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Alterations in DNAs could not reveal what happened in proteins. The accumulated alterations of DNAs would change the manifestation of proteins. Therefore, as is the case in cancer liquid biopsies, deep proteome profiling will likely provide invaluable and clinically relevant information in real-time throughout all stages of cancer progression. However, due to the great complexity of proteomes in liquid biopsy samples and the limitations of proteomic technologies compared to high-plex sequencing technologies, proteomic discoveries have yet lagged behind their counterpart, genomic technologies. Therefore, novel protein technologies are in urgent demand to fulfill the goals set out for biomarker discovery in cancer liquid biopsies. Notably, conventional and innovative technologies are being rapidly developed for proteomic analysis in cancer liquid biopsies. These advances have greatly facilitated early detection, diagnosis, prognosis, and monitoring of cancer evolution, adapted or adopted in response to therapeutic interventions. In this paper, we review the high-plex proteomics technologies that are capable of measuring at least hundreds of proteins simultaneously from liquid biopsy samples, ranging from traditional technologies based on mass spectrometry (MS) and antibody/antigen arrays to innovative technologies based on aptamer, proximity extension assay (PEA), and reverse phase protein arrays (RPPA).

Background Myeloid epithelial reproductive tyrosine kinase (MERTK) receptor is overexpressed in cancers and is associated with poor prognosis. RGX-019-MMAE, a novel humanized IgG1-MMAE antibody-drug conjugate (ADC) (Inspirna, Inc), selectively binds to MERTK with high affinity, resulting in internalization and degradation of the receptor. It then induces cytotoxicity through the release of the payload, MMAE (monomethyl auristatin E), which disrupts mitosis. Methods MERTK protein expression was analyzed in 818 AML patients using Reverse Phase Protein Arrays (RPPA). Expression was also assessed by flow cytometry in eight AML cell lines and peripheral blood or bone marrow mononuclear cells from five AML patients. Cell lines with the highest MERTK expression were treated with varying doses of RGX-019-MMAE or naked antibody for 120 h, and viability was measured using CellTiter-Glo 2.0. Similarly, primary cells from five AML patients were treated to assess the anti-leukemic effect of RGX-019-MMAE. Further, the combinatorial effects of RGX-019-MMAE with venetoclax (BCL2 inhibitor) were evaluated in vitro. Results Reverse-phase protein array in 818 primary AML samples revealed significantly high MERTK protein expression in monocytic acute myeloid leukemia (AML), especially in those with PTPN11, RAS, CEBPA mutations, t (9;11) translocation, and high WBC count, suggesting its potential as a therapeutic target in AML. We also observed varying degrees of MERTK expression in AML cell lines, with highest expression in Kasumi-1 and OCI-AML3. Treatment of these cell lines with the anti-MERTK antibody-drug conjugate RGX-019-MMAE resulted in significantly more leukemic cell killing than the control antibody in a dose-dependent manner. We validated this finding in MERTK-expressing primary AML samples expressing MERTK. Interestingly, RGX-019-MMAE had no effect on normal hematopoietic stem cells’ clonogenic potential. Further, treatment with RGX-019-MMAE inhibited AML progression in vivo and significantly prolonged survival of AML xenograft-bearing mice in a dose-dependent manner. Moreover, treatment with RGX-019-MMAE sensitized AML cells to venetoclax in a dose-dependent manner. Conclusion MERTK is overexpressed in AML and could serve as a therapeutic target. Furthermore, RGX-019 MMAE can be used as a novel therapeutic approach for treating AML, especially in treating monocytic subsets of AML.

Personalized (patient-specific) approaches have recently emerged with a precision medicine paradigm that acknowledges the fact that molecular pathway structures and activity might be considerably different within and across tumors. The functional cancer genome and proteome provide rich sources of information to identify patient-specific variations in signaling pathways and activities within and across tumors; however, current analytic methods lack the ability to exploit the diverse and multi-layered architecture of these complex biological networks. We assessed pan-cancer pathway activities for >7700 patients across 32 tumor types from The Cancer Proteome Atlas by developing a personalized cancer-specific integrated network estimation (PRECISE) model. PRECISE is a general Bayesian framework for integrating existing interaction databases, data-driven de novo causal structures, and upstream molecular profiling data to estimate cancer-specific integrated networks, infer patient-specific networks and elicit interpretable pathway-level signatures. PRECISE-based pathway signatures, can delineate pan-cancer commonalities and differences in proteomic network biology within and across tumors, demonstrates robust tumor stratification that is both biologically and clinically informative and superior prognostic power compared to existing approaches. Towards establishing the translational relevance of the functional proteome in research and clinical settings, we provide an online, publicly available, comprehensive database and visualization repository of our findings ( https://mjha.shinyapps.io/PRECISE/ ).

The primary objective was to determine if multi-omic molecular profiling (MMP) informed selection of approved cancer treatments could change the clinical course of disease for patients with previously treated metastatic breast cancer (MBC) (i.e., produce a growth modulation index (GMI) ≥1.3). GMI was calculated as the ratio of progression free survival on MMP-selected therapy/time to progression on last prior treatment. To meet the primary objective at least 35 % of the subjects should demonstrate a GMI ≥1.3. Secondary endpoints included determining the response rate (according to RECIST 1.1), the percent of patients with non-progression at 4 months, and overall survival in patients whose therapy is selected by molecular profiling and proteomic analysis. Eligible patients had MBC, with ≥3 prior lines of therapy. A multi-omic based approach was performed incorporating multiplexed immunohistochemistry, c-DNA microarray, and phosphoprotein pathway activation mapping by reverse phase protein array. MMP was performed on fresh core biopsies; results were generated and sent to a Treatment Selection Committee (TSC) for review and treatment selection. Three sites enrolled 28 patients, of which 25 were evaluable. The median range of prior treatment was 7 (range 3–12). The MMP analysis and treatment recommendation were delivered within a median of 15.5 days from biopsy (range 12–23). The TSC selected MMP-rationalized treatment in 100 % (25/25) of cases. None of the MMP-based therapies were the same as what the clinician would have selected if the MMP had not been performed. GMI ≥1.3 was reported in 11/25 (44 %) patients. Partial responses were noted in 5/25 (20 %), stable disease in 8/25 (32 %) and 9/25 (36 %) had no progression at 4 months. This pilot study demonstrates the feasibility of finding possible treatments for patients with previously treated MBC using a multiplexed MMP-rationalized treatment recommendation. This MMP approach merits further investigation.

Background In 2016, it is estimated that there will be 62,700 new cases of kidney cancer in the United States, and 14,240 patients will die from the disease. Because the incidence of kidney renal clear cell carcinoma (KIRC), the most common type of kidney cancer, is expected to continue to increase in the US, there is an urgent need to find effective diagnostic biomarkers for KIRC that could help earlier detection of and customized treatment strategies for the disease. Accordingly, in this study we systematically investigated KIRC’s prognostic biomarkers for survival using the reverse phase protein array (RPPA) data and the high throughput sequencing data from The Cancer Genome Atlas (TCGA). Results With comprehensive data available in TCGA, we systematically screened protein expression based survival biomarkers in 10 major cancer types, among which KIRC presented many protein prognostic biomarkers of survival time. This is in agreement with a previous report that expression level changes (mRNAs, microRNA and protein) may have a better performance for prognosis of KIRC. In this study, we also identified 52 prognostic genes for KIRC, many of which are involved in cell-cycle and cancer signaling, as well as 15 tumor-stage-specific prognostic biomarkers. Notably, we found fewer prognostic biomarkers for early-stage than for late-stage KIRC. Four biomarkers (the RPPA protein IDs: FASN, ACC1, Cyclin_B1 and Rad51) were found to be prognostic for survival based on both protein and mRNA expression data. Conclusions Through pan-cancer screening, we found that many protein biomarkers were prognostic for patients’ survival in KIRC. Stage-specific survival biomarkers in KIRC were also identified. Our study indicated that these protein biomarkers might have potential clinical value in terms of predicting survival in KIRC patients and developing individualized treatment strategies. Importantly, we found many biomarkers in KIRC at both the mRNA expression level and the protein expression level. These biomarkers shared a significant overlap, indicating that they were technically replicable.

Cysteine-Rich Secretory Proteins (CRiSPs) are typically found in many snake venoms; however, the role that these toxins play in the pathophysiology of snakebites is still unclear. Herein, we compared the effects of snake venom CRiSPs (svCRiSPs) from the most medically important species of North American snakes on endothelial cell permeability and vascular permeability. We used reverse phase protein array (RPPA) to identify key signaling molecules on human dermal lymphatic (HDLECs) and blood (HDBECs) endothelial cells treated with svCRiSPs. The results showed that Css-CRiSP isolated from Crotalus scutulatus scutulatus and App-CRiSP from Agkistrodon piscivorus piscivorus are the most potent causes of increase vascular and endothelial permeability in comparison with other svCRiSPs used in this study. We examined the protein expression levels and their activated phosphorylation states in HDLECs and HDBECs induced by App-CRiSP and Css-CRiSP using RPPA. Interestingly, both App-CRiSP and Css-CRiSP induced caveolin-1 expression in HDBECs. We also found that stimulating HDBECs with Css-CRiSP and App-CRiSP significantly induced the phosphorylation of mTOR and Src, respectively. In HDLECs, Css-CRiSP significantly downregulated the expression of N-Cadherin and phospholipase C-gamma, while App-CRiSP significantly enhanced Akt and JNK phosphorylation. These results suggest that the increased endothelial permeability in HDLECs and HDBECs by Css-CRiSP and App-CRiSP may occur through different pathways.

We analyze time-course protein activation data to track the changes in protein expression over time after exposure to drugs such as protein inhibitors. Protein expression is expected to change over time in response to the intervention in different ways due to biological pathways. We therefore allow for clusters of proteins with different treatment effects, and allow these clusters to change over time. As the effect of the drug wears off, protein expression may revert back to the level before treatment. In addition, different drugs, doses, and cell lines may have different effects in altering the protein expression. To model and understand this process we develop random partitions that define a refinement and coagulation of protein clusters over time. We demonstrate the approach using a time-course reverse phase protein array (RPPA) dataset consisting of protein expression measurements under different drugs, dose levels, and cell lines. The proposed model can be applied in general to time-course data where clustering of the experimental units is expected to change over time in a sequence of refinement and coagulation.

Background: Oesophageal adenocarcinomas often show resistances to chemotherapy (CTX), therefore, it would be of high interest to better understand the mechanisms of resistance. We examined the expression of heat-shock proteins (HSPs) and glucose-regulated proteins (GRPs) in pretherapeutic biopsies of oesophageal adenocarcinomas to assess their potential role in CTX response. Methods: Ninety biopsies of locally advanced adenocarcinomas before platin/5-fluorouracil (FU)-based CTX were investigated by reverse phase protein arrays (RPPAs), immunohistochemistry (IHC) and quantitative RT–PCR. Results: CTX response strongly correlated with survival ( P =0.001). Two groups of tumours with specific protein expression patterns were identified by RPPA: Group A was characterised by low expression of HSP90, HSP27 and p-HSP27 (Ser15, Ser78, Ser82) and high expression of GRP78, GRP94, HSP70 and HSP60; Group B exhibited the inverse pattern. Tumours of Group A were more likely to respond to CTX, resulting in histopathological tumour regression ( P =0.041) and post-therapeutic down-categorisation from cT3 to ypT0–T2 ( P =0.040). High HSP60 protein (IHC) and mRNA expression were also associated with tumour down-categorisation ( P =0.016 and P =0.004). Conclusion: Our findings may enhance the understanding of CTX response mechanisms, might be helpful to predict CTX response and might have translational relevance as they highlight the role of potentially targetable cellular stress proteins in the context of CTX response.

Background Mitogen-activated protein kinases (MAPKs) are signalling transduction molecules that have different functions and diverse behaviour in cancer. In breast cancer, MAPK is related to oestrogen receptor (ER) and HER2. Methods Protein expression of a large panel of MAPKs (JNK1/2, ERK, p38, C-JUN and ATF2 including phosphorylated forms) were assessed immunohistochemically in a large ( n  = 1400) and well-characterised breast cancer series prepared as tissue microarray. Moreover, reverse phase protein array was applied to quantify protein expression of MAPKs in six breast cancer cell lines with different phenotypes including HER2-transfected cells. Results MAPKs expression was associated with clinicopathological variables characteristic of good prognosis. These associations were most significant in the whole series and in the ER+ subgroup compared to other BC classes. Most of MAPKs showed a positive association with ER, BCL2 and better outcome and were negatively associated with the proliferation marker Ki67 and p53. Association of MAPK with HER2 was mainly seen in the ER- subgroup. Reverse phase protein array confirmed immunohistochemistry results and revealed differential expression of MAPK proteins in ER+ and ER− cell lines. Conclusions MAPKs are associated with good prognosis and their expression is mainly related to ER. Studying a large panel rather than individual biomarkers may provide improved understanding of the pathway.

Epithelial ovarian cancer is a highly metastatic disease and the leading cause of death among cancer of the female genital tract. Abnormal epidermal growth factor receptor (EGFR) signalling has been shown to be involved in epithelial–mesenchymal transition (EMT), an early step during metastasis. Additionally, over-expression of the E-cadherin repressor Snail, a key regulator of EMT, has previously been found to be associated with unfavourable prognostic features. Thus, the aim of our study was to elucidate the role of EGFR-dependent signalling pathways for Snail expression in ovarian cancer. For this purpose, we analysed 25 formalin-fixed and paraffin-embedded (FFPE) primary tumours and their corresponding metastases for the expression of 25 signalling pathway molecules by reverse phase protein arrays. We found a significant correlation of Snail with EGFR (Tyr1086) and p38 MAPK (Thr180/Tyr182) in primary ovarian carcinoma and with EGFR (Tyr1086) in their corresponding metastasis. Additionally, we showed that high expression levels of Snail in primary tumours combined with high expression levels of the phosphorylated p38 MAPK (Thr180/Tyr182) in metastasis lead to an increased risk for death in ovarian carcinoma patients. Thus, for future combinatorial cancer therapy, drug combinations that best target the deregulated protein network in each individual patient should be selected.