Evaluation of Protein Profiles From Treated Xenograft Tumor Models Identifies an Antibody Panel for Formalin-fixed and Paraffin-embedded (FFPE) Tissue Analysis by Reverse Phase Protein Arrays (RPPA)

Reverse phase protein arrays (RPPA) are an established tool for measuring the expression and activation status of multiple proteins in parallel using only very small amounts of tissue. Several studies have demonstrated the value of this technique for signaling pathway analysis using proteins extracted from fresh frozen (FF) tissue in line with validated antibodies for this tissue type; however, formalin fixation and paraffin embedding (FFPE) is the standard method for tissue preservation in the clinical setting. Hence, we performed RPPA to measure profiles for a set of 300 protein markers using matched FF and FFPE tissue specimens to identify which markers performed similarly using the RPPA technique in fixed and unfixed tissues. Protein lysates were prepared from matched FF and FFPE tissue specimens of individual tumors taken from three different xenograft models of human cancer. Materials from both untreated mice and mice treated with either anti-HER3 or bispecific anti-IGF-1R/EGFR monoclonal antibodies were analyzed. Correlations between signals from FF and FFPE tissue samples were investigated. Overall, 60 markers were identified that produced comparable profiles between FF and FFPE tissues, demonstrating significant correlation between the two sample types. The top 25 markers also showed significance after correction for multiple testing. The panel of markers covered several clinically relevant tumor signaling pathways and both phosphorylated and nonphosphorylated proteins were represented. Biologically relevant changes in marker expression were noted when RPPA profiles from treated and untreated xenografts were compared. These data demonstrate that, using appropriately selected antibodies, RPPA analysis from FFPE tissue is well feasible and generates biologically meaningful information. The identified panel of markers that generate similar profiles in matched fixed and unfixed tissue samples may be clinically useful for pharmacodynamic studies of drug effect using FFPE tissues.Many human diseases are characterized by abnormalities in complex signaling pathways (1). The expression and activation status of proteins from these deregulated pathways has traditionally been analyzed using single marker techniques such as immunohistochemistry and Western blotting. Although these techniques have provided valuable information on the molecular abnormalities underlying human disease, they are labor intensive, have a low throughput, and often require high sample volume. Furthermore, techniques such as Western blotting are not applicable in the routine clinical setting. Miniaturized parallel immunoassay techniques have been developed in recent years and have played a pivotal role in biomarker discovery (2). Antibody arrays enable multiple potential disease markers to be investigated in a single sample in parallel (3). Beyond this, Reverse Phase Protein Arrays (RPPA)¹ are sensitive high throughput tools that can quantify protein expression levels and activation status (posttranslational modifications such as phosphorylation) in multiple experimental samples simultaneously. The technique requires only minute amounts of samples, printed as lysate arrays onto slides, and hundreds of markers of interest can be investigated, array by array, in a miniaturized dot blot manner. Numerous reports have demonstrated that RPPA can be applied to various sources of cells and tissues to analyze protein profiles, signaling pathway networks, and for the identification of biomarkers (4–13). A recently published workshop report reviews the full potential and advances of RPPA for use in clinical, translational, and basic research (11).In oncology, the parallel profiling of multiple protein markers is particularly desirable to study tumor initiation and progression, to classify tumor disease states on the molecular level, and to discover and monitor biomarkers that can predict therapeutic response or tumor recurrence (14–16). The study of signaling response and analysis of pharmacodynamic (PD) markers upon treatment using in vitro and in vivo test systems (e.g. cell line or patient derived xenograft tumor models) is an established component of preclinical and early clinical drug development. These techniques can provide evidence of target pathway modulation for new therapeutic lead candidate compounds and provide valuable information on the drug mode of action (17), especially in the translational phase. Multiplex analyses of PD biomarkers by RPPA have been performed in vitro using cancer cell lines (18, 19) as well as in patient-derived tumor tissue and blood samples (20, 21) to assess response to treatment and target inhibition. A combination of RPPA signaling pathway mapping and functional PET imaging has recently been successfully evaluated in xenograft models as an early response PD marker for anti-cancer drug efficacy (13).Translating miniaturized multiple protein analysis platforms-such as RPPA - from preclinical to clinical applicability is highly desirable; however, issues such as the limited amount of available clinical samples and tumor heterogeneity must first be addressed. Furthermore, most studies of RPPA in tumor tissue to date have been conducted using proteins extracted from fresh-frozen (FF) tissue specimens; whereas, formalin fixation and paraffin embedding (FFPE) is the standard method for tissue preservation used in clinical pathology laboratories. FFPE yields excellent tissue architecture for histological assessment and enables analysis of individual proteins in situ by techniques such as immunohistochemistry. However, formalin fixation leads to extensive protein–protein and protein–nucleic acid cross-linking (22), which can hamper protein extraction and reduce both the overall yield of extracted protein and the profile of proteins detectable by proteomic techniques (23, 24). Furthermore, formalin-induced cross-linking induces conformational changes in protein structure that can alter the immunoreactivity of some proteins in situ by hiding or altering peptide epitopes (25, 26). Such artifacts are absent from snap-frozen tissue; therefore, protein profiles obtained from FF tissue are likely to reflect the in vivo biology of the tumor more closely. However, FF tumor tissue is not widely available because it is costly to collect and maintain in the clinical setting. FFPE tissue samples are routinely archived by nearly every hospital and offer a unique opportunity to study thousands of samples retrospectively with extensive clinical records and follow-up information.Several groups have now established protocols for retrieving cross-linked proteins from fixed tissues (27–33). These methods are mainly based on the use of concentrated ionic detergents and high temperature protocols closely related to the antigen retrieval methods developed for immunohistochemistry. These studies show that obtaining nondegraded, full-length proteins from FFPE tissues for multiplex analyses is feasible (27–33). More recently, protein extraction techniques optimized for fixed samples have been used to successfully conduct RPPA using FFPE tissue biopsies from different cancer types (34–40). Guo et al. systematically investigated several protein extraction methods and demonstrated that RPPA of FFPE materials is feasible, reproducible and can generate biologically relevant protein profiles (41). Other studies have confirmed the validity of this approach and shown that data generated from RPPA analyses of FFPE tissue demonstrate good concordance with traditional immunohistochemistry markers such as HER2 protein in breast cancer (34, 40). However, to date, analyses have been performed only for a limited set of protein markers.To evaluate whether analysis of a broader panel of protein markers is feasible and generates meaningful data from FFPE tumor tissue sections, we conducted RPPA on matched samples of FF and FFPE tissues using a set of 300 markers, the largest panel reported to date. Our aim was to identify markers that performed similarly when comparing the protein profiles measured in protein extracts from matched FF and FFPE tissue, using RPPA assays established for use in frozen tissues. Correlating selected markers and assays in such a way should qualify RPPA for further use with FFPE tissues of clinical relevance, e.g. in PD marker studies. In this paper, we have specifically focused on the technical issues relevant for using the RPPA platform in a clinical setting, and did not address the biology of the test systems used in detail. However, the models used have been pre-characterized to identify key signaling parameters in context of targeted drug treatment (42). We conducted a systematic comparison of RPPA protein profiles in matched FF and FFPE tumor tissues resected from three different xenograft models of human cancer, each treated with targeted therapeutic antibodies that have previously been shown to achieve tumor growth inhibition. Furthermore, we investigated the effect of targeted drug treatment on protein expression and activation status, and the concordance of matched FF and FFPE tissue RPPA profiles. Finally, with one of the applied tumor models, we compared a set of protein profiles measured with two different multiple assay platforms - the RPPA and the Luminex Bio-Plex system, and discuss their relevance with respect to the analysis of FFPE tissue.     

Quantification of HER2 by Targeted Mass Spectrometry in Formalin-Fixed Paraffin-Embedded (FFPE) Breast Cancer Tissues

The ability to accurately quantify proteins in formalin-fixed paraffin-embedded tissues using targeted mass spectrometry opens exciting perspectives for biomarker discovery. We have developed and evaluated a selectedreaction monitoring assay for the human receptor tyrosine-protein kinase erbB-2 (HER2) in formalin-fixed paraffin-embedded breast tumors. Peptide candidates were identified using an untargeted mass spectrometry approach in relevant cell lines. A multiplexed assay was developed for the six best candidate peptides and evaluated for linearity, precision and lower limit of quantification. Results showed a linear response over a calibration range of 0.012 to 100 fmol on column (R²: 0.99–1.00).The lower limit of quantification was 0.155 fmol on column for all peptides evaluated. The six HER2 peptides were quantified by selected reaction monitoring in a cohort of 40 archival formalin-fixed paraffin-embedded tumor tissues from women with invasive breast carcinomas, which showed different levels of HER2 gene amplification as assessed by standard methods used in clinical pathology. The amounts of the six HER2 peptides were highly and significantly correlated with each other, indicating that peptide levels can be used as surrogates of protein amounts in formalin-fixed paraffin-embedded tissues. After normalization for sample size, selected reaction monitoring peptide measurements were able to correctly predict 90% of cases based on HER2 amplification as defined by the American Society of Clinical Oncology and College of American Pathologists. In conclusion, the developed assay showed good analytical performance and a high agreement with immunohistochemistry and fluorescence in situ hybridization data. This study demonstrated that selected reaction monitoring allows to accurately quantify protein expression in formalin-fixed paraffin-embedded tissues and represents therefore a powerful approach for biomarker discovery studies. The untargeted mass spectrometry data is available via ProteomeXchange whereas the quantification data by selected reaction monitoring is available on the Panorama Public website.MS based proteomics has traditionally been used to investigate complex biological systems, such as cell lines, plasma, or fresh-frozen tissues (1, 2). In the last decade however, MS proteomics has extended to the analysis of formalin-fixed paraffin-embedded (FFPE)¹ tissues (3). Formalin fixation is the gold standard for sample storage in clinical pathology because it allows optimal preservation of the morphological features of the tissue and it is economically attractive (storage at room temperature over several years or decades) (4). Several studies have shown that although the individual peptides retrieved and identified from fresh-frozen and FFPE tissues may differ, the biological information obtained from both types of material in terms of number of proteins identified, cellular location and molecular function is very similar (5–10). A number of proteomics studies were reported, which used untargeted MS on FFPE tissues to compare diseased and healthy samples in the search for potential novel biomarkers (10). Nevertheless, these untargeted MS workflows do not allow performing accurate protein quantification on large numbers of samples. One option is to use targeted MS approaches, such as selected reaction monitoring (SRM), which are highly quantitative and reproducible over many samples (11, 12). Additionally, SRM assays allow a high level of multiplexing (several hundreds of peptides can be measured in parallel in a single analysis) (13). The lack of access to a sufficient number of high-quality samples annotated with comprehensive clinical data sets may be a limiting factor for preclinical exploratory phase biomarker studies (14). The possibility to use FFPE samples for MS-based proteomics, in particular for quantitative targeted approaches, would therefore open tremendous perspectives for performing large retrospective biomarker discovery and verification studies. Indeed, in addition to being widely available, most FFPE tissues are annotated with clinical data. Moreover, targeted MS workflows applied to FFPE samples are complementary to techniques requiring high-quality antibodies, such as immunohistochemistry (IHC) or reverse-phase protein arrays (RPPA). These techniques all rely on the measurement of the target protein, with SRM measuring one or ideally several peptides as surrogates of the protein (15, 16). In opposition to IHC and RPPA however, SRM does not rely on the presence of a specific antibody for analyte detection, thereby avoiding cross-reaction issues and making assay development relatively rapid and cost effective. Although SRM is less advanced for protein analysis than for small molecules quantification, the technique was demonstrated to be selective, reproducible, and highly quantitative over large dynamic ranges for proteins as well (17–19). However, although the equivalence of qualitative analyses performed on fresh-frozen and FFPE samples has been investigated and demonstrated, only a few studies evaluated quantitative targeted MS approaches in FFPE samples (20, 21). Targeted proteomics performed on FFPE tissues is still in its early days and known limitations of this technique include the loss of morphologic features of the tissue and an extensive sample preparation, causing a low sample throughput (20). Moreover, targeted proteomics quantifies peptides as surrogates of a protein, with the former not necessarily agreeing in absolute terms with the latter. This is true for bottom-up proteomics in general, but it is of particular importance for FFPE tissues.In this study, we critically assessed the validity of targeted MS applied to peptide quantification in FFPE tissues. We developed and evaluated an SRM assay for the quantification of six peptides of the human receptor tyrosine-protein kinase erbB-2 (HER2) and compared the obtained results with those of standard methods used in clinical pathology, namely IHC and fluorescence in situ hybridization (FISH). HER2 was chosen as a candidate protein because its overexpression is routinely assessed in breast tumors in order to determine susceptibility to anti-HER2 treatment (22). Depending on the laboratory, HER2 overexpression can be assessed by IHC or the amplification of the corresponding gene (ERBB2) can be quantified by FISH (23). Several laboratories use both techniques for decision-making, as they are complementary.In a first step, we developed an SRM assay for the quantification of HER2 peptides in archival clinical FFPE tumor tissues and assessed its analytical performance, including linearity, precision and lower limit of quantification (LLOQ). We then demonstrated the applicability of the method by quantifying HER2 peptides in a cohort of 40 FFPE tumor tissues expressing different levels of HER2 (selected based on ERBB2 gene amplification status). The samples originated from surgical resections performed on women with invasive mammary carcinomas. We thereby investigated several options to normalize the results in regard to sample size. Finally, in order to confirm the validity of SRM as a suitable method for protein quantification in FFPE tissues, we determined the agreement between data generated by SRM and data generated by IHC or FISH.     

Multipattern Consensus Regions in Multiple Aligned Protein Sequences and Their Segmentation

Decomposing a biological sequence into its functional regions is an important prerequisite to understand the molecule. Using the multiple alignments of the sequences, we evaluate a segmentation based on the type of statistical variation pattern from each of the aligned sites. To describe such a more general pattern, we introduce multipattern consensus regions as segmented regions based on conserved as well as interdependent patterns. Thus the proposed consensus region considers patterns that are statistically significant and extends a local neighborhood. To show its relevance in protein sequence analysis, a cancer suppressor gene called p53 is examined. The results show significant associations between the detected regions and tendency of mutations, location on the 3D structure, and cancer hereditable factors that can be inferred from human twin studies.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27]     

Printor, a Novel TorsinA-interacting Protein Implicated in Dystonia Pathogenesis

Early onset generalized dystonia (DYT1) is an autosomal dominant neurological disorder caused by deletion of a single glutamate residue (torsinA ΔE) in the C-terminal region of the AAA⁺ (ATPases associated with a variety of cellular activities) protein torsinA. The pathogenic mechanism by which torsinA ΔE mutation leads to dystonia remains unknown. Here we report the identification and characterization of a 628-amino acid novel protein, printor, that interacts with torsinA. Printor co-distributes with torsinA in multiple brain regions and co-localizes with torsinA in the endoplasmic reticulum. Interestingly, printor selectively binds to the ATP-free form but not to the ATP-bound form of torsinA, supporting a role for printor as a cofactor rather than a substrate of torsinA. The interaction of printor with torsinA is completely abolished by the dystonia-associated torsinA ΔE mutation. Our findings suggest that printor is a new component of the DYT1 pathogenic pathway and provide a potential molecular target for therapeutic intervention in dystonia.Early onset generalized torsion dystonia (DYT1) is the most common and severe form of hereditary dystonia, a movement disorder characterized by involuntary movements and sustained muscle spasms (1). This autosomal dominant disease has childhood onset and its dystonic symptoms are thought to result from neuronal dysfunction rather than neurodegeneration (2, 3). Most DYT1 cases are caused by deletion of a single glutamate residue at positions 302 or 303 (torsinA ΔE) of the 332-amino acid protein torsinA (4). In addition, a different torsinA mutation that deletes amino acids Phe³²³–Tyr³²⁸ (torsinA Δ323–328) was identified in a single family with dystonia (5), although the pathogenic significance of this torsinA mutation is unclear because these patients contain a concomitant mutation in another dystonia-related protein, ϵ-sarcoglycan (6). Recently, genetic association studies have implicated polymorphisms in the torsinA gene as a genetic risk factor in the development of adult-onset idiopathic dystonia (7, 8).TorsinA contains an N-terminal endoplasmic reticulum (ER)³ signal sequence and a 20-amino acid hydrophobic region followed by a conserved AAA⁺ (ATPases associated with a variety of cellular activities) domain (9, 10). Because members of the AAA⁺ family are known to facilitate conformational changes in target proteins (11, 12), it has been proposed that torsinA may function as a molecular chaperone (13, 14). TorsinA is widely expressed in brain and multiple other tissues (15) and is primarily associated with the ER and nuclear envelope (NE) compartments in cells (16–20). TorsinA is believed to mainly reside in the lumen of the ER and NE (17–19) and has been shown to bind lamina-associated polypeptide 1 (LAP1) (21), lumenal domain-like LAP1 (LULL1) (21), and nesprins (22). In addition, recent evidence indicates that a significant pool of torsinA exhibits a topology in which the AAA⁺ domain faces the cytoplasm (20). In support of this topology, torsinA is found in the cytoplasm, neuronal processes, and synaptic terminals (2, 3, 15, 23–26) and has been shown to bind cytosolic proteins snapin (27) and kinesin light chain 1 (20). TorsinA has been proposed to play a role in several cellular processes, including dopaminergic neurotransmission (28–31), NE organization and dynamics (17, 22, 32), and protein trafficking (27, 33). However, the precise biological function of torsinA and its regulation remain unknown.To gain insights into torsinA function, we performed yeast two-hybrid screens to search for torsinA-interacting proteins in the brain. We report here the isolation and characterization of a novel protein named printor (protein interactor of torsinA) that interacts selectively with wild-type (WT) torsinA but not the dystonia-associated torsinA ΔE mutant. Our data suggest that printor may serve as a cofactor of torsinA and provide a new molecular target for understanding and treating dystonia.     

Sip1, a Novel RS Domain-Containing Protein Essential for Pre-mRNA Splicing

Previous studies have shown that protein-protein interactions among splicing factors may play an important role in pre-mRNA splicing. We report here identification and functional characterization of a new splicing factor, Sip1 (SC35-interacting protein 1). Sip1 was initially identified by virtue of its interaction with SC35, a splicing factor of the SR family. Sip1 interacts with not only several SR proteins but also with U1-70K and U2AF65, proteins associated with 5′ and 3′ splice sites, respectively. The predicted Sip1 sequence contains an arginine-serine-rich (RS) domain but does not have any known RNA-binding motifs, indicating that it is not a member of the SR family. Sip1 also contains a region with weak sequence similarity to the Drosophila splicing regulator suppressor of white apricot (SWAP). An essential role for Sip1 in pre-mRNA splicing was suggested by the observation that anti-Sip1 antibodies depleted splicing activity from HeLa nuclear extract. Purified recombinant Sip1 protein, but not other RS domain-containing proteins such as SC35, ASF/SF2, and U2AF65, restored the splicing activity of the Sip1-immunodepleted extract. Addition of U2AF65 protein further enhanced the splicing reconstitution by the Sip1 protein. Deficiency in the formation of both A and B splicing complexes in the Sip1-depleted nuclear extract indicates an important role of Sip1 in spliceosome assembly. Together, these results demonstrate that Sip1 is a novel RS domain-containing protein required for pre-mRNA splicing and that the functional role of Sip1 in splicing is distinct from those of known RS domain-containing splicing factors.Pre-mRNA splicing takes place in spliceosomes, the large RNA-protein complexes containing pre-mRNA, U1, U2, U4/6, and U5 small nuclear ribonucleoprotein particles (snRNPs), and a large number of accessory protein factors (for reviews, see references 21, 22, 37, 44, and 48). It is increasingly clear that the protein factors are important for pre-mRNA splicing and that studies of these factors are essential for further understanding of molecular mechanisms of pre-mRNA splicing.Most mammalian splicing factors have been identified by biochemical fractionation and purification (3, 15, 19, 31–36, 45, 69–71, 73), by using antibodies recognizing splicing factors (8, 9, 16, 17, 61, 66, 67, 74), and by sequence homology (25, 52, 74).Splicing factors containing arginine-serine-rich (RS) domains have emerged as important players in pre-mRNA splicing. These include members of the SR family, both subunits of U2 auxiliary factor (U2AF), and the U1 snRNP protein U1-70K (for reviews, see references 18, 41, and 59). Drosophila alternative splicing regulators transformer (Tra), transformer 2 (Tra2), and suppressor of white apricot (SWAP) also contain RS domains (20, 40, 42). RS domains in these proteins play important roles in pre-mRNA splicing (7, 71, 75), in nuclear localization of these splicing proteins (23, 40), and in protein-RNA interactions (56, 60, 64). Previous studies by us and others have demonstrated that one mechanism whereby SR proteins function in splicing is to mediate specific protein-protein interactions among spliceosomal components and between general splicing factors and alternative splicing regulators (1, 1a, 6, 10, 27, 63, 74, 77). Such protein-protein interactions may play critical roles in splice site recognition and association (for reviews, see references 4, 18, 37, 41, 47 and 59). Specific interactions among the splicing factors also suggest that it is possible to identify new splicing factors by their interactions with known splicing factors.Here we report identification of a new splicing factor, Sip1, by its interaction with the essential splicing factor SC35. The predicted Sip1 protein sequence contains an RS domain and a region with sequence similarity to the Drosophila splicing regulator, SWAP. We have expressed and purified recombinant Sip1 protein and raised polyclonal antibodies against the recombinant Sip1 protein. The anti-Sip1 antibodies specifically recognize a protein migrating at a molecular mass of approximately 210 kDa in HeLa nuclear extract. The anti-Sip1 antibodies sufficiently deplete Sip1 protein from the nuclear extract, and the Sip1-depleted extract is inactive in pre-mRNA splicing. Addition of recombinant Sip1 protein can partially restore splicing activity to the Sip1-depleted nuclear extract, indicating an essential role of Sip1 in pre-mRNA splicing. Other RS domain-containing proteins, including SC35, ASF/SF2, and U2AF65, cannot substitute for Sip1 in reconstituting splicing activity of the Sip1-depleted nuclear extract. However, addition of U2AF65 further increases splicing activity of Sip1-reconstituted nuclear extract, suggesting that there may be a functional interaction between Sip1 and U2AF65 in nuclear extract.     

Identification of Tumor-associated Autoantigens for the Diagnosis of Colorectal Cancer in Serum Using High Density Protein Microarrays

There is a mounting evidence of the existence of autoantibodies associated to cancer progression. Antibodies are the target of choice for serum screening because of their stability and suitability for sensitive immunoassays. By using commercial protein microarrays containing 8000 human proteins, we examined 20 sera from colorectal cancer (CRC) patients and healthy subjects to identify autoantibody patterns and associated antigens. Forty-three proteins were differentially recognized by tumoral and reference sera (p value <0.04) in the protein microarrays. Five immunoreactive antigens, PIM1, MAPKAPK3, STK4, SRC, and FGFR4, showed the highest prevalence in cancer samples, whereas ACVR2B was more abundant in normal sera. Three of them, PIM1, MAPKAPK3, and ACVR2B, were used for further validation. A significant increase in the expression level of these antigens on CRC cell lines and colonic mucosa was confirmed by immunoblotting and immunohistochemistry on tissue microarrays. A diagnostic ELISA based on the combination of MAPKAPK3 and ACVR2B proteins yielded specificity and sensitivity values of 73.9 and 83.3% (area under the curve, 0.85), respectively, for CRC discrimination after using an independent sample set containing 94 sera representative of different stages of progression and control subjects. In summary, these studies confirmed the presence of specific autoantibodies for CRC and revealed new individual markers of disease (PIM1, MAPKAPK3, and ACVR2B) with the potential to diagnose CRC with higher specificity and sensitivity than previously reported serum biomarkers.Colorectal cancer (CRC)¹ is the second most prevalent cancer in the western world. The development of this disease takes decades and involves multiple genetic events. CRC remains a major cause of mortality in developed countries because most of the patients are diagnosed at advanced stages because of the reluctance to use highly invasive diagnostic tools like colonoscopy. Actually only a few proteins have been described as biomarkers in CRC (carcinoembryonic antigen (CEA), CA19.9, and CA125 (1–3)), although none of them is recommended for clinical screening (4). Proteomics analysis is actively used for the identification of new biomarkers. In previous studies, the use of two-dimensional DIGE and antibody microarrays allowed the identification of differentially expressed proteins in CRC tissue, including isoforms and post-translational modifications responsible for modifications in signaling pathways (5–8). Both approaches resulted in the identification of a collection of potential tumoral tissue biomarkers that is currently being investigated.However, the implementation of simpler, non-invasive methods for the early detection of CRC should be based on the identification of proteins or antibodies in serum or plasma (9–13). There is ample evidence of the existence of an immune response to cancer in humans as demonstrated by the presence of autoantibodies in cancer sera. Self-proteins (autoantigens) altered before or during tumor formation can elicit an immune response (13–19). These tumor-specific autoantibodies can be detected at early cancer stages and prior to cancer diagnosis revealing a great potential as biomarkers (14, 15, 20). Tumor proteins can be affected by specific point mutations, misfolding, overexpression, aberrant glycosylation, truncation, or aberrant degradation (e.g. p53, HER2, NY-ESO1, or MUC1 (16, 21–25)). In fact, a number of tumor-associated autoantigens (TAAs) were identified previously in different studies involving autoantibody screening in CRC (26–28).Several approaches have been used to identify TAAs in cancer, including natural protein arrays prepared with fractions obtained from two-dimensional LC separations of tumoral samples (29, 30) or protein extracts from cancer cells or tissue (9, 31) probed by Western blot with patient sera, cancer tissue peptide libraries expressed as cDNA expression libraries for serological screening (serological analysis of recombinant cDNA expression libraries (SEREX)) (22, 32), or peptides expressed on the surface of phages in combination with microarrays (17, 18, 33, 34). However, these approaches suffer from several drawbacks. In some cases TAAs have to be isolated and identified from the reactive protein lysate by LC-MS techniques, or in the phage display approach, the reactive TAA could be a mimotope without a corresponding linear amino acid sequence. Moreover, cDNA libraries might not be representative of the protein expression levels in tumors as there is a poor correspondence between mRNA and protein levels.Protein arrays provide a novel platform for the identification of both autoantibodies and their respective TAAs for diagnostic purposes in cancer serum patients. They present some advantages. Proteins printed on the microarray are known “a priori,” avoiding the need for later identifications and the discovery of mimotopes. There is no bias in protein selection as the proteins are printed at a similar concentration. This should result in a higher sensitivity for biomarker identification (13, 35, 36).In this study, we used commercially available high density protein microarrays for the identification of autoantibody signatures and tumor-associated antigens in colorectal cancer. We screened 20 CRC patient and control sera with protein microarrays containing 8000 human proteins to identify the CRC-associated autoantibody repertoire and the corresponding TAAs. Autoantibody profiles that discriminated the different types of CRC metastasis were identified. Moreover a set of TAAs showing increased or decreased expression in cancer cell lines and paired tumoral tissues was found. Finally an ELISA was set up to test the ability of the most immunoreactive proteins to detect colorectal adenocarcinoma. On the basis of the antibody response, combinations of three antigens, PIM1, MAPKAPK3, and ACVR2B, showed a great potential for diagnosis.     

Quantitative Proteomics with siRNA Screening Identifies Novel Mechanisms of Trastuzumab Resistance in HER2 Amplified Breast Cancers

HER2 is a receptor tyrosine kinase that is overexpressed in 20% to 30% of human breast cancers and which affects patient prognosis and survival. Treatment of HER2-positive breast cancer with the monoclonal antibody trastuzumab (Herceptin) has improved patient survival, but the development of trastuzumab resistance is a major medical problem. Many of the known mechanisms of trastuzumab resistance cause changes in protein phosphorylation patterns, and therefore quantitative proteomics was used to examine phosphotyrosine signaling networks in trastuzumab-resistant cells. The model system used in this study was two pairs of trastuzumab-sensitive and -resistant breast cancer cell lines. Using stable isotope labeling, phosphotyrosine immunoprecipitations, and online TiO₂ chromatography utilizing a dual trap configuration, ∼1700 proteins were quantified. Comparing quantified proteins between the two cell line pairs showed only a small number of common protein ratio changes, demonstrating heterogeneity in phosphotyrosine signaling networks across different trastuzumab-resistant cancers. Proteins showing significant increases in resistant versus sensitive cells were subjected to a focused siRNA screen to evaluate their functional relevance to trastuzumab resistance. The screen revealed proteins related to the Src kinase pathway, such as CDCP1/Trask, embryonal Fyn substrate, and Paxillin. We also identify several novel proteins that increased trastuzumab sensitivity in resistant cells when targeted by siRNAs, including FAM83A and MAPK1. These proteins may present targets for the development of clinical diagnostics or therapeutic strategies to guide the treatment of HER2+ breast cancer patients who develop trastuzumab resistance.HER2 is a member of the epidermal growth factor receptor (EGFR)/ErbB family of receptor tyrosine kinases. Under normal physiologic conditions, HER2 tyrosine kinase signaling is tightly regulated spatially and temporally by the requirement for it to heterodimerize with a ligand bound family member, such as EGFR, HER3/ErbB3, or HER4/ErbB4 (1). However, in 20% to 30% of human breast cancer cases, HER2 gene amplification is present, resulting in a high level of HER2 protein overexpression and unregulated, constitutive HER2 tyrosine kinase signaling (2, 3). HER2 gene amplified breast cancer, also termed HER2-positive breast cancer, carries a poor prognosis, but the development of the HER2 targeted monoclonal antibody trastuzumab (Herceptin) has significantly improved patient survival (2). Despite the clinical effectiveness of trastuzumab, the development of drug resistance significantly increases the risk of patient death. This poses a major medical problem, as most metastatic HER2-positive breast cancer patients develop trastuzumab resistance over the course of their cancer treatment (4). The treatment approach for HER2+ breast cancer patients after trastuzumab resistance develops is mostly a trial-and-error process that subjects the patient to increased toxicity. Therefore, there is a substantial medical need for strategies to overcome trastuzumab resistance.Multiple trastuzumab-resistance mechanisms have been identified, and they alter signaling networks and protein phosphorylation patterns in either a direct or an indirect manner. These mechanisms can be grouped into three categories. The first category is the activation of a parallel signaling network by other tyrosine kinases. These kinases include the receptor tyrosine kinases, EGFR, IGF1R, Her3, Met, EphA2, and Axl, as well as the erythropoietin-receptor-mediated activation of the cytoplasmic tyrosine kinases Jak2 and Src (5–11). The second category is the activation of downstream signaling proteins. Multiple studies have demonstrated activation of the phosphatidylinositol-3-kinase (PI3K)/AKT pathway in trastuzumab resistance, which occurs either via deletion of the PTEN lipid phosphatase or mutation of the PI3K genes (12, 13). Activation of Src family kinases or overexpression of cyclin E, which increases the cyclin E–cyclin-dependent kinase 2 signaling pathway, has also been reported (14). The third category includes mechanisms that maintain HER2 signaling even in the presence of trastuzumab. The production of a truncated isoform of HER2, p95HER2, which lacks the trastuzumab binding site, causes constitutive HER2 signaling (15, 16). Overexpression of the MUC4 sialomucin complex inhibits trastuzumab binding to HER2 and thereby maintains HER2 signaling (17, 18).Given that multiple trastuzumab-resistance mechanisms alter signaling networks and protein phosphorylation patterns, we reasoned that mapping phosphotyrosine signaling networks using quantitative proteomics would be a highly useful strategy for analyzing known mechanisms and identifying novel mechanisms of trastuzumab resistance. Quantitative proteomics and phosphotyrosine enrichment approaches have been extensively used to study the EGFR signal networks (19–23). We and others have used these approaches to map the HER2 signaling network (22, 24, 25). Multiple other tyrosine kinase signaling networks were analyzed using quantitative proteomics, including Ephrin receptor, EphB2 (26–28), platelet-derived growth factor receptor (PDGFR) (21), insulin receptor (29, 30), and the receptor for hepatocyte growth factor, c-MET (31).The goal of this study is to identify, quantify, and functionally screen proteins that might be involved in trastuzumab resistance. We used two pairs of HER2 gene amplified trastuzumab-sensitive (parental, SkBr3 and BT474) and -resistant (SkBr3^R and BT474^R) human breast cancer cell lines as models for trastuzumab resistance. These cell lines and their trastuzumab-resistant derivatives have been extensively characterized and highly cited in the breast cancer literature (32, 33). Using stable isotope labeling of amino acids in cell culture (SILAC),¹ phosphotyrosine immunoprecipitations, and online TiO₂ chromatography with dual trap configuration, we quantified the changes in phosphotyrosine containing proteins and interactors between trastuzumab-sensitive and -resistant cells. Several of the known trastuzumab-resistance mechanisms were identified, which serves as a positive control and validation of our approach, and large protein ratio changes were measured in proteins that had not been previously connected with trastuzumab resistance. We then performed a focused siRNA screen targeting the proteins with significantly increased protein ratios. This screen functionally tested the role of the identified proteins and identifies which proteins might have the largest effect on reversing trastuzumab resistance.     

Coomassie Blue as a Near-infrared Fluorescent Stain: A Systematic Comparison With Sypro Ruby for In-gel Protein Detection

Quantitative proteome analyses suggest that the well-established stain colloidal Coomassie Blue, when used as an infrared dye, may provide sensitive, post-electrophoretic in-gel protein detection that can rival even Sypro Ruby. Considering the central role of two-dimensional gel electrophoresis in top-down proteomic analyses, a more cost effective alternative such as Coomassie Blue could prove an important tool in ongoing refinements of this important analytical technique. To date, no systematic characterization of Coomassie Blue infrared fluorescence detection relative to detection with SR has been reported. Here, seven commercial Coomassie stain reagents and seven stain formulations described in the literature were systematically compared. The selectivity, threshold sensitivity, inter-protein variability, and linear-dynamic range of Coomassie Blue infrared fluorescence detection were assessed in parallel with Sypro Ruby. Notably, several of the Coomassie stain formulations provided infrared fluorescence detection sensitivity to <1 ng of protein in-gel, slightly exceeding the performance of Sypro Ruby. The linear dynamic range of Coomassie Blue infrared fluorescence detection was found to significantly exceed that of Sypro Ruby. However, in two-dimensional gel analyses, because of a blunted fluorescence response, Sypro Ruby was able to detect a few additional protein spots, amounting to 0.6% of the detected proteome. Thus, although both detection methods have their advantages and disadvantages, differences between the two appear to be small. Coomassie Blue infrared fluorescence detection is thus a viable alternative for gel-based proteomics, offering detection comparable to Sypro Ruby, and more reliable quantitative assessments, but at a fraction of the cost.Gel electrophoresis is an accessible, widely applicable and mature protein resolving technology. As the original top-down approach to proteomic analyses, among its many attributes the high resolution achievable by two dimensional gel-electrophoresis (2DE)¹ ensures that it remains an effective analytical technology despite the appearance of alternatives. However, in-gel detection remains a limiting factor for gel-based analyses; available technology generally permits the detection and quantification of only relatively abundant proteins (35). Many critical components in normal physiology and also disease may be several orders of magnitude less abundant and thus below the detection threshold of in-gel stains, or indeed most techniques. Pre- and post-fractionation technologies have been developed to address this central issue in proteomics but these are not without limitations (1–5). Thus improved detection methods for gel-based proteomics continue to be a high priority, and the literature is rich with different in-gel detection methods and innovative improvements (6–34). This history of iterative refinement presents a wealth of choices when selecting a detection strategy for a gel-based proteomic analysis (35).Perhaps the best known in-gel detection method is the ubiquitous Coomassie Blue (CB) stain; CB has served as a gel stain and protein quantification reagent for over 40 years. Though affordable, robust, easy to use, and compatible with mass spectrometry (MS), CB staining is relatively insensitive. In traditional organic solvent formulations, CB detects ∼ 10 ng of protein in-gel, and some reports suggest poorer sensitivity (27, 29, 36, 37). Sensitivity is hampered by relatively high background staining because of nonspecific retention of dye within the gel matrix (32, 36, 38, 39). The development of colloidal CB (CCB) formulations largely addressed these limitations (12); the concentration of soluble CB was carefully controlled by sequestering the majority of the dye into colloidal particles, mediated by pH, solvent, and the ionic strength of the solution. Minimizing soluble dye concentration and penetration of the gel matrix mitigated background staining, and the introduction of phosphoric acid into the staining reagent enhanced dye-protein interactions (8, 12, 40), contributing to an in-gel staining sensitivity of 5–10 ng protein, with some formulations reportedly yielding sensitivities of 0.1–1 ng (8, 12, 22, 39, 41, 42). Thus CCB achieved higher sensitivity than traditional CB staining, yet maintained all the advantages of the latter, including low cost and compatibility with existing densitometric detection instruments and MS. Although surpassed by newer methods, the practical advantages of CCB ensure that it remains one of the most common gel stains in use.Fluorescent stains have become the routine and sensitive alternative to visible dyes. Among these, the ruthenium-organometallic family of dyes have been widely applied and the most commercially well-known is Sypro Ruby (SR), which is purported to interact noncovalently with primary amines in proteins (15, 18, 19, 43). Chief among the attributes of these dyes is their high sensitivity. In-gel detection limits of < 1 ng for some proteins have been reported for SR (6, 9, 14, 44, 45). Moreover, SR staining has been reported to yield a greater linear dynamic range (LDR), and reduced interprotein variability (IPV) compared with CCB and silver stains (15, 19, 46–49). SR is easy to use, fully MS compatible, and relatively forgiving of variations in initial conditions (6, 15). The chief consequence of these advances remains high cost; SR and related stains are notoriously expensive, and beyond the budget of many laboratories. Furthermore, despite some small cost advantage relative to SR, none of the available alternatives has been consistently and quantitatively demonstrated to substantially improve on the performance of SR under practical conditions (9, 50).Notably, there is evidence to suggest that CCB staining is not fundamentally insensitive, but rather that its sensitivity has been limited by traditional densitometric detection (50, 51). When excited in the near IR at ∼650 nm, protein-bound CB in-gel emits light in the range of 700–800 nm. Until recently, the lack of low-cost, widely available and sufficiently sensitive infrared (IR)-capable imaging instruments prevented mainstream adoption of in-gel CB infrared fluorescence detection (IRFD); advances in imaging technology are now making such instruments far more accessible. Initial reports suggested that IRFD of CB-stained gels provided greater sensitivity than traditional densitometric detection (50, 51). Using CB R250, in-gel IRFD was reported to detect as little as 2 ng of protein in-gel, with a LDR of about an order of magnitude (2 to 20 ng, or 10 to 100 ng in separate gels), beyond which the fluorescent response saturated into the μg range (51). Using the G250 dye variant, it was determined that CB-IRFD of 2D gels detected ∼3 times as many proteins as densitometric imaging, and a comparable number of proteins as seen by SR (50). This study also concluded that CB-IRFD yielded a significantly higher signal to background ratio (S/BG) than SR, providing initial evidence that CB-IRFD may be superior to SR in some aspects of stain performance (50).Despite this initial evidence of the viability of CB-IRF as an in-gel protein detection method, a detailed characterization of this technology has not yet been reported. Here a more thorough, quantitative characterization of CB-IRFD is described, establishing its lowest limit of detection (LLD), IPV, and LDR in comparison to SR. Finally a wealth of modifications and enhancements of CCB formulations have been reported (8, 12, 21, 24, 26, 29, 40, 41, 52–54), and likewise there are many commercially available CCB stain formulations. To date, none of these formulations have been compared quantitatively in terms of their relative performance when detected using IRF. As a general detection method for gel-based proteomics, CB-IRFD was found to provide comparable or even slightly superior performance to SR according to most criteria, including sensitivity and selectivity (50). Furthermore, in terms of LDR, CB-IRFD showed distinct advantages over SR. However, assessing proteomes resolved by 2DE revealed critical distinctions between CB-IRFD and SR in terms of protein quantification versus threshold detection: neither stain could be considered unequivocally superior to the other by all criteria. Nonetheless, IRFD proved the most sensitive method of detecting CB-stained protein in-gel, enabling high sensitivity detection without the need for expensive reagents or even commercial formulations. Overall, CB-IRFD is a viable alternative to SR and other mainstream fluorescent stains, mitigating the high cost of large-scale gel-based proteomic analyses, making high sensitivity gel-based proteomics accessible to all labs. With improvements to CB formulations and/or image acquisition instruments, the performance of this detection technology may be further enhanced.     

Sgt1 Dimerization Is Negatively Regulated by Protein Kinase CK2-mediated Phosphorylation at Ser361

The kinetochore, which consists of centromere DNA and structural proteins, is essential for proper chromosome segregation in eukaryotes. In budding yeast, Sgt1 and Hsp90 are required for the binding of Skp1 to Ctf13 (a component of the core kinetochore complex CBF3) and therefore for the assembly of CBF3. We have previously shown that Sgt1 dimerization is important for this kinetochore assembly mechanism. In this study, we report that protein kinase CK2 phosphorylates Ser³⁶¹ on Sgt1, and this phosphorylation inhibits Sgt1 dimerization.The kinetochore is a structural protein complex located in the centromeric region of the chromosome coupled to spindle microtubules (1, 2). The kinetochore generates a signal to arrest cells during mitosis when it is not properly attached to microtubules, thereby preventing chromosome missegregation, which can lead to aneuploidy (3, 4). The molecular structure of the kinetochore complex of the budding yeast Saccharomyces cerevisiae has been well characterized; it is composed of more than 70 proteins, many of which are conserved in mammals (2).The centromere DNA in the budding yeast is a 125-bp region that contains three conserved regions, CDEI, CDEII, and CDEIII (5, 6). CDEIII (25 bp) is essential for centromere function (7) and is bound to a key component of the centromere, the CBF3 complex. The CBF3 complex contains four proteins, Ndc10, Cep3, Ctf13 (8–15), and Skp1 (14, 15), all essential for viability. Mutations in any of the CBF3 proteins abolish the ability of CDEIII to bind to CBF3 (16, 17). All of the kinetochore proteins, except the CDEI-binding Cbf1 (18–20), localize to the kinetochores in a CBF3-dependent manner (2). Thus, CBF3 is a fundamental kinetochore complex, and its mechanism of assembly is of great interest.We have previously found that Sgt1 and Skp1 activate Ctf13; thus, they are required for assembly of the CBF3 complex (21). The molecular chaperone Hsp90 is also required to form the active Ctf13-Skp1 complex (22). Sgt1 has two highly conserved motifs that are required for protein-protein interaction: the tetratricopeptide repeat (21) and the CHORD protein and Sgt1-specific motif. We and others have found that both domains are important for the interaction of Sgt1 with Hsp90 (23–26), which is required for assembly of the core kinetochore complex. This interaction is an initial step in kinetochore activation (24, 26, 27), which is conserved between yeast and humans (28, 29).We have recently shown that Sgt1 dimerization is important for Sgt1-Skp1 binding and therefore for kinetochore assembly (30). In this study, we have found that protein kinase CK2 phosphorylates Sgt1 at Ser³⁶¹, and this phosphorylation inhibits Sgt1 dimerization. Therefore, CK2 appears to regulate kinetochore assembly negatively in budding yeast.     

Analysis of Free Energy Signals Arising from Nucleotide Hybridization Between rRNA and mRNA Sequences during Translation in Eubacteria

A decoding algorithm is tested that mechanistically models the progressive alignments that arise as the mRNA moves past the rRNA tail during translation elongation. Each of these alignments provides an opportunity for hybridization between the single-stranded, -terminal nucleotides of the 16S rRNA and the spatially accessible window of mRNA sequence, from which a free energy value can be calculated. Using this algorithm we show that a periodic, energetic pattern of frequency 1/3 is revealed. This periodic signal exists in the majority of coding regions of eubacterial genes, but not in the non-coding regions encoding the 16S and 23S rRNAs. Signal analysis reveals that the population of coding regions of each bacterial species has a mean phase that is correlated in a statistically significant way with species () content. These results suggest that the periodic signal could function as a synchronization signal for the maintenance of reading frame and that codon usage provides a mechanism for manipulation of signal phase.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]