Alzheimer’s disease causes a progressive dementia that currently affects over 35 million individuals worldwide and is expected to affect 115 million by 2050 (ref. 1). There are no cures or disease-modifying therapies, and this may be due to our inability to detect the disease before it has progressed to produce evident memory loss and functional decline. Biomarkers of preclinical disease will be critical to the development of disease-modifying or even preventative therapies2. Unfortunately, current biomarkers for early disease, including cerebrospinal fluid tau and amyloid-β levels3, structural and functional magnetic resonance imaging4 and the recent use of brain amyloid imaging5 or inflammaging6, are limited because they are either invasive, time-consuming or expensive. Blood-based biomarkers may be a more attractive option, but none can currently detect preclinical Alzheimer’s disease with the required sensitivity and specificity7. Herein, we describe our lipidomic approach to detecting preclinical Alzheimer’s disease in a group of cognitively normal older adults. We discovered and validated a set of ten lipids from peripheral blood that predicted phenoconversion to either amnestic mild cognitive impairment or Alzheimer’s disease within a 2–3 year timeframe with over 90% accuracy. This biomarker panel, reflecting cell membrane integrity, may be sensitive to early neurodegeneration of preclinical Alzheimer’s disease.

Genetic alterations and etiology of thymic epithelial tumors (TETs) are largely unknown, hampering the development of effective targeted therapies for patients with TETs. Here TETs of advanced-stage patients enrolled in a clinical trial of molecularly-guided targeted therapies were employed for targeted sequencing of 197 cancer-associated genes. Comparative sequence analysis of 78 TET/blood paired samples obtained from 47 thymic carcinoma (TC) and 31 thymoma patients revealed a total of 86 somatic non-synonymous sequence variations across 39 different genes in 33 (42%) TETs. TCs (62%; 29/47) showed higher incidence of somatic non-synonymous mutations than thymomas (13%; 4/31; p < 0.0001). TP53 was the most frequently mutated gene in TETs (n = 13; 17%), especially in TCs (26%), and was associated with a poorer overall survival (p < 0.0001). Genes in histone modification [BAP1 (n = 6; 13%), SETD2 (n = 5; 11%), ASXL1 (n = 2; 4%)], chromatin remodeling [SMARCA4 (n = 2; 4%)], and DNA methylation [DNMT3A (n = 3; 7%), TET2 (n = 2; 4%), WT1 (n = 2; 4%)] pathways were recurrently mutated in TCs, but not in thymomas. Our results suggest a potential disruption of epigenetic homeostasis in TCs, and a substantial difference in genetic makeup between TCs and thymomas. Further investigation is warranted into the roles of epigenetic dysregulation in TC development and its potential for targeted therapy.

We recently documented plasma lipid dysregulation in preclinical late-onset Alzheimer's disease (LOAD). A 10 plasma lipid panel, predicted phenoconversion and provided 90% sensitivity and 85% specificity in differentiating an at-risk group from those that would remain cognitively intact. Despite these encouraging results, low positive predictive values limit the clinical usefulness of this panel as a screening tool in subjects aged 70-80 years or younger. In this report, we re-examine our metabolomic data, analyzing baseline plasma specimens from our group of phenoconverters (n = 28) and a matched set of cognitively normal subjects (n = 73), and discover and internally validate a panel of 24 plasma metabolites. The new panel provides a classifier with receiver operating characteristic area under the curve for the discovery and internal validation cohort of 1.0 and 0.995 (95% confidence intervals of 1.0-1.0, and 0.981-1.0), respectively. Twenty-two of the 24 metabolites were significantly dysregulated lipids. While positive and negative predictive values were improved compared to our 10-lipid panel, low positive predictive values provide a reality check on the utility of such biomarkers in this age group (or younger). Through inclusion of additional significantly dysregulated analyte species, our new biomarker panel provides greater accuracy in our cohort but remains limited by predictive power. Unfortunately, the novel metabolite panel alone may not provide improvement in counseling and management of at-risk individuals but may further improve selection of subjects for LOAD secondary prevention trials. We expect that external validation will remain challenging due to our stringent study design, especially compared with more diverse subject cohorts. We do anticipate, however, external validation of reduced plasma lipid species as a predictor of phenoconversion to either prodromal or manifest LOAD.

Our previous study demonstrated that conditional reprogramming (CR) allows the establishment of patient-derived normal and tumor epithelial cell cultures from a variety of tissue types including breast, lung, colon and prostate. Using CR, we have established matched normal and tumor cultures, GUMC-29 and GUMC-30 respectively, from a patient's prostatectomy specimen. These CR cells proliferate indefinitely in vitro and retain stable karyotypes. Most importantly, only tumor-derived CR cells (GUMC-30) produced tumors in xenografted SCID mice, demonstrating maintenance of the critical tumor phenotype. Characterization of cells with DNA fingerprinting demonstrated identical patterns in normal and tumor CR cells as well as in xenografted tumors. By flow cytometry, both normal and tumor CR cells expressed basal, luminal, and stem cell markers, with the majority of the normal and tumor CR cells expressing prostate basal cell markers, CD44 and Trop2, as well as luminal marker, CD13, suggesting a transit-amplifying phenotype. Consistent with this phenotype, real time RT-PCR analyses demonstrated that CR cells predominantly expressed high levels of basal cell markers (KRT5, KRT14 and p63), and low levels of luminal markers. When the CR tumor cells were injected into SCID mice, the expression of luminal markers (AR, NKX3.1) increased significantly, while basal cell markers dramatically decreased. These data suggest that CR cells maintain high levels of proliferation and low levels of differentiation in the presence of feeder cells and ROCK inhibitor, but undergo differentiation once injected into SCID mice. Genomic analyses, including SNP and INDEL, identified genes mutated in tumor cells, including components of apoptosis, cell attachment, and hypoxia pathways. The use of matched patient-derived cells provides a unique in vitro model for studies of early prostate cancer.

Pancreatic cancer (PC) is an aggressive disease with high mortality rates, however, there is no blood test for early detection and diagnosis of this disease. Several research groups have reported on metabolomics based clinical investigations to identify biomarkers of PC, however there is a lack of a centralized metabolite biomarker repository that can be used for meta-analysis and biomarker validation. Furthermore, since the incidence of PC is associated with metabolic syndrome and Type 2 diabetes mellitus (T2DM), there is a need to uncouple these common metabolic dysregulations that may otherwise diminish the clinical utility of metabolomic biosignatures. Here, we attempted to externally replicate proposed metabolite biomarkers of PC reported by several other groups in an independent group of PC subjects. Our study design included a T2DM cohort that was used as a non-cancer control and a separate cohort diagnosed with colorectal cancer (CRC), as a cancer disease control to eliminate possible generic biomarkers of cancer. We used targeted mass spectrometry for quantitation of literature-curated metabolite markers and identified a biomarker panel that discriminates between normal controls (NC) and PC patients with high accuracy. Further evaluation of our model with CRC, however, showed a drop in specificity for the PC biomarker panel. Taken together, our study underscores the need for a more robust study design for cancer biomarker studies so as to maximize the translational value and clinical implementation.

Investigations of transcript levels on a genomic scale using hybridization-based arrays have led to formidable advances in our understanding of the biology of many human illnesses. At the same time, these investigations have generated controversy because of the probabilistic nature of the conclusions and the surfacing of noticeable discrepancies between the results of studies addressing the same biological question. In this article, we present simple and effective data analysis and visualization tools for gauging the degree to which the findings of one study are reproduced by others and for integrating multiple studies in a single analysis. We describe these approaches in the context of studies of breast cancer and illustrate that it is possible to identify a substantial biologically relevant subset of the human genome within which hybridization results are reliable. The subset generally varies with the platforms used, the tissues studied, and the populations being sampled. Despite important differences, it is also possible to develop simple expression measures that allow comparison across platforms, studies, laboratories and populations. Important biological signals are often preserved or enhanced. Cross-study validation and combination of microarray results requires careful, but not overly complex, statistical thinking and can become a routine component of genomic analysis.

The impact of different culture conditions on biology of primary cancer cells is not always addressed. Here conditional reprogramming (CRC) was compared with mammary-optimized EpiCult™-B (EpiC) for primary mammary epithelial cell isolation and propagation, allograft generation and genome-wide transcriptional consequences using cancer and non-cancer mammary tissue from mice with different dosages of Brca1 and p53. Selective comparison to DMEM was included. Primary cultures were established with all three media but CRC was most efficient for initial isolation (p<0.05). Allograft development was faster using cells grown in EpiC as compared to CRC (p<0.05). Transcriptome comparison of paired CRC and EpiC cultures revealed 1700 differentially expressed genes by passage 20. CRC promoted Trp53 gene family up-regulation and increased expression of epithelial differentiation genes while EpiC elevated expression of epithelial-mesenchymal-transition genes. Differences did not persist in allografts where both methods yielded allografts with relatively similar transcriptomes. Restricting passage (<7) reduced numbers of differentially expressed genes below 50. In conclusion CRC was most efficient for initial cell isolation but EpiC was quicker for allograft generation. The extensive culture-specific gene expression patterns that emerged with longer passage could be limited by reducing passage number when both culture transcriptomes were equally similar to that of the primary tissue. Defining impact of culture condition and passage on the transcriptome of primary cells could assist experimental design and interpretation. For example differences that appear with passage and culture condition are potentially exploitable for comparative studies targeting specific biological networks in different transcriptional environments.

Although radiation-induced tissue-specific injury is well documented, the underlying molecular changes resulting in organ dysfunction and the consequences thereof on overall metabolism and physiology have not been elucidated. We previously reported the generation and characterization of a transgenic mouse strain that ubiquitously overexpresses Gfrp (GTPH-1 feedback regulatory protein) and exhibits higher oxidative stress, which is a possible result of decreased tetrahydrobiopterin (BH4) bioavailability. In this study, we report genotype-dependent changes in the metabolic profiles of liver tissue after exposure to nonlethal doses of ionizing radiation. Using a combination of untargeted and targeted quantitative mass spectrometry, we report significant accumulation of metabolites associated with oxidative stress, as well as the dysregulation of lipid metabolism in transgenic mice after radiation exposure. The radiation stress seems to exacerbate lipid peroxidation and also results in higher expression of genes that facilitate liver fibrosis, in a manner that is dependent on the genetic background and post-irradiation time interval. These findings suggest the significance of Gfrp in regulating redox homeostasis in response to stress induced by ionizing radiation affecting overall physiology.