Prevalence of somatic alterations in the colorectal cancer cell genome

Single-molecule genome-wide mutation profiles of cell-free DNA for non-invasive detection of cancer

Somatic mutations are a hallmark of tumorigenesis and may be useful for non-invasive diagnosis of cancer. We analyzed whole-genome sequencing data from 2,511 individuals in the Pan-Cancer Analysis of Whole Genomes (PCAWG) study as well as 489 individuals from four prospective cohorts and found distinct regional mutation type-specific frequencies in tissue and cell-free DNA from patients with cancer that were associated with replication timing and other chromatin features. A machine-learning model using genome-wide mutational profiles combined with other features and followed by CT imaging detected >90% of patients with lung cancer, including those with stage I and II disease. The fixed model was validated in an independent cohort, detected patients with cancer earlier than standard approaches and could be used to monitor response to therapy. This approach lays the groundwork for non-invasive cancer detection using genome-wide mutation features that may facilitate cancer screening and monitoring.

Dynamics of Acquired Resistance to Nivolumab Therapies Varies From Administration Strategies

PURPOSE

The identification of optimal drug administration schedules to overcome the emergence of resistance that causes treatment failure is a major challenge in cancer research. We report the outcomes of a computational strategy to assess the dynamics of tumor progression as a function of time under different treatment regimens.

METHODS

We developed an evolutionary game theory model that combined Lotka-Volterra equations and pharmacokinetic properties with 2 competing cancer species: nivolumab-response cells and Janus kinase (JAK1/2) mutation cells. We selected 3 therapeutic schemes that have been tested in the clinical trials: 3 mg/kg Q2w, 10 mg/kg Q2w, and 480 mg Q4w. The simulation was performed under the intervals of 75, 125, and 175 days, respectively, for each regimen. The data sources of the pharmacokinetic parameters used in this study were collected from previous published clinical trials. Other parameters in the evolutionary model come from the existing references.

FINDINGS

Predictions under various dose schedules indicated a strong selection for nivolumab-independent cells. Under the 3 mg/kg dose strategy, the reproduction rate of JAK mutation cells was highest, with strongest tumor elimination ability at a 75-day interval between treatments. Prolonged drug intervals to 125 or 175 days delayed tumor evolution but accelerated tumor recurrence. Although 10 mg/kg Q2w had an obvious clinical effect in a short time, it further promotes the progress of resistant population compared with the 3 mg/kg dose. Our model suggests that 480 mg Q4w would be more valuable in terms of clinical efficacy, but complete resistant occurs earlier regardless the interval.

IMPLICATIONS

The results of this study emphasize that increasing the dose or shortening the interval between doses accelerates the evolution of heterogeneous populations, although the short-term effect is significant. In practice, the therapeutic regimen should be balanced according to the evolutionary principle.

A systematic view of computational methods for identifying driver genes based on somatic mutation data

Abnormal changes of driver genes are serious for human health and biomedical research. Identifying driver genes, exactly from enormous genes with mutations, promotes accurate diagnosis and treatment of cancer. A lot of works about uncovering driver genes have been developed over the past decades. By analyzing previous works, we find that computational methods are more efficient than traditional biological experiments when distinguishing driver genes from massive data. In this study, we summarize eight common computational algorithms only using somatic mutation data. We first group these methods into three categories according to mutation features they apply. Then, we conclude a general process of nominating candidate cancer driver genes. Finally, we evaluate three representative methods on 10 kinds of cancer derived from The Cancer Genome Atlas Program and five Chinese projects from the International Cancer Genome Consortium. In addition, we compare results of methods with various parameters. Evaluation is performed from four perspectives, including CGC, OG/TSG, Q-value and QQQuantile-Quantileplot. To sum up, we present algorithms using somatic mutation data in order to offer a systematic view of various mutation features and lay the foundation of methods based on integration of mutation information and other types of data.

Recognition of human gastrointestinal cancer neoantigens by circulating PD-1+ lymphocytes

Tumor-resident lymphocytes can mount a response against neoantigens expressed in microsatellite-stable gastrointestinal (GI) cancers, and adoptive transfer of neoantigen-specific lymphocytes has demonstrated antitumor activity in selected patients. However, whether peripheral blood could be used as an alternative minimally invasive source to identify lymphocytes targeting neoantigens in patients with GI cancer with relatively low mutation burden is unclear. We used a personalized high-throughput screening strategy to investigate whether PD-1 expression in peripheral blood could be used to identify CD8+ or CD4+ lymphocytes recognizing neoantigens identified by whole-exome sequencing in 7 patients with GI cancer. We found that neoantigen-specific lymphocytes were preferentially enriched in the CD8+PD-1+/hi or CD4+PD-1+/hi subsets, but not in the corresponding bulk or PD-1- fractions. In 6 of 7 individuals analyzed we identified circulating CD8+ and CD4+ lymphocytes targeting 6 and 4 neoantigens, respectively. Moreover, neoantigen-reactive T cells and a T cell receptor (TCR) isolated from the CD8+PD-1+ subsets recognized autologous tumor, albeit at reduced levels, in 2 patients with available cell lines. These data demonstrate the existence of circulating T cells targeting neoantigens in GI cancer patients and provide an approach to generate enriched populations of personalized neoantigen-specific lymphocytes and isolate TCRs that could be exploited therapeutically to treat cancer.

Systemic Metabolism, Its Regulators, and Cancer: Past Mistakes and Future Potential

There has been a resurgence of interest in cancer metabolism; primarily in the resetting of metabolism within malignant cells. Metabolism within cells has always been a tightly regulated process; initially in protozoans due to metabolic enzymes, and the intracellular signaling pathways that regulate these, being directly sensitive to the availability of nutrients. With the evolution of metazoans many of these controls had been overlaid by extra-cellular regulators that ensured coordinated regulation of metabolism within the community of cells that comprised the organism. Central to these systemic regulators is the insulin/insulin-like growth factor (IGF) system that throughout evolution has integrated the control of tissue growth with metabolic status. Oncological interest in the main systemic metabolic regulators greatly subsided when pharmaceutical strategies designed to treat cancers failed in the clinic. During the same period, however the explosion of new information from genetics has revealed the complexity and heterogeneity of advanced cancers and helped explain the problems of managing cancer when it reaches such a stage. Evidence has also accumulated implying that the setting of the internal environment determines whether cancers progress to advanced disease and metabolic status is clearly an important component of this local ecology. We are in the midst of an epidemic of metabolic disorders and there is considerable research into strategies for controlling metabolism. Integrating these new streams of information suggests new possibilities for cancer prevention; both primary and secondary.

Circulating tumor DNA analysis in breast cancer: Is it ready for prime-time?

Precision Medicine is becoming the new paradigm in healthcare as it enables better resources allocation, treatment optimization with a potential side-effects reduction and consequent impact on quality of life and survival. This revolution is being catalyzed by liquid biopsy technologies, which provide prognostic and predictive information for advanced cancer patients, without the analytical and procedural drawbacks of tissue-biopsy. In particular, circulating tumor DNA (ctDNA) is gaining momentum as a clinically feasible option capable to capture both spatial and temporal tumor heterogeneity. Several techniques are currently available for ctDNA extraction and analysis, each with its preferential case scenarios and preanalytical implications which must be taken into consideration to effectively support clinical decision-making and to better highlight its clinical utility. Aim of this review is to summarize both analytical developments and clinical evidences to offer a comprehensive update on the deployment of ctDNA in breast cancer's (BC) characterization and treatment.

Cancer Etiology: A Metabolic Disease Originating from Life's Major Evolutionary Transition?

A clear understanding of the origins of cancer is the basis of successful strategies for effective cancer prevention and management. The origin of cancer at the molecular and cellular levels is not well understood. Is the primary cause of the origin of cancer the genomic instability or impaired energy metabolism? An attempt was made to present cancer etiology originating from life's major evolutionary transition. The first evolutionary transition went from simple to complex cells when eukaryotic cells with glycolytic energy production merged with the oxidative mitochondrion (The Endosymbiosis Theory first proposed by Lynn Margulis in the 1960s). The second transition went from single-celled to multicellular organisms once the cells obtained mitochondria, which enabled them to obtain a higher amount of energy. Evidence will be presented that these two transitions, as well as the decline of NAD+ and ATP levels, are the root of cancer diseases. Restoring redox homeostasis and reactivation of mitochondrial oxidative metabolism are important factors in cancer prevention.

KIF11 Functions as an Oncogene and Is Associated with Poor Outcomes from Breast Cancer

Purpose

The study aimed to search and identify genes that were differentially expressed in breast cancer, and their roles in cancer growth and progression.

Materials and Methods

The Gene Expression Omnibus (Oncomine) and The Cancer Genome Atlas databases (https://cancergenome.nih.gov/) were screened for genes that were expressed differentially in breast cancer and were closely related to a poor prognosis. Gene expressions were verified by quantitative real-time polymerase chain reaction, and genes were knocked down by a lentivirus-based system. Cell growth and motility were evaluated and in vivo nude mice were used to confirm the in vitro roles of genes. Markers of epithelial-to-mesenchymal transition and the associations of KIF11 with the classical cancer signaling pathways were detected by Western blot.

Results

A series of genes expressed differentially in patients with breast cancer. The prognosis associated with high KIF11 expression was poor, and the expression of KIF11 increased significantly in high stage and malignant tumor cells. Inhibiting KIF11 expression in lentivirus-suppressed cells revealed that KIF11 inhibition significantly reduced cell viability and colony formation, inhibited migration and invasion, but promoted apoptosis. The sizes and weights of KIF11-inhibited tumors in nude mice were significantly lower than in the negative controls. Western blot showed that E-cadherin in breast cancer was significantly upregulated in KIF-inhibited cells and tumor tissues, whereas N-cadherin and vimentin were significantly downregulated. BT549 and MDA231 cells with KIF11 knockdown exhibited decreased ERK, AMPK, AKT, and CREB phosphorylation.

Conclusion

KIF11 acts as a potential oncogene that regulates the development and progression of breast cancer.

GPNMB methylation: a new marker of potentially carcinogenic colon lesions

Background

Epigenetic plays an important role in colorectal neoplasia process. There is a need to determine sound biomarkers of colorectal cancer (CRC) progression with clinical and therapeutic implications. Therefore, we aimed to examine the role and methylation status of Glyco Protein Non-Metastatic GPNM B (GPNMB) gene in normal, adenoma and CRC in African American (AA) patients.

Methods

The methylation status of 13 CpG sites (chr7: 23287345–23,287,426) in GPNMB gene’s promoter, was analyzed by pyrosequencing in human CRC cell lines (HCT116, SW480, and HT29) and microdissected African American paraffin embedded samples (20 normal, 21 non-advanced adenoma (NA), 48 advanced adenoma (AD), and 20 cancer tissues. GPNMB expression was analyzed by immunohistochemistry (IHC) on tissue microarrays (TMA). Correlations between GPNMB methylation and expression with clinicopathological features were analyzed. GPNMB functional analysis was performed in triplicates using cell proliferation, migration and invasion assays in HCT116 colon cell line after stable transfection with a GPNMB-cDNA expression vector.

Results

GPNMB methylation was lower in normal mucosa compared to CRC samples (1/20 [5%] vs. 18/20 [90%]; P < 0.001). AD also had a significantly higher GPNMB methylation frequency than normal colon samples (42/48 [88%] vs 1/20 [5%]; P < 0.001). GPNMB was more frequently methylated in AD than in matched normal mucosa from three patients (3/3 [100%] vs 1/3 [33.3%]; P < 0.001). The frequency of GPNMB methylation in NA differed significantly from that in the normal mucosa (16/21 [76%] vs 1/20 [5%]; P = 0.008). There was statistically significant correlation of higher methylation at advanced stages and lower methylation at stage 1 CRCs (P < 0.05). In agreement with these findings, GPNMB protein expression decreased in CRC tissues compared with AD and NA colon mucosa (p < 0.05). GPNMB overexpression in HCT116 colon cancer cell line decreased cell proliferation [(24 h, P = 0.02), (48 h, P < 0.001, 72 h, P = 0.007)], invasion (p < 0.05) and migration (p > 0.05) compared to the mock-transfected cells.

Conclusion

Our data indicate a high methylation profile leading to a lower GPNMB expression in adenoma and CRC samples. The functional analysis established GPNMB as a potential tumor suppressor gene. As such, GPNMB might be useful as a biomarker of adenomas with high carcinogenic potential.

Upregulated circular RNA circ-102004 that promotes cell proliferation in prostate cancer

OBJECTIVE

Prostate cancer (PCa) is one of the most prevalent cancers affecting men worldwide. However, the biological functions of circRNAs in PCa are still largely unknown.

METHODS

Real-time PCR (RT-PCR) and immunohistochemistry were performed to characterize the circ-102004 expression in both human PCa tissues and cell lines. The apoptosis and cell cycle status of prostate immortalized cell lines that were overexpressed with circ-102004 by transfection was analyzed using flow cytometry. The scratch test and the Transwell assay were conducted to evaluate the ability of transfected cells to migrate and invade. RNA sequencing, pathway analysis, and Western blotting were performed to probe the associations of circ-102004 with the classical cancer signaling pathways after functionally evaluating circ-102004 in a xenograft tumor model.

RESULTS

In the present study, circ-102004 expression was found to be significantly higher in PCa samples than in the matched normal tissues. In functional experiments, circ-102004 is shown to play an oncogenic role in PCa by stimulating cancer cell migration and invasion. Circ-102004 overexpression was also accompanied by significant alterations in many signaling pathways, such as ERK, JNK, and Hedgehog, which are known to cause different types of cancers.

CONCLUSIONS

Circ-102004 is a potential oncogenic gene that regulates the development and progression of PCa. This study provides a scientific basis for targeting circ-102004 for either diagnosis or therapy.

Speciation Theory of Carcinogenesis Explains Karyotypic Individuality and Long Latencies of Cancers

It has been known for over 100 years that cancers have individual karyotypes and arise only years to decades after initiating carcinogens. However, there is still no coherent theory to explain these definitive characteristics of cancer. The prevailing mutation theory holds that cancers are late because the primary cell must accumulate 3–8 causative mutations to become carcinogenic and that mutations, which induce chromosomal instability (CIN), generate the individual karyotypes of cancers. However, since there is still no proven set of mutations that transforms a normal to a cancer cell, we have recently advanced the theory that carcinogenesis is a form of speciation. This theory predicts carcinogens initiate cancer by inducing aneuploidy, which automatically unbalances thousands of genes and thus catalyzes chain-reactions of progressive aneuploidizations. Over time, these aneuploidizations have two endpoints, either non-viable karyotypes or very rarely karyotypes of new autonomous and immortal cancers. Cancer karyotypes are immortalized despite destabilizing congenital aneuploidy by clonal selections for autonomy—similar to those of conventional species. This theory predicts that the very low probability of converting the karyotype of a normal cell to that of a new autonomous cancer species by random aneuploidizations is the reason for the karyotypic individuality of new cancers and for the long latencies from carcinogens to cancers. In testing this theory, we observed: (1) Addition of mutagenic and non-mutagenic carcinogens to normal human and rat cells generated progressive aneuploidizations months before neoplastic transformation. (2) Sub-cloning of a neoplastic rat clone revealed heritable individual karyotypes, rather than the non-heritable karyotypes predicted by the CIN theory. (3) Analyses of neoplastic and preneoplastic karyotypes unexpectedly identified karyotypes with sets of 3–12 new marker chromosomes without detectable intermediates, consistent with single-step origins. We conclude that the speciation theory explains logically the long latencies from carcinogen exposure and the individuality of cancers. In addition, the theory supports the single-step origins of cancers, because karyotypic autonomy is all-or-nothing. Accordingly, we propose that preneoplastic aneuploidy and clonal neoplastic karyotypes provide more reliable therapeutic indications than current analyses of thousands of mutations.

Innovative Disease Model: Zebrafish as an In Vivo Platform for Intestinal Disorder and Tumors

Colorectal cancer (CRC) is one of the world’s most common cancers and is the second leading cause of cancer deaths, causing more than 50,000 estimated deaths each year. Several risk factors are highly associated with CRC, including being overweight, eating a diet high in red meat and over-processed meat, having a history of inflammatory bowel disease, and smoking. Previous zebrafish studies have demonstrated that multiple oncogenes and tumor suppressor genes can be regulated through genetic or epigenetic alterations. Zebrafish research has also revealed that the activation of carcinogenesis-associated signal pathways plays an important role in CRC. The biology of cancer, intestinal disorders caused by carcinogens, and the morphological patterns of tumors have been found to be highly similar between zebrafish and humans. Therefore, the zebrafish has become an important animal model for translational medical research. Several zebrafish models have been developed to elucidate the characteristics of gastrointestinal diseases. This review article focuses on zebrafish models that have been used to study human intestinal disorders and tumors, including models involving mutant and transgenic fish. We also report on xenograft models and chemically-induced enterocolitis. This review demonstrates that excellent zebrafish models can provide novel insights into the pathogenesis of gastrointestinal diseases and help facilitate the evaluation of novel anti-tumor drugs.

The Epigenomics of Embryonic Pathway Signaling in Colorectal Cancer

Colorectal cancer (CRC) is the second-leading cause of cancer death in developed countries. While early detection (e.g., colonoscopy) generally yields excellent outcomes, metastatic and drug-resistant disease is uniformly fatal, and non-compliance for screening remains over 25%. Familial CRCs (10% of total cases) primarily include mutations in the gene APC. Somatic disease is linked to several environmental several risk factors, including mutations in WNT, KRAS, and TGFβ. To reflect the genesis/progression of CRC, a series of five discrete stages, from normal colon mucosa to fully invasive carcinoma, each regulated by specific “gatekeeper” genes, remains well-accepted after 20 years. However, many CRC tumors do not possess those particular mutations, suggesting alternative mechanisms. More recently, embryo-like “cancer stem cells” have been proposed to undergo self-renewal and drive tumorigenesis (and possibly, metastasis), as governed by specific “epigenomic” alterations. Here, we review recent literature describing possible mechanisms that underlie these phenotypes, including cancer “stemness,” believed by many to associate with the epithelial-to-mesenchymal transition (EMT). We further propose that the maintenance of undifferentiated phenotypes, by the activity of distinct transcription factors, facilitates chromatin remodeling and phenotypic plasticity. With that regard, we support recent assertions that EMT is not an “either/or” event, but rather a continuous spectrum of mesenchymal vs. epithelial phenotypes (in various degrees of aberrant differentiation/undifferentiation). Finally, we discuss possible methods of pharmacologically targeting such aberrant epigenomes, with regard to their possible relevance toward halting, or even reversing, colorectal cancer progression.

Natural Selection in Cancer Biology: From Molecular Snowflakes to Trait Hallmarks

Evolution by natural selection is the conceptual foundation for nearly every branch of biology and increasingly also for biomedicine and medical research. In cancer biology, evolution explains how populations of cells in tumors change over time. It is a fundamental question whether this evolutionary process is driven primarily by natural selection and adaptation or by other evolutionary processes such as founder effects and drift. In cancer biology, as in organismal evolutionary biology, there is controversy about this question and also about the use of adaptation through natural selection as a guiding framework for research. In this review, we discuss the differences and similarities between evolution among somatic cells versus evolution among organisms. We review what is known about the parameters and rate of evolution in neoplasms, as well as evidence for adaptation. We conclude that adaptation is a useful framework that accurately explains the defining characteristics of cancer. Further, convergent evolution through natural selection provides the only satisfying explanation both for how a group of diverse pathologies have enough in common to usefully share the descriptive label of "cancer" and for why this convergent condition becomes life-threatening.

Leveraging protein quaternary structure to identify oncogenic driver mutations

BACKGROUND

Identifying key "driver" mutations which are responsible for tumorigenesis is critical in the development of new oncology drugs. Due to multiple pharmacological successes in treating cancers that are caused by such driver mutations, a large body of methods have been developed to differentiate these mutations from the benign "passenger" mutations which occur in the tumor but do not further progress the disease. Under the hypothesis that driver mutations tend to cluster in key regions of the protein, the development of algorithms that identify these clusters has become a critical area of research.

RESULTS

We have developed a novel methodology, QuartPAC (Quaternary Protein Amino acid Clustering), that identifies non-random mutational clustering while utilizing the protein quaternary structure in 3D space. By integrating the spatial information in the Protein Data Bank (PDB) and the mutational data in the Catalogue of Somatic Mutations in Cancer (COSMIC), QuartPAC is able to identify clusters which are otherwise missed in a variety of proteins. The R package is available on Bioconductor at: http://bioconductor.jp/packages/3.1/bioc/html/QuartPAC.html .

CONCLUSION

QuartPAC provides a unique tool to identify mutational clustering while accounting for the complete folded protein quaternary structure.

A spatial model predicts that dispersal and cell turnover limit intratumour heterogeneity

Most cancers in humans are large, measuring centimetres in diameter, and composed of many billions of cells. An equivalent mass of normal cells would be highly heterogeneous as a result of the mutations that occur during each cell division. What is remarkable about cancers is that virtually every neoplastic cell within a large tumour often contains the same core set of genetic alterations, with heterogeneity confined to mutations that emerge late during tumour growth. How such alterations expand within the spatially constrained three-dimensional architecture of a tumour, and come to dominate a large, pre-existing lesion, has been unclear. Here we describe a model for tumour evolution that shows how short-range dispersal and cell turnover can account for rapid cell mixing inside the tumour. We show that even a small selective advantage of a single cell within a large tumour allows the descendants of that cell to replace the precursor mass in a clinically relevant time frame. We also demonstrate that the same mechanisms can be responsible for the rapid onset of resistance to chemotherapy. Our model not only provides insights into spatial and temporal aspects of tumour growth, but also suggests that targeting short-range cellular migratory activity could have marked effects on tumour growth rates.

A spatial simulation approach to account for protein structure when identifying non-random somatic mutations

Background

Current research suggests that a small set of “driver” mutations are responsible for tumorigenesis while a larger body of “passenger” mutations occur in the tumor but do not progress the disease. Due to recent pharmacological successes in treating cancers caused by driver mutations, a variety of methodologies that attempt to identify such mutations have been developed. Based on the hypothesis that driver mutations tend to cluster in key regions of the protein, the development of cluster identification algorithms has become critical.

Results

We have developed a novel methodology, SpacePAC (Spatial Protein Amino acid Clustering), that identifies mutational clustering by considering the protein tertiary structure directly in 3D space. By combining the mutational data in the Catalogue of Somatic Mutations in Cancer (COSMIC) and the spatial information in the Protein Data Bank (PDB), SpacePAC is able to identify novel mutation clusters in many proteins such as FGFR3 and CHRM2. In addition, SpacePAC is better able to localize the most significant mutational hotspots as demonstrated in the cases of BRAF and ALK. The R package is available on Bioconductor at: http://www.bioconductor.org/packages/release/bioc/html/SpacePAC.html.

Conclusion

SpacePAC adds a valuable tool to the identification of mutational clusters while considering protein tertiary structure.

An improved understanding of cancer genomics through massively parallel sequencing

DNA sequencing technology advances have enabled genetic investigation of more samples in a shorter time than has previously been possible. Furthermore, the ability to analyze and understand large sequencing datasets has improved due to concurrent advances in sequence data analysis methods and software tools. Constant improvements to both technology and analytic approaches in this fast moving field are evidenced by many recent publications of computational methods, as well as biological results linking genetic events to human disease. Cancer in particular has been the subject of intense investigation, owing to the genetic underpinnings of this complex collection of diseases. New massively-parallel sequencing (MPS) technologies have enabled the investigation of thousands of samples, divided across tens of different tumor types, resulting in new driver gene identification, mutagenic pattern characterization, and other newly uncovered features of tumor biology. This review will focus both on methods and recent results: current analytical approaches to DNA and RNA sequencing will be presented followed by a review of recent pan-cancer sequencing studies. This overview of methods and results will not only highlight the recent advances in cancer genomics, but also the methods and tools used to accomplish these advancements in a constantly and rapidly improving field.

A graph theoretic approach to utilizing protein structure to identify non-random somatic mutations

Background

It is well known that the development of cancer is caused by the accumulation of somatic mutations within the genome. For oncogenes specifically, current research suggests that there is a small set of "driver" mutations that are primarily responsible for tumorigenesis. Further, due to recent pharmacological successes in treating these driver mutations and their resulting tumors, a variety of approaches have been developed to identify potential driver mutations using methods such as machine learning and mutational clustering. We propose a novel methodology that increases our power to identify mutational clusters by taking into account protein tertiary structure via a graph theoretical approach.

Results

We have designed and implemented GraphPAC (Graph Protein Amino acid Clustering) to identify mutational clustering while considering protein spatial structure. Using GraphPAC, we are able to detect novel clusters in proteins that are known to exhibit mutation clustering as well as identify clusters in proteins without evidence of prior clustering based on current methods. Specifically, by utilizing the spatial information available in the Protein Data Bank (PDB) along with the mutational data in the Catalogue of Somatic Mutations in Cancer (COSMIC), GraphPAC identifies new mutational clusters in well known oncogenes such as EGFR and KRAS. Further, by utilizing graph theory to account for the tertiary structure, GraphPAC discovers clusters in DPP4, NRP1 and other proteins not identified by existing methods. The R package is available at: http://bioconductor.org/packages/release/bioc/html/GraphPAC.html.

Conclusion

GraphPAC provides an alternative to iPAC and an extension to current methodology when identifying potential activating driver mutations by utilizing a graph theoretic approach when considering protein tertiary structure.

Effect of dedifferentiation on time to mutation acquisition in stem cell-driven cancers

Accumulating evidence suggests that many tumors have a hierarchical organization, with the bulk of the tumor composed of relatively differentiated short-lived progenitor cells that are maintained by a small population of undifferentiated long-lived cancer stem cells. It is unclear, however, whether cancer stem cells originate from normal stem cells or from dedifferentiated progenitor cells. To address this, we mathematically modeled the effect of dedifferentiation on carcinogenesis. We considered a hybrid stochastic-deterministic model of mutation accumulation in both stem cells and progenitors, including dedifferentiation of progenitor cells to a stem cell-like state. We performed exact computer simulations of the emergence of tumor subpopulations with two mutations, and we derived semi-analytical estimates for the waiting time distribution to fixation. Our results suggest that dedifferentiation may play an important role in carcinogenesis, depending on how stem cell homeostasis is maintained. If the stem cell population size is held strictly constant (due to all divisions being asymmetric), we found that dedifferentiation acts like a positive selective force in the stem cell population and thus speeds carcinogenesis. If the stem cell population size is allowed to vary stochastically with density-dependent reproduction rates (allowing both symmetric and asymmetric divisions), we found that dedifferentiation beyond a critical threshold leads to exponential growth of the stem cell population. Thus, dedifferentiation may play a crucial role, the common modeling assumption of constant stem cell population size may not be adequate, and further progress in understanding carcinogenesis demands a more detailed mechanistic understanding of stem cell homeostasis.

Recent evidence suggests that, like many normal tissues, many cancers are maintained by a small population of immortal stem cells that divide indefinitely to produce many differentiated cells. Cancer stem cells may come directly from mutation of normal stem cells, but this route demands high mutation rates, because there are few normal stem cells. There are, however, many differentiated cells, and mutations can cause such cells to “dedifferentiate” into a stem-like state. We used mathematical modeling to study the effects of dedifferentiation on the time to cancer onset. We found that the effect of dedifferentiation depends critically on how stem cell numbers are controlled by the body. If homeostasis is very tight (due to all divisions being asymmetric), then dedifferentiation has little effect, but if homeostatic control is looser (allowing both symmetric and asymmetric divisions), then dedifferentiation can dramatically hasten cancer onset and lead to exponential growth of the cancer stem cell population. Our results suggest that dedifferentiation may be a very important factor in cancer and that more study of dedifferentiation and stem cell control is necessary to understand and prevent cancer onset.

Epithelial cancers in the post-genomic era: should we reconsider our lifestyle?

The age-related epithelial cancers of the breast, colorectum and prostate are the most prevalent and are increasing in our aging populations. Epithelial cells turnover rapidly and mutations naturally accumulate throughout life. Most epithelial cancers arise from this normal mutation rate. All elderly individuals will harbour many cells with the requisite mutations and most will develop occult neoplastic lesions. Although essential for initiation, these mutations are not sufficient for the progression of cancer to a life-threatening disease. This progression appears to be dependent on context: the tissue ecosystem within individuals and lifestyle exposures across populations of individuals. Together, this implies that the seeds may be plentiful but they only germinate in the right soil. The incidence of these cancers is much lower in Eastern countries but is increasing with Westernisation and increases more acutely in migrants to the West. A Western lifestyle is strongly associated with perturbed metabolism, as evidenced by the epidemics of obesity and diabetes: this may also provide the setting enabling the progression of epithelial cancers. Epidemiology has indicated that metabolic biomarkers are prospectively associated with cancer incidence and prognosis. Furthermore, within cancer research, there has been a rediscovery that a switch in cell metabolism is critical for cancer progression but this is set within the metabolic status of the host. The seed may only germinate if the soil is fertile. This perspective brings together the different avenues of investigation implicating the role that metabolism may play within the context of post-genomic concepts of cancer.

Direct measurements of human colon crypt stem cell niche genetic fidelity: the role of chance in non-darwinian mutation selection

Perfect human stem cell genetic fidelity would prevent aging and cancer. However, perfection would be difficult to achieve, and aging is universal and cancers common. A hypothesis is that because mutations are inevitable over a human lifetime, downstream mechanisms have evolved to manage the deleterious effects of beneficial and lethal mutations. In the colon, a crypt stem cell architecture reduces the number of mitotic cells at risk for mutation accumulation, and multiple niche stem cells ensure that a lethal mutation within any single stem cell does not lead to crypt death. In addition, the architecture of the colon crypt stem cell niche may harness probability or chance to randomly discard many beneficial mutations that might lead to cancer. An analysis of somatic chromosome copy number alterations (CNAs) reveals a lack of perfect fidelity in individual normal human crypts, with age-related increases and higher frequencies in ulcerative colitis, a proliferative, inflammatory disease. The age-related increase in somatic CNAs appears consistent with relatively normal replication error and cell division rates. Surprisingly, and similar to point mutations in cancer genomes, the types of crypt mutations were more consistent with random fixation rather than selection. In theory, a simple “non-Darwinian” way to nullify selection is to reduce the size of the reproducing population. Fates are more determined by chance rather than selection in very small populations, and therefore selection may be minimized within small crypt niches. The desired effect is that many beneficial mutations that might lead to cancer are randomly lost by drift rather than fixed by selection. The subdivision of the colon into multiple very small stem cell niches may trade Darwinian evolution for non-Darwinian somatic cell evolution, capitulating to aging but reducing cancer risks.

Utilizing protein structure to identify non-random somatic mutations

Background

Human cancer is caused by the accumulation of somatic mutations in tumor suppressors and oncogenes within the genome. In the case of oncogenes, recent theory suggests that there are only a few key “driver” mutations responsible for tumorigenesis. As there have been significant pharmacological successes in developing drugs that treat cancers that carry these driver mutations, several methods that rely on mutational clustering have been developed to identify them. However, these methods consider proteins as a single strand without taking their spatial structures into account. We propose an extension to current methodology that incorporates protein tertiary structure in order to increase our power when identifying mutation clustering.

Results

We have developed iPAC (identification of Protein Amino acid Clustering), an algorithm that identifies non-random somatic mutations in proteins while taking into account the three dimensional protein structure. By using the tertiary information, we are able to detect both novel clusters in proteins that are known to exhibit mutation clustering as well as identify clusters in proteins without evidence of clustering based on existing methods. For example, by combining the data in the Protein Data Bank (PDB) and the Catalogue of Somatic Mutations in Cancer, our algorithm identifies new mutational clusters in well known cancer proteins such as KRAS and PI3KC α. Further, by utilizing the tertiary structure, our algorithm also identifies clusters in EGFR, EIF2AK2, and other proteins that are not identified by current methodology. The R package is available at: http://www.bioconductor.org/packages/2.12/bioc/html/iPAC.html.

Conclusion

Our algorithm extends the current methodology to identify oncogenic activating driver mutations by utilizing tertiary protein structure when identifying nonrandom somatic residue mutation clusters.

Large chromosome deletions, duplications, and gene conversion events accumulate with age in normal human colon crypts

Little is known about the types and numbers of mutations that may accumulate in normal human cells with age. Such information would require obtaining enough DNA from a single cell to accurately carry out reliable analysis despite extensive amplification; and complete genomic coverage under these circumstances is difficult. We have compared colon crypts, which are putatively clonal and contain ~2000 cells each, to determine how much somatic genetic variation occurs in vivo (without ex vivo cell culturing). Using high-density SNP microarrays, we find that chromosome deletions, duplications, and gene conversions were significantly more frequent in colons from the older individuals. These changes affected lengths ranging from 73 kb to 46 Mb. Although detection requires progeny of a single mutant stem cell to reach niche dominance over neighboring stem cells, none of the deletions appear likely to confer a selective advantage. Mutations can become fixed randomly during stem cell evolution through neutral drift in normal human crypts. The fact that chromosomal changes are detected in individual crypts with increasing age suggests that either such changes accumulate with age or single stem cell dominance increases with age, and the former is more likely. This progressive genome-wide divergence of human somatic cells with age has implications for aging and disease in multicellular organisms.

Immortality of cancers: a consequence of inherent karyotypic variations and selections for autonomy

Immortality is a common characteristic of cancers, but its origin and purpose are still unclear. Here we advance a karyotypic theory of immortality based on the theory that carcinogenesis is a form of speciation. Accordingly, cancers are generated from normal cells by random karyotypic rearrangements and selection for cancer-specific reproductive autonomy. Since such rearrangements unbalance long-established mitosis genes, cancer karyotypes vary spontaneously but are stabilized perpetually by clonal selections for autonomy. To test this theory we have analyzed neoplastic clones, presumably immortalized by transfection with overexpressed telomerase or with SV40 tumor virus, for the predicted clonal yet flexible karyotypes. The following results were obtained: (1) All immortal tumorigenic lines from cells transfected with overexpressed telomerase had clonal and flexible karyotypes; (2) Searching for the origin of such karyotypes, we found spontaneously increasing, random aneuploidy in human fibroblasts early after transfection with overexpressed telomerase; (3) Late after transfection, new immortal tumorigenic clones with new clonal and flexible karyotypes were found; (4) Testing immortality of one clone during 848 unselected generations showed the chromosome number was stable, but the copy numbers of 36% of chromosomes drifted ± 1; (5) Independent immortal tumorigenic clones with individual, flexible karyotypes arose after individual latencies; (6) Immortal tumorigenic clones with new flexible karyotypes also arose late from cells of a telomerase-deficient mouse rendered aneuploid by SV40 virus. Because immortality and tumorigenicity: (1) correlated exactly with individual clonal but flexible karyotypes; (2) originated simultaneously with such karyotypes; and (3) arose in the absence of telomerase, we conclude that clonal and flexible karyotypes generate the immortality of cancers.

Overexpression of DNA polymerase iota (Polι) in esophageal squamous cell carcinoma

The present study investigated the transcriptional regulation of low-fidelity translesion DNA synthesis (TLS) polymerases in human esophageal carcinoma. Significantly higher mRNA expression of polymerase zeta (Polξ), RAD18, polymerase iota (Polι), and polymerase kappa (Polκ) was found in esophageal carcinomas. The increased expression of Polι in tumor samples was further confirmed by immunohistochemistry. The promoter of POLI that encodes Polι was found to be hypomethylated, although the overexpression of this gene was unlikely to be associated with methylation in tumors. We further identified Sp1 and Oct-1 binding sites present in the POLI promoter. We observed that the binding affinity of Sp1 to the POLI promoter was significantly increased in cancerous tissues and that Sp1 activated POLI gene transcription in cultured cell lines. The present study demonstrates overexpression of the TLS genes in esophageal carcinoma and identifies a key role for Sp1 in upregulating POLI gene expression.

Origin of metastases: subspecies of cancers generated by intrinsic karyotypic variations

Conventional mutation theories do not explain (1) why the karyotypes of metastases are related to those of parental cancers but not to those of metastases of other cancers and (2) why cancers metastasize at rates that often far exceed those of conventional mutations. To answer these questions, we advance here the theory that metastases are autonomous subspecies of cancers, rather than mutations. Since cancers are species with intrinsically flexible karyotypes, they can generate new subspecies by spontaneous karyotypic rearrangements. This phylogenetic theory predicts that metastases are karyotypically related to parental cancers but not to others. Testing these predictions on metastases from two pancreatic cancers, we found: (1) Metastases had individual karyotypes and phenotypes. The karyotypes of metastases were related to, but different from, those of parental cancers in 11 out of 37 and 26 out of 49 parental chromosomal units. Chromosomal units are defined as intact chromosomes with cancer-specific copy numbers and marker chromosomes that are > 50% clonal. (2) Metastases from the two different cancers did not share chromosomal units. Testing the view that multi-chromosomal rearrangements occur simultaneously in cancers, as opposed to sequentially, we found spontaneous non-clonal rearrangements with as many new chromosomal units as in authentic metastases. We conclude that metastases are individual autonomous species differing from each other and parental cancers in species-specific karyotypes and phenotypes. They are generated from parental cancers by multiple simultaneous karyotypic rearrangements, much like new species. The species-specific individualities of metastases explain why so many searches for commonalities have been unsuccessful.

Molecular tumor clocks to study the evolution of drug resistance

It is likely that drug resistance evolves after transformation. Exactly how these resistant cells arise is uncertain. This review outlines how the evolution of individual human cancers may be inferred by comparing genomic variation from different parts of the same tumor. The past of a tumor may help predict its responses to chemotherapy.

High DNA methylation pattern intratumoral diversity implies weak selection in many human colorectal cancers

BACKGROUND

It is possible to infer the past of populations by comparing genomes between individuals. In general, older populations have more genomic diversity than younger populations. The force of selection can also be inferred from population diversity. If selection is strong and frequently eliminates less fit variants, diversity will be limited because new, initially homogeneous populations constantly emerge.

METHODOLOGY AND RESULTS

Here we translate a population genetics approach to human somatic cancer cell populations by measuring genomic diversity within and between small colorectal cancer (CRC) glands. Control tissue culture and xenograft experiments demonstrate that the population diversity of certain passenger DNA methylation patterns is reduced after cloning but subsequently increases with time. When measured in CRC gland populations, passenger methylation diversity from different parts of nine CRCs was relatively high and uniform, consistent with older, stable lineages rather than mixtures of younger homogeneous populations arising from frequent cycles of selection. The diversity of six metastases was also high, suggesting dissemination early after transformation. Diversity was lower in DNA mismatch repair deficient CRC glands, possibly suggesting more selection and the elimination of less fit variants when mutation rates are elevated.

CONCLUSION/SIGNIFICANCE

The many hitchhiking passenger variants observed in primary and metastatic CRC cell populations are consistent with relatively old populations, suggesting that clonal evolution leading to selective sweeps may be rare after transformation. Selection in human cancers appears to be a weaker than presumed force after transformation, consistent with the observed rarity of driver mutations in cancer genomes. Phenotypic plasticity rather than the stepwise acquisition of new driver mutations may better account for the many different phenotypes within human tumors.

Human cancers express mutator phenotypes: origin, consequences and targeting

Recent data on DNA sequencing of human tumours have established that cancer cells contain thousands of mutations. These data support the concept that cancer cells express a mutator phenotype. This Perspective considers the evidence supporting the mutator phenotype hypothesis, the origin and consequences of a mutator phenotype, the implications for personalized medicine and the feasibility of ablating tumours by error catastrophe.

PathScan: a tool for discerning mutational significance in groups of putative cancer genes

MOTIVATION

The expansion of cancer genome sequencing continues to stimulate development of analytical tools for inferring relationships between somatic changes and tumor development. Pathway associations are especially consequential, but existing algorithms are demonstrably inadequate.

METHODS

Here, we propose the PathScan significance test for the scenario where pathway mutations collectively contribute to tumor development. Its design addresses two aspects that established methods neglect. First, we account for variations in gene length and the consequent differences in their mutation probabilities under the standard null hypothesis of random mutation. The associated spike in computational effort is mitigated by accurate convolution-based approximation. Second, we combine individual probabilities into a multiple-sample value using Fisher-Lancaster theory, thereby improving differentiation between a few highly mutated genes and many genes having only a few mutations apiece. We investigate accuracy, computational effort and power, reporting acceptable performance for each.

RESULTS

As an example calculation, we re-analyze KEGG-based lung adenocarcinoma pathway mutations from the Tumor Sequencing Project. Our test recapitulates the most significant pathways and finds that others for which the original test battery was inconclusive are not actually significant. It also identifies the focal adhesion pathway as being significantly mutated, a finding consistent with earlier studies. We also expand this analysis to other databases: Reactome, BioCarta, Pfam, PID and SMART, finding additional hits in ErbB and EPHA signaling pathways and regulation of telomerase. All have implications and plausible mechanistic roles in cancer. Finally, we discuss aspects of extending the method to integrate gene-specific background rates and other types of genetic anomalies.

AVAILABILITY

PathScan is implemented in Perl and is available from the Genome Institute at: http://genome.wustl.edu/software/pathscan.

Detection of somatic mutations by high-resolution DNA melting (HRM) analysis in multiple cancers

Identification of somatic mutations in cancer is a major goal for understanding and monitoring the events related to cancer initiation and progression. High resolution melting (HRM) curve analysis represents a fast, post-PCR high-throughput method for scanning somatic sequence alterations in target genes. The aim of this study was to assess the sensitivity and specificity of HRM analysis for tumor mutation screening in a range of tumor samples, which included 216 frozen pediatric small rounded blue-cell tumors as well as 180 paraffin-embedded tumors from breast, endometrial and ovarian cancers (60 of each). HRM analysis was performed in exons of the following candidate genes known to harbor established commonly observed mutations: PIK3CA, ERBB2, KRAS, TP53, EGFR, BRAF, GATA3, and FGFR3. Bi-directional sequencing analysis was used to determine the accuracy of the HRM analysis. For the 39 mutations observed in frozen samples, the sensitivity and specificity of HRM analysis were 97% and 87%, respectively. There were 67 mutation/variants in the paraffin-embedded samples, and the sensitivity and specificity for the HRM analysis were 88% and 80%, respectively. Paraffin-embedded samples require higher quantity of purified DNA for high performance. In summary, HRM analysis is a promising moderate-throughput screening test for mutations among known candidate genomic regions. Although the overall accuracy appears to be better in frozen specimens, somatic alterations were detected in DNA extracted from paraffin-embedded samples.

Mutation and epigenetic molecular clocks in cancer

A quandary of evolution is how to measure change over time. A natural inclination is to use morphologic criteria-the greater the differences between two phenotypes, the greater amount of time needed to evolve these differences. However, appearances may be deceiving, and another approach to infer time is with molecular clocks. Here, the greater the differences between two genomes, on average the greater the time since a common ancestor. Recent advances in DNA sequencing shed new light on how human cancers might evolve.

Noise-driven heterogeneity in the rate of genetic-variant generation as a basis for evolvability

Molecular biologists have long searched for molecular mechanisms responsible for tuning the rate of genetic-variant generation (RGVG) in fluctuating environments. In spite of several bacterial examples, no regulated variation in the RGVG has been identified in eukaryotic systems. Based notably on the example of industrial and pathogenic yeasts, this article proposes a nonregulated molecular evolutionary mechanism for the appearance of the transient increase of the RGVG in eukaryotic cell populations facing challenging environments. The stochastic nature of gene expression allows a model in which the RGVG in the population can be rapidly tuned as a result of a simple Darwinian process acting on noise-driven heterogeneity in the RGVG from cell to cell. The high flexibility conferred through this model could resolve paradoxical situations, especially concerning the mutator phenotype in cancer cells.

High CCND1 amplification identifies a group of poor prognosis women with estrogen receptor positive breast cancer

CCND1 encodes for the cyclin D1 protein involved in G1/S cell cycle transition. In breast cancer the mechanism of CCND1 amplification, relationship between cyclin D1 protein expression and the key clinical markers estrogen receptor (ER) and HER2 requires elucidation. Tissue microarrays of primary invasive breast cancer from 93 women were evaluated for CCND1 amplification by fluorescent in-situ hybridization and cyclin D1 protein overexpression by immunohistochemistry. CCND1 amplification was identified in 27/93 (30%) cancers and 59/93 (63%) cancers had overexpression of cyclin D1. CCND1 amplification was significantly associated with cyclin D1 protein overexpression (p < 0.001; Fisher's exact test) and both CCND1 amplification and cyclin D1 protein expression with oestrogen receptor (ER) expression (p = 0.003 and p < 0.001; Fishers exact test). Neither CCND1 amplification nor cyclinD1 expression was associated with tumor size, pathological node status or HER2 amplification, but high CCND1 amplification (Copy Number Gain (CNG) > or = 8) was associated with high tumor grade (p = 0.005; chi square 7.915, 2 df) and worse prognosis by Nottingham Prognostic Index (p = 0.001; 2 sample t-test). High CCND1 amplification (CNG > or = 8) may identify a subset of patients with poor prognosis ER-positive breast cancers who should be considered for additional therapy.

Statistical method on nonrandom clustering with application to somatic mutations in cancer

Background

Human cancer is caused by the accumulation of tumor-specific mutations in oncogenes and tumor suppressors that confer a selective growth advantage to cells. As a consequence of genomic instability and high levels of proliferation, many passenger mutations that do not contribute to the cancer phenotype arise alongside mutations that drive oncogenesis. While several approaches have been developed to separate driver mutations from passengers, few approaches can specifically identify activating driver mutations in oncogenes, which are more amenable for pharmacological intervention.

Results

We propose a new statistical method for detecting activating mutations in cancer by identifying nonrandom clusters of amino acid mutations in protein sequences. A probability model is derived using order statistics assuming that the location of amino acid mutations on a protein follows a uniform distribution. Our statistical measure is the differences between pair-wise order statistics, which is equivalent to the size of an amino acid mutation cluster, and the probabilities are derived from exact and approximate distributions of the statistical measure. Using data in the Catalog of Somatic Mutations in Cancer (COSMIC) database, we have demonstrated that our method detects well-known clusters of activating mutations in KRAS, BRAF, PI3K, and β-catenin. The method can also identify new cancer targets as well as gain-of-function mutations in tumor suppressors.

Conclusions

Our proposed method is useful to discover activating driver mutations in cancer by identifying nonrandom clusters of somatic amino acid mutations in protein sequences.

A simple algebraic cancer equation: calculating how cancers may arise with normal mutation rates

Background

The purpose of this article is to present a relatively easy to understand cancer model where transformation occurs when the first cell, among many at risk within a colon, accumulates a set of driver mutations. The analysis of this model yields a simple algebraic equation, which takes as inputs the number of stem cells, mutation and division rates, and the number of driver mutations, and makes predictions about cancer epidemiology.

Methods

The equation [p = 1 - (1 - (1 - (1 - u)^d)^k)^Nm ] calculates the probability of cancer (p) and contains five parameters: the number of divisions (d), the number of stem cells (N × m), the number of critical rate-limiting pathway driver mutations (k), and the mutation rate (u). In this model progression to cancer "starts" at conception and mutations accumulate with cell division. Transformation occurs when a critical number of rate-limiting pathway mutations first accumulates within a single stem cell.

Results

When applied to several colorectal cancer data sets, parameter values consistent with crypt stem cell biology and normal mutation rates were able to match the increase in cancer with aging, and the mutation frequencies found in cancer genomes. The equation can help explain how cancer risks may vary with age, height, germline mutations, and aspirin use. APC mutations may shorten pathways to cancer by effectively increasing the numbers of stem cells at risk.

Conclusions

The equation illustrates that age-related increases in cancer frequencies may result from relatively normal division and mutation rates. Although this equation does not encompass all of the known complexity of cancer, it may be useful, especially in a teaching setting, to help illustrate relationships between small and large cancer features.

Our changing view of the genomic landscape of cancer

Sporadic tumours, which account for the majority of all human cancers, arise from the acquisition of somatic, genetic and epigenetic alterations leading to changes in gene sequence, structure, copy number and expression. Within the last decade, the availability of a complete sequence-based map of the human genome, coupled with significant technological advances, has revolutionized the search for somatic alterations in tumour genomes. Recent landmark studies, which resequenced all coding exons within breast, colorectal, brain and pancreatic cancers, have shed new light on the genomic landscape of cancer. Within a given tumour type there are many infrequently mutated genes and a few frequently mutated genes, resulting in incredible genetic heterogeneity. However, when the altered genes are placed into biological processes and biochemical pathways, this complexity is significantly reduced and shared pathways that are affected in significant numbers of tumours can be discerned. The advent of next-generation sequencing technologies has opened up the potential to resequence entire tumour genomes to interrogate protein-encoding genes, non-coding RNA genes, non-genic regions and the mitochondrial genome. During the next decade it is anticipated that the most common forms of human cancer will be systematically surveyed to identify the underlying somatic changes in gene copy number, sequence and expression. The resulting catalogues of somatic alterations will point to candidate cancer genes requiring further validation to determine whether they have a causal role in tumourigenesis. The hope is that this knowledge will fuel improvements in cancer diagnosis, prognosis and therapy, based on the specific molecular alterations that drive individual tumours. In this review, I will provide a historical perspective on the identification of somatic alterations in the pre- and post-genomic eras, with a particular emphasis on recent pioneering studies that have provided unprecedented insights into the genomic landscape of human cancer.

Mutational heterogeneity in human cancers: origin and consequences

Cancer recapitulates Darwinian evolution. Mutations acquired during life that provide cells with a growth or survival advantage will preferentially multiply to form a tumor. As a result of The Cancer Genome Atlas Project, we have gathered detailed information on the nucleotide sequence changes in a number of human cancers. The sources of mutations in cancer are diverse, and the complexity of those found to be clonally present in tumors has increasingly made it difficult to identify key rate-limiting genes for tumor growth that could serve as potential targets for directed therapies. The impact of DNA sequencing on future cancer research and personalized therapy is likely to be profound and merits critical evaluation.

XPC is involved in genome maintenance through multiple pathways in different tissues

In an attempt to evaluate the role of the Xpc gene in maintaining genomic stability in vivo under normal conditions, the age-dependent accumulation of spontaneous mutations in different tissues was analyzed in Xpc-deficient lacZ-transgenic mice. Brain, testis, and small intestine revealed no effects from the Xpc-deficiency, whereas liver, spleen, heart, and lung showed an enhanced age-related accumulation of mutations in Xpc-deficient mice. In the spleen, the effect was not obvious at 2 and 12 months of age, but became apparent at 23 months. The magnitude of the observed effect at an advanced age was similar in the liver, spleen and heart, but was comparatively smaller in the lung. Haploinsufficiency was observed in liver and spleen but not in heart and lung. Analysis of DNA sequences in the mutants revealed that the frequency of G:C to T:A changes were elevated in the liver and heart of Xpc-deficient aged mice, supporting the possible involvement of XPC in base excision repair of oxidized guanine. The occurrence of two or more mutations within a single lacZ gene was termed a multiple mutation and was also elevated in old Xpc-deficient mice. Among the clones examined, two mutant clones showed as many as four mutations within a short stretch of DNA. This is the first demonstration to support suggestions for the existence of a role for XPC in the suppression of multiple mutations. These multiple mutations could conceivably be generated by error-prone trans-lesional DNA synthesis. Overall, these results indicate that there may be diverse roles or mechanisms through which XPC participates in genome maintenance in different tissues.

Cancer genome sequencing--an interim analysis

With the publishing of the first complete whole genome of a human cancer and its paired normal, we have passed a key milestone in the cancer genome sequencing strategy. The generation of such data will, thanks to technical advances, soon become commonplace. As a significant number of proof-of-concept studies have been published, it is important to analyze now the likely implications of these data and how this information might frame cancer research in the near future. The diversity of genes mutated within individual tumor types, the most striking feature of all studies reported to date, challenges gene-centric models of tumorigenesis. Although cancer genome sequencing will revolutionize certain aspects of personalized care, the value of these studies in facilitating the development of new therapies, their primary goal, seems less promising. Most significantly, however, the cancer genome sequencing strategy, as currently applied, fails to characterize the most relevant genomic features of cancer-the mutational heterogeneity within individual tumors.

Older individuals appear to acquire mitotically older colorectal cancers

The incidence of many common cancers increases with ageing. The purpose of this study is to infer whether cancer mitotic ages (total numbers of divisions since the zygote) also increase with chronological age. Mitotic ages may be inferred by counting numbers of replication errors or neutral passenger changes. Methylation at certain CpG-rich sequences or 'tags' appears to be proportional to mitotic age, because age-related increases in tag methylation are observed in mitotic tissues such as the colon. Such passenger tag methylation was measured by bisulphite sequencing from 16 colorectal cancers from differently aged male individuals. Both normal colon and cancers exhibited significant age-related increases in tag methylation, but cancer methylation was significantly higher. Therefore, older individuals appear to have mitotically older colorectal cancers. Cell division per se may be an important mechanism underlying the increased incidence of colorectal cancer with ageing, because the neoplastic phase appears more commonly to start from mitotically older crypt stem cells.

Mutational spectra of human cancer

The purpose of this review is to summarize the evidence that can be used to reconstruct the etiology of human cancers from mutations found in tumors. Mutational spectra of the tumor suppressor gene p53 (TP53) are tumor specific. In several cases, these mutational spectra can be linked to exogenous carcinogens, most notably for sunlight-associated skin cancers, tobacco-associated lung cancers, and aristolochic acid-related urothelial tumors. In the TP53 gene, methylated CpG dinucleotides are sequences selectively targeted by endogenous and exogenous mutagenic processes. Recent high-throughput sequencing efforts analyzing a large number of genes in cancer genomes have so far, for the most part, produced mutational spectra similar to those in TP53 but have unveiled a previously unrecognized common G to C transversion mutation signature at GpA dinucleotides in breast cancers and several other cancers. Unraveling the origin of these G to C mutations will be of importance for understanding cancer etiology.