Dataset Information

Dilution Series imputation, LC MS/MS

ABSTRACT: Missing values in proteomic data sets have real consequences on downstream data analysis and reproducibility. Although several imputation methods exist to handle missing values, no single imputation method is best suited for a diverse range of data sets, and no clear strategy exists for evaluating imputation methods for large-scale DIA-MS data sets, especially at different levels of protein quantification. To navigate through the different imputation strategies available in the literature, we have established a workflow to assess imputation methods on large-scale label-free DIA-MS data sets. We used three DIA-MS data sets with real missing values to evaluate eight different imputation methods with multiple parameters at different levels of protein quantification; dilution series data set, a small pilot data set, and a larger proteomic data set.

INSTRUMENT(S): LTQ Orbitrap Elite

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Epithelial Ovarian Cancer Cell

SUBMITTER: Kruttika Dabke

LAB HEAD: Sarah Parker

PROVIDER: PXD023012 | Pride | 2021-05-07

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	POCROC_SCX_fraction_DDAlib_SpecLib_iRT_FULLMERGE_TTOF_LUMOS_PUBLISHED_cons_decoys_R.TraML	Other
	POCROC_SCX_fraction_DDAlib_SpecLib_iRT_FULLMERGE_TTOF_LUMOS_PUBLISHED_cons_decoys_R_featurealignment_output.tsv	Tabular
	SP_190530_SWATH_Total_2ugLoad_POCROCPool_Run1.wiff	Wiff
	SP_190530_SWATH_Total_2ugLoad_POCROCPool_Run1_profile.mzML	Mzml
	SP_190530_SWATH_Total_2ugLoad_POCROCPool_Run2.wiff	Wiff

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Publications

A Simple Optimization Workflow to Enable Precise and Accurate Imputation of Missing Values in Proteomic Data Sets.

Dabke Kruttika K Kreimer Simion S Jones Michelle R MR Parker Sarah J SJ

Journal of proteome research 20210503 6

Missing values in proteomic data sets have real consequences on downstream data analysis and reproducibility. Although several imputation methods exist to handle missing values, no single imputation method is best suited for a diverse range of data sets, and no clear strategy exists for evaluating imputation methods for clinical DIA-MS data sets, especially at different levels of protein quantification. To navigate through the different imputation strategies available in the literature, we have ...[more]

PMID: 33939434

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Project description:The global rise of HPV(+) oropharyngeal squamous cell carcinoma (OPSCC) has generated considerable interest underlying its etiology and management. Despite an overall decline in head and neck malignancies, the incidence in OPSCC has by contrast sharply risen, with HPV(+) subtypes now comprising 80% of all OPSCC.1,2 Relative to HPV(-) OPSCC, HPV(+) patients are more responsive to chemoradiation and harbor a 52% risk-of-death reduction.3 Despite this distinct outcome, treatment regimens remain the same for all OPSCC subtypes (smoking-driven and virus-driven), rather than an adaptive approach to what most consider distinct diseases. The short-term effects (e.g., mucositis, odynophagia) and long-term toxicities (e.g., xerostomia, dysphagia, ototoxicity) from treatment substantially affect quality of life, and rival the impact of the cancer itself. Recently published as well as ongoing trials are actively examining deintensification approaches2,4-9, with the goal of diminishing treatment sequelae for HPV(+) subtypes. While deintensification may decrease chemoradiation-related toxicities, it nonetheless may also undertreat a meaningful percentage of HPV(+) patients who may then recur. In examining national trials, 19% of HPV(+) patients had disease progression after therapy, with a two-year survival rate of 60%.10 Current methods to ascertain HPV status involve p16 staining. However, on comparison with gold standard E6/E7 expression by qPCR, p16 harbored a 15% false positivity rate11, suggesting limited utility as a sole biomarker for deintensification. Improved molecular stratification would greatly enhance the clinician’s ability to precisely tailor treatment while minimizing the risk of jeopardizing outcomes. One approach encompasses in-depth proteomic profiling of HPV(+) OPSCC to reveal distinct protein expression profiles and delineate clinically relevant upstream pathways. In turn, these proteomic differences may distinguish higher-risk disease (cases predisposed to recurrence that may benefit from treatment intensification) from lower-risk phenotypes (cases whose treatment response is sufficiently robust to warrant deintensification). Here, two HPV(+) OPSCC cohorts stratified by treatment response are compared via a hybrid data dependent acquisition/data independent acquisition (DDA/DIA) approach via mass spectrometry. We focused on detection of low-abundance proteins to highlight proteomic signatures that can be potentially exploited for treatment stratification.