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Distinct and Common Features of Numerical and Structural Chromosomal Instability across Different Cancer Types.

ABSTRACT: A large proportion of tumours is characterised by numerical or structural chromosomal instability (CIN), defined as an increased rate of gaining or losing whole chromosomes (W-CIN) or of accumulating structural aberrations (S-CIN). Both W-CIN and S-CIN are associated with tumourigenesis, cancer progression, treatment resistance and clinical outcome. Although W-CIN and S-CIN can co-occur, they are initiated by different molecular events. By analysing tumour genomic data from 33 cancer types, we show that the majority of tumours with high levels of W-CIN underwent whole genome doubling, whereas S-CIN levels are strongly associated with homologous recombination deficiency. Both CIN phenotypes are prognostic in several cancer types. Most drugs are less efficient in high-CIN cell lines, but we also report compounds and drugs which should be investigated as targets for W-CIN or S-CIN. By analysing associations between CIN and bio-molecular entities with pathway and gene expression levels, we complement gene signatures of CIN and report that the drug resistance gene CKS1B is strongly associated with S-CIN. Finally, we propose a potential copy number-dependent mechanism to activate the PI3K pathway in high-S-CIN tumours.

SUBMITTER: Zhang X

PROVIDER: S-EPMC8946057 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Distinct and Common Features of Numerical and Structural Chromosomal Instability across Different Cancer Types.

Zhang Xiaoxiao X Kschischo Maik M

Cancers 20220310 6

A large proportion of tumours is characterised by numerical or structural chromosomal instability (CIN), defined as an increased rate of gaining or losing whole chromosomes (W-CIN) or of accumulating structural aberrations (S-CIN). Both W-CIN and S-CIN are associated with tumourigenesis, cancer progression, treatment resistance and clinical outcome. Although W-CIN and S-CIN can co-occur, they are initiated by different molecular events. By analysing tumour genomic data from 33 cancer types, we s ...[more]

PMID: 35326573

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Project description:BackgroundDespite our substantial understanding of molecular mechanisms and gene mutations involved in cancer, the technical approaches for diagnosis and prognosis of cancer are limited. In routine clinical diagnosis of cancer, the procedure is very basic: nuclear morphology is used as a common assessment of the degree of malignancy, and hence acts as a prognostic and predictive indicator of the disease. Furthermore, though the atypical nuclear morphology of cancer cells is believed to be a consequence of oncogenic signaling, the molecular basis remains unclear. Another common characteristic of human cancer is aneuploidy, but the causes and its role in carcinogenesis are not well established.MethodsWe investigated the expression of the nuclear envelope proteins lamin A/C in ovarian cancer by immunohistochemistry and studied the consequence of lamin A/C suppression using siRNA in primary human ovarian surface epithelial cells in culture. We used immunofluorescence microscopy to analyze nuclear morphology, flow cytometry to analyze cellular DNA content, and fluorescence in situ hybridization to examine cell ploidy of the lamin A/C-suppressed cells.ResultsWe found that nuclear lamina proteins lamin A/C are often absent (47%) in ovarian cancer cells and tissues. Even in lamin A/C-positive ovarian cancer, the expression is heterogeneous within the population of tumor cells. In most cancer cell lines, a significant fraction of the lamin A/C-negative population was observed to intermix with the lamin A/C-positive cells. Down regulation of lamin A/C in non-cancerous primary ovarian surface epithelial cells led to morphological deformation and development of aneuploidy. The aneuploid cells became growth retarded due to a p53-dependent induction of the cell cycle inhibitor p21.ConclusionsWe conclude that the loss of nuclear envelope structural proteins, such as lamin A/C, may underlie two of the hallmarks of cancer--aberrations in nuclear morphology and aneuploidy.

Project description:Cancer cells frequently undergo chromosome missegregation events during mitosis, whereby the copies of a given chromosome are not distributed evenly among the two daughter cells, thus creating cells with heterogeneous karyotypes. A stochastic model tracing cellular karyotypes derived from clonal populations over hundreds of generations was recently developed and experimentally validated, and it was capable of predicting favorable karyotypes frequently observed in cancer. Here, we construct and study a Markov chain that precisely describes karyotypic evolution during clonally expanding cancer cell populations. The Markov chain allows us to directly predict the distribution of karyotypes and the expected size of the tumor after many cell divisions without resorting to computationally expensive simulations. We determine the limiting karyotype distribution of an evolving tumor population, and quantify its dependency on several key parameters including the initial karyotype of the founder cell, the rate of whole chromosome missegregation, and chromosome-specific cell viability. Using this model, we confirm the existence of an optimal rate of chromosome missegregation probabilities that maximizes karyotypic heterogeneity, while minimizing the occurrence of nullisomy. Interestingly, karyotypic heterogeneity is significantly more dependent on chromosome missegregation probabilities rather than the number of cell divisions, so that maximal heterogeneity can be reached rapidly (within a few hundred generations of cell division) at chromosome missegregation rates commonly observed in cancer cell lines. Conversely, at low missegregation rates, heterogeneity is constrained even after thousands of cell division events. This leads us to conclude that chromosome copy number heterogeneity is primarily constrained by chromosome missegregation rates and the risk for nullisomy and less so by the age of the tumor. This model enables direct integration of karyotype information into existing models of tumor evolution based on somatic mutations.

Dataset Information

Distinct and Common Features of Numerical and Structural Chromosomal Instability across Different Cancer Types.

Publications

Distinct and Common Features of Numerical and Structural Chromosomal Instability across Different Cancer Types.

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