Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing

Leveraging cancer genome information in hematologic malignancies

The use of candidate gene and genome-wide discovery studies in the last several years has led to an expansion of our knowledge of the spectrum of recurrent, somatic disease alleles, which contribute to the pathogenesis of hematologic malignancies. Notably, these studies have also begun to fundamentally change our ability to develop informative prognostic schema that inform outcome and therapeutic response, yielding substantive insights into mechanisms of hematopoietic transformation in different tissue compartments. Although these studies have already had important biologic and translational impact, significant challenges remain in systematically applying these findings to clinical decision making and in implementing new technologies for genetic analysis into clinical practice to inform real-time decision making. Here, we review recent major genetic advances in myeloid and lymphoid malignancies, the impact of these findings on prognostic models, our understanding of disease initiation and evolution, and the implication of genomic discoveries on clinical decision making. Finally, we discuss general concepts in genetic modeling and the current state-of-the-art technology used in genetic investigation.

Revising a personal genome by comparing and combining data from two different sequencing platforms

For the robust practice of genomic medicine, sequencing results must be compatible, regardless of the sequencing technologies and algorithms used. Presently, genome sequencing is still an imprecise science and is complicated by differences in the chemistry, coverage, alignment, and variant-calling algorithms. We identified ~3.33 million single nucleotide variants (SNVs) and ~3.62 million SNVs in the SJK genome using SOLiD and Illumina data, respectively. Approximately 3 million SNVs were concordant between the two platforms while 68,532 SNVs were discordant; 219,616 SNVs were SOLiD-specific and 516,080 SNVs were Illumina-specific (i.e., platform-specific). Concordant, discordant, and platform-specific SNVs were further analyzed and characterized. Overall, a large portion of heterozygous SNVs that were discordant with genotyping calls of single nucleotide polymorphism chips were highly confident. Approximately 70% of the platform-specific SNVs were located in regions containing repetitive sequences. Such platform-specificity may arise from differences between platforms, with regard to read length (36 bp and 72 bp vs. 50 bp), insert size (~100-300 bp vs. ~1-2 kb), sequencing chemistry (sequencing-by-synthesis using single nucleotides vs. ligation-based sequencing using oligomers), and sequencing quality. When data from the two platforms were merged for variant calling, the proportion of callable regions of the reference genome increased to 99.66%, which was 1.43% higher than the average callability of the two platforms, representing ~40 million bases. In this study, we compared the differences in sequencing results between two sequencing platforms. Approximately 90% of the SNVs were concordant between the two platforms, yet ~10% of the SNVs were either discordant or platform-specific, indicating that each platform had its own strengths and weaknesses. When data from the two platforms were merged, both the overall callability of the reference genome and the overall accuracy of the SNVs improved, demonstrating the likelihood that a re-sequenced genome can be revised using complementary data.

Developmental timing of mutations revealed by whole-genome sequencing of twins with acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) is the major pediatric cancer. At diagnosis, the developmental timing of mutations contributing critically to clonal diversification and selection can be buried in the leukemia's covert natural history. Concordance of ALL in monozygotic, monochorionic twins is a consequence of intraplacental spread of an initiated preleukemic clone. Studying monozygotic twins with ALL provides a unique means of uncovering the timeline of mutations contributing to clonal evolution, pre- and postnatally. We sequenced the whole genomes of leukemic cells from two twin pairs with ALL to comprehensively characterize acquired somatic mutations in ALL, elucidating the developmental timing of all genetic lesions. Shared, prenatal, coding-region single-nucleotide variants were limited to the putative initiating lesions. All other nonsynonymous single-nucleotide variants were distinct between tumors and, therefore, secondary and postnatal. These changes occurred in a background of noncoding mutational changes that were almost entirely discordant in twin pairs and likely passenger mutations acquired during leukemic cell proliferation.

Genome evolution during progression to breast cancer

Cancer evolution involves cycles of genomic damage, epigenetic deregulation, and increased cellular proliferation that eventually culminate in the carcinoma phenotype. Early neoplasias, which are often found concurrently with carcinomas and are histologically distinguishable from normal breast tissue, are less advanced in phenotype than carcinomas and are thought to represent precursor stages. To elucidate their role in cancer evolution we performed comparative whole-genome sequencing of early neoplasias, matched normal tissue, and carcinomas from six patients, for a total of 31 samples. By using somatic mutations as lineage markers we built trees that relate the tissue samples within each patient. On the basis of these lineage trees we inferred the order, timing, and rates of genomic events. In four out of six cases, an early neoplasia and the carcinoma share a mutated common ancestor with recurring aneuploidies, and in all six cases evolution accelerated in the carcinoma lineage. Transition spectra of somatic mutations are stable and consistent across cases, suggesting that accumulation of somatic mutations is a result of increased ancestral cell division rather than specific mutational mechanisms. In contrast to highly advanced tumors that are the focus of much of the current cancer genome sequencing, neither the early neoplasia genomes nor the carcinomas are enriched with potentially functional somatic point mutations. Aneuploidies that occur in common ancestors of neoplastic and tumor cells are the earliest events that affect a large number of genes and may predispose breast tissue to eventual development of invasive carcinoma.

1,8-Naphthyridine-2,7-diamine: a potential universal reader of Watson-Crick base pairs for DNA sequencing by electron tunneling

With the aid of Density Functional Theory (DFT), we designed 1,8-naphthyridine-2,7-diamine as a recognition molecule to read DNA base pairs for genomic sequencing by electron tunneling. NMR studies show that it can form stable triplets with both A : T and G : C base pairs through hydrogen bonding. Our results suggest that the naphthyridine molecule should be able to function as a universal base pair reader in a tunneling gap, generating distinguishable signatures under electrical bias for each of DNA base pairs.

Assessing the order of critical alterations in prostate cancer development and progression by IHC: further evidence that PTEN loss occurs subsequent to ERG gene fusion

BACKGROUND

ERG rearrangements and PTEN (phosphatase and tensin homolog deleted on chromosome 10) loss are two of the most common genetic alterations in prostate cancer. However, there is still significant controversy regarding the order of events of these two changes during the carcinogenic process. We used immunohistochemistry (IHC) to determine ERG and PTEN status, and calculated the fraction of cases with homogeneous/heterogeneous ERG and PTEN staining in a given tumor.

METHODS

Using a single standard tissue section from the index tumor from radical prostatectomies (N=77), enriched for relatively high grade and stage tumors, we examined ERG and PTEN status by IHC. We determined whether ERG or PTEN staining was homogeneous (all tumor cells staining positive) or heterogeneous (focal tumor cell staining) in a given tumor focus.

RESULTS

Fifty-seven percent (N=44/77) of tumor foci showed ERG positivity, with 93% of these (N=41/44) cases showing homogeneous ERG staining in which all tumor cells stained positively. Fifty-three percent (N=41/77) of tumor foci showed PTEN loss, and of these 66% (N=27/41) showed heterogeneous PTEN loss. In ERG homogeneously positive cases, any PTEN loss occurred in 56% (N=23/41) of cases, and of these 65% (N=15/23) showed heterogeneous loss. In ERG-negative tumors, 51.5% (N=17/33) showed PTEN loss, and of these 64.7% (N=11/17) showed heterogeneous PTEN loss. In a subset of cases, genomic deletions of PTEN were verified by fluorescence in situ hybridization in regions with PTEN protein loss as compared with regions with intact PTEN protein, which did not show PTEN genomic loss.

CONCLUSIONS

These results support the concept that PTEN loss tends to occur as a subclonal event within a given established prostatic carcinoma clone after ERG gene fusion. The combination of ERG and PTEN IHC staining can be used as a simple test to ascertain PTEN and ERG gene rearrangement status within a given prostate cancer in either a research or clinical setting.

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.

Genome-wide analyses on high-grade osteosarcoma: making sense of a genomically most unstable tumor

High-grade osteosarcoma is an extremely genomically unstable tumor. This, together with other challenges, such as the heterogeneity within and between tumor samples, and the rarity of the disease, renders it difficult to study this tumor on a genome-wide level. Now that most laboratories change from genome-wide microarray experiments to Next-Generation Sequencing it is important to discuss the lessons we have learned from microarray studies. In this review, we discuss the challenges of high-grade osteosarcoma data analysis. We give an overview of microarray studies that have been conducted so far on both osteosarcoma tissue samples and cell lines. We discuss recent findings from integration of different data types, which is particularly relevant in a tumor with such a complex genomic profile. Finally, we elaborate on the translation of results obtained with bioinformatics into functional studies, which has lead to valuable findings, especially when keeping in mind that no new therapies with a significant impact on survival have been developed in the past decades.

Identification of emerging FLT3 ITD-positive clones during clinical remission and kinetics of disease relapse in acute myeloid leukaemia with mutated nucleophosmin

FLT3 internal tandem duplication (ITD) mutations are frequently detected at diagnosis in cytogenetically normal acute myeloid leukaemia (CN-AML) and predict unfavourable outcome. FLT3 ITD is an unstable aberration and may be lost or acquired at relapse. Recent whole genome sequencing studies have suggested that FLT3 ITD(+)ve AML relapse may evolve from small subclones undetectable at diagnosis by routine polymerase chain reaction (PCR). We developed a patient-specific real-time quantitative-PCR (RQ-PCR) to implement FLT3 ITD detection in six AML patients whose blasts carried wild-type FLT3 at diagnosis and who relapsed with FLT3 ITD by routine PCR. Patient-specific forward primers were designed after cloning and sequencing the FLT3 ITD in each case. The assay allowed retrospective detection of FLT3 ITD in diagnostic samples of 4/6 cases and to establish the kinetics of clonal evolution preceding relapse. After conventional chemotherapy, all patients had early relapse despite having been classified as NPM1(+)ve/FLT3 ITD(-)ve at presentation, with shorter remissions being observed in four patients re-classified as FLT3 ITD(+)ve by the new assay. Notably, FLT3 ITD clone became detectable by conventional PCR in three patients tested during remission after initial treatment. Our data underscore the need of identifying low FLT3 ITD levels, which are probably associated with relapse in otherwise good prognosis CN-AML.

Low-level processing of Illumina Infinium DNA Methylation BeadArrays

We propose a novel approach to background correction for Infinium HumanMethylation data to account for technical variation in background fluorescence signal. Our approach capitalizes on a new use for the Infinium I design bead types to measure non-specific fluorescence in the colour channel opposite of their design (Cy3/Cy5). This provides tens of thousands of features for measuring background instead of the much smaller number of negative control probes on the platforms (n = 32 for HumanMethylation27 and n = 614 for HumanMethylation450, respectively). We compare the performance of our methods with existing approaches, using technical replicates of both mixture samples and biological samples, and demonstrate that within- and between-platform artefacts can be substantially reduced, with concomitant improvement in sensitivity, by the proposed methods.

An empirical Bayesian framework for somatic mutation detection from cancer genome sequencing data

Recent advances in high-throughput sequencing technologies have enabled a comprehensive dissection of the cancer genome clarifying a large number of somatic mutations in a wide variety of cancer types. A number of methods have been proposed for mutation calling based on a large amount of sequencing data, which is accomplished in most cases by statistically evaluating the difference in the observed allele frequencies of possible single nucleotide variants between tumours and paired normal samples. However, an accurate detection of mutations remains a challenge under low sequencing depths or tumour contents. To overcome this problem, we propose a novel method, Empirical Bayesian mutation Calling (https://github.com/friend1ws/EBCall), for detecting somatic mutations. Unlike previous methods, the proposed method discriminates somatic mutations from sequencing errors based on an empirical Bayesian framework, where the model parameters are estimated using sequencing data from multiple non-paired normal samples. Using 13 whole-exome sequencing data with 87.5–206.3 mean sequencing depths, we demonstrate that our method not only outperforms several existing methods in the calling of mutations with moderate allele frequencies but also enables accurate calling of mutations with low allele frequencies (≤10%) harboured within a minor tumour subpopulation, thus allowing for the deciphering of fine substructures within a tumour specimen.

How animal models of leukaemias have already benefited patients

The relative genetic simplicity of leukaemias, the development of which likely relies on a limited number of initiating events has made them ideal for disease modelling, particularly in the mouse. Animal models provide incomparable insights into the mechanisms of leukaemia development and allow exploration of the molecular pillars of disease maintenance, an aspect often biased in cell lines or ex vivo systems. Several of these models, which faithfully recapitulate the characteristics of the human disease, have been used for pre-clinical purposes and have been instrumental in predicting therapy response in patients. We plea for a wider use of genetically defined animal models in the design of clinical trials, with a particular focus on reassessment of existing cancer or non-cancer drugs, alone or in combination.

Using functional genomics to overcome therapeutic resistance in hematological malignancies

Despite great advances in our understanding of the driving events involved in malignant transformation, only a small number of oncogenic drivers have been targeted and translated into tangible clinical benefit. Moreover, even when a targeted therapy can be shown to effectively inhibit an oncogenic driver, leading to cancer remission, disease persistence and/or relapse is typically inevitable. Reemergence of the cancer can result from either intrinsic or acquired resistance mechanisms that result in failure to eliminate all cancer cells. Intrinsic mechanisms of resistance include tumor heterogeneity and pathways that can compensate for the inhibition of the oncogenic driver. Acquired resistance mechanisms include mutation of the oncogenic driver to directly prevent drug-mediated inhibition and the activation of compensatory survival pathways. RNA interference (RNAi)-based screening provides a powerful approach for the interrogation of both intrinsic and acquired resistance mechanisms. The availability of short interfering (si)RNA libraries targeting all human and mouse genes has made it possible to perform large-scale unbiased screens to identify pathways that are specifically required in cancer cells of particular genotypes or following particular treatments, facilitating the design of potential new therapeutic strategies that may limit resistance mechanisms. In this review, we will discuss how RNAi screens can be used to uncover critical growth and survival pathways and aid in the identification of novel therapeutic targets for improved treatment of hematological malignancies.

Replication stress links structural and numerical cancer chromosomal instability

Cancer chromosomal instability (CIN) results in an increased rate of change of chromosome number and structure and generates intratumour heterogeneity. CIN is observed in most solid tumours and is associated with both poor prognosis and drug resistance. Understanding a mechanistic basis for CIN is therefore paramount. Here we find evidence for impaired replication fork progression and increased DNA replication stress in CIN(+) colorectal cancer (CRC) cells relative to CIN(-) CRC cells, with structural chromosome abnormalities precipitating chromosome missegregation in mitosis. We identify three new CIN-suppressor genes (PIGN (also known as MCD4), MEX3C (RKHD2) and ZNF516 (KIAA0222)) encoded on chromosome 18q that are subject to frequent copy number loss in CIN(+) CRC. Chromosome 18q loss was temporally associated with aneuploidy onset at the adenoma-carcinoma transition. CIN-suppressor gene silencing leads to DNA replication stress, structural chromosome abnormalities and chromosome missegregation. Supplementing cells with nucleosides, to alleviate replication-associated damage, reduces the frequency of chromosome segregation errors after CIN-suppressor gene silencing, and attenuates segregation errors and DNA damage in CIN(+) cells. These data implicate a central role for replication stress in the generation of structural and numerical CIN, which may inform new therapeutic approaches to limit intratumour heterogeneity.

High-resolution mutational profiling suggests the genetic validity of glioblastoma patient-derived pre-clinical models

Recent advances in the ability to efficiently characterize tumor genomes is enabling targeted drug development, which requires rigorous biomarker-based patient selection to increase effectiveness. Consequently, representative DNA biomarkers become equally important in pre-clinical studies. However, it is still unclear how well these markers are maintained between the primary tumor and the patient-derived tumor models. Here, we report the comprehensive identification of somatic coding mutations and copy number aberrations in four glioblastoma (GBM) primary tumors and their matched pre-clinical models: serum-free neurospheres, adherent cell cultures, and mouse xenografts. We developed innovative methods to improve the data quality and allow a strict comparison of matched tumor samples. Our analysis identifies known GBM mutations altering PTEN and TP53 genes, and new actionable mutations such as the loss of PIK3R1, and reveals clear patient-to-patient differences. In contrast, for each patient, we do not observe any significant remodeling of the mutational profile between primary to model tumors and the few discrepancies can be attributed to stochastic errors or differences in sample purity. Similarly, we observe ∼96% primary-to-model concordance in copy number calls in the high-cellularity samples. In contrast to previous reports based on gene expression profiles, we do not observe significant differences at the DNA level between in vitro compared to in vivo models. This study suggests, at a remarkable resolution, the genome-wide conservation of a patient’s tumor genetics in various pre-clinical models, and therefore supports their use for the development and testing of personalized targeted therapies.

Next generation sequencing in cancer research and clinical application

The wide application of next-generation sequencing (NGS), mainly through whole genome, exome and transcriptome sequencing, provides a high-resolution and global view of the cancer genome. Coupled with powerful bioinformatics tools, NGS promises to revolutionize cancer research, diagnosis and therapy. In this paper, we review the recent advances in NGS-based cancer genomic research as well as clinical application, summarize the current integrative oncogenomic projects, resources and computational algorithms, and discuss the challenge and future directions in the research and clinical application of cancer genomic sequencing.

Genetic biomarkers in acute myeloid leukemia: will the promise of improving treatment outcomes be realized?

Recent progress in the molecular genetics of acute myeloid leukemia (AML) has shown this disease to be more heterogeneous than previously realized. Recurrent cytogenetic and mutational changes in leukemic blasts have been confirmed to have high prognostic significance. High-throughput techniques to analyze the AML genome in greater depth have revealed novel mutations with putative roles in leukemogenesis. The use of prognostic biomarkers has allowed for a more detailed categorization of AML based on risk. Despite this tremendous progress, the understanding of the mechanisms by which these changes influence leukemia growth and response to treatment is still limited, which in turn has hindered the development of rationally targeted therapies for AML. The integration of clinical, cytogenetic and molecular data will be essential to translate the current research momentum into better outcomes for patients with AML.

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.

Acute myeloid leukaemia in adults

The outlook for patients with acute myeloid leukaemia has improved in the past 30 years. Unlike other cancers, much of this progress is attributable to refinement of supportive treatment, rather than the introduction of new drugs. New antibacterial and antifungal agents, antiemetics, and improved transfusion support have decreased the rate of early death, and morbidity and mortality from allogeneic stem cell transplantation has been substantially reduced. However, more than half of young adult patients and about 90% of older patients still die from their disease. Refractoriness to initial induction treatment and, more frequently, relapse after complete remission, are still the main obstacles to cure. Accordingly, new treatment approaches with mechanisms of action different from those of conventional chemotherapy are needed. Our knowledge of the various chromosomal and molecular abnormalities implicated in the pathogenesis of the many subtypes of the disease has greatly expanded; as a result, clinical research is moving towards the investigation of new non-cytotoxic agents in combination with chemotherapy. The goal is to target the molecular abnormalities identified at diagnosis; however, several aberrations can coexist in subclones of acute myeloid leukaemia, making the disease less likely to be inhibited by a single agent.

Circulating miR-150 and miR-342 in plasma are novel potential biomarkers for acute myeloid leukemia

BACKGROUND

MicroRNAs (miRNAs) are small (19-22-nt) single-stranded noncoding RNA molecules whose deregulation of expression can contribute to human disease including the multistep processes of carcinogenesis in human. Circulating miRNAs are emerging biomarkers in many diseases and cancers such as type 2 diabetes, pulmonary disease, colorectal cancer, and gastric cancer among others; however, defining a plasma miRNA signature in acute myeloblastic leukemia (AML) that could serve as a biomarker for diagnosis or in the follow-up has not been done yet.

METHODS

TaqMan miRNA microarray was performed to identify deregulated miRNAs in the plasma of AML patients. Quantitative real-time RT-PCR was used to validate the results. Receiver-operator characteristic (ROC) curve analysis was conducted to evaluate the diagnostic accuracy of the highly and significantly identified deregulated miRNA(s) as potential candidate biomarker(s).

RESULTS

The plasma expression level of let-7d, miR-150, miR-339, and miR-342 was down-regulated whilst that of let-7b, and miR-523 was up-regulated in the AML group at diagnosis compared to healthy controls. ROC curve analyses revealed an AUC (the areas under the ROC curve) of 0.835 (95% CI: 0.7119- 0.9581; P<0.0001) and 0.8125 (95% CI: 0.6796-0.9454; P=0.0005) for miR-150, and miR-342 respectively. Combined ROC analyses using these 2 miRNAs revealed an elevated AUC of 0.86 (95% CI: 0.7819-0.94; P<0.0001) indicating the additive effect in the diagnostic value of these 2 miRNAs. QRT-PCR results showed that the expression level of these two miRs in complete remission AML patients resembled that of healthy controls.

CONCLUSIONS

Our findings indicated that plasma miR-150 and miR-342 are novel important promising biomarkers in the diagnosis of AML. These novel and promising markers warrant validation in larger prospective studies.

Single-cell protein secretomic signatures as potential correlates to tumor cell lineage evolution and cell-cell interaction

Secreted proteins including cytokines, chemokines, and growth factors represent important functional regulators mediating a range of cellular behavior and cell-cell paracrine/autocrine signaling, e.g., in the immunological system (Rothenberg, 2007), tumor microenvironment (Hanahan and Weinberg, 2011), or stem cell niche (Gnecchi etal., 2008). Detection of these proteins is of great value not only in basic cell biology but also for diagnosis and therapeutic monitoring of human diseases such as cancer. However, due to co-production of multiple effector proteins from a single cell, referred to as polyfunctionality, it is biologically informative to measure a panel of secreted proteins, or secretomic signature, at the level of single cells. Recent evidence further indicates that a genetically identical cell population can give rise to diverse phenotypic differences (Niepel etal., 2009). Non-genetic heterogeneity is also emerging as a potential barrier to accurate monitoring of cellular immunity and effective pharmacological therapies (Cohen etal., 2008; Gascoigne and Taylor, 2008), but can hardly assessed using conventional approaches that do not examine cellular phenotype at the functional level. It is known that cytokines, for example, in the immune system define the effector functions and lineage differentiation of immune cells. In this article, we hypothesize that protein secretion profile may represent a universal measure to identify the definitive correlate in the larger context of cellular functions to dissect cellular heterogeneity and evolutionary lineage relationship in human cancer.

Clonal heterogeneity of mantle cell lymphoma revealed by array comparative genomic hybridization

Mantle cell lymphoma (MCL) is an aggressive B-cell non-Hodgkin lymphoma (NHL) characterized by the translocation t(11;14)(q13;q32). This lymphoma exhibits a poor prognosis and remains incurable with standard chemotherapy approaches. Recently, we have shown that a majority of patients with acute-type adult T-cell leukemia/lymphoma (ATLL) have multiple subclones that were likely produced in lymph nodes. We investigated whether MCL has multiple subclones as identified in ATLL by high-resolution oligo-array comparative genomic hybridization (CGH). Eleven of 20 (55%) evaluable MCL cases had a log2 ratio imbalance, suggesting the existence of multiple subclones in MCL. Based on the proportion of every subclone relative to the main clone, we were able to speculate clonal evolution in each MCL case with multiple subclones. Our analysis gave new insights into the clonal heterogeneity quantitatively and accurately. Furthermore, genomic copy number alterations are not hierarchical events and not necessarily the initial or later events for cells to become MCL.

Gene mutations of acute myeloid leukemia in the genome era

Ten years ago, gene mutations found in acute myeloid leukemia (AML) were conceptually grouped into class I mutation, which causes constitutive activation of intracellular signals that contribute to the growth and survival, and class II mutation, which blocks differentiation and/or enhance self-renewal by altered transcription factors. A cooperative model between two classes of mutations has been suggested by murine experiments and partly supported by epidemiological findings. In the last 5 years, comprehensive genomic analysis proceeded to find new gene mutations, which are found in the epigenome-associated enzymes and the molecules never noticed so far. These new mutations apparently increase the complexity and heterogeneity of AML. Although a long list of gene mutations might have been compiled, the entire picture of molecular pathogenesis in AML remains to be elucidated because gene rearrangement, gene copy number, DNA methylation and expression profiles are not fully studied in conjunction with gene mutations. Comprehensive genome research will deepen the understanding of AML to promote the development of new classification and treatment. This review focuses on gene mutations that were recently discovered by genome sequencing.

Diagnostic applications of high-throughput DNA sequencing

Advances in DNA sequencing technology have allowed comprehensive investigation of the genetics of human beings and human diseases. Insights from sequencing the genomes, exomes, or transcriptomes of healthy and diseased cells in patients are already enabling improved diagnostic classification, prognostication, and therapy selection for many diseases. Understanding the data obtained using new high-throughput DNA sequencing methods, choices made in sequencing strategies, and common challenges in data analysis and genotype-phenotype correlation is essential if pathologists, geneticists, and clinicians are to interpret the growing scientific literature in this area. This review highlights some of the major results and discoveries stemming from high-throughput DNA sequencing research in our understanding of Mendelian genetic disorders, hematologic cancer biology, infectious diseases, the immune system, transplant biology, and prenatal diagnostics. Transition of new DNA sequencing methodologies to the clinical laboratory is under way and is likely to have a major impact on all areas of medicine.

Concise review: preleukemic stem cells: molecular biology and clinical implications of the precursors to leukemia stem cells

Recent experimental evidence has shown that acute myeloid leukemia (AML) and myelodysplastic syndromes (MDS) arise from transformed immature hematopoietic cells following the accumulation of multiple stepwise genetic and epigenetic changes in hematopoietic stem cells and committed progenitors. The series of transforming events initially gives rise to preleukemic stem cells (pre-LSC), preceding the formation of fully transformed leukemia stem cells (LSC). Despite the established use of poly-chemotherapy, relapse continues to be the most common cause of death in AML and MDS. The therapeutic elimination of all LSC, as well as pre-LSC, which provide a silent reservoir for the re-formation of LSC, will be essential for achieving lasting cures. Conventional sequencing and next-generation genome sequencing have allowed us to describe many of the recurrent mutations in the bulk cell populations in AML and MDS, and recent work has also focused on identifying the initial molecular changes contributing to leukemogenesis. Here we review recent and ongoing advances in understanding the roles of pre-LSC, and the aberrations that lead to pre-LSC formation and subsequent LSC transformation.

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.

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.

Residue mutations and their impact on protein structure and function: detecting beneficial and pathogenic changes

The present review focuses on the evolution of proteins and the impact of amino acid mutations on function from a structural perspective. Proteins evolve under the law of natural selection and undergo alternating periods of conservative evolution and of relatively rapid change. The likelihood of mutations being fixed in the genome depends on various factors, such as the fitness of the phenotype or the position of the residues in the three-dimensional structure. For example, co-evolution of residues located close together in three-dimensional space can occur to preserve global stability. Whereas point mutations can fine-tune the protein function, residue insertions and deletions (‘decorations’ at the structural level) can sometimes modify functional sites and protein interactions more dramatically. We discuss recent developments and tools to identify such episodic mutations, and examine their applications in medical research. Such tools have been tested on simulated data and applied to real data such as viruses or animal sequences. Traditionally, there has been little if any cross-talk between the fields of protein biophysics, protein structure–function and molecular evolution. However, the last several years have seen some exciting developments in combining these approaches to obtain an in-depth understanding of how proteins evolve. For example, a better understanding of how structural constraints affect protein evolution will greatly help us to optimize our models of sequence evolution. The present review explores this new synthesis of perspectives.

Cancer heterogeneity: implications for targeted therapeutics

Developments in genomic techniques have provided insight into the remarkable genetic complexity of malignant tumours. There is increasing evidence that solid tumours may comprise of subpopulations of cells with distinct genomic alterations within the same tumour, a phenomenon termed intra-tumour heterogeneity. Intra-tumour heterogeneity is likely to have implications for cancer therapeutics and biomarker discovery, particularly in the era of targeted treatment, and evidence for a relationship between intra-tumoural heterogeneity and clinical outcome is emerging. Our understanding of the processes that exacerbate intra-tumoural heterogeneity, both iatrogenic and tumour specific, is likely to increase with the development and more widespread implementation of advanced sequencing technologies, and adaptation of clinical trial design to include comprehensive tissue collection protocols. The current evidence for intra-tumour heterogeneity and its relevance to cancer therapeutics will be presented in this mini-review.

Next-generation sequencing - feasibility and practicality in haematology

Next-generation sequencing platforms have evolved to provide an accurate and comprehensive means for the detection of molecular mutations in heterogeneous tumour specimens. Here, we review the feasibility and practicality of this novel laboratory technology. In particular, we focus on the utility of next-generation sequencing technology in characterizing haematological neoplasms and the landmark findings in key haematological malignancies. We also discuss deep-sequencing strategies to analyse the constantly increasing number of molecular markers applied for disease classification, patient stratification and individualized monitoring of minimal residual disease. Although many facets of this assay need to be taken into account, amplicon deep-sequencing has already demonstrated a promising technical performance and is being continuously developed towards routine application in diagnostic laboratories so that an impact on clinical practice can be achieved.

DNA methylation changes are a late event in acute promyelocytic leukemia and coincide with loss of transcription factor binding

The origin of aberrant DNA methylation in cancer remains largely unknown. In the present study, we elucidated the DNA methylome in primary acute promyelocytic leukemia (APL) and the role of promyelocytic leukemia–retinoic acid receptor α (PML-RARα) in establishing these patterns. Cells from APL patients showed increased genome-wide DNA methylation with higher variability than healthy CD34+ cells, promyelocytes, and remission BM cells. A core set of differentially methylated regions in APL was identified. Age at diagnosis, Sanz score, and Flt3-mutation status characterized methylation subtypes. Transcription factor–binding sites (eg, the c-myc–binding sites) were associated with low methylation. However, SUZ12- and REST-binding sites identified in embryonic stem cells were preferentially DNA hypermethylated in APL cells. Unexpectedly, PML-RARα–binding sites were also protected from aberrant DNA methylation in APL cells. Consistent with this, myeloid cells from preleukemic PML-RARα knock-in mice did not show altered DNA methylation and the expression of PML-RARα in hematopoietic progenitor cells prevented differentiation without affecting DNA methylation. Treatment of APL blasts with all-trans retinoic acid also did not result in immediate DNA methylation changes. The results of the present study suggest that aberrant DNA methylation is associated with leukemia phenotype but is not required for PML-RARα–mediated initiation of leukemogenesis.

Tumor dormancy: long-term survival in a hostile environment

Tumor dormancy occurs when cancer cells are present but the tumor does not grow. Following treatment, patients may enter complete remission in which persistent cells represent the minimal residual disease (MRD). Experimental models and clinical data suggest that the absolute quantity of this MRD is extremely low. Very few cancer cells can persist for years or decades under these hostile conditions that include continuous exposure to maintenance treatment, autologous anti-tumor immune response, and a nonpermissive microenvironment. Dormant tumor cells may survive despite these destruction factors if they adapt and develop strategies to escape from cell death. Escape may result in a state of equilibrium between MRD and the patient. Equilibrium between the immune response and tumor cells can result in long-term tumor dormancy; however, after variable lengths of time, tumor dormancy ends, and the disease progresses. Experimental models have shown that dormant tumor cells may over-express B7-H1 and B7.1 and inhibit cytotoxic T-cell mediated lysis. This resistance could be therapeutically targeted using drugs like MEK inhibitors that modulate pathways involved in B7-H1 expression. Dormant tumor cells may also develop nonspecific resistance mechanisms to cell death, such as deregulation of JAK/STAT and mTORC2/AKT pathways or autocrine and paracrine production of cytokines. This deregulation leads to cross-resistance between the immune response and cytotoxic drugs, indicating that the long-term selection that occurs in vivo during tumor dormancy may ultimately result in resistant relapse. Long-term selection of cancer cells in vitro using tyrosine kinase inhibitors selects cells that harbor the same resistance mechanisms as dormant tumor cells. Elucidating the mechanisms underlying the equilibrium that allows for the persistence of dormant tumor cells presents a novel strategy for targeted drug treatment in the context of maintenance therapy.

ABCs of genomics

Our genome, the 6 billion bp of DNA that contain the blueprint of a human being, has become the focus of intense interest in medicine in the past two decades. Two developments have contributed to this situation: (1) the genetic basis of more and more diseases has been discovered, especially of malignant diseases, and (2) at the same time, our abilities to analyze our genome have increased exponentially through technological breakthroughs. We can expect genomics to become ever more relevant for day-to-day treatment decisions and patient management. It is therefore of great importance for physicians, especially those who are treating patients with malignant diseases, to become familiar with our genome and the technologies that are currently available for genomics analysis. This review provides a brief overview of the organization of our genome, high-throughput sequence analysis methods, and the analysis of leukemia genomes using next-generation sequencing (NGS) technologies.

Genomic applications in the clinic: use in treatment paradigm of acute myeloid leukemia

In recent years, research in genomics has resulted in the rapid uncovering of the molecular pathogenesis of acute myeloid leukemia (AML). The identification of the genetic determinants of response to standard-but also to experimental-treatment is increasingly used for patient counseling, to guide clinical decision making, and for resource-efficient care provision at diagnosis, during consolidation treatment and follow-up, and after relapse. Gene mutations now allow us to explore the enormous diversity among cytogenetically defined subsets of AML, in particular the large subset of cytogenetically normal AML. Nonetheless, there are several challenges in evaluating the prognostic value of a specific mutation in the concert of the various concurrent mutations and determining the relative prognostic value of the genetic profile during the disease course. In particular, changes in the genetic profile in relapse compared with that at diagnosis will increasingly affect the treatment strategy at relapse, but also will give us the possibility of learning which treatment strategy during frontline therapy is best to prevent them.

Defining principles of combination drug mechanisms of action

Combination chemotherapies have been a mainstay in the treatment of disseminated malignancies for almost 60 y, yet even successful regimens fail to cure many patients. Although their single-drug components are well studied, the mechanisms by which drugs work together in clinical combination regimens are poorly understood. Here, we combine RNAi-based functional signatures with complementary informatics tools to examine drug combinations. This approach seeks to bring to combination therapy what the knowledge of biochemical targets has brought to single-drug therapy and creates a statistical and experimental definition of "combination drug mechanisms of action." We show that certain synergistic drug combinations may act as a more potent version of a single drug. Conversely, unlike these highly synergistic combinations, most drugs average extant single-drug variations in therapeutic response. When combined to form multidrug regimens, averaging combinations form averaging regimens that homogenize genetic variation in mouse models of cancer and in clinical genomics datasets. We suggest surprisingly simple and predictable combination mechanisms of action that are independent of biochemical mechanism and have implications for biomarker discovery as well as for the development of regimens with defined genetic dependencies.

A proteomics and transcriptomics approach to identify leukemic stem cell (LSC) markers

Interactions between hematopoietic stem cells and their niche are mediated by proteins within the plasma membrane (PM) and changes in these interactions might alter hematopoietic stem cell fate and ultimately result in acute myeloid leukemia (AML). Here, using nano-LC/MS/MS, we set out to analyze the PM profile of two leukemia patient samples. We identified 867 and 610 unique CD34(+) PM (-associated) proteins in these AML samples respectively, including previously described proteins such as CD47, CD44, CD135, CD96, and ITGA5, but also novel ones like CD82, CD97, CD99, PTH2R, ESAM, MET, and ITGA6. Further validation by flow cytometry and functional studies indicated that long-term self-renewing leukemic stem cells reside within the CD34(+)/ITGA6(+) fraction, at least in a subset of AML cases. Furthermore, we combined proteomics with transcriptomics approaches using a large panel of AML CD34(+) (n = 60) and normal bone marrow CD34(+) (n = 40) samples. Thus, we identified eight subgroups of AML patients based on their specific PM expression profile. GSEA analysis revealed that these eight subgroups are enriched for specific cellular processes.

Variation in dicer gene is associated with increased survival in T-cell lymphoma

Dicer, an endonuclease in RNase III family, is essential for the RNA interference (RNAi) pathway. Aberrant expression of Dicer has been shown in various cancers including some subtypes of T cell lymphoma (TCL), which influences patient prognosis. A single-nucleotide polymorphism (SNP) rs3742330A>G has been identified in the Dicer gene, located in the 3′ untranslated region (3′ UTR) that is important for mRNA transcript stability. We investigated whether rs3742330 is associated with the survival in 163 TCL patients. Significant association between Dicer rs3742330 and TCL survival were found. Patients carrying the GG genotype (n = 12) had a significantly increased overall survival (OS) compared with those carrying the GA and AA genotypes (n = 70 and n = 81, respectively; p = 0.031). Moreover, the significant association was maintained for patients with mature T type (n = 134; p = 0.026). In multivariate Cox-regression analysis, rs3742330 proved to be an independent predictor for OS, together with the commonly used International Prognostic Index (IPI) and BAFF rs9514828, another SNP we have previously reported to be associated with TCL survival, with hazard ratios (HRs) for patient death rate of 8.956 (95% CI, 1.210 to 66.318; p = 0.032) for the GA genotype and 10.145 (95% CI, 1.371 to 75.084; p = 0.023) for the AA genotype. Furthermore, we observed cumulative effects of Dicer rs3742330 and BAFF rs9514828 on TCL survival. Compared with patients carrying zero unfavorable genotype, those carrying one and two unfavorable genotypes had an increased risk of death with a HR of 7.104 (95% CI, 0.969–53.086; p = 0.054) and 14.932 (95% CI, 1.950–114.354; p = 0.009), respectively, with a significant dose-response trend (p_trend  = 0.004). In conclusion, Dicer rs3742330 is associated with TCL survival, suggesting that genetic variation might play a role in predicting prognosis of TCL patients.

How do novel molecular genetic markers influence treatment decisions in acute myeloid leukemia?

Acute myeloid leukemia (AML) is the most common acute leukemia diagnosed in adults, and the majority of patients with AML die from relapsed disease. Although many studies over the past 4 decades have identified disease alleles in AML, recent genome-wide and candidate gene studies have identified additional recurrent somatic mutations in AML patients with biologic, clinical, and therapeutic importance. Herein we review our current understanding of the molecular pathogenesis of AML and discuss how mutational profiling can be used to refine prognostication in AML and to inform therapeutic approaches. We also review the current challenges in translating genomic studies to the clinical setting, which remains a significant challenge and an urgent priority.

The prevalent predicament of relapsed acute myeloid leukemia

A majority of patients with acute myeloid leukemia (AML) will relapse after achieving complete remission. At relapse, patients should be risk stratified and a decision made about the appropriateness of intensive therapy and whether a potentially curative allogeneic stem cell transplantation (allo-SCT) is possible. Risk factors include duration of first complete remission and adverse cytogenetics, as well as age and FLT3 mutation status. Available therapies are steadily increasing, but for the most part should be regarded as either best palliation or as a bridge to allo-SCT. Simple symptomatic therapies for patients with extreme age or the worst prognosis should also be considered. Newer therapeutic options include novel cytotoxic chemotherapies including clofarabine, immunomodulatory agents, targeted therapies against FLT3 and mTOR, and immunoconjugates. All patients with relapsed AML should be considered for an appropriate clinical trial.

U-2932: two clones in one cell line, a tool for the study of clonal evolution

Genetic heterogeneity is common in tumors, explicable by the development of subclones with distinct genetic and epigenetic alterations. We describe an in vitro model for cancer heterogeneity, comprising the diffuse large B-cell lymphoma cell line U-2932 which expresses two sets of cell surface markers representing twin populations flow-sorted by CD20 vs CD38 expression. U-2932 populations were traced to subclones of the original tumor with clone-specific immunoglobulin IgVH4-39 hypermutation patterns. BCL6 was overexpressed in one subpopulation (R1), MYC in the other (R2), both clones overexpressed BCL2. According to the combined results of immunoglobulin hypermutation and cytogenetic analysis, R1 and R2 derive from a mother clone with genomic BCL2 amplification, which acquired secondary rearrangements leading to the overexpression of BCL6 (R1) or MYC (R2). Some 200 genes were differentially expressed in R1/R2 microarrays including transcriptional targets of the aberrantly expressed oncogenes. Other genes were regulated by epigenetic means as shown by DNA methylation analysis. Ectopic expression of BCL6 in R2 variously modulated new candidate target genes, confirming dual silencing and activating functions. In summary, stable retention of genetically distinct subclones in U-2932 models tumor heterogeneity in vitro permitting functional analysis of oncogenes against a syngenic background.