Biology subjects

Christensen, C. L.

Publications and source records attributed to Christensen, C. L..

3 recordsLinked to original sources

bioRxiv2023

Acquired Cross-resistance in Small Cell Lung Cancer due to Extrachromosomal DNA Amplification of MYC paralogs

Small cell lung cancer (SCLC) presents as a highly chemosensitive malignancy but acquires cross-resistance after relapse. This transformation is nearly inevitable in patients but has been difficult to capture in laboratory models. Here we present a pre-clinical system that recapitulates acquired cross-resistance in SCLC, developed from 51 patient-derived xenografts (PDXs). Each model was tested for in vivo sensitivity to three clinical regimens: cisplatin plus etoposide, olaparib plus temozolomide, and topotecan. These functional profiles captured hallmark clinical features, such as the emergence of treatment-refractory disease after early relapse. Serially derived PDX models from the same patient revealed that cross-resistance was acquired through a MYC amplification on extrachromosomal DNA (ecDNA). Genomic and transcriptional profiles of the full PDX panel revealed that this was not unique to one patient, as MYC paralog amplifications on ecDNAs were recurrent among cross-resistant models derived from patients after relapse. We conclude that ecDNAs with MYC paralogs are recurrent drivers of cross-resistance in SCLC. SIGNIFICANCESCLC is initially chemosensitive, but acquired cross-resistance renders this disease refractory to further treatment and ultimately fatal. The genomic drivers of this transformation are unknown. We use a population of PDX models to discover that amplifications of MYC paralogs on ecDNA are recurrent drivers of acquired cross-resistance in SCLC.

cancer biology↗

bioRxiv2023

Cell-type deconvolution of bulk RNA-Seq from kidney using opensource bioinformatic tools

Traditional bulk RNA-Seq pipelines do not assess cell-type composition within heterogeneous tissues. Therefore, it is difficult to determine whether conflicting findings among samples or datasets are the result of biological differences or technical differences due to variation in sample collections. This report provides a user-friendly, open source method to assess cell-type composition in bulk RNA-Seq datasets for heterogeneous tissues using published single cell (sc)RNA-Seq data as a reference. As an example, we apply the method to analysis of kidney cortex bulk RNA-Seq data from female (N=8) and male (N=9) baboons to assess whether observed transcriptome sex differences are biological or technical, i.e., variation due to ultrasound guided biopsy collections. We found cell-type composition was not statistically different in female versus male transcriptomes based on expression of 274 kidney cell-type specific transcripts, indicating differences in gene expression are not due to sampling differences. This method of cell-type composition analysis is recommended for providing rigor in analysis of bulk RNA-Seq datasets from complex tissues. It is clear that with reduced costs, more analyses will be done using scRNA-Seq; however, the approach described here is relevant for data mining and meta analyses of the thousands of bulk RNA-Seq data archived in the NCBI GEO public database. Author SummaryThis method, which provides a simple method for assessing sampling biases in bulk RNA-Seq datasets with evaluation of cell-type composition, will aid researchers in assessing whether bulk RNA-Seq from different studies of the same heterogeneous tissue are comparable. The additional layer of information can help determine if differential gene expression observed is biological or technical, i.e., cell composition variation among study samples. The described method uses publicly available bioinformatics resources and does not require coding expertise or high-capacity computational processing. Development of tools accessible to scientists without computing expertise will contribute to greater rigor and reproducibility for bioinformatic analyses of transcriptome data.

genomics↗

bioRxiv2021

Blood pressure and the kidney cortex transcriptome response to high sodium diet challenge in female nonhuman primates

Blood pressure (BP) is influenced by genetic variation and sodium intake. Ninety percent of Americans consume more than the AHA recommended amount of sodium. Studies on the impact of genetic variation and sodium intake on BP in nonhuman primates (NHP) to date have focused on males. We hypothesized that variation in renal transcriptional networks correlate with BP response to high dietary sodium in female baboons. Sodium-naive female baboons (n=7) were fed a low-sodium (LS) diet for 6 weeks followed by a high sodium (HS) diet for 6 weeks. Sodium intake, serum 17 betaestradiol, and ultrasound-guided kidney biopsies for RNA-Seq BP were collected at the end of each diet. BP was continuously measured for 64-hour periods throughout the study by implantable telemetry devices. On the LS diet, Na+ intake and serum 17 beta-estradiol concentration correlated with BP. Kidney transcriptomes differed by diet; analysis by unbiased weighted gene co-expression network analysis revealed modules of genes correlated with BP on the HS diet. Cell type composition of renal biopsies was consistent among all animals for both diets. Network analysis of module genes showed causal networks linking hormone receptors, proliferation and differentiation, methylation, hypoxia, insulin and lipid regulation, and inflammation as regulators underlying variation in BP on the HS diet. Our results show variation in BP correlated with novel kidney gene networks with master regulators PPARG and MYC in female baboons on a HS diet. Identification of mechanisms underlying regulators that influence BP will inform better therapies towards greater precision medicine for women.

genomics↗