Biology subjects

Andersson, N.

Publications and source records attributed to Andersson, N..

6 recordsLinked to original sources

bioRxiv2022

Inherent evolutionary unpredictability in cancer model system

Despite the advent of personalized medicine, it is still difficult to predict how a cancer develops over time at the level of the individual patient or even in cancer model systems which begs the question whether certain aspects of cancer can ever be predicted or if there is an inherent unpredictability in cancer, similar to other complex biological systems, We demonstrate by a combination of agent-based mathematical modelling and analysis of data from patient-derived xenograft systems from multiple cancer types that certain conditions may invoke chaotic fluctuations in the clonal landscape of cancer cells. Our findings indicate that under those conditions, the cancer genome behaves as a complex dynamic system, making its long-term evolution inherently unpredictable.

evolutionary biology↗

bioRxiv2022

Microarray profiling of hypothalamic gene expression changes in Huntington's disease mouse models

Structural changes and neuropathology in the hypothalamus have been suggested to contribute to the non-motor manifestations of Huntingtons disease (HD), a neurodegenerative disorder caused by an expanded CAG repeat in the huntingtin (HTT) gene. In the present study, we investigated whether transcriptional changes would be part of hypothalamic pathology induced by the disease-causing huntingtin (HTT) protein. We performed microarray analysis using the Affymetrix platform on total hypothalamic RNA isolated from two HD mouse models and their littermate controls; BACHD mice with ubiquitous expression of full-length mutant HTT (mHTT) and wild-type mice with targeted hypothalamic overexpression of either wild-type HTT (wtHTT) or mHTT fragments. To analyze microarray datasets (34760 variables) and obtain functional implications of differential expression patterns, we used Linear Models for Microarray Data (limma) followed by Gene Set Enrichment Analysis (GSEA) using ClusterProfiler. Limma identified 735 and 721 significantly differentially expressed genes (adjusted p < 0.05) in hypothalamus of AAV datasets wtHTT vs control and mHTT vs control. In contrast, for BACHD datasets and the AAV mHTT vs. wtHTT dataset, none of the genes were differentially expressed (adjusted p-value > 0.05 for all probe IDs). In AAV groups, from the combined limma with GSEA using ClusterProfiler, we found both shared and unique gene sets and pathways for mice with wtHTT overexpression compared to mice with mHTT overexpression. mHTT caused widespread suppression of neuroendocrine networks, as evident by GSEA enrichment of GO-terms related to neurons and/or specific neuroendocrine populations. Using qRT-PCR, we confirmed that mHTT overexpression caused significant downregulation of key enzymes involved in neuropeptide synthesis, including histidine and dopa decarboxylases, compared to wtHTT overexpression. Multiple biosynthetic pathways such as sterol synthesis were among the top shared processes, where both unique and shared genes constituted leading-edge subsets. In conclusion, mice with targeted overexpression of HTT (wtHTT or mHTT) in the hypothalamus show dysregulation of pathways, of which there are subsets of shared pathways and pathways unique to either wtHTT or mHTT overexpression.

neuroscience↗

bioRxiv2020

Tracing the evolution of aneuploid cancers from multiregional sequencing

Clonal deconvolution of mutational landscapes is crucial to understand the evolutionary dynamics of cancer. Two limiting factors for clonal deconvolution that have remained unresolved are variation in tumor cell fraction (TCF) and chromosomal copy number across different samples of the same tumor. We developed a semi-supervised algorithm that tracks variant calls through multi-sample spatiotemporal tumor data. While normalizing allele frequencies based on TCF it also adjusts for copy number changes at clonal deconvolution. Absent a priori copy number data, it renders in silico copy number estimations from bulk sequences. Using published and simulated tumor sequences, we reliably segregated clonal/subclonal variants even at low sequencing depth ([~]50x). Given at least one pure tumor sample (>70% TCF), we could normalize and deconvolve paired samples down to a TCF of 40%. This renders a reliable clonal reconstruction well adapted to multi-regionally sampled solid tumor, which are often aneuploid and contaminated by non-cancer cells.

bioinformatics↗

bioRxiv2020

Analysis of single-cell RNA-seq data from ovarian cancer samples before and after chemotherapy links stress-related transcriptional profile withchemotherapy resistance

Chemotherapy resistance is the greatest contributor to cancer mortality and the most urgent unmet challenge in oncology. In order to reveal transcriptomics changes due to platinum-based chemotherapy we analyzed single-cell RNA-seq data from fresh tissue samples taken at the time of diagnosis and after neoadjuvant chemotherapy from 11 high-grade serous ovarian cancer (HGSOC) patients. With a novel clustering method accounting for patient-specific variability and technical confounders, we identified 12 clusters. Of these, a stress-related transcriptional profile was enriched during chemotherapy and associated significantly to poor progression-free survival (PFS) and disease-free interval (DFI) in deconvoluted bulk RNA-seq data analysis of treatment-naive samples in TCGA cohort. Pan-cancer cell line analysis suggests that patients with high stress-related transcriptional profile may benefit from MEK1/2 inhibitors instead of platinum. Further, high proportion of stromal components and high interaction score between tumor and stromal suggest the tumor cells with high-stress profile actively interact with and potentially recruit stromal cells to their microenvironment already prior to chemotherapy, potentially facilitating protection from chemotherapeutic treatments. In summary, we have identified a stress-related transcriptional profile, which is present at the time of diagnosis, enriched during platinum treatment, independent predictor for poor PFS and DFI, and, based on in vitro data, targetable with MEK1/2 inhibitors. Translational relevanceWe discovered a stress-related transcriptional profile that is significantly enriched in fresh tissue samples after chemotherapy and is significantly associated with poor progression-free survival in an independent patient cohort. The survival association is independent of age, tumor purity or BRCAness. Therefore, this chemotherapy resistance associated profile is intrinsic and could thus be targeted already in treatment-naive patients. The translation potential of the stress-related transcriptomics profile was further supported by pan-cancer cell line analysis that showed that cell lines with high stress-related transcriptional profile are not affected by platinum, corroborating our results, whereas they were more sensitive to MEK1/2 inhibitors. Taken together, the stress-related transcriptional profile, quantifiable with a set of 35 marker genes, provides a basis for improved prediction of platinum response as well as novel avenues to treat this patient group more effectively.

bioinformatics↗

bioRxiv2019

Multiplexed single-cell transcriptomic analysis of normal and impaired lung development in the mouse

During late lung development alveolar and microvascular development is finalized to enable sufficient gas exchange. Impaired late lung development manifests as bronchopulmonary dysplasia (BPD) in preterm infants. Single-cell RNA sequencing (scRNA-seq) allows for assessment of complex cellular dynamics during biological processes, such as development. Here, we use MULTI-seq to generate scRNA-seq profiles of over 66,000 cells from 36 mice during normal or impaired lung development secondary to hyperoxia. We observed dynamic populations of cells, including several rare cell types and putative progenitors. Hyperoxia exposure, which mimics the BPD phenotype, alters the composition of all cellular compartments, particularly alveolar epithelium, capillary endothelium and macrophage populations. We identified several BPD-associated signatures, including Pdgfra in fibroblasts, Activin A in capillary endothelial cells, and Csf1-Csf1r and Ccl2-Ccr2 signaling in macrophages and neutrophils. Our data provides a novel single-cell view of cellular changes associated with late lung development in health and in disease.

cell biology↗

bioRxiv2019

PRISM: Recovering cell type specific expression profilesfrom composite RNA-seq data

A major challenge in analyzing cancer patient transcriptomes is that the tumors are inherently heterogeneous and evolving. We analyzed 214 bulk RNA samples of a longitudinal, prospective ovarian cancer cohort and found that the sample composition changes systematically due to chemotherapy and between the anatomical sites, preventing direct comparison of treatment-naive and treated samples. To overcome this, we developed PRISM, a latent statistical framework to simultaneously extract the sample composition and cell type specific whole-transcriptome profiles adapted to each individual sample. Our results indicate that the PRISM-derived composition-free transcriptomic profiles and signatures derived from them predict the patient response better than the composite raw bulk data. We validated our findings in independent ovarian cancer and melanoma cohorts, and verified that PRISM accurately estimates the composition and cell type specific expression through whole-genome sequencing and RNA in situ hybridization experiments. PRISM is freely available with full source code and documentation.

bioinformatics↗