Exploring the effectiveness of molecular subtypes, biomarkers, and genetic variations as first-line treatment predictors in Asian breast cancer patients: a systematic review and meta-analysis

Abstract

Background: Breast cancer incidence has been on the rise significantly in the Asian population, occurring at an earlier age and a later stage. The potential predictive value of molecular subtypes, biomarkers, and genetic variations has not been deeply explored in the Asian population. This study evaluated the effect of molecular subtype classification and the presence or absence of biomarkers and genetic variations on pathological complete response (pCR) after neoadjuvant treatment in Asian breast cancer patients.

Methods: A systematic search was conducted in MEDLINE (PubMed), Science Direct, Scopus, and Cochrane Library databases. Studies were selected if they included Asian breast cancer patients treated with neoadjuvant chemotherapy and contained data for qualitative or quantitative analyses. The quality of the included studies was assessed using the Newcastle Ottawa Scale. Following the random effects model, pooled odds ratios or hazard ratios with 95% confidence intervals for pCR were analysed using Review Manager Software. Heterogeneity between studies was assessed using Cochran's Q-test and I² test statistics.

Results: In total, 19,708 Asian breast cancer patients were pooled from 101 studies. In the neoadjuvant setting, taxane-anthracycline (TA) chemotherapy showed better pCR outcomes in triple-negative breast cancer (TNBC) (p<0.0001) and human epidermal growth factor receptor 2 enriched (HER2E) (p<0.0001) than luminal breast cancer patients. Similarly, taxane-platinum (TP) chemotherapy also showed better pCR outcomes in TNBC (p<0.0001) and HER2E (p<0.0001). Oestrogen receptor (ER)-negative, progesterone receptor (PR)-negative, HER2-positive and high Ki-67 were significantly associated with better pCR outcomes when treated with either TA or TP. Asian breast cancer patients harbouring wildtype PIK3CA were significantly associated with better pCR outcomes when treated with TA in the neoadjuvant setting (p=0.001).

Conclusions: In the neoadjuvant setting, molecular subtypes (HER2E and TNBC), biomarkers (ER, PR, HER2, HR, Ki-67, nm23-H1, CK5/6, and Tau), and gene (PIK3CA) are associated with increased pCR rates in Asian breast cancer patients. Hence, they could be further explored for their possible role in first-line treatment response, which can be utilised to treat breast cancer more efficiently in the Asian population. However, it needs to be further validated with additional powered studies.

Systematic review registration: PROSPERO CRD42021246295.

Keywords: Asian patients; Biomarkers; Breast cancer; Genetic variation; Molecular subtypes; Neoadjuvant treatment; Pathological complete response; Systematic review.

Fig. 1

The PRISMA flowchart of literature search and study eligibility strategy

Fig. 2

Pooled pCR outcome of TA-treated Asian breast cancer patients according to molecular subtypes. Forest plots describing the random effect ORs and 95% CIs from studies assessing the association of pCR outcome in NAC TA-treated breast cancer patients between (A) HER2E and luminal-like; (B) HER2E and luminal A; (C) HER2E and luminal B; (D) HER2E and luminal, combined; and (E) TNBC and luminal-like; (F) TNBC and luminal A; (G) TNBC and luminal B; and (H) TNBC and luminal, combined. I² and p-value for X² of heterogeneity are reported for each group analysis

Fig. 3

Pooled pCR outcome of TP-treated Asian breast cancer patients according to molecular subtypes. Forest plots describing the random effect ORs and 95% CIs from studies assessing the association of pCR outcome in NAC TP-treated Asian breast cancer patients between (A) HER2E and luminal-like; (B) HER2E and luminal A; (C) HER2E and luminal B; (D) HER2E and luminal, combined; (E) Luminal B and luminal A; (F) TNBC and luminal A; (G) TNBC and luminal B. I² and p-value for X² of heterogeneity are reported for each group analysis

Fig. 4

Pooled pCR outcome of NAC-treated Asian breast cancer patients according to molecular subtypes and biomarkers. Forest plots describing the random effect ORs and 95% CIs from studies assessing the association of pCR outcome in (A) NAC TP-treated Asian breast cancer patients between TNBC and luminal, combined; (B) between Asian TNBC patients treated with NAC TP and TA; Asian breast cancer patients treated with anthracycline-based chemotherapy with (C) ER; (D) PR; and (E) HER2 biomarkers; (F) Asian breast cancer patients treated with taxane-based chemotherapy and HR biomarker; (G) Asian breast cancer patients treated with TA and ER biomarker. I² and p-value for X² of heterogeneity are reported for each group analysis

Fig. 5

Pooled pCR outcome of TA-treated Asian breast cancer patients according to biomarkers. Forest plots describing the random effect ORs and 95% CIs from studies assessing the association of pCR outcome in NAC TA-treated breast cancer patients in biomarkers (A) PR; (B) HR; (C) HER2; (D) nm23-H1; and (E) CK5/6. I² and p-value for X² of heterogeneity are reported for each group analysis

Fig. 6

Pooled pCR outcome of NAC-treated Asian breast cancer patients according to biomarkers. Forest plots describing the random effect ORs and 95% CIs from studies assessing the association of pCR outcome in (A) NAC TA-treated breast cancer patients in biomarkers Ki-67; NAC TP-treated breast cancer patients in biomarkers (B) ER; (C) PR; (D) HR; and (E) Ki-67. I² and p-value for X² of heterogeneity are reported for each group analysis

Fig. 7

Pooled pCR outcome of NAC-treated Asian patients according to biomarkers and genetic variation. Forest plots describing the random effect ORs and 95% CIs from studies assessing the (A) Association of pCR outcome in NAC TP-treated Asian breast cancer patients in HER2; (B) Association between pCR in Asian patients with HER2+ biomarker treated with NAC TP and TA; and (C) Association of pCR outcome in NAC TA-treated Asian breast cancer patients in PIK3CA gene. I² and p-value for X² of heterogeneity are reported for each group analysis