Proteome and phospho-proteome profiling for deeper phenotype characterization of colorectal cancer heterogeneity.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e15536-e15536
Author(s):  
Jakob Vowinckel ◽  
Thomas Corwin ◽  
Jonathan Woodsmith ◽  
Tobias Treiber ◽  
Roland Bruderer ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cancer Biology ◽  
Molecular Mechanisms ◽  
Deep Understanding ◽  
Peptide Fragmentation ◽  
Spectrometric Method ◽  
Molecular Heterogeneity ◽  
Precision Oncology ◽  
Mass Spectrometric Method

e15536 Background: The rise of precision oncology therapeutics requires deep understanding of the molecular mechanisms implicated in cancer biology. Colorectal cancer (CRC) is one of the first solid tumors to be molecularly characterized by defined genes and pathways. Advances in tumor profiling have revealed a profound molecular heterogeneity in CRC leading to the definition of several consensus molecular subtypes (CMS). However, this molecular heterogeneity is still largely defined on the genomic and transcriptomics level. To complement the understanding of genetically defined molecular subgroups, we performed large-scale deep proteomic and phospho-proteomic profiling of CRC patient biopsies and adjacent healthy control tissue, which has enabled to explore the phenotype and obtain more functional insights in cancer biology. Methods: Sample processing from 5-10 mg of tissue per sample was performed using a liquid handling robot. Phospho-peptide enrichment was carried out with a Kingfisher Flex device and MagReSyn Ti-IMAC magnetic beads. Data-Independent Acquisition (DIA) LC-MS/MS was performed on multiple platforms consisting of a Thermo Scientific Q Exactive HF-X mass spectrometer coupled to a Waters M-Class LC. Chromatography was operating at 5 µL/min, and separation was achieved using 45 min (whole proteome) and 60 min (phospho-proteome) gradients. Results: Indivumed has built IndivuType, the world’s first multi-omics database for individualized cancer therapy, analyzing the highest quality cancer biospecimens to generate the most comprehensive dataset, including genomics, transcriptomics, proteomics, and clinical outcome information. Enabled by the DIA technology, a mass spectrometric method developed by Biognosys that obtains peptide fragmentation data in a highly parallelized way with high sensitivity, more than 7,000 proteins in the whole proteome and 20,000 phospho-peptides in the phospho-proteome workflow were profiled across more than 900 resected tissue samples of various CMS of CRC. The resulting proteome and phospho-proteome data were integrated into the IndivuType database and cross-analyzed with genomic and transcriptomic markers. Through this combined analysis, novel insights in clinically relevant signaling pathways in CRC subtypes were revealed. Conclusions: The deep phenotypic profiling of cancer samples, using next generation proteomics and phospho-proteomics, has enabled us to go beyond the genomic level in the characterization of tumor molecular heterogeneity. This multi-omics approach provides a solid foundation to advance the understanding of cancer biology, unravel key molecular events, and support the identification of novel therapeutic targets for precision medicine in CRC.

Next generation proteomics in precision oncology: 1000s of proteome and phosphoproteome profiles of tumors and matching healthy tissues as meaningful layer in multi-omics database.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e15672-e15672
Author(s):  
Claudia Escher ◽  
Jakob Vowinckel ◽  
Karel Novy ◽  
Thomas Corwin ◽  
Tobias Treiber ◽  
...  
Keyword(s):  
Protein Expression ◽  
Cancer Biology ◽  
Molecular Mechanisms ◽  
Magnetic Beads ◽  
Deep Understanding ◽  
Peptide Fragmentation ◽  
Spectrometric Method ◽  
Precision Oncology ◽  
Mass Spectrometric Method ◽  
Data Set

e15672 Background: The rise of precision oncology therapeutics requires deep understanding of all molecular mechanisms involved in cancer biology. IndivuType offers the world’s first multi-omics database for individualized cancer therapy, analyzing the highest quality cancer biospecimens to generate the most comprehensive dataset, including genomics (WGS), transcriptomics, proteomics, and clinical outcome information. Indivumed is committed to the quality of the IndivuType ecosystem starting with stringent SOP-driven sample collection combined with thorough validation of clinical information and data integrity. The availability of multi-omics data from the same tumor can provide a comprehensive molecular picture of cancer for a given patient. Protein expression and activation are directly related to cellular function and hence provide actionable information about druggable targets. Until recently, the proteomics technology could not match the scale of next-gen sequencing and consequently precision medicine has almost exclusively been based on gene level data. Here we present the first large-scale data set for protein expression and phosphorylation. Enabled by the data independent acquisition (DIA) workflow, a mass spectrometric method provided by Biognosys that obtains peptide fragmentation data in a highly parallelized way with high sensitivity, more than 7,000 proteins in the whole proteome (WP) and 20,000 phospho-peptides in the phospho-proteome (PP) workflow were profiled. Methods: Sample processing from 5 mg of tissue per sample was performed using liquid handling robot. Phospho-peptide enrichment was carried out with a Kingfisher Flex device and MagReSyn Ti-IMAC magnetic beads. DIA LC-MS/MS was performed on multiple platforms consisting of a Thermo Scientific Q Exactive HF-X mass spectrometer coupled to a Waters M-Class LC. Chromatography was operating at 5 µL/min, and separation was achieved using 45 min (WP) and 60 min (PP) gradients. Results: Several thousands of high-quality patient samples of various cancer types have been analyzed to date. The resulting proteome and phospho-proteome data has been integrated into the IndivuType database, thereby providing a solid foundation to advance our understanding of cancer. Conclusions: With the ongoing addition of more samples and associated deep and rich data, the platform could unravel key molecular events and is expected to transform knowledge into actionable treatments and personalized therapies.

scholarly journals Mass Spectrometric Calibration Procedure for Real-Time Detection of Lighter Hydrocarbons

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2123
Author(s):  
Makuachukwu F. Mbaegbu ◽  
Puspa L. Adhikari ◽  
Ipsita Gupta ◽  
Mathew Rowe
Keyword(s):  
Real Time ◽  
Mass Spectrometer ◽  
Calibration Procedure ◽  
Mass Spectrometric ◽  
Spectrometric Method ◽  
Mass Selection ◽  
Mass Spectrometric Method ◽  
Mass Spectrometers ◽  
Drilling Rig

Determining gas compositions from live well fluids on a drilling rig is critical for real time formation evaluation. Development and utilization of a reliable mass spectrometric method to accurately characterize these live well fluids are always challenging due to lack of a robust and effectively selective instrument and procedure. The methods currently utilized need better calibration for the characterization of light hydrocarbons (C1–C6) at lower concentrations. The primary goal of this research is to develop and optimize a powerful and reliable analytical method to characterize live well fluid using a quadruple mass spectrometer (MS). The mass spectrometers currently being used in the field have issues with detection, spectra deconvolution, and quantification of analytes at lower concentrations (10–500 ppm), particularly for the lighter (<30 m/z) hydrocarbons. The objectives of the present study are thus to identify the detection issues, develop and optimize a better method, calibrate and QA/QC the MS, and validate the MS method in lab settings. In this study, we used two mass spectrometers to develop a selective and precise method to quantitatively analyze low level lighter analytes (C1–C6 hydrocarbons) with masses <75 m/z at concentrations 10–500 ppm. Our results suggest that proper mass selection like using base peaks with m/z 15, 26, 41, 43, 73, and 87, respectively, for methane, ethane, propane, butane, pentane, and hexane can help detect and accurately quantify hydrocarbons from gas streams. This optimized method in quadrupole mass spectrometer (QMS) will be invaluable for early characterization of the fluid components from a live hydrocarbon well in the field in real time.

Mass Spectrometric Method for the Rapid Characterization of Transition Metal Carbonyl Cluster Reaction Mixtures

2001 ◽  
Vol 20 (19) ◽  
pp. 3970-3974 ◽  
Author(s):  
Paul J. Dyson ◽  
Andrew K. Hearley ◽  
Brian F. G. Johnson ◽  
Tetyana Khimyak ◽  
J. Scott McIndoe ◽  
...  
Keyword(s):  
Transition Metal ◽  
Metal Carbonyl ◽  
Mass Spectrometric ◽  
Spectrometric Method ◽  
Mass Spectrometric Method ◽  
Carbonyl Cluster ◽  
Cluster Reaction ◽  
Rapid Characterization ◽  
Metal Carbonyl Cluster

Multi-omics characterization of left-right colorectal cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 3542-3542
Author(s):  
John Marshall ◽  
Takayuki Yoshino ◽  
Sun Young Rha ◽  
David N. Church ◽  
Anelisa Kruschewsky Coutinho ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Colon Cancer ◽  
Cancer Biology ◽  
Normal Tissue ◽  
Molecular Mechanisms ◽  
Clinical Information ◽  
Molecular Testing ◽  
Primary Tumors ◽  
Ischemia Time ◽  
Normal Tissues

3542 Background: Right (R) vs left (L) sided colorectal cancers are clinically distinguishable based on prognosis and response to certain therapies, but as of yet, limited data have emerged to explain these differences. The science of molecular testing has evolved rapidly. Enabled by improved technologies and computing power, it is now feasible to obtain to systematic multi-omic datasets covering DNA, RNA, proteins, phospho-proteins and metabolomics on large numbers of patients. Multi-omic analysis can further define disease specific subgroups but pre-analytic quality of the tissues (ischemia time) and comparison to normal tissue controls is paramount to optimize results. Methods: Following informed consent, 450 colorectal cancer primary tumors and paired normal tissues were collected following an SOP to minimize ischemia time, and were analyzed using comprehensive genomics, transcriptomics, proteomics, phosphoproteomics, morphology and annual clinical information. Right (C18.0,2,3) and left (C18.6,7) CRC tumors, normal tissue were compared using machine learning tools to unravel the molecular mechanisms that underpin these clinically distinguishable phenotypes as well as correlating with known genomic metrics such MSI and KRAS mutation status. Results: Through leveraging the tumor and paired normal patient samples, systematic differences between left and right tumor samples were observed including specific molecular events associated with these anatomical differences. The detailed results will be presented at the meeting. Conclusions: Progress in precision medicine requires the inclusion of multi-omics which in turn requires changes to our current SOPs of tissue collection. The ability to define molecular distinctions such as between R and L colon cancer will permit the rapid discovery of clinically useful prognostic and predictive markers, dramatically adding to our fundamental understanding to colon cancer biology. Future work will focus on the discovery of novel targets and signatures, creating innovative tools that depict multi-omic results for clinicians.

scholarly journals Systematic detection of functional proteoform groups from bottom-up proteomic datasets

2020 ◽  
Author(s):  
Isabell Bludau ◽  
Max Frank ◽  
Christian Dörig ◽  
Yujia Cai ◽  
Moritz Heusel ◽  
...  
Keyword(s):  
Alternative Splicing ◽  
Molecular Mechanisms ◽  
Data Matrix ◽  
Spectrometric Method ◽  
Mass Spectrometric Method ◽  
Bottom Up ◽  
Tissue Samples ◽  
Protein Coding ◽  
Systematic Assessment ◽  
A Genome

AbstractThe cellular proteome, the ensemble of proteins derived from a genome, catalyzes and controls thousands of biochemical functions that are the basis of living cells. Whereas the protein coding regions of the genome of the human and many other species are well known, the complexity and composition of proteomes largely remains to be explored. This task is challenging because mechanisms including alternative splicing and post-translational modifications generally give rise to multiple distinct, but related proteins – proteoforms – per coding gene that expand the functional capacity of a cell.Bottom-up proteomics is a mass spectrometric method that infers the identity and quantity of proteins from the measurement of peptides derived from these proteins by proteolytic digestion. Whereas bottom-up proteomics has become the method of choice for the detection of translation products from essentially any gene, the inherent missing link between measured peptides and their parental proteins has so far precluded the systematic assessment of proteoforms and thus limited the resolution of proteome maps. Here we present a novel, data-driven strategy to assign peptides to unique functional proteoform groups based on peptide correlation patterns across large bottom-up proteomic datasets. Our strategy does not fully characterize specific proteoforms, as is achievable in top-down approaches. Rather, it clusters peptides into functional proteoform groups that are directly linked to the biological context of the study. This allows the detection of tens to hundreds of proteoform groups in an untargeted fashion from bottom-up proteomics experiments.We applied the strategy to two types of bottom-up proteomic datasets. The first is a protein complex co-fractionation dataset where native complexes across two different cell cycle stages were resolved and analyzed. Here, our approach enabled the systematic detection and evaluation of assembly specific proteoforms at an unprecedented scale. The second is a protein abundance vs. sample data matrix typical for bottom-up cohort studies consisting of tissue samples from the mouse BXD genetic reference panel. In either data type the method detected state-specific proteoform groups that could be linked to distinct molecular mechanisms including proteolytic cleavage, alternative splicing and phosphorylation. We envision that the presented approach lays the foundation for a systematic assessment of proteoforms and their functional implications directly from bottom-up proteomic datasets.

Celecoxib repurposing in cancer therapy: molecular mechanisms and nanomedicine-based delivery technologies

Nanomedicine ◽  
2021 ◽  
Author(s):  
Asmaa F Khafaga ◽  
Rehab N Shamma ◽  
Ahmed Abdeen ◽  
Abdelmonem M Barakat ◽  
Ahmed E Noreldin ◽  
...  
Keyword(s):  
Cancer Therapy ◽  
Anticancer Agents ◽  
Cancer Biology ◽  
Molecular Mechanisms ◽  
Deep Understanding ◽  
Drug Repurposing ◽  
Special Focus ◽  
Drug Classes ◽  
Cox 2 ◽  
Cox 2 Inhibitors

While cancer remains a significant global health problem, advances in cancer biology, deep understanding of its underlaying mechanism and identification of specific molecular targets allowed the development of new therapeutic options. Drug repurposing poses several advantages as reduced cost and better safety compared with new compounds development. COX-2 inhibitors are one of the most promising drug classes for repurposing in cancer therapy. In this review, we provide an overview of the detailed mechanism and rationale of COX-2 inhibitors as anticancer agents and we highlight the most promising research efforts on nanotechnological approaches to enhance COX-2 inhibitors delivery with special focus on celecoxib as the most widely studied agent for chemoprevention or combined with chemotherapeutic and herbal drugs for combating various cancers.

scholarly journals Heterogeneity in Colorectal Cancer: A Challenge for Personalized Medicine?

2018 ◽  
Vol 19 (12) ◽  
pp. 3733 ◽  
Author(s):  
Chiara Molinari ◽  
Giorgia Marisi ◽  
Alessandro Passardi ◽  
Laura Matteucci ◽  
Giulia De Maio ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Residual Disease ◽  
False Negative ◽  
Response To Therapy ◽  
Molecular Heterogeneity ◽  
Non Invasive ◽  
Negative Results ◽  
False Negative Results ◽  
Essential Components

High inter-patient variability and high spatial heterogeneity are features of colorectal cancer (CRC). This may influence the molecular characterization of tumor tissue, now mandatory for patients with metastatic CRC who are candidates for treatment with an anti-EGFR mAb, as false-negative results can occur, leading to non optimal therapy. Moreover, temporal molecular heterogeneity during treatment is known to influence the response to therapy and prognosis. We present a literature overview of advances made in characterizing molecular heterogeneity in CRC, underlining that the analysis of liquid biopsy could represent an efficient non-invasive tool to overcome the problem. We believe that understanding CRC heterogeneity is fundamental for a more accurate diagnosis, for selecting the best targets to ensure prolonged antitumor response, and for monitoring minimal residual disease and the onset of resistance to therapy, all essential components of successful personalized treatment.

Sign in / Sign up

Export Citation Format

Share Document