scholarly journals Use of Circulating Tumour DNA (ctDNA) for Measurement of Therapy Predictive Biomarkers in Patients with Cancer

2022 ◽  
Vol 12 (1) ◽  
pp. 99
Author(s):  
Michael J. Duffy ◽  
John Crown
Keyword(s):  
Gold Standard ◽  
Kinase Inhibitors ◽  
Predictive Biomarkers ◽  
Lower Sensitivity ◽  
Cancer Therapies ◽  
Gold Standard Method ◽  
Biomarker Analysis ◽  
Egfr Tyrosine Kinase Inhibitors ◽  
Biomarker Testing ◽  
Circulating Tumour Dna

Biomarkers that predict likely response or resistance to specific therapies are critical in personalising treatment for cancer patients. Such biomarkers are now available for an increasing number of anti-cancer therapies, especially targeted therapy and immunotherapy. The gold-standard method for determining predictive biomarkers requires tumour tissue. Obtaining tissue, however, is not always possible and even if possible, the amount or quality of tissue obtained may be inadequate for biomarker analysis. Tumour DNA, however, can be released into the bloodstream, giving rise to what is referred to as circulating tumour DNA (ctDNA). In contrast to tissue, blood can be obtained from effectively all patients in a minimally invasive and safe manner. Other advantages of blood over tissue for biomarker testing include a shorter turn-around time and an ability to perform serial measurements. Furthermore, blood should provide a more complete profile of mutations present in heterogeneous tumours than a single-needle tissue biopsy. A limitation of blood vis-à-vis tissue, however, is lower sensitivity and, thus, the possibility of missing an actionable mutation. Despite this limitation, blood-based predictive biomarkers, such as mutant EGFR for predicting response to EGFR tyrosine kinase inhibitors in advanced non-small-cell lung cancer and mutant PIK3CA for predicting response to alpelisib in combination with fulvestrant in advanced breast cancer, may be used when tissue is unavailable. Although tissue remains the gold standard for detecting predictive biomarkers, it is likely that several further blood-based assays will soon be validated and used when tissue is unavailable or unsuitable for analysis.

scholarly journals Predictive biomarkers in nonsmall cell carcinoma and their clinico-pathological association

2019 ◽  
Vol 08 (04) ◽  
pp. 250-254 ◽  
Author(s):  
Anurag Mehta ◽  
Nayana N. Sriramanakoppa ◽  
Poojan Agarwal ◽  
Gayatri Viswakarma ◽  
Smreti Vasudevan ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Kinase Inhibitors ◽  
Growth Factor Receptor ◽  
Stage Iv ◽  
Predictive Biomarkers ◽  
Progression Free Survival ◽  
Predictive Biomarker ◽  
First Line ◽  
Anaplastic Lymphoma ◽  
Biomarker Testing

Abstract Background: Lung cancer is the leading cause of cancer-related mortality worldwide. Genome-directed therapy is less toxic, prolongs survival and provides a better quality of life. Predictive biomarker testing, therefore, has become a standard of care in advanced lung cancers. The objective of this study was to relate clinical and pathological features, including response to targeted therapy (TT) and progression-free survival (PFS) with positive driver mutation. Materials and Methods: Archival data of nonsmall cell carcinoma patients with Stage IV disease were retrieved. Those who tested positive for one of the four biomarkers (epidermal growth factor receptor [EGFR], anaplastic lymphoma kinase [ALK], MET, and ROS) were included. Patient demographics and clinical features were reviewed. Tumor histomorphology was correlated with oncological drivers. Treatment response, PFS, and overall survival were studied in three subcohorts of patients who received computed tomography (CT), CT followed by TT and those who received TT in the first line. Results: A total of 900 patients underwent biomarker evaluation of which 288 tested positive. Frequency of the four biomarkers observed was 26.6% (229/860), 6.6% (51/775), 6.6% (5/75), and 5.1% (3/59) for EGFR, ALK, MET, and ROS-1, respectively. The median PFS for EGFR-mutated cohort was 12 months, whereas it was 21 months for ALK protein overexpressing cases. Patients treated with first-line tyrosine kinase inhibitors performed better compared to those who were switched from chemotherapy to TT or those who received chemotherapy alone (P < 0.05). Conclusion: Biomarker testing has improved patient outcome. Genome-directed therapy accords best PFS with an advantage of nearly 10 months over cytotoxic therapy.

scholarly journals From Biology to Therapy: Improvements of Therapeutic Options in Lung Cancer

2019 ◽  
Vol 18 (9) ◽  
pp. 1235-1240 ◽  
Author(s):  
Luigi Formisano ◽  
Valerie M. Jansen ◽  
Roberta Marciano ◽  
Roberto Bianco
Keyword(s):  
Lung Cancer ◽  
Kinase Inhibitors ◽  
Growth Factor Receptor ◽  
Initial Response ◽  
Chemotherapeutic Agents ◽  
Cancer Therapies ◽  
In Vivo Models ◽  
Egfr Tyrosine Kinase Inhibitors

Lung cancer is the leading cause of cancer-related mortality around the world, despite effective chemotherapeutic agents, the prognosis has remained poor for a long time. The discovery of molecular changes that drive lung cancer has led to a dramatic shift in the therapeutic landscape of this disease. In “in vitro” and “in vivo” models of NSCLC (Non-Small Cell Lung Cancer), angiogenesis blockade has demonstrated an excellent anti-tumor activity, thus, a number of anti-angiogenic drugs have been approved by regulatory authorities for use in clinical practice. Much more interesting is the discovery of EGFR (Epithelial Growth Factor Receptor) mutations that predict sensitivity to the anti-EGFR Tyrosine Kinase Inhibitors (TKIs), a class of drugs that has shown to significantly improve survival when compared with standard chemotherapy in the first-line treatment of metastatic NSCLC. Nevertheless, after an initial response, resistance often occurs and prognosis becomes dismal. Biomolecular studies on cell line models have led to the discovery of mutations (e.g., T790M) that confer resistance to anti-EGFR inhibitors. Fortunately, drugs that are able to circumvent this mechanism of resistance have been developed and have been recently approved for clinical use. The discovery of robust intratumor lymphocyte infiltration in NSCLC has paved the way to several strategies able to restore the immune response. Thus, agents interfering with PD-1/PD-L1 (Programmed Death) pathways make up a significant portion of the armamentarium of cancer therapies for NSCLC. In all the above-mentioned situations, the basis of the success in treating NSCLC has started from understanding of the mutational landscape of the tumor.

scholarly journals Evaluation of the methods to identify patients who may benefit from PARP inhibitor use

2016 ◽  
Vol 23 (6) ◽  
pp. R267-R285 ◽  
Author(s):  
Diana Lim ◽  
Joanne Ngeow
Keyword(s):  
Anticancer Agents ◽  
Germ Line ◽  
Synthetic Lethality ◽  
Parp Inhibitor ◽  
Predictive Biomarkers ◽  
Clinical Diagnostics ◽  
Gold Standard Method ◽  
Biomarker Testing ◽  
Molecular Aberrations ◽  
No Gold Standard

The effectiveness of poly (ADP-ribose) polymerase inhibitors (PARPi) in treating cancers associated withBRCA1/2mutations hinges upon the concept of synthetic lethality and exemplifies the principles of precision medicine. Currently, most clinical trials are recruiting patients based on pathological subtypes or have includedBRCAmutation analysis (germ line and/or somatic) as part of the selection criteria. Mounting evidence, however, suggests that these drugs may also be efficacious in tumors with defects in other genes involved in the homologous recombination repair pathway. Advances in molecular profiling techniques together with increased research efforts have led to a better understanding of the molecular aberrations underlying this BRCA-like phenotype and helped broaden the concept of BRCAness. Hence, it is likely that the list of predictive biomarkers for PARPi therapy will increase in future. There is currently no gold standard method of testing for PARPi response and no universal guidelines are in place on how to incorporate biomarker testing into routine clinical diagnostics. In this review, we explore the concept of BRCAness and highlight the different methods that have been used to identify patients who may benefit from the use of these anticancer agents. The identification of predictive biomarkers is crucial in improving patient selection and expanding the clinical applications of PARPi therapy.

scholarly journals Circulating tumour DNA as a cancer biomarker

2018 ◽  
Vol 56 (1) ◽  
pp. 42-48 ◽  
Author(s):  
Michael J Duffy
Keyword(s):  
Kinase Inhibitors ◽  
Cancer Biomarkers ◽  
Therapy Resistance ◽  
Genetic Alterations ◽  
Clinical Use ◽  
Dna Assays ◽  
Egfr Tyrosine Kinase Inhibitors ◽  
Polymerase Chain ◽  
New Generation ◽  
Circulating Tumour Dna

Measurement of genetically altered DNA shed from tumours into the circulation can potentially provide a new generation of blood-based cancer biomarkers. Compared with tissue DNA biomarkers which require surgery or biopsy, samples for circulating tumour DNA assays can be obtained with minimal inconvenience and at lower cost. Furthermore, in contrast to tissue, the use of circulating tumour DNA allows serial monitoring, faster delivery of results and potentially provides an integrative representation of genetic alterations across all tumour sites within a patient. In contrast to existing protein-based cancer biomarkers, all of which can be produced by benign disease, circulating tumour DNA biomarkers would be expected to be more specific for malignancy. Furthermore, unlike the available blood cancer biomarkers, circulating tumour DNA can be used to predict response to specific therapies, identify mechanisms of therapy resistance and detect potentially actionable mutations. One of the first circulating tumour DNA assays recommended for clinical use involves EGFR mutation testing for predicting response to EGFR tyrosine kinase inhibitors in patients with advanced non-small cell lung cancer, especially when tumour tissue is unavailable. In order to accelerate the introduction of circulating tumour DNA assays into routine clinical use, laboratory medicine staff will have to undergo training in the use of polymerase chain reaction and DNA sequencing. Furthermore, existing circulating tumour DNA assays will need to be simplified, standardized, shown to have clinical utility, be made available at reasonable costs and be reimbursable.

Effect of Dasatinib on Genes Related to Mitotic Catastrophe Pathway in Chronic Myeloid Leukemia Cells

2020 ◽  
Vol 20 ◽  
Author(s):  
Sezgi Kipcak ◽  
Buket Ozel ◽  
Cigir B. Avci ◽  
Leila S. Takanlou ◽  
Maryam S. Takanlou ◽  
...  
Keyword(s):  
Chronic Myeloid Leukemia ◽  
Myeloid Leukemia ◽  
Kinase Inhibitors ◽  
Fusion Gene ◽  
Philadelphia Chromosome ◽  
K562 Cells ◽  
Mitotic Catastrophe ◽  
Control Group ◽  
Cancer Therapies ◽  
Apoptosis Pathways

Background: Chronic myeloid leukemia (CML), is characterized by a reciprocal translocation t(9;22) and forms the BCR/ABL1 fusion gene, which is called the Philadelphia chromosome. The therapeutic targets for CML patients which are mediated with BCR/ABL1 oncogenic are tyrosine kinase inhibitors such as imatinib, dasatinib, and nilotinib. The latter two of which have been approved for the treatment of imatinib-resistant or intolerance CML patients. Mitotic catastrophe (MC) is one of the non-apoptotic mechanisms which frequently initiated in types of cancer cells in response to anti-cancer therapies; pharmacological inhibitors of G2 checkpoint members or genetic suppression of PLK1, PLK2, ATR, ATM, CHK1, and CHK2 can trigger DNA-damage-stimulated mitotic catastrophe. PLK1, AURKA/B anomalously expressed in CML cells, that phosphorylation and activation of PLK1 occur by AURKB at centromeres and kinetochores. Objective: The purpose of this study was to investigate the effect of dasatinib on the expression of genes in MC and apoptosis pathways in K562 cells. Methods: Total RNA was isolated from K-562 cells treated with the IC50 value of dasatinib and untreated cells as a control group. The expression of MC and apoptosis-related genes were analyzed by the qRT-PCR system. Results: The array-data demonstrated that dasatinib-treated K562 cells significantly caused the decrease of several genes (AURKA, AURKB, PLK, CHEK1, MYC, XPC, BCL2, and XRCC2). Conclusion: The evidence supply a basis to support clinical researches for the suppression of oncogenes such as PLKs with AURKs in the treatment of types of cancer especially chronic myeloid leukemia.

Kinase Inhibitors Targeting Anti-angiogenesis as Anti-cancer Therapies

2012 ◽  
Vol 1 (4) ◽  
pp. 335-346 ◽  
Author(s):  
Jing Liu ◽  
Feiyang Liu ◽  
David L. Waller ◽  
Junfeng Wang ◽  
Qingsong Liu
Keyword(s):  
Kinase Inhibitors ◽  
Cancer Therapies ◽  
Anti Cancer

scholarly journals Quantum chemical benchmark databases of gold-standard dimer interaction energies

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Alexander G. Donchev ◽  
Andrew G. Taube ◽  
Elizabeth Decolvenaere ◽  
Cory Hargus ◽  
Robert T. McGibbon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gold Standard ◽  
Training Data ◽  
Structure Theory ◽  
Cluster Method ◽  
Coupled Cluster ◽  
Interaction Energies ◽  
Gold Standard Method ◽  
Representative Subset ◽  
Semi Empirical

AbstractAdvances in computational chemistry create an ongoing need for larger and higher-quality datasets that characterize noncovalent molecular interactions. We present three benchmark collections of quantum mechanical data, covering approximately 3,700 distinct types of interacting molecule pairs. The first collection, which we refer to as DES370K, contains interaction energies for more than 370,000 dimer geometries. These were computed using the coupled-cluster method with single, double, and perturbative triple excitations [CCSD(T)], which is widely regarded as the gold-standard method in electronic structure theory. Our second benchmark collection, a core representative subset of DES370K called DES15K, is intended for more computationally demanding applications of the data. Finally, DES5M, our third collection, comprises interaction energies for nearly 5,000,000 dimer geometries; these were calculated using SNS-MP2, a machine learning approach that provides results with accuracy comparable to that of our coupled-cluster training data. These datasets may prove useful in the development of density functionals, empirically corrected wavefunction-based approaches, semi-empirical methods, force fields, and models trained using machine learning methods.

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