Significance of testing for TP53 gene mutations in lung adenocarcinoma using targeted gene sequencing

Matsumura, Yuki; Owada-Ozaki, Yuki; Suzuki, Hiroyuki

doi:10.21037/jtd.2018.10.66

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…Different types of TP53 mutations were detectable at different time-points in all the 20 subjects (overall 10/20, 50%), although TP53 mutations appeared to be more prevalent in EGFR wildtype subjects on chemotherapy than in patients treated with EGFR-TKI. TP53 point mutations of various types have been reported to result in loss of tumor suppressor function and promote tumor growth, hence disease progression while on treatment (28,29). The emergence of serial changes in the various different types of TP53 mutations as well as other non-EGFR mutations such as BRCA1, BRCA2, MYC or MET mutations along treatment course probably reflected the evolution of tumor heterogeneity.…”

Section: Discussionmentioning

confidence: 99%

Longitudinal multi-gene panel assessment of circulating tumor DNA revealed tumor burden and molecular characteristics along treatment course of non-small cell lung cancer

Wang

Kwok

et al. 2020

Transl Lung Cancer Res

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Background: Most studies associating circulating tumor DNA (ctDNA) with outcome in lung cancer treatment were either cross-sectional or, if longitudinal, only analyzed a limited number of genes. This study evaluated the potential of utilizing ctDNA profiled by a panel of common cancer genes to monitor tumor burden and to reveal molecular characteristics of tumor along treatment course.Methods: Twenty Chinese non-small cell lung cancer (NSCLC) patients with serial plasma samples collected (I) before starting on either first-or second-line treatment, (II) at stable disease on treatment, and (III) upon disease progression, were analyzed for mutations in ctDNA using the PGDx 64-gene panel. Paired statistics compared mutation profiles between any two of the three time points.Results: Proportions with detectable ctDNA decreased from 65% at baseline to 35% at stable disease and rose to 80% at progression (P=0.012, between stable disease and progression); median ctDNA levels (mutated fragments per mL) were 7.8, 0, and 24.7 at the three time points, respectively (P=0.013 between baseline and progression; P=0.007 between stable disease and progression). Although plasma epidermal growth factor receptor (EGFR) mutations were commonly detected, 15% of patients had mutations other than EGFR detected during progression, such as various types of TP53 mutations.Conclusions: ctDNA profiling in serial blood samples reflected tumor burden over time, and a multi-gene panel was more sensitive in indicating lung cancer progression on treatment than a single gene approach.The detection of additional oncogenic mutations or their disappearance suggested evolution of tumor heterogeneity along treatment course.

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Section: Discussionmentioning

confidence: 99%

Longitudinal multi-gene panel assessment of circulating tumor DNA revealed tumor burden and molecular characteristics along treatment course of non-small cell lung cancer

Wang

Kwok

et al. 2020

Transl Lung Cancer Res

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“…Gene expression arrays (used in melanoma classifications) [ 61 ], and next-generation sequencing (NGS) are helping physicians determine how patients will respond to a particular therapy. These arrays and NGS will advance gene profiling technology to develop patient-specific treatments [ 62 , 63 ]. PM is very effective in treating some types of brain tumors, such as glioblastoma [ 64 ], using photodynamic therapy (PDT) [ 65 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and Precision Medicine: A New Frontier for the Treatment of Brain Tumors

Philip

Samuel

Bhatia

et al. 2022

Life

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Brain tumors are a widespread and serious neurological phenomenon that can be life- threatening. The computing field has allowed for the development of artificial intelligence (AI), which can mimic the neural network of the human brain. One use of this technology has been to help researchers capture hidden, high-dimensional images of brain tumors. These images can provide new insights into the nature of brain tumors and help to improve treatment options. AI and precision medicine (PM) are converging to revolutionize healthcare. AI has the potential to improve cancer imaging interpretation in several ways, including more accurate tumor genotyping, more precise delineation of tumor volume, and better prediction of clinical outcomes. AI-assisted brain surgery can be an effective and safe option for treating brain tumors. This review discusses various AI and PM techniques that can be used in brain tumor treatment. These new techniques for the treatment of brain tumors, i.e., genomic profiling, microRNA panels, quantitative imaging, and radiomics, hold great promise for the future. However, there are challenges that must be overcome for these technologies to reach their full potential and improve healthcare.

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Significance of testing for TP53 gene mutations in lung adenocarcinoma using targeted gene sequencing

Abstract: Cite this article as: Matsumura Y, Owada-Ozaki Y, Suzuki H. Significance of testing for TP53 gene mutations in lung adenocarcinoma using targeted gene sequencing.

Cited by 2 publications

References 23 publications

Longitudinal multi-gene panel assessment of circulating tumor DNA revealed tumor burden and molecular characteristics along treatment course of non-small cell lung cancer

Longitudinal multi-gene panel assessment of circulating tumor DNA revealed tumor burden and molecular characteristics along treatment course of non-small cell lung cancer

Artificial Intelligence and Precision Medicine: A New Frontier for the Treatment of Brain Tumors

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