Alzheimer's disease affects millions of people worldwide and incidence is expected to rise as the population ages, but no effective therapies exist despite decades of research and more than 20 known disease markers. Research has shown that Alzheimer's disease's missing heritability remains extensive with an estimated 25% of phenotypic variance unexplained by known variants. The missing heritability may be explained by missing variants or by epistasis. Researchers often focus on individual loci rather than epistatic interactions, which is likely an oversimplification of the underlying biology since most phenotypes are affected by multiple genes. Focusing research efforts on epistasis will be critical to resolving Alzheimer's disease etiology, and a major key to identifying and properly interpreting key epistatic interactions will be bridging the gap between statistical and biological epistasis. This review covers the current state of epistasis research in Alzheimer's disease and how researchers can bridge the gap between statistical and biological epistasis to help resolve Alzheimer's disease etiology.
Reported odds ratios and population attributable fractions (PAF) for late-onset Alzheimer’s disease (LOAD) risk loci (BIN1, ABCA7, CR1, MS4A4E, CD2AP, PICALM, MS4A6A, CD33, and CLU) come from clinically ascertained samples. Little is known about the combined PAF for these LOAD risk alleles and the utility of these combined markers for case-control prediction. Here we evaluate these loci in a large population-based sample to estimate PAF and explore the effects of additive and non-additive interactions on LOAD status prediction performance.
2,419 samples from the Cache County Memory Study were genotyped for APOE and nine LOAD risk loci from AlzGene.org. We used logistic regression and ROC analysis to assess the LOAD status prediction performance of these loci using additive and non-additive models, and compared ORs and PAFs between AlzGene.org and Cache County.
Odds ratios were comparable between Cache County and AlzGene.org when identical SNPs were genotyped. PAFs from AlzGene.org ranged from 2.25–37%; those from Cache County ranged from 0.05–20%. Including non-APOE alleles significantly improved LOAD status prediction performance (AUC = 0.80) over APOE alone (AUC = 0.78) when not constrained to an additive relationship (p < 0.03). We identified potential allelic interactions (p-values uncorrected): CD33-MS4A4E (Synergy Factor = 5.31; p < 0.003) and CLU-MS4A4E (SF = 3.81; p < 0.016).
While non-additive interactions between loci significantly improve diagnostic ability, the improvement does not reach the desired sensitivity or specificity for clinical use. Nevertheless, these results suggest that understanding gene-gene interactions may be important in resolving Alzheimer’s disease etiology.
Alzheimer’s disease; epistasis; genetic interactions; population attributable fraction; odds ratio; risk
Data are lacking to describe gene expression-based breast cancer intrinsic subtype patterns for population-based patient groups.
We studied a diverse cohort of women with breast cancer from the Life After Cancer Epidemiology (LACE) and Pathways studies. RNA was extracted from 1 mm punches from fixed tumor tissue. Quantitative reverse-transcriptase polymerase chain reaction (RT-qPCR) was conducted for the 50 genes that comprise the PAM50 intrinsic subtype classifier.
In a subcohort of 1,319 women, the overall subtype distribution based on PAM50 was 53.1% Luminal A, 20.5% Luminal B, 13.0% HER2-enriched, 9.8% Basal-like, and 3.6% Normal-like. Among low-risk endocrine positive tumors (i.e. estrogen and progesterone receptor positive by immunohistochemistry, Her2 negative, and low histologic grade), only 76.5% were categorized as Luminal A by PAM50. Continuous-scale Luminal A, Luminal B, HER2-enriched, and Normal-like scores from PAM50 were mutually positively correlated; Basal-like score was inversely correlated with other subtypes. The proportion with non-Luminal A subtype decreased with older age at diagnosis, p trend < 0.0001. Compared with non-Hispanic whites, African-American women were more likely to have Basal-like tumors, age-adjusted odds ratio (OR) 4.4 (95% CI 2.3,8.4), whereas Asian and Pacific Islander women had reduced odds of Basal-like subtype, OR 0.5 (95% CI 0.3,0.9).
Our data indicate that over 50% of breast cancers treated in the community have Luminal A subtype. Gene expression-based classification shifted some tumors categorized as low risk by surrogate clinicopathological criteria to higher-risk subtypes.
Subtyping in a population-based cohort revealed distinct profiles by age and race.
breast neoplasms; cohort studies; intrinsic subtypes; PAM50
The use of cerebrospinal fluid levels of Aβ42 and pTau181 as endophenotypes for genetic studies of Alzheimer's disease (AD) has led to successful identification of both rare and common AD risk variants. In addition, this approach has provided meaningful hypotheses for the biological mechanisms by which known AD risk variants modulate the disease process. In this article we discuss these successes and outline challenges to effective and continued applications of this approach. We contrast the statistical power of this approach with traditional case-control designs and discuss solutions to address challenges in quality control and data analysis for these phenotypes. Finally, we discuss the potential for the use of this approach with larger samples as well as the incorporation of next generation sequencing and for future work with other endophenotypes for AD.
Cerebrospinal fluid; CSF; amyloid beta; Abeta; Abeta42; tau; ptau; Alzheimer's disease; LOAD; endophenotype; GWAS
Recent studies have identified the R47H variant in TREM2 as an Alzheimer’s disease (AD) risk factor with estimated odds ratio ranging from 2.9–5.1. The Cache County Memory Study is a large, population-based sample designed for the study of memory and aging. We genotyped rs75932628 (R47H) in 2974 samples (427 cases and 2540 controls) from the Cache County study using a custom Taqman Assay. We observed 7 heterozygous cases and 12 heterozygous controls with an odds ratio of 3.5 (95% confidence interval, 1.3–8.8; p = 0.0076). The minor allele frequency and population attributable fraction for R47H were 0.0029 and 0.004, respectively. This study replicates the association between R47H and AD risk in a large, population-based sample and estimates the population frequency and attributable risk of this rare variant.
Alzheimer’s disease; human; TREM2; rare variant; R47H
Since the advent of next-generation sequencing many previously untestable hypotheses have been realized. Next-generation sequencing has been used for a wide range of studies in diverse fields such as population and medical genetics, phylogenetics, microbiology, and others. However, this novel technology has created unanticipated challenges such as the large numbers of genetic variants. Each caucasian genome has more than four million single nucleotide variants, insertions and deletions, copy number variants, and structural variants. Several formats have been suggested for storing these variants; however, the variant call format (VCF) has become the community standard.
We developed new software called the Variant Tool Chest (VTC) to provide much needed tools to work with VCF files. VTC provides a variety of tools for manipulating, comparing, and analyzing VCF files beyond the functionality of existing tools. In addition, VTC was written to be easily extended with new tools.
Variant Tool Chest brings new and important functionality that complements and integrates well with existing software. VTC is available at https://github.com/mebbert/VariantToolChest
Recent studies suggest that intrinsic breast cancer subtypes may differ in their responsiveness to specific chemotherapy regimens. We examined this hypothesis on NCIC.CTG MA.5, a clinical trial randomizing premenopausal women with node-positive breast cancer to adjuvant CMF (cyclophosphamide-methotrexate-fluorouracil) versus CEF (cyclophosphamide-epirubicin-fluorouracil) chemotherapy.
Intrinsic subtype was determined for 476 tumors using the quantitative reverse transcriptase PCR PAM50 gene expression test. Luminal A, luminal B, HER2-enriched (HER2-E), and basal-like subtypes were correlated with relapse-free survival (RFS) and overall survival (OS), estimated using Kaplan-Meier plots and log-rank testing. Multivariable Cox regression analyses determined significance of interaction between treatment and intrinsic subtypes.
Intrinsic subtypes were associated with RFS (P = 0005) and OS (P < 0.0001) on the combined cohort. The HER2-E showed the greatest benefit from CEF versus CMF, with absolute 5-year RFS and OS differences exceeding 20%, whereas there was a less than 2% difference for non-HER2-E tumors (interaction test P = 0.03 for RFS and 0.03 for OS). Within clinically defined Her2+ tumors, 79% (72 of 91) were classified as the HER2-E subtype by gene expression and this subset was strongly associated with better response to CEF versus CMF (62% vs. 22%, P = 0.0006). There was no significant difference in benefit between CEF and CMF in basal-like tumors [n = 94; HR, 1.1; 95% confidence interval (CI), 0.6−.1 for RFS and HR, 1.3; 95% CI, 0.7−2.5 for OS].
HER2-E strongly predicted anthracycline sensitivity. The chemotherapy-sensitive basal- like tumors showed no added benefit for CEF over CMF, suggesting that nonanthracycline regimens may be adequate in this subtype although further investigation is required.
Alzheimer's disease is the most common form of dementia and is the only top 10 cause of death in the United States that lacks disease-altering treatments. It is a complex disorder with environmental and genetic components. There are two major types of Alzheimer's disease, early onset and the more common late onset. The genetics of early-onset Alzheimer's disease are largely understood with variants in three different genes leading to disease. In contrast, while several common alleles associated with late-onset Alzheimer's disease, including APOE, have been identified using association studies, the genetics of late-onset Alzheimer's disease are not fully understood. Here we review the known genetics of early- and late-onset Alzheimer's disease.
Risk assignment in breast cancer patients using the PAM50 Breast Cancer Intrinsic Classifier™ and the Oncotype DX® Recurrence Score in the same population was compared. There is good agreement between the two assays for high and low prognostic risk assignment but PAM50 assigns more patients to the low risk category. About half of the intermediate risk RS group was reclassified as low risk luminal A by PAM50, which suggests a potential complementary use for the assays.
To compare risk assignment by PAM50 Breast Cancer Intrinsic Classifier™ and Oncotype DX_Recurrence Score (RS) in the same population.
RNA was extracted from 151 estrogen receptor (ER)+ stage I–II breast cancers and gene expression profiled using PAM50 “intrinsic” subtyping test.
One hundred eight cases had complete molecular information; 103 (95%) were classified as luminal A (n = 76) or luminal B (n = 27). Ninety-two percent (n = 98) had a low (n = 59) or intermediate (n = 39) RS. Among luminal A cancers, 70% had low (n = 53) and the remainder (n = 23) had an intermediate RS. Among luminal B cancers, nine were high (33%) and 13 were intermediate (48%) by the RS. Almost all cancers with a high RS were classified as luminal B (90%, n = 9). One high RS cancer was identified as basal-like and had low ER/ESR1 and low human epidermal growth factor receptor 2 (HER2) expression by quantitative polymerase chain reaction in both assays. The majority of low RS cases were luminal A (83%, n = 53). Importantly, half of the intermediate RS cancers were re-categorized as low risk luminal A subtype by PAM50.
There is good agreement between the two assays for high (i.e., luminal B or RS > 31) and low (i.e., luminal B or RS < 18) prognostic risk assignment but PAM50 assigns more patients to the low risk category. About half of the intermediate RS group was reclassified as luminal A by PAM50.
Oncotype DX®; PAM50 assay; Gene expression profiles; Breast cancer; Prognosis
To identify a group of patients who might benefit from the addition of weekly paclitaxel to conventional anthracycline-containing chemotherapy as adjuvant therapy of node-positive operable breast cancer. The predictive value of PAM50 subtypes and the 11-gene proliferation score contained within the PAM50 assay were evaluated in 820 patients from the GEICAM/9906 randomized phase III trial comparing adjuvant FEC to FEC followed by weekly paclitaxel (FEC-P). Multivariable Cox regression analyses of the secondary endpoint of overall survival (OS) were performed to determine the significance of the interaction between treatment and the (1) PAM50 subtypes, (2) PAM50 proliferation score, and (3) clinical and pathological variables. Similar OS analyses were performed in 222 patients treated with weekly paclitaxel versus paclitaxel every 3 weeks in the CALGB/9342 and 9840 metastatic clinical trials. In GEICAM/9906, with a median follow up of 8.7 years, OS of the FEC-P arm was significantly superior compared to the FEC arm (unadjusted HR = 0.693, p = 0.013). A benefit from paclitaxel was only observed in the group of patients with a low PAM50 proliferation score (unadjusted HR = 0.23, p < 0.001; and interaction test, p = 0.006). No significant interactions between treatment and the PAM50 subtypes or the various clinical–pathological variables, including Ki-67 and histologic grade, were identified. Finally, similar OS results were obtained in the CALGB data set, although the interaction test did not reach statistical significance (p = 0.109). The PAM50 proliferation score identifies a subset of patients with a low proliferation status that may derive a larger benefit from weekly paclitaxel.
Electronic supplementary material
The online version of this article (doi:10.1007/s10549-013-2416-2) contains supplementary material, which is available to authorized users.
Breast cancer; Paclitaxel; PAM50 subtypes; PAM50 proliferation score; Prediction of paclitaxel efficacy
Despite improvements in early detection and treatment, cancer remains a major cause of mortality. Death from cancer is largely due to metastasis, which results in spreading of tumor cells to other parts of the body. The metastatic process is poorly understood, is often unpredictable, and usually results in incurable disease. There are no therapies specifically designed to target metastases or to block the metastatic process. Development and pre-clinical testing of new cancer therapies is limited by the scarcity of in vivo models that authentically reproduce human tumor growth and metastatic progression. Here, we report development of novel models for breast tumor growth and metastasis, which exist in the form of transplantable tumors derived directly from patients. These tumor grafts not only represent the diversity of human breast cancer, but also maintain essential features of the original patients’ tumors, including histopathology, clinical markers, hormone responsiveness, and metastasis to specific sites. Genomic features, such as gene expression profiles and DNA copy number variants, are also well maintained between the original specimens and the tumor grafts. We found that co-engraftment of primary human mesenchymal stem cells with tumor grafts helps to maintain the phenotypic stability of the tumors, and increases tumor growth by promoting angiogenesis and reducing necrosis. Remarkably, tumor engraftment is also a prognostic indicator of disease outcome: newly diagnosed women whose primary breast tumor successfully engrafted in mouse mammary glands had significantly reduced survival compared to patients whose tumors did not engraft. Thus, orthotopic breast tumor grafting marks a first step toward personalized medicine by replicating the diversity of human breast cancer through patient-centric models for tumor growth, metastasis, drug efficacy, and prognosis.
Mutations in TP53 lead to a defective G1 checkpoint and the dependence on checkpoint kinase 1 (Chk1) for G2 or S phase arrest in response to DNA damage. In preclinical studies, Chk1 inhibition resulted in enhanced cytotoxicity of several chemotherapeutic agents. The high frequency of TP53 mutations in triple negative breast cancer (TNBC: negative for estrogen receptor, progesterone receptor, and HER2) make Chk1 an attractive therapeutic target. UCN-01, a non-selective Chk1 inhibitor, combined with irinotecan demonstrated activity in advanced TNBC in our Phase I study. The goal of this trial was to further evaluate this treatment in women with TNBC. Patients with metastatic TNBC previously treated with anthracyclines and taxanes received irinotecan (100–125 mg/m2 IV days 1, 8, 15, 22) and UCN-01 (70 mg/m2 IV day 2, 35 mg/m2 day 23 and subsequent doses) every 42-day cycle. Peripheral blood mononuclear cells (PBMC) and tumor specimens were collected. Twenty five patients were enrolled. The overall response (complete response (CR) + partial response (PR)) rate was 4 %. The clinical benefit rate (CR + PR + stable disease ≥6 months) was 12 %. Since UCN-01 inhibits PDK1, phosphorylated ribosomal protein S6 (pS6) in PBMC was assessed. Although reduced 24 h post UCN-01, pS6 levels rose to baseline by day 8, indicating loss of UCN-01 bioavailability. Immunostains of γH2AX and pChk1S296 on serial tumor biopsies from four patients demonstrated an induction of DNA damage and Chk1 activation following irinotecan. However, Chk1 inhibition by UCN-01 was not observed in all tumors. Most tumors were basal-like (69 %), and carried mutations in TP53 (53 %). Median overall survival in patients with TP53 mutant tumors was poor compared to wild type (5.5 vs. 20.3 months, p = 0.004). This regimen had limited activity in TNBC. Inconsistent Chk1 inhibition was likely due to the pharmacokinetics of UCN-01. TP53 mutations were associated with a poor prognosis in metastatic TNBC.
Irinotecan; UCN-01; Chk1; Metastatic triple negative breast cancer; TP53; p53
Many methodologies have been used in research to identify the “intrinsic” subtypes of breast cancer commonly known as Luminal A, Luminal B, HER2-Enriched (HER2-E) and Basal-like. The PAM50 gene set is often used for gene expression-based subtyping; however, surrogate subtyping using panels of immunohistochemical (IHC) markers are still widely used clinically. Discrepancies between these methods may lead to different treatment decisions.
We used the PAM50 RT-qPCR assay to expression profile 814 tumors from the GEICAM/9906 phase III clinical trial that enrolled women with locally advanced primary invasive breast cancer. All samples were scored at a single site by IHC for estrogen receptor (ER), progesterone receptor (PR), and Her2/neu (HER2) protein expression. Equivocal HER2 cases were confirmed by chromogenic in situ hybridization (CISH). Single gene scores by IHC/CISH were compared with RT-qPCR continuous gene expression values and “intrinsic” subtype assignment by the PAM50. High, medium, and low expression for ESR1, PGR, ERBB2, and proliferation were selected using quartile cut-points from the continuous RT-qPCR data across the PAM50 subtype assignments.
ESR1, PGR, and ERBB2 gene expression had high agreement with established binary IHC cut-points (area under the curve (AUC) ≥ 0.9). Estrogen receptor positivity by IHC was strongly associated with Luminal (A and B) subtypes (92%), but only 75% of ER negative tumors were classified into the HER2-E and Basal-like subtypes. Luminal A tumors more frequently expressed PR than Luminal B (94% vs 74%) and Luminal A tumors were less likely to have high proliferation (11% vs 77%). Seventy-seven percent (30/39) of ER-/HER2+ tumors by IHC were classified as the HER2-E subtype. Triple negative tumors were mainly comprised of Basal-like (57%) and HER2-E (30%) subtypes. Single gene scoring for ESR1, PGR, and ERBB2 was more prognostic than the corresponding IHC markers as shown in a multivariate analysis.
The standard immunohistochemical panel for breast cancer (ER, PR, and HER2) does not adequately identify the PAM50 gene expression subtypes. Although there is high agreement between biomarker scoring by protein immunohistochemistry and gene expression, the gene expression determinations for ESR1 and ERBB2 status was more prognostic.
Multivariate assays (MVAs) for assisting clinical decisions are becoming commonly available, but due to complexity, are often considered a high-risk approach. A key concern is that uncertainty on the assay's final results is not well understood. This study focuses on developing a process to characterize error introduced in the MVA's results from the intrinsic error in the laboratory process: sample preparation and measurement of the contributing factors, such as gene expression.
Using the PAM50 Breast Cancer Intrinsic Classifier, we show how to characterize error within an MVA, and how these errors may affect results reported to clinicians. First we estimated the error distribution for measured factors within the PAM50 assay by performing repeated measures on four archetypal samples representative of the major breast cancer tumor subtypes. Then, using the error distributions and the original archetypal sample data, we used Monte Carlo simulations to generate a sufficient number of simulated samples. The effect of these errors on the PAM50 tumor subtype classification was estimated by measuring subtype reproducibility after classifying all simulated samples. Subtype reproducibility was measured as the percentage of simulated samples classified identically to the parent sample. The simulation was thereafter repeated on a large, independent data set of samples from the GEICAM 9906 clinical trial. Simulated samples from the GEICAM sample set were used to explore a more realistic scenario where, unlike archetypal samples, many samples are not easily classified.
All simulated samples derived from the archetypal samples were classified identically to the parent sample. Subtypes for simulated samples from the GEICAM set were also highly reproducible, but there were a non-negligible number of samples that exhibit significant variability in their classification.
We have developed a general methodology to estimate the effects of intrinsic errors within MVAs. We have applied the method to the PAM50 assay, showing that the PAM50 results are resilient to intrinsic errors within the assay, but also finding that in non-archetypal samples, experimental errors can lead to quite different classification of a tumor. Finally we propose a way to provide the uncertainty information in a usable way for clinicians.
Multivariate Assays; PAM50; Monte Carlo Simulations; Breast Cancer
To compare clinical, immunohistochemical and gene expression models of prognosis applicable to formalin-fixed, paraffin-embedded blocks in a large series of estrogen receptor positive breast cancers, from patients uniformly treated with adjuvant tamoxifen.
qRT-PCR assays for 50 genes identifying intrinsic breast cancer subtypes were completed on 786 specimens linked to clinical (median followup 11.7 years) and immunohistochemical (ER, PR, HER2, Ki67) data. Performance of predefined intrinsic subtype and Risk-Of-Relapse scores was assessed using multivariable Cox models and Kaplan-Meier analysis. Harrell’s C index was used to compare fixed models trained in independent data sets, including proliferation signatures.
Despite clinical ER positivity, 10% of cases were assigned to non-Luminal subtypes. qRT-PCR signatures for proliferation genes gave more prognostic information than clinical assays for hormone receptors or Ki67. In Cox models incorporating standard prognostic variables, hazard ratios for breast cancer disease specific survival over the first 5 years of followup, relative to the most common Luminal A subtype, are 1.99 (95% CI: 1.09–3.64) for Luminal B, 3.65 (1.64–8.16) for HER2-enriched and 17.71 (1.71–183.33) for the basal like subtype. For node-negative disease, PAM50 qRT-PCR based risk assignment weighted for tumor size and proliferation identifies a group with >95% 10 yr survival without chemotherapy. In node positive disease, PAM50-based prognostic models were also superior.
The PAM50 gene expression test for intrinsic biological subtype can be applied to large series of formalin-fixed paraffin-embedded breast cancers, and gives more prognostic information than clinical factors and immunohistochemistry using standard cutpoints.
Metabolism of energy nutrients by the mitochondrial electron transport chain (ETC) is implicated in the aging process. Polymorphisms in core ETC proteins may have an effect on longevity. Here we investigate the cytochrome b (cytb) polymorphism at amino acid 7 (cytbI7T) that distinguishes human mitochondrial haplogroup H from haplogroup U.
We compared longevity of individuals in these two haplogroups during historical extremes of caloric intake. Haplogroup H exhibits significantly increased longevity during historical caloric restriction compared to haplogroup U (p = 0.02) while during caloric abundance they are not different. The historical effects of natural selection on the cytb protein were estimated with the software TreeSAAP using a phylogenetic reconstruction for 107 mammal taxa from all major mammalian lineages using 13 complete protein-coding mitochondrial gene sequences. With this framework, we compared the biochemical shifts produced by cytbI7T with historical evolutionary pressure on and near this polymorphic site throughout mammalian evolution to characterize the role cytbI7T had on the ETC during times of restricted caloric intake.
Our results suggest the relationship between caloric restriction and increased longevity in human mitochondrial haplogroup H is determined by cytbI7T which likely enhances the ability of water to replenish the Qi binding site and decreases the time ubisemiquinone is at the Qo site, resulting in a decrease in the average production rate of radical oxygen species (ROS).