Serum discrimination of early-stage lung cancer patients using electrospray-ionization mass spectrometry

pH-Adjusted Liquid SERS Approach: Toward a Reliable Plasma-Based Early Stage Lung Cancer Detection

Surface-enhanced Raman spectroscopy (SERS) provides a rapid and nondestructive method for biological plasma analysis, offering unparalleled sensitivity and specificity. However, most current studies predominantly employ the drop-cast method, where liquid samples are dried on the SERS substrate for spectral recording. While effective, this method is both time-consuming and inconsistent. Additionally, the Raman spectral signal of uric acid in plasma often dominates, overshadowing other crucial biomarkers necessary for disease differentiation. In this study, we present the application of label-free SERS detection for distinguishing early stage lung cancer (LC) from healthy controls (HC) using plasma samples. Plasma was diluted, adjusted to a weakly acidic pH, and combined with silver nanoparticles (Ag NPs) for SERS analysis. To maintain the plasma in a liquid state and prevent evaporation, liquid paraffin was employed. Our findings indicate that a 10-fold dilution of plasma under weakly acidic conditions significantly enhanced diagnostic performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.975. This method is featured by its simplicity and high efficiency, presenting a promising strategy for the minimally invasive diagnosis of early stage LC using SERS technology.

Metabolomics-based search for lung cancer markers among patients with different smoking status

Tobacco smoking is the main etiological factor of lung cancer (LC), which can also cause metabolome disruption. This study aimed to investigate whether the observed metabolic shift in LC patients was also associated with their smoking status. Untargeted metabolomics profiling was applied for the initial screening of changes in serum metabolic profile between LC and chronic obstructive pulmonary disease (COPD) patients, selected as a non-cancer group. Differences in metabolite profiles between current and former smokers were also tested. Then, targeted metabolomics methods were applied to verify and validate the proposed LC biomarkers. For untargeted metabolomics, a single extraction-dual separation workflow was applied. The samples were analyzed using a liquid chromatograph-high resolution quadrupole time-of-flight mass spectrometer. Next, the selected metabolites were quantified using liquid chromatography-triple-quadrupole mass spectrometry. The acquired data confirmed that patients' stratification based on smoking status impacted the discriminating ability of the identified LC marker candidates. Analyzing a validation set of samples enabled us to determine if the putative LC markers were truly robust. It demonstrated significant differences in the case of four metabolites: allantoin, glutamic acid, succinic acid, and sphingosine-1-phosphate. Our research showed that studying the influence of strong environmental factors, such as tobacco smoking, should be considered in cancer marker research since it reduces the risk of false positives and improves understanding of the metabolite shifts in cancer patients.

Sera Protein Signatures of Endometrial Cancer Lymph Node Metastases

The presence of lymph node metastases in endometrial cancer patients is a critical factor guiding treatment decisions; however, surgical and imaging methods for their detection are limited by morbidity and inaccuracy. To determine if sera can predict the presence of positive lymph nodes, sera collected from endometrial cancer patients with or without lymph node metastases, and benign gynecology surgical patients (N = 20 per group) were subjected to electron spray ionization mass spectrometry (ES-MS). Peaks that were significantly different among the groups were evaluated by leave one out cross validation (LOOCV) for their ability to differentiation between the groups. Proteins in the peaks were identified by MS/MS of five specimens in each group. Ingenuity Pathway Analysis was used to predict pathways regulated by the protein profiles. LOOCV of sera protein discriminated between each of the group comparisons and predicted positive lymph nodes. Pathways implicated in metastases included loss of PTEN activation and PI3K, AKT and PKA activation, leading to calcium signaling, oxidative phosphorylation and estrogen receptor-induced transcription, leading to platelet activation, epithelial-to-mesenchymal transition and senescence. Upstream activators implicated in these events included neurostimulation and inflammation, activation of G-Protein-Coupled Receptor Gβγ, loss of HER-2 activation and upregulation of the insulin receptor.

OKN-007 Alters Protein Expression Profiles in High-Grade Gliomas: Mass Spectral Analysis of Blood Sera

Current therapies for high-grade gliomas, particularly glioblastomas (GBM), do not extend patient survival beyond 16–22 months. OKN-007 (OKlahoma Nitrone 007), which is currently in phase II (multi-institutional) clinical trials for GBM patients, and has demonstrated efficacy in several rodent and human xenograft glioma models, shows some promise as an anti-glioma therapeutic, as it affects most aspects of tumorigenesis (tumor cell proliferation, angiogenesis, migration, and apoptosis). Combined with the chemotherapeutic agent temozolomide (TMZ), OKN-007 is even more effective by affecting chemo-resistant tumor cells. In this study, mass spectrometry (MS) methodology ESI-MS, mass peak analysis (Leave One Out Cross Validation (LOOCV) and tandem MS peptide sequence analyses), and bioinformatics analyses (Ingenuity^® Pathway Analysis (IPA^®)), were used to identify up- or down-regulated proteins in the blood sera of F98 glioma-bearing rats, that were either untreated or treated with OKN-007. Proteins of interest identified by tandem MS-MS that were decreased in sera from tumor-bearing rats that were either OKN-007-treated or untreated included ABCA2, ATP5B, CNTN2, ITGA3, KMT2D, MYCBP2, NOTCH3, and VCAN. Conversely, proteins of interest in tumor-bearing rats that were elevated following OKN-007 treatment included ABCA6, ADAMTS18, VWA8, MACF1, and LAMA5. These findings, in general, support our previous gene analysis, indicating that OKN-007 may be effective against the ECM. These findings also surmise that OKN-007 may be more effective against oligodendrogliomas, other brain tumors such as medulloblastoma, and possibly other types of cancers.

Clinical Application of Mass Spectrometry-Based Proteomics in Lung Cancer Early Diagnosis

The current knowledge on proteomic biomarker analysis for the early diagnosis of lung cancer is summarized, underlining the diversity among the results and the current interest in translating research results into clinical practice. A MEDLINE/PubMed literature search to retrieve all the papers published in the last 10 years is performed. Proteomics studies on lung cancer have gathered evidence on the potential role of biomarkers in early diagnosis. Although promising, none of them have proved to be sufficiently reliable to achieve validation. Future research should evolve toward a multipanel analysis of proteins, considering the possibility that individual biomarkers might not be specific enough to diagnose lung cancer, but could be related to oncological conditions.

Absolute quantification of selenoproteins and selenometabolites in lung cancer human serum by column switching coupled to triple quadrupole inductively coupled plasma mass spectrometry

One of the most important causes of the high mortality rate and low life expectancy of lung cancer is the detection at advanced stages. Thus, there is an urgent need for early diagnosis and the search of new selective biomarkers. Selenium is an important constituent of selenoproteins and a powerful antioxidant able to protect against cancer. In this work, the absolute quantification of selenium in selenoproteins and the total content in selenometabolites has been performed for the first time in serum from lung cancer patients (LC) and healthy controls (HC). To this end, a method for the simultaneous speciation of selenoproteins using size exclusion chromatography (SEC) and affinity chromatography (AF) with detection by ICP-QQQ-MS, and quantification by isotopic dilution (IDA) (SEC-AF-HPLC-SUID-ICP-QQQ-MS) was developed to determine the selenium concentration in eGPx, SEPP1 and SeAlb, as well as total selenometabolites, to find alterations that may serve as biomarkers of this disease. In the same way, a method based on anion-exchange chromatography coupled to ICP-QQQ-MS was developed to quantify selenometabolites (SeCys2, SeMeSeCys, SeMet, selenite and selenate) in the same LC and HC serum samples. The results showed that the averaged concentrations of selenium in eGPx, SeAlb and selenite were significantly higher in LC patients (LC (eGPx: 21.24 ± 0.77 ng g^-1; SeAlb: 49.56 ± 3.16 ng g^-1 and Se(IV): 6.20 ± 1.22 ng g^-1) than in HC group (eGPx: 16.96 ± 0.53 ng g^-1; SeAlb: 38.33 ± 2.66 ng g^-1 and Se(IV): 3.56 ± 0.55 ng g^-1). In addition, the ratios between selenoproteins and selenometabolites have been calculated for the first to study their potential use as LC biomarkers. The rates eGPx/SEPP1, SEPP1/SeAlb, eGPx/Se(IV) and SEPP1/Se(IV) were significantly different between LC and HC groups.

Exploratory study on application of MALDI‑TOF‑MS to detect serum and urine peptides related to small cell lung carcinoma

Matrix‑assisted laser desorption/ionization time‑of‑flight mass spectrometry (MALDI‑TOF‑MS) was employed to analyze differential serum and urine peptides in patients with small cell lung cancer (SCLC) and healthy individuals, and SCLC diagnostic classification models were constructed. Serum and urine samples from 72 patients with SCLC, age‑ and gender‑matched with 72 healthy individuals, were divided into training and testing sets in a 3:1 ratio. Serum and urine peptides were extracted using copper ion‑chelating nanomagnetic beads, and mass spectra were obtained using MALDI‑TOF‑MS. Peptide spectra for the training set were analyzed, and the classification model was constructed using ClinProTools (CPT). The testing set was used for blinded model validation. For training‑set sera, 122 differential peptide signal peaks with a mass of 0.8‑10 kDa were observed, and 19 peptides showed significantly different expression [P<0.0005; area under curve (AUC) ≥0.80]. CPT screened 5 peptide peaks (0.8114, 0.83425, 1.86655, 4.11133 and 5.81192 kDa) to construct the classification model. The testing set was used for the blinded validation, which had 95.0% sensitivity and 90.0% specificity. For the training‑set urine, 132 differential peptide signal peaks with m/z ratios of 0.8‑10 kDa were observed, and 8 peptides had significantly different expression (P<0.0005; AUC ≥0.80). Then, 5 peaks (1.0724, 2.37692, 2.7554, 4.75475 and 4.7949 kDa) were used for classification model construction. The testing set was used for 36 blinded validation, which had 85.0% sensitivity and 80.0% specificity. Among the differential peptides, 3 had the same significant peaks at 2.3764, 0.8778 and 0.8616 kDa, identified as fibrinogen α, glucose‑6‑phosphate isomerase and cyclin‑dependent kinase‑1, respectively. The present study highlighted the differences that exist in serum and urine peptides between patients with SCLC and healthy individuals. Serum and urine peptide diagnostic classification models could be constructed using MALDI‑TOF‑MS, and showed high sensitivity and specificity.

Study of the metabolomic relationship between lung cancer and chronic obstructive pulmonary disease based on direct infusion mass spectrometry

The high prevalence of lung cancer (LC) has triggered the search of biomarkers for early diagnosis of this disease. For this purpose the study of metabolic changes related to the development of lung cancer could provide interesting information about its early diagnosis. In this sense, chronic obstructive pulmonary disease (COPD), a disease associated with tumor development, is a comorbidity that increases the risk of onset and progression of lung neoplasia and has also to be considered in the study of pathology related to lung cancer. This work develop a metabolomic approach based on direct infusion mass spectrometry using a hybrid triple quadrupole-time of flight mass spectrometer (DI-ESI-QqQ-TOF-MS) in order to identify altered metabolites from serum of LC and COPD patients and evaluate its relationship and implication in the progression of LC. This methodology has been applied to 30 serum samples from LC, 30 healthy patients used as controls (HC) and 30 serum samples from COPD to found altered metabolites from both LC and COPD diseases. In addition, some metabolic differences and similarities were found in Pulmonary Emphysema and Chronic Bronchitis patients. On the other hand, altered metabolites were studied in different stages of LC (II, III and IV) to evaluate the perturbation of them throughout the progression of disease. The sample treatment consisted of the extraction of polar and non-polar metabolites from serum that was later infused into the mass spectrometer using an electrospray ionization source in positive and negative mode. Partial least squares discriminant analysis (PLS-DA) allowed a classification between LC, HC and COPD groups in all acquisition modes. A total of 35 altered and common metabolites between LC and COPD, including amino acids, fatty acids, lysophospholipids, phospholipids and triacylglycerides were identified, being alanine, aspartate and glutamate metabolism the most altered. Finally, ROC curves were applied to the dataset and metabolites with AUC value higher than 0.70 were considered as relevant in the progression of LC.

Label-free and stable serum analysis based on Ag-NPs/PSi surface-enhanced Raman scattering for noninvasive lung cancer detection

Surface-enhanced Raman scattering (SERS) has a broad application prospect in the field of tumor detection owing to its ultrahigh detective sensitivity. However, SERS analysis of serum remain a challenge in terms of repeatability and stability due to the maldistribution of the silver nanoparticles (Ag-NPs)-serum. With the aim to make up for this shortcoming, we report a new method for obtaining stable serum Raman signals utilizing the ordered arrays of pyramidal silicon (PSi) and Ag-NPs. We prove the practicability of this method by detecting the samples of serum from 50 lung cancer patients and 50 normal healthy people. Principal component analysis (PCA) of the serum SERS spectra shows that the spectral data of the two sample groups can form obvious and completely separated clusters. The receiver operating characteristic curve provides the sensitivity (100%) and specificity (90%) from the PCA-LDA method. This research indicates that a stable and label-free analysis technique of serum SERS based on Ag-NPs/PSi and PCA-LDA is promising for noninvasive lung cancer diagnoses.

Serum Monitoring and Phenotype Identification of Stage I Non-Small Cell Lung Cancer Patients

A stage I non-small cell lung cancer (NSCLC) serum profiling platform is presented which is highly efficient and accurate. Test sensitivity (0.95) for stage I NSCLC is the highest reported so far. Test metrics are reported for discriminating stage I adenocarcinoma vs squamous cell carcinoma subtypes. Blinded analysis identified 23 out of 24 stage I NSCLC and control serum samples. Group-discriminating mass peaks were targeted for tandem mass spectrometry peptide/protein identification, and yielded a lung cancer phenotype. Bioinformatic analysis revealed a novel lymphocyte adhesion pathway involved with early-stage lung cancer.

Simultaneous Quantification of Serum Multi-Phospholipids as Potential Biomarkers for Differentiating Different Pathophysiological states of lung, stomach, intestine, and pancreas

Background: Aberrant lipid metabolism is closely associated with cancer.

Materials & Methods: Serum levels of sphingomyelins (SM) (34:1), phosphatidylcholine (PC) (34:2), PC(34:1), PC(36:4), PC(36:3), and PC(36:2) in 1449 serum samples (including 599 normal controls, 69 patients with benign lung diseases (BLDs), 61 with benign colorectal diseases, 54 with benign gastric diseases, 67 with benign pancreatic diseases, and 246 with lung cancer (LC), 144 with colorectal cancer, 94 with gastric cancer, 115 with pancreatic cancer) were quantified simultaneously based on their respective calibration equations with correlation coefficient of >0.98.

Results: Receiver operating characteristic (ROC) analysis indicated that 18 panels obtained from these six phospholipids have high diagnostic ability to differentiate between different pathophysiological states. For example, a combination of SM(34:1), PC(34:2), PC(34:1), PC(36:3), and PC(36:2) to differentiating male patients with early stage LC from male normal controls plus male BLDs with a value under ROC curve (AUC) of 0.957, a sensitivity of 88.9%, and a specificity of 90.8%. SM(34:1) and PC(34:1) to differentiating female patients with early stage LC from female normal controls plus female BLDs with an AUC of 0.903, a sensitivity of 90.0%, and a specificity of 77.5%.

Conclusion: Change trends of these six phospholipids were significantly correlated with gender, physiological states, and cancer stages.

Identification of Serum Peptidome Signatures of Non-Small Cell Lung Cancer

Due to high mortality rates of lung cancer, there is a need for identification of new, clinically useful markers, which improve detection of this tumor in early stage of disease. In the current study, serum peptide profiling was evaluated as a diagnostic tool for non-small cell lung cancer patients. The combination of the ZipTip technology with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) for the analysis of peptide pattern of cancer patients (n = 153) and control subjects (n = 63) was presented for the first time. Based on the observed significant differences between cancer patients and control subjects, the classification model was created, which allowed for accurate group discrimination. The model turned out to be robust enough to discriminate a new validation set of samples with satisfactory sensitivity and specificity. Two peptides from the diagnostic pattern for non-small cell lung cancer (NSCLC) were identified as fragments of C3 and fibrinogen α chain. Since ELISA test did not confirm significant differences in the expression of complement component C3, further study will involve a quantitative approach to prove clinical utility of the other proteins from the proposed multi-peptide cancer signature.

Label-free blood serum detection by using surface-enhanced Raman spectroscopy and support vector machine for the preoperative diagnosis of parotid gland tumors

BACKGROUND

It is difficult for the parotid gland neoplasms to make an accurate preoperative diagnosis due to the restriction of biopsy in the parotid gland neoplasms. The aim of this study is to apply the surface-enhanced Raman spectroscopy (SERS) method for the blood serum biochemical detection and use the support vector machine for the analysis in order to develop a simple but accurate blood serum detection for preoperative diagnosis of the parotid gland neoplasms.

METHODS

The blood serums were collected from four groups: the patients with pleomorphic adenoma, the patients with Warthin's tumor, the patients with mucoepidermoid carcinoma and the volunteers without parotid gland neoplasms. Au nanoparticles (Au NPs) were mixed with the blood serum as the SERS active nanosensor to enhance the Raman scattering signals produced by the various biochemical materials and high quality SERS spectrum were obtained by using the Raman microscope system. Then the support vector machine was utilized to analyze the differences of the SERS spectrum from the blood serum of different groups and established a diagnostic model to discriminate the different groups.

RESULTS

It was demonstrated that there were different intensities of SERS peaks assigned to various biochemical changes in the blood serum between the parotid gland tumor groups and normal control group. Compared with the SERS spectra of the normal serums, the intensities of peaks assigned to nucleic acids and proteins increased in the SERS spectra of the parotid gland tumor serums, which manifested the differences of the biochemical metabolites in the serum from the patients with parotid gland tumors. When the leave-one-sample-out method was used, support vector machine (SVM) played an outstanding performance in the classification of the SERS spectra with the high accuracy (84.1 % ~ 88.3 %), sensitivity (82.2 % ~ 97.4 %) and specificity (73.7 % ~ 86.7 %). Though the accuracy, sensitivity and specificity decreased in the leave-one-patient-out cross validation, the mucoepidermoid carcinoma was still easier to diagnose than other tumors.

DISCUSSION

The specific molecular differences of parotid gland tumors and normal serums were significantly demonstrated through the comparison between the various SERS spectra.But compared with the serum SERS spectra reported in the other studies, some differences exist between the spectra in this study and the ones reported in the lietratures. These differences may result from the various nano-particles, the different preparation of serum and equipment parameters, and we could need a further research to find an exact explanation.Based on the SERS spectra of the serum samples, SVM have shown a giant potential to diagnose the parotid gland tumors in our preliminary study. However, different cross validaiton methods could effect the accuracy and a further study involing a great number of samples should be needed.

CONCLUSIONS

This exploratory research demonstrated the great potential of SERS combined with SVM into a non-invasive clinical diagnostic method for preoperative diagnosis of parotid gland tumors. And the internal relation between the spectra and patients should be established in the further study.

Discriminating patients with early-stage pancreatic cancer or chronic pancreatitis using serum electrospray mass profiling

Blood tests are needed to aid in the early detection of pancreatic ductal adenocarcinoma (PDAC), and monitoring pancreatitis development into malignancy especially in high risk patients. This study exhibits efforts and progress toward developing such blood tests, using electrospray-mass spectrometry (MS) serum profiling to distinguish patients with early-stage PDAC or pancreatitis from each other and from controls. Identification of significant serum mass peak differences between these individuals was performed using t tests and "leave one out" cross validation. Serum mass peak distributions of control individuals were distinguished from those of patients with chronic pancreatitis or early-stage PDAC with P values <10(-15), and patients with chronic pancreatitis were distinguished from those of patients with early-stage PDAC with a P value <10(-12). Sera from 12 out of 12 patients with PDAC stages I, IIA and IIB were blindly validated from controls. Tandem MS/MS identified a cancer phenotype with elements of PDAC involved in early-stage PDAC/control discrimination. These studies indicate electrospray-MS mass profiling can detect serum changes in patients with pancreatitis or early-stage pancreatic cancer. Such technology has the potential to aid in early detection of pancreatic cancer, biomarker development, and in monitoring development of pancreatitis into PDAC.

[Preliminary study of MALDI-TOF mass spectrometry-based screening of patients with the NSCLC serum-specific peptides]

背景与目的

早期诊断是提高肺癌生存率的关键，传统的肺癌诊断技术仍存在一定局限性。鉴于近年来以质谱为核心技术的肿瘤蛋白组学在癌症诊断方面的初步研究，本研究探索性应用基质辅助激光解析电离飞行时间质谱（matrix assisted laser desorption ionization-time of flight-mass spectrometry, MALDI-TOF-MS）分析非小细胞肺癌（non-small cell lung cancer, NSCLC）患者和健康人群的血清差异多肽，以建立NSCLC的血清分类模型。

方法

将年龄和性别匹配的133例NSCLC患者和132例健康者血清标本按照3:1的比例随机分为两组：训练组由100例NSCLC患者和100例健康者血清标本组成，用以建立分类模型；测试组由33例NSCLC患者和32例健康者血清标本组成，用以验证模型。采用铜离子鳌合纳米磁珠提取血清多肽、MALDI-TOF-MS技术检测得到质谱图。ClinProTools^TM统计软件分析训练组NSCLC患者与健康者之间的多肽图谱，从中筛选出一组差异多肽并建立分类模型，最后用测试组对模型进行盲样验证。

结果

在训练组中观察到血清质荷比（m/z）在1, 000 Da-10, 000 Da范围内有131个差异多肽信号峰，在此范围内共得到14个有统计学意义的差异多肽峰（P < 0.000, 001; AUC≥0.9），其中NSCLC患者与健康者相比，表达上调的多肽有2个，表达下调的有12个，由统计软件筛选出3个多肽峰（7, 478.59 Da、2, 271.44 Da、4, 468.38 Da）建立分类模型，然后对测试组进行验证，其盲样验证敏感性100%，特异性96.9%，准确率98.5%。

结论

本组研究显示NSCLC患者与健康人群的血清多肽存在差异，应用MALDI-TOF-MS技术可建立NSCLC的血清多肽分类模型且小规模验证具有较好的敏感性和特异性，希望大规模验证模型，并与传统诊断方法对照或结合，进而尝试建立一种新的NSCLC早期诊断模式。

Distinguishing patients with stage I lung cancer versus control individuals using serum mass profiling

Serum mass profiling can discern physiological changes associated with specific disease states and their progression. Sera (86 total) from control individuals and patients with stage I nonsmall cell lung cancer or benign small pulmonary nodules were discriminated retrospectively by serum changes discerned by mass profiling. Control individuals were distinguished from patients with Stage I lung cancer or benign nodules with test sensitivities of 89% and 83%. Lung cancer patients versus those with benign nodules were distinguished with 80% sensitivity. This study exhibits progress toward a minimally-invasive aid in early detection of lung cancer and monitoring small pulmonary nodules for malignancy.

Mass profiling of serum to distinguish mice with pancreatic cancer induced by a transgenic Kras mutation

Mass spectrometry (MS) has the unique ability to profile, in an easily accessible body tissue (peripheral blood/serum,) the sizes and relative amounts of a wide variety of biomolecules in a single platform setting. Using electrospray ionization (ESI)-MS, we distinguished individual serum from wild-type control mice from serum of mice containing an oncogenic Kras mutation, which leads to development of pancreatic ductal adenocarcinoma (PDAC) similar to that observed in humans. Identification of differences in significant ESI-MS sera mass peaks between Kras-activated mice and control mice was performed using t tests and a "nested leave one out" cross-validation procedure. Peak distributions in serum of control mice from mice with Kras-mutant-dependent PDAC were distinguished from those of pancreatic intraepithelial neoplasia (PanIN) lesions (p = 0.00024). In addition, Kras mutant mice with PDAC were distinguished from Kras mutant mice with PanIN alone (p = 0.0057). Test specificity, a measure of the false positives, was greater for the control vs. Kras mutated mice, and the test sensitivity, a measure of false negatives, was greater for the PDAC vs. PanIN containing mice. Receiver-operating characteristic (ROC) curve discriminatory values were 0.85 for both comparisons. These studies indicate ESI-MS serum mass profiling can detect physiological changes associated with pancreatic cancer initiation and development in a GEM (genetic engineered mouse) model that mimics pancreatic cancer development in humans. Such technology has the potential to aid in early detection of pancreatic cancer and in developing therapeutic drug interventions.

Serum profiling to distinguish early- and late-stage ovarian cancer patients from disease-free individuals

Sera mass spectrometry (MS) peak differences were analyzed from 35 ovarian cancer patients and 16 disease-free individuals. "Leave one out" cross validation was used to assign "% cancer peaks" in control and ovarian cancer sera samples. Sera MS discriminated stage I/II and stage III/V ovarian cancer patients versus controls with ROC curve area values of 0.82 and 0.92. Test sensitivities for ovarian cancer stage I/II and III/V were 80% and 93% respectively. These results indicate that MS is useful for distinguishing sera from early-stage ovarian cancer patients, and has potential as a test for early detection of this disease.

Distinguishing non-small cell lung adenocarcinoma patients from squamous cell carcinoma patients and control individuals using serum profiling

Goals of this study were to analyze the ability of mass spectrometry serum profiling to distinguish non-small cell lung adenocarcinoma from squamous cell carcinoma patients and healthy controls. Sera were obtained from 19 adenocarcinoma patients, 24 squamous cell carcinoma patients, and 21 controls. Identifications of significant mass-to-charge ratio (m/z) peak differences between these groups were performed using t-tests. A "leave one out" cross-validation procedure yielded discriminatory lung adenocarcinoma versus squamous cell carcinoma p and ROC curve values of <.0001 and 0.92, respectively. Test sensitivity and specificity were 84% and 79%, respectively. This approach could aid in lung cancer diagnosis and sub-typing.