Chromatin Profiles Are Prognostic of Clinical Response to Bortezomib-Containing Chemotherapy in Pediatric Acute Myeloid Leukemia: Results from the COG AAML1031 Trial

The addition of the proteasome inhibitor bortezomib to standard chemotherapy did not improve survival in pediatric acute myeloid leukemia (AML) when all patients were analyzed as a group in the Children's Oncology Group phase 3 trial AAML1031 (NCT01371981). Proteasome inhibition influences the chromatin landscape and proteostasis, and we hypothesized that baseline proteomic analysis of histone- and chromatin-modifying enzymes (HMEs) would identify AML subgroups that benefitted from bortezomib addition. A proteomic profile of 483 patients treated with AAML1031 chemotherapy was generated using a reverse-phase protein array. A relatively high expression of 16 HME was associated with lower EFS and higher 3-year relapse risk after AML standard treatment compared to low expressions (52% vs. 29%, p = 0.005). The high-HME profile correlated with more transposase-accessible chromatin, as demonstrated via ATAC-sequencing, and the bortezomib addition improved the 3-year overall survival compared with standard therapy (62% vs. 75%, p = 0.033). These data suggest that there are pediatric AML populations that respond well to bortezomib-containing chemotherapy.

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.

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

INTRODUCTION

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

AREAS COVERED

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

EXPERT OPINION

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

Pathway profiling of a novel SRC inhibitor, AZD0424, in combination with MEK inhibitors for cancer treatment

A more comprehensive understanding of how cells respond to drug intervention, the likely immediate signalling responses and how resistance may develop within different microenvironments will help inform treatment regimes. The nonreceptor tyrosine kinase SRC regulates many cellular signalling processes, and pharmacological inhibition has long been a target of cancer drug discovery projects. Here, we describe the in vitro and in vivo characterisation of the small-molecule SRC inhibitor AZD0424. We show that AZD0424 potently inhibits the phosphorylation of tyrosine-419 of SRC (IC50 ~ 100 nm) in many cancer cell lines; however, inhibition of cell viability, via a G1 cell cycle arrest, was observed only in a subset of cancer cell lines in the low (on target) micromolar range. We profiled the changes in intracellular pathway signalling in cancer cells following exposure to AZD0424 and other targeted therapies using reverse-phase protein array (RPPA) analysis. We demonstrate that SRC is activated in response to treatment of KRAS-mutant colorectal cell lines with MEK inhibitors (trametinib or AZD6244) and that AZD0424 abrogates this. Cell lines treated with trametinib or AZD6244 in combination with AZD0424 had reduced EGFR, FAK and SRC compensatory activation, and cell viability was synergistically inhibited. In vivo, trametinib treatment of mice-bearing HCT116 tumours increased phosphorylation of SRC on Tyr419, and, when combined with AZD0424, inhibition of tumour growth was greater than with trametinib alone. We also demonstrate that drug-induced resistance to trametinib is not re-sensitised by AZD0424 treatment in vitro, likely as a result of multiple compensatory signalling mechanisms; however, inhibition of SRC remains an effective way to block invasion of trametinib-resistant tumour cells. These data imply that SRC inhibition may offer a useful addition to MEK inhibitor combination strategies.

A proteomic study of myeloproliferative neoplasms using reverse-phase protein arrays

Myeloproliferative neoplasms polycythemia vera (PV), essential thrombocythaemia (ET) and primary myelofibrosis constitute a group of haematological diseases. The comprehensive assessment of signaling pathway activation in blood cells may aid the understanding of MPN pathophysiology. Thus, levels of post-translational protein modifications and total protein expression were determined in MPN patients and control leukocytes by using reverse-phase protein arrays (RPPA). Compared to control samples, p-SRC, p-CTNNB1, c-MYC, MCL-1, p-MDM2, BAX and CCNB1 showed higher expression in PV samples than controls. P-JAK2/JAK2 and pro-apoptotic BIM showed differential expression between JAK2V617F-positive and -negative ET patients. Apoptosis, cancer and PI3K/AKT pathways proteins showed differential expression among the studied groups. For most of the proteins analyzed using Western-Blot and RPPA, RPPA showed higher sensitivity to detect subtle differences. Taken together, our data indicate deregulated protein expression in MPN patients compared to controls. Thus, RPPA may be a useful method for broad proteome analysis in MPN patients´ leukocytes.

Credentialing Individual Samples for Proteogenomic Analysis

An integrated analysis of DNA, RNA and protein, so called proteogenomic studies, has the potential to greatly increase our understanding of both normal physiology and disease development. However, such studies are challenged by a lack of a systematic approach to credential individual samples resulting in the introduction of noise into the system that limits the ability to identify important biological signals. Indeed, a recent proteogenomic CPTAC study identified 26% of samples as unsatisfactory, resulting in a marked increase in cost and loss of information content. Based on a large-scale analysis of RNA-seq and proteomic data generated by reverse phase protein arrays (RPPA) and by mass spectrometry, we propose a protein-mRNA correlation-based (PMC) score as a robust metric to credential single samples for integrated proteogenomic studies. Samples with high PMC scores have significantly higher protein-mRNA correlation, total protein content and tumor purity. Our results highlight the importance of credentialing individual samples prior to proteogenomic analysis.

Effects of low dose ionizing radiation on DNA damage-caused pathways by reverse-phase protein array and Bayesian networks

Ionizing radiation (IR) causing damages to Deoxyribonucleic acid (DNA) constitutes a broad range of base damage and double strand break, and thereby, it induces the operation of relevant signaling pathways such as DNA repair, cell cycle control, and cell apoptosis. The goal of this paper is to study how the exposure to low dose radiation affects the human body by observing the signaling pathway associated with Ataxia Telangiectasia mutated (ATM) using Reverse-Phase Protein Array (RPPA) and isogenic human Ataxia Telangiectasia (A-T) cells under different amounts and durations of IR exposure. In order to verify which proteins could be involved in a DNA damage-caused pathway, only proteins that highly interact with each other under IR are selected by using correlation coefficient. The pathway inference is derived from learning Bayesian networks in combination with prior knowledge such as Protein-Protein Interactions (PPIs) and signaling pathways from well-known databases. Learning Bayesian networks is based on a score and search scheme that provides the highest scored network structure given a score function, and the prior knowledge is included in the score function as a prior probability by using Dempster-Shafer theory (DST). In this way, the inferred network can be more likely to be similar to already discovered pathways and consistent with confirmed PPIs for more reliable inference. The experimental results show which proteins are involved in signaling pathways under IR, how the inferred pathways are different under low and high doses of IR, and how the selected proteins regulate each other in the inferred pathways. As our main contribution, overall results confirm that low dose IR could cause DNA damage and thereby induce and affect related signaling pathways such as apoptosis, cell cycle, and DNA repair.

Characterization of Human Cancer Cell Lines by Reverse-phase Protein Arrays

Cancer cell lines are major model systems for mechanistic investigation and drug development. However, protein expression data linked to high-quality DNA, RNA, and drug-screening data have not been available across a large number of cancer cell lines. Using reverse-phase protein arrays, we measured expression levels of ∼230 key cancer-related proteins in >650 independent cell lines, many of which have publically available genomic, transcriptomic, and drug-screening data. Our dataset recapitulates the effects of mutated pathways on protein expression observed in patient samples, and demonstrates that proteins and particularly phosphoproteins provide information for predicting drug sensitivity that is not available from the corresponding mRNAs. We also developed a user-friendly bioinformatic resource, MCLP, to help serve the biomedical research community.

Modified SuperCurve Method for Analysis of Reverse-Phase Protein Array Data

Reverse-phase protein arrays (RPPAs) are widely used in biological and biomedical fields of study. One of the most popular analytic methods in RPPA data analysis is the SuperCurve method, which requires estimation of the background fluorescence level. This estimation is usually not accurate and has sample bias and spatial bias. Here, we propose a taking-the-difference method to overcome this problem. Briefly, for each two consecutive RPPA cycles, we subtract the later cycle from the earlier cycle, transforming the m-cycle data into m-1 cycle of data. This removes most of the background fluorescence noise. We then use the m-1 cycle of data to fit a new model accordingly derived from the SuperCurve model. To evaluate our proposed method, we compare the accuracy and precision between our proposed model and the original SuperCurve model by testing them on both real and simulated datasets. For both situations, our modified model shows improved results. The modified SuperCurve method is easy to perform and the taking-the-difference idea is recommended for application to all current methods of RPPA data analysis.

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

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

High-throughput screens identify microRNAs essential for HER2 positive breast cancer cell growth

MicroRNAs (miRNAs) are non-coding RNAs regulating gene expression post-transcriptionally. We have characterized the role of miRNAs in regulating the human epidermal growth factor receptor 2 (HER2)-pathway in breast cancer. We performed miRNA gain-of-function assays by screening two HER2 amplified cell lines (KPL-4 and JIMT-1) with a miRNA mimic library consisting of 810 human miRNAs. The levels of HER2, phospho-AKT, phospho-ERK1/2, cell proliferation (Ki67) and apoptosis (cPARP) were analyzed with reverse-phase protein arrays. Rank product analyses identified 38 miRNAs (q < 0.05) as inhibitors of HER2 signaling and cell growth, the most effective being miR-491-5p, miR-634, miR-637 and miR-342-5p. We also characterized miRNAs directly targeting HER2 and identified seven novel miRNAs (miR-552, miR-541, miR-193a-5p, miR-453, miR-134, miR-498, and miR-331-3p) as direct regulators of the HER2 3'UTR. We demonstrated the clinical relevance of the miRNAs and identified miR-342-5p and miR-744* as significantly down-regulated in HER2-positive breast tumors as compared to HER2-negative tumors from two cohorts of breast cancer patients (101 and 1302 cases). miR-342-5p specifically inhibited HER2-positive cell growth, as it had no effect on the growth of HER2-negative control cells in vitro. Furthermore, higher expression of miR-342-5p was associated with better survival in both breast cancer patient cohorts. In conclusion, we have identified miRNAs which are efficient negative regulators of the HER2 pathway that may play a role in vivo during breast cancer progression. These results give mechanistic insights in HER2 regulation which may open potential new strategies towards prevention and therapeutic inhibition of HER2-positive breast cancer.

Prognostic impact of phosphorylated HER-2 in HER-2+ primary breast cancer

PURPOSE

Tyrosine 1248 is one of the autophosphorylation sites of human epidermal growth factor receptor (HER)-2. We determined the prognostic value of the expression level of tyrosine 1248-phosphorylated HER-2 (pHER-2) in patients with HER-2(+) primary breast cancer using a reverse-phase protein array.

PATIENTS AND METHODS

The optimal cutoff value of pHER-2 for segregating disease-free survival (DFS) was determined by receiver operating characteristic (ROC) curve analysis. Five-year DFS for pHER-2 expression level was estimated with the Kaplan-Meier method using both derivation (n = 162) and validation (n = 227) cohorts.

RESULTS

Of the 162 patients in the derivation cohort, 26 had high HER-2 expression levels. The area under the ROC curve for pHER-2 level and DFS was 0.662. Nineteen of the 162 patients (11.7%) had high pHER-2 expression levels (pHER-2(high)); 143 patients (88.3%) had low pHER-2 expression levels (pHER-2(low)). Among the 26 patients with high HER-2 expression levels, the 17 pHER-2(high) patients had a significantly lower 5-year DFS rate than the nine pHER-2(low) patients (23.5% versus 77.8%). On multivariate analysis, only pHER-2(high) independently predicted DFS in the Cox proportional hazards model. In the validation cohort, among 61 patients with high HER-2 expression, the difference in 5-year DFS rates between pHER-2(high) (n = 7) and pHER-2(low) (n = 54) patients was marginal (57.1% versus 81.5%).

CONCLUSION

In patients with HER-2(+) primary breast cancer, pHER-2(high) patients had a lower 5-year DFS rate than pHER-2(low) patients. Quantification of pHER-2 expression level may provide prognostic information beyond the current standard HER-2 status.

A well-based reverse-phase protein array applicable to extracts from formalin-fixed paraffin-embedded tissue

Proteomic analysis of formalin-fixed paraffin-embedded (FFPE) tissue offers significant diagnostic utility but is complicated due to the high level of covalently crosslinked proteins arising from formalin fixation. To address these challenges, we developed a reliable protein extraction method for FFPE tissue, based on heat-induced antigen retrieval within a pressure cooker. The protein extraction yield from archival FFPE tissue section is approximately 90% of that recovered from frozen tissue. This method demonstrates preservation of immunoreactivity and recovery of full-length proteins by Western blotting. Additionally, we developed a well-based RP protein array platform utilizing an electrochemiluminescence detection system. Protein samples derived from FFPE tissue by means of laser capture dissection, with as few as 500 shots demonstrate measurable signal differences for different proteins. The lysates coated to the array plate, remain stable over 1 month at room temperature. Theses data suggest that this new protein-profiling platform coupled with the protein extraction method can be used for molecular profiling analysis in FFPE tissue, and contribute to the validation and development of biomarkers in clinical studies.