Cancer progression by non-clonal chromosome aberrations.
ABSTRACT The establishment of the correct conceptual framework is vital to any scientific discipline including cancer research. Influenced by hematologic cancer studies, the current cancer concept focuses on the stepwise patterns of progression as defined by specific recurrent genetic aberrations. This concept has faced a tough challenge as the majority of cancer cases follow non-linear patterns and display stochastic progression. In light of the recent discovery that genomic instability is directly linked to stochastic non-clonal chromosome aberrations (NCCAs), and that cancer progression can be characterized as a dynamic relationship between NCCAs and recurrent clonal chromosome aberrations (CCAs), we propose that the dynamics of NCCAs is a key element for karyotypic evolution in solid tumors. To support this viewpoint, we briefly discuss various basic elements responsible for cancer initiation and progression within an evolutionary context. We argue that even though stochastic changes can be detected at various levels of genetic organization, such as at the gene level and epigenetic level, it is primarily detected at the chromosomal or genome level. Thus, NCCA-mediated genomic variation plays a dominant role in cancer progression. To further illustrate the involvement of NCCA/CCA cycles in the pattern of cancer evolution, four cancer evolutionary models have been proposed based on the comparative analysis of karyotype patterns of various types of cancer.
Article: Cancer-causing karyotypes: chromosomal equilibria between destabilizing aneuploidy and stabilizing selection for oncogenic function.[show abstract] [hide abstract]
ABSTRACT: The chromosomes of cancer cells are unstable, because of aneuploidy. Despite chromosomal instability, however, cancer karyotypes are individual and quasi-stable, as is evident especially from clonal chromosome copy numbers and marker chromosomes. This paradox would be resolved if the karyotypes in cancers represent chromosomal equilibria between destabilizing aneuploidy and stabilizing selection for oncogenic function. To test this hypothesis, we analyzed the initial and long-term karyotypes of seven clones of newly transformed human epithelial, mammary, and muscle cells. Approximately 1 in 100,000 such cells generates transformed clones at 2-3 months after introduction of retrovirus-activated cellular genes or the tumor virus SV40. These frequencies are too low for direct transformation, so we postulated that virus-activated genes initiate transformation indirectly, via specific karyotypes. Using multicolor fluorescence in situ hybridization with chromosome-specific DNA probes, we found individual clonal karyotypes that were stable for at least 34 cell generations-within limits, as follows. Depending on the karyotype, average clonal chromosome numbers were stable within +/- 3%, and chromosome-specific copy numbers were stable in 70-100% cells. At any one time, however, relative to clonal means, per-cell chromosome numbers varied +/-18% and chromosome-specific copy numbers varied +/-1 in 0-30% of cells; unstable nonclonal markers were found within karyotype-specific quotas of <1% to 20% of the total chromosome number. For two clones, karyotypic ploidies also varied. With these rates of variation, the karyotypes of transformed clones would randomize in a few generations unless selection occurs. We conclude that individual aneuploid karyotypes initiate and maintain cancers, much like new species. These cancer-causing karyotypes are in flexible equilibrium between destabilizing aneuploidy and stabilizing selection for transforming function. Karyotypes as a whole, rather than specific mutations, explain the individuality, fluidity, and phenotypic complexity of cancers.Cancer genetics and cytogenetics 02/2009; 188(1):1-25. · 1.54 Impact Factor
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ABSTRACT: This review focuses on the recent patents and use of small-molecule inhibitors (SMIs) of Bcl-2 family proteins as therapeutic agents against cancer. Bcl-2 members are crucial regulators of apoptotic cell death. Apoptosis is an evolutionarily conserved process of programmed cell death that plays an essential role in organism development and tissue homeostasis. Several mechanisms exist allowing cells to escape programmed cell death among them is the overexpression of the antiapoptotic proteins. Cancer cells are often found to overexpress many of these members such as Bcl-2, Bcl-X(L), Mcl-1, Bcl-w and A1/Bfl1 and are usually resistant to a wide range of anti-cancer drugs and treatments. Many groups have been working to develop anti-cancer drugs that block the function of anti-apoptotic Bcl-2 members, thus favoring cell death. Methods include the downregulation of Bcl-2 expression or the use of peptides or small organic molecules to the Bcl-2 binding pocket, preventing its sequestration of proapoptotic proteins such as Bid and Bim. One of the most promising aspects of SMIs in treating cancer is that their targets and mechanisms of action are different from those of cytotoxic drugs and radiation. This makes it feasible to combine SMIs with other treatments, creating a synergistic therapy, without likely development of cross-resistance or increased toxicity. A broad-spectrum or "pan" SMI which targets multiple Bcl-2 family proteins is the goal.Recent Patents on Anti-Cancer Drug Discovery 02/2008; 3(1):20-30. · 2.72 Impact Factor
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ABSTRACT: In contrast to conventional data mining, which searches for specific subsets of genes (extensive variables) to correlate with specific phenotypes, DATE analysis correlates intensive state variables calculated from the same datasets. At the heart of DATE analysis are two biological equations of state not dependent on genetic pathways. This result distinguishes DATE analysis from other bioinformatics approaches. The dimensionless state variable F quantifies the relative overall cellular activity of test cells compared to well-chosen reference cells. The variable pi(i) is the fold-change in the expression of the ith gene of test cells relative to reference. It is the fraction phi of the genome undergoing differential expression-not the magnitude pi-that controls biological change. The state variable phi is equivalent to the control strength of metabolic control analysis. For tractability, DATE analysis assumes a linear system of enzyme-connected networks and exploits the small average contribution of each cellular component. This approach was validated by reproducible values of the state variables F, RNA index, and phi calculated from random subsets of transcript microarray data. Using published microarray data, F, RNA index, and phi were correlated with: (1) the blood-feeding cycle of the malaria parasite, (2) embryonic development of the fruit fly, (3) temperature adaptation of Killifish, (4) exponential growth of cultured S. pneumoniae, and (5) human cancers. DATE analysis was applied to aCGH data from the great apes. A good example of the power of DATE analysis is its application to genomically unstable cancers, which have been refractory to data mining strategies.Biotechnology Progress 09/2009; 25(5):1275-88. · 2.34 Impact Factor