Higher Expression Levels In Patients Research Articles

Abstract 1208: Plasma proteomics-based models for predicting therapeutic benefit and immune-related adverse events in non-small cell lung cancer patients treated with immunotherapy

Abstract Background: Immune checkpoint inhibitors (ICIs) have significantly improved outcomes for patients with metastatic non-small cell lung cancer (NSCLC). However, clinical management is challenging. The PD-L1 biomarker does not optimally guide the choice between ICI monotherapy and combination ICI-chemotherapy, and there are currently no biomarkers to predict immune-related adverse events (irAEs). Here, we describe a versatile platform based on plasma proteomics and machine learning for generating predictive tools to support treatment decisions. Methods: Pre-treatment plasma samples were collected from 616 NSCLC patients receiving ICI-based therapy (NCT04056247). Clinical benefit (CB) data and irAEs were recorded. Proteomic profiling of plasma samples was performed using the SomaScan® assay, covering ~7000 proteins per sample. Proteins associated with Clinical Benefit (progression-free survival at 12 months) were classified as resistance-associated proteins (RAPs). Proteins associated with grade ≥3 irAEs occurring within the first 100 days of treatment were classified as toxicity-associated proteins (TAPs). RAPs and TAPs were integrated into two separate machine learning-based models, yielding CB and irAE probabilities, respectively. The CB model used a CB probability threshold to assign patients a PROphet-positive or PROphet-negative result. Bioinformatic analysis was performed on RAPs and TAPs. Results: CB and irAE models displayed strong predictive performance with a high correlation between predicted probability and observed CB or irAE rate (CB model: R2=0.98; p-value<0.0001; irAE model: R2= 0.92; p-value <0.0001). In the CB model, patients classified as PROphet-positive achieved significantly longer overall survival (OS) than PROphet-negative patients, with a median OS of 25.9 vs. 10.8 months (hazard ratio, HR = 0.51; p-value<0.001). Furthermore, patients with PD-L1 ≥50% tumors and a PROphet-negative result had significantly longer OS with ICI-chemotherapy in comparison to ICI monotherapy (HR=0.23, p-value = 0.0003), suggesting that combination therapy is preferable for such patients despite high PD-L1 levels. Bioinformatic analysis showed that RAPs related to immune regulation, angiogenesis, chemo-resistance, and other potential resistance mechanisms displayed higher expression levels in patients who did not benefit from treatment. Neutrophil- and inflammation-related proteins were significantly enriched in patients who experienced irAEs. Conclusions: We describe two machine-learning based predictive tools that may be used separately or in combination to inform treatment decisions based on the patient’s likelihood to benefit from ICI therapy and to develop serious irAEs. Our findings also provide biological insights related to treatment resistance and immune-related toxicity. Citation Format: Michal Harel, Petros Christopoulos, Igor Puzanov, Jair Bar, Iris Kamer, Niels Reinmuth, Ina Koch, Mor Moskovitz, Adva Levy-Barda, Alona Zer, Michal Lotem, David Farrugia, Rivka Katzenelson, Gillian Price, Abed Agbarya, Helen Cheley, Adam Hassani, Mahmoud Abu-Amna, Tom Geldart, Anirban Chatterjee, Andreas Polychronis, Maya Gottfried, Yanyan Lou, Tatiana Harkovsky, Alison Brewster, Ido Wolf, Ella Tepper, Ben Yellin, Itamar Sela, Coren Lahav, Yehonatan Elon, Raya Leibowitz, Adam P. Dicker, Young Kwang Chae, Ryan J. Sullivan, David Carbone, David Gandara, Jarushka Naidoo. Plasma proteomics-based models for predicting therapeutic benefit and immune-related adverse events in non-small cell lung cancer patients treated with immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1208.

Identification of immunological characteristics and cuproptosis-related molecular clusters in Rheumatoid arthritis

BackgroundRheumatoid arthritis (RA) is a chronic autoimmune disease characterised by progressive articular damage, functional loss, and comorbidities. The relationship between cuproptosis, a form of programmed cell death, and RA remains unknown. Therefore, this study aimed to explore cuproptosis-related molecular clusters in RA. MethodsGene expression profiles of GSE93272 were downloaded from the Gene Expression Omnibus to identify the expression profiles of cuproptosis regulators and the immune infiltration characteristics of RA. The molecular clusters of cuproptosis-related genes and the related immune cell infiltration were explored. Cluster-specific differentially expressed genes were identified using the weighted gene co-expression network analysis. Further, an external dataset (GSE15573) was used, and an enzyme-linked immunosorbent assay was performed to validate the predictive efficiency. ResultsThirteen cuproptosis-related genes and activated immune responses were identified between patients with RA and controls. Immune infiltration revealed significant immunological heterogeneity in the two cuproptosis-related molecular clusters in RA. Functional enrichment indicated that Cluster1 and Cluster2 were predominantly enriched in the toll-like receptor signalling pathway and regulation of autophagy, respectively. Further, the performance of FAM96A and CGRRF1 genes in the external validation dataset was observed to be relatively satisfactory (area under the receiver operating characteristic curve = 0.687 and 0.674, respectively). Based on our serum samples, FAM96A and CGRRF1 both exhibited higher expression levels in patients with RA (p = 0.001; p = 0.000). ConclusionsOur study systematically illustrated the involvement of cuproptosis in the progression of RA, and explored the pathogenic mechanisms and novel therapeutic strategies for RA, targeting FAM96A and CGRRF1.

Overexpressed kinetochore genes are used by cancer cells as genome destabilizers and transformation catalysts

Cancer cells have an altered transcriptome, which contributes to their abnormal behavior. Many tumors have high levels of kinetochore genes, which play important roles in genome stability. This overexpression could be utilized to destabilize cancer cell genomes, however this has not been proven specifically. We investigated the link between kinetochore gene overexpression, chromosomal number variations (CNVs) and genomic instability.Data on RNA expression and CNV from 12 different cancer types were evaluated using information theory. In all cancer types, we looked at the relationship between RNA expression and CNVs. Kinetochore gene expression was found to be substantially linked with CNV levels. In all cancer types, with the exception of thyroid cancer, highly expressed kinetochore genes were enriched in the most dominant cancer-specific co-expression subnetworks characterizing the largest patient subgroups. Except for thyroid cancer, kinetochore inner protein CENPA was among the transcripts most strongly associated with CNV values in all cancer types studied, with significantly higher expression levels in patients with high CNVs than in patients with low CNVs. CENPA function was investigated further in cell models by transfecting genomically stable (HCT116) and unstable (MCF7 and HT29) cancer cell lines using CENPA overexpression vectors. This overexpression increased the number of abnormal cell divisions in the stable cancer cell line HCT116 and, to a lesser extent, in the unstable cell lines MCF7 and HT29. Overexpression improved anchorage-independent growth properties of all cell lines.Our findings suggest that overexpression of kinetochore genes in general, and CENPA in particular, can cause genomic instability and cancer progression.

Expression profiling analysis reveals key microRNA-mRNA interactions in patients with transposition of the great arteries and systemic left and right ventricles.

Patients with transposition of the great arteries (TGA) have different connected systemic chambers and this determines the long-term morbidities and survival. Limited findings have been reported to systematically identify miRNA and mRNA expression levels in such cohorts of patients. In this study, we aimed to characterize miRNAs, mRNAs, and miRNA-mRNA interaction networks in patients with TGA, with a systemic left (LV) and right ventricle (RV). Large panel of human miRNA and mRNA microarrays were conducted to determine the genome-wide expression profiles in the blood of 16 TGA-RV patients, 16 TGA-LV patients, and 16 age and gender-matched controls. Using real-time quantitative PCR (RT-qPCR), the differential expression level of a single miRNA was validated. Enrichment analyses of altered miRNA and mRNA expression levels were identified using bioinformatics tools. Altered miRNA and mRNA expression levels were observed between TGA-RV and TGA-LV patients, together or separated, compared to controls. Among the deregulated miRNAs and mRNAs, 39 and 101 miRNAs were identified as significantly differentially expressed in patients with TGA (both TGA-RV and TGA-LV) and TGA-RV, when compared to matched controls. Furthermore, 51 miRNAs were identified as significantly differentially expressed in patients with TGA-RV when compared to patients with TGA-LV. RT-qPCR relative expression level was highly consistent with microarray analysis results. Similarly, 36 and 164 mRNAs were identified as significantly differentially expressed in patients with TGA (both TGA-RV and TGA-LV) and TGA-RV, when compared to matched controls. Additionally, miR-140-3p showed a higher expression level in patients with overt heart failure (FC = 1.54; P = 0.001) and miR-502-3p showed a higher expression level in patients died due to cardiac death (FC = 1.41; P = 0.011). Integrative analysis resulted in 21 and 23 target genes with higher and lower expression levels, respectively (r ≥ 0.50 and P < 0.05). These target genes (i.e., 21 and 23 target genes) showed an inverse direction of regulation with miRNA and exhibited a miRNA binding site position within the 3'UTR of the target gene. Our findings provide new insights into a potential molecular biomarker(s) for patients with TGA that may guide better risk stratification and the development of novel targeting therapies. Future studies are needed to investigate the potential significance of miRNAs and mRNAs in TGA-related cardiovascular diseases.

Association between Gene Expression Levels of Candidate Cytarabine Pathway and Treatment Outcome Using Publically Available Data from Target Project

Acute myeloid leukemia (AML) is the second most common form of childhood leukemia and has the worst prognosis of all major childhood cancers. Improving the treatment outcome for patients with AML remains a major clinical challenge. The nucleoside analog, cytarabine (Ara-C), has been the mainstay of AML chemotherapy for more than 40 years. However, extensive inter-patient variation in treatment response, development of resistance, and severe toxicity remain as major hurdles to effective Ara-C chemotherapy. It is a pro-drug that requires activation to Ara-CTP by multiple phosphorylation steps. Thus, inter-patient variation in gene expression levels of candidate genes involved in cytarabine metabolism can impact therapeutic outcome. In last few years' significant efforts have been invested in generating genomic, transcriptomic and epigenetic data in various cancer patients including AML. TCGA (The Cancer Genome Atlas) is one of the most commonly available such resource, recently another resource available to scientific community TARGET (Therapeutically Applicable Research to Generate Effective Treatments), a project initiated by the National Cancer Institute (NCI) in 2006. This databased was created with the aim to take multi-omics integrative approach to characterize molecular alterations associated with poor outcomes in some common children's cancers including AML. In this study, we leveraged TARGET database to extract gene expression levels (generated using Affymetrix Gene ST Array) to investigate whether inter-patient differences in expression levels of 11 key candidate genes in cytarabine metabolic pathway are predictive of minimal residual disease (MRD: positive or negative) and thus outcome. These genes included DCK and CMPK1 encoding for drug activating enzymes, CDA, DCTD, NT5C2, NT5C3A, important for cytarabine inactivation; drug influx transporter SLC29A1; RRM1 and RRM2 as ribonucleotide inhibitors; CTPS1 and NME3 which play a key role in the synthesis of nucleoside tri-phosphates. We also investigated correlation of the expression levels of these genes with each other to get insight into whether these genes could be co-regulated. In order to have consistency we only included data (gene expression and clinical and patient demographic, characteristics) from 105 AML patients where expression levels were available from bone marrow specimens and also restricted our analysis to AAML03P1 and AAML0531 clinical cohorts. Our resultsshow significant correlation in expression levels between cytarabine metabolic pathway genes resulting in three clusters: First cluster included DCK, NT5C2, NT5C3A, RRM1 and CMPK1 with positive correlation with each other's ranging between (r=0.619 to 0.720 p-value Our results from association analysis with MRD showed that cluster 1 genes had higher expression levels in patients with positive MRD. On the other hand, cluster 2 genes have all a higher expression in the MRD negative. To get the combinatorial effect of gene-expression on different clinical endpoints we defined a combination score (CS) using CS = (DCK*NT5C2*NT5C3A*CMPK1*CTPS1*RRM1*RRM2*DCTD)/(CDA*NME3*SLC29A1). Higher CS was associated with positive MRD (P= 0.012) (Figure 2) and lower overall survival rates (r= - 0.232, P-value = 0.017). By generating a combinatorial expression score for cytarabine pathway genes, we have been able to identify features that might predict MRD, overall survival and thus outcome in patients treated with cytarabine. An interesting fact that we observed positive correlations between activating genes - DCK, CMPK with inactivating genes such as NT5C2 or reductases RRM1/RRM2 that negatively impact the drug activation. One possible explanation, when DCK is highly expressed, CMPK1 responsible for the second phosphorylation step increases. At the same time, RRM1 and RRM2 known as DCK inhibitors and NT5C2 become highly expressed to balance high DCK level. This might also be a plausible reason for not observing consistent correlations between expression levels of ara-C pathway genes with outcome. Download : Download high-res image (178KB) Download : Download full-size image Disclosures No relevant conflicts of interest to declare.

CAD increases the long noncoding RNA PUNISHER in small extracellular vesicles and regulates endothelial cell function via vesicular shuttling

Long noncoding RNAs (lncRNAs) have emerged as biomarkers and regulators of cardiovascular disease. However, the expression pattern of circulating extracellular vesicle (EV)-incorporated lncRNAs in patients with coronary artery disease (CAD) is still poorly investigated. A human lncRNA array revealed that certain EV-lncRNAs are significantly dysregulated in CAD patients. Circulating small EVs (sEVs) from patients with (n = 30) or without (n = 30) CAD were used to quantify PUNISHER (also known as AGAP2-antisense RNA 1 [AS1]), GAS5, MALAT1, and H19 RNA levels. PUNISHER (p = 0.002) and GAS5 (p = 0.02) were significantly increased in patients with CAD, compared to non-CAD patients. Fluorescent labeling and quantitative real-time PCR of sEVs demonstrated that functional PUNISHER was transported into the recipient cells. Mechanistically, the RNA-binding protein, heterogeneous nuclear ribonucleoprotein K (hnRNPK), interacts with PUNISHER, regulating its loading into sEVs. Knockdown of PUNISHER abrogated the EV-mediated effects on endothelial cell (EC) migration, proliferation, tube formation, and sprouting. Angiogenesis-related gene profiling showed that the expression of vascular endothelial growth factor A (VEGFA) RNA was significantly increased in EV recipient cells. Protein stability and RNA immunoprecipitation indicated that the PUNISHER-hnRNPK axis regulates the stability and binding of VEGFA mRNA to hnRNPK. Loss of PUNISHER in EVs abolished the EV-mediated promotion of VEGFA gene and protein expression. Intercellular transfer of EV-incorporated PUNISHER promotes a pro-angiogenic phenotype via a VEGFA-dependent mechanism.

Identification and validation of prognostic signature for breast cancer based on genes potentially involved in autophagy.

We aimed to identify prognostic signature based on autophagy-related genes (ARGs) for breast cancer patients. The datasets of breast cancer were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Least absolute shrinkage and selection operator (LASSO) Cox regression was conducted to construct multiple-ARG risk signature. In total, 32 ARGs were identified as differentially expressed between tumors and adjacent normal tissues based on TCGA. Six ARGs (IFNG, TP63, PPP1R15A, PTK6, EIF4EBP1 and NKX2-3) with non-zero coefficient were selected from the 32 ARGs using LASSO regression. The 6-ARG signature divided patients into high-and low-risk group. Survival analysis indicated that low-risk group had longer survival time than high-risk group. We further validated the 6-ARG signature using dataset from GEO and found similar results. We analyzed the associations between ARGs and breast cancer survival in TCGA and nine GEO datasets, and obtained 170 ARGs with significant associations. EIF4EBP1, FOS and FAS were the top three ARGs with highest numbers of significant associations. EIF4EBP1 may be a key ARG which had a higher expression level in patients with more malignant molecular subtypes and higher grade breast cancer. In conclusion, our 6-ARG signature was of significance in predicting of overall survival of patients with breast cancer. EIF4EBP1 may be a key ARG associated with breast cancer survival.

Proteomics Reveals Plasma Biomarkers for Ischemic Stroke Related to the Coagulation Cascade.

Stroke has a high incidence rate and often leads to permanent disability, particularly if it is not treated promptly. However, no blood biomarkers for early diagnosis are available to date. Therefore, we sought to detect stroke-specific blood biomarkers by identifying proteins associated with the underlying coagulation mechanism, which accounts for more than 80% of all stroke cases. Protein profiling was performed using blood samples from 16 healthy controls and 18 patients who suffered a stroke as the discovery set. We identified upregulated proteins (> 1.5-fold change and p value < 0.05) in patients who suffered a stroke relative to the corresponding levels in healthy controls by nano-liquid chromatography-tandem mass spectrometry using data-independent acquisition based on sequential window acquisition of all theoretical mass spectra, which was developed to improve the consistency and accuracy of candidate proteins. Pathway analysis confirmed that the upregulated proteins were mainly involved in blood coagulation. Among these, we selected prothrombin, plasminogen, fibrinogen alpha-chain, and histidine-rich glycoprotein as candidate biomarkers. Multiple reaction monitoring analysis was performed on a validation set of 61 serum samples (31 healthy controls and 30 stroke patients) to assess the diagnostic value of the candidate biomarkers. All four proteins showed higher expression levels in patients with stroke than in healthy controls. The areas under the receiver operating characteristic curve were greater than 0.9, confirming their clinical value. These four blood coagulation proteins may help in diagnosing stroke more accurately and quickly.

TBX2 Identified as a Potential Predictor of Bone Metastasis in Lung Adenocarcinoma via Integrated Bioinformatics Analyses and Verification of Functional Assay.

Objective: Bone metastasis from patients with advanced lung adenocarcinoma (LAC) is a very serious complication. To better understand the molecular mechanism, our current study sheds light on identification of hub genes mediating bone metastatic spread by combining bioinformatic analysis with functional verification.Methods: First, we downloaded a lung adenocarcinoma dataset (GSE76194) from Gene Expression Omnibus, analyzed differentially expressed genes (DEGs) through Limma package in R software and constructed a protein-protein interaction network. Based on that preliminary data, we further performed modular and topological analysis using Cystoscope to obtain biological connected genes. Through literature searching and performing mRNA expression analysis on the other independent public dataset (GSE10799), we finally focused on TBX2. Functional effects of TBX2 were performed in tumorigenicity assays including migration and invasion assays, cell proliferation assay, and cell cycle assay. In addition, mechanically, we found enriched pathways related to bone metastasis using Gene Set Enrichment Analysis (GSEA) and validated our results by western blot.Result: A total of 1132 significant genes were sorted initially. We selected common significant genes (log FC>2; p<0.01) from both the biological network data and microarray data. In total, 44 such genes were identified. we found TBX2, along with 10 other genes, to be reported with relevance to bone metastasis in other cancer types. Moreover, TBX2 showed significantly higher expression levels in patients that were found positive for metastasis to bone marrow compared to patients that did not exhibit this type of metastasis in the other separated cohort (GSE10799). Thus, we finally focused on TBX2. We found that TBX2 had detectable expression in LAC cell lines and silencing endogenous TBX2 expression in A549 and H1299 cell lines markedly suppressed migration and invasion, cell proliferation and arrested cell-cycle. Pathway enrichment analyses suggested that TBX2 drove LAC oncogenesis and metastasis through various pathways with epithelial mesenchymal transition (EMT) figuring prominently in the bone metastatic group, which was evidenced by western blot.Conclusion: Collectively, TBX2 plays as a potential predictor of bone metastasis from LAC, yielding a better promise view towards “driver” gene responsible for bone metastasis.

Abstract 4922: Utility of evidence-based gene expression panels in predicting outcome in stage matched colorectal cancer patients

Abstract Purpose: The aim of our study is to create an evidence-based gene expression analysis panel and use it to identify genes that can predict overall survival of stage matched colorectal cancer patients. Procedure: We performed in silico analysis on 222 colorectal patients in TCGA using the nCounter PanCancer Human Progression Panel (genes n=742) on the cBioPortal platform. After extensive analysis of the individual genes and deep machine learning we selected 8 genes (NFKB1, ELK3, SEMA3E, ADRA2B, WNT5A, MMP14, PKM, and SDC4) associated with decreased survival (p&amp;lt;.05) and 7 genes (VSIG4, SORD, CCR4, VEGFC, SETD2, RUNX1T1, and EPAS1) associated with increased survival (p&amp;lt;.1) to include in our assay. Under an approved IRB protocol, we identified 750 colorectal cancer patients at the Georgia Cancer Center with a 5-year follow-up. The patients were divided into experimental groups based on tumor stage (AJCC stages 1-4), survival (+/-3 years), tumor grade (AJCC grades 1-3), and age(+/-76 years). RNA was isolated from FFPE blocks and analyzed by Nanostring’s custom 23-gene multiplex assay (15 experimental and 8 control). Statistical analyses included ANOVAs, Kaplan-Meier survival analysis, and subgroup analysis using Student’s t-test. Results: The CCR4 gene, part of the increased survival panel, showed multiple significant associations with better prognosis. It had higher expression levels in patients with better survivals compared to those with worse survivals (p=.0119*). On survival analysis, higher expression was associated with increased survival (p=.004*). Subgroup analysis showed that patients with stage 4 cancer and better survivals had higher expression levels than those who had worse survivals and stage 4 (p=.0372*) or stage 1 (p=.0389*) tumors. The same trend was found when patients with stage 3 and better survivals were compared to patients with worse survivals and stage 4 (p=.0462*) or stage 1 (p=.0464*) tumors. Analysis of tumor grade plus survival and patient age plus survival gave more associations. Patients with grade 1 tumors and better survivals had higher expression levels than ones with low survival and either grade 1 (p=.0164*) or grade 2 tumors (p=.0026*). Young patients with better survivals had higher expression levels than those with worse survivals who were young (p=.0241*) or old (p=.0115*). Discussion: These results show a significant correlation between high levels of CCR4 gene expression and longer survivals in patients with high stage tumors, a group rarely associated with such outcomes. The trend between the split survivals of stage 4 is interesting as a potential indicator of prognosis. Associations of age and grade could serve as prognostic indicators as well, especially the split survivals seen in grade 1 tumors and young patients. Future studies should include larger sample sizes as well as investigations into mechanistic explanations for these associations. Citation Format: Chance Bloomer, Pankaj Ahluwalia, Chetan Pundkar, Saleh Heneidi, Kimya Jones, Ashis Mondal, Ravindra Kolhe. Utility of evidence-based gene expression panels in predicting outcome in stage matched colorectal cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4922.

Validity and clinical impact of glucose transporter 1 expression in colorectal cancer

Background/Aim:There is no doubt that colorectal cancer (CRC) poses a major threat to public health worldwide, and despite improvement in managements, prognosis still remains an irritating question with no definite answer. Being a fundamental player in cancer metabolism, glucose transporter 1 (GLUT1) could be utilized as a prognostic biomarker that could fuel development of new treatment strategies. The aim of this study was to assess the validity of GLUT1 expression as a prognostic biomarker and to elucidate to what extent it is immersed in poor clinical outcome among CRC patients.Patients and Methods:GLUT1 expression in peripheral blood specimens was analyzed by quantitative real-time polymerase chain reaction in 47 CRC patients and 20 healthy controls.Results:There was significantly elevated GLUT1 expression in peripheral blood of CRC patients than in controls (P < 0.001). The cutoff value of 0.605 provided 98% sensitivity and 100% specificity. There were significantly higher values of GLUT1 expression in patients under 50 years (P = 0.003), performance status 2 (P = 0.009), stage IV (P < 0.001), and presence of metastasis (P < 0.001). GLUT1 expression showed nonsignificant association with overall survival (P = 0.068), while tumor stage (P = 0.01) and metastasis (P = 0.009) were significantly associated with lower overall survival.Conclusion:GLUT1 is sensitive and specific marker for CRC. It is overexpressed in young age patients, poor performance status, and stage IV patients. Although this was not statistically significant, GLUT 1 showed higher expression level in patients with lesser survival.

Identification of ENO1 As a Potential Sputum Biomarker for Early-Stage Lung Cancer by Shotgun Proteomics

BackgroundLung cancer is the leading cancer killer. Early detection will reduce the related deaths. The objective of this study was to identify potential biomarkers for early-stage lung cancer in sputum supernatant. Materials and MethodsUsing shotgun proteomics, we detected changes in protein profiles that were associated with lung cancer by analyzing sputum supernatants from 6 patients with early-stage lung cancer and 5 cancer-free controls. Using western blotting, we validated the proteomic results in 22 lung cancer cases and 22 controls. Using enzyme-linked immunosorbent assay (ELISA), we evaluated the diagnostic performance of the biomarker candidates in an independent set of 35 cases and 36 controls. ResultsProteomics identified 8 biomarker candidates for lung cancer. Western blotting validation of the candidates showed that enolase 1 (ENO1) displayed a higher expression level in patients with cancer than in cancer-free individuals (P = .015). ELISA revealed that the assessment of ENO1 expression in sputum supernatant had 58.33% sensitivity and 80.00% specificity in distinguishing patients with stage I lung cancer from cancer-free individuals. ConclusionThe analysis of protein biomarkers in sputum may provide a potential approach for the early detection of lung cancer. Future validation of all the candidates defined by shotgun proteomics in a large cohort study may help develop additional biomarkers that can be added to ENO1 to provide more diagnostic efficacy for lung cancer.

Identification of Prognostic Markers by Gene Expression Profiling In Myelodysplastic Syndrome Hematopoietic Stem Cells

AbstractAbstract 298The myelodysplastic syndromes (MDS) are a heterogeneous group of clonal hematopoietic stem cell malignancies that are characterized by ineffective hematopoiesis resulting in peripheral cytopenias and a hypercellular bone marrow. Approximately 40% of patients with MDS will develop an acute myeloid leukemia. It is important to establish prognosis of MDS patients since the treatment options vary from supportive care to bone marrow transplantation. In order to determine the relationship of gene expression levels to prognosis and so identify new molecular markers, we have used gene expression profiling to study the transcriptome of the hematopoietic stem cells of 125 MDS patients with a minimum 12 month follow up. The CD34+ cells obtained from MDS patients and healthy individuals were analyzed using Affymetrix U133 Plus2.0 arrays. The patients were split randomly in a training set (n=84) and a test set (n=41). Supervised principal components analysis was used to identify genes correlated with survival. Using the 84 patients in the training set, the Cox scores were computed for each gene, and the principal components calculated on the genes with the highest Cox scores. The first of the principal components was then used to generate a regression model to predict the survival in the test set. Finally, for each probe set an importance score was calculated equal to its correlation with the supervised principal component predictor. This approach returned a list of 150 top ranked probe sets correlated with survival. Patients in the training set were split into tertiles based on the predictor (low, medium and high score) and patients in the test set were assigned to their predicted class, and Kaplan-Meier plots were generated for both training and test set. The differences in survival for both training and test set were statistically significant (Figure 1). Top ranked genes showing lower expression levels in patients with shorter survival include CDH1, LEF1 and AKAP12/Gravin. Top ranked genes showing higher expression levels in patients with shorter survival include IL23A, WT1 and PTHR2. Figure 2 shows survival of patients divided into tertiles of expression for the individual genes CDH1, LEF1 and WT1. It is probable that the genes identified in this study will become the first validated molecular markers for MDS prognosis. Multivariate analysis is currently being performed. [Display omitted] [Display omitted] Disclosure:No relevant conflicts of interest to declare.

Plasma Proteomic Analysis May Identify New Markers for Radiation-Induced Lung Toxicity in Patients With Non–Small-Cell Lung Cancer

To study whether radiation induces differential changes in plasma proteomics in patients with and without radiation-induced lung toxicity (RILT) of Grade >/=2 (RILT2). A total of 57 patients with NSCLC received radiation therapy (RT) were eligible. Twenty patients, 6 with RILT2 with tumor stage matched to 14 without RILT2, were enrolled for this analysis. Platelet-poor plasma was obtained before RT, at 2, 4, 6 weeks during RT, and 1 and 3 months after RT. Plasma proteomes were compared using a multiplexed quantitative proteomics approach involving ExacTag labeling, reverse-phase high-performance liquid chromatography and nano-LC electrospray tandem mass spectrometry. Variance components models were used to identify the differential protein expression between patients with and without RILT2. More than 100 proteins were identified and quantified. After excluding proteins for which there were not at least 2 subjects with data for at least two time points, 76 proteins remained for this preliminary analysis. C4b-binding protein alpha chain, Complement C3, and Vitronectin had significantly higher expression levels in patients with RILT2 compared with patients without RILT2, based on both the data sets of RT start to 3 months post-RT (p < 0.01) and RT start to the end of RT (p < 0.01). The expression ratios of patients with RILT2 vs. without RILT2 were 1.60, 1.36, 1.46, and 1.66, 1.34, 1.46, for the above three proteins, respectively. This proteomic approach identified new plasma protein markers for future studies on RILT prediction.

Gene Expression Profiling of CD34+ Cells in Patients with Myelodysplastic Syndromes

The myelodysplastic syndromes (MDS) are a heterogeneous group of hematopoietic malignancies, characterized by blood cytopenias, ineffective hematopoiesis and a hypercellular bone marrow. We have investigated the gene expression profiles of a large group of patients with MDS in order to better understand the molecular pathogenesis of this disorder. The CD34+ cells obtained from 154 MDS patients and 17 healthy individuals were analyzed using Affymetrix U133 Plus2.0 arrays. 38 genes were up-regulated by >2-fold in at least 77 MDS patients, and pathway analysis using these genes showed that the interferon signalling pathway was significantly deregulated (p=0.0006). Indeed IFIT1, the most up-regulated gene (up-regulated in 110 of 154 MDS patients), is an interferon-stimulated gene (ISG). Other ISGs, which mediate growth inhibitory effects of interferon, such as IFITM1, IFI44L and IFIT3, were markedly up-regulated in the majority of MDS patients. Up-regulation of ISGs is a major feature of MDS and may be responsible for some of the hematological characteristics of this disorder, such as peripheral blood cytopenias. We investigated differences in gene expression that could distinguish MDS patients according to their FAB subtype classification (48 patients with RA, 44 patients with RARS and 62 patients with RAEB). Hierarchical clustering performed using the 773 significantly differentially expressed probe sets identified showed that MDS patients with RARS constitute the most homogeneous group, while MDS patients with RA and RAEB show more overlap. RARS gene expression profile was characterized by up-regulation of mitochondrial-related genes and by down-regulation of ABCB7, a gene mutated in the rare inherited X-linked sideroblastic anemia with ataxia (XLSA/A). Moreover, a good separation between the 20 patients with RARS and the 20 patients with RCMD-RS was obtained by hierarchical clustering using the 86 significantly differentially expressed genes between these two WHO subgroups. One of the most significant genes was MFN1, which is essential for mitochondrial fusion and maintenance of mitochondrial morphology. The association of distinct gene expression profiles with specific cytogenetic groups was also determined, and we were able to separate by hierarchical clustering MDS patients with del(5q), patients with −7/del(7q) and patients with trisomy 8. The expression profile of patients with the del(5q) was characterized by down-regulation of genes mapping to chromosome 5q. Genes differentially expressed in patients with −7/del(7q) include LOX and UBE2H, while genes differentially expressed in patients with trisomy 8 include HRSP12 and TPM4. These findings suggest distinct molecular pathogenetic pathways for MDS patients with del(5q), −7/del(7q) and trisomy 8. In order to identify differences in gene expression associated with MDS disease progression, we compared the 48 patients with early MDS (RA) and the 35 patients with advanced MDS (RAEB2). Hierarchical clustering performed using 1081 significantly differentially expressed probe sets resulted in a good separation between MDS patients with RA and patients with RAEB2. LEF1, a regulator of neutrophilic granulopoiesis, was the most significant differentially expressed gene with higher expression levels in patients with RA and decreasing in patients with RAEB2. Other genes showing higher expression levels in patients with RA, decreasing in patients with RAEB2, include CASC5, a cancer susceptibility candidate gene, and RBBP8, a gene that plays a role in DNA-damage-induced cell cycle checkpoint control. Several genes mapping to the cell cycle pathway were significantly deregulated between early and advanced MDS. This study provides new important insights into the pathophysiology of MDS and represents a first step towards determining pathway signatures in MDS as a guide to targeted therapies.