Literature DB >> 33753452

Cancer Cell Intrinsic and Immunologic Phenotypes Determine Clinical Outcomes in Basal-like Breast Cancer.

Christopher I Li1, Yuping Zhang2, Marcin Cieślik2,3,4,5, Yi-Mi Wu2,3, Lanbo Xiao2,3, Erin Cobain6, Mei-Tzu C Tang7, Xuhong Cao2,3,8, Peggy Porter9, Jamie Guenthoer9, Dan R Robinson2,3, Arul M Chinnaiyan10,3,5,8,11.   

Abstract

PURPOSE: Basal-like breast cancer (BLBC) is a particularly aggressive intrinsic molecular subtype of breast cancer that lacks targeted therapies. There is also no clinically useful test to risk stratify patients with BLBC. We hypothesized that a transcriptome-based phenotypic characterization of BLBC tumors and their microenvironments may overcome these challenges. EXPERIMENTAL
DESIGN: We conducted a retrospective correlative genomic sequencing study using a matched pairs design with validation in five independent cohorts. The study was conducted on a large population-based prospective cohort of the major molecular subtypes of breast cancer conducted in the greater Seattle-Puget Sound metropolitan area. Cases consisted of women 20-69 years of age first diagnosed with invasive breast cancer identified through the population-based Surveillance Epidemiology and End Results program. Patients for this analysis (n = 949) were identified from the 1,408 patients with stage I-III triple-negative breast cancer [estrogen receptor-negative (ER-), progesterone receptor-negative (PR-), HER2-]. Of the 949 women, 248 developed a recurrence after their initial diagnosis. A matched set of 67 recurrent and nonrecurrent BLBC tumors was subjected to transcriptome sequencing. Through RNA sequencing of the matched sets of recurrent and nonrecurrent BLBC tumors, we aimed to identify prognostic phenotypes.To identify nonredundant and uncorrelated prognostic genes, we used an ensemble of variable selection algorithms, which resulted in a ranking of genes on the basis of their expected utility in classification. Using leave-one-out cross-validation, we trained a random forest classifier on the basis of the top 21 genes (BRAVO-DX). Validations were performed in five independent triple-negative or BLBC cohorts, and biomarker robustness and transferability were demonstrated by employing real-time PCR.
RESULTS: We found that cancer cell intrinsic and immunologic phenotypes are independent predictors of recurrence. By simultaneously interrogating the tumor and its microenvironment, we developed a compound risk model that stratified patients into low-, medium-, and high-risk groups, with a 14%/56%/74% chance of recurrence, respectively. Biologically, the primary tumors of patients who developed a recurrence had increased growth factor signaling and stem-like features, while nonrecurrent tumors showed high lymphocyte infiltration with clonal expansion of T and B cells, as well as antitumor polarization of macrophages. We validated our model in five independent cohorts, including three large cohorts, where BRAVO-DX was highly informative in identifying patients with disease recurrence [HR, 6.79 (95% confidence interval (CI), 1.89-24.37); HR, 3.45 (95% CI, 2.41-4.93); and HR, 1.69 (95% CI, 1.17-2.46)]. A smaller gene set focused on the tumor immunophenotype, BRAVO-IMMUNE, was highly prognostic in all five cohorts.
CONCLUSIONS: Together, these results indicate that phenotypic characteristics of BLBCs and their microenvironment are associated with recurrence-free survival and demonstrate the utility of intrinsic and extrinsic phenotypes as independent prognostic biomarkers in BLBC. Pending further evaluation and validation, our prognostic model has the potential to inform clinical decision-making for patients with BLBC as it identifies those at high risk of rapidly progressing on standard chemotherapy, as well as those who may benefit from alternative first-line therapies. ©2021 American Association for Cancer Research.

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Year:  2021        PMID: 33753452     DOI: 10.1158/1078-0432.CCR-20-3890

Source DB:  PubMed          Journal:  Clin Cancer Res        ISSN: 1078-0432            Impact factor:   12.531


  5 in total

1.  Immune response pathways enriched in breast cancer samples with brain metastasis.

Authors:  Peng Wang; Zengfeng Sun; Zhen Zhang; Qiang Yin
Journal:  Gland Surg       Date:  2021-12

2.  RSPO2 and RANKL signal through LGR4 to regulate osteoclastic premetastatic niche formation and bone metastasis.

Authors:  Zhiying Yue; Xin Niu; Zengjin Yuan; Qin Qin; Wenhao Jiang; Liang He; Jingduo Gao; Yi Ding; Yanxi Liu; Ziwei Xu; Zhenxi Li; Zhengfeng Yang; Rong Li; Xiwen Xue; Yankun Gao; Fei Yue; Xiang H-F Zhang; Guohong Hu; Yi Wang; Yi Li; Geng Chen; Stefan Siwko; Alison Gartland; Ning Wang; Jianru Xiao; Mingyao Liu; Jian Luo
Journal:  J Clin Invest       Date:  2022-01-18       Impact factor: 19.456

3.  A Novel Seven Gene Signature-Based Prognostic Model to Predict Distant Metastasis of Lymph Node-Negative Triple-Negative Breast Cancer.

Authors:  Wenting Peng; Caijin Lin; Shanshan Jing; Guanhua Su; Xi Jin; Genhong Di; Zhiming Shao
Journal:  Front Oncol       Date:  2021-09-16       Impact factor: 6.244

4.  Berberine Attenuates Cell Motility via Inhibiting Inflammation-Mediated Lysyl Hydroxylase-2 and Glycolysis.

Authors:  Yishan Du; Muhammad Khan; Nana Fang; Fang Ma; Hongzhi Du; Zhenya Tan; Hua Wang; Shi Yin; Xiaohui Wei
Journal:  Front Pharmacol       Date:  2022-04-26       Impact factor: 5.988

Review 5.  Heterogeneity of triple negative breast cancer: Current advances in subtyping and treatment implications.

Authors:  Karama Asleh; Nazia Riaz; Torsten O Nielsen
Journal:  J Exp Clin Cancer Res       Date:  2022-09-01
  5 in total

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