Investigation of altered urinary metabolomic profiles of invasive ductal carcinoma of breast using targeted and untargeted approaches

More, Tushar H.; Taware, Ravindra; Taunk, Khushman; Chanukuppa, Venkatesh; Naik, Venkateshwarlu; Mane, Anupama; Rapole, Srikanth

doi:10.1007/s11306-018-1405-1

Cited by 3 publications

(6 citation statements)

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“…These metabolites were selected based on the previous literature survey of various cancer metabolomic studies. 22 The chromatography parameters and details of MRM library are discussed in our previous report 23 and the same have been utilized for this study as well. Metabolites extraction was executed by the addition of 200 mL methanol to 25 mL of every serum samples, vortexed vigorously and incubated at À20 C overnight.…”

Section: Targeted Lc-mrm/ms Based Metabolomic Prolingmentioning

confidence: 99%

“…The LC buffers and chromatography parameters along with MS sample acquisition parameters were adopted from our earlier studies as mentioned elsewhere. 23 Positive ionization chromatographic separation was carried out using 32 min gradient program of mobile phase A (10 mM ammonium formate with 0.1% formic acid in water) and mobile phase B (acetonitrile with 0.1% formic acid) with 700 mL min À1 elution rate. The gradient started with 95% B and further decreased to 40% B over next 15 min and held constant for 6 min, and then the column was gradually returned to 95% B and equilibrated for another 9 min.…”

Section: Targeted Lc-mrm/ms Based Metabolomic Prolingmentioning

confidence: 99%

“…25 Univariate and multivariate statistical analyses were applied to these normalized datasets in order to identify the differentially expressed metabolites that can unambiguously discriminate MM subjects from their respective controls. 23 Similarly, PLSDA model was also veried. Furthermore, variable importance in projection (VIP) score of OPLS-DA model was used to rank the metabolites as per their contribution in segregation of MM subjects from the controls in the score plot.…”

Section: Data Pre-processing and Statistical Analysismentioning

confidence: 99%

“…23,26 Univariate statistical methods such as Wilcoxon rank t-test (p < 0.05) and log 2 fold change ($0.58/ #À0.58) were used to select statistically signicant metabolites in both the datasets. Furthermore, multivariate statistical methods like principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were employed to visualize the overall distribution of serum metabolites among the study cohort as well as their efficacy in discrimination of MM subjects from the respective controls.…”

mentioning

confidence: 99%

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Serum metabolomic alterations in multiple myeloma revealed by targeted and untargeted metabolomics approaches: a pilot study

Chanukuppa

Taunk

et al. 2019

RSC Adv.

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Section: Targeted Lc-mrm/ms Based Metabolomic Prolingmentioning

confidence: 99%

Section: Targeted Lc-mrm/ms Based Metabolomic Prolingmentioning

confidence: 99%

Section: Data Pre-processing and Statistical Analysismentioning

confidence: 99%

mentioning

confidence: 99%

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Serum metabolomic alterations in multiple myeloma revealed by targeted and untargeted metabolomics approaches: a pilot study

Chanukuppa

Taunk

et al. 2019

RSC Adv.

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“…Metabolomic analyses of primary tumours have also demonstrated predictive value in relation to neoadjuvant treatment of patients with locally advanced disease 28. So, there is proof of principle showing the use of preoperative blood and urine samples for metabolic profiling in patients with breast cancer 25 32…”

Section: Introductionmentioning

confidence: 99%

Liquid biopsies and patient-reported outcome measures for integrative monitoring of patients with early-stage breast cancer: a study protocol for the longitudinal observational Prospective Breast Cancer Biobanking (PBCB) study

et al. 2022

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IntroductionBreast cancer is still the most common malignancy among women worldwide. The Prospective Breast Cancer Biobank (PBCB) collects blood and urine from patients with breast cancer every 6 or 12 months for 11 years from 2011 to 2030 at two university hospitals in Western Norway. The project aims to identify new biomarkers that enable detection of systemic recurrences at the molecular level. As blood represents the biological interface between the primary tumour, the microenvironment and distant metastases, liquid biopsies represent the ideal medium to monitor the patient‘s cancer biology for identification of patients at high risk of relapse and for early detection systemic relapse.Including patient-reported outcome measures (PROMs) allows for a vast number of possibilities to compare PROM data with biological information, enabling the study of fatigue and Quality of Life in patients with breast cancer.Methods and analysisA total of 1455 patients with early-stage breast cancer are enrolled in the PBCB study, which has a one-armed prospective observational design. Participants consent to contribute liquid biopsies (i.e., peripheral blood and urine samples) every 6 or 12 months for 11 years. The liquid biopsies are the basis for detection of circulating tumour cells, circulating tumour DNA (ctDNA), exosomal micro-RNA (miRNA), miRNA in Tumour Educated Platelet and metabolomic profiles. In addition, participants respond to 10 PROM questionnaires collected annually. Moreover, a control group comprising 200 women without cancer aged 25–70 years will provide the same data.Ethics and disseminationThe general research biobank PBCB was approved by the Ministry of Health and Care Services in 2007, by the Regional Ethics Committee (REK) in 2010 (#2010/1957). The PROM (#2011/2161) and the biomarker study PerMoBreCan (#2015/2010) were approved by REK in 2011 and 2015 respectively. Results will be published in international peer reviewed journals. Deidentified data will be accessible on request.Trial registration numberNCT04488614.

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Biomarker Reproducibility Challenge: A Review of Non-Nucleotide Biomarker Discovery Protocols from Body Fluids in Breast Cancer Diagnosis

Safari

Kehelpannala

Safarchi

et al. 2023

Cancers

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Breast cancer has now become the most commonly diagnosed cancer, accounting for one in eight cancer diagnoses worldwide. Non-invasive diagnostic biomarkers and associated tests are superlative candidates to complement or improve current approaches for screening, early diagnosis, or prognosis of breast cancer. Biomarkers detected from body fluids such as blood (serum/plasma), urine, saliva, nipple aspiration fluid, and tears can detect breast cancer at its early stages in a minimally invasive way. The advancements in high-throughput molecular profiling (omics) technologies have opened an unprecedented opportunity for unbiased biomarker detection. However, the irreproducibility of biomarkers and discrepancies of reported markers have remained a major roadblock to clinical implementation, demanding the investigation of contributing factors and the development of standardised biomarker discovery pipelines. A typical biomarker discovery workflow includes pre-analytical, analytical, and post-analytical phases, from sample collection to model development. Variations introduced during these steps impact the data quality and the reproducibility of the findings. Here, we present a comprehensive review of methodological variations in biomarker discovery studies in breast cancer, with a focus on non-nucleotide biomarkers (i.e., proteins, lipids, and metabolites), highlighting the pre-analytical to post-analytical variables, which may affect the accurate identification of biomarkers from body fluids.

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Investigation of altered urinary metabolomic profiles of invasive ductal carcinoma of breast using targeted and untargeted approaches

Cited by 3 publications

References 56 publications

Serum metabolomic alterations in multiple myeloma revealed by targeted and untargeted metabolomics approaches: a pilot study

Serum metabolomic alterations in multiple myeloma revealed by targeted and untargeted metabolomics approaches: a pilot study

Liquid biopsies and patient-reported outcome measures for integrative monitoring of patients with early-stage breast cancer: a study protocol for the longitudinal observational Prospective Breast Cancer Biobanking (PBCB) study

Biomarker Reproducibility Challenge: A Review of Non-Nucleotide Biomarker Discovery Protocols from Body Fluids in Breast Cancer Diagnosis

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