Clinical proteomics最新文献

Background: Immunotherapy is applied to breast cancer to resolve the limitations of survival gain in existing treatment modalities. With immunotherapy, a tumor can be classified into immune-inflamed, excluded and desert based on the distribution of immune cells. We assessed the clinicopathological features, each subtype's prognostic value and differentially expressed proteins between immune subtypes.

Methods: Immune subtyping and proteomic analysis were performed on 56 breast cancer cases with neoadjuvant chemotherapy. The immune subtyping was based on the level of tumor-infiltrating lymphocytes (TILs) and Klintrup criteria. If the level of TILs was ≥ 10%, it was classified as immune-inflamed type without consideration of the Klintrup criteria. In cases of 1-9% TIL, Klintrup criteria 1-3 were classified as the immune-excluded subtype and Klintrup criteria not available (NA) was classified as NA. Cases of 1% TILs and Klintrup 0 were classified as the immune-desert subtype. Mass spectrometry was used to identify differentially expressed proteins in formalin-fixed paraffin-embedded biopsy tissues.

Results: Of the 56 cases, 31 (55%) were immune-inflamed, 21 (38%) were immune-excluded, 2 (4%) were immune-desert and 2 (4%) were NA. Welch's t-test revealed two differentially expressed proteins between immune-inflamed and immune-excluded/desert subtypes. Coronin-1A was upregulated in immune-inflamed tumors (adjusted p = 0.008) and α-1-antitrypsin was upregulated in immune-excluded/desert tumors (adjusted p = 0.008). Titin was upregulated in pathologic complete response (pCR) than non-pCR among immune-inflamed tumors (adjusted p = 0.036).

Conclusions: Coronin-1A and α-1-antitrypsin were upregulated in immune-inflamed and immune-excluded/desert subtypes, respectively. Titin's elevated expression in pCR within the immune-inflamed subtype may indicate a favorable prognosis. Further studies involving large representative cohorts are necessary to validate these findings.

Background: Presently, antibody concentration measurements for patients undergoing treatment are predominantly determined by ELISA, which still comes with known disadvantages. Therefore, our aim was to establish a targeted mass-spectrometric assay enabling the reproducible absolute quantification of peptides from the hypervariable and interaction regions of infliximab.

Methods: Peptides of infliximab were measured post-trypsin digestion and subsequent separation on a Vanquish Horizon UHPLC coupled to a TSQ Altis Triple-Quad mass spectrometer. Normalization and absolute quantification were conducted using stable isotope-synthesized peptides. Calibration curves covering a range of 0.25-50 µg/ml were employed for quantitation.

Results: We demonstrated the substantial influence of peptide selection, choice of hydrolase for digestion, and digestion time on absolute peptide yield (28-44% for peptide 1 and 64-97% for peptide 2). Furthermore, we showed that the generated calibration curves for absolute quantification were highly reproducible and robust (LLOQ1 0.72 µg/ml and LLOQ2 1.00 µg/ml) over several months. In comparison to ELISA values, the absolute values obtained by mass spectrometry often yielded lower results for both targeted peptides.

Conclusions: In this study, a semi-automated workflow was employed and tested with 8 patients and corresponding replicates (n = 3-4). We demonstrated the robust implementation of calibration curves for the absolute quantification of infliximab in patient samples, with coefficients of variation ranging from 0.5 to 9%. Taken together, we have developed a platform enabling the rapid (2 days of sample preparation and 30 min of measurement time per sample) and robust quantification of Infliximab antibody concentration in patients. The use of mass spectrometry also facilitates the straightforward expansion of the method to include additional antibody peptides.

Background: Hypertension is an important public health priority with a high prevalence in Africa. It is also an independent risk factor for kidney outcomes. We aimed to identify potential proteins and pathways involved in hypertension-associated albuminuria by assessing urinary proteomic profiles in black South African participants with combined hypertension and albuminuria compared to those who have neither condition.

Methods: The study included 24 South African cases with both hypertension and albuminuria and 49 control participants who had neither condition. Protein was extracted from urine samples and analysed using ultra-high-performance liquid chromatography coupled with mass spectrometry. Data were generated using data-independent acquisition (DIA) and processed using Spectronaut™ 15. Statistical and functional data annotation were performed on Perseus and Cytoscape to identify and annotate differentially abundant proteins. Machine learning was applied to the dataset using the OmicLearn platform.

Results: Overall, a mean of 1,225 and 915 proteins were quantified in the control and case groups, respectively. Three hundred and thirty-two differentially abundant proteins were constructed into a network. Pathways associated with these differentially abundant proteins included the immune system (q-value [false discovery rate] = 1.4 × 10^- 45), innate immune system (q = 1.1 × 10^- 32), extracellular matrix (ECM) organisation (q = 0.03) and activation of matrix metalloproteinases (q = 0.04). Proteins with high disease scores (76-100% confidence) for both hypertension and chronic kidney disease included angiotensinogen (AGT), albumin (ALB), apolipoprotein L1 (APOL1), and uromodulin (UMOD). A machine learning approach was able to identify a set of 20 proteins, differentiating between cases and controls.

Conclusions: The urinary proteomic data combined with the machine learning approach was able to classify disease status and identify proteins and pathways associated with hypertension-associated albuminuria.

Serum or plasma is frequently utilized in biomedical research; however, its application is impeded by the requirement for invasive sample collection. The non-invasive nature of urine collection makes it an attractive alternative for disease characterization and biomarker discovery. Mass spectrometry-based protein profiling of urine has led to the discovery of several disease-associated biomarkers. Proteomic analysis of urine has not only been applied to disorders of the kidney and urinary bladder but also to conditions affecting distant organs because proteins excreted in the urine originate from multiple organs. This review provides a progress update on urinary proteomics carried out over the past decade. Studies summarized in this review have expanded the catalog of proteins detected in the urine in a variety of clinical conditions. The wide range of applications of urine analysis-from characterizing diseases to discovering predictive, diagnostic and prognostic markers-continues to drive investigations of the urinary proteome.

Background: COPD is a complex respiratory disorder with high morbidity and mortality rates. Even with the current conventional diagnostic methods, including circulating inflammatory biomarkers, underdiagnosis rates in COPD remain as high as 70%. Our study was a comparative cross-sectional study that aimed to address the diagnostic challenges by identifying future biomarker candidates in COPD variants.

Methods: This study used a label-free plasma proteomics approach that combined mass spectrometric data with bioinformatics to shed light on the functional roles of differentially expressed proteins in the COPD lung microenvironment. The predictive capacity of the screened proteins was assessed using Receiver Operating Characteristic (ROC) curves, with Western blot analysis validating protein expression patterns in an independent cohort.

Results: Our study identified three DEPs-reticulocalbin-1, sideroflexin-4, and liprinα-3 that consistently exhibited altered expression in COPD exacerbation. ROC analysis indicated strong predictive potential, with AUC values of 0.908, 0.715, and 0.856 for RCN1, SFXN4, and LIPα-3, respectively. Validation through Western blot analysis confirmed their expression patterns in an independent validation cohort.

Conclusions: Our study discovered a novel duo of proteins reticulocalbin-1, and sideroflexin-4 that showed potential as valuable future biomarkers for the diagnosis and clinical management of COPD exacerbations.

Background: Dynein axonemal intermediate chain 1 protein (DNAI1) plays an essential role in cilia structure and function, while its mutations lead to primary ciliary dyskinesia (PCD). Accurate quantitation of DNAI1 in lung tissue is crucial for comprehensive understanding of its involvement in PCD, as well as for developing the potential PCD therapies. However, the current protein quantitation method is not sensitive enough to detect the endogenous level of DNAI1 in complex biological matrix such as lung tissue.

Methods: In this study, a quantitative method combining immunoprecipitation with nanoLC-MS/MS was developed to measure the expression level of human wild-type (WT) DNAI1 protein in lung tissue. To our understanding, it is the first immunoprecipitation (IP)-MS based method for absolute quantitation of DNAI1 protein in lung tissue. The DNAI1 quantitation was achieved through constructing a standard curve with recombinant human WT DNAI1 protein spiked into lung tissue matrix.

Results: This method was qualified with high sensitivity and accuracy. The lower limit of quantitation of human DNAI1 was 4 pg/mg tissue. This assay was successfully applied to determine the endogenous level of WT DNAI1 in human lung tissue.

Conclusions: The results clearly demonstrate that the developed assay can accurately quantitate low-abundance WT DNAI1 protein in human lung tissue with high sensitivity, indicating its high potential use in the drug development for DNAI1 mutation-caused PCD therapy.