Charlotte H. Clarke

Quality Control and Peak Finding for Proteomics Data Collected from Nipple Aspirate Fluid by Surface-Enhanced Laser Desorption and Ionization

BACKGROUND Recently, researchers have been using mass spectroscopy to study cancer. For use of proteomics spectra in a clinical setting, stringent quality-control procedures will be needed. METHODS We pooled samples of nipple aspirate fluid from healthy breasts and breasts with cancer to prepare a control sample. Aliquots of the control sample were used on two spots on each of three IMAC ProteinChip arrays (Ciphergen Biosystems, Inc.) on 4 successive days to generate 24 SELDI spectra. In 36 subsequent experiments, the control sample was applied to two spots of each ProteinChip array, and the resulting spectra were analyzed to determine how closely they agreed with the original 24 spectra. RESULTS We describe novel algorithms that (a) locate peaks in unprocessed proteomics spectra and (b) iteratively combine peak detection with baseline correction. These algorithms detected approximately 200 peaks per spectrum, 68 of which are detected in all 24 original spectra. The peaks were highly correlated across samples. Moreover, we could explain 80% of the variance, using only six principal components. Using a criterion that rejects a chip if the Mahalanobis distance from both control spectra to the center of the six-dimensional principal component space exceeds the 95% confidence limit threshold, we rejected 5 of the 36 chips. CONCLUSIONS Mahalanobis distance in principal component space provides a method for assessing the reproducibility of proteomics spectra that is robust, effective, easily computed, and statistically sound.

Significant differences in nipple aspirate fluid protein expression between healthy women and those with breast cancer demonstrated by time-of-flight mass spectrometry

New approaches are needed for the early detection of breast cancer. Proteomic profiling technologies, such as surface-enhanced laser desorption ionization mass spectrometry (SELDI-MS), may be able to identify tumor markers in biological fluids. The objective of this study was to determine whether there are differences in protein expression patterns in nipple aspirate fluid (NAF) from the cancerous and noncancerous breasts of patients with unilateral breast cancer and the breasts of healthy volunteers. Paired NAF samples were obtained from 23 women with stage I or II unilateral invasive breast carcinoma and five healthy female volunteers. Aliquots of the samples were applied to SELDI Protein-chip arrays (WCX2 and IMAC3-Cu++), and protein expression was analyzed using time-of-flight MS. A total of 463 distinct peaks were detected and analyzed. In breast cancer patients, no differences in protein expression were identified between the breast with the intact primary carcinoma and the contralateral noncancerous breast. Seventeen peaks were overexpressed in cancer-bearing breasts compared to breasts of healthy volunteers (p < 0.0005). When spectra from the nontumor-bearing breasts of breast cancer patients were compared with spectra from breasts of healthy volunteers, two peaks that were overexpressed in breast cancer patients and one peak that was underexpressed in breast cancer patients were detected (p < 0.0027). SELDI-MS was able to identify differences in the phenotypic proteomic profile of NAF samples obtained from patients with early-stage breast cancer and healthy women. Proteomic screening techniques such as SELDI-MS analysis of NAF may be useful for breast cancer screening and diagnosis.

SELDI-TOF-MS proteomics of breast cancer.

Abstract The detection, diagnosis, and management of breast cancer rely on an integrated approach using clinical history, physical examination, imaging, and histopathology. The discovery and validation of novel biomarkers will aid the physician in more effectively achieving this integration. This review discusses efforts in surface-enhanced laser desorption/ionization (SELDI)-based proteomics to address various clinical questions surrounding breast cancer, including diagnosis, monitoring, and stratification for treatment. Emphasis is placed on examining how study design and execution influence the discovery and validation process, which is critical to the proper development of potential clinical tests.

Proteomic biomarkers apolipoprotein A1, truncated transthyretin and connective tissue activating protein III enhance the sensitivity of CA125 for detecting early stage epithelial ovarian cancer

OBJECTIVE The low prevalence of ovarian cancer demands both high sensitivity (>75%) and specificity (99.6%) to achieve a positive predictive value of 10% for successful early detection. Utilizing a two stage strategy where serum marker(s) prompt the performance of transvaginal sonography (TVS) in a limited number (2%) of women could reduce the requisite specificity for serum markers to 98%. We have attempted to improve sensitivity by combining CA125 with proteomic markers. METHODS Sera from 41 patients with early stage (I/II) and 51 with late stage (III/IV) epithelial ovarian cancer, 40 with benign disease and 99 healthy individuals, were analyzed to measure 7 proteins [Apolipoprotein A1 (Apo-A1), truncated transthyretin (TT), transferrin, hepcidin, ß-2-microglobulin (ß2M), Connective Tissue Activating Protein III (CTAPIII), and Inter-alpha-trypsin inhibitor heavy chain 4 (ITIH4)]. Statistical models were fit by logistic regression, followed by optimization of factors retained in the models determined by optimizing the Akaike Information Criterion. A validation set included 136 stage I ovarian cancers, 140 benign pelvic masses and 174 healthy controls. RESULTS In a training set analysis, the 3 most effective biomarkers (Apo-A1, TT and CTAPIII) exhibited 54% sensitivity at 98% specificity, CA125 alone produced 68% sensitivity and the combination increased sensitivity to 88%. In a validation set, the marker panel plus CA125 produced a sensitivity of 84% at 98% specificity (P=0.015, McNemars test). CONCLUSION Combining a panel of proteomic markers with CA125 could provide a first step in a sequential two-stage strategy with TVS for early detection of ovarian cancer.

ARHI ( DIRAS3 ) induces autophagy in ovarian cancer cells by downregulating the epidermal growth factor receptor, inhibiting PI3K and Ras/MAP signaling and activating the FOXo3a-mediated induction of Rab7

The process of autophagy has been described in detail at the molecular level in normal cells, but less is known of its regulation in cancer cells. Aplasia Ras homolog member I (ARHI; DIRAS3) is an imprinted tumor suppressor gene that is downregulated in multiple malignancies including ovarian cancer. Re-expression of ARHI slows proliferation, inhibits motility, induces autophagy and produces tumor dormancy. Our previous studies have implicated autophagy in the survival of dormant ovarian cancer cells and have shown that ARHI is required for autophagy induced by starvation or rapamycin treatment. Re-expression of ARHI in ovarian cancer cells blocks signaling through the PI3K and Ras/MAP pathways, which, in turn, downregulates mTOR and initiates autophagy. Here we show that ARHI is required for autophagy-meditated cancer cell arrest and ARHI inhibits signaling through PI3K/AKT and Ras/MAP by enhancing internalization and degradation of the epidermal growth factor receptor. ARHI-mediated downregulation of PI3K/AKT and Ras/ERK signaling also decreases phosphorylation of FOXo3a, which sequesters this transcription factor in the nucleus. Nuclear retention of FOXo3a induces ATG4 and MAP-LC3-I, required for maturation of autophagosomes, and also increases the expression of Rab7, required for fusion of autophagosomes with lysosomes. Following the knockdown of FOXo3a or Rab7, autophagolysosome formation was observed but was markedly inhibited, resulting in numerous enlarged autophagosomes. ARHI expression correlates with LC3 expression and FOXo3a nuclear localization in surgical specimens of ovarian cancer. Thus, ARHI contributes to the induction of autophagy through multiple mechanisms in ovarian cancer cells.

DIRAS3 regulates the autophagosome initiation complex in dormant ovarian cancer cells.

DIRAS3 is an imprinted tumor suppressor gene that is downregulated in 60% of human ovarian cancers. Re-expression of DIRAS3 at physiological levels inhibits proliferation, decreases motility, induces autophagy, and regulates tumor dormancy. Functional inhibition of autophagy with choroquine in dormant xenografts that express DIRAS3 significantly delays tumor regrowth after DIRAS3 levels are reduced, suggesting that autophagy sustains dormant ovarian cancer cells. This study documents a newly discovered role for DIRAS3 in forming the autophagosome initiation complex (AIC) that contains BECN1, PIK3C3, PIK3R4, ATG14, and DIRAS3. Participation of BECN1 in the AIC is inhibited by binding of BECN1 homodimers to BCL2. DIRAS3 binds BECN1, disrupting BECN1 homodimers and displacing BCL2. Binding of DIRAS3 to BECN1 increases the association of BECN1 with PIK3C3 and ATG14, facilitating AIC activation. Amino acid starvation of cells induces DIRAS3 expression, reduces BECN1-BCL2 interaction and promotes autophagy, whereas DIRAS3 depletion blocks amino acid starvation-induced autophagy. In primary ovarian cancers, punctate expression of DIRAS3, BECN1, and the autophagic biomarker MAP1LC3 are highly correlated (P < 0.0001), underlining the clinical relevance of these mechanistic studies. Punctate expression of DIRAS3 and MAP1LC3 was detected in only 21–23% of primary ovarian cancers but in 81–84% of tumor nodules found on the peritoneal surface at second-look operations following primary chemotherapy. This reflects a 4-fold increase (P < 0.0001) in autophagy between primary disease and post-treatment recurrence. We suggest that DIRAS3 not only regulates the AIC, but induces autophagy in dormant, nutrient-deprived ovarian cancer cells that remain after conventional chemotherapy, facilitating their survival.

Reproducibility of SELDI Spectra Across Time and Laboratories

The reproducibility of mass spectrometry (MS) data collected using surface enhanced laser desorption/ionization-time of flight (SELDI-TOF) has been questioned. This investigation was designed to test the reproducibility of SELDI data collected over time by multiple users and instruments. Five laboratories prepared arrays once every week for six weeks. Spectra were collected on separate instruments in the individual laboratories. Additionally, all of the arrays produced each week were rescanned on a single instrument in one laboratory. Lab-to-lab and array-to-array variability in alignment parameters were larger than the variability attributable to running samples during different weeks. The coefficient of variance (CV) in spectrum intensity ranged from 25% at baseline, to 80% in the matrix noise region, to about 50% during the exponential drop from the maximum matrix noise. Before normalization, the median CV of the peak heights was 72% and reduced to about 20% after normalization. Additionally, for the spectra from a common instrument, the CV ranged from 5% at baseline, to 50% in the matrix noise region, to 20% during the drop from the maximum matrix noise. Normalization reduced the variability in peak heights to about 18%. With proper processing methods, SELDI instruments produce spectra containing large numbers of reproducibly located peaks, with consistent heights.

Validation of a Biomarker Panel and Longitudinal Biomarker Performance for Early Detection of Ovarian Cancer.

Objectives Longitudinal multimarker combinations have the potential to improve sensitivity while maintaining the high specificity required for the early detection of ovarian cancer. The use of multiple markers to improve sensitivity over cancer antigen 125 (CA125) in longitudinal algorithms for early ovarian cancer detection requires the selection of markers with optimal discriminatory power and low longitudinal variance relative to disease-initiated changes. Our objective was to identify a multimarker panel suitable for ovarian cancer, where each individual marker has its own baseline, permitting longitudinal algorithm development. Materials and Methods In this retrospective study, we measured CA125, human epididymis protein 4 (HE4), matrix metalloproteinase-7 (MMP-7), CA72-4, CA19-9, CA15-3, carcinoembryonic antigen, and soluble vascular cell adhesion molecule (sVCAM) concentrations using immunoassays in pretreatment sera from 142 stage I ovarian cancer cases and 5 annual samples each from 217 healthy controls. After random division into training and validation sets, all possible biomarker combinations were explored exhaustively using linear classifiers to identify the panel with the greatest sensitivity for stage I disease at a high specificity of 98%. To evaluate longitudinal performance of the individual markers, the within-person over time and the between-person coefficient of variation (CV) were estimated. Hierarchical modeling across women of log-concentrations enabled the borrowing of information across subjects to moderate variance estimates given the small number of observations per subject. Results The 4-marker panel comprising CA125, HE4, MMP-7, and CA72-4 performed with the highest sensitivity (83.2%) at 98% specificity. The within-person CVs were lower for CA125, HE4, MMP-7, and CA72-4 (15%, 25%, 25%, and 21%, respectively) compared with their corresponding between-person CV (49%, 20%, 35%, and 84%, respectively) indicating baselines in healthy volunteers. After simple log-transformations, the within-volunteer variation across volunteers was modeled with a normal distribution permitting parsimonious hierarchical modeling. Conclusions The multiplex panel chosen is suitable for the early detection of ovarian cancer and the individual markers have their own baseline permitting longitudinal algorithm development.

Optimized Conditions for a Quantitative SELDI TOF MS Protein Assay

The development of peptide/protein analyte assays for the purpose of diagnostic tests is driven by multiple factors, including sample availability, required throughput, and quantitative reproducibility. Laser Desorption/ionization mass spectrometry methods (LDI-MS) are particularly well suited for both peptide and protein characterization, and combining chromatographic surfaces directly onto the MS probe in the form of surface enhanced laser desorption/ionization (SELDI)-biochips has improved the reproducibility of analyte detection and provided effective relative quantitation. Here, we provide methods for developing reproducible SELDI-based assays by providing a complex artificial protein matrix background within the sample to be analyzed that allows for a common and reproducible ionization background as well as internal normalization standards. Using this approach, quantitative assays can be developed with CVs typically less than 10% across assays and days. Although the method has been extensively and successfully implemented in association with a protein matrix from E. coli, any other source for the complex protein matrix can be considered as long as it adheres to a set of conditions including the following: (1) the protein matrix must not provide interferences with the analyte to be detected, (2) the protein matrix must be sufficiently complex such that a majority of ion current generated from the desorption of the sample comes from the complex protein matrix, and (3) specific and well-resolved protein matrix peaks must be present within the mass range of the analyte of interest for appropriate normalization.

Erratum to: Lu Z, Baquero MT, Yang H, Yang M, Reger AS, Kim C, Levine DA, Clarke CH, Liao WS, Bast Jr RC. DIRAS3 regulates the autophagosome initiation complex in dormant ovarian cancer cells. Autophagy 2014; 10:1071-92

The authors have not changed the content of the original article, but would like to make the following corrections to the acknowledgments section of the published manuscript.