Discovery and Evaluation of Protein Biomarkers as a Signature of Wellness in Late-Stage Cancer Patients in Early Phase Clinical Trials.
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ABSTRACT:
SUBMITTER: Geary B
PROVIDER: S-EPMC8157875 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:TARGET (Tumour characterisation to Guide Experimental Targeted therapy) seeks to optimise the pathway for molecular characterisation of all patients entering early phase cancer trials to inform clinical decision-making on optimal therapy. Performance status (PS) was developed to assess a patient’s ability to perform a variety of activities to further inform clinical decision making on optimal therapy including appropriate inclusion in clinical trials. However, the quality of present algorithms to assess performance status are limited and based on a semi-quantitative clinician assessment. In particular, a common eligibility criterion for entry into early phase cancer clinical trials is a life expectancy of >3 or 6 months. Here we investigate proteomics as a means of more objective and quantitative assessment of both performance status and thus likely prognosis prior to clinical trial enrolment. Improved patient stratification at enrolment would reduce the number of patients unnecessarily exposed to potentially toxic drugs and decrease withdrawal from trials. Plasma samples from patients enrolled into the TARGET trial were analysed using the mass spectrometry (MS) technique: Sequential window acquisition of all theoretical fragment ion spectra (SWATH) MS. SWATH-MS was used on a discovery cohort of 73 patients to identify proteins that could differentiate patients into either a good or poor prognosis. A three-protein panel showed a stronger prediction of overall survival (p = 0.000086) compared to PS (p = 0.001). This panel was then tested against a validation cohort of 77 patients. The panel was determined as being a significant (p = 0.000451) predictor of overall survival whereas PS was not significant. The panel had a receiver operating characteristic curve area under the curve (AUC) of 0.73 in the validation set; the AUC of PS was 0.54. This signature can now be developed further in larger patient populations to assess its utility in a clinical setting.
2022-07-05 | PXD023553 | Pride
Project description:BACKGROUND: Transmissible spongiform encephalopathies are fatal neurodegenerative disease occurring in animals and humans for which no ante-mortem diagnostic test in biological fluids is available. In such pathologies, detection of the pathological form of the prion protein (i.e., the causative factor) in blood is difficult and therefore identification of new biomarkers implicated in the pathway of prion infection is relevant. METHODS: In this study we used the SELDI-TOF MS technology to analyze a large number of serum samples from control sheep and animals with early phase or late phase scrapie. A few potential low molecular weight biomarkers were selected by statistical methods and, after a training analysis, a protein signature pattern, which discriminates between early phase scrapie samples and control sera was identified. RESULTS: The combination of early phase biomarkers showed a sensitivity of 87% and specificity of 90% for all studied sheep in the early stage of the disease. One of these potential biomarkers was identified and validated in a SELDI-TOF MS kinetic study of sera from Syrian hamsters infected by scrapie, by western blot analysis and ELISA quantitation. CONCLUSIONS: Differential protein expression profiling allows establishing a TSE diagnostic in scrapie sheep, in the early phase of the disease. Some proteic differences observed in scrapie sheep exist in infected hamsters. Further studies are being performed to identify all the discriminant biomarkers of interest and to test our potential markers in a new cohort of animals.
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Project description:We use a systems biology approach to construct protein-protein interaction networks (PPINs) for early and late stage bladder cancer. By comparing the networks of these two stages, we find that both networks showed very significantly different mechanisms. To obtain the differential network structures between cancer and noncancer PPINs, we constructed cancer PPIN and noncancer PPIN network structures for the two bladder cancer stages using microarray data from cancer cells and their adjacent noncancer cells, respectively. With their carcinogenesis relevance values (CRVs), we identified 152 and 50 significant proteins and their PPI networks (network markers) for early and late stage bladder cancer by statistical assessment. To investigate the evolution of network biomarkers in the carcinogenesis process, primary pathway analysis showed that the significant pathways of early stage bladder cancer are related to ordinary cancer mechanisms, while the ribosome pathway and spliceosome pathway are most important for late stage bladder cancer. Their only intersection is the ubiquitin mediated proteolysis pathway in the whole stage of bladder cancer. The evolution of network biomarkers from early to late stage can reveal the carcinogenesis of bladder cancer. The findings in this study are new clues specific to this study and give us a direction for targeted cancer therapy, and it should be validated in vivo or in vitro in the future.
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Project description:Drug repositioning is a popular approach in the pharmaceutical industry for identifying potential new uses for existing drugs and accelerating the development time. Non-small-cell lung cancer (NSCLC) is one of the leading causes of death worldwide. To reduce the biological heterogeneity effects among different individuals, both normal and cancer tissues were taken from the same patient, hence allowing pairwise testing. By comparing early- and late-stage cancer patients, we can identify stage-specific NSCLC genes. Differentially expressed genes are clustered separately to form up- and downregulated communities that are used as queries to perform enrichment analysis. The results suggest that pathways for early- and late-stage cancers are different. Sets of up- and downregulated genes were submitted to the cMap web resource to identify potential drugs. To achieve high confidence drug prediction, multiple microarray experimental results were merged by performing meta-analysis. The results of a few drug findings are supported by MTT assay or clonogenic assay data. In conclusion, we have been able to assess the potential existing drugs to identify novel anticancer drugs, which may be helpful in drug repositioning discovery for NSCLC.
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Project description:Simon's optimal two-stage design has been widely used in early phase clinical trials for Oncology and AIDS studies with binary endpoints. With this approach, the second-stage sample size is fixed when the trial passes the first stage with sufficient activity. Adaptive designs, such as those due to Banerjee and Tsiatis (2006) and Englert and Kieser (2013), are flexible in the sense that the second-stage sample size depends on the response from the first stage, and these designs are often seen to reduce the expected sample size under the null hypothesis as compared with Simon's approach. An unappealing trait of the existing designs is that they are not associated with a second-stage sample size, which is a non-increasing function of the first-stage response rate. In this paper, an efficient intelligent process, the branch-and-bound algorithm, is used in extensively searching for the optimal adaptive design with the smallest expected sample size under the null, while the type I and II error rates are maintained and the aforementioned monotonicity characteristic is respected. The proposed optimal design is observed to have smaller expected sample sizes compared to Simon's optimal design, and the maximum total sample size of the proposed adaptive design is very close to that from Simon's method. The proposed optimal adaptive two-stage design is recommended for use in practice to improve the flexibility and efficiency of early phase therapeutic development.
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Project description:Despite significant improvements in life expectancy of kidney transplant patients due to advances in surgery and immunosuppression, Chronic Allograft Nephropathy (CAN) remains a daunting problem. A complex network of cellular mechanisms in both graft and peripheral immune compartments complicates the non-invasive diagnosis of CAN, which still requires biopsy histology. This is compounded by non-immunological factors contributing to graft injury. There is a pressing need to identify and validate minimally invasive biomarkers for CAN to serve as early predictors of graft loss and as metrics for managing long-term immunosuppression.We used DNA microarrays, tandem mass spectroscopy proteomics and bioinformatics to identify genomic and proteomic markers of mild and moderate/severe CAN in peripheral blood of two distinct cohorts (n = 77 total) of kidney transplant patients with biopsy-documented histology.Gene expression profiles reveal over 2400 genes for mild CAN, and over 700 for moderate/severe CAN. A consensus analysis reveals 393 (mild) and 63 (moderate/severe) final candidates as CAN markers with predictive accuracy of 80% (mild) and 92% (moderate/severe). Proteomic profiles show over 500 candidates each, for both stages of CAN including 302 proteins unique to mild and 509 unique to moderate/severe CAN.This study identifies several unique signatures of transcript and protein biomarkers with high predictive accuracies for mild and moderate/severe CAN, the most common cause of late allograft failure. These biomarkers are the necessary first step to a proteogenomic classification of CAN based on peripheral blood profiling and will be the targets of a prospective clinical validation study.
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Project description:In this study, an attempt has been made to identify expression-based gene biomarkers that can discriminate early and late stage of clear cell renal cell carcinoma (ccRCC) patients. We have analyzed the gene expression of 523 samples to identify genes that are differentially expressed in the early and late stage of ccRCC. First, a threshold-based method has been developed, which attained a maximum accuracy of 71.12% with ROC 0.67 using single gene NR3C2. To improve the performance of threshold-based method, we combined two or more genes and achieved maximum accuracy of 70.19% with ROC of 0.74 using eight genes on the validation dataset. These eight genes include four underexpressed (NR3C2, ENAM, DNASE1L3, FRMPD2) and four overexpressed (PLEKHA9, MAP6D1, SMPD4, C11orf73) genes in the late stage of ccRCC. Second, models were developed using state-of-art techniques and achieved maximum accuracy of 72.64% and 0.81 ROC using 64 genes on validation dataset. Similar accuracy was obtained on 38 genes selected from subset of genes, involved in cancer hallmark biological processes. Our analysis further implied a need to develop gender-specific models for stage classification. A web server, CancerCSP, has been developed to predict stage of ccRCC using gene expression data derived from RNAseq experiments.
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Project description:Breast cancer (BC) is the most predominant type of cancer among women. The aim of this study is to find new biomarkers that can help in early detection of BC, especially for those who are too young to be screened using mammography as per guidelines. Using microRNA microarray, we previously showed dysregulation of 74 microRNAs in tumors from early BC patients as compared with normal adjacent tissues, which we were interested in studying in blood circulation. In this study, we investigated the expression of 12 microRNA (miR-21/miR-155/miR-23a/miR-130a/miR-145/miR-425-5p/miR-139-5p/miR-451/miR-195/miR-125b/miR-100, and miR-182) in the plasma of 41 newly diagnosed Lebanese BC patients with early invasive ductal carcinoma as compared with 32 healthy controls. Total RNA was extracted from plasma, and expression levels of miRNA of interest were measured using RT-qPCR followed by statistical analysis; miR-21, miR-155, miR-23a, miR-130a, miR-145, miR-425-5p, and miR-139-5p were significantly upregulated and miR-451 was significantly downregulated, in the plasma of BC patients as compared with healthy controls. The positively correlated miR-23a, miR-21, and miR-130a had a high diagnostic accuracy (86%). Importantly, the combination of miR-145/miR-425-5p/miR-139-5p/miR-130a scored the highest diagnostic accuracy of 95% with AUC = 0.97 (sensitivity 97% and specificity 91%). MicroRNAs are promising non-invasive diagnostic biomarkers for early-stage BC with the panel of miR-145/miR-425-5p/miR-139-5p/miR-130a having the highest diagnostic accuracy.
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Project description:Diabetic kidney disease (DKD) is a devastating complication that affects an estimated third of patients with type 1 diabetes mellitus (DM). There is no cure once the disease is diagnosed, but early treatment at a sub-clinical stage can prevent or at least halt the progression. DKD is clinically diagnosed as abnormally high urinary albumin excretion rate (AER). We hypothesize that subtle changes in the urine metabolome precede the clinically significant rise in AER. To test this, 52 type 1 diabetic patients were recruited by the FinnDiane study that had normal AER (normoalbuminuric). After an average of 5.5 years of follow-up half of the subjects (26) progressed from normal AER to microalbuminuria or DKD (macroalbuminuria), the other half remained normoalbuminuric. The objective of this study is to discover urinary biomarkers that differentiate the progressive form of albuminuria from non-progressive form of albuminuria in humans. Metabolite profiles of baseline 24 h urine samples were obtained by gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) to detect potential early indicators of pathological changes. Multivariate logistic regression modeling of the metabolomics data resulted in a profile of metabolites that separated those patients that progressed from normoalbuminuric AER to microalbuminuric AER from those patients that maintained normoalbuminuric AER with an accuracy of 75% and a precision of 73%. As this data and samples are from an actual patient population and as such, gathered within a less controlled environment it is striking to see that within this profile a number of metabolites (identified as early indicators) have been associated with DKD already in literature, but also that new candidate biomarkers were found. The discriminating metabolites included acyl-carnitines, acyl-glycines and metabolites related to tryptophan metabolism. We found candidate biomarkers that were univariately significant different. This study demonstrates the potential of multivariate data analysis and metabolomics in the field of diabetic complications, and suggests several metabolic pathways relevant for further biological studies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0291-6) contains supplementary material, which is available to authorized users.
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Project description:Physical activity can improve health as well as reduce stress and the risk of developing several widespread diseases. However, there exists no accepted standard biomedical examination-method for stress evaluation. The purpose of this study was to investigate the effect of regular physical activity on stress and wellness as well as the evaluation of potential biomarkers in this field. This study included 105 people (mean age = 36.57 ± 1.4 years) who were randomly assigned into the exercise group 1 (EG-1) (n = 41), the exercise group 2 (EG-2) (n = 30), and the control group (CG) (n = 34). Measurements of stress and wellness were obtained by Multiscan BC-OXI before and after experimental period. This device presents a multifrequency segmental body composition 3D analyser with digital pulse oximeter. The key indicators of stress as well as for wellness were significantly improved in the EG-1. Parasympathetic activity showed significant changes as potential stress biomarker. Statistically significant gender differences were not observed in the comparable groups. The results suggest that the stress resistance and well-being significantly improved in the EG-1 due to regular physical activity. However, further research is necessary to determine effects of physical activity on integral health indicators.
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