Variable slope normalization of reverse phase protein arrays

The Proteomics of T-Cell and Early T-Cell Precursor (ETP) Acute Lymphocytic Leukemia: Prognostic Patterns in Adult and Pediatric-ETP ALL

BACKGROUND

The 5-year overall survival (OS) rates of T-cell lymphocytic leukemia (T-ALL) are better for children (>90%) compared to adults (~57%). The early T-cell precursor (ETP) T-ALL subtype is prognostically unfavorable in adults, but less significant in pediatric T-ALL, and the diagnosis and prognosis of "near"-ETP is controversial. We compared protein and RNA expression patterns in pediatric and adult T-ALL to identify prognostic subgroups, and to further characterize ETP and near-ETP T-ALL in both age groups.

METHODS

Protein expression was assessed using RPPA methodology for 321 target proteins in 361 T-ALL patient samples from 292 pediatrics and 69 adults, including 103 ETP-ALL. RNA-sequencing was performed on 81 pediatric T-ALL samples.

RESULTS

We identified recurrent protein expression patterns that classified patients into ten protein expression signatures using the "MetaGalaxy" analysis. In adults, Cox regression analysis identified two risk-groups associated with OS (p = 0.0002) and complete remission duration (p < 0.001). Cluster analysis of adults and pediatric-ETP patients identified three ETP-clusters strongly associated with age. Pediatric ETP-patients with a pediatric-dominant expression profile were associated with a shorter OS (p = 0.04) and event-free survival (p = 0.05) compared to pediatric ETP-patients with an ETP expression profile that was also identified in adults.

CONCLUSION

Our study demonstrates that proteomics are predictive of outcome in adult T-ALL and that we can identify a small subset of pediatric ETP with an inferior outcome. The observation that there are age-specific patterns supports the idea that the origin of T-ALL in most pediatric and adult patients is different, while overlapping patterns suggests that there are some with a common pathophysiology. Proteomics could enhance risk stratification in both pediatric and adults with T-ALL.

Valosin-containing protein (VCP/p97) is prognostically unfavorable in pediatric AML, and negatively correlates with unfolded protein response proteins IRE1 and GRP78: A report from the Children's Oncology Group

PURPOSE

The endoplasmic reticulum (ER) is the major site of protein synthesis and folding in the cell. ER-associated degradation (ERAD) and unfolded protein response (UPR) are the main mechanisms of ER-mediated cell stress adaptation. Targeting the cell stress response is a promising therapeutic approach in acute myeloid leukemia (AML).

EXPERIMENTAL DESIGN

Protein expression levels of valosin-containing protein (VCP), a chief element of ERAD, were measured in peripheral blood samples from in 483 pediatric AML patients using reverse phase protein array methodology. Patients participated in the Children's Oncology Group AAML1031 phase 3 clinical trial that randomized patients to standard chemotherapy (cytarabine (Ara-C), daunorubicin, and etoposide [ADE]) versus ADE plus bortezomib (ADE+BTZ).

RESULTS

Low-VCP expression was significantly associated with favorable 5-year overall survival (OS) rate compared to middle-high-VCP expression (81% versus 63%, p < 0.001), independent of additional bortezomib treatment. Multivariable Cox regression analysis identified VCP as independent predictor of clinical outcome. UPR proteins IRE1 and GRP78 had significant negative correlation with VCP. Five-year OS in patients characterized by low-VCP, moderately high-IRE1 and high-GRP78 improved after treatment with ADE+BTZ versus ADE (66% versus 88%, p = 0.026).

CONCLUSION AND CLINICAL RELEVANCE

Our findings suggest the potential of the protein VCP as biomarker in prognostication prediction in pediatric AML.

Tissue-Specific Downregulation of Fatty Acid Synthase Suppresses Intestinal Adenoma Formation via Coordinated Reprograming of Transcriptome and Metabolism in the Mouse Model of Apc-Driven Colorectal Cancer

Altered lipid metabolism is a potential target for therapeutic intervention in cancer. Overexpression of Fatty Acid Synthase (FASN) correlates with poor prognosis in colorectal cancer (CRC). While multiple studies show that upregulation of lipogenesis is critically important for CRC progression, the contribution of FASN to CRC initiation is poorly understood. We utilize a C57BL/6-Apc/Villin-Cre mouse model with knockout of FASN in intestinal epithelial cells to show that the heterozygous deletion of FASN increases mouse survival and decreases the number of intestinal adenomas. Using RNA-Seq and gene set enrichment analysis, we demonstrate that a decrease in FASN expression is associated with inhibition of pathways involved in cellular proliferation, energy production, and CRC progression. Metabolic and reverse phase protein array analyses demonstrate consistent changes in alteration of metabolic pathways involved in both anabolism and energy production. Downregulation of FASN expression reduces the levels of metabolites within glycolysis and tricarboxylic acid cycle with the most significant reduction in the level of citrate, a master metabolite, which enhances ATP production and fuels anabolic pathways. In summary, we demonstrate the critical importance of FASN during CRC initiation. These findings suggest that targeting FASN is a potential therapeutic approach for early stages of CRC or as a preventive strategy for this disease.

The metabolic enzyme hexokinase 2 localizes to the nucleus in AML and normal haematopoietic stem and progenitor cells to maintain stemness

Mitochondrial metabolites regulate leukaemic and normal stem cells by affecting epigenetic marks. How mitochondrial enzymes localize to the nucleus to control stem cell function is less understood. We discovered that the mitochondrial metabolic enzyme hexokinase 2 (HK2) localizes to the nucleus in leukaemic and normal haematopoietic stem cells. Overexpression of nuclear HK2 increases leukaemic stem cell properties and decreases differentiation, whereas selective nuclear HK2 knockdown promotes differentiation and decreases stem cell function. Nuclear HK2 localization is phosphorylation-dependent, requires active import and export, and regulates differentiation independently of its enzymatic activity. HK2 interacts with nuclear proteins regulating chromatin openness, increasing chromatin accessibilities at leukaemic stem cell-positive signature and DNA-repair sites. Nuclear HK2 overexpression decreases double-strand breaks and confers chemoresistance, which may contribute to the mechanism by which leukaemic stem cells resist DNA-damaging agents. Thus, we describe a non-canonical mechanism by which mitochondrial enzymes influence stem cell function independently of their metabolic function.

Thomas, Egan et al. report that hexokinase 2 localizes to the nucleus of leukaemic and normal haematopoietic cells to maintain stemness by interacting with nuclear proteins and modulating chromatin accessibility independently of its kinase activity.

Adaptation and Changes in Actin Dynamics and Cell Motility as Early Responses of Cultured Mammalian Cells to Altered Gravitational Vector

Cultured mammalian cells have been shown to respond to microgravity (μG), but the molecular mechanism is still unknown. The study we report here is focused on molecular and cellular events that occur within a short period of time, which may be related to gravity sensing by cells. Our assumption is that the gravity-sensing mechanism is activated as soon as cells are exposed to any new gravitational environment. To study the molecular events, we exposed cells to simulated μG (SμG) for 15 min, 30 min, 1 h, 2 h, 4 h, and 8 h using a three-dimensional clinostat and made cell lysates, which were then analyzed by reverse phase protein arrays (RPPAs) using a panel of 453 different antibodies. By comparing the RPPA data from cells cultured at 1G with those of cells under SμG, we identified a total of 35 proteomic changes in the SμG samples and found that 20 of these changes took place, mostly transiently, within 30 min. In the 4 h and 8 h samples, there were only two RPPA changes, suggesting that the physiology of these cells is practically indistinguishable from that of cells cultured at 1 G. Among the proteins involved in the early proteomic changes were those that regulate cell motility and cytoskeletal organization. To see whether changes in gravitational environment indeed activate cell motility, we flipped the culture dish upside down (directional change in gravity vector) and studied cell migration and actin cytoskeletal organization. We found that compared with cells grown right-side up, upside-down cells transiently lost stress fibers and rapidly developed lamellipodia, which was supported by increased activity of Ras-related C3 botulinum toxin substrate 1 (Rac1). The upside-down cells also increased their migratory activity. It is possible that these early molecular and cellular events play roles in gravity sensing by mammalian cells. Our study also indicated that these early responses are transient, suggesting that cells appear to adapt physiologically to a new gravitational environment.

Characterization of anticancer drug resistance by reverse-phase protein array: new targets and strategies

INTRODUCTION

Drug resistance is the main barrier to achieving cancer cures with medical therapy. Cancer drug resistance occurs, in part, due to adaptation of the tumor and microenvironment to therapeutic stress at a proteomic level. Reverse-phase protein arrays (RPPA) are well suited to proteomic analysis of drug resistance due to high sample throughput, sensitive detection of phosphoproteins, and validation for a large number of critical cellular pathways.

AREAS COVERED

This review summarizes contributions of RPPA to understanding and combating drug resistance. In particular, contributions of RPPA to understanding resistance to PARP inhibitors, BRAF inhibitors, immune checkpoint inhibitors, and breast cancer investigational therapies are discussed. Articles reviewed were identified by MEDLINE, Scopus, and Cochrane search for keywords 'proteomics,' 'reverse-phase protein array,' 'drug resistance,' 'PARP inhibitor,' 'BRAF inhibitor,' 'immune checkpoint inhibitor,' and 'I-SPY' spanning October 1, 1960 - October 1, 2021.

EXPERT OPINION

Precision oncology has thus far failed to convert the armament of targeted therapies into durable responses for most patients, highlighting that genetic sequencing alone is insufficient to guide therapy selection and overcome drug resistance. Combined genomic and proteomic analyses paired with creative drug combinations and dosing strategies hold promise for maturing precision oncology into an era of improved patient outcomes.

Proteomic profiling based classification of CLL provides prognostication for modern therapy and identifies novel therapeutic targets

Protein expression for 384 total and post-translationally modified proteins was assessed in 871 CLL and MSBL patients and was integrated with clinical data to identify strategies for improving diagnostics and therapy, making this the largest CLL proteomics study to date. Proteomics identified six recurrent signatures that were highly prognostic of survival and time to first or second treatment at three levels: individual proteins, when grouped into 40 functionally related groups (PFGs), and systemically in signatures (SGs). A novel SG characterized by hairy cell leukemia like proteomics but poor therapy response was discovered. SG membership superseded other prognostic factors (Rai Staging, IGHV Status) and were prognostic for response to modern (BTK inhibition) and older CLL therapies. SGs and PFGs membership provided novel drug targets and defined optimal candidates for Watch and Wait vs. early intervention. Collectively proteomics demonstrates promise for improving classification, therapeutic strategy selection, and identifying novel therapeutic targets.

Bortezomib is significantly beneficial for de novo pediatric AML patients with low phosphorylation of the NF-κB subunit RelA

PURPOSE

The addition of the proteasome inhibitor (PI) bortezomib to standard chemotherapy (ADE: cytarabine [Ara-C], daunorubicin, and etoposide) did not improve overall outcome of pediatric AML patients in the Children's Oncology Group AAML1031 phase 3 randomized clinical trial (AAML1031) . Bortezomib prevents protein degradation, including RelA via the intracellular NF-kB pathway. In this study, we hypothesized that subgroups of pediatric AML patients benefitting from standard therapy plus bortezomib (ADEB) could be identified based on pre-treatment RelA expression and phosphorylation status.

EXPERIMENTAL DESIGN

RelA-total and phosphorylation at serine 536 (RelA-pSer⁵³⁶ ) were measured in 483 patient samples using reverse phase protein array technology.

RESULTS

In ADEB-treated patients, low-RelA-pSer⁵³⁶ was favorably prognostic when compared to high-RelA-pSer⁵³⁶ (3-yr overall survival (OS): 81% vs. 68%, p = 0.032; relapse risk (RR): 30% vs. 49%, p = 0.004). Among low-RelA-pSer⁵³⁶ patients, RR significantly decreased with ADEB compared to ADE (RR: 30% vs. 44%, p = 0.035). Correlation between RelA-pSer⁵³⁶ and 295 other assayed proteins identified a strong correlation with HSF1-pSer³²⁶ , another protein previously identified as modifying ADEB response. The combination of low-RelA-pSer⁵³⁶ and low-HSF1-pSer³²⁶ was a significant predictor of ADEB response (3-yr OS: 86% vs. 67%, p = 0.013).

CONCLUSION AND CLINICAL RELEVANCE

Bortezomib may improve clinical outcome in a subgroup of AML patients identified by low-RelA-pSer⁵³⁶ and low-HSF1-pSer³²⁶ .

Reverse phase protein arrays in acute leukemia: investigative and methodological challenges

INTRODUCTION

Acute leukemia results from a series of mutational events that alter cell growth and proliferation. Mutations result in protein changes that orchestrate growth alterations characteristic of leukemia. Proteomics is a methodology appropriate for study of protein changes found in leukemia. The high-throughput reverse phase protein array (RPPA) technology is particularly well-suited for the assessment of protein changes in samples derived from clinical trials.

AREAS COVERED

This review discusses the technical, methodological, and analytical issues related to the successful development of acute leukemia RPPAs.

EXPERT COMMENTARY

To obtain representative protein sample lysates, samples should be prepared from freshly collected blood or bone marrow material. Variables such as sample shipment, transit time, and holding temperature only have minimal effects on protein expression. CellSave preservation tubes are preferred for cells collected after exposure to chemotherapy, and incorporation of standardized guidelines for antibody validation is recommended. A more systematic biological approach to analyze protein expression is desired, searching for recurrent patterns of protein expression that allow classification of patients into risk groups, or groups of patients that may be treated similarly. Comparing RPPA protein analysis between cell lines and primary samples shows that cell lines are not representative of patient proteomic patterns.

Heat shock factor 1 (HSF1-pSer326) predicts response to bortezomib-containing chemotherapy in pediatric AML: a COG report

Bortezomib (BTZ) was recently evaluated in a randomized phase 3 clinical trial by the Children's Oncology Group (COG) that compared standard chemotherapy (cytarabine, daunorubicin, and etoposide [ADE]) vs standard therapy with BTZ (ADEB) for de novo pediatric acute myeloid leukemia (AML). Although the study concluded that BTZ did not improve outcome overall, we examined patient subgroups benefiting from BTZ-containing chemotherapy using proteomic analyses. The proteasome inhibitor BTZ disrupts protein homeostasis and activates cytoprotective heat shock responses. Total heat shock factor 1 (HSF1) and phosphorylated HSF1 (HSF1-pSer326) were measured in leukemic cells from 483 pediatric patients using reverse phase protein arrays. HSF1-pSer326 phosphorylation was significantly lower in pediatric AML compared with CD34+ nonmalignant cells. We identified a strong correlation between HSF1-pSer326 expression and BTZ sensitivity. BTZ significantly improved outcome of patients with low-HSF1-pSer326 with a 5-year event-free survival of 44% (ADE) vs 67% for low-HSF1-pSer326 treated with ADEB (P = .019). To determine the effect of HSF1 expression on BTZ potency in vitro, cell viability with HSF1 gene variants that mimicked phosphorylated (S326A) and nonphosphorylated (S326E) HSF1-pSer326 were examined. Those with increased HSF1 phosphorylation showed clear resistance to BTZ vs those with wild-type or reduced HSF1-phosphorylation. We hypothesize that HSF1-pSer326 expression could identify patients who benefit from BTZ-containing chemotherapy.

Integrative analysis of multi-platform reverse-phase protein array data for the pharmacodynamic assessment of response to targeted therapies

Reverse-phase protein array (RPPA) technology uses panels of high-specificity antibodies to measure proteins and protein post-translational modifications in cells and tissues. The approach offers sensitive and precise quantification of large numbers of samples and has thus found applications in the analysis of clinical and pre-clinical samples. For effective integration into drug development and clinical practice, robust assays with consistent results are essential. Leveraging a collaborative RPPA model, we set out to assess the variability between three different RPPA platforms using distinct instrument set-ups and workflows. Employing multiple RPPA-based approaches operated across distinct laboratories, we characterised a range of human breast cancer cells and their protein-level responses to two clinically relevant cancer drugs. We integrated multi-platform RPPA data and used unsupervised learning to identify protein expression and phosphorylation signatures that were not dependent on RPPA platform and analysis workflow. Our findings indicate that proteomic analyses of cancer cell lines using different RPPA platforms can identify concordant profiles of response to pharmacological inhibition, including when using different antibodies to measure the same target antigens. These results highlight the robustness and the reproducibility of RPPA technology and its capacity to identify protein markers of disease or response to therapy.

The effects of sample handling on proteomics assessed by reverse phase protein arrays (RPPA): Functional proteomic profiling in leukemia

Reverse phase protein arrays (RPPA) can assess protein expression and activation states in large numbers of samples (n > 1000) and evidence suggests feasibility in the setting of multi-institution clinical trials. Despite evidence in solid tumors, little is known about protein stability in leukemia. Proteins collected from leukemia cells in blood and bone marrow biopsies must be sufficiently stable for analysis. Using 58 leukemia samples, we initially assessed protein/phospho-protein integrity for the following preanalytical variables: 1) shipping vs local processing, 2) temperature (4 °C vs ambient temperature), 3) collection tube type (heparin vs Cell Save (CS) preservation tubes), 4) treatment effect (pre- vs post-chemotherapy) and 5) transit time. Next, we assessed 1515 samples from the Children's Oncology Group Phase 3 AML clinical trial (AAML1031, NCT01371981) for the effects of transit time and tube type. Protein expression from shipped blood samples was stable if processed in ≤72 h. While protein expression in pre-chemotherapy samples was stable in both heparin and CS tubes, post-chemotherapy samples were stable in only CS tubes. RPPA protein extremes is a successful quality control measure to identify and exclude poor quality samples. These data demonstrate that a majority of shipped proteins can be accurately assessed using RPPA. SIGNIFICANCE: RPPA can assess protein abundance and activation states in large numbers of samples using small amounts of material, making this method ideal for use in multi-institution clinical trials. However, there is little known about the effect of preanalytical handling variables on protein stability and the integrity of protein concentrations after sample collection and shipping. In this study, we used RPPA to assess preanalytical variables that could potentially affect protein concentrations. We found that the preanalytical variables of shipping, transit time, and temperature had minimal effects on RPPA protein concentration distributions in peripheral blood and bone marrow, demonstrating that these preanalytical variables could be successfully managed in a multi-site clinical trial setting.

Towards understanding the cell surface phenotype, metabolic properties and immune functions of resident macrophages of the peritoneal cavity and splenic red pulp using high resolution quantitative proteomics

Background:Resident macrophages (Mϕs) are distributed throughout the body and are important for maintaining tissue homeostasis and for defence against infections. Tissue Mϕs are highly adapted to their microenvironment and thought to mediate tissue-specific functions involving metabolism and immune defence that are not fully elucidated. Methods:We have used high resolution quantitative proteomics to gain insights into the functions of two types of resident tissue Mϕs: peritoneal cavity Mϕs and splenic red pulp Mϕs.  The cellular expression levels of many proteins were validated by flow cytometry and were consistently in agreement with the proteomics data.Results:Peritoneal and splenic red pulp macrophages displayed major differences in cell surface phenotype reflecting their adaptation to different tissue microenvironments and tissue-specific functions. Peritoneal Mϕs were shown to be enriched in a number of key enzymes and metabolic pathways normally associated with the liver, such as metabolism of fructose, detoxification, nitrogen homeostasis and the urea cycle. Supporting these observations, we show that peritoneal Mϕs are able to utilise glutamine and glutamate which are rich in peritoneum for urea generation.  In comparison, splenic red pulp Mϕs were enriched in proteins important for adaptive immunity such as antigen presenting MHC molecules, in addition to proteins required for erythrocyte homeostasis and iron turnover. We also show that these tissue Mϕs may utilise carbon and nitrogen substrates for different metabolic fates to support distinct tissue-specific roles.Conclusions:This study provides new insights into the functions of tissue Mϕs in immunity and homeostasis.  The comprehensive proteomics data sets are a valuable resource for biologists and immunologists.

RSK inhibitor BI-D1870 inhibits acute myeloid leukemia cell proliferation by targeting mitotic exit

The 90 kDa Ribosomal S6 Kinase (RSK) drives cell proliferation and survival in cancers, although its oncogenic mechanism has not been well characterized. Phosphorylated level of RSK (T573) was increased in acute myeloid leukemia (AML) patients and associated with poor survival. To examine the role of RSK in AML, we analyzed apoptosis and the cell cycle profile following treatment with BI-D1870, a potent inhibitor of RSK. BI-D1870 treatment increased the G2/M population and induced apoptosis in AML cell lines and patient AML cells. Characterization of mitotic phases showed that the metaphase/anaphase transition was significantly inhibited by BI-D1870. BI-D1870 treatment impeded the association of activator CDC20 with APC/C, but increased binding of inhibitor MAD2 to CDC20, preventing mitotic exit. Moreover, the inactivation of spindle assembly checkpoint or MAD2 knockdown released cells from BI-D1870-induced metaphase arrest. Therefore, we investigated whether BI-D1870 potentiates the anti-leukemic activity of vincristine by targeting mitotic exit. Combination treatment of BI-D1870 and vincristine synergistically increased mitotic arrest and apoptosis in acute leukemia cells. These data show that BI-D1870 induces apoptosis of AML cells alone and in combination with vincristine through blocking mitotic exit, providing a novel approach to overcoming vincristine resistance in AML cells.

Evaluation of antibody responses to the early transcribed membrane protein family in Plasmodium vivax

Background

Malaria parasites form intracellular membranes that separate the parasite from the internal space of erythrocytes, and membrane proteins from the parasites are exported to the host via the membrane. In our previous study, Plasmodium vivax early transcribed membrane protein (PvETRAMP) 11.2, an intracellular membrane protein that is highly expressed in blood-stage parasites, was characterized as a highly immunogenic protein in P. vivax malaria patients. However, the other PvETRAMP family proteins have not yet been investigated. In this study, PvETRAMPs were expressed and evaluated to determine their immunological profiles.

Methods

The protein structure and amino acid alignment were carried out using bioinformatics analysis software. A total of six PvETRAMP family proteins were successfully expressed and purified using a wheat germ cell free protein expression system and the purified proteins were used for protein microarray and immunization of mice. The localization of the protein was determined with serum against PvETRAMP4. IgG subclasses were assessed from the immunized mice.

Results

In silico analysis showed that P. vivax exhibits nine genes encoding the ETRAMP family. The ETRAMP family proteins are relatively small molecules with conserved structural features. A total of 6 recombinant ETRAMP proteins were successfully expressed and purified. The serum positivity of P. vivax malaria patients and healthy individuals was evaluated using a protein microarray method. Among the PvETRAMPs, ETRAMP4 showed the highest positivity rate of 62%, comparable to that of PvETRAMP11.2, which served as the positive control, and a typical export pattern of PvETRAMP4 was observed in the P. vivax parasite. The assessment of IgG subclasses in mice immunized with PvETRAMP4 showed high levels of IgG1 and IgG2b. PvETRAMP family proteins were identified and characterized as serological markers.

Conclusions

The relatively high antibody responses to PvETRAMP4 as well as the specific IgG subclasses observed in immunized mice suggest that the ETRAMP family is immunogenic in pathogens and can be used as a protein marker and for vaccine development.

Analytical Platforms 1: Use of Cultured Cells and Fluorescent Read-Out Coupled to NormaCurve Normalization in RPPA

The analytic platform described in this chapter uses proteins extracted from cultured cells as an infinite source of material to set up, validate, and quality control an RPPA platform. Readout of the arrays uses near-infrared fluorescence labeling and data normalization is performed using the bioinformatics package NormaCurve.In the first part, we will describe the advantages, drawbacks, and different applications of cell line material for RPPA. In the second part, we will describe how the staining protocol, the method of readout, and the normalization method applied afterward are interconnected and should be considered together. Finally, we will describe the NormaCurve package, which is freely available, and its requirements for implementation.Four protocols are provided in this chapter: (1) Protein lysis of cell lines using a homemade Laemmli buffer, (2) RPPA staining for fluorescent readout including a signal amplification step, (3) total protein staining in the visible spectrum for normalization purposes, and (4) total protein staining in the near-infrared spectrum for normalization purposes.

Proteomic Profiling of Acute Promyelocytic Leukemia Identifies Two Protein Signatures Associated with Relapse

PURPOSE

Acute promyelocytic leukemia (APL) is the most prognostically favorable subtype of Acute myeloid leukemia (AML). Defining the features that allow identification of APL patients likely to relapse after therapy remains challenging.

EXPERIMENTAL DESIGN

Proteomic profiling is performed on 20 newly diagnosed APL, 205 non-APL AML, and 10 normal CD34+ samples using Reverse Phase Protein Arrays probed with 230 antibodies.

RESULTS

Comparison between APL and non-APL AML samples identifies 8.3% of the proteins to be differentially expressed. Proteins higher expressed in APL are involved in the pro-apoptotic pathways or are linked to higher proliferation. The "MetaGalaxy" approach that considers proteins in relation to other assayed proteins stratifies the APL patients into two protein signatures. All of the relapse patients (n = 4/4) are in protein signature 2 (S2). Comparison of proteins between the signatures shows significant differences in relative expression for 38 proteins. Protein expression summary plots suggest less translational activity in combination with a less proliferative character for S2 compared to signature 1.

CONCLUSIONS AND CLINICAL RELEVANCE

This study provides a potential proteomic-based classification of APL patients that may be useful for risk stratification and therapeutic guidance. Validation in a larger independent cohort is required.

A quantitative analysis of heterogeneities and hallmarks in acute myelogenous leukaemia

Acute myelogenous leukaemia (AML) is associated with risk factors that are largely unknown and with a heterogeneous response to treatment. Here, we provide a comprehensive quantitative understanding of AML proteomic heterogeneities and hallmarks by using the AML proteome atlas, a proteomics database that we have newly derived from MetaGalaxy analyses, for the proteomic profiling of 205 AML patients and 111 leukaemia cell lines. The analysis of the dataset revealed 154 functional patterns based on common molecular pathways, 11 constellations of correlated functional patterns, and 13 signatures that stratify the patients’ outcomes. We find limited overlap between proteomics data and both cytogenetics and genetic mutations, and also that leukaemia cell lines show limited proteomic similarities with cells from AML patients, suggesting that a deeper focus on patient-derived samples is needed to gain disease-relevant insights. The AML proteome atlas provides a knowledge base for proteomic patterns in AML, a guide to leukaemia cell-line selection, and a broadly applicable computational approach for quantifying the heterogeneities of protein expression and proteomic hallmarks in AML.

Analytical Platforms 3: Processing Samples via the RPPA Pipeline to Generate Large-Scale Data for Clinical Studies

Reverse phase protein array (RPPA) is a functional proteomics technology amenable to moderately high throughputs of samples and antibodies. The University of Texas MD Anderson Cancer Center RPPA Core Facility has implemented various processes and techniques to maximize RPPA throughput; key among them are maximizing array configuration and relying on database management and automation. One major tool used by the RPPA Core is a semi-automated RPPA process management system referred to as the RPPA Pipeline. The RPPA Pipeline, developed with the aid of MD Avnderson's Department of Bioinformatics and Computational Biology and InSilico Solutions, has streamlined sample and antibody tracking as well as advanced quality control measures of various RPPA processes. This chapter covers RPPA Core processes associated with the RPPA Pipeline workflow from sample receipt to sample printing to slide staining and RPPA report generation that enables the RPPA Core to process at least 13,000 samples per year with approximately 450 individual RPPA-quality antibodies. Additionally, this chapter will cover results of large-scale clinical sample processing, including The Cancer Genome Atlas Project and The Cancer Proteome Atlas.

Reproducibility and Crossplatform Validation of Reverse-Phase Protein Array Data

Reverse-phase protein array (RPPA) technology is a high-throughput antibody- and microarray-based approach for the rapid profiling of levels of proteins and protein posttranslational modifications in biological specimens. The technology consumes small amounts of samples, can sensitively detect low-abundance proteins and posttranslational modifications, enables measurements of multiple signaling pathways in parallel, has the capacity to analyze large sample numbers, and offers robust interexperimental reproducibility. These features of RPPA experiments have motivated and enabled the use of RPPA technology in various biomedical, translational, and clinical applications, including the delineation of molecular mechanisms of disease, profiling of druggable signaling pathway activation, and search for new prognostic markers. Owing to the complexity of many of these applications, such as developing multiplex protein assays for diagnostic laboratories or integrating posttranslational modification-level data using large-scale proteogenomic approaches, robust and well-validated data are essential. There are many distinct components of an RPPA workflow, and numerous possible technical setups and analysis parameter options exist. The differences between RPPA platform setups around the world offer opportunities to assess and minimize interplatform variation. Crossplatform validation may also aid in the evaluation of robust, platform-independent protein markers of disease and response to therapy.

Mycoplasma contamination of leukemic cell lines alters protein expression determined by reverse phase protein arrays

Mycoplasma contamination is a major problem in cell culturing, potentially altering the results of cell line-based experiments in largely uncharacterized ways. To define the consequences of mycoplasma infection at the level of protein expression we utilized the reverse phase protein array technology to analyze the expression of 235 proteins in mycoplasma infected, uninfected post treatment, and never-infected leukemic cell lines. Overall, protein profiles of cultured cells remained relatively stable after mycoplasma infection. However, paired comparisons for individual proteins identified that 18.7% of the proteins significantly changed between the infected and the never-infected cell line samples, and that 14.0% of the proteins significantly altered between the infected and the post treatment samples. Six percent of the proteins were affected in the post treatment samples compared to the never-infected samples, and 7.2% compared to treated cells that had never had mycoplasma infection before. Proteins that were significantly altered in the infected cells were enriched for apoptotic signaling processes and auto-phosphorylation, suggesting an increased cellular stress and a decreased growth rate. In conclusion, this study shows that mycoplasma infection of leukemic cell lines alters the proteins expression levels, potentially confounding experimental results. This reinforces the need for regular testing of mycoplasma.

RPPAware: A software suite to preprocess, analyze and visualize reverse phase protein array data

Reverse Phase Protein Arrays (RPPA) is a high-throughput technology used to profile levels of protein expression. Handling the large datasets generated by RPPA can be facilitated by appropriate software tools. Here, we describe RPPAware, a free and intuitive software suite that was developed specifically for analysis and visualization of RPPA data. RPPAware is a portable tool that requires no installation and was built using Java. Many modules of the tool invoke R to utilize the statistical features. To demonstrate the utility of RPPAware, data generated from screening brain regions of a mouse model of Down syndrome with 62 antibodies were used as a case study. The ease of use and efficiency of RPPAware can accelerate data analysis to facilitate biological discovery. RPPAware 1.0 is freely available under GNU General Public License from the project website at http://downsyndrome.ucdenver.edu/iddrc/rppaware/home.htm along with a full documentation of the tool.

Recurrent Patterns of Protein Expression Signatures in Pediatric Acute Lymphoblastic Leukemia: Recognition and Therapeutic Guidance

Pediatric acute lymphoblastic leukemia (ALL) is the most common pediatric malignancy, and the second leading cause of pediatric cancer-related death in developed countries. While the cure rate for newly diagnosed ALL is excellent, the genetic heterogeneity and chemoresistance of leukemia cells at relapse makes individualized curative treatment plans difficult. We hypothesize that genetic events would coalesce into a finite number of protein signatures that could guide the design of individualized therapy. Custom reverse-phase protein arrays were produced from pediatric ALL (n = 73) and normal CD34⁺ (n = 10) samples with 194 validated antibodies. Proteins were allocated into 31 protein functional groups (PFG) to analyze them in the context of other proteins, based on known associations from the literature. The optimal number of protein clusters was determined for each PFG. Protein networks showed distinct transition states, revealing "normal-like" and "leukemia-specific" protein patterns. Block clustering identified strong correlation between various protein clusters that formed 10 protein constellations. Patients that expressed similar recurrent combinations of constellations comprised 7 distinct signatures, correlating with risk stratification, cytogenetics, and laboratory features. Most constellations and signatures were specific for T-cell ALL or pre-B-cell ALL; however, some constellations showed significant overlap. Several signatures were associated with Hispanic ethnicity, suggesting that ethnic pathophysiologic differences likely exist. In addition, some constellations were enriched for "normal-like" protein clusters, whereas others had exclusively "leukemia-specific" patterns.Implications: Recognition of proteins that have universally altered expression, together with proteins that are specific for a given signature, suggests targets for directed combinatorial inhibition or replacement to enable personalized therapy. Mol Cancer Res; 16(8); 1263-74. ©2018 AACRSee related article by Hoff et al., p. 1275.

Recognition of Recurrent Protein Expression Patterns in Pediatric Acute Myeloid Leukemia Identified New Therapeutic Targets

Heterogeneity in the genetic landscape of pediatric acute myeloid leukemia (AML) makes personalized medicine challenging. As genetic events are mediated by the expression and function of proteins, recognition of recurrent protein patterns could enable classification of pediatric AML patients and could reveal crucial protein dependencies. This could help to rationally select combinations of therapeutic targets. To determine whether protein expression levels could be clustered into functionally relevant groups, custom reverse-phase protein arrays were performed on pediatric AML (n = 95) and CD34⁺ normal bone marrow (n = 10) clinical specimens using 194 validated antibodies. To analyze proteins in the context of other proteins, all proteins were assembled into 31 protein functional groups (PFG). For each PFG, an optimal number of protein clusters was defined that represented distinct transition states. Block clustering analysis revealed strong correlations between various protein clusters and identified the existence of 12 protein constellations stratifying patients into 8 protein signatures. Signatures were correlated with therapeutic outcome, as well as certain laboratory and demographic characteristics. Comparison of acute lymphoblastic leukemia specimens from the same array and AML pediatric patient specimens demonstrated disease-specific signatures, but also identified the existence of shared constellations, suggesting joint protein deregulation between the diseases.Implication: Recognition of altered proteins in particular signatures suggests rational combinations of targets that could facilitate stratified targeted therapy. Mol Cancer Res; 16(8); 1275-86. ©2018 AACRSee related article by Hoff et al., p. 1263.

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smoking and/or human papillomavirus (HPV). SCCs harbor 3q, 5p, and other recurrent chromosomal copy-number alterations (CNAs), DNA mutations, and/or aberrant methylation of genes and microRNAs, which are correlated with the expression of multi-gene programs linked to squamous cell stemness, epithelial-to-mesenchymal differentiation, growth, genomic integrity, oxidative damage, death, and inflammation. Low-CNA SCCs tended to be HPV(+) and display hypermethylation with repression of TET1 demethylase and FANCF, previously linked to predisposition to SCC, or harbor mutations affecting CASP8, RAS-MAPK pathways, chromatin modifiers, and immunoregulatory molecules. We uncovered hypomethylation of the alternative promoter that drives expression of the ΔNp63 oncogene and embedded miR944. Co-expression of immune checkpoint, T-regulatory, and Myeloid suppressor cells signatures may explain reduced efficacy of immune therapy. These findings support possibilities for molecular classification and therapeutic approaches.

Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer

We report a comprehensive analysis of 412 muscle-invasive bladder cancers characterized by multiple TCGA analytical platforms. Fifty-eight genes were significantly mutated, and the overall mutational load was associated with APOBEC-signature mutagenesis. Clustering by mutation signature identified a high-mutation subset with 75% 5-year survival. mRNA expression clustering refined prior clustering analyses and identified a poor-survival "neuronal" subtype in which the majority of tumors lacked small cell or neuroendocrine histology. Clustering by mRNA, long non-coding RNA (lncRNA), and miRNA expression converged to identify subsets with differential epithelial-mesenchymal transition status, carcinoma in situ scores, histologic features, and survival. Our analyses identified 5 expression subtypes that may stratify response to different treatments.

Statistical Contributions to Bioinformatics: Design, Modeling, Structure Learning, and Integration

The advent of high-throughput multi-platform genomics technologies providing whole-genome molecular summaries of biological samples has revolutionalized biomedical research. These technologiees yield highly structured big data, whose analysis poses significant quantitative challenges. The field of Bioinformatics has emerged to deal with these challenges, and is comprised of many quantitative and biological scientists working together to effectively process these data and extract the treasure trove of information they contain. Statisticians, with their deep understanding of variability and uncertainty quantification, play a key role in these efforts. In this article, we attempt to summarize some of the key contributions of statisticians to bioinformatics, focusing on four areas: (1) experimental design and reproducibility, (2) preprocessing and feature extraction, (3) unified modeling, and (4) structure learning and integration. In each of these areas, we highlight some key contributions and try to elucidate the key statistical principles underlying these methods and approaches. Our goals are to demonstrate major ways in which statisticians have contributed to bioinformatics, encourage statisticians to get involved early in methods development as new technologies emerge, and to stimulate future methodological work based on the statistical principles elucidated in this article and utilizing all availble information to uncover new biological insights.

Clustering and Network Analysis of Reverse Phase Protein Array Data

Molecular profiling of proteins and phosphoproteins using a reverse phase protein array (RPPA) platform, with a panel of target-specific antibodies, enables the parallel, quantitative proteomic analysis of many biological samples in a microarray format. Hence, RPPA analysis can generate a high volume of multidimensional data that must be effectively interrogated and interpreted. A range of computational techniques for data mining can be applied to detect and explore data structure and to form functional predictions from large datasets. Here, two approaches for the computational analysis of RPPA data are detailed: the identification of similar patterns of protein expression by hierarchical cluster analysis and the modeling of protein interactions and signaling relationships by network analysis. The protocols use freely available, cross-platform software, are easy to implement, and do not require any programming expertise. Serving as data-driven starting points for further in-depth analysis, validation, and biological experimentation, these and related bioinformatic approaches can accelerate the functional interpretation of RPPA data.

Characterization of protein expression levels with label-free detected reverse phase protein arrays

In reverse-phase protein arrays (RPPA), one immobilizes complex samples (e.g., cellular lysate, tissue lysate or serum etc.) on solid supports and performs parallel reactions of antibodies with immobilized protein targets from the complex samples. In this work, we describe a label-free detection of RPPA that enables quantification of RPPA data and thus facilitates comparison of studies performed on different samples and on different solid supports. We applied this detection platform to characterization of phosphoserine aminotransferase (PSAT) expression levels in Acanthamoeba lysates treated with artemether and the results were confirmed by Western blot studies.

Characterization of Pv92, a Novel Merozoite Surface Protein of Plasmodium vivax

The discovery and understanding of antigenic proteins are essential for development of a vaccine against malaria. In Plasmodium falciparum, Pf92 have been characterized as a merozoite surface protein, and this protein is expressed at the late schizont stage, but no study of Pv92, the orthologue of Pf92 in P. vivax, has been reported. Thus, the protein structure of Pv92 was analyzed, and the gene sequence was aligned with that of other Plasmodium spp. using bioinformatics tools. The recombinant Pv92 protein was expressed and purified using bacterial expression system and used for immunization of mice to gain the polyclonal antibody and for evaluation of antigenicity by protein array. Also, the antibody against Pv92 was used for subcellular analysis by immunofluorescence assay. The Pv92 protein has a signal peptide and a sexual stage s48/45 domain, and the cysteine residues at the N-terminal of Pv92 were completely conserved. The N-terminal of Pv92 was successfully expressed as soluble form using a bacterial expression system. The antibody raised against Pv92 recognized the parasites and completely merged with PvMSP1-19, indicating that Pv92 was localized on the merozoite surface. Evaluation of the human humoral immune response to Pv92 indicated moderate antigenicity, with 65% sensitivity and 95% specificity by protein array. Taken together, the merozoite surface localization and antigenicity of Pv92 implicate that it might be involved in attachment and invasion of a merozoite to a new host cell or immune evasion during invasion process.

A Crowdsourcing Approach to Developing and Assessing Prediction Algorithms for AML Prognosis

Acute Myeloid Leukemia (AML) is a fatal hematological cancer. The genetic abnormalities underlying AML are extremely heterogeneous among patients, making prognosis and treatment selection very difficult. While clinical proteomics data has the potential to improve prognosis accuracy, thus far, the quantitative means to do so have yet to be developed. Here we report the results and insights gained from the DREAM 9 Acute Myeloid Prediction Outcome Prediction Challenge (AML-OPC), a crowdsourcing effort designed to promote the development of quantitative methods for AML prognosis prediction. We identify the most accurate and robust models in predicting patient response to therapy, remission duration, and overall survival. We further investigate patient response to therapy, a clinically actionable prediction, and find that patients that are classified as resistant to therapy are harder to predict than responsive patients across the 31 models submitted to the challenge. The top two performing models, which held a high sensitivity to these patients, substantially utilized the proteomics data to make predictions. Using these models, we also identify which signaling proteins were useful in predicting patient therapeutic response.

Essential role for cyclic-AMP responsive element binding protein 1 (CREB) in the survival of acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) relapse remains a leading cause of cancer related death in children, therefore, new therapeutic options are needed. Recently, we showed that a peptide derived from Cyclic-AMP Responsive Element Binding Protein (CREB) was highly phosphorylated in pediatric leukemias.

In this study, we determined CREB phosphorylation and mRNA levels showing that CREB expression was significantly higher in ALL compared to normal bone marrow (phosphorylation: P < 0.0001, mRNA: P = 0.004). High CREB and phospho-CREB expression was correlated with a lower median overall survival in a cohort of 140 adult ALL patients. ShRNA mediated knockdown of CREB in ALL cell lines blocked leukemic cell growth by inducing cell cycle arrest and apoptosis. Gene expression array analysis showed downregulation of CREB target genes regulating cell proliferation and glucose metabolism and upregulation of apoptosis inducing genes. Similar to CREB knockdown, the CREB inhibitor KG-501 decreased leukemic cell viability and induced apoptosis in ALL cell lines, as well as primary T-ALL samples, with cases showing high phospho-CREB levels being more sensitive than those with lower phospho-CREB levels.

Together, these in vitro findings support an important role for CREB in the survival of ALL cells and identify this transcription factor as a potential target for treatment.

Novel technologies and emerging biomarkers for personalized cancer immunotherapy

The culmination of over a century's work to understand the role of the immune system in tumor control has led to the recent advances in cancer immunotherapies that have resulted in durable clinical responses in patients with a variety of malignancies. Cancer immunotherapies are rapidly changing traditional treatment paradigms and expanding the therapeutic landscape for cancer patients. However, despite the current success of these therapies, not all patients respond to immunotherapy and even those that do often experience toxicities. Thus, there is a growing need to identify predictive and prognostic biomarkers that enhance our understanding of the mechanisms underlying the complex interactions between the immune system and cancer. Therefore, the Society for Immunotherapy of Cancer (SITC) reconvened an Immune Biomarkers Task Force to review state of the art technologies, identify current hurdlers, and make recommendations for the field. As a product of this task force, Working Group 2 (WG2), consisting of international experts from academia and industry, assembled to identify and discuss promising technologies for biomarker discovery and validation. Thus, this WG2 consensus paper will focus on the current status of emerging biomarkers for immune checkpoint blockade therapy and discuss novel technologies as well as high dimensional data analysis platforms that will be pivotal for future biomarker research. In addition, this paper will include a brief overview of the current challenges with recommendations for future biomarker discovery.

Novel technologies and emerging biomarkers for personalized cancer immunotherapy

The culmination of over a century’s work to understand the role of the immune system in tumor control has led to the recent advances in cancer immunotherapies that have resulted in durable clinical responses in patients with a variety of malignancies. Cancer immunotherapies are rapidly changing traditional treatment paradigms and expanding the therapeutic landscape for cancer patients. However, despite the current success of these therapies, not all patients respond to immunotherapy and even those that do often experience toxicities. Thus, there is a growing need to identify predictive and prognostic biomarkers that enhance our understanding of the mechanisms underlying the complex interactions between the immune system and cancer. Therefore, the Society for Immunotherapy of Cancer (SITC) reconvened an Immune Biomarkers Task Force to review state of the art technologies, identify current hurdlers, and make recommendations for the field. As a product of this task force, Working Group 2 (WG2), consisting of international experts from academia and industry, assembled to identify and discuss promising technologies for biomarker discovery and validation. Thus, this WG2 consensus paper will focus on the current status of emerging biomarkers for immune checkpoint blockade therapy and discuss novel technologies as well as high dimensional data analysis platforms that will be pivotal for future biomarker research. In addition, this paper will include a brief overview of the current challenges with recommendations for future biomarker discovery.

Analysis of Reverse Phase Protein Array Data: From Experimental Design towards Targeted Biomarker Discovery

Mastering the systematic analysis of tumor tissues on a large scale has long been a technical challenge for proteomics. In 2001, reverse phase protein arrays (RPPA) were added to the repertoire of existing immunoassays, which, for the first time, allowed a profiling of minute amounts of tumor lysates even after microdissection. A characteristic feature of RPPA is its outstanding sample capacity permitting the analysis of thousands of samples in parallel as a routine task. Until today, the RPPA approach has matured to a robust and highly sensitive high-throughput platform, which is ideally suited for biomarker discovery. Concomitant with technical advancements, new bioinformatic tools were developed for data normalization and data analysis as outlined in detail in this review. Furthermore, biomarker signatures obtained by different RPPA screens were compared with another or with that obtained by other proteomic formats, if possible. Options for overcoming the downside of RPPA, which is the need to steadily validate new antibody batches, will be discussed. Finally, a debate on using RPPA to advance personalized medicine will conclude this article.

Studying Cellular Signal Transduction with OMIC Technologies

In the gulf between genotype and phenotype exists proteins and, in particular, protein signal transduction systems. These systems use a relatively limited parts list to respond to a much longer list of extracellular, environmental, and/or mechanical cues with rapidity and specificity. Most signaling networks function in a highly non-linear and often contextual manner. Furthermore, these processes occur dynamically across space and time. Because of these complexities, systems and "OMIC" approaches are essential for the study of signal transduction. One challenge in using OMIC-scale approaches to study signaling is that the "signal" can take different forms in different situations. Signals are encoded in diverse ways such as protein-protein interactions, enzyme activities, localizations, or post-translational modifications to proteins. Furthermore, in some cases, signals may be encoded only in the dynamics, duration, or rates of change of these features. Accordingly, systems-level analyses of signaling may need to integrate multiple experimental and/or computational approaches. As the field has progressed, the non-triviality of integrating experimental and computational analyses has become apparent. Successful use of OMIC methods to study signaling will require the "right" experiments and the "right" modeling approaches, and it is critical to consider both in the design phase of the project. In this review, we discuss common OMIC and modeling approaches for studying signaling, emphasizing the philosophical and practical considerations for effectively merging these two types of approaches to maximize the probability of obtaining reliable and novel insights into signaling biology.

Perturbation biology nominates upstream-downstream drug combinations in RAF inhibitor resistant melanoma cells

Resistance to targeted cancer therapies is an important clinical problem. The discovery of anti-resistance drug combinations is challenging as resistance can arise by diverse escape mechanisms. To address this challenge, we improved and applied the experimental-computational perturbation biology method. Using statistical inference, we build network models from high-throughput measurements of molecular and phenotypic responses to combinatorial targeted perturbations. The models are computationally executed to predict the effects of thousands of untested perturbations. In RAF-inhibitor resistant melanoma cells, we measured 143 proteomic/phenotypic entities under 89 perturbation conditions and predicted c-Myc as an effective therapeutic co-target with BRAF or MEK. Experiments using the BET bromodomain inhibitor JQ1 affecting the level of c-Myc protein and protein kinase inhibitors targeting the ERK pathway confirmed the prediction. In conclusion, we propose an anti-cancer strategy of co-targeting a specific upstream alteration and a general downstream point of vulnerability to prevent or overcome resistance to targeted drugs.

Integrative Protein-Based Prognostic Model for Early-Stage Endometrioid Endometrial Cancer

PURPOSE

Endometrioid endometrial carcinoma (EEC) is the major histologic type of endometrial cancer, the most prevalent gynecologic malignancy in the United States. EEC recurrence or metastasis is associated with a poor prognosis. Early-stage EEC is generally curable, but a subset has high risk of recurrence or metastasis. Prognosis estimation for early-stage EEC mainly relies on clinicopathologic characteristics, but is unreliable. We aimed to identify patients with high-risk early-stage EEC who are most likely to benefit from more extensive surgery and adjuvant therapy by building a prognostic model that integrates clinical variables and protein markers.

EXPERIMENTAL DESIGN

We used two large, independent early-stage EEC datasets as training (n = 183) and validation cohorts (n = 333), and generated the levels of 186 proteins and phosphoproteins using reverse-phase protein arrays. By applying an initial filtering and the elastic net to the training samples, we developed a prognostic model for overall survival containing two clinical variables and 18 protein markers and optimized the risk group classification.

RESULTS

The Kaplan-Meier survival analyses in the validation cohort confirmed an improved discriminating power of our prognostic model for patients with early-stage EEC over key clinical variables (log-rank test, P = 0.565 for disease stage, 0.567 for tumor grade, and 1.3 × 10(-4) for the integrative model). Compared with clinical variables (stage, grade, and patient age), only the risk groups defined by the integrative model were consistently significant in both univariate and multivariate analyses across both cohorts.

CONCLUSIONS

Our prognostic model is potentially of high clinical value for stratifying patients with early-stage EEC and improving their treatment strategies.

Phosphorylation of GSK3α/β correlates with activation of AKT and is prognostic for poor overall survival in acute myeloid leukemia patients

Background

Acute myeloid leukemia (AML) patients with highly active AKT tend to do poorly. Cell cycle arrest and apoptosis are tightly regulated by AKT via phosphorylation of GSK3α and β isoforms which inactivates these kinases. In the current study we examine the prognostic role of AKT mediated GSK3 phosphorylation in AML.

Methods

We analyzed GSK3α/β phosphorylation by reverse phase protein analysis (RPPA) in a cohort of 511 acute myeloid leukemia (AML) patients. Levels of phosphorylated GSK3 were correlated with patient characteristics including survival and with expression of other proteins important in AML cell survival.

Results

High levels of p-GSK3α/β correlated with adverse overall survival and a lower incidence of complete remission duration in patients with intermediate cytogenetics, but not in those with unfavorable cytogenetics. Intermediate cytogenetic patients with FLT3 mutation also fared better respectively when p-GSK3α/β levels were lower. Phosphorylated GSK3α/β expression was compared and contrasted with that of 229 related cell cycle arrest and/or apoptosis proteins. Consistent with p-GSK3α/β as an indicator of AKT activation, RPPA revealed that p-GSK3α/β positively correlated with phosphorylation of AKT, BAD, and P70S6K, and negatively correlated with β-catenin and FOXO3A. PKCδ also positively correlated with p-GSK3α/β expression, suggesting crosstalk between the AKT and PKC signaling pathways in AML cells.

Conclusions

These findings suggest that AKT-mediated phosphorylation of GSK3α/β may be beneficial to AML cell survival, and hence detrimental to the overall survival of AML patients. Intrinsically, p-GSK3α/β may serve as an important adverse prognostic factor for a subset of AML patients.

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Phospho-GSK3 is prognostic for poor survival in a subset of AML patients.

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Phospho-GSK3 is a biomarker for active AKT in AML.

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AKT is a PKCδ kinase in AML cells.

Reverse Phase Protein Arrays-Quantitative Assessment of Multiple Biomarkers in Biopsies for Clinical Use

Reverse Phase Protein Arrays (RPPA) represent a very promising sensitive and precise high-throughput technology for the quantitative measurement of hundreds of signaling proteins in biological and clinical samples. This array format allows quantification of one protein or phosphoprotein in multiple samples under the same experimental conditions at the same time. Moreover, it is suited for signal transduction profiling of small numbers of cultured cells or cells isolated from human biopsies, including formalin fixed and paraffin embedded (FFPE) tissues. Owing to the much easier sample preparation, as compared to mass spectrometry based technologies, and the extraordinary sensitivity for the detection of low-abundance signaling proteins over a large linear range, RPPA have the potential for characterization of deregulated interconnecting protein pathways and networks in limited amounts of sample material in clinical routine settings. Current aspects of RPPA technology, including dilution curves, spotting, controls, signal detection, antibody validation, and calculation of protein levels are addressed.

Integrated genomic characterization of papillary thyroid carcinoma

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. Here, we describe the genomic landscape of 496 PTCs. We observed a low frequency of somatic alterations (relative to other carcinomas) and extended the set of known PTC driver alterations to include EIF1AX, PPM1D, and CHEK2 and diverse gene fusions. These discoveries reduced the fraction of PTC cases with unknown oncogenic driver from 25% to 3.5%. Combined analyses of genomic variants, gene expression, and methylation demonstrated that different driver groups lead to different pathologies with distinct signaling and differentiation characteristics. Similarly, we identified distinct molecular subgroups of BRAF-mutant tumors, and multidimensional analyses highlighted a potential involvement of oncomiRs in less-differentiated subgroups. Our results propose a reclassification of thyroid cancers into molecular subtypes that better reflect their underlying signaling and differentiation properties, which has the potential to improve their pathological classification and better inform the management of the disease.

Genetic events that limit the efficacy of MEK and RTK inhibitor therapies in a mouse model of KRAS-driven pancreatic cancer

Mutated KRAS (KRAS*) is a fundamental driver in the majority of pancreatic ductal adenocarcinomas (PDAC). Using an inducible mouse model of KRAS*-driven PDAC, we compared KRAS* genetic extinction with pharmacologic inhibition of MEK1 in tumor spheres and in vivo. KRAS* ablation blocked proliferation and induced apoptosis, whereas MEK1 inhibition exerted cytostatic effects. Proteomic analysis evidenced that MEK1 inhibition was accompanied by a sustained activation of the PI3K-AKT-MTOR pathway and by the activation of AXL, PDGFRa, and HER1-2 receptor tyrosine kinases (RTK) expressed in a large proportion of human PDAC samples analyzed. Although single inhibition of each RTK alone or plus MEK1 inhibitors was ineffective, a combination of inhibitors targeting all three coactivated RTKs and MEK1 was needed to inhibit proliferation and induce apoptosis in both mouse and human low-passage PDAC cultures. Importantly, constitutive AKT activation, which may mimic the fraction of AKT2-amplified PDAC, was able to bypass the induction of apoptosis caused by KRAS* ablation, highlighting a potential inherent resistance mechanism that may inform the clinical application of MEK inhibitor therapy. This study suggests that combinatorial-targeted therapies for pancreatic cancer must be informed by the activation state of each putative driver in a given treatment context. In addition, our work may offer explanative and predictive power in understanding why inhibitors of EGFR signaling fail in PDAC treatment and how drug resistance mechanisms may arise in strategies to directly target KRAS.

Microarray R-based analysis of complex lysate experiments with MIRACLE

Motivation: Reverse-phase protein arrays (RPPAs) allow sensitive quantification of relative protein abundance in thousands of samples in parallel. Typical challenges involved in this technology are antibody selection, sample preparation and optimization of staining conditions. The issue of combining effective sample management and data analysis, however, has been widely neglected.

Results: This motivated us to develop MIRACLE, a comprehensive and user-friendly web application bridging the gap between spotting and array analysis by conveniently keeping track of sample information. Data processing includes correction of staining bias, estimation of protein concentration from response curves, normalization for total protein amount per sample and statistical evaluation. Established analysis methods have been integrated with MIRACLE, offering experimental scientists an end-to-end solution for sample management and for carrying out data analysis. In addition, experienced users have the possibility to export data to R for more complex analyses. MIRACLE thus has the potential to further spread utilization of RPPAs as an emerging technology for high-throughput protein analysis.

Availability: Project URL: http://www.nanocan.org/miracle/

Contact: mlist@health.sdu.dk

Supplementary information:Supplementary data are available at Bioinformatics online.

A comprehensive comparison of normalization methods for loading control and variance stabilization of reverse-phase protein array data

Loading control (LC) and variance stabilization of reverse-phase protein array (RPPA) data have been challenging mainly due to the small number of proteins in an experiment and the lack of reliable inherent control markers. In this study, we compare eight different normalization methods for LC and variance stabilization. The invariant marker set concept was first applied to the normalization of high-throughput gene expression data. A set of “invariant” markers are selected to create a virtual reference sample. Then all the samples are normalized to the virtual reference. We propose a variant of this method in the context of RPPA data normalization and compare it with seven other normalization methods previously reported in the literature. The invariant marker set method performs well with respect to LC, variance stabilization and association with the immunohistochemistry/florescence in situ hybridization data for three key markers in breast tumor samples, while the other methods have inferior performance. The proposed method is a promising approach for improving the quality of RPPA data.

BAYESIAN SPARSE GRAPHICAL MODELS FOR CLASSIFICATION WITH APPLICATION TO PROTEIN EXPRESSION DATA

Reverse-phase protein array (RPPA) analysis is a powerful, relatively new platform that allows for high-throughput, quantitative analysis of protein networks. One of the challenges that currently limit the potential of this technology is the lack of methods that allow for accurate data modeling and identification of related networks and samples. Such models may improve the accuracy of biological sample classification based on patterns of protein network activation and provide insight into the distinct biological relationships underlying different types of cancer. Motivated by RPPA data, we propose a Bayesian sparse graphical modeling approach that uses selection priors on the conditional relationships in the presence of class information. The novelty of our Bayesian model lies in the ability to draw information from the network data as well as from the associated categorical outcome in a unified hierarchical model for classification. In addition, our method allows for intuitive integration of a priori network information directly in the model and allows for posterior inference on the network topologies both within and between classes. Applying our methodology to an RPPA data set generated from panels of human breast cancer and ovarian cancer cell lines, we demonstrate that the model is able to distinguish the different cancer cell types more accurately than several existing models and to identify differential regulation of components of a critical signaling network (the PI3K-AKT pathway) between these two types of cancer. This approach represents a powerful new tool that can be used to improve our understanding of protein networks in cancer.