The purpose of the present study was therefore to determine

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This analysis also classified the samples into buy AP24534 three main groups corresponding to AML, lymphoma, and multiple myeloma, and clustered repli cates for each cell line within those groups. Interestingly lymphoma and multiple myeloma cells were clustered together, consistent with AML cells being the most dif ferentiated group, This is consistent with the data obtained by PCA, Taken together, the data in Figure 1 indicate that although there are dif ferences in global phosphoprotein abundance within cell lines of the same pathology, the differences in the phos phoproteomes of hematological cancer cells are greater across pathology groups than within a given disease.

Stringent statistical analysis identified 609 phosphopeptides AT7519 844442-38-2 differentially regulated in the AML cell lines, of which 544 showed increased phosphorylation and 65 decreased phosphoryla tion, 46 were different in lymphoma cells and 53 in multiple myeloma, Representative examples of phosphopeptides increased or decreased in AML, lymphoma, and multiple myeloma cells are shown in Additional file 5, Figure S3. Taken together, these data show that label free quantitative phosphoproteomic data can be used to reproducibly classify hematological cancer cell lines according to their pathological origin.

Phosphorylation patterns in hematological cancer cells associated with sensitivity resistance to kinase inhibitors In order to assess whether phosphoproteomicscould also be used to classify cells according to their responses to kinase inhibitor treatment, we compared phosphoryla tion patterns with the sensitivity of a panel of FDA approved Akt 阻害剤 seven AML cell lines to a PI3K mTOR inhibitor, a MEK inhibitor, and a JAK inhibitor, Cell lines were ranked accord ing to the percentage reduction in MTS signals when incubated with 1 uM PI 103, 1 uM JAK i, or 10 uM MEK i, MTS measures mitochondrial redox activity, a parameter that often correlates with cell viabi lity. In our assay a reduction in MTS signal as a func tion of compound treatment may be taken to indicate a reduction in the number of viable cells relative to the control as a combined action of the drugs in inhibiting proliferation and inducing cell death. We then performed phosphoproteomics analysis on these cell lines, These experiments were performed in three independent cell cultures per cell line and further data analysis was based on phosphopeptide intensities from individual replicates rather than the averages values of the three replicates.

Assessing the potential of phosphoproteomics data in predicting drug responses We assessed the ability of phosphoproteomics data in predicting the drug sensitivity profiles of our panel of cell lines against the three tested inhibitors through a leave one cell line out approach based on lasso regression, detailed in the methods section. For each of the three tested drugs, we trained seven dif ferent models by leaving out the samples corresponding to each of the seven cell lines in turn, and composing each time a test set with them. The trained models were finally used to make predictions on the test set. Each training phase was composed by a three fold cross validation estimation of the lasso shrinkage para meter followed by an optimization phase of the regressor coefficients and was repeated 20 times.