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  • G01N33/57484—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites
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  • G01N2800/50—Determining the risk of developing a disease
• • G—PHYSICS
  • G01—MEASURING; TESTING
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  • G01N2800/00—Detection or diagnosis of diseases
  • G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N2800/00—Detection or diagnosis of diseases
  • G01N2800/56—Staging of a disease; Further complications associated with the disease

Definitions

• the invention relates to the malignant primary brain tumor most common in adults, glioblastoma multiforme (GBM). In particular, it concerns identification of bloodborne markers for this condition as well as markers that indicate the viability of potential therapies.
• GBM glioblastoma multiforme
• GBM Glioblastoma multiforme
• CSCs GBM cancer stem cells
• Antibodies targeting the cell surface transmembrane protein CD 133 have been used to isolate CSCs from bulk tumor populations, but several recent studies have suggested significant limitations of CD 133 as a stand-alone CSC marker and have highlighted the need for additional cell-surface
• the invention provides a set of protein markers that are accessible by assaying blood samples to provide an assessment of the probability that a subject is afflicted with GBM.
• a method for assessing the probability that a human subject is afflicted with glioblastoma multiform (GBM) which method comprises:
• a decreased level of CD44 and/or an increased level of HMOXl and/or a decreased level of VCAM1 and/or a decreased level of TGFBI in the test subject as compared to normal subjects indicates the probability that said test subject is afflicted with GBM.
• the invention is directed to a method for assessing the probability that a human subject is afflicted with glioblastoma multiform (GBM) which method comprises: assessing the level of at least one protein selected from the group consisting of ABCA1, ASPH, CA12, CADMl, CAVl, CD109, CD151, CD276, CD44, CD47, CD97, CD99, CLCCl, CRTAP, DDR2, EGFR, HMOXl, ITGA7, MGSTl, MRC2, MYOF, NRPl, PDIA4, PTGFRN, RTN4, S100A10, SCAMP3, SLC16A1, SLC16A3, TGFBI, TMX1, TNC, and VCAM1 in the brain tissue, tumor cells or blood or fraction thereof of a test subject; and
• a difference in the level in the test subject as compared to normal subjects indicates the probability that the test subject is afflicted with GBM.
• the invention is directed to ordered panels of reagents designed to detect these and additional proteins that have been identified as described below as indicative of the presence of GBM in a test subject.
• the invention is directed to a method to assess whether a subject will respond to treatment for GBM by administering an inhibitor of TGF- ⁇ .
• the invention is directed to a method to treat GBM by modulating the expression or activity of proteins identified as promoting invasiveness upon TGF- ⁇ stimulation and to a method to classify GBM.
• GBMSig GBM-specific membrane signature
• CSPs cell surface proteins
• PCA principal component analysis
• Figure 3a shows the results of d-SRM assays employed to identify unique GBMSig that can distinguish cancer stem cells (CSCs) (from Celprogen) and healthy neural stem cells (NSCs) (from Millipore).
• CSCs cancer stem cells
• NSCs healthy neural stem cells
• Figure 3b shows the validation of SRM results of GBMSig expression by an alternate method flow cytometry.
• An independent primary cancer stem cell obtained from GBM patient also revealed higher expressions of HMOX1, SLC16A1, but lower expression of SLC16A3 relative to healthy neural stem cells.
• Figure 5a shows association of GBMSig proteins with TGF- ⁇ signaling network.
• Figure 5b shows effects the association of GBMSig proteins with cancer invasion.
• Figure 6a shows ELISA assays of 21 healthy and 21 GBM plasmas for the indicated GBMSig proteins HMOX1, CD44, VCAM1, and TGFBI.
• Figure 6b shows ROC analysis (10,000x10 folds cross validation) of HMOX1, CD44, VCAM1, and TGFBI ELISA results, which offer a basis to diagnose GBM from blood analyses.
• the invention is directed to the methods and compositions that are indicated useful in diagnosis and selection of treatment method based on the nature and level of surface proteins that are characteristic of GBM as opposed to normal tissue, as well as to methods to treat GBM.
• the invention herein relates to 1) cell surface GBMSig classifiers (33 cell surface proteins such as ABCA1, ASPH, CA12, CADM1, CAV1, CD109, CD151, CD276, CD44, CD47, CD97, CD99, CLCC1, CRTAP, DDR2, EGFR, HMOX1, ITGA7, MGST1, MRC2, MYOF, NRP1, PDIA4, PTGFRN, RTN4, S100A10, SCAMP3,
• cell surface GBMSig classifiers 33 cell surface proteins such as ABCA1, ASPH, CA12, CADM1, CAV1, CD109, CD151, CD276, CD44, CD47, CD97, CD99, CLCC1, CRTAP, DDR2, EGFR, HMOX1, ITGA7, MGST1, MRC2, MYOF, NRP1, PDIA4, PTGFRN, RTN4, S100A10, SCAMP3,
• SLC16A1,SLC16A3,TGFBI,TMX1,TNC, and VCAM1 that can accurately distinguish GBM tissues from healthy tissues at both transcript and proteome level, 2) blood biomarkers among GBMSig proteins, a subset of which was validated by d-SRM and ELISA, 3) disrupted TGF- ⁇ network components represented by key GBMSig proteins in GBM and 4) representative cell surface markers for GCSCs.
• the present inventors analyzed cell surface proteins in GBM through comparative analysis of a representative GBM CSCs, healthy NSCs, and bulk tumor cell populations exemplified by U87 and T98 cell lines.
• Cell-surface proteomics data were combined with the large-scale GBM tissue transcriptomic array analyses from REMBRANDT and TCGA tumor compendiums. This integrative approach resulted in a GBMSig comprising 33 cell surface proteins that characterize of GBM tissues.
• the cell-surface proteins from four cell lines that have relevance in GBM were analyzed.
• cell lines that represent bulk tumor populations U-87 and T-98
• a representative healthy NSC line positive for putative stem cell markers tub iii, oct-4, sox-2 and CD133
• a GBM CSC line positive for CD133 expression.
• the membrane impermeable sulfo-NHS-SS-biotin strategy was used to capture cell-surface proteins from intact cells.
• Cell-surface composition of each cell line appears significantly different from the others, which suggests that these four cell lines may be functionally different as well or the heterogeneity might just reflect the increased mutational process fundamental to all cancers.
• Captured cell surface proteins were subjected to high resolution mass spectrometry in triplicates and the proteins were identified using the Global Proteome Machine [(the GPM) (located on the World Wide Web at: theGPM.org)] with minimum log expectation scores of ⁇ 10 .
• GPM Global Proteome Machine
• a total of 868, 813, 541, and 564 non-redundant proteins were identified from U87, T98, NSC, and CSC populations, respectively.
• TMHMM transmembrane
• GBM-membrane unique signature comprised of 33 cell-surface proteins was obtained as shown in Figure 1.
• the classifier was (i.e. the 33 proteins of GBMSig) evaluated by support vector machines (SVM)- supervised learning models. After 10-fold cross validation(CV) and fitting the model on training dataset (REMBRANDT) the hyperparameters (parameters tuned after 10-fold CV) were identified and the discerning capabilities of the classifier on validation set (TCGA dataset) was predicted. This resulted in 99.85% sensitivity, 75% specificity, 99.54% positive predictive value, and 90.69% negative predictive value for the classifier. Principal component analysis (PCA) of the classifier and individual specificities and sensitivities on both test set and validation set is presented in
• FIGS. 2a- 2d Figures 2a- 2d.
• GBMSig effectively distinguishes GBM from non-tumor counterparts.
• GBMSig proteins viz. ASPH, SCAMP3, CLCC1, and CADM1 representing proneuronal; CD44, ITGA7, and EGFR representing classical; CAV1 and TGFBI representing mesenchymal subtypes with high degree of specificity (> 80%).
• GBM cell-surface composition of various GBM cell lines including U87, T98, CD 133 + CSC (Celprogen) and a NSC line (Millipore) were examined by high resolution mass spectrometry that led to the identification of cell-surface proteins especially those with transmembrane domains.
• the sequence of peptides showed the mass spec compatibility of the peptides required to set-up SRM assays.
• Integrated cell-surface proteomics data was integrated with large scale GBM tissue transcriptome repositories in REMBRANDT (228 GBM and 9 non-tumors) and TCGA (547 GBM and 10 non-tumors) repositories. From these integrated analyses, a GBM membrane signature (GBMSig) was developed.
• Tissue SRM analysis showed that a number of GBMSig proteins— possibly deregulated as a consequence of the disease were co-overexpressed with TGFBI a TGF- ⁇ inducible protein, indicating a putative regulation of these GBMSig proteins through TGF- ⁇ signaling.
• Novel TGF- ⁇ responsive elements were identified among GBMSig through experimental validation. Modular roles of 19 GBMSig proteins were demonstrated in TGF- ⁇ responsiveness through in vitro analysis using the U87 cell line. U87 cells were treated with TGF- ⁇ or its inhibitor alone or sequentially with inhibitor followed by TGF- ⁇ . Changes in expressions of GBMSig proteins following such treatments were measured by SRM assays.
• EGFRVIII are overexpressed alone or in combination with PTEN were tested for molecular responsiveness of isogenic cell lines towards TGF- ⁇ treatment.
• EGFR and EGFRVIII isogenic cell lines there was elevated surface expression of SLC16A1 and HMOXl in response to TGF- ⁇ treatment.
• PTEN expression inhibited this effect, indicating possible involvement of a tumor suppressor PTEN in modulating the surface expression of these proteins in GBM.
• MRC2 and CD47 were up regulated in response to TGF- ⁇ treatment when PTEN was overexpressed.
• SN143 tumor-derived GCSC populations exhibited TGF- ⁇ responsiveness different from the isogenic cell lines from U87. They showed a 30 -increase in surface expression of HMOXl in response to TGF ⁇ -inhibitor treatment relative to TGF- ⁇ treatment, though an increase in expression of MRC2 in response to TGF- ⁇ treatment over its inhibitor was similar to that of U87 cell lines.
• HMOXl has been known to protect cells during oxidative damage and thus by regulating the expression of this protein, a GCSC may escape damage caused by therapeutic agents.
• the observed increase in expression of HMOXl— the cell-surface protein which was found to be enriched on proliferating SN143 cells— may be related to this defense response.
• SN143-tumor derived GCSCs In NSCs, there were no significant changes in the cell-surface expression of SLC16A1, HMOXl, MRC2, CD47, SLC16A3 and CD97 in response to TGF- ⁇ treatment. This result may favor an inactivated and unperturbed TGF- ⁇ network in healthy NSCs that is poised to dampen neurogenesis and proliferative capabilities as mentioned in earlier reports. Additionally, TGF-P-inhibitor treatment of NSCs resulted in increased expression of MRC2, CD47, SLC16A3 and CD97, i.e., GBMSig proteins that were inhibited in GBM cells following TGF-P-inhibitor treatment.
• siRNA-mediated inhibition of SLC16A1, HMOXl, and MRC2 resulted in reduced cell invasion (>50% in comparison to scrambled siRNA treated cells) similar to that of a known invasive marker CD47, pointing to direct involvement of these proteins in GBM invasion and TGF- ⁇ responsiveness. It is, therefore, likely that the invasive nature of SLC16A1, HMOXl, CD47 and MRC2 and the overexpression of these proteins on GCSCs may enable these cells to contribute to metastasis in response to TGF- ⁇ and therefore negatively impact patient survival. Thus, characterizing expression of these proteins or inhibiting their activity would be an effective treatment. Survival analysis of TCGA datasets support this notion as patients co-expressing five GBMSig proteins viz.
• SLC16A1, HMOXl, MRC2, CD47 and SLC16A3 revealed poor survival by 30% (p ⁇ 0.08) while 10 GBMSig proteins viz. CA12, MRC2, CD44, TNC, SLC16A1, S100A10, HMOXl, ITGA7, SLC16A3, and CLCC1 revealed poor survival by 50% (p ⁇ 0.003) in REMBRANDT dataset.
• the additional markers that may occur in blood include DDR2, PDIA4, CADM1, ITGA7, MRC2, MYOF, NRP1, RTN4, TNC, SCAMP3 and CD47.
• a number of the GBMSig proteins were identified as upregulated by TGF- ⁇ stimulation. These proteins appear to enhance the effect of TGF- ⁇ in promoting invasiveness. Thus, GBM tumor tissue that is obtained from subjects that have high levels of these proteins indicate that the subject is a promising candidate for therapy based on administration of TGF- ⁇ inhibitors. These proteins include SLC16A1, HMOX1, MRC2 and CD47.
• Such methods include the use of expression inhibitors such as siRNA, antisense constructs, and the like, and methods to inhibit activities include administering binding agents for the proteins themselves, such as antibodies, aptamers, antibody mimics and the like.
• GBMSig proteins are shown to be characteristic of various forms of GBM.
• mesenchymal, classical/proliferative, and pre-neuronal subtypes of GBM can be distinguished based on the expression patterns of specific subsets of these proteins.
• GBM plasmas were analyzed for circulating GBMSig by SRM mass spectrometry. Fourteen of 33 GBMSig proteins were detected independently in triplicate SRM runs. Four circulating GBMSig proteins HMOX1, CD44, VCAM1, and TGFBI were also evaluated by ELISA. Using 42 plasma samples (21 healthy and 21 GBM, age and gender matched) statistically significant differences were observed in the concentrations of these proteins. Comparing healthy plasma vs. GBM plasma, the concentrations were:
• CD44 149.31 healthy versus 75.09 ng/ml GBM, (p ⁇ 3.69E-08, two-tailed), for HMOX1: 10.70 healthy versus 17.52 ng/ml GBM, (p ⁇ 9.21E-05, two-tailed), for VCAM1: 583.22 healthy versus 436.40 ng/ml GBM, (p ⁇ 0.02, two-tailed), and for TGFBI: 2482.51 healthy versus 931.74 ng/ml GBM, (p ⁇ 5.68E-10).
• HMOX1 is increased.
• Figure 6a shows ROC analysis (10,000x10 folds cross validation) of HMOX1, CD44, VCAM1, and TGFBI ELISA results, which offer a basis to diagnose GBM from blood analyses.
• TGFBI is a TGF- ⁇ inducible protein that plays important role in cancer invasion.
• TGF- ⁇ is an inducer of epithelial to mesenchymal transition (EMT) and plays cardinal role in several aspects of GBM biology including the local metastasis of tumor cells, maintenance of cancer stem cell niche and therapeutic resistance of cancer cells.
• EMT epithelial to mesenchymal transition
• GBMSig protein levels that are correlated with stimulation by TGF- ⁇ in a subject indicate that inhibitors of TGF- ⁇ may be beneficial in treating GBM in such subjects.
• astrocytoma cell line U87 was serum starved overnight and treated with 10 ng/ml TGF- ⁇ for 40 hrs.
• TGF- ⁇ treatment increased the C-terminal phosphorylation of SMAD2 in comparison to cells grown in serum-free media, suggesting the activation of TGF- ⁇ signaling.
• serum contains many essential elements and growth factors, the effect of serum starvation on cells might not be specific to the inhibition of TGF- ⁇ signaling. Therefore, we employed a TGF ⁇ -inhibitor (SB 431542) known to interfere with the C-terminal phosphorylation of SMAD2.
• TGF- ⁇ -inhibitor Cells grown in normal media (DMEM + 10% FCS) supplemented with TGF- ⁇ -inhibitor dampened or diminished C-terminal phosphorylation of SMAD2 similarly to what was observed for cells grown in serum-free media. Thus in subsequent SRM analysis, TGF ⁇ -inhibitor was used instead of serum starving.
• GBMSig proteins viz. SLC16A1, MRC2, CD47, SLC16A3, HMOXl, and CD97 were tested as downstream factors of TGF- ⁇ signaling by flow cytometry.
• TGF- ⁇ or TGF- ⁇ inhibitor treated intact U-87 cells were analyzed by flow cytometry and the ratio of the respective GBMSig expression in response to TGF- ⁇ over its inhibitor was obtained.
• the ratio of protein on the cell-surface TGF- ⁇ over TGF- ⁇ inhibitor was found to increase by the following amounts in each case.
• CD47 40% (p ⁇ 3.68E-08),
• the discrepancy in HMOXl expression may be individual protein-specific and related to differential partitioning of proteins on the cell surface in comparison to total internal pools.
• GBMSig molecules that enhance TGF- ⁇ signaling has been identified: CD47, SLC16A3, MRC2, SLC16A1, and HMOXl.
• TGF- ⁇ is an inducer of the EMT process
• the subset of GBMSig that were identified in Example 2 as TGF- ⁇ responders may contribute to the invasiveness of
• TGF- ⁇ responsive GBMSig genes were silenced using si-RNA in U87 cells and the ability of cells in which these genes were silenced to invade through extracellular matrix was assessed.
• siRNAs were directed against SLC16A1, HMOXl, MRC2, and CD47
• siRNA mediated gene silencing was evaluated by both qPCR-at the transcript level and by flow cytometry on the cell surface. Greater than two fold reduced expression of the target genes in comparison to non-targeting RNAs was found. To account for any effect on the cell viability before and after siRNA treatments, viability was tested with calcein AM assay. No change in cell viability was found.
• siRNA or non-targeting RNA treated cells were seeded in transwell chambers and the degree of cell invasion was evaluated as percentage of cells invaded by silenced vs. non-silenced cells.
• GBM genes include EGFR, EGFRVIII, and PTEN.
• EGFRVIII EGFRVIII
• PTEN a stably integrated retroviral vector
• NSCs or their progenitors can undergo mutational changes and give rise to GCSCs with sustained self-renewal capabilities to propel tumor growth, drug resistance and recurrence.
• Differentially expressed GBMSig proteins between NSCs and GCSCs serve as cell-surface markers to distinguish these populations.
• Equal quantities (5.7 ⁇ g) of cell lysates from NSCs and GCSCs were enzymatically digested and clarified, and spiked with equal quantities of SRM peptide standards (labeled
• d-SRM dynamic-SRM assays
• GBMSig proteins viz. SLC16A1, HMOXl, MRC2, and SLC16A3 exhibiting differential expression between GCSC and NSC cells, were further validated by flow cytometry. Intact NSC and GCSC cells were labeled with appropriate primary antibodies, and the bound antibodies were detected by FITC or PE conjugated secondary antibodies. Mean fluorescence intensities (MFI) were calculated from four replicates of each antibody type after isotype subtraction, and presented as mean values + S.E. of mean difference (SEM).
• SLC16A1, HMOXl, MRC2, and SLC16A3 were all found to be highly expressed on CSCs compared to NSCs.
• GCSC cells expressed SLC16A1 and HMOXl at 26- and 8-fold higher levels, respectively, in comparison to NSC cells (p ⁇ 0.001). These data are in good agreement with d-SRM assays, which also indicated higher expression of SLC16A1 and HMOXl in GCSCs over NSCs.
• Reduced expressions of SLC16A3 and MRC2 on the surface of NSCs in comparison to GCSCs were evident from flow cytometry analysis.
• the distinctiveness in quantitative measurement of the cell-surface proteins from two alternate sources such as cell lysates and cell surface may be related to additional regulation in subcellular partitioning of these molecules on the surface of GCSC cells.
• GBMSig proteins as potential GCSC markers on primary GBM cells, distinct from cancer stem cells from commercial source.
• a subset of GBMSig proteins including SLC16A1, HMOXl, MRC2, CD47, SLC16A3, and CD97 were further evaluated for surface expression levels in relation to stem-like properties of primary GBM cells. These cells were isolated from SN143 tissue (also used for targeted tissue and serum proteomics) and maintained in stem-cell mimicking conditions to enrich GCSC populations.
• SN143 tissue also used for targeted tissue and serum proteomics
• Nestin enrichment on GCSCs was similar to that of NSCs.
• proliferating SN143 cells grown in stem cell-enrichment media were allowed to differentiate by withdrawing growth factors.
• Cellular differentiation of GCSC cells was confirmed from increased expression of known differentiation marker GFAP and diminished surface expression of known the GCSC marker CD 133.
• GBMSig expressions were analyzed by flow cytometry.
• the discrepancy that arose from flow cytometry analysis of SLC16A1 positive cells may be related to the averaging of SLC16A1 signal in flow cytometry due to rarity of the SLC16A1 -positive cells among SN143-derived GCSC populations.
• d-SRM assays were developed by determining the retention time of each surrogate peptide in presence of corresponding tissue or serum isolates in prior runs, thus reducing the peptide retention time window during SRM run and improving the confidence in peptide identification across multiple isolates as could be evident from high correlation (R > 0.99) of peptide retention time among different isolates.
• analysis of multiple transitions (Q1-Q3) for individual peptides improved the precision and quality of SRM analysis.
• GBMSig expression in the four GBM tissues could only be compared with that in two of the non-tumor tissues that came from SN132 and SN154 subjects.
• Z score transformation of SRM trace ratio of endogenous to surrogate peptide
• 12 GBMSig proteins overexpressed in all four GBM tissues were observed in comparison to both the non-tumor tissues, represented as a heat-map as shown in Figure 4.
• CAV1, TGFBI, and CA12 were found relatively overexpressed in gliosarcoma SN143 similar to the mesenchymal- subtypes underlined in TCGA datasets.
• classical/ proliferative SN154 relative overexpression of EGFR and reduced expression of S100A10 and NRP1 or in case of proneuronal SN186, relative overexpression of SLC16A3 and SCAMP3 at both the transcriptome and proteome levels was observed.
• intermediate subtype SN132 expression patterns of both mesenchymal as evident from the overexpression of CAV1, TGFBl, and CA12 proteins, and proliferative as evident from the overexpression of EGFR were clearly visible.
• the expression pattern of selected GBMSig proteins is thus reminiscent of GBM heterogeneities at both transcriptome and proteome levels so as to enable GBM stratification.

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