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Posts Tagged ‘aneuploidy’

Genomic data can predict miscarriage and IVF failure

Posted in Advanced Computing Platform, Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence - General, Artificial Intelligence Applications in Health Care, Artificial Intelligence in Health Care - Tools & Innovations, Artificial Intelligence in Medicine - Application for Diagnosis, Artificial Intelligence in Medicine - Applications in Therapeutics, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Biological Engineering, Biological Networks, Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, Cell Level, Computational Biology/Systems and Bioinformatics, Computational Histopathology, Deep Learning in Pathology, Developmental biology, Diagnostics and Lab Tests, Digital HealthCare – biotech & internet joint ventures, Disease Biology, Genomic Testing: Methodology for Diagnosis, Genomics Pharmacy, HealthCare IT, Healthcare Reform, Machine Learning, MicroEngineering Cell-Tissue & Systems, Placenta, Preimplantation Genetic Diagnosis and Reproductive Genomics, Reproductive Andrology, Embryology, Genomic Endocrinology, Preimplantation Genetic Diagnosis and Reproductive Genomics, Reproductive Biology & Bio Instrumentation, therapeutics, Tissue Engineering, Translational Research, Women Health, tagged aneuploidy, Diagnosis, Genomic data, IVF, Miscarriage, treatment on June 15, 2022| 1 Comment »

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

Infertility is a major reproductive health issue that affects about 12% of women of reproductive age in the United States. Aneuploidy in eggs accounts for a significant proportion of early miscarriage and in vitro fertilization failure. Recent studies have shown that genetic variants in several genes affect chromosome segregation fidelity and predispose women to a higher incidence of egg aneuploidy. However, the exact genetic causes of aneuploid egg production remain unclear, making it difficult to diagnose infertility based on individual genetic variants in mother’s genome. Although, age is a predictive factor for aneuploidy, it is not a highly accurate gauge because aneuploidy rates within individuals of the same age can vary dramatically.

Researchers described a technique combining genomic sequencing with machine-learning methods to predict the possibility a woman will undergo a miscarriage because of egg aneuploidy—a term describing a human egg with an abnormal number of chromosomes. The scientists were able to examine genetic samples of patients using a technique called “whole exome sequencing,” which allowed researchers to home in on the protein coding sections of the vast human genome. Then they created software using machine learning, an aspect of artificial intelligence in which programs can learn and make predictions without following specific instructions. To do so, the researchers developed algorithms and statistical models that analyzed and drew inferences from patterns in the genetic data.

As a result, the scientists were able to create a specific risk score based on a woman’s genome. The scientists also identified three genes—MCM5, FGGY and DDX60L—that when mutated and are highly associated with a risk of producing eggs with aneuploidy. So, the report demonstrated that sequencing data can be mined to predict patients’ aneuploidy risk thus improving clinical diagnosis. The candidate genes and pathways that were identified in the present study are promising targets for future aneuploidy studies. Identifying genetic variations with more predictive power will serve women and their treating clinicians with better information.

References:

https://medicalxpress-com.cdn.ampproject.org/c/s/medicalxpress.com/news/2022-06-miscarriage-failure-vitro-fertilization-genomic.amp

https://pubmed.ncbi.nlm.nih.gov/35347416/

https://pubmed.ncbi.nlm.nih.gov/31552087/

https://pubmed.ncbi.nlm.nih.gov/33193747/

https://pubmed.ncbi.nlm.nih.gov/33197264/

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Aneuploidy and Carcinogenesis

Posted in Biological Networks, Gene Regulation and Evolution, CANCER BIOLOGY & Innovations in Cancer Therapy, Chemical Genetics, Genome Biology, Pharmacogenomics, Population Health Management, Genetics & Pharmaceutical, tagged aneuploidy, Brigham and Women's, Cancer - General, chromosomal deletions, driver mutation, Harvard Medical School, oncogene, potency, promoter gene, Stephen Elledge, tumor suppressor, Tumor suppressor gene on October 31, 2013| Leave a Comment »

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,

concentrations of oncogenes and fewer tumor suppressors coincided with
more chromosome duplication.

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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Whole-genome sequencing in probing the meiotic recombination and aneuploidy of single sperm cells

Posted in Biological Networks, Gene Regulation and Evolution, Chemical Genetics, Genome Biology, Genomic Testing: Methodology for Diagnosis, Medical and Population Genetics, Molecular Genetics & Pharmaceutical, Personalized and Precision Medicine & Genomic Research, Population Health Management, Genetics & Pharmaceutical, Reproductive Andrology, Embryology, Genomic Endocrinology, Preimplantation Genetic Diagnosis and Reproductive Genomics, Reproductive Biology & Bio Instrumentation, tagged amplification, aneuploidy, chromosome, DNA, Genetic recombination, genome, Homologous chromosome, Meiosis, meiotic recombination, Multiple displacement amplification, sperm on February 6, 2013| 3 Comments »

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, whole–genome 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.