Lisa H. Cazares

Tissue Imaging Using Nanospray Desorption Electrospray Ionization Mass Spectrometry

Ambient ionization imaging mass spectrometry is uniquely suited for detailed spatially resolved chemical characterization of biological samples in their native environment. However, the spatial resolution attainable using existing approaches is limited by the ion transfer efficiency from the ionization region into the mass spectrometer. Here, we present a first study of ambient imaging of biological samples using nanospray desorption ionization (nano-DESI). Nano-DESI is a new ambient pressure ionization technique that uses minute amounts of solvent confined between two capillaries comprising the nano-DESI probe and the solid analyte for controlled desorption of molecules present on the substrate followed by ionization through self-aspirating nanospray. We demonstrate highly sensitive spatially resolved analysis of tissue samples without sample preparation. Our first proof-of-principle experiments indicate the potential of nano-DESI for ambient imaging with a spatial resolution of better than 12 μm. The significant improvement of the spatial resolution offered by nano-DESI imaging combined with high detection efficiency will enable new imaging mass spectrometry applications in clinical diagnostics, drug discovery, molecular biology, and biochemistry.

Imaging Mass Spectrometry of a Specific Fragment of Mitogen-Activated Protein Kinase/Extracellular Signal-Regulated Kinase Kinase Kinase 2 Discriminates Cancer from Uninvolved Prostate Tissue

Purpose: Histopathology is the standard approach for tissue diagnostics and centerpiece of pathology. Although the current system provides prognostic information, there is need for molecular markers that enhance diagnosis and better predict clinical prognosis. The ability to localize disease-specific molecular changes in biopsy tissue would help improve critical pathology decision making. Direct profiling of proteins from tissue using matrix-assisted laser desorption/ionization imaging mass spectrometry has the potential to supplement morphology with underlying molecular detail. Experimental Design: A discovery set of 11 prostate cancer (PCa)–containing and 10 benign prostate tissue sections was evaluated for protein expression differences. A separate validation set of 54 tissue sections (23 PCa and 31 benign) was used to verify the results. Cryosectioning was done to yield tissue sections analyzed by a pathologist to determine tissue morphology and mirror sections for imaging mass spectrometry. Spectra were acquired and the intensity of signals was plotted as a function of the location within the tissue. Results: An expression profile was found that discriminates between PCa and normal tissue. The overexpression of a single ion at m/z 4,355 was able to discriminate cancer from uninvolved tissue. Tandem mass spectrometry identified this marker as a fragment of mitogen-activated protein kinase/extracellular signal-regulated kinase kinase kinase 2 (MEKK2). The ability of MEKK2 to discriminate tumor from normal cells was orthogonally confirmed. Conclusions: This study highlights the potential of this approach to uncover molecular detail that can be correlated with pathology decision making. In addition, the identification of MEKK2 shows the ability to discover proteins of relevance to PCa biology. (Clin Cancer Res 2009;15(17):5541–51)

SELDI-TOF MS profiling of serum for detection of the progression of chronic hepatitis C to hepatocellular carcinoma†

Proteomic profiling of serum is an emerging technique to identify new biomarkers indicative of disease severity and progression. The objective of our study was to assess the use of surface‐enhanced laser desorption/ionization time‐of‐flight mass spectrometry (SELDI‐TOF MS) to identify multiple serum protein biomarkers for detection of liver disease progression to hepatocellular carcinoma (HCC). A cohort of 170 serum samples obtained from subjects in the United States with no liver disease (n = 39), liver diseases not associated with cirrhosis (n = 36), cirrhosis (n = 38), or HCC (n = 57) were applied to metal affinity protein chips for protein profiling by SELDI‐TOF MS. Across the four test groups, 38 differentially expressed proteins were used to generate multiple decision classification trees to distinguish the known disease states. Analysis of a subset of samples with only hepatitis C virus (HCV)‐related disease was emphasized. The serum protein profiles of control patients were readily distinguished from each HCV‐associated disease state. Two‐way comparisons of chronic hepatitis C, HCV cirrhosis, or HCV‐HCC versus healthy had a sensitivity/specificity range of 74% to 95%. For distinguishing chronic HCV from HCV‐HCC, a sensitivity of 61% and a specificity of 76% were obtained. However, when the values of known serum markers α fetoprotein, des‐gamma carboxyprothrombin, and GP73 were combined with the SELDI peak values, the sensitivity and specifity improved to 75% and 92%, respectively. In conclusion, SELDI‐TOF MS serum profiling is able to distinguish HCC from liver disease before cirrhosis as well as cirrhosis, especially in patients with HCV infection compared with other etiologies. (HEPATOLOGY 2005;41:634–642.)

Serum Protein Profiles to Identify Head and Neck Cancer

Purpose: New and more consistent biomarkers of head and neck squamous cell carcinoma (HNSCC) are needed to improve early detection of disease and to monitor successful patient management. The purpose of this study was to determine whether a new proteomic technology could correctly identify protein expression profiles for cancer in patient serum samples. Experimental Design: Surface-enhanced laser desorption/ionization-time of flight-mass spectrometry ProteinChip system was used to screen for differentially expressed proteins in serum from 99 patients with HNSCC and 102 normal controls. Protein peak clustering and classification analyses of the surface-enhanced laser desorption/ionization spectral data were performed using the Biomarker Wizard and Biomarker Patterns software (version 3.0), respectively (Ciphergen Biosystems, Fremont, CA). Results: Several proteins, with masses ranging from 2,778 to 20,800 Da, were differentially expressed between HNSCC and the healthy controls. The serum protein expression profiles were used to develop and train a classification and regression tree algorithm, which reliably achieved a sensitivity of 83.3% and a specificity of 100% in discriminating HNSCC from normal controls. Conclusions: We propose that this technique has potential for the development of a screening test for the detection of HNSCC.

SELDI-TOF serum profiling for prognostic and diagnostic classification of breast cancers

Surface enhanced laser desorption/ionization (SELDI) time-of-flight mass spectrometry has emerged as a successful tool for serum based detection and differentiation of many cancer types, including breast cancers. In this study, we have applied the SELDI technology to evaluate three potential applications that could extend the effectiveness of established procedures and biomarkers used for prognostication of breast cancers. Paired serum samples obtained from women with breast cancers prior to surgery and post-surgery (6–9 mos.) were examined. In 14/16 post-treatment patients, serum protein profiles could be used to distinguish these samples from the pre-treatment cancer samples. When compared to serum samples from normal healthy women, 11 of these post-treatment samples retained global protein profiles not found in healthy women, including five low-mass proteins that remained elevated in both pre-treatment and post-treatment serum groups. In another pilot study, serum profiles were compared for a group of 30 women who were known BRCA-1 mutation carriers, half of whom subsequently developed breast cancer within three years of the sample procurement. SELDI protein profiling accurately classified 13/15 women with BRCA-1 breast cancers from the 15 non-cancer BRCA-1 carriers. Additionally, the ability of SELDI to distinguish between the serum profiles from sentinel lymph node positive and sentinel lymph node negative patients was evaluated. In sentinel lymph node positive samples, 22/27 samples were correctly classified, in comparison to the correct classification of 55/71 sentinel lymph node negative samples. These initial results indicate the utility of protein profiling approaches for developing new diagnostic and prognostic assays for breast cancers.

Surfaced-Enhanced Laser Desorption/Ionization Time-of-Flight (SELDI-TOF) Differentiation of Serum Protein Profiles of BRCA-1 and Sporadic Breast Cancer

AbstractBackground: BRCA-1 mutations predispose women to early onset breast cancer, but ∼20% never develop cancer. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) profiling can differentiate protein signatures of cancer and normal subjects. Our objective was to distinguish women with BRCA-1 mutations who developed breast cancer (BRCA-1 Ca) from those who did not (Carrier), normal volunteers (NL), and women with sporadic breast cancer (SBC), using SELDI-TOF. Methods: Baseline serum specimens were obtained from women with BRCA-1 mutations without cancer, SBC, and NL. BRCA-1 women were later divided into two cohorts, pending cancer development. The sera were spotted onto protein chips for SELDI-TOF analysis and analyzed with classification algorithm software. Results: BRCA-1 Ca patients (n = 15) developed cancer within 3 years of baseline, while BRCA-1 carriers (n = 15) were cancer-free in 7 years of follow-up. SELDI-TOF analysis revealed differentially expressed proteins (P < .05) between BRCA-1 Ca, Carrier, and SBC patients (n = 16), such that 13/15 BRCA-1 Ca vs. Carrier women were correctly identified (sensitivity/specificity of 87%/87%) and 14/15 BRCA-1 Ca vs. SBC patients were correctly identified (sensitivity/specificity 94%/100%). Profiles of Carriers resembled NL profiles (n = 16). Conclusions: SELDI-TOF protein profiles from this small pilot study distinguished between women with BRCA-1 Ca, Carriers, and women with SBC. Whether BRCA-1 Ca represents earlier detection of occult cancer or other risk factors is unknown. Follow-up studies with larger numbers and longer follow-up are required to validate these findings but may allow more timely prophylactic or therapeutic strategies.

Data reduction using a discrete wavelet transform in discriminant analysis of very high dimensionality data.

We present a method of data reduction using a wavelet transform in discriminant analysis when the number of variables is much greater than the number of observations. The method is illustrated with a prostate cancer study, where the sample size is 248, and the number of variables is 48,538 (generated using the ProteinChip technology). Using a discrete wavelet transform, the 48,538 data points are represented by 1271 wavelet coefficients. Information criteria identified 11 of the 1271 wavelet coefficients with the highest discriminatory power. The linear classifier with the 11 wavelet coefficients detected prostate cancer in a separate test set with a sensitivity of 97% and specificity of 100%.

MALDI tissue imaging: from biomarker discovery to clinical applications

Matrix-assisted laser desorption ionization (MALDI) imaging mass spectrometry (IMS) is a powerful tool for the generation of multidimensional spatial expression maps of biomolecules directly from a tissue section. From a clinical proteomics perspective, this method correlates molecular detail to histopathological changes found in patient-derived tissues, enhancing the ability to identify candidates for disease biomarkers. The unbiased analysis and spatial mapping of a variety of molecules directly from clinical tissue sections can be achieved through this method. Conversely, targeted IMS, by the incorporation of laser-reactive molecular tags onto antibodies, aptamers, and other affinity molecules, enables analysis of specific molecules or a class of molecules. In addition to exploring tissue during biomarker discovery, the integration of MALDI-IMS methods into existing clinical pathology laboratory practices could prove beneficial to diagnostics. Querying tissue for the expression of specific biomarkers in a biopsy is a critical component in clinical decision-making and such markers are a major goal of translational research. An important challenge in cancer diagnostics will be to assay multiple parameters in a single slide when tissue quantities are limited. The development of multiplexed assays that maximize the yield of information from a small biopsy will help meet a critical challenge to current biomarker research. This review focuses on the use of MALDI-IMS in biomarker discovery and its potential as a clinical diagnostic tool with specific reference to our application of this technology to prostate cancer.

Differential Capture of Serum Proteins for Expression Profiling and Biomarker Discovery in Pre‐ and Posttreatment Head and Neck Cancer Samples

Introduction: A long‐term goal of our group is to develop proteomic‐based approaches to the detection and use of protein biomarkers for improvement in diagnosis, prognosis, and tailoring of treatment for head and neck squamous cell cancer (HNSCC). We have previously demonstrated that protein expression profiling of serum can identify multiple protein biomarker events that can serve as molecular fingerprints for the assessment of HNSCC disease state and prognosis.

Serum, salivary and tissue proteomics for discovery of biomarkers for head and neck cancers.

Initial clinically oriented applications of emerging proteomic technologies that aim to identify biomarkers for head and neck squamous cell carcinoma diagnostics have yielded promising results. The development of new proteomic diagnostics remains critical for the early detection of head and neck squamous cell carcinoma at more treatable stages. Prognostic markers for disease recurrence and treatment sensitivities are also required. In this overview of current biomarker identification strategies for head and neck squamous cell carcinoma, different combinations of mass spectrometry platforms, laser capture microscopy and 2D gel electrophoresis procedures are summarized as applied to readily available clinical specimens (tissue, blood and saliva). Issues related to assay reproducibility, management of large data sets and future improvements in clinical proteomics are also addressed.