Literature DB >> 35971249

Prognostic value of baseline genetic features and newly identified TP53 mutations in advanced breast cancer.

Lanxin Zhang1, Siwen Sun1, Xiaotian Zhao2, Jingwen Liu2, Yang Xu2, Lingzhi Xu1, Chen Song1, Na Li1, Jing Yu1, Shanshan Zhao1, Peiyao Yu3, Fengqi Fang3, Jiping Xie4, Xuening Ji5, Ruoying Yu2, Qiuxiang Ou2, Zuowei Zhao1,6, Man Li1.   

Abstract

Approximately 30% of breast cancer (BC) patients suffer from disease relapse after definitive treatment. Monitoring BC at baseline and disease progression using comprehensive genomic profiling would facilitate the prediction of prognosis. We retrospectively studied 101 BC patients ultimately experiencing relapse and/or metastases. The baseline and circulating tumor DNA-monitoring cohorts included patients with baseline tumor tissue and serial plasma samples, respectively. Samples were analyzed with targeted next-generation sequencing of 425 cancer-relevant genes. Of 35 patients in the baseline cohort, patients with TP53 mutations (P < 0.01), or CTCF/GNAS mutations (P < 0.01) displayed inferior disease-free survival, and patients harboring TP53 (P = 0.06) or NOTCH1 (P = 0.06) mutations showed relatively poor overall survival (OS), compared to patients with wild-type counterparts. Of the 59 patients with serial plasma samples, 11 patients who were newly detected with TP53 mutations had worse OS than patients whose TP53 mutational status remained negative (P < 0.01). These results indicate that an inferior prognosis of advanced breast cancer was potentially associated with baseline TP53, CTCF, and NOTCH1 alterations. Newly identified TP53 mutations after relapse and/or metastasis was another potential prognostic biomarker of poor prognosis.
© 2022 The Authors. Molecular Oncology published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.

Entities:  

Keywords:  TP53 mutations; advanced breast cancer; baseline genetic features; prognosis

Mesh:

Substances:

Year:  2022        PMID: 35971249      PMCID: PMC9580879          DOI: 10.1002/1878-0261.13297

Source DB:  PubMed          Journal:  Mol Oncol        ISSN: 1574-7891            Impact factor:   7.449


  50 in total

1.  Assessment of Molecular Relapse Detection in Early-Stage Breast Cancer.

Authors:  Isaac Garcia-Murillas; Neha Chopra; Iñaki Comino-Méndez; Matthew Beaney; Holly Tovey; Rosalind J Cutts; Claire Swift; Divya Kriplani; Maria Afentakis; Sarah Hrebien; Giselle Walsh-Crestani; Peter Barry; Stephen R D Johnston; Alistair Ring; Judith Bliss; Simon Russell; Abigail Evans; Anthony Skene; Duncan Wheatley; Mitch Dowsett; Ian E Smith; Nicholas C Turner
Journal:  JAMA Oncol       Date:  2019-10-01       Impact factor: 31.777

2.  Implications of Selection Bias Due to Delayed Study Entry in Clinical Genomic Studies.

Authors:  Samantha Brown; Jessica A Lavery; Ronglai Shen; Axel S Martin; Kenneth L Kehl; Shawn M Sweeney; Eva M Lepisto; Hira Rizvi; Caroline G McCarthy; Nikolaus Schultz; Jeremy L Warner; Ben Ho Park; Philippe L Bedard; Gregory J Riely; Deborah Schrag; Katherine S Panageas
Journal:  JAMA Oncol       Date:  2022-02-01       Impact factor: 33.006

3.  Physical Activity and Survival in Women With Advanced Breast Cancer.

Authors:  Oxana Palesh; Charles Kamen; Susan Sharp; Ashleigh Golden; Eric Neri; David Spiegel; Cheryl Koopman
Journal:  Cancer Nurs       Date:  2018 Jul/Aug       Impact factor: 2.592

4.  TP53 mutation spectrum in breast cancer is subtype specific and has distinct prognostic relevance.

Authors:  Laxmi Silwal-Pandit; Hans Kristian Moen Vollan; Suet-Feung Chin; Oscar M Rueda; Steven McKinney; Tomo Osako; David A Quigley; Vessela N Kristensen; Samuel Aparicio; Anne-Lise Børresen-Dale; Carlos Caldas; Anita Langerød
Journal:  Clin Cancer Res       Date:  2014-05-06       Impact factor: 12.531

5.  c-myc amplification is a better prognostic factor than HER2/neu amplification in primary breast cancer.

Authors:  E M Berns; J G Klijn; W L van Putten; I L van Staveren; H Portengen; J A Foekens
Journal:  Cancer Res       Date:  1992-03-01       Impact factor: 12.701

6.  RB1CC1 together with RB1 and p53 predicts long-term survival in Japanese breast cancer patients.

Authors:  Tokuhiro Chano; Kaichiro Ikebuchi; Yasuhiko Tomita; Yufen Jin; Hideo Inaji; Makoto Ishitobi; Koji Teramoto; Yasuko Ochi; Hitosuke Tameno; Ichiro Nishimura; Kahori Minami; Hirokazu Inoue; Takahiro Isono; Masao Saitoh; Taketoshi Shimada; Yasuo Hisa; Hidetoshi Okabe
Journal:  PLoS One       Date:  2010-12-22       Impact factor: 3.240

7.  Expression of Notch1 Correlates with Breast Cancer Progression and Prognosis.

Authors:  Xun Yuan; Mingsheng Zhang; Hua Wu; Hanxiao Xu; Na Han; Qian Chu; Shiying Yu; Yuan Chen; Kongming Wu
Journal:  PLoS One       Date:  2015-06-29       Impact factor: 3.240

8.  Effects of BRCA Germline Mutations on Triple-Negative Breast Cancer Prognosis.

Authors:  Katarzyna Pogoda; Anna Niwińska; Elżbieta Sarnowska; Dorota Nowakowska; Agnieszka Jagiełło-Gruszfeld; Janusz Siedlecki; Zbigniew Nowecki
Journal:  J Oncol       Date:  2020-01-27       Impact factor: 4.375

9.  Fast and accurate short read alignment with Burrows-Wheeler transform.

Authors:  Heng Li; Richard Durbin
Journal:  Bioinformatics       Date:  2009-05-18       Impact factor: 6.937

10.  CoNVEX: copy number variation estimation in exome sequencing data using HMM.

Authors:  Kaushalya C Amarasinghe; Jason Li; Saman K Halgamuge
Journal:  BMC Bioinformatics       Date:  2013-01-21       Impact factor: 3.169

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