A novel approach of homozygous haplotype sharing identifies candidate genes in autism spectrum disorder

Autism spectrum disorder (ASD) is a highly heritable disorder of complex and heterogeneous aetiology. It is primarily characterized by altered cognitive ability including impaired language and communication skills and fundamental deficits in social reciprocity. Despite some notable successes in neuropsychiatric genetics, overall, the high heritability of ASD (~90%) remains poorly explained by common genetic risk variants. However, recent studies suggest that rare genomic variation, in particular copy number variation, may account for a significant proportion of the genetic basis of ASD. We present a large scale analysis to identify candidate genes which may contain low-frequency recessive variation contributing to ASD while taking into account the potential contribution of population differences to the genetic heterogeneity of ASD. Our strategy, homozygous haplotype (HH) mapping, aims to detect homozygous segments of identical haplotype structure that are shared at a higher frequency amongst ASD patients compared to parental controls. The analysis was performed on 1,402 Autism Genome Project trios genotyped for 1 million single nucleotide polymorphisms (SNPs). We identified 25 known and 1,218 novel ASD candidate genes in the discovery analysis including CADM2, ABHD14A, CHRFAM7A, GRIK2, GRM3, EPHA3, FGF10, KCND2, PDZK1, IMMP2L and FOXP2. Furthermore, 10 of the previously reported ASD genes and 300 of the novel candidates identified in the discovery analysis were replicated in an independent sample of 1,182 trios. Our results demonstrate that regions of HH are significantly enriched for previously reported ASD candidate genes and the observed association is independent of gene size (odds ratio 2.10). Our findings highlight the applicability of HH mapping in complex disorders such as ASD and offer an alternative approach to the analysis of genome-wide association data

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders

Antitumor activity of the polo-like kinase inhibitor, TAK-960, against preclinical models of colorectal cancer

Abstract Background Polo-like kinase 1 (Plk1) is a serine/threonine kinase that is a key regulator of multiple stages of mitotic progression. Plk1 is upregulated in many tumor types including colorectal cancer (CRC) and portends a poor prognosis. TAK-960 is an ATP-competitive Plk1 inhibitor that has demonstrated efficacy across a broad range of cancer cell lines, including CRC. In this study, we investigated the activity of TAK-960 against a large collection of CRC models including 55 cell lines and 18 patient-derived xenografts. Methods Fifty-five CRC cell lines and 18 PDX models were exposed to TAK-960 and evaluated for proliferation (IC50) and Tumor Growth Inhibition Index, respectively. Additionally, 2 KRAS wild type and 2 KRAS mutant PDX models were treated with TAK-960 as single agent or in combination with cetuximab or irinotecan. TAK-960 mechanism of action was elucidated through immunoblotting and cell cycle analysis. Results CRC cell lines demonstrated a variable anti-proliferative response to TAK-960 with IC50 values ranging from 0.001 to > 0.75 μmol/L. Anti-proliferative effects were sustained after removal of drug. Following TAK-960 treatment a highly variable accumulation of mitotic (indicating cell cycle arrest) and apoptotic markers was observed. Cell cycle analysis demonstrated that TAK-960 treatment induced G2/M arrest and polyploidy. Six out of the eighteen PDX models responded to single agent TAK-960 therapy (TGII< 20). The addition of TAK-960 to standard of care chemotherapy resulted in largely additive antitumor effects. Conclusion TAK-960 is an active anti-proliferative agent against CRC cell lines and PDX models. Collectively, these data suggest that TAK-960 may be of therapeutic benefit alone or in combination with other agents, although future work should focus on the development of predictive biomarkers and hypothesis-driven rational combinations

Dual Pharmacological Targeting of the MAP Kinase and PI3K/mTOR Pathway in Preclinical Models of Colorectal Cancer

<div><p>Background</p><p>The activation of the MAPK and PI3K/AKT/mTOR pathways is implicated in the majority of cancers. Activating mutations in both of these pathways has been described in colorectal cancer (CRC), thus indicating their potential as therapeutic targets. This study evaluated the combination of a PI3K/mTOR inhibitor (PF-04691502/PF-502) in combination with a MEK inhibitor (PD-0325901/PD-901) in CRC cell lines and patient-derived CRC tumor xenograft models (PDTX).</p><p>Materials and Methods</p><p>The anti-proliferative effects of PF-502 and PD-901 were assessed as single agents and in combination against a panel of CRC cell lines with various molecular backgrounds. Synergy was evaluated using the Bliss Additivity method. In selected cell lines, we investigated the combination effects on downstream effectors by immunoblotting. The combination was then evaluated in several fully genetically annotated CRC PDTX models.</p><p>Results</p><p>The <i>in vitro</i> experiments demonstrated a wide range of IC₅₀ values for both agents against a cell line panel. The combination of PF-502 and PD-901 demonstrated synergistic anti-proliferative activity with Bliss values in the additive range. As expected, p-AKT and p-ERK were downregulated by PF-502 and PD-901, respectively. In PDTX models, following a 30-day exposure to PF-502, PD-901 or the combination, the combination demonstrated enhanced reduction in tumor growth as compared to either single agent regardless of KRAS or PI3K mutational status.</p><p>Conclusions</p><p>The combination of a PI3K/mTOR and a MEK inhibitor demonstrated enhanced anti-proliferative effects against CRC cell lines and PDTX models.</p></div

Growth inhibition of the PI3K/mTORi, PF-04691502 (PF-502) combined with the MEKi, PD-0325901 (PD-901) on four colorectal cancer cell lines.

<p>LOVO (KRAS^G13D), HCT116 (KRAS^G13D/PIK3CA^H1047R), WIDR (BRAF^V600E/PIK3CA^P449T), GEO (KRAS^G13D). Cells were exposed to various combinations of PF-502 and PD-901 for 72 hours. (A) Fraction inhibited was graphed following exposure to single agent and combination. (B) Bliss additivity was calculated and to assess combinatorial effects. Average bliss scores were graphed.</p

Tumor growth rate analysis on patient-derived tumor xenograft models (PDTX). Tumor growth rates were determined for each individual tumor by fitting tumor volume data over the course of the treatment period to an exponential growth rate equation.

<p>Each point represents a single tumor. Mean tumor growth rate ± standard deviation are represented by the bar and handles. All data presented as mean±SD, ANOVA Tukey’s adjusted p values: *P<0.05 vs. Control; ^†P<0.05 vs. 901; ^‡P<0.05 vs. 502.</p

Effect of single agent the PI3K/mTORi, PF-04691502, the MEKi, PD-0325901 or the combination on Caspase 3/7 activity.

<p>All data presented as mean±SD, ANOVA Tukey’s adjusted p values: *<i>p</i>,0.05, **<i>p</i><0.01, ***<i>p</i><0.001 vs vehicle, #<i>p</i>,0.05, ##<i>p</i><0.01, ###<i>p</i><0.001 vs PF-502, &<i>p</i>,0.05, &&<i>p</i><0.01, &&&<i>p</i><0.001 vs PD-901. All data represents three independent experiments.</p