Stochastic variation of transcript abundance in C57BL/6J mice

<p>Abstract</p> <p>Background</p> <p>Transcripts can exhibit significant variation in tissue samples from inbred laboratory mice. We have designed and carried out a microarray experiment to examine transcript variation across samples from adipose, heart, kidney, and liver tissues of C57BL/6J mice and to partition variation into within-mouse and between-mouse components. Within-mouse variance captures variation due to heterogeneity of gene expression within tissues, RNA-extraction, and array processing. Between-mouse variance reflects differences in transcript abundance between genetically identical mice.</p> <p>Results</p> <p>The nature and extent of transcript variation differs across tissues. Adipose has the largest total variance and the largest within-mouse variance. Liver has the smallest total variance, but it has the most between-mouse variance. Genes with high variability can be classified into groups with correlated patterns of expression that are enriched for specific biological functions. Variation between mice is associated with circadian rhythm, growth hormone signaling, immune response, androgen regulation, lipid metabolism, and the extracellular matrix. Genes showing correlated patterns of within-mouse variation are also associated with biological functions that largely reflect heterogeneity of cell types within tissues.</p> <p>Conclusions</p> <p>Genetically identical mice can experience different individual outcomes for medically important traits. Variation in gene expression observed between genetically identical mice can identify functional classes of genes that are likely to vary in the absence of experimental perturbations, can inform experimental design decisions, and provides a baseline for the interpretation of gene expression data in interventional studies. The extent of transcript variation among genetically identical mice underscores the importance of stochastic and micro-environmental factors and their phenotypic consequences.</p

Integration of multiomics data shows down regulation of mismatch repair and tubulin pathways in triple-negative chemotherapy-resistant breast tumors

Abstract Background Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype. Patients with TNBC are primarily treated with neoadjuvant chemotherapy (NAC). The response to NAC is prognostic, with reductions in overall survival and disease-free survival rates in those patients who do not achieve a pathological complete response (pCR). Based on this premise, we hypothesized that paired analysis of primary and residual TNBC tumors following NAC could identify unique biomarkers associated with post-NAC recurrence. Methods and results We investigated 24 samples from 12 non-LAR TNBC patients with paired pre- and post-NAC data, including four patients with recurrence shortly after surgery ( 48 months). These tumors were collected from a prospective NAC breast cancer study (BEAUTY) conducted at the Mayo Clinic. Differential expression analysis of pre-NAC biopsies showed minimal gene expression differences between early recurrent and nonrecurrent TNBC tumors; however, post-NAC samples demonstrated significant alterations in expression patterns in response to intervention. Topological-level differences associated with early recurrence were implicated in 251 gene sets, and an independent assessment of microarray gene expression data from the 9 paired non-LAR samples available in the NAC I-SPY1 trial confirmed 56 gene sets. Within these 56 gene sets, 113 genes were observed to be differentially expressed in the I-SPY1 and BEAUTY post-NAC studies. An independent (n = 392) breast cancer dataset with relapse-free survival (RFS) data was used to refine our gene list to a 17-gene signature. A threefold cross-validation analysis of the gene signature with the combined BEAUTY and I-SPY1 data yielded an average AUC of 0.88 for six machine-learning models. Due to the limited number of studies with pre- and post-NAC TNBC tumor data, further validation of the signature is needed. Conclusion Analysis of multiomics data from post-NAC TNBC chemoresistant tumors showed down regulation of mismatch repair and tubulin pathways. Additionally, we identified a 17-gene signature in TNBC associated with post-NAC recurrence enriched with down-regulated immune genes

Mutational Landscapes of Sequential Prostate Metastases and Matched Patient Derived Xenografts during Enzalutamide Therapy

<div><p>Developing patient derived models from individual tumors that capture the biological heterogeneity and mutation landscape in advanced prostate cancer is challenging, but essential for understanding tumor progression and delivery of personalized therapy in metastatic castrate resistant prostate cancer stage. To demonstrate the feasibility of developing patient derived xenograft models in this stage, we present a case study wherein xenografts were derived from cancer metastases in a patient progressing on androgen deprivation therapy and prior to initiating pre-chemotherapy enzalutamide treatment. Tissue biopsies from a metastatic rib lesion were obtained for sequencing before and after initiating enzalutamide treatment over a twelve-week period and also implanted subcutaneously as well as under the renal capsule in immuno-deficient mice. The genome and transcriptome landscapes of xenografts and the original patient tumor tissues were compared by performing whole exome and transcriptome sequencing of the metastatic tumor tissues and the xenografts at both time points. After comparing the somatic mutations, copy number variations, gene fusions and gene expression we found that the patient’s genomic and transcriptomic alterations were preserved in the patient derived xenografts with high fidelity. These xenograft models provide an opportunity for predicting efficacy of existing and potentially novel drugs that is based on individual metastatic tumor expression signature and molecular pharmacology for delivery of precision medicine.</p></div

Comparison of genome and transcriptome landscapes between patient tumor tissue and patient derived xenograft models (PDXs).

<p>(A) Recall (grey) and precision (tan) of detected somatic mutations. Recall = number of somatic mutations called from both patient tissue and xenograft divided by total somatic mutations called from patient tissue; Precision = number of somatic mutations called from both patient tissue and xenograft divided by total mutations called from xenograft. (B) Heatmap showing the concordance of relative allele frequency of somatic mutations between patient tissues and xenografts. Rows correspond to patients and xenograft samples and columns correspond to 60 selected somatic mutations. (C) Circos plot showing profiles of copy number variation for patient tumor tissues and xenografts. From outside to inside, tracks correspond to 1 = V1/met, 1A = V1/xeno/A, 1B = V1/xeno/B, 1C = V1/xeno/C, 1AA = V1/xeno/A/A, 1BA = V1/xeno/B/A, V1/xeno/C/A, V2/met, V2/xeno/A, V2/xeno/A/A1, V2/xeno/A/A2 and V2/xeno/A/B. (D) Pair-wise gene expression correlation between patient tissues and xenografts. Correlation was measured by Pearson correlation coefficient. Gene expression was measured by log10 (RPKM, Reads Per Kilobase exon per Million mapped reads).</p

Classification of xenograft whole exome sequencing (panels A and B) and RNA sequencing reads (panels C and D).

<p>Reads generated from xenograft samples were divided into five groups including “graft”, “host”, “both”, “neither” and “ambiguous” using tool developed by Conway et al. (A) Reads assignments for 10 xenograft whole exome sequencing data. (B) Average proportion of whole exome sequencing reads assigned to the 5 groups mentioned above. (C) Reads assignments for 10 xenograft RNA-seq data. (D) Average proportion of RNA-seq reads assigned to the 5 groups mentioned above.</p