Adaptive protein and phosphoprotein networks which promote therapeutic sensitivity or acquired resistance

Integrating phosphoproteomics into kinase-targeted cancer therapies in precision medicine

Protein phosphorylation is a post-translational modification that is involved in the regulation of all major biological processes in cells. As a rapid and reversible means to modulate protein activity and transduce signals, aberrant protein phosphorylation is implicated in the onset and progression of most cancer types. Therefore, pharmacological inhibitors against protein kinases are highly pursued therapeutic approaches for treating cancer. Phosphoproteomics has become an important approach for investigating protein phosphorylation, and it is a technique that provides measurements of kinase pathway activation and the circuitry of signalling networks with both spatial and temporal resolution. Combined with the recent advances in mass spectrometry and development in biochemical procedures for phosphopeptide enrichment and computational approaches, high-throughput phosphoproteomics enables the investigation of kinase signalling networks with unprecedented depth. Here, we review the recent progresses in phosphoproteomics methodology and how phosphoproteomics profiling could be implemented in translational research to aid cancer therapies, facilitate novel drug target discovery and overcome the therapeutic obstacles caused by drug resistance. SIGNIFICANCE: In this review, we summarized the recent progress in mass spectrometry-based phosphoproteomics and discussed how phosphoproteomics profiling can be implemented in translational research to aid cancer therapies, facilitate novel drug target discovery and overcome the therapeutic obstacles caused by drug resistance due to the rapid remodelling of signalling networks in response to kinase inhibitor treatment. In addition, we addressed the insights and challenges of applying MS phosphoproteomics in clinical routine practice in precision medicine. This review will help readers become more familiar with the recent advancements and applications of phosphoproteomics, especially in the field of kinase-targeted cancer therapy.

Integrated analysis of proteome, phosphotyrosine-proteome, tyrosine-kinome, and tyrosine-phosphatome in acute myeloid leukemia

Reversible protein-tyrosine phosphorylation is catalyzed by the antagonistic actions of protein-tyrosine kinases (PTKs) and phosphatases (PTPs), and represents a major form of cell regulation. Acute myeloid leukemia (AML) is an aggressive hematological malignancy that results from the acquisition of multiple genetic alterations, which in some instances are associated with deregulated protein-phosphotyrosine (pY) mediated signaling networks. However, although individual PTKs and PTPs have been linked to AML and other malignancies, analysis of protein-pY networks as a function of activated PTKs and PTPs has not been done. In this study, MS was used to characterize AML proteomes, and phospho-proteome-subsets including pY proteins, PTKs, and PTPs. AML proteomes resolved into two groups related to high or low degrees of maturation according to French-American-British classification, and reflecting differential expression of cell surface antigens. AML pY proteomes reflect canonical, spatially organized signaling networks, unrelated to maturation, with heterogeneous expression of activated receptor and nonreceptor PTKs. We present the first integrated analysis of the pY-proteome, activated PTKs, and PTPs. Every PTP and most PTKs have both positive and negative associations with the pY-proteome. pY proteins resolve into groups with shared PTK and PTP correlations. These findings highlight the importance of pY turnover and the PTP phosphatome in shaping the pY-proteome in AML.

An 80-gene set to predict response to preoperative chemoradiotherapy for rectal cancer by principle component analysis

Preoperative chemoradiotherapy (CRT) for locally advanced rectal cancer not only improves the postoperative local control rate, but also induces downstaging. However, it has not been established how to individually select patients who receive effective preoperative CRT. The aim of this study was to identify a predictor of response to preoperative CRT for locally advanced rectal cancer. This study is additional to our multicenter phase II study evaluating the safety and efficacy of preoperative CRT using oral fluorouracil (UMIN ID: 03396). From April, 2009 to August, 2011, 26 biopsy specimens obtained prior to CRT were analyzed by cyclopedic microarray analysis. Response to CRT was evaluated according to a histological grading system using surgically resected specimens. To decide on the number of genes for dividing into responder and non-responder groups, we statistically analyzed the data using a dimension reduction method, a principle component analysis. Of the 26 cases, 11 were responders and 15 non-responders. No significant difference was found in clinical background data between the two groups. We determined that the optimal number of genes for the prediction of response was 80 of 40,000 and the functions of these genes were analyzed. When comparing non-responders with responders, genes expressed at a high level functioned in alternative splicing, whereas those expressed at a low level functioned in the septin complex. Thus, an 80-gene expression set that predicts response to preoperative CRT for locally advanced rectal cancer was identified using a novel statistical method.

Role of phosphoproteomics in the development of personalized cancer therapies

Cell signalling pathways driven by protein and lipid kinases contribute to the onset and progression of virtually all cancer types. Consequently, several inhibitors against these enzymes have clinical utility for the treatment of different forms of cancer. A problem that hampers further development is that not all patients respond equally well to kinase inhibitors and a significant proportion of those that initially respond eventually develop resistance. This review considers how an integrative analysis of kinase signalling may be used to address this issue. Advances in the biophysics of mass spectrometry, in biochemical procedures for phosphopeptide enrichment, and in computational approaches for label-free quantification have contributed to the development of phosphoproteomics workflows compatible with the analysis of clinical material. These developments, together with new bioinformatics tools to derive information on signalling circuitry from phosphoproteomics data, allow investigating kinase networks with unprecedented depth. Phosphoproteomics technology is starting to be used in translational research and, with further developments, such methods may also be able to measure the circuitry of cancer signalling networks in routine clinical assays. This review reflects on how this information could be used to accurately predict the best kinase inhibitor for each individual cancer patient.