An Eighteen-Gene Classifier Predicts Locoregional Recurrence in Post-Mastectomy Breast Cancer Patients

Cheng, Skye Hung-Chun; Horng, Chen-Fang; Huang, Tzu‐Ting; Huang, Erich; Tsou, Mei-Hua; Shi, Li-Sun; Yu, Ben-Long; Chen, Chii‐Ming; Huang, Andrew T.

doi:10.1016/j.ebiom.2016.02.022

Cited by 21 publications

(35 citation statements)

References 40 publications

(51 reference statements)

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“…18-GC development has been reported previously [ 17 ]. Each gene was assigned a weight according to the Cox proportional hazards model to assemble the 18-gene scoring algorithm ( S1 and S2 Tables).…”

Section: Methods and Patientsmentioning

confidence: 70%

“…18-gene score = 4 × TRPV6 + 3 × DDX39 + 8 × BUB1B + CCR1 + STIL + 3 × BLM + 11 × C16ORF7 + 4 × PIM1 + TPX2 + 2 × PTI1 + 2 × TCF3 + CCNB1 + DTX2 + 2 × ENSA + 5 × RCHY1 + 4 × NFATC2IP + OBSL1 + 2 × MMP15 [ 17 ]. We defined patients with scores of ≥21 as high-risk and <21 as low-risk for distant metastasis.…”

Section: Methods and Patientsmentioning

confidence: 99%

“…The optimal cutoff to predict LRR after mastectomy was set at ≥44. Patients with scores <21 were generally free from recurrence (both locally and distantly) [ 17 ]. LRR has long been thought to be a sign of distant metastasis; patients with LRR would eventually develop systemic recurrence [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

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Validation of the 18-gene classifier as a prognostic biomarker of distant metastasis in breast cancer

et al. 2017

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We validated an 18-gene classifier (GC) initially developed to predict local/regional recurrence after mastectomy in estimating distant metastasis risk. The 18-gene scoring algorithm defines scores as: <21, low risk; ≥21, high risk. Six hundred eighty-three patients with primary operable breast cancer and fresh frozen tumor tissues available were included. The primary outcome was the 5-year probability of freedom from distant metastasis (DMFP). Two external datasets were used to test the predictive accuracy of 18-GC. The 5-year rates of DMFP for patients classified as low-risk (n = 146, 21.7%) and high-risk (n = 537, 78.6%) were 96.2% (95% CI, 91.1%–98.8%) and 80.9% (74.6%–81.9%), respectively (median follow-up interval, 71.8 months). The 5-year rates of DMFP of the low-risk group in stage I (n = 62, 35.6%), stage II (n = 66, 20.1%), and stage III (n = 18, 10.3%) were 100%, 94.2% (78.5%–98.5%), and 90.9% (50.8%–98.7%), respectively. Multivariate analysis revealed that 18-GC is an independent prognostic factor of distant metastasis (adjusted hazard ratio, 5.1; 95% CI, 1.8–14.1; p = 0.0017) for scores of ≥21. External validation showed that the 5-year rate of DMFP in the low- and high-risk patients was 94.1% (82.9%–100%) and 80.3% (70.7%–89.9%, p = 0.06) in a Singapore dataset, and 89.5% (81.9%–94.1%) and 73.6% (67.2%–79.0%, p = 0.0039) in the GEO-GSE20685 dataset, respectively. In conclusion, 18-GC is a viable prognostic biomarker for breast cancer to estimate distant metastasis risk.

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Section: Methods and Patientsmentioning

confidence: 70%

Section: Methods and Patientsmentioning

confidence: 99%

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Validation of the 18-gene classifier as a prognostic biomarker of distant metastasis in breast cancer

et al. 2017

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“…as an 18-gene classifier (18-GC) with more sensitivity, specificity, and accuracy-not only in predicting LRR but also distant recurrence 9,10 . This 18-GC utilises the genes BLM, TCF3, PIM1, RCHY1, PTI1, DDX39, BUB1B, STIL, TPX2, CCNB1, MMP15, CCR1, NFATC2IP, TRPV6, OBSL1, C16ORF7, DTX2, and ENSA, among which 17 were included in the distant genomic model (DGM).…”

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confidence: 99%

A new clinical-genomic model to predict 10-year recurrence risk in primary operable breast cancer patients

Huang

Lei

Chen

et al. 2020

Sci Rep

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This study aimed to validate the long-term prognostic value of a new clinical-genomic model, Distant Genetic Model-Clinical Variable Model 6 (DGM-CM6), developed in Asia as a prognostic panel for all subtypes of breast cancer. We included 752 operable stage I-III breast cancer patients representing all subtypes treated from 2005 to 2014 as the validation cohort. The median follow-up was 95.8 months. The low-and high-risk patients classified by DGM-CM6 (RI-DR) had significant differences in 10-year distant recurrence-free interval (DRFI) (94.1% vs. 85.0%, P < 0.0001) and relapse-free survival (RFS) (90.0% vs. 80.5%, P = 0.0003). External validation using EMTAB-365 dataset showed similar observation (P < 0.0001). DGM-CM6 was an independent prognostic factor by multivariate analysis with hazard ratios of 3.1 (1.6-6.0) for RFS (P = 0.0009) and 3.8 (1.6-9.0) for DRFI (P = 0.0028). Comparing the C-index of DGM-CM6 and PAM50-ROR scores, the former performed better than the latter in predicting long-term DRFI and RFS, especially in N0, ER/PR-positive, and HER2-negative patients. Breast cancer is the most prevalent and deadly malignant disease among women throughout the world. Despite recent advances in early breast cancer (EBC) management, recurrent events remain inevitable in high-risk populations 1. A reliable prognostic algorithm combining clinical and genomic information to help determine treatment strategies for EBC patients is urgently needed. The Oncotype DX (ODx) 21-gene recurrence score (RS) is known to be a sound prognostic and predictive assay in node-negative hormonal receptor-positive (HR-positive) and human epidermal growth factor receptor 2 (HER2) negative breast cancer patients. However, its prognostic value in HR-negative patients is unknown. According to the Trial Assigning Individualized Options for Treatment (TAILORx), the RS cutoff at which chemotherapy in patients 50 years of age or younger can safely be avoided is under investigated 2,3. Women from Asian-Pacific countries experience earlier onset of breast cancer than women from Western countries, nearly 50% suffering from breast cancer under the age of 50 4. However, they usually have a better survival rate. One study using data from Surveillance, Epidemiology and End Results (SEER) demonstrated that the actuarial risk of death 7 years post-onset for women with stage I breast cancer was lower among Asian women than that of non-Hispanic white women 5. This difference may be related to biological differences in tumour characteristics between the races. The ODx was mainly developed based on Western populations and may not be fully applicable to Asian ethnic groups; the TAILORx trial included only 151 Asian women out of a total of 11,248 (1.3%) patients 2. Overestimated prediction for the recurrence risk by ODx in Japanese populations has been reported, with no recurrence in the intermediate-risk group (cutoff 18-30) 6. Considering the potential impact caused by differences in race and ethnicity, a Korean group developed a multi-gene assay in 2019, wh...

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“…In their recent work published in EBioMedicine , Cheng and colleagues developed an 18-gene classifier capable of stratifying breast cancer patients with 0–3 positive lymph nodes (N0–1) after mastectomy into low and high risk groups to better determine the need for post-mastectomy radiation therapy (PMRT)( Cheng et al, 2016 ). Through their work, they found that patients, who were defined by the 18-gene classifier as high risk, had significantly reduced rates of 5-year loco-regional recurrence, distant metastasis-free survival, and overall survival, regardless of lymph node status and breast cancer subtype based on estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2/neu status.…”

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confidence: 99%

An Emerging Role for Radiation Oncology in Precision Oncology

Goyal

Haffty

2016

EBioMedicine

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An Eighteen-Gene Classifier Predicts Locoregional Recurrence in Post-Mastectomy Breast Cancer Patients

Cited by 21 publications

References 40 publications

Validation of the 18-gene classifier as a prognostic biomarker of distant metastasis in breast cancer

Validation of the 18-gene classifier as a prognostic biomarker of distant metastasis in breast cancer

A new clinical-genomic model to predict 10-year recurrence risk in primary operable breast cancer patients

An Emerging Role for Radiation Oncology in Precision Oncology

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