Severe neutropenia is the major dose-liming toxicity of irinotecan-based chemotherapy. The objective was to assess to what extent a population pharmacokinetic/pharmacodynamic model including patient-specific demographic/clinical characteristics, individual pharmacokinetics, and absolute neutrophil counts (ANCs) can predict irinotecan-induced grade 4 neutropenia. A semimechanistic population pharmacokinetic/pharmacodynamic model was developed to describe neutrophil response over time in 197 patients with cancer receiving irinotecan. For covariate analysis, sex, race, age, pretreatment total bilirubin, and body surface area were evaluated to identify significant covariates on system-related parameters (mean transit time (MTT) and ɣ) and sensitivity to neutropenia effects of irinotecan and SN-38 (SLOPE). The model-based simulation was performed to assess the contribution of the identified covariates, individual pharmacokinetics, and baseline ANC alone or with incremental addition of weekly ANC up to 3 weeks on predicting irinotecan-induced grade 4 neutropenia. The time course of neutrophil response was described using the model assuming that irinotecan and SN-38 have toxic effects on bone marrow proliferating cells. Sex and pretreatment total bilirubin explained 10.5% of interindividual variability in MTT. No covariates were identified for SLOPE and γ. Incorporating sex and pretreatment total bilirubin (area under the receiver operating characteristic curve (AUC-ROC): 50%, 95% CI 50-50%) or with the addition of individual pharmacokinetics (AUC-ROC: 62%, 95% CI 53-71%) in the model did not result in accurate prediction of grade 4 neutropenia. However, incorporating ANC only at baseline and week 1 in the model achieved a good prediction (AUC-ROC: 78%, 95% CI 69-88%). These results demonstrate the potential applicability of a model-based approach to predict irinotecan-induced neutropenia, which ultimately allows for personalized intervention to maximize treatment outcomes.
The rate of recombination is a key genomic parameter that displays considerable variation among taxa. Species comparisons have demonstrated that the rate of evolution in recombination rate is strongly dependent on the physical scale of measurement. Individual recombination hotspots are poorly conserved among closely related taxa, whereas genomic-scale recombination rate variation bears a strong signature of phylogenetic history. In contrast, the mode and tempo of evolution in recombination rates measured on intermediate physical scales is poorly understood. Here, we conduct a detailed statistical comparison between two whole-genome F₂ genetic linkage maps constructed from experimental intercrosses between closely related house mouse subspecies (Mus musculus). Our two maps profile a common wild-derived inbred strain of M. m. domesticus crossed to distinct wild-derived inbred strains representative of two other house mouse subspecies, M. m. castaneus and M. m. musculus. We identify numerous orthologous genomic regions with significant map length differences between these two crosses. Because the genomes of these recently diverged house mice are highly collinear, observed differences in map length (centimorgans) are suggestive of variation in broadscale recombination rate (centimorgans per megabase) within M. musculus. Collectively, these divergent intervals span 19% of the house mouse genome, disproportionately aggregating on the X chromosome. In addition, we uncover strong statistical evidence for a large effect, sex-linked, site-specific modifier of recombination rate segregating within M. musculus. Our findings reveal considerable variation in the megabase-scale recombination landscape among recently diverged taxa and underscore the continued importance of genetic linkage maps in the post-genome era.
The role of the immune system in response to chemotherapeutic agents remains elusive. The interpatient variability observed in immune and chemotherapeutic cytotoxic responses is likely, at least in part, due to complex genetic differences. Through the use of a panel of genetically diverse mouse inbred strains, we developed a drug screening platform aimed at identifying genes underlying these chemotherapeutic cytotoxic effects on immune cells. Using genome-wide association studies (GWAS), we identified four genome-wide significant quantitative trait loci (QTL) that contributed to the sensitivity of doxorubicin and idarubicin in immune cells. Of particular interest, a locus on chromosome 16 was significantly associated with cell viability following idarubicin administration (p = 5.01 × 10(-8)). Within this QTL lies App, which encodes amyloid beta precursor protein. Comparison of dose-response curves verified that T-cells in App knockout mice were more sensitive to idarubicin than those of C57BL/6J control mice (p < 0.05). In conclusion, the cellular screening approach coupled with GWAS led to the identification and subsequent validation of a gene involved in T-cell viability after idarubicin treatment. Previous studies have suggested a role for App in in vitro and in vivo cytotoxicity to anticancer agents; the overexpression of App enhances resistance, while the knockdown of this gene is deleterious to cell viability. Further investigations should include performing mechanistic studies, validating additional genes from the GWAS, including Ppfia1 and Ppfibp1, and ultimately translating the findings to in vivo and human studies.
In Huntington's disease (HD), an expanded CAG repeat produces characteristic striatal neurodegeneration. Interestingly, the HD CAG repeat, whose length determines age at onset, undergoes tissue-specific somatic instability, predominant in the striatum, suggesting that tissue-specific CAG length changes could modify the disease process. Therefore, understanding the mechanisms underlying the tissue specificity of somatic instability may provide novel routes to therapies. However progress in this area has been hampered by the lack of sensitive high-throughput instability quantification methods and global approaches to identify the underlying factors. Here we describe a novel approach to gain insight into the factors responsible for the tissue specificity of somatic instability. Using accurate genetic knock-in mouse models of HD, we developed a reliable, high-throughput method to quantify tissue HD CAG repeat instability and integrated this with genome-wide bioinformatic approaches. Using tissue instability quantified in 16 tissues as a phenotype and tissue microarray gene expression as a predictor, we built a mathematical model and identified a gene expression signature that accurately predicted tissue instability. Using the predictive ability of this signature we found that somatic instability was not a consequence of pathogenesis. In support of this, genetic crosses with models of accelerated neuropathology failed to induce somatic instability. In addition, we searched for genes and pathways that correlated with tissue instability. We found that expression levels of DNA repair genes did not explain the tissue specificity of somatic instability. Instead, our data implicate other pathways, particularly cell cycle, metabolism and neurotransmitter pathways, acting in combination to generate tissue-specific patterns of instability. Our study clearly demonstrates that multiple tissue factors reflect the level of somatic instability in different tissues. In addition, our quantitative, genome-wide approach is readily applicable to high-throughput assays and opens the door to widespread applications with the potential to accelerate the discovery of drugs that alter tissue instability.
Abstract The discovery of quantitative trait loci (QTL) in model organisms has relied heavily on the ability to perform controlled breeding to generate genotypic and phenotypic diversity. Recently, we and others have demonstrated the use of an existing set of diverse inbred mice (referred to here as the mouse diversity panel, MDP) as a QTL mapping population. The use of the MDP population has many advantages relative to traditional F2 mapping populations, including increased phenotypic diversity, a higher recombination frequency, and the ability to collect genotype and phenotype data in community databases. However, these methods are complicated by population structure inherent in the MDP and the lack of an analytical framework to assess statistical power. To address these issues, we measured gene expression levels in hypothalamus across the MDP. We then mapped these phenotypes as quantitative traits with our association algorithm, resulting in a large set of expression QTL (eQTL). We utilized these eQTL, and specifically cis-eQTL, to develop a novel nonparametric method for association analysis in structured populations like the MDP. These eQTL data confirmed that the MDP is a suitable mapping population for QTL discovery and that eQTL results can serve as a gold standard for relative measures of statistical power.
Deafness is the most common form of sensory impairment in the human population and is frequently caused by recessive mutations. To obtain animal models for recessive forms of deafness and to identify genes that control the development and function of the auditory sense organs, we performed a forward genetics screen in mice. We identified 13 mouse lines with defects in auditory function and six lines with auditory and vestibular defects. We mapped several of the affected genetic loci and identified point mutations in four genes. Interestingly, all identified genes are expressed in mechanosensory hair cells and required for their function. One mutation maps to the pejvakin gene, which encodes a new member of the gasdermin protein family. Previous studies have described two missense mutations in the human pejvakin gene that cause nonsyndromic recessive deafness (DFNB59) by affecting the function of auditory neurons. In contrast, the pejvakin allele described here introduces a premature stop codon, causes outer hair cell defects, and leads to progressive hearing loss. We also identified a novel allele of the human pejvakin gene in an Iranian pedigree that is afflicted with progressive hearing loss. Our findings suggest that the mechanisms of pathogenesis associated with pejvakin mutations are more diverse than previously appreciated. More generally, our findings demonstrate that recessive screens in mice are powerful tools for identifying genes that control the development and function of mechanosensory hair cells and cause deafness in humans, as well as generating animal models for disease.
