Phosphoproteomics data classify hematological cancer cell lines according to tumor type and sensitivity to kinase inhibitors

Abstract

BackgroundTumor classification based on their predicted responses to kinase inhibitors is a major goal for advancing targeted personalized therapies. Here, we used a phosphoproteomic approach to investigate biological heterogeneity across hematological cancer cell lines including acute myeloid leukemia, lymphoma, and multiple myeloma.ResultsMass spectrometry was used to quantify 2,000 phosphorylation sites across three acute myeloid leukemia, three lymphoma, and three multiple myeloma cell lines in six biological replicates. The intensities of the phosphorylation sites grouped these cancer cell lines according to their tumor type. In addition, a phosphoproteomic analysis of seven acute myeloid leukemia cell lines revealed a battery of phosphorylation sites whose combined intensities correlated with the growth-inhibitory responses to three kinase inhibitors with remarkable correlation coefficients and fold changes (> 100 between the most resistant and sensitive cells). Modeling based on regression analysis indicated that a subset of phosphorylation sites could be used to predict response to the tested drugs. Quantitative analysis of phosphorylation motifs indicated that resistant and sensitive cells differed in their patterns of kinase activities, but, interestingly, phosphorylations correlating with responses were not on members of the pathway being targeted; instead, these mainly were on parallel kinase pathways.ConclusionThis study reveals that the information on kinase activation encoded in phosphoproteomics data correlates remarkably well with the phenotypic responses of cancer cells to compounds that target kinase signaling and could be useful for the identification of novel markers of resistance or sensitivity to drugs that target the signaling network.

Empirical inference of circuitry and plasticity in a kinase signaling network

“…Following quantile normalization of the data (39), the magnitude and statistical significance of differences between conditions were computed by means of empirical Bayes shrinkage of SDs (40) using the limma package within the R computing environment (41,42). The abundance of CTAMs was monitored systematically by using KSEA (14,18,19,30).…”

“…S1. Each phosphopeptide was quantified across all of the experimental conditions by using a previously described labelfree methodology (18,19), generating 1,930,320 data points (Dataset S1).…”

Environmental Stress Affects the Activity of Metabolic and Growth Factor Signaling Networks and Induces Autophagy Markers in MCF7 Breast Cancer Cells

“…The experiment included two completely independent replicates and each experiment was performed twice, giving rise to four biological replicates. Phosphopeptides were quantified by label-free phosphoproteomics using extracted ion chromatograms (XIC) constructed by Pescal (13,14,26,30). In this experimental set up, peptides are quantified in all samples provided they give a positive identification in database search in at least one of the LC-MS/MS runs (30) (Fig.…”

“…As a consequence, signaling processes can now be studied with unprecedented depth and coverage (7)(8)(9)(10). Phosphoproteomics has also been applied to investigate how signaling networks are modulated during disease progression and for the identification of biomarkers that classify patients according to prognosis or treatment response (11)(12)(13)(14)(15). A potential caveat in the interpretation of such experiments is that protein phosphorylation is a dynamic modification that can be affected by variables difficult to control including cell confluence, circadian rhythms, shear stress and other types of environmental stresses including exposure to ambient conditions (16 -22).…”

Analysis of contribution of protein phosphorylation in the development of the diseases

“…даже между опухолевыми клетками одного вида. Кластерный анализ уровня фосфорилирования белков позволил типировать и дифференцировать колоректальные опухоли [32,33], немелкоклеточный рак лёгкого [16,34,35], рак печени [36], гематологические виды рака [37]. В таблице приведены некоторые белки, у которых изменение уровня ПТМ достоверно ассоциировано с развитием рака.…”