Clinical utility of reverse phase protein array for molecular classification of breast cancer

Integrative analysis of the cuproptosis-related gene ATP7B in the prognosis and immune infiltration of IDH1 wild-type glioma

Glioma is the most common malignant tumor in the brain and the central nervous system with a poor prognosis, and wild-type isocitrate dehydrogenase (IDH) glioma indicates a worse prognosis. Cuproptosis is a recently discovered form of cell death regulated by copper-dependent mitochondrial respiration. However, the effect of cuproptosis on tumor prognosis and immune infiltration is not clear. In this research, we analyzed of public databases to show the correlation between cuproptosis-related genes and the prognosis of IDH1 wild-type glioma. Nine out of 12 genes were upregulated in IDH1 wild-type glioma patients, and 6 genes were significantly associated with overall survival (OS), while 5 genes were associated with progression-free survival (PFS). Then, we constructed a prognostic cuproptosis-related gene signature for IDH1 wild-type glioma patients. ATP7B was considered an independent prognostic indicator, and a low expression level of ATP7B was related to a shorter period of OS and PFS. Moreover, downregulation of ATP7B was correlated not only with the infiltration of activated NK cells, CD8 + T cells and M2 macrophages; but also with high expression of immune checkpoint genes and tumor mutation burden (TMB). In the IDH1 wild-type glioma tissues we collected, our data also confirmed that high tumor grade was accompanied by low expression of ATP7B and high expression of PD-L1, which was associated with increasing infiltration of CD8 + immune cells. In conclusion, our research constructed a prognostic cuproptosis-related gene signature model to predict the prognosis of IDH1 wild-type glioma. ATP7B is deemed to be a potential prognostic indicator and novel immunotherapy biomarker for IDH1 wild-type glioma patients.

Delineating intra-tumoral heterogeneity and tumor evolution in breast cancer using precision-based approaches

The burden of breast cancer continues to increase worldwide as it remains the most diagnosed tumor in females and the second leading cause of cancer-related deaths. Breast cancer is a heterogeneous disease characterized by different subtypes which are driven by aberrations in key genes such as BRCA1 and BRCA2, and hormone receptors. However, even within each subtype, heterogeneity that is driven by underlying evolutionary mechanisms is suggested to underlie poor response to therapy, variance in disease progression, recurrence, and relapse. Intratumoral heterogeneity highlights that the evolvability of tumor cells depends on interactions with cells of the tumor microenvironment. The complexity of the tumor microenvironment is being unraveled by recent advances in screening technologies such as high throughput sequencing; however, there remain challenges that impede the practical use of these approaches, considering the underlying biology of the tumor microenvironment and the impact of selective pressures on the evolvability of tumor cells. In this review, we will highlight the advances made thus far in defining the molecular heterogeneity in breast cancer and the implications thereof in diagnosis, the design and application of targeted therapies for improved clinical outcomes. We describe the different precision-based approaches to diagnosis and treatment and their prospects. We further propose that effective cancer diagnosis and treatment are dependent on unpacking the tumor microenvironment and its role in driving intratumoral heterogeneity. Underwriting such heterogeneity are Darwinian concepts of natural selection that we suggest need to be taken into account to ensure evolutionarily informed therapeutic decisions.

A reverse phase protein array based phospho-antibody characterization approach and its applicability for clinical derived tissue specimens

Systematic quantification of phosphoprotein within cell signaling networks in solid tissues remains challenging and precise quantification in large scale samples has great potential for biomarker identification and validation. We developed a reverse phase protein array (RPPA) based phosphor-antibody characterization approach by taking advantage of the lysis buffer compatible with alkaline phosphatase (AP) treatment that differs from the conventional RPPA antibody validation procedure and applied it onto fresh frozen (FF) and formalin-fixed and paraffin-embedded tissue (FFPE) to test its applicability. By screening 106 phospho-antibodies using RPPA, we demonstrated that AP treatment could serve as an independent factor to be adopted for rapid phospho-antibody selection. We also showed desirable reproducibility and specificity in clincical specimens indicating its potential for tissue-based phospho-protein profiling. Of further clinical significance, using the same approach, based on melanoma and lung cancer FFPE samples, we showed great interexperimental reproducibility and significant correlation with pathological markers in both tissues generating meaningful data that match clinical features. Our findings set a benchmark of an efficient workflow for phospho-antibody characterization that is compatible with high-plex clinical proteomics in precison oncology.

Protein Profiling of Breast Cancer for Treatment Decision-Making

The increasing use of neoadjuvant therapy has resulted in therapeutic decisions being made on the basis of diagnostic needle core biopsy. For many patients, this method might yield the only fragment of tumor available for biomarker analysis, necessitating judicious use. Many multiplex protein analytic methods have been developed that employ fluorescence or other tags to overcome the limitations of immunohistochemistry while still retaining the spatial annotation. Interpretation of the data can be difficult because of the limitations of the human eye. Computational deconvolution of the signals may be necessary for some of these methods to enable identification of cell-specific localization and coexpression of biomarkers. Herein, we present the different methods that are coming of age and their application in cancer research, with a focus on breast cancer. We also discuss the limitations, which include high costs and long turnaround times. The methods are also based on the premise that preanalytical factors will have identical impact on all proteins analyzed. There is a need to establish standards to normalize the data and enable cross-sample comparisons. In spite of these limitations, the multiplex technologies are extremely valuable discovery tools and can provide novel insights into the biology of cancer and mechanisms of drug resistance.

Systematic evaluation and optimization of protein extraction parameters in diagnostic FFPE specimens

Objectives

Formalin-fixed paraffin-embedded (FFPE) tissue is the standard material for diagnostic pathology but poses relevant hurdles to accurate protein extraction due to cross-linking and chemical alterations. While numerous extraction protocols and chemicals have been described, systematic comparative analyses are limited. Various parameters were thus investigated in their qualitative and quantitative effects on protein extraction (PE) efficacy. Special emphasis was put on preservation of membrane proteins (MP) as key subgroup of functionally relevant proteins.

Methods

Using the example of urothelial carcinoma, FFPE tissue sections were subjected to various deparaffinization, protein extraction and antigen retrieval protocols and buffers as well as different extraction techniques. Performance was measured by protein concentration and western blot analysis of cellular compartment markers as well as liquid chromatography-coupled mass spectrometry (LC–MS).

Results

Commercially available extraction buffers showed reduced extraction of MPs and came at considerably increased costs. On-slide extraction did not improve PE whereas several other preanalytical steps could be simplified. Systematic variation of temperature and exposure duration demonstrated a quantitatively relevant corridor of optimal antigen retrieval.

Conclusions

Preanalytical protein extraction can be optimized at various levels to improve unbiased protein extraction and to reduce time and costs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12014-022-09346-0.

Laser Capture Proteomics: spatial tissue molecular profiling from the bench to personalized medicine

INTRODUCTION

Laser Capture Microdissection (LCM) uses a laser to isolate, or capture, specific cells of interest in a complex heterogeneous tissue section, under direct microscopic visualization. Recently, there has been a surge of publications using LCM for tissue spatial molecular profiling relevant to a wide range of research topics.

AREAS COVERED

We summarize the many advances in tissue Laser Capture Proteomics (LCP) using mass spectrometry for discovery, and protein arrays for signal pathway network mapping. This review emphasizes: a) transition of LCM phosphoproteomics from the lab to the clinic for individualized cancer therapy, and b) the emerging frontier of LCM single cell molecular analysis combining proteomics with genomic, and transcriptomic analysis. The search strategy was based on the combination of MeSH terms with expert refinement.

EXPERT OPINION

LCM is complemented by a rich set of instruments, methodology protocols, and analytical A.I. (artificial intelligence) software for basic and translational research. Resolution is advancing to the tissue single cell level. A vision for the future evolution of LCM is presented. Emerging LCM technology is combining digital and AI guided remote imaging with automation, and telepathology, to a achieve multi-omic profiling that was not previously possible.

Magnetic Resonance Features of Lower-grade Gliomas in Prediction of the Reverse Phase Protein A

OBJECTIVES

The Cancer Genome Atlas Research Network identified 4 novel protein expression-defined subgroups in patients with lower-grade gliomas (LGGs). The RPPA3 subtype had high levels of Epidermal Growth Factor Receptor and Human epidermal growth factor receptor-2, further increasing the chances for targeted therapy. In this study, we aimed to explore the relationships between magnetic resonance features and reverse phase protein array (RPPA) subtypes (R1-R4).

METHODS

Survival estimates for the Cancer Genome Atlas cohort were generated using the Kaplan-Meier method and time-dependent receiver operating characteristic curves. A total of 153 patients with LGG with brain magnetic resonance imaging from The Cancer Imaging Archive were retrospectively analyzed. Least absolute shrinkage and selection operator algorithm was used to reduce the feature dimensions of the RPPA3 subtype.

RESULTS

A total of 51 (33.3%) RPPA1 subtype, 42 (27.4) RPPA2 subtype, 19 (12.4%) RPPA3 subtype, and 38 (24.8%) RPPA4 subtype were identified. On multivariate logistic regression analysis, subventricular zone involvement [odds ratio (OR), 0.370; P = 0.006; 95% confidence interval (CI), 0.181-0.757) was associated with RPPA1 subtype [area under the curve (AUC), 0.598]. Volume of 60 cm3 or greater (OR, 5.174; P < 0.001; 95% CI, 2.182-12.267) was associated with RPPA2 subtype (AUC, 0.684). Proportion contrast-enhanced tumor greater than 5% (OR, 4.722; P = 0.010; 95% CI, 1.456-15.317), extranodular growth (OR, 5.524; P = 0.010; 95% CI, 1.509-20.215), and L/CS ratio equal to or greater than median (OR, 0.132; P = 0.003; 95% CI, 0.035-0.500) were associated with RPPA3 subtype (AUC, 0.825). Proportion contrast-enhanced tumor greater than 5% (OR, 0.206; P = 0.005; 95% CI, 0.068-0.625) was associated with RPPA4 subtype (AUC, 0.638). For the prediction of RPPA3 subtype, the nomogram showed good discrimination, with an AUC of 0.825 (95% CI, 0.711-0.939) and was well calibrated. The RPPA3 subtype was associated with shortest mean overall survival (RPPA3 subtype vs other: 613 vs 873 days; P < 0.05). The time-dependent receiver operating characteristic curves for the RPPA3 subtype was 0.72 (95% CI, 0.60-0.84) for survival at 1 year. Decision curve analysis indicated that prediction for the RPPA3 model was clinically useful.

CONCLUSIONS

The RPPA3 subtype is an unfavorable prognostic biomarker for overall survival in patients with LGG. Radiogenomics analysis of magnetic resonance features can predict the RPPA subtype preoperatively and may be of clinical value in tailoring the management strategies in patients with LGG.

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.

A Machine Learning Platform to Optimize the Translation of Personalized Network Models to the Clinic

PURPOSE

Dynamic network models predict clinical prognosis and inform therapeutic intervention by elucidating disease-driven aberrations at the systems level. However, the personalization of model predictions requires the profiling of multiple model inputs, which hampers clinical translation.

PATIENTS AND METHODS

We applied APOPTO-CELL, a prognostic model of apoptosis signaling, to showcase the establishment of computational platforms that require a reduced set of inputs. We designed two distinct and complementary pipelines: a probabilistic approach to exploit a consistent subpanel of inputs across the whole cohort (Ensemble) and a machine learning approach to identify a reduced protein set tailored for individual patients (Tree). Development was performed on a virtual cohort of 3,200,000 patients, with inputs estimated from clinically relevant protein profiles. Validation was carried out in an in-house stage III colorectal cancer cohort, with inputs profiled in surgical resections by reverse phase protein array (n = 120) and/or immunohistochemistry (n = 117).

RESULTS

Ensemble and Tree reproduced APOPTO-CELL predictions in the virtual patient cohort with 92% and 99% accuracy while decreasing the number of inputs to a consistent subset of three proteins (40% reduction) or a personalized subset of 2.7 proteins on average (46% reduction), respectively. Ensemble and Tree retained prognostic utility in the in-house colorectal cancer cohort. The association between the Ensemble accuracy and prognostic value (Spearman ρ = 0.43; P = .02) provided a rationale to optimize the input composition for specific clinical settings. Comparison between profiling by reverse phase protein array (gold standard) and immunohistochemistry (clinical routine) revealed that the latter is a suitable technology to quantify model inputs.

CONCLUSION

This study provides a generalizable framework to optimize the development of network-based prognostic assays and, ultimately, to facilitate their integration in the routine clinical workflow.

Utility of a Reverse Phase Protein Array to Evaluate Multiple Biomarkers in Diffuse Large B-Cell Lymphoma

Purpose

Diffuse large B‐cell lymphoma (DLBCL), the most common non‐Hodgkin lymphoma, is a heterogeneous lymphoma with different clinical manifestations and molecular alterations, and several markers are currently being measured routinely for its diagnosis, subtyping, or prognostication by immunohistochemistry (IHC). Here, the utility of a reverse‐phase‐protein‐array (RPPA) as a novel supportive tool to measure multiple biomarkers for DLBCL diagnosis is validated.

Experimental design

The expression of seven markers (CD5, CD10, BCL2, BCL6, MUM1, Ki‐67, and C‐MYC) is analyzed by RPPA and IHC using 37 DLBCL tissues, and the correlation between the two methods is determined. To normalize tumor content ratio in the tissues, the raw RPPA values of each marker are adjusted by that of CD20 or PAX‐5.

Results

The CD20‐adjusted data for CD5, MUM1, BCL2, Ki‐67, and C‐MYC has better correlation with IHC results than PAX‐5‐adjusted data. Receiver operating characteristic (ROC) analysis reveals that CD5, MUM1, BCL2, and C‐MYC exhibit a better sensitivity and specificity >0.750. Furthermore, the CD20‐adjusted C‐MYC value strongly correlates with that of IHC, and has a particularly high specificity (0.882).

Conclusions and clinical relevance

Although further investigation using a large number of DLBCL specimens needs to be conducted, these results suggest that RPPA could be applicable as a supportive tool for determining lymphoma prognosis.

Identification of spleen tyrosine kinase as a potential therapeutic target for esophageal squamous cell carcinoma using reverse phase protein arrays

The vast majority of esophageal cancers in China, India and Iran are esophageal squamous cell carcinomas (ESCC). A timely diagnosis provides surgical removal as the main therapeutic option for patients with ESCC. Currently, there are no targeted therapies available for ESCC. We carried out reverse phase protein array-based protein expression profiling of seven ESCC-derivedcell lines and a non-neoplastic esophageal epithelial cell line (Het-1A) to identify differentially expressed proteins in ESCC. SYK non-receptortyrosine kinase was overexpressed in six out of seven ESCC cell lines that were used in the study. We evaluated the role of SYK in ESCC using the pharmacological inhibitor entospletinib (GS-9973) and siRNA-based knock down studies. Entospletinib is a selective inhibitor of SYK, which is currently being evaluated in phase II clinical trials for hematological malignancies. Using in vivo subcutaneous tumor xenografts in mice, we demonstrate that treatment with entospletinib significantly inhibits tumor growth. Further clinical studies are needed to prove the efficacy of entospletinib as a targeted therapeutic agent for treating ESCC.

Identification of spleen tyrosine kinase as a potential therapeutic target for esophageal squamous cell carcinoma using reverse phase protein arrays

The vast majority of esophageal cancers in China, India and Iran are esophageal squamous cell carcinomas (ESCC). A timely diagnosis provides surgical removal as the main therapeutic option for patients with ESCC. Currently, there are no targeted therapies available for ESCC. We carried out reverse phase protein array-based protein expression profiling of seven ESCC-derivedcell lines and a non-neoplastic esophageal epithelial cell line (Het-1A) to identify differentially expressed proteins in ESCC. SYK non-receptortyrosine kinase was overexpressed in six out of seven ESCC cell lines that were used in the study. We evaluated the role of SYK in ESCC using the pharmacological inhibitor entospletinib (GS-9973) and siRNA-based knock down studies. Entospletinib is a selective inhibitor of SYK, which is currently being evaluated in phase II clinical trials for hematological malignancies. Using in vivo subcutaneous tumor xenografts in mice, we demonstrate that treatment with entospletinib significantly inhibits tumor growth. Further clinical studies are needed to prove the efficacy of entospletinib as a targeted therapeutic agent for treating ESCC.

Impact of Phosphoproteomics in the Era of Precision Medicine for Prostate Cancer

Prostate cancer is the most common malignancy in men in the United States. While androgen deprivation therapy results in tumor responses initially, there is relapse and progression to metastatic castration-resistant prostate cancer. Currently, all prostate cancer patients receive essentially the same treatment, and there is a need for clinically applicable technologies to provide predictive biomarkers toward personalized therapies. Genomic analyses of tumors are used for clinical applications, but with a paucity of obvious driver mutations in metastatic castration-resistant prostate cancer, other applications, such as phosphoproteomics, may complement this approach. Immunohistochemistry and reverse phase protein arrays are limited by the availability of reliable antibodies and evaluates a preselected number of targets. Mass spectrometry-based phosphoproteomics has been used to profile tumors consisting of thousands of phosphopeptides from individual patients after surgical resection or at autopsy. However, this approach is time consuming, and while a large number of candidate phosphopeptides are obtained for evaluation, limitations are reduced reproducibility, sensitivity, and precision. Targeted mass spectrometry can help eliminate these limitations and is more cost effective and less time consuming making it a practical platform for future clinical testing. In this review, we discuss the use of phosphoproteomics in prostate cancer and other clinical cancer tissues for target identification, hypothesis testing, and possible patient stratification. We highlight the majority of studies that have used phosphoproteomics in prostate cancer tissues and cell lines and propose ways forward to apply this approach in basic and clinical research. Overall, the implementation of phosphoproteomics via targeted mass spectrometry has tremendous potential to aid in the development of more rational, personalized therapies that will result in increased survival and quality of life enhancement in patients suffering from metastatic castration-resistant prostate cancer.

Overcoming intratumoural heterogeneity for reproducible molecular risk stratification: a case study in advanced kidney cancer

BACKGROUND

Metastatic clear cell renal cell cancer (mccRCC) portends a poor prognosis and urgently requires better clinical tools for prognostication as well as for prediction of response to treatment. Considerable investment in molecular risk stratification has sought to overcome the performance ceiling encountered by methods restricted to traditional clinical parameters. However, replication of results has proven challenging, and intratumoural heterogeneity (ITH) may confound attempts at tissue-based stratification.

METHODS

We investigated the influence of confounding ITH on the performance of a novel molecular prognostic model, enabled by pathologist-guided multiregion sampling (n = 183) of geographically separated mccRCC cohorts from the SuMR trial (development, n = 22) and the SCOTRRCC study (validation, n = 22). Tumour protein levels quantified by reverse phase protein array (RPPA) were investigated alongside clinical variables. Regularised wrapper selection identified features for Cox multivariate analysis with overall survival as the primary endpoint.

RESULTS

The optimal subset of variables in the final stratification model consisted of N-cadherin, EPCAM, Age, mTOR (NEAT). Risk groups from NEAT had a markedly different prognosis in the validation cohort (log-rank p = 7.62 × 10^-7; hazard ratio (HR) 37.9, 95% confidence interval 4.1-353.8) and 2-year survival rates (accuracy = 82%, Matthews correlation coefficient = 0.62). Comparisons with established clinico-pathological scores suggest favourable performance for NEAT (Net reclassification improvement 7.1% vs International Metastatic Database Consortium score, 25.4% vs Memorial Sloan Kettering Cancer Center score). Limitations include the relatively small cohorts and associated wide confidence intervals on predictive performance. Our multiregion sampling approach enabled investigation of NEAT validation when limiting the number of samples analysed per tumour, which significantly degraded performance. Indeed, sample selection could change risk group assignment for 64% of patients, and prognostication with one sample per patient performed only slightly better than random expectation (median logHR = 0.109). Low grade tissue was associated with 3.5-fold greater variation in predicted risk than high grade (p = 0.044).

CONCLUSIONS

This case study in mccRCC quantitatively demonstrates the critical importance of tumour sampling for the success of molecular biomarker studies research where ITH is a factor. The NEAT model shows promise for mccRCC prognostication and warrants follow-up in larger cohorts. Our work evidences actionable parameters to guide sample collection (tumour coverage, size, grade) to inform the development of reproducible molecular risk stratification methods.

Establishment of an orthotopic patient-derived xenograft mouse model using uveal melanoma hepatic metastasis

Background

Metastatic uveal melanoma is a highly fatal disease; most patients die from their hepatic metastasis within 1 year. A major drawback in the development of new treatments for metastatic uveal melanoma is the difficulty in obtaining appropriate cell lines and the lack of appropriate animal models. Patient-derived xenograft (PDX) tumor models, bearing ectopically implanted tumors at a subcutaneous site, have been developed. However, these ectopically implanted PDX models have obstacles to translational research, including a low engraftment rate, slow tumor growth, and biological changes after multiple passages due to the different microenvironment. To overcome these limitations, we developed a new method to directly transplant biopsy specimens to the liver of immunocompromised mice.

Results

By using two metastatic uveal melanoma cell lines, we demonstrated that the liver provides a more suitable microenvironment for tumor growth compared to subcutaneous sites and that surgical orthotopic implantation (SOI) of tumor pieces allows the creation of a liver tumor in immunocompromised mice. Subsequently, 10 of 12 hepatic metastasis specimens from patients were successfully xenografted into the immunocompromised mice (83.3% success rate) using SOI, including 8 of 10 needle biopsy specimens (80%). Additionally, four cryopreserved PDX tumors were re-implanted to new mice and re-establishment of PDX tumors was confirmed in all four mice. The serially passaged xenograft tumors as well as the re-implanted tumors after cryopreservation were similar to the original patient tumors in histologic, genomic, and proteomic expression profiles. CT imaging was effective for detecting and monitoring PDX tumors in the liver of living mice. The expression of Ki67 in original patient tumors was a predictive factor for implanted tumor growth and the success of serial passages in PDX mice.

Conclusions

Surgical orthotopic implantation of hepatic metastasis from uveal melanoma is highly successful in the establishment of orthotopic PDX models, enhancing their practical utility for research applications. By using CT scan, tumor growth can be monitored, which is beneficial to evaluate treatment effects in interventional studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12967-017-1247-z) contains supplementary material, which is available to authorized users.