PIONEER: Pipeline for Generating High‐Quality Spectral Libraries for DIA‐MS Data

Manda, Srikanth S.; Noor, Zainab; Hains, Peter G.; Zhong, Qing

doi:10.1002/cpz1.69

Cited by 4 publications

(6 citation statements)

References 60 publications

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“…Proteomic profiles of all samples including controls were acquired by DIA-MS in technical duplicate at ProCan 36 using operating conditions that enable reproducible and high throughput data acquisition across six SCIEX™TripleTOF ® 6600 mass spectrometers 31,35 . We quantified 5,803 proteins ( Supplementary Fig.…”

Section: Resultsmentioning

confidence: 99%

“…To address this limitation, we have compiled a cohort of 290 patients procured from the Prostate Cancer Outcomes Cohort Study (ProCOC) 30 to generate large-scale proteomic measurement of PCa tissue samples using data-independent acquisition mass spectrometry (DIA-MS). The data have been analysed through purpose-built computational workflows at the Australian Cancer Research Foundation International Centre for the Proteome of Human Cancer (ProCan ® ) in Westmead, Australia 31,32,33,34,35,36,37 . We have identified differentially expressed proteins and pathways involved in PCa development and biochemical recurrence (BCR), including the identification of possible new therapeutic targets.…”

Section: Introductionmentioning

confidence: 99%

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Proteomic-based stratification of intermediate-risk prostate cancer patients

Zhong

Sun

Aref

et al. 2023

Preprint

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Gleason grading is an important prognostic indicator for prostate adenocarcinoma and is crucial for patient treatment decisions. However, intermediate-risk patients diagnosed in Gleason Grade Groups (GG) 2 and GG3 can harbour either aggressive or non-aggressive disease, resulting in under- or over-treatment of a significant number of patients. Here, we performed proteomic, differential expression, machine learning, and survival analyses for 1,348 matched tumour and benign sample runs from 278 patients. Three proteins (F5, TMEM126B and EARS2) were identified as candidate biomarkers in patients with biochemical recurrence. Multivariate Cox regression yielded 18 proteins, from which a risk score was constructed to dichotomise prostate cancer patients into low- and high-risk groups. This 18-protein signature is prognostic for the risk of biochemical recurrence and completely independent of the intermediate GG. Our results suggest that markers generated by computational proteomic profiling have the potential for clinical applications including integration into prostate cancer management.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Proteomic-based stratification of intermediate-risk prostate cancer patients

Zhong

Sun

Aref

et al. 2023

Preprint

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“…There are a range of DIA-MS approaches including SWATH-MS [25], MS E [27], and diaPASEF [28], among others (reviewed in [29]). The utility of DIA-MS is increasing due to associated developments in software [29] such as tools for processing raw data [30][31][32], building a high-quality spectral reference library [33] and downstream analysis steps such as batch correction, normalisation, and missing value imputation [7,[34][35][36]. These advances have enabled the integration of proteomic and drug response datasets at scale [9,37,38].…”

Section: Advances In Ms Technology and Implications For Pharmacoprote...mentioning

confidence: 99%

Opportunities for pharmacoproteomics in biomarker discovery

et al. 2022

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Proteomic data are a uniquely valuable resource for drug response prediction and biomarker discovery because most drugs interact directly with proteins in target cells rather than with DNA or RNA. Recent advances in mass spectrometry and associated processing methods have enabled the generation of large‐scale proteomic datasets. Here we review the significant opportunities that currently exist to combine large‐scale proteomic data with drug‐related research, a field termed pharmacoproteomics. We describe successful applications of drug response prediction using molecular data, with an emphasis on oncology. We focus on technical advances in data‐independent acquisition mass spectrometry (DIA‐MS) that can facilitate the discovery of protein biomarkers for drug responses, alongside the increased availability of big biomedical data. We spotlight new opportunities for machine learning in pharmacoproteomics, driven by the combination of these large datasets and improved high‐performance computing. Finally, we explore the value of pre‐clinical models for pharmacoproteomic studies and the accompanying challenges of clinical validation. We propose that pharmacoproteomics offers the potential for novel discovery and innovation within the cancer landscape.

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“…These methods are currently the most productive ones because of their high-throughput nature and reproducibility. A significant drawback of this technology is the very short length of resulting peptides [typically 7-40 amino acids (aa)] on one hand and the inability to detect proteins of low relative abundance on the other hand [3,[62][63][64][65]. To prove the mosaic nature of a protein, multiple ribosomal frameshifting events have to be captured in a single continuous polypeptide, not in its fragments.…”

Section: Experimental Demonstration Of Mosaic Translation Is a Challengementioning

confidence: 99%

Mosaic translation hypothesis: chimeric polypeptides produced via multiple ribosomal frameshifting as a basis for adaptability

et al. 2021

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How many different proteins can be produced from a single spliced transcript? Genome annotation projects overlook the coding potential of reading frames other than that of the reference open reading frames (refORFs). Recently, alternative open reading frames (altORFs) and their translational products, alternative proteins, have been shown to carry out important functions in various organisms. AltORFs overlapping refORFs or other altORFs in a different reading frame may be involved in one fundamental mechanism so far overlooked. A few years ago, it was proposed that altORFs may act as building blocks for chimeric (mosaic) polypeptides, which are produced via multiple ribosomal frameshifting events from a single mature transcript. We adopt terminology from that earlier discussion and call this mechanism mosaic translation. This way of extracting and combining genetic information may significantly increase proteome diversity. Thus, we hypothesize that this mechanism may have contributed to the flexibility and adaptability of organisms to a variety of environmental conditions. Specialized ribosomes acting as sensors probably played a central role in this process. Importantly, mosaic translation may be the main source of protein diversity in genomes that lack alternative splicing. The idea of mosaic translation is a testable hypothesis, although its direct demonstration is challenging. Should mosaic translation occur, we would currently highly underestimate the complexity of translation mechanisms and thus the proteome.

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PIONEER: Pipeline for Generating High‐Quality Spectral Libraries for DIA‐MS Data

Cited by 4 publications

References 60 publications

Proteomic-based stratification of intermediate-risk prostate cancer patients

Proteomic-based stratification of intermediate-risk prostate cancer patients

Opportunities for pharmacoproteomics in biomarker discovery

Mosaic translation hypothesis: chimeric polypeptides produced via multiple ribosomal frameshifting as a basis for adaptability

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