Biology subjects

Kschischo, M.

Publications and source records attributed to Kschischo, M..

5 recordsLinked to original sources

bioRxiv2021

Distinct and common features of numerical and structural chromosomal instability across different cancer types

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. 1. Simple summaryMany cancer cells are chromosomally unstable, a phenotype describing a tendency for accumulating chromosomal aberrations. Entire chromosomes tend to be gained or lost, which is called whole chromosome instability (W-CIN). Structural chromosomal instability (S-CIN) describes an increased rate of gaining, losing or translocating smaller parts of chromosomes. Here, we analyse data from 33 cancer types to find differences and commonalities between W-CIN and S-CIN. We find that W-CIN is strongly linked to whole genome doubling (WGD), whereas S-CIN is associated with a specific DNA damage repair pathway. Both W-CIN and S-CIN are difficult to target using currently available compounds and have distinct prognostic values. The activity of the drug resistance gene CKS1B is associated with S-CIN, which merits further investigation. In addition, we identify a potential copy number-based mechanism promoting signalling of the important PI3K cancer pathway in high-S-CIN tumours.

genetics↗

bioRxiv2021

Dormant replication origin firing links replication stress to whole chromosomal instability in human cancer

Chromosomal instability (CIN) is a hallmark of cancer and comprises structural CIN (S-CIN) and whole chromosome instability (W-CIN). Replication stress (RS), a condition of slowed or stalled DNA replication during S phase, has been linked to S-CIN, whereas defects in mitosis leading to chromosome missegregation and aneuploidy can account for W-CIN. It is well established that RS can activate additional replication origin firing that is considered as a rescue mechanism to suppress chromosomal instability in the presence of RS. In contrast, we show here that an increase in replication origin firing during S phase can contribute to W-CIN in human cancer cells. Increased origin firing can be specifically triggered by overexpression of origin firing genes including GINS1 and CDC45, whose elevated expression significantly correlates with W-CIN in human cancer specimens. Moreover, endogenous mild RS present in cancer cells characterized by W-CIN or modulation of the origin firing regulating ATR-CDK1-RIF1 axis induces dormant origin firing, which is sufficient to trigger chromosome missegregation and W-CIN. Importantly, chromosome missegregation upon increased dormant origin firing is mediated by increased microtubule growth rates leading to the generation of lagging chromosomes in mitosis, a condition prevalent in chromosomally unstable cancer cells. Thus, our study identified increased or dormant replication origin firing as a hitherto unrecognized, but cancer-relevant trigger for chromosomal instability.

cell biology↗

bioRxiv2021

MFmap: A semi-supervised generative model matching cell lines to tumours and cancer subtypes

Translating in vitro results from experiments with cancer cell lines to clinical applications requires the selection of appropriate cell line models. Here we present MFmap (model fidelity map), a machine learning model to simultaneously predict the cancer subtype of a cell line and its similarity to an individual tumour sample. The MFmap is a semi-supervised generative model, which compresses high dimensional gene expression, copy number variation and mutation data into cancer subtype informed low dimensional latent representations. The accuracy (test set F1 score > 90%) of the MFmap subtype prediction is validated in ten different cancer datasets. We use breast cancer and glioblastoma cohorts as examples to show how subtype specific drug sensitivity can be translated to individual tumour samples. The low dimensional latent representations extracted by MFmap explain known and novel subtype specific features and enable the analysis of cell-state transformations between different subtypes. From a methodological perspective, we report that MFmap is a semi-supervised method which simultaneously achieves good generative and predictive performance and thus opens opportunities in other areas of computational biology. Author summaryCancer researchers perform experiments with cell lines to better understand the biology of cancer and to develop new anti-cancer treatments. A prerequisite to translate promising results from these in vitro experiments to clinical applications is to use the most appropriate cell line for a given tumour or cancer subtype. We present MFmap (model fidelity map), a deep learning technique to integrate cancer genomic data from patients with cell line data. The MFmap neural network compresses complex genomic features from thousands of genes into a small set of features called latent representations. This makes cell line and tumour data comparable and allows cancer researchers to select the best cell line which closely resembles a specific type of tumours or even an individual tumour. By classifying cancer cell lines into subtypes, MFmap offers a new possibility to predict the effect of therapeutic compounds in a particular tumour subtype. For the example of an aggressive brain tumour we demonstrate that MFmap can be used to study cell-state transformations during the disease course. In addition, MFmap is a promising machine learning method with potential applications in many other areas of biology and medicine.

bioinformatics↗

bioRxiv2021

Systems approaches identify the consequences ofmonosomy in somatic human cells

Chromosome loss that results in monosomy is detrimental to viability, yet, it is frequently observed in cancers. How cancers survive with monosomy is unknown. Using p53 deficient monosomic cell lines, we found that chromosome loss impairs proliferation and genomic stability. Transcriptome and proteome analysis revealed a partial compensation of the gene dosage changes that mitigates the effects of chromosome loss. Monosomy triggers global gene expression changes that differ from the effects of trisomy. We show that ribosome biogenesis and translation were commonly downregulated in monosomic cells, likely due to haploinsufficiency of ribosomal genes. The ensuing ribosome biogenesis stress triggers the p53 pathway and G1 arrest when TP53 is reintroduced into monosomic cells. Accordingly, impaired ribosome biogenesis and p53 inactivation are associated with monosomy in cancer. Our first systematic study of monosomy in human cells explains why monosomy is so detrimental and how loss of p53 enables its incidence in cancer.

systems biology↗

bioRxiv2020

USP28 and SPINT2 mediate cell cycle arrest after whole genome doubling

Tetraploidy is frequent in cancer and whole genome doubling shapes the evolution of cancer genomes, thereby driving the transformation, metastasis and drug resistance. Yet, human cells usually arrest when they become tetraploid due to p53 activation that leads to CDKN1A expression, cell cycle arrest, senescence or apoptosis. To uncover the barriers that block proliferation of tetraploids, we performed an RNAi mediated genome-wide screen in a human cancer cell line. We identified 140 genes whose depletion improved survival of tetraploids and characterized in depth two of them: SPINT2 and USP28. We show that SPINT2 is a general regulator of CDKN1A, regulating its transcription via histone acetylation. By mass spectrometry and immunoprecipitation, we show that USP28 interacts with NuMA1 and affects centrosome clustering. Moreover, tetraploid cells accumulate DNA damage and loss of USP28 reduces checkpoint activation. Our results indicate three aspects that contribute to survival of tetraploid cells: i) increased mitogenic signaling and reduced expression of cell cycle inhibitors, ii) the ability to establish functional bipolar spindle, and iii) reduced DNA damage signaling.

cell biology↗