Benchmarking common quantification strategies for large-scale phosphoproteomics

Quantitative phosphoproteomics has become a standard method in molecular and cell biology. Here, the authors compare performance and parameters of phosphoproteome quantification by LFQ, SILAC, and MS2-/MS3-based TMT and introduce a TMT-adapted algorithm for calculating phosphorylation site stoichiometry

CT versus FDG-PET/CT response evaluation in patients with metastatic colorectal cancer treated with irinotecan and cetuximab

We compared morphologic computed tomography (CT)-based to metabolic fluoro-deoxy-glucose (FDG) positron emission tomography (PET)/CT-based response evaluation in patients with metastatic colorectal cancer and correlated the findings with survival and KRAS status. From 2006 to 2009, patients were included in a phase II trial and treated with cetuximab and irinotecan every second week. They underwent FDG-PET/CT examination at baseline and after every fourth treatment cycle. Response evaluation was performed prospectively according to Response Evaluation Criteria in Solid Tumors (RECIST 1.0) and retrospectively according to Positron Emission Tomography Response Criteria in Solid Tumors (PERCIST). Best overall responses were registered. Sixty-one patients were eligible for response evaluation. Partial response (PR) rate was 18%, stable disease (SD) rate 64%, and progressive disease (PD) rate 18%. Partial metabolic response (PMR) rate was 56%, stable metabolic disease rate 33%, and progressive metabolic disease (PMD) rate 11%. Response agreement was poor, κ-coefficient 0.19. Hazard ratio for overall survival for responders (PR/PMR) versus nonresponders (PD/PMD) was higher for CT- than for FDG-PET/CT evaluation. Within patients with KRAS mutations, none had PR but 44% had PMR. In conclusion, morphologic and metabolic response agreement was poor primarily because a large part of the patients shifted from SD with CT evaluation to PMR when evaluated with FDG-PET/CT. Furthermore, a larger fraction of the patients with KRAS mutations had a metabolic treatment response

Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses.

Multi-omics datasets can provide molecular insights beyond the sum of individual omics. Various tools have been recently developed to integrate such datasets, but there are limited strategies to systematically extract mechanistic hypotheses from them. Here, we present COSMOS (Causal Oriented Search of Multi-Omics Space), a method that integrates phosphoproteomics, transcriptomics, and metabolomics datasets. COSMOS combines extensive prior knowledge of signaling, metabolic, and gene regulatory networks with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. COSMOS provides mechanistic hypotheses for experimental observations across multi-omics datasets. We applied COSMOS to a dataset comprising transcriptomics, phosphoproteomics, and metabolomics data from healthy and cancerous tissue from eleven clear cell renal cell carcinoma (ccRCC) patients. COSMOS was able to capture relevant crosstalks within and between multiple omics layers, such as known ccRCC drug targets. We expect that our freely available method will be broadly useful to extract mechanistic insights from multi-omics studies