cancer is a lethal disease where specific early detection
biomarkers would be very valuable to improve outcomes in patients.
Many previous studies have compared biosamples from pancreatic cancer
patients with healthy controls to find potential biomarkers. However,
a range of related disease conditions can influence the performance
of these putative biomarkers, including pancreatitis and diabetes.
In this study, quantitative proteomics methods were applied to discover
potential serum glycoprotein biomarkers that distinguish pancreatic
cancer from other pancreas related conditions (diabetes, cyst, chronic
pancreatitis, obstructive jaundice) and healthy controls. Aleuria aurantia lectin (AAL) was used to extract
fucosylated glycoproteins and then both TMT protein-level labeling
and label-free quantitative analysis were performed to analyze glycoprotein
differences from 179 serum samples across the six different conditions.
A total of 243 and 354 serum proteins were identified and quantified
by label-free and TMT protein-level quantitative strategies, respectively.
Nineteen and 25 proteins were found to show significant differences
in samples between the pancreatic cancer and other conditions using
the label-free and TMT strategies, respectively, with 7 proteins considered
significant in both methods. Significantly different glycoproteins
were further validated by lectin-ELISA and ELISA assays. Four candidates
were identified as potential markers with profiles found to be highly
complementary with CA 19–9 (p < 0.001).
Obstructive jaundice (OJ) was found to have a significant impact on
the performance of every marker protein, including CA 19–9.
The combination of α-1-antichymotrypsin (AACT), thrombospondin-1
(THBS1), and haptoglobin (HPT) outperformed CA 19–9 in distinguishing
pancreatic cancer from normal controls (AUC = 0.95), diabetes (AUC
= 0.89), cyst (AUC = 0.82), and chronic pancreatitis (AUC = 0.90).
A marker panel of AACT, THBS1, HPT, and CA 19–9 showed a high
diagnostic potential in distinguishing pancreatic cancer from other
conditions with OJ (AUC = 0.92) or without OJ (AUC = 0.95).
serum; pancreatic cancer; glycoprotein
biomarker; quantitative proteomics; TMT protein-level
The purpose of this pilot study is to investigate the utility of, and areas of refinement for, digital photography as an educational tool for food logging in obese patients with type 2 diabetes (T2DM).
Thirty-three patients aged 18-70 with T2DM, BMI at least 30 kg/m2, and A1C 7.5-9% were recruited from an endocrinology clinic and randomized to a week of food logging using a digital camera (DC) or paper diary (PD), crossing over for week two. Patients then viewed a presentation about dietary effects on blood glucose, using patient DC and blood glucose entries. Outcomes of adherence (based on number of weekly entries), changes in mean blood glucose and frequency of blood glucose checks, and patient satisfaction were compared between methods. Patient feedback on the DC intervention and presentation was also analyzed.
Thirty patients completed the study. Adherence was identical across methods. The mean difference in number of entries was not significant between methods. This difference increased and neared statistical significance (favoring DC) among patients who were adherent for at least one week (21 entries, with 2 entries per day for 5 of 7 days, n=25). Mean blood glucose did not significantly decrease in either method. Patient satisfaction was similar between interventions. Feedback indicated concerns over photograph accuracy, forgetting to use the cameras, and embarrassment using them in public.
Though comparable to PD in adherence, blood glucose changes, and patient satisfaction in this pilot trial, patient feedback suggested specific areas of refinement to maximize utility of DC-based food logging as an educational tool in T2DM.
The identification of gene mutations and structural genomic aberrations that are critically involved in CLL pathogenesis is still evolving. One may postulate that genomic driver lesions with effects on CLL cell proliferation, apoptosis thresholds or chemotherapy resistance should increase in frequency over time when measured sequentially in a large CLL cohort.
We sequentially sampled a large well-characterized CLL cohort at a mean of 4 years between samplings and measured acquired copy number aberrations (aCNA) and LOH using SNP 6.0 array profiling and the mutational state of TP53, NOTCH1 and SF3B1 using Sanger sequencing. The paired analysis included 156 patients, of whom 114 remained untreated and 42 received intercurrent therapies, predominantly potent chemo-immunotherapy, during the sampling interval.
We identify a strong effect of intercurrent therapies on the frequency of acquisition of aCNAs in CLL. Importantly, the spectrum of acquired genomic changes was largely similar in patients that did or did not receive intercurrent therapies; therefore, various genomic changes that become part of the dominant clones are often already present in CLL cell populations prior to therapy. Further, we provide evidence that therapy of CLL with pre-existing TP53 mutations results in outgrowth of genomically very complex clones which dominate at relapse.
Using complementary technologies directed at the detection of genomic events that are present in substantial proportions of the clinically relevant CLL disease bulk, we capture aspects of genomic evolution in CLL over time, including increases in the frequency of genomic complexity, specific recurrent aCNAs and TP53 mutations.
CLL; genomic copy number aberrations; mutations
A subset of chronic lymphocytic leukemia (CLL) carries mutations in ATM. Such ATM mutations may be particularly relevant in the setting of del11q, which invariably results in the deletion of one ATM allele. To improve our understanding of the frequency and type of ATM mutations that exist in CLL, we resequenced all ATM coding exons in 24 CLL with del11q using direct sequencing. We detected 2 missense mutations, resulting in an ATM mutation frequency of 8%; nonsense and frameshift mutations were not identified. Given the low ATM mutation frequency detected in this cohort, we proceeded with measurements of non-mutational ATM aberrations in CLL through analysis of the activation state of ATM in response to external irradiation. The phosphorylation state of ATM at Ser-1981 was measured using quantitative immunoblotting in purified CLL cells isolated from 251 CLL patients; data were normalized to simultaneous measurements of total ATM protein and actin. Resulting p-ATM/ATM and p-ATM/actin ratios were subsequently analyzed for prognostic significance inclusive and exclusive of TP53 exons 2-10 mutations. From these analyses, conducted in a large prospectively enrolled CLL patient cohort, neither the p-ATM/ATM nor the p-ATM/actin ratios were found to be prognostic for short survival. These data in aggregate demonstrate a low frequency of ATM aberrations in an unselected CLL cohort and do not support a major prognostic role for ATM aberrations in CLL, thus motivating renewed research efforts aimed at understanding the pathobiology of 11q deletions in CLL.
CLL; ATM; outcome
Proteinuria and/or albuminuria are widely used for noninvasive assessment of kidney diseases. However, proteinuria is a nonspecific marker of diverse forms of kidney injury, physiologic processes and filtration of small proteins of monoclonal and other pathologic processes. The opportunity to develop new glomerular disease biomarkers follows the realization that the degree of podocyte depletion determines the degree of glomerulosclerosis, and if persistent, determines the progression to end-stage kidney disease (ESKD). Podocyte cell lineage-specific mRNAs can be recovered in urine pellets of model systems and in humans. In model systems, progressive glomerular disease is associated with decreased nephrin mRNA steady-state levels compared with podocin mRNA. Thus, the urine podocin:nephrin mRNA ratio (PNR) could serve as a useful progression biomarker. The use of podocyte-specific transcript ratios also circumvents many problems inherent to urine assays.
To test this hypothesis, the human diphtheria toxin receptor (hDTR) rat model of progression was used to evaluate potentially useful urine mRNA biomarkers. We compared histologic progression parameters (glomerulosclerosis score, interstitial fibrosis score and percent of podocyte depletion) with clinical biomarkers [serum creatinine, systolic blood pressure (BP), 24-h urine volume, 24-h urine protein excretion and the urine protein:creatinine ratio(PCR)] and with the novel urine mRNA biomarkers.
The PNR correlated with histologic outcome as well or better than routine clinical biomarkers and other urine mRNA biomarkers in the model system with high specificity and sensitivity, and a low coefficient of assay variation.
We concluded that the PNR, used in combination with proteinuria, will be worth testing for its clinical diagnostic and decision-making utility.
glomerular disease; podocyte; proteinuria; urine biomarker; urine podocin:nephrin mRNA ratio
To study the chemical determinants of small molecule transport inside cells, it is crucial to visualize relationships between the chemical structure of small molecules and their associated subcellular distribution patterns. For this purpose, we experimented with cells incubated with a synthetic combinatorial library of fluorescent, membrane-permeant small molecule chemical agents. With an automated high content screening instrument, the intracellular distribution patterns of these chemical agents were microscopically captured in image data sets, and analyzed off-line with machine vision and cheminformatics algorithms. Nevertheless, it remained challenging to interpret correlations linking the structure and properties of chemical agents to their subcellular localization patterns in large numbers of cells, captured across large number of images.
To address this challenge, we constructed a Multidimensional Online Virtual Image Display (MOVID) visualization platform using off-the-shelf hardware and software components. For analysis, the image data set acquired from cells incubated with a combinatorial library of fluorescent molecular probes was sorted based on quantitative relationships between the chemical structures, physicochemical properties or predicted subcellular distribution patterns. MOVID enabled visual inspection of the sorted, multidimensional image arrays: Using a multipanel desktop liquid crystal display (LCD) and an avatar as a graphical user interface, the resolution of the images was automatically adjusted to the avatar’s distance, allowing the viewer to rapidly navigate through high resolution image arrays, zooming in and out of the images to inspect and annotate individual cells exhibiting interesting staining patterns. In this manner, MOVID facilitated visualization and interpretation of quantitative structure-localization relationship studies. MOVID also facilitated direct, intuitive exploration of the relationship between the chemical structures of the probes and their microscopic, subcellular staining patterns.
MOVID can provide a practical, graphical user interface and computer-assisted image data visualization platform to facilitate bioimage data mining and cheminformatics analysis of high content, phenotypic screening experiments.
Machine vision; Cheminformatics; Virtual reality; Data mining; Optical probes; Multivariate analysis; Human-computer interaction; Graphical user interface
The initial report of an interaction between a serotonin transporter promoter polymorphism (5-HTTLPR) and stress in the development of depression is perhaps the best-known and most cited finding in psychiatric genetics. Two recent meta-analyses explored the studies seeking to replicate this initial report and concluded that the evidence did not support the presence of the interaction. However, even the larger of the meta-analyses included only 14 of the 56 studies that have explored the relationship between 5-HTTLPR, stress and depression.
We sought to perform a meta-analysis including all relevant studies assessing whether 5-HTTLPR moderates the relationship between stress and depression.
We identified relevant articles from previous meta-analyses and reviews and a PubMed database search.
We excluded two studies presenting data that were included in other, larger, studies already included in our meta-analysis to avoid duplicate counting of subjects.
In order to perform a more inclusive meta-analysis, we used the Liptak-Stouffer Z-score method to combine findings of primary studies at the significance test level rather than raw data level.
We included 54 studies and found strong evidence that 5-HTTLPR moderates the relationship between stress and depression, with the 5-HTTLPR s allele associated with an increased risk of developing depression under stress (p<0.0001). When restricting our analysis to the studies included in the previous meta-analyses, we found no evidence of association (Munafo studies p=0.16; Risch studies p=0.11). This suggests that the difference in results between previous meta-analyses and ours was not due to the difference in meta-analytic technique but instead to the expanded set of studies included in this analysis.
Contrary to the results of the smaller earlier meta-analyses, we find strong evidence that 5-HTTLPR moderates the relationship between stress and depression in the studies published to date.
Graduate; Medical; Education; Residency; Serotonin; Transporter
Podocyte depletion is a major mechanism driving glomerulosclerosis. Progression is the process by which progressive glomerulosclerosis leads to End Stage Kidney Disease (ESKD). We therefore tested the hypothesis that progression to ESKD can be caused by persistent podocyte loss using the human diphtheria toxin transgenic rat model. After initial podocyte injury causing >30% loss of podocytes glomeruli became destabilized, resulting in continuous podocyte loss over time until global podocyte depletion (ESKD) occured. Similar patterns of podocyte depletion were observed in the puromycin aminonucleoside and 5/6 nephrectomy rat models of progression. Angiotensin II blockade prevented continuous podocyte loss and progression (restabilized glomeruli). Discontinuing angiotensin II blockade resulted in recurrent glomerular destabilization, podocyte loss and progression. Reduction in blood pressure alone did not reduce proteinuria or prevent podocyte loss from destabilized glomeruli. The protective effect of angiotensin II blockade could be entirely accounted for by reduction in podocyte loss. These data demonstrate that an initiating event that results in a critical degree of podocyte depletion can destabilize glomeruli by setting in train a superimposed angiotensin II-dependent podocyte loss cycle that accelerates the progression process and results in global podocyte depletion and progression to ESKD. These events can be monitored non-invasively through urine mRNA assays.
High-throughput microscopic screening instruments can generate huge collections of images of live cells incubated with combinatorial libraries of fluorescent molecules. Organizing and visualizing these images to discern biologically important patterns that link back to chemical structure is a challenge. We present an analysis and visualization methodology - Cheminformatic Assisted Image Array (CAIA) - that greatly facilitates data mining efforts. For illustration, we considered a collection of microscopic images acquired from cells incubated with each member of a combinatorial library of styryl molecules being screened for candidate bioimaging probes. By sorting CAIAs based on quantitative image features, the relative contribution of each combinatorial building block on probe intracellular distribution could be visually discerned. The results revealed trends hidden in the dataset: most interestingly, the building blocks of the styryl molecules appeared to behave as chemical address tags, additively and independently encoding spatial patterns of intracellular fluorescence. Translated into practice, CAIA facilitated discovery of several outstanding styryl molecules for live cell nuclear imaging applications.
Cheminformatics; high content screening; combinatorial library; styryl; fluorescence; bioimaging; chemical address tags; QSAR; CAIA
Advances in modern neuroimaging in combination with behavioral genetics have allowed neuroscientists to investigate how genetic and environmental factors shape human brain structure and function. Estimating the heritability of brain structure and function via twin studies has become one of the major approaches in studying the genetics of the brain. In a classical twin study, heritability is estimated by computing genetic and phenotypic variation based on the similarity of monozygotic and dizygotic twins. However, heritability has traditionally been measured for univariate, scalar traits, and it is challenging to assess the heritability of a spatial process, such as a pattern of neural activity. In this work, we develop a statistical method to estimate phenotypic variance and covariance at each location in a spatial process, which in turn can be used to estimate the heritability of a spatial dataset. The method is based on a dimensionally-reduced model of spatial variation in paired images, in which adjusted least squares estimates can be used to estimate the key model parameters. The advantage of the proposed method compared to conventional methods such as a voxelwise or mean-ROI approaches is demonstrated in both a simulation study and a real data study assessing genetic influence on patterns of brain activity in the visual and motor cortices in response to a simple visuomotor task.
Heritability; Intraclass Correlation; Twin Study; Spatial Analysis; Genetics
Vaccine adverse events (VAEs) are adverse bodily changes occurring after vaccination. Understanding the adverse event (AE) profiles is a crucial step to identify serious AEs. Two different types of seasonal influenza vaccines have been used on the market: trivalent (killed) inactivated influenza vaccine (TIV) and trivalent live attenuated influenza vaccine (LAIV). Different adverse event profiles induced by these two groups of seasonal influenza vaccines were studied based on the data drawn from the CDC Vaccine Adverse Event Report System (VAERS). Extracted from VAERS were 37,621 AE reports for four TIVs (Afluria, Fluarix, Fluvirin, and Fluzone) and 3,707 AE reports for the only LAIV (FluMist). The AE report data were analyzed by a novel combinatorial, ontology-based detection of AE method (CODAE). CODAE detects AEs using Proportional Reporting Ratio (PRR), Chi-square significance test, and base level filtration, and groups identified AEs by ontology-based hierarchical classification. In total, 48 TIV-enriched and 68 LAIV-enriched AEs were identified (PRR>2, Chi-square score >4, and the number of cases >0.2% of total reports). These AE terms were classified using the Ontology of Adverse Events (OAE), MedDRA, and SNOMED-CT. The OAE method provided better classification results than the two other methods. Thirteen out of 48 TIV-enriched AEs were related to neurological and muscular processing such as paralysis, movement disorders, and muscular weakness. In contrast, 15 out of 68 LAIV-enriched AEs were associated with inflammatory response and respiratory system disorders. There were evidences of two severe adverse events (Guillain-Barre Syndrome and paralysis) present in TIV. Although these severe adverse events were at low incidence rate, they were found to be more significantly enriched in TIV-vaccinated patients than LAIV-vaccinated patients. Therefore, our novel combinatorial bioinformatics analysis discovered that LAIV had lower chance of inducing these two severe adverse events than TIV. In addition, our meta-analysis found that all previously reported positive correlation between GBS and influenza vaccine immunization were based on trivalent influenza vaccines instead of monovalent influenza vaccines.
To further our understanding of the biology and prognostic significance of various chromosomal 13q14 deletions in CLL.
We have analyzed data from SNP 6.0 arrays to define the anatomy of various 13q14 deletions in a cohort of 255 CLL patients and have correlated two subsets of 13q14 deletions (type I: exclusive of RB1 and type II: inclusive of RB1) with patient survival. Further, we have measured the expression of the 13q14-resident microRNAs by Q-PCR in 242 CLL patients and subsequently assessed their prognostic significance. We have sequenced all coding exons of RB1 in patients with monoallelic Rb1 deletion and have sequenced the 13q14-resident miR locus in all patients.
Large 13q14 (type II) deletions were detected in ~20% of all CLL patients and were associated with shortened survival. A strong association between 13q14 type II deletions and elevated genomic complexity, as measured through CLL-FISH or SNP 6.0 array profiling, was identified, suggesting that these lesions may contribute to CLL disease evolution through genomic destabilization. Sequence and copy number analysis of the RB1 gene identified a small CLL subset that is RB1 null. Finally, neither the expression levels of the 13q14-resident microRNAs nor the degree of 13q14 deletion, as measured through SNP 6.0 array-based copy number analysis, had significant prognostic importance.
Our data suggest that the clinical course of CLL is accelerated in patients with large (type II) 13q14 deletions that span the RB1 gene, therefore justifying routine identification of 13q14 subtypes in CLL management.
CLL; 13q14 deletion subtypes; survival
To explore the extent to which current knowledge about the organelle-targeting features of small molecules may be applicable towards controlling the accumulation and distribution of exogenous chemical agents inside cells, molecules with known subcellular localization properties (as reported in the scientific literature) were compiled into a single data set. This data set was compared to a reference data set of approved drug molecules derived from the DrugBank database, and to a reference data set of random organic molecules derived from the PubChem database. Cheminformatic analysis revealed that molecules with reported subcellular localizations were comparably diverse. However, the calculated physicochemical properties of molecules reported to accumulate in different organelles were markedly overlapping. In relation to the reference sets of Drug Bank and Pubchem molecules, molecules with reported subcellular localizations were biased towards larger, more complex chemical structures possessing multiple ionizable functional groups and higher lipophilicity. Stratifying molecules based on molecular weight revealed that many physicochemical properties trends associated with specific organelles were reversed in smaller vs. larger molecules. Most likely, these reversed trends are due to the different transport mechanisms determining the subcellular localization of molecules of different sizes. Molecular weight can be dramatically altered by tagging molecules with fluorophores or by incorporating organelle targeting motifs. Generally, in order to better exploit structure-localization relationships, subcellular targeting strategies would benefit from analysis of the biodistribution effects resulting from variations in the size of the molecules.
drug transport; pharmacokinetics; biodistribution; drug targeting; databases; mathematical modeling; drug delivery; cheminformatics
An active area in cancer biomarker research is the development of statistical methods to identify expression signatures reflecting the heterogeneity of cancer across affected individuals. Tomlins et al.  observed heterogeneous patterns of oncogene activation within several cancer types, and introduced a statistical method called Cancer Outlier Profile Analysis (COPA) to identify “cancer outlier genes”. Several related statistical approaches have since been developed, but the operating characteristics of these procedures (e.g. power, false positive rate), have not yet been fully characterized, especially in a proteomics setting. Here, we use simulation to identify the degree to which an outlier pattern of differential expression must hold in order for outlier-based approaches to be more effective than mean-based approaches. We also propose a diagnostic procedure that characterizes the potentially unequal levels of differential expression in the tails and in the center of a distribution of expression values. We find that for sample sizes and effect sizes typical of proteomics studies, the outlier pattern must be strong in order for outlier-based analysis to provide a meaningful benefit. This is corroborated by analysis of proteomics data from a melanoma study, in which the differential expression is most often present throughout the distribution, rather than being concentrated in the tails, albeit with a few proteins showing expression patterns consistent with outlier expression.
Cancers of the urinary bladder are the fifth most commonly diagnosed malignancy in the US. Early clinical diagnosis of bladder cancer remains a major challenge and the development of non-invasive methods for detection and surveillance is desirable for both patients and health care providers.
In order to identify urinary proteins with potential clinical utility we enriched and profiled the glycoprotein component of urine samples using a dual-lectin affinity chromatography and LC-MS/MS platform.
From a primary sample set obtained from 54 cancer patients and 46 controls a total of 265 distinct glycoproteins were identified with high confidence, and changes in glycoprotein abundance between groups were quantified by a label-free spectral counting method. Validation of candidate biomarker alpha-1-antitrypsin (A1AT) for disease association was performed on an independent set of 70 samples (35 cancer cases) using an ELISA. Increased levels of urinary alpha-1-antitrypsin (A1AT) glycoprotein were indicative of the presence of bladder cancer (p value < 0.0001) and augmented voided urine cytology results. A1AT detection classified bladder cancer patients with a sensitivity of 74% and specificity of 80%.
The described strategy can enable higher resolution profiling of the proteome in biological fluids by reducing complexity. Application of glycoprotein enrichment provided novel candidates for further investigation as biomarkers for the non-invasive detection of bladder cancer.
bladder cancer; glycoprotein profiling; diagnostic profile; A1AT
A mass spectrometric method was developed to elucidate the N-glycan structures of serum glycoproteins and utilize fucosylated glycans as potential markers for pancreatic cancer. This assay was applied to haptoglobin in human serum where N-glycans derived from the serum of 16 pancreatic cancer patients were compared with those from 15 individuals with benign conditions (5 normals, 5 chronic pancreatitis, and 5 type II diabetes). This assay used only 10uL of serum where haptoglobin was extracted using a monoclonal antibody and quantitative permethylation was performed on desialylated N-glycans followed by MALDI-QIT-TOF MS analysis. Eight desialylated N-glycan structures of haptoglobin were identified where a bifucosylated tri-antennary structure was reported for the first time in pancreatic cancer samples. Both core and antennary fucosylation were elevated in pancreatic cancer samples compared to samples from benign conditions. Fucosylation degree indices were calculated and show a significant difference between pancreatic cancer patients of all stages and the benign conditions analyzed. This study demonstrates that a serum assay based on haptoglobin fucosylation patterns using mass spectrometric analysis may serve as a novel method for the diagnosis of pancreatic cancer.
The chromosomal deletion 11q affects biology and clinical outcome in CLL but del11q-deregulated genes remain incompletely characterized.
We have employed integrated genomic profiling approaches upon CLL cases with and without del11q to identify 11q-relevant genes.
We have identified differential expression of the insulin receptor (INSR) in CLL, including high-level INSR expression in the majority of CLL with del11q. High INSR mRNA expression in 11q CLL (~10-fold higher mean levels than other genomic categories) was confirmed by Q-PCR in 247 CLL cases. INSR protein measurements in 257 CLL cases through FACS, compared with measurements in normal CD19+ B-cells and monocytes, confirmed that a subset of CLL aberrantly expresses high INSR levels. INSR stimulation by insulin in CLL cells ex vivo resulted in the activation of canonical INSR signaling pathways, including the AKT-mTOR and Ras/Raf/Erk pathways, and INSR activation partially abrogated spontaneous CLL cell apoptosis ex vivo. Higher INSR levels correlated with shorter time to first therapy (TTFT) and shorter overall survival (OS). In bivariate analysis, INSR expression predicted for rapid initial disease progression and shorter OS in ZAP-70 low/negative CLL. Finally, in multivariate analysis (ZAP-70 status, IgVH status and INSR expression), we detected elevated hazard ratios and trends for short OS for CLL cases with high INSR expression (analyzed inclusive or exclusive of cases with del11q).
Our aggregate biochemical and clinical outcome data suggest biologically meaningful elevated INSR expression in a substantial subset of all CLL cases, including many cases with del11q.
CLL; Insulin receptor; apoptosis; deletion 11q; disease progression
Using the exome sequencing data from 697 unrelated individuals and their simulated disease phenotypes from Genetic Analysis Workshop 17, we develop and apply a gene-based method to identify the relationship between a gene with multiple rare genetic variants and a phenotype. The method is based on the Mantel test, which assesses the correlation between two distance matrices using a permutation procedure. Using up to 100,000 permutations to estimate the statistical significance in 200 replicate data sets, we found that the method had 5.1% type I error at an α level of 0.05 and had various power to detect genes with simulated genetic associations. FLT1 and KDR had the most significant correlations with Q1 and were replicated 170 and 24 times, respectively, in 200 simulated data sets using a Bonferroni corrected p-value of 0.05 as a threshold. These results suggest that the distance correlation method can be used to identify genotype-phenotype association when multiple rare genetic variants in a gene are involved.
This study was conducted to identify novel genes with importance to the biology of adult acute myelogenous leukemia (AML).
We analyzed DNA from highly purified AML blasts and paired buccal cells from 95 patients for recurrent genomic microdeletions using ultra-high density Affymetrix SNP 6.0 array-based genomic profiling.
Through fine mapping of microdeletions on 17q, we derived a minimal deleted region of ~0.9Mb length that harbors 11 known genes; this region includes Neurofibromin 1 (NF1). Sequence analysis of all NF1 coding exons in the 11 AML cases with NF1 copy number changes identified acquired truncating frameshift mutations in 2 patients. These NF1 mutations were already present in the hematopoetic stem cell compartment. Subsequent expression analysis of NF1 mRNA in the entire AML cohort using FACS sorted blasts as a source of RNA identified 6 patients (one with a NF1 mutation) with absent NF1 expression. The NF1 null states were associated with increased Ras-bound GTP, and shRNA-mediated NF1 suppression in primary AML blasts with wild type NF1 facilitated colony formation in methylcellulose. Primary AML blasts without functional NF1, unlike blasts with functional NF1, displayed sensitivity to rapamycin-induced apoptosis, thus identifying a dependence on mTOR signaling for survival. Finally, colony formation in methylcellulose ex vivo of NF1 null CD34+/CD38− cells sorted from AML bone marrow samples was inhibited by low dose rapamycin.
NF1 null states are present in 7/95=7% of adult AML and delineate a disease subset that could be preferentially targeted by Ras or mTOR-directed therapeutics.
AML; genomic microdeletions; NF1 mutations
Analyzing subpopulations of tumor cells in tissue is a challenging subject in proteomic studies. Pancreatic cancer stem cells (CSCs) are such a group of cells that only constitute 0.2-0.8% of the total tumor cells but have been found to be the origin of pancreatic cancer carcinogenesis and metastasis. Global proteome profiling of pancreatic CSCs from xenograft tumors in mice is a promising way to unveil the molecular machinery underlying the signaling pathways. However, the extremely low availability of pancreatic tissue CSCs (around 10,000 cells per xenograft tumor or patient sample) has limited the utilization of currently standard proteomic approaches which do not work effectively with such a small amount of material. Herein, we describe the profiling of the proteome of pancreatic CSCs using a capillary scale shotgun technique by coupling offline capillary isoelectric focusing(cIEF) with nano reversed phase liquid chromatography(RPLC) followed by spectral counting peptide quantification. A whole cell lysate from 10,000 cells which corresponds to ∼1ug protein material is equally divided for three repeated cIEF separations where around 300ng peptide material is used in each run. In comparison with a non-tumorigenic tumor cell sample, among 1159 distinct proteins identified with FDR less than 0.2%, 169 differentially expressed proteins are identified after multiple testing corrections where 24% of the proteins are upregulated in the CSCs group. Ingenuity Pathway analysis of these differential expression signatures further suggests significant involvement of signaling pathways related to apoptosis, cell proliferation, inflammation and metastasis.
cancer stem cells; capillary isoelectric focusing; two-dimensional separation; proteome profiling; differential expression; spectral count; pathway analysis
Chemical address tags can be defined as specific structural features shared by a set of bioimaging probes having a predictable influence on cell-associated visual signals obtained from these probes. Here, using a large image dataset acquired with a high content screening instrument, machine vision and cheminformatics analysis have been applied to reveal chemical address tags. With a combinatorial library of fluorescent molecules, fluorescence signal intensity, spectral, and spatial features characterizing each one of the probes' visual signals were extracted from images acquired with the three different excitation and emission channels of the imaging instrument. With multivariate regression, the additive contribution from each one of the different building blocks of the bioimaging probes towards each measured, cell-associated image-based feature was calculated. In this manner, variations in the chemical features of the molecules were associated with the resulting staining patterns, facilitating quantitative, objective analysis of chemical address tags. Hierarchical clustering and paired image-cheminformatics analysis revealed key structure-property relationships amongst many building blocks of the fluorescent molecules. The results point to different chemical modifications of the bioimaging probes that can exert similar (or different) effects on the probes' visual signals. Inspection of the clustered structures suggests intramolecular charge migration or partial charge distribution as potential mechanistic determinants of chemical address tag behavior.
Cheminformatics; machine vision; bioimaging; fluorescence; high content screening; image cytometry; combinatorial chemistry
Genomic complexity is present in ~15–30% of all CLL and has emerged as a strong independent predictor of rapid disease progression and short remission duration in CLL. We conducted this study to advance our understanding of the causes of genomic complexity in CLL.
We have obtained quantitative measurements of radiation-induced apoptosis and radiation-induced ATM auto-phosphorylation in purified CLL cells from 158 and 140 patients, respectively, and have employed multi variate analysis to identify independent contributions of various biological variables on genomic complexity in CLL.
Here, we identify a strong independent effect of radiation resistance on elevated genomic complexity in CLL and describe radiation resistance as a predictor for shortened CLL survival. Further, using multivariate analysis, we identify del17p/p53 aberrations, del11q, del13q14 type II (invariably resulting in Rb loss) and CD38 expression as independent predictors of genomic complexity in CLL, with aberrant p53 as a predictor of ~50% of genomic complexity in CLL. Focusing on del11q, we determined that normalized ATM activity was a modest predictor of genomic complexity but was not independent of del11q. Through SNP array-based fine mapping of del11q, we identified frequent mono-allelic loss of Mre11 and H2AFX in addition to ATM, indicative of compound del11q-resident gene defects in the DNA-ds-break response.
Our quantitative analysis links multiple molecular defects, including for the first time del11q and large 13q14 deletions (type II), to elevated genomic complexity in CLL, thereby suggesting mechanisms for the observed clinical aggressiveness of CLL in patients with unstable genomes.
CLL; genomic complexity; DNA double strand break response
The NCI60 human tumor cell line screen is a public resource for studying selective and non-selective growth inhibition of small molecules against cancer cells. By coupling growth inhibition screening data with biological characterizations of the different cell lines, it becomes possible to infer mechanisms of action underlying some of the observable patterns of selective activity. Using these data, mechanistic relationships have been identified including specific associations between single genes and small families of closely related compounds, and less specific relationships between biological processes involving several cooperating genes and broader families of compounds. Here we aim to characterize the degree to which such specific and general relationships are present in these data. A related question is whether genes tend to act with a uniform mechanism for all associated compounds, or whether multiple mechanisms are commonly involved. We address these two issues in a statistical framework placing special emphasis on the effects of measurement error in the gene expression and chemical screening data. We find that as measurement accuracy increases, the pattern of apparent associations shifts from one dominated by isolated gene/compound pairs, to one in which families consisting of an average of 25 compounds are associated to the same gene. At the same time, the number of genes that appear to play a role in influencing compound activities decreases. For less than half of the genes, the presence of both positive and negative correlations indicates pleiotropic associations with molecules via different mechanisms of action.
High throughput screen; gene expression; chemical biology; measurement error; false discovery rate; toxicity
Ovarian cancer, the second most common gynecological malignancy, accounts for 3% of all cancers among women in the United States, and has a high mortality rate, largely because existing therapies for widespread disease are rarely curative. Ovarian endometrioid adenocarcinoma (OEA) accounts for about 20% of the overall incidence of all ovarian cancer. We have used proteomics profiling to characterize low stage (FIGO stage 1 or 2) versus high stage (FIGO stage 3 or 4) human OEAs. In general, the low stage tumors lacked p53 mutations and had frequent CTNNB1, PTEN, and/or PIK3CA mutations. The high stage tumors had mutant p53, were usually high grade, and lacked mutations predicted to deregulate Wnt/β-catenin and PI3K/Pten/Akt signaling. We utilized 2-D liquid-based separation/mass mapping techniques to elucidate molecular weight and pI measurements of the differentially expressed intact proteins. We generated 2-D protein mass maps to facilitate the analysis of protein expression between both the low stage and high stage tumors. These mass maps (over a pI range of 5.6–4.6) revealed that the low stage OEAs demonstrated protein over-expression at the lower pI ranges (pI 4.8–4.6) in comparison to the high stage tumors, which demonstrated protein over-expression in the higher pI ranges (pI 5.4–5.2). These data suggest that both low and high stage OEAs have characteristic pI signatures of abundant protein expression probably reflecting, at least in part, the different signaling pathway defects that characterize each group. In this study, the low stage OEAs were distinguishable from high stage tumors based upon the proteomic profiles. Interestingly, when only high-grade (grade 2 or 3) OEAs were included in the analysis, the tumors still tended to cluster according to stage, suggesting that the altered protein expression was not solely dependent upon tumor cell differentiation. Further, these protein profiles clearly distinguish OEA from other types of ovarian cancer at the protein level.
Endometrioid ovarian cancer; Liquid-based protein separation; Mass mapping