Cancer Biomarker Discovery for Precision Medicine: New Progress

2020 ◽  
Vol 26 (42) ◽  
pp. 7655-7671 ◽  
Author(s):  
Jinfeng Zou ◽  
Edwin Wang
Keyword(s):  
Precision Medicine ◽  
Cancer Patients ◽  
Clinical Application ◽  
Liquid Biopsy ◽  
Biomarker Discovery ◽  
Deep Understanding ◽  
Genetic Mutation ◽  
Data Driven ◽  
Novel Approaches ◽  
Functional Biomarkers

Background: Precision medicine puts forward customized healthcare for cancer patients. An important way to accomplish this task is to stratify patients into those who may respond to a treatment and those who may not. For this purpose, diagnostic and prognostic biomarkers have been pursued. Objective: This review focuses on novel approaches and concepts of exploring biomarker discovery under the circumstances that technologies are developed, and data are accumulated for precision medicine. Results: The traditional mechanism-driven functional biomarkers have the advantage of actionable insights, while data-driven computational biomarkers can fulfill more needs, especially with tremendous data on the molecules of different layers (e.g. genetic mutation, mRNA, protein etc.) which are accumulated based on a plenty of technologies. Besides, the technology-driven liquid biopsy biomarker is very promising to improve patients’ survival. The developments of biomarker discovery on these aspects are promoting the understanding of cancer, helping the stratification of patients and improving patients’ survival. Conclusion: Current developments on mechanisms-, data- and technology-driven biomarker discovery are achieving the aim of precision medicine and promoting the clinical application of biomarkers. Meanwhile, the complexity of cancer requires more effective biomarkers, which could be accomplished by a comprehensive integration of multiple types of biomarkers together with a deep understanding of cancer.

scholarly journals Towards precision medicine: advances in 5-hydroxymethylcytosine cancer biomarker discovery in liquid biopsy

2019 ◽  
Vol 39 (1) ◽  
pp. 12 ◽  
Author(s):  
Chang Zeng ◽  
Emily Kunce Stroup ◽  
Zhou Zhang ◽  
Brian C.-H. Chiu ◽  
Wei Zhang
Keyword(s):  
Precision Medicine ◽  
Liquid Biopsy ◽  
Biomarker Discovery ◽  
Cancer Biomarker

scholarly journals Data Driven Mathematical Model of FOLFIRI Treatment for Colon Cancer

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2632
Author(s):  
Aparajita Budithi ◽  
Sumeyye Su ◽  
Arkadz Kirshtein ◽  
Leili Shahriyari
Keyword(s):  
Mathematical Model ◽  
Colon Cancer ◽  
Cancer Patients ◽  
Immune Cell ◽  
Treatment Options ◽  
Data Driven ◽  
T Helper ◽  
Primary Tumors ◽  
Cell Fractions

Many colon cancer patients show resistance to their treatments. Therefore, it is important to consider unique characteristic of each tumor to find the best treatment options for each patient. In this study, we develop a data driven mathematical model for interaction between the tumor microenvironment and FOLFIRI drug agents in colon cancer. Patients are divided into five distinct clusters based on their estimated immune cell fractions obtained from their primary tumors’ gene expression data. We then analyze the effects of drugs on cancer cells and immune cells in each group, and we observe different responses to the FOLFIRI drugs between patients in different immune groups. For instance, patients in cluster 3 with the highest T-reg/T-helper ratio respond better to the FOLFIRI treatment, while patients in cluster 2 with the lowest T-reg/T-helper ratio resist the treatment. Moreover, we use ROC curve to validate the model using the tumor status of the patients at their follow up, and the model predicts well for the earlier follow up days.

scholarly journals Human Plasma Metabolomics for Biomarker Discovery: Targeting the Molecular Subtypes in Breast Cancer

Cancers ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 147
Author(s):  
Leticia Díaz-Beltrán ◽  
Carmen González-Olmedo ◽  
Natalia Luque-Caro ◽  
Caridad Díaz ◽  
Ariadna Martín-Blázquez ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Biomarker Discovery ◽  
Molecular Subtypes ◽  
Breast Cancer Subtype ◽  
P Value ◽  
Plasma Samples ◽  
Breast Cancer Patients ◽  
Clinical Value ◽  
Molecular Phenotypes

Purpose: The aim of this study is to identify differential metabolomic signatures in plasma samples of distinct subtypes of breast cancer patients that could be used in clinical practice as diagnostic biomarkers for these molecular phenotypes and to provide a more individualized and accurate therapeutic procedure. Methods: Untargeted LC-HRMS metabolomics approach in positive and negative electrospray ionization mode was used to analyze plasma samples from LA, LB, HER2+ and TN breast cancer patients and healthy controls in order to determine specific metabolomic profiles through univariate and multivariate statistical data analysis. Results: We tentatively identified altered metabolites displaying concentration variations among the four breast cancer molecular subtypes. We found a biomarker panel of 5 candidates in LA, 7 in LB, 5 in HER2 and 3 in TN that were able to discriminate each breast cancer subtype with a false discovery range corrected p-value < 0.05 and a fold-change cutoff value > 1.3. The model clinical value was evaluated with the AUROC, providing diagnostic capacities above 0.85. Conclusion: Our study identifies metabolic profiling differences in molecular phenotypes of breast cancer. This may represent a key step towards therapy improvement in personalized medicine and prioritization of tailored therapeutic intervention strategies.

scholarly journals Identification of Novel Mutations in Colorectal Cancer Patients Using AmpliSeq Comprehensive Cancer Panel

2021 ◽  
Vol 11 (6) ◽  
pp. 535
Author(s):  
Bader Almuzzaini ◽  
Jahad Alghamdi ◽  
Alhanouf Alomani ◽  
Saleh AlGhamdi ◽  
Abdullah A. Alsharm ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Personalized Medicine ◽  
Cancer Patients ◽  
Biomarker Discovery ◽  
Local Population ◽  
Network Analyses ◽  
Functional Perspective ◽  
Novel Variants ◽  
Colorectal Cancer Patients ◽  
Cancer Panel

Biomarker discovery would be an important tool in advancing and utilizing the concept of precision and personalized medicine in the clinic. Discovery of novel variants in local population provides confident targets for developing biomarkers for personalized medicine. We identified the need to generate high-quality sequencing data from local colorectal cancer patients and understand the pattern of occurrence of variants. In this report, we used archived samples from Saudi Arabia and used the AmpliSeq comprehensive cancer panel to identify novel somatic variants. We report a comprehensive analysis of next-generation sequencing results with a coverage of >300X. We identified 466 novel variants which were previously unreported in COSMIC and ICGC databases. We analyzed the genes associated with these variants in terms of their frequency of occurrence, probable pathogenicity, and clinicopathological features. Among pathogenic somatic variants, 174 were identified for the first time in the large intestine. APC, RET, and EGFR genes were most frequently mutated. A higher number of variants were identified in the left colon. Occurrence of variants in ERBB2 was significantly correlated with those of EGFR and ATR genes. Network analyses of the identified genes provide functional perspective of the identified genes and suggest affected pathways and probable biomarker candidates. This report lays the ground work for biomarker discovery and identification of driver gene mutations in local population.

scholarly journals L-Carnitine and Acylcarnitines: Mitochondrial Biomarkers for Precision Medicine

Metabolites ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 51
Author(s):  
Marc R. McCann ◽  
Mery Vet George De la Rosa ◽  
Gus R. Rosania ◽  
Kathleen A. Stringer
Keyword(s):  
Precision Medicine ◽  
Inborn Errors Of Metabolism ◽  
Clinical Utility ◽  
Biomarker Discovery ◽  
Genetic Disorders ◽  
Acid Oxidation ◽  
Inborn Errors ◽  
Therapeutic Decisions ◽  
Literature Evidence ◽  
Clinical Biomarker

Biomarker discovery and implementation are at the forefront of the precision medicine movement. Modern advances in the field of metabolomics afford the opportunity to readily identify new metabolite biomarkers across a wide array of disciplines. Many of the metabolites are derived from or directly reflective of mitochondrial metabolism. L-carnitine and acylcarnitines are established mitochondrial biomarkers used to screen neonates for a series of genetic disorders affecting fatty acid oxidation, known as the inborn errors of metabolism. However, L-carnitine and acylcarnitines are not routinely measured beyond this screening, despite the growing evidence that shows their clinical utility outside of these disorders. Measurements of the carnitine pool have been used to identify the disease and prognosticate mortality among disorders such as diabetes, sepsis, cancer, and heart failure, as well as identify subjects experiencing adverse drug reactions from various medications like valproic acid, clofazimine, zidovudine, cisplatin, propofol, and cyclosporine. The aim of this review is to collect and interpret the literature evidence supporting the clinical biomarker application of L-carnitine and acylcarnitines. Further study of these metabolites could ultimately provide mechanistic insights that guide therapeutic decisions and elucidate new pharmacologic targets.

scholarly journals The Origin of Tumor DNA in Urine of Urogenital Cancer Patients: Local Shedding and Transrenal Excretion

Cancers ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 535
Author(s):  
Anouk E. Hentschel ◽  
Rianne van den Helder ◽  
Nienke E. van Trommel ◽  
Annina P. van Splunter ◽  
Robert A. A. van Boerdonk ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Bladder Cancer ◽  
Cancer Patients ◽  
Liquid Biopsy ◽  
Bladder Tumor ◽  
Great Promise ◽  
Tumor Tissues ◽  
Catheter Urine ◽  
Tumor Dna ◽  
Urogenital Cancers

In urogenital cancers, urine as a liquid biopsy for non-invasive cancer detection holds great promise for future clinical application. Their anatomical position allows for the local shedding of tumor DNA, but recent data indicate that tumor DNA in urine might also result from transrenal excretion. This study aims to assess the origin of tumor-associated DNA in the urine of 5 bladder and 25 cervical cancer patients. Besides natural voided urine, paired urine samples were collected in which contact with the local tumor was circumvented to bypass local shedding. The latter concerned nephrostomy urine in bladder cancer patients, and catheter urine in cervical cancer patients. Methylation levels of GHSR, SST, and ZIC1 were determined using paired bladder tumor tissues and cervical scrapes as a reference. Urinary methylation levels were compared to natural voided urine of matched controls. To support methylation results, mutation analysis was performed in urine and tissue samples of bladder cancer patients. Increased methylation levels were not only found in natural voided urine from bladder and cervical cancer patients, but also in the corresponding nephrostomy and catheter urine. DNA mutations detected in bladder tumor tissues were also detectable in all paired natural voided urine as well as in a subset of nephrostomy urine. These results provide the first evidence that the suitability of urine as a liquid biopsy for urogenital cancers relies both on the local shedding of tumor cells and cell fragments, as well as the transrenal excretion of tumor DNA into the urine.

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