Reverse phase protein arrays: mapping the path towards personalized medicine

Histopathology and proteomics are synergistic for High-Grade Serous Ovarian Cancer platinum response prediction

Patients with High-Grade Serous Ovarian Cancer (HGSOC) exhibit varied responses to treatment, with 20-30% showing de novo resistance to platinum-based chemotherapy. While hematoxylin-eosin (H&E) pathological slides are used for routine diagnosis of cancer type, they may also contain diagnostically useful information about treatment response. Our study demonstrates that combining H&E-stained Whole Slide Images (WSIs) with proteomic signatures using a multimodal deep learning framework significantly improves the prediction of platinum response in both discovery and validation cohorts. This method outperforms the Homologous Recombination Deficiency (HRD) score in predicting platinum response and overall patient survival. The study sets new performance benchmarks and explores the intersection of histology and proteomics, highlighting phenotypes related to treatment response pathways, including homologous recombination, DNA damage response, nucleotide synthesis, apoptosis, and ER stress. This integrative approach has the potential to improve personalized treatment and provide insights into the therapeutic vulnerabilities of HGSOC.

Harnessing the potential of reverse-phase protein array technology: Advancing precision oncology strategies

The last few decades have seen remarkable strides in the field of cancer therapy. Precision oncology coupled with comprehensive genomic profiling has become routine clinical practice for solid tumors, the advent of immune checkpoint inhibitors has transformed the landscape of oncology treatment, and the number of cancer drug approvals has continued to increase. Nevertheless, the application of genomics-driven precision oncology has thus far benefited only 10%-20% of cancer patients, leaving the majority without matched treatment options. This limitation underscores the need to explore alternative avenues with regard to selecting patients for targeted therapies. In contrast with genomics-based approaches, proteomics-based strategies offer a more precise understanding of the intricate biological processes driving cancer pathogenesis. This perspective underscores the importance of integrating complementary proteomic analyses into the next phase of precision oncology to establish robust biomarker-drug associations and surmount challenges related to drug resistance. One promising technology in this regard is the reverse-phase protein array (RPPA), which excels in quantitatively detecting protein modifications, even with limited amounts of sample. Its cost-effectiveness and rapid turnaround time further bolster its appeal for application in clinical settings. Here, we review the current status of genomics-driven precision oncology, as well as its limitations, with an emphasis on drug resistance. Subsequently, we explore the application of RPPA technology as a catalyst for advancing precision oncology. Through illustrative examples drawn from clinical trials, we demonstrate its utility for unraveling the molecular mechanisms underlying drug responses and resistance.

Global proteomic identifies multiple cancer-related signaling pathways altered by a gut pathobiont associated with colorectal cancer

In this work, we investigated the oncogenic role of Streptococcus gallolyticus subsp. gallolyticus (SGG), a gut bacterium associated with colorectal cancer (CRC). We showed that SGG UCN34 accelerates colon tumor development in a chemically induced CRC murine model. Full proteome and phosphoproteome analysis of murine colons chronically colonized by SGG UCN34 revealed that 164 proteins and 725 phosphorylation sites were differentially regulated. Ingenuity Pathway Analysis (IPA) indicates a pro-tumoral shift specifically induced by SGG UCN34, as ~ 90% of proteins and phosphoproteins identified were associated with digestive cancer. Comprehensive analysis of the altered phosphoproteins using ROMA software revealed up-regulation of several cancer hallmark pathways such as MAPK, mTOR and integrin/ILK/actin, affecting epithelial and stromal colonic cells. Importantly, an independent analysis of protein arrays of human colon tumors colonized with SGG showed up-regulation of PI3K/Akt/mTOR and MAPK pathways, providing clinical relevance to our findings. To test SGG's capacity to induce pre-cancerous transformation of the murine colonic epithelium, we grew ex vivo organoids which revealed unusual structures with compact morphology. Taken together, our results demonstrate the oncogenic role of SGG UCN34 in a murine model of CRC associated with activation of multiple cancer-related signaling pathways.

Gene expression profiles of ischemic stroke clots retrieved by mechanical thrombectomy are associated with disease etiology

BACKGROUND

Determining stroke etiology is crucial for secondary prevention, but intensive workups fail to classify ~30% of strokes that are cryptogenic.

OBJECTIVE

To examine the hypothesis that the transcriptomic profiles of clots retrieved during mechanical thrombectomy are unique to strokes of different subtypes.

METHODS

We isolated RNA from the clots of 73 patients undergoing mechanical thrombectomy. Samples of sufficient quality were subjected to 100-cycle, paired-end RNAseq, and transcriptomes with less than 10 million unique reads were excluded from analysis. Significant differentially expressed genes (DEGs) between subtypes (defined by the Trial of Org 10 172 in Acute Stroke Treatment) were identified by expression analysis in edgeR. Gene ontology enrichment analysis was used to study the biologic differences between stroke etiologies.

RESULTS

In all, 38 clot transcriptomes were analyzed; 6 from large artery atherosclerosis (LAA), 21 from cardioembolism (CE), 5 from strokes of other determined origin, and 6 from cryptogenic strokes. Among all comparisons, there were 816 unique DEGs, 174 of which were shared by at least two comparisons, and 20 of which were shared by all three. Gene ontology analysis showed that CE clots reflected high levels of inflammation, LAA clots had greater oxidoreduction and T-cell processes, and clots of other determined origin were enriched for aberrant platelet and hemoglobin-related processes. Principal component analysis indicated separation between these subtypes and showed cryptogenic samples clustered among several different groups.

CONCLUSIONS

Expression profiles of stroke clots were identified between stroke etiologies and reflected different biologic responses. Cryptogenic thrombi may be related to multiple etiologies.

Reversed-phase allergen microarrays on optical discs for multiplexed diagnostics of food allergies

A high percentage of the population suffers from multiple food allergies justifying  the importance of reliable diagnostic methods. Single-analyte solutions based on the determination of specific immunoglobulins E (sIgE) are safe and fast but are generally time-consuming and expensive. Thus sustainable microanalytical methods that provide multianalyte profiling information are highly demanded. This work presents the in vitro biosensing of specific IgE levels based on a reversed-phase allergen array. The approach consists of optical biosensing supported by direct multiplex immunoassays and on-disc technology. It identifies 12 sIgE associated with food allergies in a single analysis with a low serum sample volume (25 µL). After processing captured images, specific signals for each target biomarker correlate to their concentration. The assay analytically performs well with 0.3 IU/mL and 0.41 IU/mL as the detection and quantification limits in serum, respectively. This novel method achieves excellent clinical specificity (100%) and high sensitivity (91.1%), considering the diagnosis obtained by clinical history and ImmunoCAP analysis. The results demonstrate that microanalytical systems based on allergen arrays can potentially diagnose multiple food allergies and are easily implemented in primary care laboratory settings.

Analysis of neuroendocrine clones in NSCLCs using an immuno-guided laser-capture microdissection-based approach

Clonal evolution and lineage plasticity are key contributors to tumor heterogeneity and response to treatment in cancer. However, capturing signal transduction events in coexisting clones remains challenging from a technical perspective. In this study, we developed and tested a signal-transduction-based workflow to isolate and profile coexisting clones within a complex cellular system like non-small cell lung cancers (NSCLCs). Cooccurring clones were isolated under immunohistochemical guidance using laser-capture microdissection, and cell signaling activation portraits were measured using the reverse-phase protein microarray. To increase the translational potential of this work and capture druggable vulnerabilities within different clones, we measured expression/activation of a panel of key drug targets and downstream substrates of FDA-approved or investigational agents. We isolated intermixed clones, including poorly represented ones (<5% of cells), within the tumor microecology and identified molecular characteristics uniquely attributable to cancer cells that undergo lineage plasticity and neuroendocrine transdifferentiation in NSCLCs.

The active kinome: The modern view of how active protein kinase networks fit in biological research

Biological regulatory networks are dynamic, intertwined, and complex systems making them challenging to study. While quantitative measurements of transcripts and proteins are key to investigate the state of a biological system, they do not inform the "active" state of regulatory networks. In consideration of that fact, "functional" proteomics assessments are needed to decipher active regulatory processes. Phosphorylation, a key post-translation modification, is a reversible regulatory mechanism that controls the functional state of proteins. Recent advancements of high-throughput protein kinase activity profiling platforms allow for a broad assessment of protein kinase networks in complex biological systems. In conjunction with sophisticated computational modeling techniques, these profiling platforms provide datasets that inform the active state of regulatory systems in disease models and highlight potential drug targets. Taken together, system-wide profiling of protein kinase activity has become a critical component of modern molecular biology research and presents a promising avenue for drug discovery.

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.

Uncovering the Depths of the Human Proteome: Antibody-based Technologies for Ultrasensitive Multiplexed Protein Detection and Quantification

Probing the human proteome in tissues and biofluids such as plasma is attractive for biomarker and drug target discovery. Recent breakthroughs in multiplex, antibody-based, proteomics technologies now enable the simultaneous quantification of thousands of proteins at as low as sub fg/ml concentrations with remarkable dynamic ranges of up to 10-log. We herein provide a comprehensive guide to the methodologies, performance, technical comparisons, advantages, and disadvantages of established and emerging technologies for the multiplexed ultrasensitive measurement of proteins. Gaining holistic knowledge on these innovations is crucial for choosing the right multiplexed proteomics tool for applications at hand to critically complement traditional proteomics methods. This can bring researchers closer than ever before to elucidating the intricate inner workings and cross talk that spans multitude of proteins in disease mechanisms.

Proteomic Analysis of Cardioembolic and Large Artery Atherosclerotic Clots Using Reverse Phase Protein Array Technology Reveals Key Cellular Interactions Within Clot Microenvironments

Thrombus characteristics are dependent on clot composition, but identification of the etiology based on histological analysis has proved inconclusive. Identification of proteomic signatures may help to differentiate between clots of different etiologies such as cardioembolic, large artery atherosclerotic, and other known etiologies, information that could enhance an individualized medicine approach to secondary stroke prevention. In this study, total protein extracts from cardioembolic (n=25) and large artery atherosclerotic (n=23) thrombus specimens were arrayed in quadruplicate on nitrocellulose slides and immunostained for 31 proteins using a Dako Autostainer (Agilent Technologies, Inc., Santa Clara, USA). We quantified 31 proteins involved in platelet and/or endothelial function, inflammation, oxidative stress, and metabolism. Pathway analysis showed more heterogeneity and protein network interactions in the cardioembolic clots but no specific correlations with clot etiology. Reverse-phase protein arrays are a powerful tool for assessing cellular interactions within the clot microenvironment and may enhance understanding of clot formation and origination. This tool could be further explored to help in identifying stroke etiology in large vessel occlusion patients with embolic stroke of an undetermined source.

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.

Evaluation of the RAS signaling network in response to MEK inhibition using organoids derived from a familial adenomatous polyposis patient

RAS signaling is a promising target for colorectal cancer (CRC) therapy, and a variety of selective inhibitors have been developed. However, their use has often failed to demonstrate a significant benefit in CRC patients. Here, we used patient-derived organoids (PDOs) derived from a familial adenomatous polyposis (FAP) patient to analyze the response to chemotherapeutic agents targeting EGFR, BRAF and MEK. We found that PDOs carrying KRAS mutations were resistant to MEK inhibition, while those harboring the BRAF class 3 mutation were hypersensitive. We used a systematic approach to examine the phosphorylation of RAS effectors using reverse-phase protein array (RPPA) and found increased phosphorylation of MEK induced by binimetinib. A high basal level of ERK phosphorylation and its rebound activation after MEK inhibition were detected in KRAS-mutant PDOs. Notably, the phosphorylation of EGFR and AKT was more closely correlated with that of MEK than that of ERK. Transcriptome analysis identified MYC-mediated transcription and IFN signaling as significantly correlated gene sets in MEK inhibition. Our experiments demonstrated that RPPA analysis of PDOs, in combination with the genome and transcriptome, is a useful preclinical research platform to understand RAS signaling and provides clues for the development of chemotherapeutic strategies.

A pre-existing population of ZEB2+ quiescent cells with stemness and mesenchymal features dictate chemoresistance in colorectal cancer

Background

Quiescent/slow cycling cells have been identified in several tumors and correlated with therapy resistance. However, the features of chemoresistant populations and the molecular factors linking quiescence to chemoresistance are largely unknown.

Methods

A population of chemoresistant quiescent/slow cycling cells was isolated through PKH26 staining (which allows to separate cells on the basis of their proliferation rate) from colorectal cancer (CRC) xenografts and subjected to global gene expression and pathway activation analyses. Factors expressed by the quiescent/slow cycling population were analyzed through lentiviral overexpression approaches for their ability to induce a dormant chemoresistant state both in vitro and in mouse xenografts. The correlation between quiescence-associated factors, CRC consensus molecular subtype and cancer prognosis was analyzed in large patient datasets.

Results

Untreated colorectal tumors contain a population of quiescent/slow cycling cells with stem cell features (quiescent cancer stem cells, QCSCs) characterized by a predetermined mesenchymal-like chemoresistant phenotype. QCSCs expressed increased levels of ZEB2, a transcription factor involved in stem cell plasticity and epithelial-mesenchymal transition (EMT), and of antiapototic factors pCRAF and pASK1. ZEB2 overexpression upregulated pCRAF/pASK1 levels resulting in increased chemoresistance, enrichment of cells with stemness/EMT traits and proliferative slowdown of tumor xenografts. In parallel, chemotherapy treatment of tumor xenografts induced the prevalence of QCSCs with a stemness/EMT phenotype and activation of the ZEB2/pCRAF/pASK1 axis, resulting in a chemotherapy-unresponsive state. In CRC patients, increased ZEB2 levels correlated with worse relapse-free survival and were strongly associated to the consensus molecular subtype 4 (CMS4) characterized by dismal prognosis, decreased proliferative rates and upregulation of EMT genes.

Conclusions

These results show that chemotherapy-naive tumors contain a cell population characterized by a coordinated program of chemoresistance, quiescence, stemness and EMT. Such population becomes prevalent upon drug treatment and is responsible for chemotherapy resistance, thus representing a key target for more effective therapeutic approaches.

Proteomics for cancer drug design

Introduction: Signal transduction cascades drive cellular proliferation, apoptosis, immune, and survival pathways. Proteins have emerged as actionable drug targets because they are often dysregulated in cancer, due to underlying genetic mutations, or dysregulated signaling pathways. Cancer drug development relies on proteomic technologies to identify potential biomarkers, mechanisms-of-action, and to identify protein binding hot spots. Areas covered: Brief summaries of proteomic technologies for drug discovery include mass spectrometry, reverse phase protein arrays, chemoproteomics, and fragment based screening. Protein-protein interface mapping is presented as a promising method for peptide therapeutic development. The topic of biosimilar therapeutics is presented as an opportunity to apply proteomic technologies to this new class of cancer drug. Expert opinion: Proteomic technologies are indispensable for drug discovery. A suite of technologies including mass spectrometry, reverse phase protein arrays, and protein-protein interaction mapping provide complimentary information for drug development. These assays have matured into well controlled, robust technologies. Recent regulatory approval of biosimilar therapeutics provides another opportunity to decipher the molecular nuances of their unique mechanisms of action. The ability to identify previously hidden protein hot spots is expanding the gamut of potential drug targets. Proteomic profiling permits lead compound evaluation beyond the one drug, one target paradigm.

Combining the DNA Repair Inhibitor Dbait With Radiotherapy for the Treatment of High Grade Glioma: Efficacy and Protein Biomarkers of Resistance in Preclinical Models

High grade glioma relapses occur often within the irradiated volume mostly due to a high resistance to radiation therapy (RT). Dbait (which stands for DNA strand break bait) molecules mimic DSBs and trap DNA repair proteins, thereby inhibiting repair of DNA damage induced by RT. Here we evaluate the potential of Dbait to sensitize high grade glioma to RT. First, we demonstrated the radiosensitizer properties of Dbait in 6/9 tested cell lines. Then, we performed animal studies using six cell derived xenograft and five patient derived xenograft models, to show the clinical potential and applicability of combined Dbait+RT treatment for human high grade glioma. Using a RPPA approach, we showed that Phospho-H2AX/H2AX and Phospho-NBS1/NBS1 were predictive of Dbait efficacy in xenograft models. Our results provide the preclinical proof of concept that combining RT with Dbait inhibition of DNA repair could be of benefit to patients with high grade glioma.

Utility of Reverse-Phase Protein Array for Refining Precision Oncology

Despite the early successes of targeted therapies and continuous improvements in next-generation sequencing technology over the last two decades, genomics-driven precision oncology has helped only a minority of cancer patients; thus treatment regimens are still not matched to the vast majority of cancer patients. It has become apparent that genomic profiling in itself is limited with respect to optimal selection of patients for targeted therapy. Proteomics-based approaches (in contrast to genomics-based and transcriptomics-based approaches) capture biological processes (e.g., diversity of protein expression patterns and post-translational modifications) directly contributing to cancer pathogenesis. This encourages incorporation of concordant proteomic analyses into the next stage of precision oncology. Reverse-phase protein array (RPPA) is well suited to pharmacodynamic analysis due to its ability to precisely map signaling status using limited amounts of clinical sample. In addition, the cost-effectiveness and rapid turnaround time of the RPPA platform offer a substantial advantage over existing molecular profiling technologies in a clinical setting. In this chapter, we begin by reviewing the current status of genomics-driven precision oncology, along with its limitations and challenges. Finally, we discuss the utility of RPPA technology as a means of improving precision oncology.

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.

Dual-Color, Multiplex Analysis of Protein Microarrays for Precision Medicine

Generating molecular information in a clinically relevant time frame is the first hurdle to truly integrating precision medicine in health care. Reverse phase protein microarrays are being utilized in clinical trials for quantifying posttranslationally modified signal transduction proteins and cellular signaling pathways, allowing direct comparison of the activation state of proteins from multiple specimens, or individual patient specimens, within the same array. This technology provides diagnostic and therapeutic information critical to precision medicine. To enhance accessibility of this technology, two hurdles must be overcome: data normalization and data acquisition. Herein we describe an unamplified, dual-color signal detection methodology for reverse phase protein microarrays that allows multiplex, within spot data normalization, reduces data acquisition time, simplifies automated spot detection, and provides a stable signal output. This method utilizes Quantum Nanocrystal fluorophore labels (Qdot) substituted for organic fluorophores coupled with an imager (ArrayCAM) that captures images of the microarray rather than sequentially scanning the array. Streamlining and standardizing the data analysis steps with ArrayCAM high-resolution, dual mode chromogenic/fluorescent array imaging overcomes the data acquisition hurdle. The spot location and analysis algorithm provides certain parameter settings that can be tailored to the particular microarray type (fluorescent vs. colorimetric), resulting in greater than 99 % spot location sensitivity. The described method demonstrates equivalent sensitivity for a non-amplified Qdot immunoassay when using automated vs. manual immunostaining procedures.

Predictive biomarkers of resistance to hypofractionated radiotherapy in high grade glioma

Background

Radiotherapy plays a major role in the management of high grade glioma. However, the radioresistance of glioma cells limits its efficiency and drives recurrence inside the irradiated tumor volume leading to poor outcome for patients. Stereotactic hypofractionated radiotherapy is one option for recurrent high grade gliomas. Optimization of hypofractionated radiotherapy with new radiosensitizing agents requires the identification of robust druggable targets involved in radioresistance.

Methods

We generated 11 xenografted glioma models: 6 were derived from cell lines (1 WHO grade III and 5 grade IV) and 5 were patient derived xenografts (2 WHO grade III and 3 grade IV). Xenografts were treated by hypofractionated radiotherapy (6x5Gy). We searched for 89 biomarkers of radioresistance (39 total proteins, 26 phosphoproteins and 24 ratios of phosphoproteins on total proteins) using Reverse Phase Protein Array.

Results

Both type of xenografted models showed equivalent spectrum of sensitivity and profile of response to hypofractionated radiotherapy. We report that Phospho-EGFR/EGFR, Phospho-Chk1/Chk1 and VCP were associated to resistance to hypofractionated radiotherapy.

Conclusions

Several compounds targeting EGFR or CHK1 are already in clinical use and combining them with stereotactic hypofractionated radiotherapy for recurrent high grade gliomas might be of particular interest.

Electronic supplementary material

The online version of this article (doi:10.1186/s13014-017-0858-0) contains supplementary material, which is available to authorized users.

Signaling pathway profiling using reverse-phase protein array and its clinical applications

INTRODUCTION

Increased accessibility to next-generation sequencing within the last decade has led to a paradigm shift in cancer treatment from one-size-fits-all medicine to precision medicine providing therapeutic strategies tailored to the requirements of individual patients. However, the effect of even the most successful agent yet tested is only transient, and durable efficacy has yet to be achieved. Genome- and transcriptome-based approaches cannot fully predict the diversity of protein expression patterns or post-translational modifications that directly contribute to cancer pathogenesis and physiology. This underscores the need for concordant proteomic analysis in the next stage of precision medicine. Areas covered: This review begins with an overview of the recent advances and trends in precision medicine that currently rely on genomics, and highlights the utility of antibody-based reverse-phase protein array (RPPA) technology as a proteomic tool in this context. Expert commentary: RPPA is well suited for pharmacodynamics analysis in view of its ability to precisely map signaling status using limited amounts of clinical samples. In addition, the cost-effectiveness and rapid turn-around time of the RPPA platform offer a substantial advantage over existing molecular profiling technologies in clinical settings.

Cyclophilin D regulates lifespan and protein expression of aging markers in the brain of mice

Cyclophilin D (cypD) modulates the properties of the permeability transition pore, a phenomenon implicated in the manifestation of many diseases including aging. Here, we examined the effects of partial or complete deletion of cypD on i) lifespan, ii) forebrain protein expression of 18 aging markers as well as regional expression of GFAP, mGluR1, and alpha-synuclein, and iii) behaviour of aged (>24month) male and female mice. Both male and female cypD heterozygous but not KO mice exhibited increased lifespans compared to WT littermates, associated with alterations in the protein expression of some markers, albeit without exhibiting changes in behaviour.

Fluorescence Sensing of Circulating Diagnostic Biomarkers Using Molecular Probes and Nanoparticles

The interplay of photonics, nanotechnology, and biochemistry has significantly improved the identification and characterization of multiple types of biomarkers by optical biosensors. Great achievements in fluorescence-based technologies have been realized, for example, by the advancement of multiplexing techniques or the introduction of nanoparticles to biochemical and clinical research. This review presents a concise overview of recent advances in fluorescence sensing techniques for the detection of circulating disease biomarkers. Detection principles of representative approaches, including fluorescence detection using molecular fluorophores, quantum dots, and metallic and silica nanoparticles, are explained and illustrated by pertinent examples from the recent literature. Advanced detection technologies and material development play a major role in modern biosensing and consistently provide significant improvements toward robust, sensitive, and versatile platforms for early detection of circulating diagnostic biomarkers.

Global Conservation of Protein Status between Cell Lines and Xenografts

Common preclinical models for testing anticancer treatment include cultured human tumor cell lines in monolayer, and xenografts derived from these cell lines in immunodeficient mice. Our goal was to determine how similar the xenografts are compared with their original cell line and to determine whether it is possible to predict the stability of a xenograft model beforehand. We studied a selection of 89 protein markers of interest in 14 human cell cultures and respective subcutaneous xenografts using the reverse-phase protein array technology. We specifically focused on proteins and posttranslational modifications involved in DNA repair, PI3K pathway, apoptosis, tyrosine kinase signaling, stress, cell cycle, MAPK/ERK signaling, SAPK/JNK signaling, NFκB signaling, and adhesion/cytoskeleton. Using hierarchical clustering, most cell culture-xenograft pairs cluster together, suggesting a global conservation of protein signature. Particularly, Akt, NFkB, EGFR, and Vimentin showed very stable protein expression and phosphorylation levels highlighting that 4 of 10 pathways were highly correlated whatever the model. Other proteins were heterogeneously conserved depending on the cell line. Finally, cell line models with low Akt pathway activation and low levels of Vimentin gave rise to more reliable xenograft models. These results may be useful for the extrapolation of cell culture experiments to in vivo models in novel targeted drug discovery.

Using reverse-phase protein arrays as pharmacodynamic assays for functional proteomics, biomarker discovery, and drug development in cancer

The majority of the targeted therapeutic agents in clinical use target proteins and protein function. Although DNA and RNA analyses have been used extensively to identify novel targets and patients likely to benefit from targeted therapies, these are indirect measures of the levels and functions of most therapeutic targets. More importantly, DNA and RNA analysis is ill-suited for determining the pharmacodynamic effects of target inhibition. Assessing changes in protein levels and function is the most efficient way to evaluate the mechanisms underlying sensitivity and resistance to targeted agents. Understanding these mechanisms is necessary to identify patients likely to benefit from treatment and to develop rational drug combinations to prevent or bypass therapeutic resistance. There is an urgent need for a robust approach to assess protein levels and protein function in model systems and across patient samples. While "shot gun" mass spectrometry can provide in-depth analysis of proteins across a limited number of samples, and emerging approaches such as multiple reaction monitoring have the potential to analyze candidate markers, mass spectrometry has not entered into general use because of the high cost, requirement of extensive analysis and support, and relatively large amount of material needed for analysis. Rather, antibody-based technologies, including immunohistochemistry, radioimmunoassays, enzyme-linked immunosorbent assays (ELISAs), and more recently protein arrays, remain the most common approaches for multiplexed protein analysis. Reverse-phase protein array (RPPA) technology has emerged as a robust, sensitive, cost-effective approach to the analysis of large numbers of samples for quantitative assessment of key members of functional pathways that are affected by tumor-targeting therapeutics. The RPPA platform is a powerful approach for identifying and validating targets, classifying tumor subsets, assessing pharmacodynamics, and identifying prognostic and predictive markers, adaptive responses and rational drug combinations in model systems and patient samples. Its greatest utility has been realized through integration with other analytic platforms such as DNA sequencing, transcriptional profiling, epigenomics, mass spectrometry, and metabolomics. The power of the technology is becoming apparent through its use in pathology laboratories and integration into trial design and implementation.

Cancer Stem Cell-Based Models of Colorectal Cancer Reveal Molecular Determinants of Therapy Resistance

Colorectal cancer (CRC) therapy mainly relies on the use of conventional chemotherapeutic drugs combined, in a subset of patients, with epidermal growth factor receptor [EGFR]-targeting agents. Although CRC is considered a prototype of a cancer stem cell (CSC)-driven tumor, the effects of both conventional and targeted therapies on the CSC compartment are largely unknown. We have optimized a protocol for colorectal CSC isolation that allowed us to obtain CSC-enriched cultures from primary tumor specimens, with high efficiency. CSC isolation was followed by in vitro and in vivo validation, genetic characterization, and drug sensitivity analysis, thus generating panels of CSC lines with defined patterns of genetic mutations and therapy sensitivity. Colorectal CSC lines were polyclonal and maintained intratumor heterogeneity in terms of somatically acquired mutations and differentiation state. Such CSC-enriched cultures were used to investigate the effects of both conventional and targeted therapies on the CSC compartment in vivo and to generate a proteomic picture of signaling pathways implicated in sensitivity/resistance to anti-EGFR agents. We propose CSC lines as a sound preclinical framework to test the effects of therapies in vitro and in vivo and to identify novel determinants of therapy resistance.

SIGNIFICANCE

Colorectal cancer stem cells (CSCs) have been shown to be responsible for tumor propagation, metastatic dissemination, and relapse. However, molecular pathways present in CSCs, as well as mechanisms of therapy resistance, are mostly unknown. Taking advantage of genetically characterized CSC lines derived from colorectal tumors, this study provides an extensive analysis of CSC response to EGFR-targeted therapy in vivo and an overview of factors implicated in therapy response or resistance. Furthermore, the implementation of a biobank of molecularly annotated CSC lines provides an innovative resource for future investigations in colorectal cancer.

ZBIT Bioinformatics Toolbox: A Web-Platform for Systems Biology and Expression Data Analysis

Bioinformatics analysis has become an integral part of research in biology. However, installation and use of scientific software can be difficult and often requires technical expert knowledge. Reasons are dependencies on certain operating systems or required third-party libraries, missing graphical user interfaces and documentation, or nonstandard input and output formats. In order to make bioinformatics software easily accessible to researchers, we here present a web-based platform. The Center for Bioinformatics Tuebingen (ZBIT) Bioinformatics Toolbox provides web-based access to a collection of bioinformatics tools developed for systems biology, protein sequence annotation, and expression data analysis. Currently, the collection encompasses software for conversion and processing of community standards SBML and BioPAX, transcription factor analysis, and analysis of microarray data from transcriptomics and proteomics studies. All tools are hosted on a customized Galaxy instance and run on a dedicated computation cluster. Users only need a web browser and an active internet connection in order to benefit from this service. The web platform is designed to facilitate the usage of the bioinformatics tools for researchers without advanced technical background. Users can combine tools for complex analyses or use predefined, customizable workflows. All results are stored persistently and reproducible. For each tool, we provide documentation, tutorials, and example data to maximize usability. The ZBIT Bioinformatics Toolbox is freely available at https://webservices.cs.uni-tuebingen.de/.

Proteomic Approaches for Biomarker Panels in Cancer

Proteomic technologies remain the main backbone of biomarkers discovery in cancer. The continuous development of proteomic technologies also enlarges the bioinformatics domain, thus founding the main pillars of cancer therapy. The main source for diagnostic/prognostic/therapy monitoring biomarker panels are molecules that have a dual role, being both indicators of disease development and therapy targets. Proteomic technologies, such as mass-spectrometry approaches and protein array technologies, represent the main technologies that can depict these biomarkers. Herein, we will illustrate some of the most recent strategies for biomarker discovery in cancer, including the development of immune-markers and the use of cancer stem cells as target therapy. The challenges of proteomic biomarker discovery need new forms of cross-disciplinary conglomerates that will result in increased and tailored access to treatments for patients; diagnostic companies would benefit from the enhanced co-development of companion diagnostics and pharmaceutical companies. In the technology optimization in biomarkers, immune assays are the leaders of discovery machinery.

Role of Proteomics in the Development of Personalized Medicine

Advances in proteomic technologies have made import contribution to the development of personalized medicine by facilitating detection of protein biomarkers, proteomics-based molecular diagnostics, as well as protein biochips and pharmacoproteomics. Application of nanobiotechnology in proteomics, nanoproteomics, has further enhanced applications in personalized medicine. Proteomics-based molecular diagnostics will have an important role in the diagnosis of certain conditions and understanding the pathomechanism of disease. Proteomics will be a good bridge between diagnostics and therapeutics; the integration of these will be important for advancing personalized medicine. Use of proteomic biomarkers and combination of pharmacoproteomics with pharmacogenomics will enable stratification of clinical trials and improve monitoring of patients for development of personalized therapies. Proteomics is an important component of several interacting technologies used for development of personalized medicine, which is depicted graphically. Finally, cancer is a good example of applications of proteomic technologies for personalized management of cancer.

Reverse phase protein arrays in signaling pathways: a data integration perspective

The reverse phase protein array (RPPA) data platform provides expression data for a prespecified set of proteins, across a set of tissue or cell line samples. Being able to measure either total proteins or posttranslationally modified proteins, even ones present at lower abundances, RPPA represents an excellent way to capture the state of key signaling transduction pathways in normal or diseased cells. RPPA data can be combined with those of other molecular profiling platforms, in order to obtain a more complete molecular picture of the cell. This review offers perspective on the use of RPPA as a component of integrative molecular analysis, using recent case examples from The Cancer Genome Altas consortium, showing how RPPA may provide additional insight into cancer besides what other data platforms may provide. There also exists a clear need for effective visualization approaches to RPPA-based proteomic results; this was highlighted by the recent challenge, put forth by the HPN-DREAM consortium, to develop visualization methods for a highly complex RPPA dataset involving many cancer cell lines, stimuli, and inhibitors applied over time course. In this review, we put forth a number of general guidelines for effective visualization of complex molecular datasets, namely, showing the data, ordering data elements deliberately, enabling generalization, focusing on relevant specifics, and putting things into context. We give examples of how these principles can be utilized in visualizing the intrinsic subtypes of breast cancer and in meaningfully displaying the entire HPN-DREAM RPPA dataset within a single page.

Emerging understanding of multiscale tumor heterogeneity

Cancer is a multifaceted disease characterized by heterogeneous genetic alterations and cellular metabolism, at the organ, tissue, and cellular level. Key features of cancer heterogeneity are summarized by 10 acquired capabilities, which govern malignant transformation and progression of invasive tumors. The relative contribution of these hallmark features to the disease process varies between cancers. At the DNA and cellular level, germ-line and somatic gene mutations are found across all cancer types, causing abnormal protein production, cell behavior, and growth. The tumor microenvironment and its individual components (immune cells, fibroblasts, collagen, and blood vessels) can also facilitate or restrict tumor growth and metastasis. Oncology research is currently in the midst of a tremendous surge of comprehension of these disease mechanisms. This will lead not only to novel drug targets but also to new challenges in drug discovery. Integrated, multi-omic, multiplexed technologies are essential tools in the quest to understand all of the various cellular changes involved in tumorigenesis. This review examines features of cancer heterogeneity and discusses how multiplexed technologies can facilitate a more comprehensive understanding of these features.