phenomena at omics levels in turn is expected to lead to significant advances in personalized and precision medicine. Patients can be treated according to their own molecular characteristics. Individual omes as well as the integrated profiles of multiple omes, such as the genome, the epigenome, the transcriptome, the proteome, the metabolome, the antibodyome, and other omics information are expected to be valuable for health monitoring, preventative measures, and precision medicine. Moreover, omics technologies have the potential to transform medicine from traditional symptom-oriented diagnosis and treatment of diseases toward disease prevention and early diagnostics. We discuss here the advances and challenges in systems biology-powered personalized medicine at its current stage, as well as a prospective view of future personalized health care at the end of this review.

Next-generation sequencing in the clinic: promises and challenges

The advent of next generation sequencing (NGS) technologies has revolutionized the field of genomics, enabling fast and cost-effective generation of genome-scale sequence data with exquisite resolution and accuracy. Over the past years, rapid technological advances led by academic institutions and companies have continued to broaden NGS applications from research to the clinic. A recent crop of discoveries have highlighted the medical impact of NGS technologies on Mendelian and complex diseases, particularly cancer. However, the ever-increasing pace of NGS adoption presents enormous challenges in terms of data processing, storage, management and interpretation as well as sequencing quality control, which hinder the translation from sequence data into clinical practice. In this review, we first summarize the technical characteristics and performance of current NGS platforms. We further highlight advances in the applications of NGS technologies towards the development of clinical diagnostics and therapeutics. Common issues in NGS workflows are also discussed to guide the selection of NGS platforms and pipelines for specific research purposes.

Translating genomics to the clinic: implications of cancer heterogeneity

BACKGROUND

Sequencing of cancer genomes has become a pivotal method for uncovering and understanding the deregulated cellular processes driving tumor initiation and progression. Whole-genome sequencing is evolving toward becoming less costly and more feasible on a large scale; consequently, thousands of tumors are being analyzed with these technologies. Interpreting these data in the context of tumor complexity poses a challenge for cancer genomics.

CONTENT

The sequencing of large numbers of tumors has revealed novel insights into oncogenic mechanisms. In particular, we highlight the remarkable insight into the pathogenesis of breast cancers that has been gained through comprehensive and integrated sequencing analysis. The analysis and interpretation of sequencing data, however, must be considered in the context of heterogeneity within and among tumor samples. Only by adequately accounting for the underlying complexity of cancer genomes will the potential of genome sequencing be understood and subsequently translated into improved management of patients.

SUMMARY

The paradigm of personalized medicine holds promise if patient tumors are thoroughly studied as unique and heterogeneous entities and clinical decisions are made accordingly. Associated challenges will be ameliorated by continued collaborative efforts among research centers that coordinate the sharing of mutation, intervention, and outcomes data to assist in the interpretation of genomic data and to support clinical decision-making.

High-throughput sequencing and rare genetic diseases

High-throughput sequencing has drastically changed the research of genes responsible for genetic disorders and is now gradually introduced as an additional genetic diagnostic testing in clinical practice. The current debates on the emerging technical, medical and ethical issues as well as the potential optimum use of the available technology are discussed.

Next-generation sequencing in breast cancer: first take home messages

PURPOSE OF REVIEW

We are currently on the threshold of a revolution in breast cancer research, thanks to the emergence of novel technologies based on next-generation sequencing (NGS). In this review, we will describe the different sequencing technologies and platforms, and summarize the main findings from the latest sequencing articles in breast cancer.

RECENT FINDINGS

Firstly, the sequencing of a few hundreds of breast tumors has revealed new cancer genes. Although these were not frequently mutated, mutated genes from different patients could be grouped into the deregulation of similar pathways. Secondly, NGS allowed further exploration of intratumor heterogeneity and revealed that although subclonal mutations were present in all tumors, there was always a dominant clone, which comprised at least 50% of the tumor cells. Finally, tumor-specific DNA rearrangements could be detected in the patient's plasma, suggesting that NGS could be used to personalize the monitoring of the disease.

SUMMARY

The application of NGS to breast cancer has been associated with tremendous advances and promises for increasing the understanding of the disease. However, there still remain many unanswered questions, such as the role of structural changes of tumor genomes in cancer progression and treatment response/resistance.

Evolution of the cancer genome

The advent of massively parallel sequencing technologies has allowed the characterization of cancer genomes at an unprecedented resolution. Investigation of the mutational landscape of tumours is providing new insights into cancer genome evolution, laying bare the interplay of somatic mutation, adaptation of clones to their environment and natural selection. These studies have demonstrated the extent of the heterogeneity of cancer genomes, have allowed inferences to be made about the forces that act on nascent cancer clones as they evolve and have shown insight into the mutational processes that generate genetic variation. Here we review our emerging understanding of the dynamic evolution of the cancer genome and of the implications for basic cancer biology and the development of antitumour therapy.

Cellular heterogeneity and molecular evolution in cancer

Intratumor heterogeneity represents a major obstacle to effective cancer treatment and personalized medicine. However, investigators are now elucidating intratumor heterogeneity at the single-cell level due to improvements in technologies. Better understanding of the composition of tumors, and monitoring changes in cell populations during disease progression and treatment, will improve cancer diagnosis and therapeutic design. Measurements of intratumor heterogeneity may also be used as biomarkers to predict the risk of progression and therapeutic resistance. We summarize important considerations related to intratumor heterogeneity during tumor evolution. We also discuss experimental approaches that are commonly used to infer intratumor heterogeneity and describe how these methodologies can be translated into clinical practice.

Developmental pathways in colon cancer: crosstalk between WNT, BMP, Hedgehog and Notch

A hallmark of cancer is reactivation/alteration of pathways that control cellular differentiation during developmental processes. Evidence indicates that WNT, Notch, BMP and Hedgehog pathways have a role in normal epithelial cell differentiation, and that alterations in these pathways accompany establishment of the tumorigenic state. Interestingly, there is recent evidence that these pathways are intertwined at the molecular level, and these nodes of intersection may provide opportunities for effective targeted therapies. This review will highlight the role of the WNT, Notch, BMP and Hedgehog pathways in colon cancer.

Genome-wide single-cell analysis of recombination activity and de novo mutation rates in human sperm

Meiotic recombination and de novo mutation are the two main contributions toward gamete genome diversity, and many questions remain about how an individual human's genome is edited by these two processes. Here, we describe a high-throughput method for single-cell whole-genome analysis that was used to measure the genomic diversity in one individual's gamete genomes. A microfluidic system was used for highly parallel sample processing and to minimize nonspecific amplification. High-density genotyping results from 91 single cells were used to create a personal recombination map, which was consistent with population-wide data at low resolution but revealed significant differences from pedigree data at higher resolution. We used the data to test for meiotic drive and found evidence for gene conversion. High-throughput sequencing on 31 single cells was used to measure the frequency of large-scale genome instability, and deeper sequencing of eight single cells revealed de novo mutation rates with distinct characteristics.

Intratumor heterogeneity: seeing the wood for the trees

Most advanced solid tumors remain incurable, with resistance to chemotherapeutics and targeted therapies a common cause of poor clinical outcome. Intratumor heterogeneity may contribute to this failure by initiating phenotypic diversity enabling drug resistance to emerge and by introducing tumor sampling bias. Envisaging tumor growth as a Darwinian tree with the trunk representing ubiquitous mutations and the branches representing heterogeneous mutations may help in drug discovery and the development of predictive biomarkers of drug response.

Challenges in the enumeration and phenotyping of CTC

PURPOSE

Presence of circulating tumor cells (CTC) in metastatic carcinoma is associated with poor survival. Phenotyping and genotyping of CTC may permit "real-time" treatment decisions, provided CTCs are available for examination. Here, we investigate what is needed to detect CTC in all patients.

EXPERIMENTAL DESIGN

CTCs enumerated in 7.5 mL of blood together with survival from 836 patients with metastatic breast, colorectal, and prostate cancer were used to predict the CTC concentration in the 42% of these patients in whom no CTCs were found and to establish the relation of concentration of CTCs with survival. Influence of different CTC definitions were investigated by automated cell recognition and a flow cytometric assay without an enrichment or permeabilization step.

RESULTS

A log-logistic regression of the log of CTC yielded a good fit to the CTC frequency distribution. Extrapolation of the blood volume to 5 L predicted that 99% of patients had at least one CTC before therapy initiation. Survival of patients with EpCAM+, cytokeratin+, CD45- nucleated CTCs is reduced by 6.6 months for each 10-fold CTC increase. Using flow cytometry, the potential three-fold recovery improvement is not sufficient to detect CTC in all patients in 7.5 mL of blood.

CONCLUSIONS

EpCAM+, cytokeratin+, CD45- nucleated CTCs are present in all patients with metastatic breast, prostate, and colorectal cancer and their frequency is proportional to survival. To serve as a liquid biopsy for the majority of patients, a substantial improvement of CTC yield is needed, which can only be achieved by a dramatic increase in sample volume.

Measurement and modeling of signaling at the single-cell level

It has long been recognized that a deeper understanding of cell function, with respect to execution of phenotypic behaviors and their regulation by the extracellular environment, is likely to be achieved by analyzing the underlying molecular processes for individual cells selected from across a population, rather than averages of many cells comprising that population. In recent years, experimental and computational methods for undertaking these analyses have advanced rapidly. In this review, we provide a perspective on both measurement and modeling facets of biochemistry at a single-cell level. Our central focus is on receptor-mediated signaling networks that regulate cell phenotypic functions.

Cancer in light of experimental evolution

Cancer initiation, progression, and the emergence of therapeutic resistance are evolutionary phenomena of clonal somatic cell populations. Studies in microbial experimental evolution and the theoretical work inspired by such studies are yielding deep insights into the evolutionary dynamics of clonal populations, yet there has been little explicit consideration of the relevance of this rapidly growing field to cancer biology. Here, we examine how the understanding of mutation, selection, and spatial structure in clonal populations that is emerging from experimental evolution may be applicable to cancer. Along the way, we discuss some significant ways in which cancer differs from the model systems used in experimental evolution. Despite these differences, we argue that enhanced prediction and control of cancer may be possible using ideas developed in the context of experimental evolution, and we point out some prospects for future research at the interface between these traditionally separate areas.

A simple and novel method for RNA-seq library preparation of single cell cDNA analysis by hyperactive Tn5 transposase

BACKGROUND

Deep sequencing of single cell-derived cDNAs offers novel insights into oncogenesis and embryogenesis. However, traditional library preparation for RNA-seq analysis requires multiple steps with consequent sample loss and stochastic variation at each step significantly affecting output. Thus, a simpler and better protocol is desirable. The recently developed hyperactive Tn5-mediated library preparation, which brings high quality libraries, is likely one of the solutions.

RESULTS AND CONCLUSIONS

Here, we tested the applicability of hyperactive Tn5-mediated library preparation to deep sequencing of single cell cDNA, optimized the protocol, and compared it with the conventional method based on sonication. This new technique does not require any expensive or special equipment, which secures wider availability. A library was constructed from only 100 ng of cDNA, which enables the saving of precious specimens. Only a few steps of robust enzymatic reaction resulted in saved time, enabling more specimens to be prepared at once, and with a more reproducible size distribution among the different specimens. The obtained RNA-seq results were comparable to the conventional method. Thus, this Tn5-mediated preparation is applicable for anyone who aims to carry out deep sequencing for single cell cDNAs.

Single-cell sequencing analysis characterizes common and cell-lineage-specific mutations in a muscle-invasive bladder cancer

BACKGROUND

Cancers arise through an evolutionary process in which cell populations are subjected to selection; however, to date, the process of bladder cancer, which is one of the most common cancers in the world, remains unknown at a single-cell level.

RESULTS

We carried out single-cell exome sequencing of 66 individual tumor cells from a muscle-invasive bladder transitional cell carcinoma (TCC). Analyses of the somatic mutant allele frequency spectrum and clonal structure revealed that the tumor cells were derived from a single ancestral cell, but that subsequent evolution occurred, leading to two distinct tumor cell subpopulations. By analyzing recurrently mutant genes in an additional cohort of 99 TCC tumors, we identified genes that might play roles in the maintenance of the ancestral clone and in the muscle-invasive capability of subclones of this bladder cancer, respectively.

CONCLUSIONS

This work provides a new approach of investigating the genetic details of bladder tumoral changes at the single-cell level and a new method for assessing bladder cancer evolution at a cell-population level.

Cancer heterogeneity: origins and implications for genetic association studies

Genetic association studies have become standard approaches to characterize the genetic and epigenetic variability associated with cancer development, including predispositions and mutations. However, the bewildering genetic and phenotypic heterogeneity inherent in cancer both magnifies the conceptual and methodological problems associated with these approaches and renders difficult the translation of available genetic information into a knowledge that is both biologically sound and clinically relevant. Here, we elaborate on the underlying causes of this complexity, illustrate why it represents a challenge for genetic association studies, and briefly discuss how it can be reconciled with the ultimate goals of identifying targetable disease pathways and successfully treating individual patients.

Systems biology: personalized medicine for the future?

Systems biology is actively transforming the field of modern health care from symptom-based disease diagnosis and treatment to precision medicine in which patients are treated based on their individual characteristics. Development of high-throughput technologies such as high-throughout sequencing and mass spectrometry has enabled scientists and clinicians to examine genomes, transcriptomes, proteomes, metabolomes, and other omics information in unprecedented detail. The combined 'omics' information leads to a global profiling of health and disease, and provides new approaches for personalized health monitoring and preventative medicine. In this article, we review the efforts of systems biology in personalized medicine in the past 2 years, and discuss in detail achievements and concerns, as well as highlights and hurdles for future personalized health care.

Cell lineage analysis of acute leukemia relapse uncovers the role of replication-rate heterogeneity and microsatellite instability

Human cancers display substantial intratumoral genetic heterogeneity, which facilitates tumor survival under changing microenvironmental conditions. Tumor substructure and its effect on disease progression and relapse are incompletely understood. In the present study, a high-throughput method that uses neutral somatic mutations accumulated in individual cells to reconstruct cell lineage trees was applied to hundreds of cells of human acute leukemia harvested from multiple patients at diagnosis and at relapse. The reconstructed cell lineage trees of patients with acute myeloid leukemia showed that leukemia cells at relapse were shallow (divide rarely) compared with cells at diagnosis and were closely related to their stem cell subpopulation, implying that in these instances relapse might have originated from rarely dividing stem cells. In contrast, among patients with acute lymphoid leukemia, no differences in cell depth were observed between diagnosis and relapse. In one case of chronic myeloid leukemia, at blast crisis, most of the cells at relapse were mismatch-repair deficient. In almost all leukemia cases, > 1 lineage was observed at relapse, indicating that diverse mechanisms can promote relapse in the same patient. In conclusion, diverse relapse mechanisms can be observed by systematic reconstruction of cell lineage trees of patients with leukemia.

From single cells to deep phenotypes in cancer

In recent years, major advances in single-cell measurement systems have included the introduction of high-throughput versions of traditional flow cytometry that are now capable of measuring intracellular network activity, the emergence of isotope labels that can enable the tracking of a greater variety of cell markers and the development of super-resolution microscopy techniques that allow measurement of RNA expression in single living cells. These technologies will facilitate our capacity to catalog and bring order to the inherent diversity present in cancer cell populations. Alongside these developments, new computational approaches that mine deep data sets are facilitating the visualization of the shape of the data and enabling the extraction of meaningful outputs. These applications have the potential to reveal new insights into cancer biology at the intersections of stem cell function, tumor-initiating cells and multilineage tumor development. In the clinic, they may also prove important not only in the development of new diagnostic modalities but also in understanding how the emergence of tumor cell clones harboring different sets of mutations predispose patients to relapse or disease progression.