PubMed · 3556680
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L Shivashankar, D Borgaonkar. 1987. Trisomy 13 update.. https://pubmed.ncbi.nlm.nih.gov/3556680/
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Preeclampsia has a familial component suggesting that one or more common alleles may act as susceptibility genes. Some families may have "private" predisposing mutations. The central role of the placenta in the pathogenesis of preeclampsia implies that fetal genes contribute to the disease process. Twin studies support the role of maternal and fetal gene interaction. Candidate gene studies have not yielded consistent results. Genome-wide linkage studies provide powerful means to study disease susceptibility genes, and several loci have been mapped. Parent-of-origin effect of the STOX1 gene has been suggested on chromosome 10q22 locus in the Dutch population. Maternally inherited missense mutations in the STOX1 gene of the fetus have been shown to co-segregate with the maternal preeclamptic phenotype. Up-regulation of placental leptin expression has been found in several studies and might be of importance in the pathogenesis of preeclampsia. DNA microarray is ideal tool for screening gene expression in preeclamptic tissues, but critical attitude is needed when interpreting the results. The placental DNA and mRNA in maternal plasma hold great promise as novel biomarkers for the prediction of preeclampsia. Finding genes prediposing to preeclampsia will enhance our understanding of the disease mechanism, and might allow identification of prognostic and therapeutic subgroups.
Angioimmunoblastic T-cell lymphoma (AILT) is a histopathologically well-defined entity. However, despite a number of cytogenetic studies, the genetic basis of this lymphoma entity is not clear. Moreover, there is an overlap to some cases of peripheral T-cell lymphoma unspecified (PTCL-u) in respect to morphological and genetic features. We used array-based comparative genomic hybridization (CGH) to study genetic imbalances in 39 AILT and 20 PTCL-u. Array-based CGH revealed complex genetic imbalances in both AILT and PTCL-u. Chromosomal imbalances were more frequent in PTCL-u than in AILT and gains exceeded the losses. The most recurrent changes in AILT were gains of 22q, 19, and 11p11-q14 (11q13) and losses of 13q. The most frequent changes in PTCL-u were gains of 17 (17q11-q25), 8 (involving the MYC locus at 8q24), and 22q and losses of 13q and 9 (9p21-q33). Interestingly, gains of 4q (4q28-q31 and 4q34-qtel), 8q24, and 17 were significantly more frequent in PTCL-u than in AILT. The regions 6q (6q16-q22) and 11p11 were predominantly lost in PTCL-u. Moreover, we could identify a recurrent gain of 11q13 in both AILT and PTCL-u, which has previously not been described in AILT. Trisomies 3 and 5, which have been described as typical aberrations in AILT, were identified only in a small number of cases. In conclusion, CGH revealed common genetic events in peripheral T-cell lymphomas as well as peculiar differences between AILT and PTCL-u.
PURPOSE OF REVIEW: Classifying tumors and identifying therapeutic targets requires a description of the genetic changes underlying cancer. Single nucleotide polymorphism (SNP) arrays provide a high-resolution platform for describing several types of genetic changes simultaneously. With the resolution of these arrays increasing exponentially, they are becoming increasingly powerful tools for describing the genetic events underlying cancer. RECENT FINDINGS: The ability to map loss of heterozygosity (LOH) and overall copy number variations using SNP arrays is known. Techniques have recently been developed to map LOH at high resolution in the absence of paired normal data. Copy number variations described by SNP array studies are now reaching resolutions enabling the identification of novel oncogenes and tumor suppressor genes. The ability to determine allele-specific copy number changes has only recently been described. Moreover, SNP arrays offer a high-throughput platform for large-scale association studies that are likely to lead to the identification of multiple germline variants that predispose to cancer. SUMMARY: SNP arrays are an ideal platform for identifying both somatic and germline genetic variants that lead to cancer. They provide a basis for DNA-based cancer classification and help to define the genes being modulated, improving understanding of cancer genesis and potential therapeutic targets.