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Aneuploidy and Carcinogenesis

Curator and Reporter: Larry H. Berntein, MD, FCAP

and

Curator: Stephen J Williams, PhD

 

New Theory of Cancer Development

Researchers have been unable to explain why cancer cells contain abnormal numbers of chromosomes for over a century. The phenomenon known as aneuploidy is associated with all types of cancer. Harvard Medical School researchers have hypothesized why cancer cells contain many more chromosome abnormalities than healthy cells. They have devised a way to understand

  • patterns of aneuploidy in tumors and
  • predict which genes in the affected chromosomes are likely to be cancer suppressors or promoters, and
  • they propose that aneuploidy is a driver of cancer, rather than a result of it.

The study, to be published online in Cell, offers a new theory of cancer development and could lead to new treatment targets.  This would be feasible if they could identify key cancers suppressors.

The cancer cell characteristically has many gene deletions and amplifications, chromosome gains and losses. Although it has the appearance of randomness, previous research has shown that there is a pattern to the alterations in chromosomes and chromosome arms, which suggests that we can decipher that pattern and perhaps learn how or if it drives the cancer, according to the senior author, Stephen Elledge, Gregor Mendel professor of Genetics and of Medicine at HMS and professor of medicine at Brigham and Women’s Hospital.  Having proposed the theory about how these cellular genetic changes occur, the team set out to prove it using mathematical analysis.

See “Related Links” for full-size image. (Source: HMS/ University of Cambridge/Joanne Davidson, Mira Grigorova and Paul Edwards)

Mining for answers

Cancer research has focused on mutations for decades since the “oncogene revolution.”  Changes in the DNA code that abnormally activate genes, called oncogenes, either promote cancer or deactivate genes that suppress cancer. The role of aneuploidy— in which entire chromosomes or chromosome arms are added or deleted— has remained largely unstudied.

Elledge and his team, including research fellow and first author Teresa Davoli, suspected that aneuploidy has a significant role to play in cancer because missing or extra chromosomes likely affect genes involved in tumor-related processes such as cell division and DNA repair.

To test their hypothesis, the researchers developed a computer program called TUSON (Tumor Suppressor and Oncogene) Explorer together with Wei Xu and Peter Park at HMS and Brigham and Women’s. The program analyzed genome sequence data from more than 8,200 pairs of cancerous and normal tissue samples in three preexisting databases.

They found many more potential cancer drivers than anticipated

  • after generating a list of suspected oncogenes and tumor suppressor genes based on their mutation patterns.

They ranked the suspects by how powerful an effect their deletion or duplication was likely to have on cancer development.  The team then looked at where the suspects normally appear in chromosomes.

They discovered that

  • the number of tumor suppressor genes or oncogenes in a chromosome
  • correlated with how often the whole chromosome or part of the chromosome was deleted or duplicated in cancers.

Where there were concentrations of tumor suppressor genes alongside

  • fewer oncogenes and fewer genes essential to survival,
  • there was more chromosome deletion.

Conversely,

When the team factored in gene potency, the correlations got even stronger. A cluster of highly potent tumor suppressors was

  • more likely to mean chromosome deletion than a cluster of weak suppressors.

Number matters

Since 1971, the standard tumor suppressor model has held that

  • cancer is caused by a “two-hit” cascade in which first one copy and
  • then the second copy of a gene becomes mutated.

Elledge argues that simply losing or gaining one copy of a gene through aneuploidy can influence tumor growth as well. However, the loss or gain of multiple cancer driver genes that individually have low potency

  • can have big effects by accretion of potency

These novel algorithms that identify tumor suppressors and oncogenes give experimentally verifiable basis for how  aneuploidies evolve in cancer cells, and

  • Indicate that subtle changes in the activity of many different genes at the same time can contribute to tumorigenesis

These findings also may have answered a long-standing question about whether aneuploidy is a cause or effect of cancer, leaving researchers free to pursue the question of how.  “Aneuploidy is driving cancer, not simply a consequence of it,” said Elledge. “Other things also matter, such as gene mutations, rearrangements and changes in expression. We don’t know what the weighting is, but now we should be able to figure it out.”  Elledge and Davoli plan to gather experimental evidence to support their mathematical findings. That will include validating some of the new predicted tumor suppressors and oncogenes as well as “making some deletions and amplifications and seeing if they have the properties we think they do”.

Source: Harvard Medical School

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Reporter and Curator: Dr. Sudipta Saha, Ph.D.

Meiosis plays a crucial role in generating haploid gametes for sexual reproduction. In most organisms, the presence of crossovers between homologous chromosomes, in combination with connections between sister chromatids, creates a physical connection that ensures regular segregation of homologs at the first of the two meiotic divisions.

Abnormality in generating crossovers is the leading cause of miscarriage and birth defects. Crossovers also create new combinations of alleles, thus contributing to genetic diversity and evolution. Recent linkage disequilibrium and pedigree studies have shown that the distribution of recombination is highly uneven across the human genome, as in all studied organisms. Substantial recombination active regions are not conserved between humans and chimpanzees or among different human populations, suggesting that these regions are quickly evolving and might even be individual-specific. However, such variation in the human population would be masked by the population average, and resolution of this variation would require comparison of recombination genome-wide among many single genomes.

Whole-genome amplification (WGA) of single sperm cells was proposed decades ago to facilitate mapping recombination at the individual level. With the development of highthroughput genotyping technologies, wholegenome mapping of recombination events in single gametes of an individual is achievable and was recently demonstrated by performing WGA by multiple displacement amplification (MDA) on single sperm cells, followed by genotyping with DNA microarrays recently demonstrated by Wang et al.. However, due to the amplification bias and, consequently, insufficient marker density, the resolution of crossover locations has been limited to ~150 kb thus far. In addition, in their recent work, Wang et al. relied on prior knowledge of the chromosome-level haplotype information of the analyzed individual, which is experimentally inconvenient to obtain and is currently available for only a few individuals.

Meiotic recombination creates genetic diversity and ensures segregation of homologous chromosomes. Previous population analyses yielded results averaged among individuals and affected by evolutionary pressures. In this study 99 sperm from an Asian male was sequenced by using the newly developed amplification method—multiple annealing and looping-based amplification cycles—to phase the personal genome and map recombination events at high resolution, which are non-uniformly distributed across the genome in the absence of selection pressure. The paucity of recombination near transcription start sites observed in individual sperm indicates that such a phenomenon is intrinsic to the molecular mechanism of meiosis. Interestingly, a decreased crossover frequency combined with an increase of autosomal aneuploidy is observable on a global per-sperm basis.

Source References:

http://www.ncbi.nlm.nih.gov/pubmed/23258895

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