Dataset Information

Mass spectrometry-based serum and plasma peptidome profiling for prediction of treatment outcome in patients with solid malignancies.

ABSTRACT:

Introduction

Treatment selection tools are needed to enhance the efficacy of targeted treatment in patients with solid malignancies. Providing a readout of aberrant signaling pathways and proteolytic events, mass spectrometry-based (MS-based) peptidomics enables identification of predictive biomarkers, whereas the serum or plasma peptidome may provide easily accessible signatures associated with response to treatment. In this systematic review, we evaluate MS-based peptide profiling in blood for prompt clinical implementation.

Methods

PubMed and Embase were searched for studies using a syntax based on the following hierarchy: (a) blood-based matrix-assisted or surface-enhanced laser desorption/ionization time-of-flight MS peptide profiling (b) in patients with solid malignancies (c) prior to initiation of any treatment modality, (d) with availability of outcome data.

Results

Thirty-eight studies were eligible for review; the majority were performed in patients with non-small cell lung cancer (NSCLC). Median classification prediction accuracy was 80% (range: 66%-93%) in 11 models from 14 studies reporting an MS-based classification model. A pooled analysis of 9 NSCLC studies revealed clinically significant median progression-free survival in patients classified as "poor outcome" and "good outcome" of 2.0 ± 1.06 months and 4.6 ± 1.60 months, respectively; median overall survival was also clinically significant at 4.01 ± 1.60 months and 10.52 ± 3.49 months, respectively.

Conclusion

Pretreatment MS-based serum and plasma peptidomics have shown promising results for prediction of treatment outcome in patients with solid tumors. Limited sample sizes and absence of signature validation in many studies have prohibited clinical implementation thus far. Our pooled analysis and recent results from the PROSE study indicate that this profiling approach enables treatment selection, but additional prospective studies are warranted.

SUBMITTER: Labots M

PROVIDER: S-EPMC4200992 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundAlthough pemetrexed plus cis/carboplatin has become the most effective chemotherapy regimen for patients with advanced lung adenocarcinoma, predictive biomarkers are not yet available, and new tools to identify chemosensitive patients who would likely benefit from this treatment are desperately needed. In this study, we constructed and validated predictive peptide models using the serum peptidome profiles of two datasets.MethodsOne hundred eighty-three patients treated with first-line platinum-based pemetrexed treatment for advanced lung adenocarcinoma were retrospectively enrolled and randomized into the training (n = 92) or validation (n = 91) set, and pre-treatment serum samples were analyzed using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and ClinProTools software. Serum peptidome profiles from the training set were used to identify potential predictive peptide biomarkers and construct a predictive peptide model for accurate group discrimination; which was then used to classify validation samples into "good" and "poor" outcome groups. The clinical outcomes of objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), and overall survival (OS) were analyzed based on the classification result.ResultsEight potential peptide biomarkers were identified. A predictive peptide model based on four distinct m/z features (2,142.12, 3,316.19, 4,281.94, and 6,624.02 Da) was developed based on the clinical outcomes of training set patients after first-line pemetrexed plus platinum treatment. In the validation set, the good group had significantly higher ORR (49.1% vs. 8.3%, P <0.001) and DCR (96.4% vs. 47.2%, P <0.001), and longer PFS (7.3 months vs. 2.7 months, P <0.001) vs. the poor group. However, the model did not predict OS (13.6 months vs. 12.7 months, P = 0.0675).ConclusionOur predictive peptide model could predict pemetrexed plus platinum treatment outcomes in patients with advanced lung adenocarcinoma and might thus facilitate appropriate patient selection. Further studies are needed to confirm these findings.

Project description:BackgroundFeline mammary carcinoma (FMC) is a common aggressive and highly metastatic cancer affecting female cats. Early detection is essential for preventing local and distant metastasis, thereby improving overall survival rates. While acquiring molecular data before surgery offers significant potential benefits, the current protein biomarkers for monitoring disease progression in non-metastatic FMC (NmFMC) and metastatic FMC (mFMC) are limited. The objective of this study was to investigate the serum peptidome profiles of NmFMC and mFMC using liquid chromatography-tandem mass spectrometry. A cross-sectional study was conducted to compare serum peptidome profiles in 13 NmFMC, 23 mFMC and 18 healthy cats. The liquid chromatography-tandem mass spectrometry analysis was performed on non-trypsinized samples.ResultsOut of a total of 8284 expressed proteins observed, several proteins were found to be associated with human breast cancer. In NmFMC, distinctive protein expressions encompassed double-stranded RNA-binding protein Staufen homolog 2 (STAU2), associated with cell proliferation, along with bromodomain adjacent to zinc finger domain 2A (BAZ2A) and gamma-aminobutyric acid type A receptor subunit epsilon (GABRE), identified as potential treatment targets. Paradoxically, positive prognostic markers emerged, such as complement C1q like 3 (C1QL3) and erythrocyte membrane protein band 4.1 (EPB41 or 4.1R). Within the mFMC group, overexpressed proteins associated with poor prognosis were exhibited, including B-cell lymphoma 6 transcription repressor (BCL6), thioredoxin reductase 3 (TXNRD3) and ceruloplasmin (CP). Meanwhile, the presence of POU class 5 homeobox (POU5F1 or OCT4) and laminin subunit alpha 1 (LAMA1), reported as metastatic biomarkers, was noted.ConclusionThe presence of both pro- and anti-proliferative proteins was observed, potentially indicating a distinctive characteristic of NmFMC. Conversely, proteins associated with poor prognosis and metastasis were noted in the mFMC group.