Detecting Chromosome Condensation Defects in Gulf War Illness Patients.

BACKGROUND: Gulf War Illness (GWI) impacts 25-30% of gulf war veterans. Due to its heterogeneity in both etiology and symptoms, it has been challenging to establish the commonly accepted case definition for GWI. Equally challenging are the understanding of the general mechanism of GWI and the development of biomarkers useful for its clinical diagnosis and treatment. OBJECTIVE: We have observed that chromosome condensation defects can be detected in GWI patients. To document this phenomenon in GWI, we aim to describe and compare different types of chromosomal condensation defects in GWI patients, if possible. Since chromosomal condensation represents an important step of ensuring genome integrity, condensation defects could be used as a potential biomarker of GWI. METHODS: Lymphocytes from GWI patients have been used for short term cell culture followed by chromosome slide preparation. Both Giemsa staining and multiple color spectral karyotyping (SKY) were applied to study chromosome aberrations, focusing on different types of condensation defects. RESULTS: At least three subtypes of Defective Mitotic Figures (DMFs) were observed. Some individuals displayed elevated frequencies of DMFs. Another type of condensation defect identified as sticky chromosomes were also observed. CONCLUSION: Various types of condensation defects have been observed in GWI patients. It is rather surprising that some GWI patients exhibited a high level of chromosomal condensation defects. Previously, the elevated frequency of DMFs was only observed in cancer patients. Since chromosome condensation can be linked to other types of chromosome aberrations, as well as cellular stress conditions, the detailed mechanism and clinical impact should be further studied, especially with increased sample size.

A Postgenomic Perspective on Molecular Cytogenetics.

BACKGROUND: The postgenomic era is featured by massive data collection and analyses from various large scale-omics studies. Despite the promising capability of systems biology and bioinformatics to handle large data sets, data interpretation, especially the translation of -omics data into clinical implications, has been challenging. DISCUSSION: In this perspective, some important conceptual and technological limitations of current systems biology are discussed in the context of the ultimate importance of the genome beyond the collection of all genes. Following a brief summary of the contributions of molecular cytogenetics/cytogenomics in the pre- and post-genomic eras, new challenges for postgenomic research are discussed. Such discussion leads to a call to search for a new conceptual framework and holistic methodologies. CONCLUSION: Throughout this synthesis, the genome theory of somatic cell evolution is highlighted in contrast to gene theory, which ignores the karyotype-mediated higher level of genetic information. Since "system inheritance" is defined by the genome context (gene content and genomic topology) while "parts inheritance" is defined by genes/epigenes, molecular cytogenetics and cytogenomics (which directly study genome structure, function, alteration and evolution) will play important roles in this postgenomic era.

Evolutionary mechanism unifies the hallmarks of cancer.

The basis for the gene mutation theory of cancer that dominates current molecular cancer research consists of: the belief that gene-level aberrations such as mutations are the main cause of cancers, the concept that stepwise gene mutation accumulation drives cancer progression, and the hallmarks of cancer. The research community swiftly embraced the hallmarks of cancer, as such synthesis has supported the notions that common cancer genes are responsible for the majority of cancers and the complexity of cancer can be dissected into simplified molecular principles. The gene/pathway classification based on individual hallmarks provides explanation for the large number of diverse gene mutations, which is in contrast to the original estimation that only a handful of gene mutations would be discovered. Further, these hallmarks have been highly influential as they also provide the rationale and research direction for continued gene-based cancer research. While the molecular knowledge of these hallmarks is drastically increasing, the clinical implication remains limited, as cancer dynamics cannot be summarized by a few isolated/fixed molecular principles. Furthermore, the highly heterogeneous genetic signature of cancers, including massive stochastic genome alterations, challenges the utility of continuously studying each individual gene mutation under the framework of these hallmarks. It is therefore necessary to re-evaluate the concept of cancer hallmarks through the lens of cancer evolution. In this analysis, the evolutionary basis for the hallmarks of cancer will be discussed and the evolutionary mechanism of cancer suggested by the genome theory will be employed to unify the diverse molecular mechanisms of cancer.

Unstable genomes elevate transcriptome dynamics.

The challenge of identifying common expression signatures in cancer is well known, however the reason behind this is largely unclear. Traditionally variation in expression signatures has been attributed to technological problems, however recent evidence suggests that chromosome instability (CIN) and resultant karyotypic heterogeneity may be a large contributing factor. Using a well-defined model of immortalization, we systematically compared the pattern of genome alteration and expression dynamics during somatic evolution. Co-measurement of global gene expression and karyotypic alteration throughout the immortalization process reveals that karyotype changes influence gene expression as major structural and numerical karyotypic alterations result in large gene expression deviation. Replicate samples from stages with stable genomes are more similar to each other than are replicate samples with karyotypic heterogeneity. Karyotypic and gene expression change during immortalization is dynamic as each stage of progression has a unique expression pattern. This was further verified by comparing global expression in two replicates grown in one flask with known karyotypes. Replicates with higher karyotypic instability were found to be less similar than replicates with stable karyotypes. This data illustrates the karyotype, transcriptome, and transcriptome determined pathways are in constant flux during somatic cellular evolution (particularly during the macroevolutionary phase) and this flux is an inextricable feature of CIN and essential for cancer formation. The findings presented here underscore the importance of understanding the evolutionary process of cancer in order to design improved treatment modalities.

Chromosomal instability and transcriptome dynamics in cancer.

Whole transcriptome profiling has long been proposed as a method of identifying cancer-specific gene expression profiles. Indeed, a multitude of these studies have generated vast amounts of expression data for many types of cancer, and most have identified specific gene signatures associated with a given cancer. These studies however, often contradict with each other, and gene lists only rarely overlap, challenging clinical application of cancer gene signatures. To understand this issue, the biological basis of transcriptome dynamics needs to be addressed. Chromosome instability (CIN) is the main contributor to genome heterogeneity and system dynamics, therefore the relationship between CIN, genome heterogeneity, and transcriptome dynamics has important implications for cancer research. In this review, we discuss CIN and its effects on the transcriptome during cancer progression, specifically how stochastic chromosome change results in transcriptome dynamics. This discussion is further applied to metastasis and drug resistance both of which have been linked to multiple diverse molecular mechanisms but are in fact driven by CIN. The diverse molecular mechanisms that drive each process are linked to karyotypic heterogeneity through the evolutionary mechanism of cancer. Karyotypic change and the resultant transcriptome change alter network function within cells increasing the evolutionary potential of the tumor. Future studies must embrace this instability-induced heterogeneity in order to devise new research and treatment modalities that focus on the evolutionary process of cancer rather than the individual genes that are uniquely changed in each tumor. Care is also needed in evaluating results from experimental systems which measure average values of a population.

Chromosomal instability (CIN): what it is and why it is crucial to cancer evolution.

Results of various cancer genome sequencing projects have "unexpectedly" challenged the framework of the current somatic gene mutation theory of cancer. The prevalence of diverse genetic heterogeneity observed in cancer questions the strategy of focusing on contributions of individual gene mutations. Much of the genetic heterogeneity in tumors is due to chromosomal instability (CIN), a predominant hallmark of cancer. Multiple molecular mechanisms have been attributed to CIN but unifying these often conflicting mechanisms into one general mechanism has been challenging. In this review, we discuss multiple aspects of CIN including its definitions, methods of measuring, and some common misconceptions. We then apply the genome-based evolutionary theory to propose a general mechanism for CIN to unify the diverse molecular causes. In this new evolutionary framework, CIN represents a system behavior of a stress response with adaptive advantages but also serves as a new potential cause of further destabilization of the genome. Following a brief review about the newly realized functions of chromosomes that defines system inheritance and creates new genomes, we discuss the ultimate importance of CIN in cancer evolution. Finally, a number of confusing issues regarding CIN are explained in light of the evolutionary function of CIN.

Why imatinib remains an exception of cancer research.

The archetype driving the drug targeting approach to cancer therapy is the success of imatinib against chronic phase chronic myeloid leukemia (CML-CP). Molecular targeting success of this magnitude has yet to be repeated for most solid tumors. To answer why imatinib remains an exception of cancer research, we summarize key features and patterns of evolution that contrast CML-CP from prostate cancer, an example of a solid tumor that also shares a signature fusion gene. Distinctive properties of CML-CP include: a large cell population size that is not geographically constrained, a highly penetrant dominant oncogene that sweeps the entire cell population, subsequent progressive and ordered clonal genetic changes, and the effectiveness of molecular targeting within the chronic phase, which is comparable to the benign phase of solid tumors. CML-CP progression resembles a clonal, stepwise model of evolution, whereas the pattern of solid tumor evolution is highly dynamic and stochastic. The distinguishing features and evolutionary pattern of CML-CP support why the success of imatinib does not carry over to most solid tumors. Changing the focus of cancer research from a gene-based view to a genome-based theory will provide insight into solid tumor evolutionary dynamics.

Decoding the genome beyond sequencing: the new phase of genomic research.

While our understanding of gene-based biology has greatly improved, it is clear that the function of the genome and most diseases cannot be fully explained by genes and other regulatory elements. Genes and the genome represent distinct levels of genetic organization with their own coding systems; Genes code parts like protein and RNA, but the genome codes the structure of genetic networks, which are defined by the whole set of genes, chromosomes and their topological interactions within a cell. Accordingly, the genetic code of DNA offers limited understanding of genome functions. In this perspective, we introduce the genome theory which calls for the departure of gene-centric genomic research. To make this transition for the next phase of genomic research, it is essential to acknowledge the importance of new genome-based biological concepts and to establish new technology platforms to decode the genome beyond sequencing.

Stress, genomic adaptation, and the evolutionary trade-off.

Cells are constantly exposed to various internal and external stresses. The importance of cellular stress and its implication to disease conditions have become popular research topics. Many ongoing investigations focus on the sources of stress, their specific molecular mechanisms and interactions, especially regarding their contributions to many common and complex diseases through defined molecular pathways. Numerous molecular mechanisms have been linked to endoplasmic reticulum stress along with many unexpected findings, drastically increasing the complexity of our molecular understanding and challenging how to apply individual mechanism-based knowledge in the clinic. A newly emergent genome theory searches for the synthesis of a general evolutionary mechanism that unifies different types of stress and functional relationships from a genome-defined system point of view. Herein, we discuss the evolutionary relationship between stress and somatic cell adaptation under physiological, pathological, and somatic cell survival conditions, the multiple meanings to achieve adaptation and its potential trade-off. In particular, we purposely defocus from specific stresses and mechanisms by redirecting attention toward studying underlying general mechanisms.

Ovarian cancer evolution through stochastic genome alterations: defining the genomic role in ovarian cancer.

Ovarian cancer is the fifth leading cause of death among women worldwide. Characterized by complex etiology and multi-level heterogeneity, its origins are not well understood. Intense research efforts over the last decade have furthered our knowledge by identifying multiple risk factors that are associated with the disease. However, it is still unclear how genetic heterogeneity contributes to tumor formation, and more specifically, how genome-level heterogeneity acts as the key driving force of cancer evolution. Most current genomic approaches are based on 'average molecular profiling.' While effective for data generation, they often fail to effectively address the issue of high level heterogeneity because they mask variation that exists in a cell population. In this synthesis, we hypothesize that genome-mediated cancer evolution can effectively explain diverse factors that contribute to ovarian cancer. In particular, the key contribution of genome replacement can be observed during major transitions of ovarian cancer evolution including cellular immortalization, transformation, and malignancy. First, we briefly review major updates in the literature, and illustrate how current gene-mediated research will offer limited insight into cellular heterogeneity and ovarian cancer evolution. We next explain a holistic framework for genome-based ovarian cancer evolution and apply it to understand the genomic dynamics of a syngeneic ovarian cancer mouse model. Finally, we employ single cell assays to further test our hypothesis, discuss some predictions, and report some recent findings.

Genome chaos: survival strategy during crisis.

Genome chaos, a process of complex, rapid genome re-organization, results in the formation of chaotic genomes, which is followed by the potential to establish stable genomes. It was initially detected through cytogenetic analyses, and recently confirmed by whole-genome sequencing efforts which identified multiple subtypes including "chromothripsis", "chromoplexy", "chromoanasynthesis", and "chromoanagenesis". Although genome chaos occurs commonly in tumors, both the mechanism and detailed aspects of the process are unknown due to the inability of observing its evolution over time in clinical samples. Here, an experimental system to monitor the evolutionary process of genome chaos was developed to elucidate its mechanisms. Genome chaos occurs following exposure to chemotherapeutics with different mechanisms, which act collectively as stressors. Characterization of the karyotype and its dynamic changes prior to, during, and after induction of genome chaos demonstrates that chromosome fragmentation (C-Frag) occurs just prior to chaotic genome formation. Chaotic genomes seem to form by random rejoining of chromosomal fragments, in part through non-homologous end joining (NHEJ). Stress induced genome chaos results in increased karyotypic heterogeneity. Such increased evolutionary potential is demonstrated by the identification of increased transcriptome dynamics associated with high levels of karyotypic variance. In contrast to impacting on a limited number of cancer genes, re-organized genomes lead to new system dynamics essential for cancer evolution. Genome chaos acts as a mechanism of rapid, adaptive, genome-based evolution that plays an essential role in promoting rapid macroevolution of new genome-defined systems during crisis, which may explain some unwanted consequences of cancer treatment.

RAS/MEK-independent gene expression reveals BMP2-related malignant phenotypes in the Nf1-deficient MPNST.

Malignant peripheral nerve sheath tumor (MPNST) is a type of soft tissue sarcoma that occurs in carriers of germline mutations in Nf1 gene as well as sporadically. Neurofibromin, encoded by the Nf1 gene, functions as a GTPase-activating protein (GAP) whose mutation leads to activation of wt-RAS and mitogen-activated protein kinase (MAPK) signaling in neurofibromatosis type I (NF1) patients' tumors. However, therapeutic targeting of RAS and MAPK have had limited success in this disease. In this study, we modulated NRAS, mitogen-activated protein/extracellular signal-regulated kinase (MEK)1/2, and neurofibromin levels in MPNST cells and determined gene expression changes to evaluate the regulation of signaling pathways in MPNST cells. Gene expression changes due to neurofibromin modulation but independent of NRAS and MEK1/2 regulation in MPNST cells indicated bone morphogenetic protein 2 (Bmp2) signaling as a key pathway. The BMP2-SMAD1/5/8 pathway was activated in NF1-associated MPNST cells and inhibition of BMP2 signaling by LDN-193189 or short hairpin RNA (shRNA) to BMP2 decreased the motility and invasion of NF1-associated MPNST cells. The pathway-specific gene changes provide a greater understanding of the complex role of neurofibromin in MPNST pathology and novel targets for drug discovery.

Genome constraint through sexual reproduction: application of 4D-Genomics in reproductive biology.

Assisted reproductive technologies have been used to achieve pregnancies since the first successful test tube baby was born in 1978. Infertile couples are at an increased risk for multiple miscarriages and the application of current protocols are associated with high first-trimester miscarriage rates. Among the contributing factors of these higher rates is a high incidence of fetal aneuploidy. Numerous studies support that protocols including ovulation-induction, sperm cryostorage, density-gradient centrifugation, and embryo culture can induce genome instability, but the general mechanism is less clear. Application of the genome theory and 4D-Genomics recently led to the establishment of a new paradigm for sexual reproduction; sex primarily constrains genome integrity that defines the biological system rather than just providing genetic diversity at the gene level. We therefore propose that application of assisted reproductive technologies can bypass this sexual reproduction filter as well as potentially induce additional system instability. We have previously demonstrated that a single-cell resolution genomic approach, such as spectral karyotyping to trace stochastic genome level alterations, is effective for pre- and post-natal analysis. We propose that monitoring overall genome alteration at the karyotype level alongside the application of assisted reproductive technologies will improve the efficacy of the techniques while limiting stress-induced genome instability. The development of more single-cell based cytogenomic technologies are needed in order to better understand the system dynamics associated with infertility and the potential impact that assisted reproductive technologies have on genome instability. Importantly, this approach will be useful in studying the potential for diseases to arise as a result of bypassing the filter of sexual reproduction.

Single cell heterogeneity: why unstable genomes are incompatible with average profiles.

Multi-level heterogeneity is a fundamental but underappreciated feature of cancer. Most technical and analytical methods either completely ignore heterogeneity or do not fully account for it, as heterogeneity has been considered noise that needs to be eliminated. We have used single-cell and population-based assays to describe an instability-mediated mechanism where genome heterogeneity drastically affects cell growth and cannot be accurately measured using conventional averages. First, we show that most unstable cancer cell populations exhibit high levels of karyotype heterogeneity, where it is difficult, if not impossible, to karyotypically clone cells. Second, by comparing stable and unstable cell populations, we show that instability-mediated karyotype heterogeneity leads to growth heterogeneity, where outliers dominantly contribute to population growth and exhibit shorter cell cycles. Predictability of population growth is more difficult for heterogeneous cell populations than for homogenous cell populations. Since "outliers" play an important role in cancer evolution, where genome instability is the key feature, averaging methods used to characterize cell populations are misleading. Variances quantify heterogeneity; means (averages) smooth heterogeneity, invariably hiding it. Cell populations of pathological conditions with high genome instability, like cancer, behave differently than karyotypically homogeneous cell populations. Single-cell analysis is thus needed when cells are not genomically identical. Despite increased attention given to single-cell variation mediated heterogeneity of cancer cells, continued use of average-based methods is not only inaccurate but deceptive, as the "average" cancer cell clearly does not exist. Genome-level heterogeneity also may explain population heterogeneity, drug resistance, and cancer evolution.