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Clinicogenomic radiotherapy classifier predicting the need for intensified locoregional treatment after breast-conserving surgery for early-stage breast cancer

Sjöström, Martin LU ; Laura Chang, S. ; Fishbane, Nick ; Davicioni, Elai ; Zhao, Shuang G. ; Hartman, Linda LU ; Holmberg, Erik LU ; Feng, Felix Y. ; Speers, Corey W. and Pierce, Lori J. , et al. (2019) In Journal of Clinical Oncology 37(35). p.3340-3349
Abstract

PURPOSE Most patients with early-stage breast cancer are treated with adjuvant radiotherapy (RT) after breast-conserving surgery (BCS) to prevent locoregional recurrence (LRR). However, no genomic tools are used currently to select the optimal RT strategy. METHODS We profiled the transcriptome of primary tumors on a clinical grade assay from the SweBCG91-RT trial, in which patients with node-negative breast cancer were randomly assigned to either whole-breast RT after BCS or no RT. We derived a new classifier, Adjuvant Radiotherapy Intensification Classifier (ARTIC), comprising 27 genes and patient age, in three publicly available cohorts, then independently validated ARTIC for LRR in 748 patients in SweBCG91-RT. We also compared... (More)

PURPOSE Most patients with early-stage breast cancer are treated with adjuvant radiotherapy (RT) after breast-conserving surgery (BCS) to prevent locoregional recurrence (LRR). However, no genomic tools are used currently to select the optimal RT strategy. METHODS We profiled the transcriptome of primary tumors on a clinical grade assay from the SweBCG91-RT trial, in which patients with node-negative breast cancer were randomly assigned to either whole-breast RT after BCS or no RT. We derived a new classifier, Adjuvant Radiotherapy Intensification Classifier (ARTIC), comprising 27 genes and patient age, in three publicly available cohorts, then independently validated ARTIC for LRR in 748 patients in SweBCG91-RT. We also compared previously published genomic signatures for ability to predict benefit from RT in SweBCG91-RT. RESULTS ARTIC was highly prognostic for LRR in patients treated with RT (hazard ratio [HR], 3.4; 95% CI, 2.0 to 5.9; P, .001) and predictive of RT benefit (Pinteraction = .005). Patients with low ARTIC scores had a large benefit from RT (HR, 0.33 [95% CI, 0.21 to 0.52], P, .001; 10-year cumulative incidence of LRR, 6% v 21%), whereas those with high ARTIC scores benefited less from RT (HR, 0.73 [95% CI, 0.44 to 1.2], P = .23; 10-year cumulative incidence of LRR, 25% v 32%). In contrast, none of the eight previously published signatures were predictive of benefit from RT in SweBCG91-RT. CONCLUSION ARTIC identified women with a substantial benefit from RT as well as women with a particularly elevated LRR risk in whom whole-breast RT was not sufficiently effective and, thus, in whom intensified treatment strategies such as tumor-bed boost, and possibly regional nodal RT, should be considered. To our knowledge, ARTIC is the first classifier validated as predictive of benefit from RT in a phase III clinical trial with patients randomly assigned to receive or not receive RT.

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Journal of Clinical Oncology
volume
37
issue
35
pages
10 pages
publisher
American Society of Clinical Oncology
external identifiers
  • pmid:31618132
  • scopus:85076038344
ISSN
0732-183X
DOI
10.1200/JCO.19.00761
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English
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yes
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8df7655a-51b6-4e3d-9ada-a1842794852d
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2020-04-15 15:10:44
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2020-05-24 06:31:17
@article{8df7655a-51b6-4e3d-9ada-a1842794852d,
  abstract     = {<p>PURPOSE Most patients with early-stage breast cancer are treated with adjuvant radiotherapy (RT) after breast-conserving surgery (BCS) to prevent locoregional recurrence (LRR). However, no genomic tools are used currently to select the optimal RT strategy. METHODS We profiled the transcriptome of primary tumors on a clinical grade assay from the SweBCG91-RT trial, in which patients with node-negative breast cancer were randomly assigned to either whole-breast RT after BCS or no RT. We derived a new classifier, Adjuvant Radiotherapy Intensification Classifier (ARTIC), comprising 27 genes and patient age, in three publicly available cohorts, then independently validated ARTIC for LRR in 748 patients in SweBCG91-RT. We also compared previously published genomic signatures for ability to predict benefit from RT in SweBCG91-RT. RESULTS ARTIC was highly prognostic for LRR in patients treated with RT (hazard ratio [HR], 3.4; 95% CI, 2.0 to 5.9; P, .001) and predictive of RT benefit (P<sub>interaction</sub> = .005). Patients with low ARTIC scores had a large benefit from RT (HR, 0.33 [95% CI, 0.21 to 0.52], P, .001; 10-year cumulative incidence of LRR, 6% v 21%), whereas those with high ARTIC scores benefited less from RT (HR, 0.73 [95% CI, 0.44 to 1.2], P = .23; 10-year cumulative incidence of LRR, 25% v 32%). In contrast, none of the eight previously published signatures were predictive of benefit from RT in SweBCG91-RT. CONCLUSION ARTIC identified women with a substantial benefit from RT as well as women with a particularly elevated LRR risk in whom whole-breast RT was not sufficiently effective and, thus, in whom intensified treatment strategies such as tumor-bed boost, and possibly regional nodal RT, should be considered. To our knowledge, ARTIC is the first classifier validated as predictive of benefit from RT in a phase III clinical trial with patients randomly assigned to receive or not receive RT.</p>},
  author       = {Sjöström, Martin and Laura Chang, S. and Fishbane, Nick and Davicioni, Elai and Zhao, Shuang G. and Hartman, Linda and Holmberg, Erik and Feng, Felix Y. and Speers, Corey W. and Pierce, Lori J. and Malmström, Per and Fernö, Mårten and Karlsson, Per},
  issn         = {0732-183X},
  language     = {eng},
  month        = {01},
  number       = {35},
  pages        = {3340--3349},
  publisher    = {American Society of Clinical Oncology},
  series       = {Journal of Clinical Oncology},
  title        = {Clinicogenomic radiotherapy classifier predicting the need for intensified locoregional treatment after breast-conserving surgery for early-stage breast cancer},
  url          = {http://dx.doi.org/10.1200/JCO.19.00761},
  doi          = {10.1200/JCO.19.00761},
  volume       = {37},
  year         = {2019},
}