Pilot clinical trial and phenotypic analysis in chemotherapy-pretreated, metastatic triple-negative breast cancer patients treated with oral TAK-228 and TAK-117 (PIKTOR) to increase DNA damage repair deficiency followed by cisplatin and nab paclitaxel.

BACKGROUND: A subset of triple-negative breast cancers (TNBCs) have homologous recombination deficiency with upregulation of compensatory DNA repair pathways. PIKTOR, a combination of TAK-228 (TORC1/2 inhibitor) and TAK-117 (PI3Kα inhibitor), is hypothesized to increase genomic instability and increase DNA damage repair (DDR) deficiency, leading to increased sensitivity to DNA-damaging chemotherapy and to immune checkpoint blockade inhibitors. METHODS: 10 metastatic TNBC patients received 4 mg TAK-228 and 200 mg TAK-117 (PIKTOR) orally each day for 3 days followed by 4 days off, weekly, until disease progression (PD), followed by intravenous cisplatin 75 mg/m2 plus nab paclitaxel 220 mg/m2 every 3 weeks for up to 6 cycles. Patients received subsequent treatment with pembrolizumab and/or chemotherapy. Primary endpoints were objective response rate with cisplatin/nab paclitaxel and safety. Biopsies of a metastatic lesion were collected prior to and at PD on PIKTOR. Whole exome and RNA-sequencing and reverse phase protein arrays (RPPA) were used to phenotype tumors pre- and post-PIKTOR for alterations in DDR, proliferation, and immune response. RESULTS: With cisplatin/nab paclitaxel (cis/nab pac) therapy post PIKTOR, 3 patients had clinical benefit (1 partial response (PR) and 2 stable disease (SD) ≥ 6 months) and continued to have durable benefit in progression-free survival with pembrolizumab post-cis/nab pac for 1.2, 2, and 3.6 years. Their post-PIKTOR metastatic tissue displayed decreased mismatch repair (MMR), increased tumor mutation burden, and significantly lower levels of 53BP1, DAG Lipase ß, GCN2, AKT Ser473, and PKCzeta Thr410/403 compared to pre-PIKTOR tumor tissue. CONCLUSIONS: Priming patients' chemotherapy-pretreated metastatic TNBC with PIKTOR led to very prolonged response/disease control with subsequent cis/nab pac, followed by pembrolizumab, in 3 of 10 treated patients. Our multi-omics approach revealed a higher number of genomic alterations, reductions in MMR, and alterations in immune and stress response pathways post-PIKTOR in patients who had durable responses. TRIAL REGISTRATION: This clinical trial was registered on June 21, 2017, at ClinicalTrials.gov using identifier NCT03193853.

Ponatinib Shows Potent Antitumor Activity in Small Cell Carcinoma of the Ovary Hypercalcemic Type (SCCOHT) through Multikinase Inhibition.

Purpose: Small cell carcinoma of the ovary, hypercalcemic type (SCCOHT) is a rare, aggressive ovarian cancer in young women that is universally driven by loss of the SWI/SNF ATPase subunits SMARCA4 and SMARCA2. A great need exists for effective targeted therapies for SCCOHT.Experimental Design: To identify underlying therapeutic vulnerabilities in SCCOHT, we conducted high-throughput siRNA and drug screens. Complementary proteomics approaches profiled kinases inhibited by ponatinib. Ponatinib was tested for efficacy in two patient-derived xenograft (PDX) models and one cell-line xenograft model of SCCOHT.Results: The receptor tyrosine kinase (RTK) family was enriched in siRNA screen hits, with FGFRs and PDGFRs being overlapping hits between drug and siRNA screens. Of multiple potent drug classes in SCCOHT cell lines, RTK inhibitors were only one of two classes with selectivity in SCCOHT relative to three SWI/SNF wild-type ovarian cancer cell lines. We further identified ponatinib as the most effective clinically approved RTK inhibitor. Reexpression of SMARCA4 was shown to confer a 1.7-fold increase in resistance to ponatinib. Subsequent proteomic assessment of ponatinib target modulation in SCCOHT cell models confirmed inhibition of nine known ponatinib target kinases alongside 77 noncanonical ponatinib targets in SCCOHT. Finally, ponatinib delayed tumor doubling time 4-fold in SCCOHT-1 xenografts while reducing final tumor volumes in SCCOHT PDX models by 58.6% and 42.5%.Conclusions: Ponatinib is an effective agent for SMARCA4-mutant SCCOHT in both in vitro and in vivo preclinical models through its inhibition of multiple kinases. Clinical investigation of this FDA-approved oncology drug in SCCOHT is warranted. Clin Cancer Res; 24(8); 1932-43. ©2018 AACR.

Small cell carcinoma of the ovary, hypercalcemic type, displays frequent inactivating germline and somatic mutations in SMARCA4.

Small cell carcinoma of the ovary of hypercalcemic type (SCCOHT) is an extremely rare, aggressive cancer affecting children and young women. We identified germline and somatic inactivating mutations in the SWI/SNF chromatin-remodeling gene SMARCA4 in 75% (9/12) of SCCOHT cases in addition to SMARCA4 protein loss in 82% (14/17) of SCCOHT tumors but in only 0.4% (2/485) of other primary ovarian tumors. These data implicate SMARCA4 in SCCOHT oncogenesis.

Loss of the tumor suppressor SMARCA4 in small cell carcinoma of the ovary, hypercalcemic type (SCCOHT).

Small cell carcinoma of the ovary, hypercalcemic type (SCCOHT), is a rare and understudied cancer with a dismal prognosis. SCCOHT's infrequency has hindered empirical study of its biology and clinical management. However, we and others have recently identified inactivating mutations in the SWI/SNF chromatin remodeling gene SMARCA4 with concomitant loss of SMARCA4 protein in the majority of SCCOHT tumors.(1-4) Here we summarize these findings and report SMARCA4 status by targeted sequencing and/or immunohistochemistry (IHC) in an additional 12 SCCOHT tumors, 3 matched germlines, and the cell line SCCOHT-1. We also report the identification of a homozygous inactivating mutation in the gene SMARCB1 in one SCCOHT tumor with wild-type SMARCA4, suggesting that SMARCB1 inactivation may also play a role in the pathogenesis of SCCOHT. To date, SMARCA4 mutations and protein loss have been reported in the majority of 69 SCCOHT cases (including 2 cell lines). These data firmly establish SMARCA4 as a tumor suppressor whose loss promotes the development of SCCOHT, setting the stage for rapid advancement in the biological understanding, diagnosis, and treatment of this rare tumor type.

Whole genome association analysis shows that ACE is a risk factor for Alzheimer's disease and fails to replicate most candidates from Meta-analysis.

For late onset Alzheimer's disease (LOAD), the only confirmed, genetic association is with the apolipoprotein E (APOE) locus on chromosome 19. Meta-analysis is often employed to sort the true associations from the false positives. LOAD research has the advantage of a continuously updated meta-analysis of candidate gene association studies in the web-based AlzGene database. The top 30 AlzGene loci on May 1(st), 2007 were investigated in our whole genome association data set consisting of 1411 LOAD cases and neuropathoiogicaiiy verified controls genotyped at 312,316 SNPs using the Affymetrix 500K Mapping Platform. Of the 30 "top AlzGenes", 32 SNPs in 24 genes had odds ratios (OR) whose 95% confidence intervals that did not include 1. Of these 32 SNPs, six were part of the Affymetrix 500K Mapping panel and another ten had proxies on the Affymetrix array that had >80% power to detect an association with α=0.001. Two of these 16 SNPs showed significant association with LOAD in our sample series. One was rs4420638 at the APOE locus (uncorrected p-value=4.58E-37) and the other was rs4293, located in the angiotensin converting enzyme (ACE) locus (uncorrected p-value=0.014). Since this result was nominally significant, but did not survive multiple testing correction for 16 independent tests, this association at rs4293 was verified in a geographically distinct German cohort (p-value=0.03). We present the results of our ACE replication aiongwith a discussion of the statistical limitations of multiple test corrections in whole genome studies.

Evidence for an association between KIBRA and late-onset Alzheimer's disease.

We recently reported evidence for an association between the individual variation in normal human episodic memory and a common variant of the KIBRA gene, KIBRA rs17070145 (T-allele). Since memory impairment is a cardinal clinical feature of Alzheimer's disease (AD), we investigated the possibility of an association between the KIBRA gene and AD using data from neuronal gene expression, brain imaging studies, and genetic association tests. KIBRA was significantly over-expressed and three of its four known binding partners under-expressed in AD-affected hippocampal, posterior cingulate and temporal cortex regions (P<0.010, corrected) in a study of laser-capture microdissected neurons. Using positron emission tomography in a cohort of cognitively normal, late-middle-aged persons genotyped for KIBRA rs17070145, KIBRA T non-carriers exhibited lower glucose metabolism than did carriers in posterior cingulate and precuneus brain regions (P<0.001, uncorrected). Lastly, non-carriers of the KIBRA rs17070145 T-allele had increased risk of late-onset AD in an association study of 702 neuropathologically verified expired subjects (P=0.034; OR=1.29) and in a combined analysis of 1026 additional living and expired subjects (P=0.039; OR=1.26). Our findings suggest that KIBRA is associated with both individual variation in normal episodic memory and predisposition to AD.

Genetic control of human brain transcript expression in Alzheimer disease.

We recently surveyed the relationship between the human brain transcriptome and genome in a series of neuropathologically normal postmortem samples. We have now analyzed additional samples with a confirmed pathologic diagnosis of late-onset Alzheimer disease (LOAD; final n = 188 controls, 176 cases). Nine percent of the cortical transcripts that we analyzed had expression profiles correlated with their genotypes in the combined cohort, and approximately 5% of transcripts had SNP-transcript relationships that could distinguish LOAD samples. Two of these transcripts have been previously implicated in LOAD candidate-gene SNP-expression screens. This study shows how the relationship between common inherited genetic variants and brain transcript expression can be used in the study of human brain disorders. We suggest that studying the transcriptome as a quantitative endo-phenotype has greater power for discovering risk SNPs influencing expression than the use of discrete diagnostic categories such as presence or absence of disease.

Sorl1 as an Alzheimer's disease predisposition gene?

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressively disabling impairments in memory, cognition, and non-cognitive behavioural symptoms. Sporadic AD is multifactorial and genetically complex. While several monogenic mutations cause early-onset AD and gene alleles have been suggested as AD susceptibility factors, the only extensively validated susceptibility gene for late-onset AD is the apolipoprotein E (APOE) epsilon4 allele. Alleles of the APOE gene do not account for all of the genetic load calculated to be responsible for AD predisposition. Recently, polymorphisms across the neuronal sortilin-related receptor (SORL1) gene were shown to be significantly associated with AD in several cohorts. Here we present the results of our large case-control whole-genome scan at over 500,000 polymorphisms which presents weak evidence for association and potentially narrows the association interval.

A survey of genetic human cortical gene expression.

It is widely assumed that genetic differences in gene expression underpin much of the difference among individuals and many of the quantitative traits of interest to geneticists. Despite this, there has been little work on genetic variability in human gene expression and almost none in the human brain, because tools for assessing this genetic variability have not been available. Now, with whole-genome SNP genotyping arrays and whole-transcriptome expression arrays, such experiments have become feasible. We have carried out whole-genome genotyping and expression analysis on a series of 193 neuropathologically normal human brain samples using the Affymetrix GeneChip Human Mapping 500K Array Set and Illumina HumanRefseq-8 Expression BeadChip platforms. Here we present data showing that 58% of the transcriptome is cortically expressed in at least 5% of our samples and that of these cortically expressed transcripts, 21% have expression profiles that correlate with their genotype. These genetic-expression effects should be useful in determining the underlying biology of associations with common diseases of the human brain and in guiding the analysis of the genomic regions involved in the control of normal gene expression.

GAB2 alleles modify Alzheimer's risk in APOE epsilon4 carriers.

The apolipoprotein E (APOE) epsilon4 allele is the best established genetic risk factor for late-onset Alzheimer's disease (LOAD). We conducted genome-wide surveys of 502,627 single-nucleotide polymorphisms (SNPs) to characterize and confirm other LOAD susceptibility genes. In epsilon4 carriers from neuropathologically verified discovery, neuropathologically verified replication, and clinically characterized replication cohorts of 1411 cases and controls, LOAD was associated with six SNPs from the GRB-associated binding protein 2 (GAB2) gene and a common haplotype encompassing the entire GAB2 gene. SNP rs2373115 (p = 9 x 10(-11)) was associated with an odds ratio of 4.06 (confidence interval 2.81-14.69), which interacts with APOE epsilon4 to further modify risk. GAB2 was overexpressed in pathologically vulnerable neurons; the Gab2 protein was detected in neurons, tangle-bearing neurons, and dystrophic neuritis; and interference with GAB2 gene expression increased tau phosphorylation. Our findings suggest that GAB2 modifies LOAD risk in APOE epsilon4 carriers and influences Alzheimer's neuropathology.

A high-density whole-genome association study reveals that APOE is the major susceptibility gene for sporadic late-onset Alzheimer's disease.

OBJECTIVE: While the apolipoprotein E (APOE) epsilon allele is a well-established risk factor for late-onset Alzheimer's disease (AD), initial genome scans using microsatellite markers in late-onset AD failed to identify this locus on chromosome 19. Recently developed methods for the simultaneous assessment of hundreds of thousands of single nucleotide polymorphisms (SNPs) promise to help more precisely identify loci that contribute to the risk of AD and other common multigenic conditions. We sought here to demonstrate that more precise identification of loci that are associated with complex, multi-genic genetic disorders can be achieved using ultra-high-density whole-genome associations by demonstrating their ability to identify the APOE locus as a major susceptibility gene for late-onset AD, despite the absence of SNPs within the APOE locus itself, as well as to refine odds ratios (ORs) based on gold-standard phenotyping of the study population. METHOD: An individualized genome-wide association study using 502,627 SNPs was performed in 1086 his-topathologically verified AD cases and controls to determine the OR associated with genes predisposing to Alzheimer's disease. RESULTS: As predicted, ultra-high-density SNP genotyping, in contrast to traditional microsatellite-based genome screening approaches, precisely identified the APOE locus as having a significant association with late-onset AD. SNP rs4420638 on chromosome 19, located 14 kilobase pairs distal to the APOE epsilon variant, significantly distinguished between AD cases and controls (Bonferroni corrected p value = 5.30 x 10(-34), OR = 4.01) and was far more strongly associated with the risk of AD than any other SNP of the 502,627 tested. CONCLUSION: This study provides empirical support for the suggestion that the APOE locus is the major susceptibility gene for late-onset AD in the human genome, with an OR significantly greater than any other locus in the human genome. It also supports the feasibility of the ultra-high-density whole-genome association approach to the study of AD and other heritable phenotypes. These whole-genome association studies show great promise to identify additional genes that contribute to the risk of AD.

Identification of a novel risk locus for progressive supranuclear palsy by a pooled genomewide scan of 500,288 single-nucleotide polymorphisms.

To date, only the H1 MAPT haplotype has been consistently associated with risk of developing the neurodegenerative disease progressive supranuclear palsy (PSP). We hypothesized that additional genetic loci may be involved in conferring risk of PSP that could be identified through a pooling-based genomewide association study of >500,000 SNPs. Candidate SNPs with large differences in allelic frequency were identified by ranking all SNPs by their probe-intensity difference between cohorts. The MAPT H1 haplotype was strongly detected by this methodology, as was a second major locus on chromosome 11p12-p11 that showed evidence of association at allelic (P<.001), genotypic (P<.001), and haplotypic (P<.001) levels and was narrowed to a single haplotype block containing the DNA damage-binding protein 2 (DDB2) and lysosomal acid phosphatase 2 (ACP2) genes. Since DNA damage and lysosomal dysfunction have been implicated in aging and neurodegenerative processes, both genes are viable candidates for conferring risk of disease.

SNP-based chromosomal copy number ascertainment following multiple displacement whole-genome amplification.

Whole genome amplification by multiple displacement amplification (MDA) offers investigators using precious genomic DNA samples a high fidelity method for amplifying nanogram quantities of DNA several thousandfold. This becomes especially important for the modemrn day genomics researcher who more and more commonly is applying today's genome scanning technologies to patient cohort samples collected years ago that are irrecoverable and invariably in short supply. We present evidence here that MDA-prepared genomic DNA includes artifacts of chromosomal copy number that resemble copy number polymorphisms (CNPs) upon analysis of the DNA on the Affymetrix 10K GeneChip. The study of CNPs in both health and disease is a rapidly growing area of research, however our current understanding of the relevance of CNPs is incomplete. Our data indicate that utilization of whole genome-amplified samples for analysis heavily reliant on accurate copy number retention could be confounded if the genomic DNA sample was subjected to MDA. We recommend that small amounts of patient cohort DNA stocks be set aside and not subjected to whole genome amplification in order to facilitate the unbiased determination of chromosomal copy numbers when desired.

Identification of the genetic basis for complex disorders by use of pooling-based genomewide single-nucleotide-polymorphism association studies.

We report the development and validation of experimental methods, study designs, and analysis software for pooling-based genomewide association (GWA) studies that use high-throughput single-nucleotide-polymorphism (SNP) genotyping microarrays. We first describe a theoretical framework for establishing the effectiveness of pooling genomic DNA as a low-cost alternative to individually genotyping thousands of samples on high-density SNP microarrays. Next, we describe software called "GenePool," which directly analyzes SNP microarray probe intensity data and ranks SNPs by increased likelihood of being genetically associated with a trait or disorder. Finally, we apply these methods to experimental case-control data and demonstrate successful identification of published genetic susceptibility loci for a rare monogenic disease (sudden infant death with dysgenesis of the testes syndrome), a rare complex disease (progressive supranuclear palsy), and a common complex disease (Alzheimer disease) across multiple SNP genotyping platforms. On the basis of these theoretical calculations and their experimental validation, our results suggest that pooling-based GWA studies are a logical first step for determining whether major genetic associations exist in diseases with high heritability.

Identification of disease causing loci using an array-based genotyping approach on pooled DNA.

BACKGROUND: Pooling genomic DNA samples within clinical classes of disease followed by genotyping on whole-genome SNP microarrays, allows for rapid and inexpensive genome-wide association studies. Key to the success of these studies is the accuracy of the allelic frequency calculations, the ability to identify false-positives arising from assay variability and the ability to better resolve association signals through analysis of neighbouring SNPs. RESULTS: We report the accuracy of allelic frequency measurements on pooled genomic DNA samples by comparing these measurements to the known allelic frequencies as determined by individual genotyping. We describe modifications to the calculation of k-correction factors from relative allele signal (RAS) values that remove biases and result in more accurate allelic frequency predictions. Our results show that the least accurate SNPs, those most likely to give false-positives in an association study, are identifiable by comparing their frequencies to both those from a known database of individual genotypes and those of the pooled replicates. In a disease with a previously identified genetic mutation, we demonstrate that one can identify the disease locus through the comparison of the predicted allelic frequencies in case and control pools. Furthermore, we demonstrate improved resolution of association signals using the mean of individual test-statistics for consecutive SNPs windowed across the genome. A database of k-correction factors for predicting allelic frequencies for each SNP, derived from several thousand individually genotyped samples, is provided. Lastly, a Perl script for calculating RAS values for the Affymetrix platform is provided. CONCLUSION: Our results illustrate that pooling of DNA samples is an effective initial strategy to identify a genetic locus. However, it is important to eliminate inaccurate SNPs prior to analysis by comparing them to a database of individually genotyped samples as well as by comparing them to replicates of the pool. Lastly, detection of association signals can be improved by incorporating data from neighbouring SNPs.