Saliva-omics in plasma cell disorders- Proof of concept and potential as a non-invasive tool for monitoring disease burden

Tierney, Ciara; Bazou, Despina; Lê, Giao; Dowling, Paul; O’Gorman, Peter

doi:10.1016/j.jprot.2020.104015

Cited by 10 publications

(7 citation statements)

References 30 publications

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“…Such changes in the composition of saliva could then be developed into a biomarker. The proteomic approach to saliva was initially introduced in studies of dental and oral diseases and is now applied in generalized conditions such as oncological disorders, addictions, or pediatric disorders [ 24 , 25 , 26 , 27 ]. The aim of this study is to compare the proteome of PD saliva vs. healthy controls (HC), as well as to further analyze specific pathways leading to different expression of proteins.…”

Section: Introductionmentioning

confidence: 99%

Proteomic Profile of Saliva in Parkinson’s Disease Patients: A Proof of Concept Study

et al. 2021

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Parkinson’s disease (PD) is a progressive neurodegenerative disorder. It affects many organs. Lewy bodies—a histopathological “hallmark” of PD—are detected in about 75% of PD submandibular gland samples. We hypothesize that saliva can be a source of biomarkers of PD. The aim of the study was to evaluate and compare the salivary proteome of PD patients and healthy controls (HC). Salivary samples from 39 subjects (24 PD patients, mean age 61.6 ± 8.2; 15 HC, mean age 60.9 ± 6.7) were collected. Saliva was collected using RNA-Pro-Sal kits. Label-free LC-MS/MS mass spectrometry was performed to characterize the proteome of the saliva. IPA analysis of upstream inhibitors was performed. A total of 530 proteins and peptides were identified. We observed lower concentrations of S100-A16, ARP2/3, and VPS4B in PD group when compared to HC. We conclude that the salivary proteome composition of PD patients is different than that of healthy controls. We observed a lower concentration of proteins involved in inflammatory processes, exosome formation, and adipose tissue formation. The variability of expression of proteins between the two groups needs to be considered.

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Section: Introductionmentioning

confidence: 99%

Proteomic Profile of Saliva in Parkinson’s Disease Patients: A Proof of Concept Study

et al. 2021

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“…It is possible to determine these biomarkers in blood, urine, and saliva that saliva can provide the appropriate way to detect patients, better prognosis, prevent recurrence, and control patient mortality [ 67 ]. Several studies have used salivary proteins as potential diagnostic markers to monitor disease, prognosis, patient survival, and treatment [ 68 – 70 ]. It has also been shown that there are blood transfusions in saliva; therefore, it is almost equal to serum [ 71 , 72 ].…”

Section: Discussionmentioning

confidence: 99%

Evaluation of CSTB and DMBT1 expression in saliva of gastric cancer patients and controls

et al. 2022

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Background Gastric cancer (GC) is the fifth most common cancer and the third cause of cancer deaths globally, with late diagnosis, low survival rate, and poor prognosis. This case-control study aimed to evaluate the expression of cystatin B (CSTB) and deleted in malignant brain tumor 1 (DMBT1) in the saliva of GC patients with healthy individuals to construct diagnostic algorithms using statistical analysis and machine learning methods. Methods Demographic data, clinical characteristics, and food intake habits of the case and control group were gathered through a standard checklist. Unstimulated whole saliva samples were taken from 31 healthy individuals and 31 GC patients. Through ELISA test and statistical analysis, the expression of salivary CSTB and DMBT1 proteins was evaluated. To construct diagnostic algorithms, we used the machine learning method. Results The mean salivary expression of CSTB in GC patients was significantly lower (115.55 ± 7.06, p = 0.001), and the mean salivary expression of DMBT1 in GC patients was significantly higher (171.88 ± 39.67, p = 0.002) than the control. Multiple linear regression analysis demonstrated that GC was significantly correlated with high levels of DMBT1 after controlling the effects of age of participants (R2 = 0.20, p < 0.001). Considering salivary CSTB greater than 119.06 ng/mL as an optimal cut-off value, the sensitivity and specificity of CSTB in the diagnosis of GC were 83.87 and 70.97%, respectively. The area under the ROC curve was calculated as 0.728. The optimal cut-off value of DMBT1 for differentiating GC patients from controls was greater than 146.33 ng/mL (sensitivity = 80.65% and specificity = 64.52%). The area under the ROC curve was up to 0.741. As a result of the machine learning method, the area under the receiver-operating characteristic curve for the diagnostic ability of CSTB, DMBT1, demographic data, clinical characteristics, and food intake habits was 0.95. The machine learning model’s sensitivity, specificity, and accuracy were 100, 70.8, and 80.5%, respectively. Conclusion Salivary levels of DMBT1 and CSTB may be accurate in diagnosing GCs. Machine learning analyses using salivary biomarkers, demographic, clinical, and nutrition habits data simultaneously could provide affordability models with acceptable accuracy for differentiation of GC by a cost-effective and non-invasive method.

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“…LFQ intensity values were log2 transformed and each sample was assigned to its corresponding group. ANOVA-based multisample t-test was performed using a cut-off of p < 0.05 on the post imputated dataset to identify statistically significant differentially abundant proteins 38 . Receiver‐operating characteristic (ROC) curve analysis was performed as it is a useful tool for the assessment of biomarker accuracy.…”

Section: Methodsmentioning

confidence: 99%

Next generation proteomics with drug sensitivity screening identifies sub-clones informing therapeutic and drug development strategies for multiple myeloma patients

Tierney

Bazou

Majumder

et al. 2021

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With the introduction of novel therapeutic agents, survival in Multiple Myeloma (MM) has increased in recent years. However, drug-resistant clones inevitably arise and lead to disease progression and death. The current International Myeloma Working Group response criteria are broad and make it difficult to clearly designate resistant and responsive patients thereby hampering proteo-genomic analysis for informative biomarkers for sensitivity. In this proof-of-concept study we addressed these challenges by combining an ex-vivo drug sensitivity testing platform with state-of-the-art proteomics analysis. 35 CD138-purified MM samples were taken from patients with newly diagnosed or relapsed MM and exposed to therapeutic agents from five therapeutic drug classes including Bortezomib, Quizinostat, Lenalidomide, Navitoclax and PF-04691502. Comparative proteomic analysis using liquid chromatography-mass spectrometry objectively determined the most and least sensitive patient groups. Using this approach several proteins of biological significance were identified in each drug class. In three of the five classes focal adhesion-related proteins predicted low sensitivity, suggesting that targeting this pathway could modulate cell adhesion mediated drug resistance. Using Receiver Operating Characteristic curve analysis, strong predictive power for the specificity and sensitivity of these potential biomarkers was identified. This approach has the potential to yield predictive theranostic protein panels that can inform therapeutic decision making.

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Saliva-omics in plasma cell disorders- Proof of concept and potential as a non-invasive tool for monitoring disease burden

Cited by 10 publications

References 30 publications

Proteomic Profile of Saliva in Parkinson’s Disease Patients: A Proof of Concept Study

Proteomic Profile of Saliva in Parkinson’s Disease Patients: A Proof of Concept Study

Evaluation of CSTB and DMBT1 expression in saliva of gastric cancer patients and controls

Next generation proteomics with drug sensitivity screening identifies sub-clones informing therapeutic and drug development strategies for multiple myeloma patients

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