The cancer proteomic landscape and the HUPO Cancer Proteome Project

Jiménez, Connie R.; Zhang, Hui; Kinsinger, Christopher R.; Nice, Edouard C.

doi:10.1186/s12014-018-9180-6

Cited by 21 publications

(15 citation statements)

References 27 publications

(33 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, one CPTAC CRC proteomic dataset generated by 2D LC-MS was compared to the proteome of another 40 CRC tissues prepared by GeLC-MS in a separate laboratory. The proteomes shared an 80% overlap in protein detections, of which quantitative measurements were strongly correlated (R 2 = 0.8) [116]. Of course, any conclusions from this meta-analysis would require rigorous validation, but it does demonstrate that comparisons of independently-generated tissue proteomic datasets will be possible in the future [92].…”

Section: Targeted and Multi-site Validationmentioning

confidence: 84%

Recent advances in mass spectrometry based clinical proteomics: applications to cancer research

2020

View full text Add to dashboard Cite

Cancer biomarkers have transformed current practices in the oncology clinic. Continued discovery and validation are crucial for improving early diagnosis, risk stratification, and monitoring patient response to treatment. Profiling of the tumour genome and transcriptome are now established tools for the discovery of novel biomarkers, but alterations in proteome expression are more likely to reflect changes in tumour pathophysiology. In the past, clinical diagnostics have strongly relied on antibody-based detection strategies, but these methods carry certain limitations. Mass spectrometry (MS) is a powerful method that enables increasingly comprehensive insights into changes of the proteome to advance personalized medicine. In this review, recent improvements in MS-based clinical proteomics are highlighted with a focus on oncology. We will provide a detailed overview of clinically relevant samples types, as well as, consideration for sample preparation methods, protein quantitation strategies, MS configurations, and data analysis pipelines currently available to researchers. Critical consideration of each step is necessary to address the pressing clinical questions that advance cancer patient diagnosis and prognosis. While the majority of studies focus on the discovery of clinically-relevant biomarkers, there is a growing demand for rigorous biomarker validation. These studies focus on high-throughput targeted MS assays and multi-centre studies with standardized protocols. Additionally, improvements in MS sensitivity are opening the door to new classes of tumour-specific proteoforms including posttranslational modifications and variants originating from genomic aberrations. Overlaying proteomic data to complement genomic and transcriptomic datasets forges the growing field of proteogenomics, which shows great potential to improve our understanding of cancer biology. Overall, these advancements not only solidify MS-based clinical proteomics' integral position in cancer research, but also accelerate the shift towards becoming a regular component of routine analysis and clinical practice.

show abstract

Section: Targeted and Multi-site Validationmentioning

confidence: 84%

Recent advances in mass spectrometry based clinical proteomics: applications to cancer research

2020

View full text Add to dashboard Cite

show abstract

“…Using this approach, we were able to analyze proteomes of 20 human tissue and 40 plasma proteomes per MS instrument per day. We will continue to generate additional DDA files from more types of human tumors with the ambition of incorporating internal and external data to create a comprehensive resource reflecting tumor heterogeneity that enables biomarker discovery as a mission of the Human Proteome Organization Cancer HPP project [50]. By appending these results to the DPHL, we can increase the human proteome coverage.…”

Section: Resultsmentioning

confidence: 99%

DPHL: A pan-human protein mass spectrometry library for robust biomarker discovery

Zhu

Xuan

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

To answer the increasing need for detecting and validating protein biomarkers in clinical specimens, proteomic techniques are required that support the fast, reproducible and quantitative analysis of large clinical sample cohorts. Targeted mass spectrometry techniques, specifically SRM, PRM and the massively parallel SWATH/DIA technique have emerged as a powerful method for biomarker research. For optimal performance, they require prior knowledge about the fragment ion spectra of targeted peptides. In this report, we describe a mass spectrometric (MS) pipeline and spectral resource to support data-independent acquisition (DIA) and parallel reaction monitoring (PRM) based biomarker studies. To build the spectral resource we integrated common open-source MS computational tools to assemble an open source computational workflow based on Docker. It was then applied to generate a comprehensive DIA pan-human library (DPHL) from 1,096 data dependent acquisition (DDA) MS raw files, and it comprises 242,476 unique peptide sequences from 14,782 protein groups and 10,943 SwissProt-annotated proteins expressed in 16 types of cancer samples. In particular, tissue specimens from patients with prostate cancer, cervical cancer, colorectal cancer, hepatocellular carcinoma, gastric cancer, lung adenocarcinoma, squamous cell lung carcinoma, diseased thyroid, glioblastoma multiforme, sarcoma and diffuse large B-cell lymphoma (DLBCL), as well as plasma samples from a range of hematologic malignancies were collected from multiple clinics in China, the Netherlands and Singapore and included in the resource. This extensive spectral resource was then applied to a prostate cancer cohort of 17 patients, consisting of 8 patients with prostate cancer (PCa) and 9 with benign prostate hyperplasia (BPH), respectively. Data analysis of DIA data from these samples identified differential expressions of FASN, TPP1 and SPON2 in prostate tumors. Thereafter, PRM validation was applied to a larger PCa cohort of 57 patients and the differential expressions of FASN, TPP1 and SPON2 in prostate tumors were validated. As a second application, the DPHL spectral resource was applied to a patient cohort consisting of samples from 19 DLBCL patients and 18 healthy individuals. Differential expressions of CRP, CD44 and SAA1 between DLBCL cases and healthy controls were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supported that DIA-PRM MS pipeline enables robust protein biomarker discoveries.

show abstract

“…The international proteomics community has acknowledged this by promulgating a commitment to transparency and quality in proteomics, chiefly through sharing data, detailed documentation of study metadata, reference datasets, and reagents . These efforts are supported by national and international consortia such as the Clinical Proteomic Tumor Analysis Consortium and the International Cancer Proteogenomics Consortium (of which ProCan is a member) that were given impetus in 2016 by the U.S. Cancer Moonshot initiative's focus on proteogenomics . Standards set by journal editors have an important role here, with a recent guideline for submission of DIA‐MS studies a useful exemplar .…”

Section: Procanmentioning

confidence: 99%

Addressing the Challenges of High‐Throughput Cancer Tissue Proteomics for Clinical Application: ProCan

et al. 2019

View full text Add to dashboard Cite

The cancer tissue proteome has enormous potential as a source of novel predictive biomarkers in oncology. Progress in the development of mass spectrometry (MS)‐based tissue proteomics now presents an opportunity to exploit this by applying the strategies of comprehensive molecular profiling and big‐data analytics that are refined in other fields of ‘omics research. ProCan (ProCan is a registered trademark) is a program aiming to generate high‐quality tissue proteomic data across a broad spectrum of cancer types. It is based on data‐independent acquisition–MS proteomic analysis of annotated tissue samples sourced through collaboration with expert clinical and cancer research groups. The practical requirements of a high‐throughput translational research program have shaped the approach that ProCan is taking to address challenges in study design, sample preparation, raw data acquisition, and data analysis. The ultimate goal is to establish a large proteomics knowledge‐base that, in combination with other cancer ‘omics data, will accelerate cancer research.

show abstract

The cancer proteomic landscape and the HUPO Cancer Proteome Project

Cited by 21 publications

References 27 publications

Recent advances in mass spectrometry based clinical proteomics: applications to cancer research

Recent advances in mass spectrometry based clinical proteomics: applications to cancer research

DPHL: A pan-human protein mass spectrometry library for robust biomarker discovery

Addressing the Challenges of High‐Throughput Cancer Tissue Proteomics for Clinical Application: ProCan

Contact Info

Product

Resources

About