Efficacy of Metformin in Patients With Breast Cancer Receiving Chemotherapy or Endocrine Therapy: Systematic Review and Meta-analysis

New evidence: Metformin unsuitable as routine adjuvant for breast cancer: a drug-target mendelian randomization analysis

PURPOSE

The potential efficacy of metformin in breast cancer (BC) has been hotly discussed but never conclusive. This genetics-based study aimed to evaluate the relationships between metformin targets and BC risk.

METHODS

Metformin targets from DrugBank and genome-wide association study (GWAS) data from IEU OpenGWAS and FinnGen were used to investigate the breast cancer (BC)-metformin causal link with various Mendelian Randomization (MR) methods (e.g., inverse-variance-weighting). The genetic association between type 2 diabetes (T2D) and the drug target of metformin was also analyzed as a positive control. Sensitivity and pleiotropic tests ensured reliability.

RESULTS

The primary targets of metformin are PRKAB1, ETFDH and GPD1L. We found a causal association between PRKAB1 and T2D (odds ratio [OR] 0.959, P = 0.002), but no causal relationship was observed between metformin targets and overall BC risk (PRKAB1: OR 0.990, P = 0.530; ETFDH: OR 0.986, P = 0.592; GPD1L: OR 1.002, P = 0.806). A noteworthy causal relationship was observed between ETFDH and estrogen receptor (ER)-positive BC (OR 0.867, P = 0.018), and between GPD1L and human epidermal growth factor receptor 2 (HER2)-negative BC (OR 0.966, P = 0.040). Other group analyses did not yield positive results.

CONCLUSION

The star target of metformin, PRKAB1, does not exhibit a substantial causal association with the risk of BC. Conversely, metformin, acting as an inhibitor of ETFDH and GPD1L, may potentially elevate the likelihood of developing ER-positive BC and HER2-negative BC. Consequently, it is not advisable to employ metformin as a standard supplementary therapy for BC patients without T2D.

Metformin and Breast Cancer: Current Findings and Future Perspectives from Preclinical and Clinical Studies

Over the last several decades, a growing body of research has investigated the potential to repurpose the anti-diabetic drug metformin for breast cancer prevention and/or treatment. Observational studies in the early 2000s demonstrated that patients with diabetes taking metformin had decreased cancer risk, providing the first evidence supporting the potential role of metformin as an anti-cancer agent. Despite substantial efforts, two decades later, the exact mechanisms and clinical efficacy of metformin for breast cancer remain ambiguous. Here, we have summarized key findings from studies examining the effect of metformin on breast cancer across the translational spectrum including in vitro, in vivo, and human studies. Importantly, we discuss critical factors that may help explain the significant heterogeneity in study outcomes, highlighting how metformin dose, underlying metabolic health, menopausal status, tumor subtype, membrane transporter expression, diet, and other factors may play a role in modulating metformin's anti-cancer effects. We hope that these insights will help with interpreting data from completed studies, improve the design of future studies, and aid in the identification of patient subsets with breast cancer or at high risk for the disease who are most likely to benefit from metformin treatment.

Metformin and long non-coding RNAs in breast cancer

Breast cancer (BC) is the second most common cancer and cause of death in women. In recent years many studies investigated the association of long non-coding RNAs (lncRNAs), as novel genetic factors, on BC risk, survival, clinical and pathological features. Recent studies also investigated the roles of metformin treatment as the firstline treatment for type 2 diabetes (T2D) played in lncRNAs expression/regulation or BC incidence, outcome, mortality and survival, separately. This comprehensive study aimed to review lncRNAs associated with BC features and identify metformin-regulated lncRNAs and their mechanisms of action on BC or other types of cancers. Finally, metformin affects BC by regulating five BC-associated lncRNAs including GAS5, HOTAIR, MALAT1, and H19, by several molecular mechanisms have been described in this review. In addition, metformin action on other types of cancers by regulating ten lncRNAs including AC006160.1, Loc100506691, lncRNA-AF085935, SNHG7, HULC, UCA1, H19, MALAT1, AFAP1-AS1, AC026904.1 is described.

Efficacy of metformin therapy in patients with cancer: a meta-analysis of 22 randomised controlled trials

BACKGROUND

To investigate whether metformin monotherapy or adjunctive therapy improves the prognosis in patients with any type of cancer compared to non-metformin users (age ≥18).

METHODS

Databases (Medline, Embase, and the Cochrane Central Register of Controlled Trials) and clinical trial registries ( ClinicalTrials.gov ; the World Health Organization International Clinical Trials Registry Platform) were screened for randomized, controlled trials (RCT) reporting at least progression-free survival (PFS) and/or overall survival (OS). Main outcome measures included hazard ratios (HR), and combined HRs and 95% confidence intervals (CI) were calculated using random-effects models.

RESULTS

Of the 8419 records screened, 22 RCTs comprising 5943 participants were included. Pooled HRs were not statistically significant in both PFS (HR 0.97, 95% CI 0.82-1.15, I² = 50%) and OS (HR 0.98, 95% CI 0.86-1.13, I² = 33%) for patients with cancer between the metformin and control groups. Subgroup analyses demonstrated that metformin treatment was associated with a marginally significant improvement in PFS in reproductive system cancers (HR 0.86, 95% CI 0.74-1.00) and a significantly worse PFS in digestive system cancers (HR 1.45, 95% CI 1.03-2.04). The PFS or OS was observed consistently across maintenance dose, diabetes exclusion, median follow-up, risk of bias, and combined antitumoral therapies.

CONCLUSION

Metformin treatment was not associated with cancer-related mortality in adults compared with placebo or no treatment. However, metformin implied beneficial effects in the PFS of the patients with reproductive system cancers but was related to a worse PFS in digestive system cancers.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO registration number CRD42022324672.

Efficacy of Metformin as Adjuvant Therapy in Metastatic Breast Cancer Treatment

Background: Metformin has been reported to have an anti-tumorigenic impact against metastatic breast cancer (MBC) cells through several mechanisms. Its effect can be evaluated by using many variables such as the response rate (RR) as well as the progression-free survival (PFS). Materials and methods: A prospective study was conducted to investigate and estimate the metformin effect on MBC. About 107 subjects were included in the study and were divided into two groups: Group A included non-diabetic MBC patients treated with metformin in conjunction with chemotherapy and group B included those treated with chemotherapy alone. Both PFS and RR were used as a criteria to evaluate the treatment outcome. Associated adverse effects of metformin were also assessed. Results: The average age of the participants in group A and group B was 50 vs. 47.5, respectively. No significant differences were detected between both cohorts concerning RR levels (regression disease (RD) 27.8% vs. 12.5%, stationary disease (SD) 44.4% vs. 41.7%, progression disease (PD) 27.8% vs. 45.8%, respectively, p = 0.074). Moreover, PFS showed no significant difference between both groups (p = 0.753). There was no significant correlation between metformin concentration and their adverse effects on the study participants. Conclusion: Metformin as an adjuvant therapy to MBC undergoing chemotherapy showed no significant survival benefit as determined by RR and PFS.

Metformin: A Narrative Review of Its Potential Benefits for Cardiovascular Disease, Cancer and Dementia

The biguanide metformin has been used as first-line therapy in type 2 diabetes mellitus (T2DM) treatment for several decades. In addition to its glucose-lowering properties and its prevention of weight gain, the landmark UK Prospective Diabetes Study (UKPDS) demonstrated cardioprotective properties in obese T2DM patients. Coupled with a favorable side effect profile and low cost, metformin has become the cornerstone in the treatment of T2DM worldwide. In addition, metformin is increasingly being investigated for its potential anticancer and neuroprotective properties both in T2DM patients and non-diabetic individuals. In the meantime, new drugs with powerful cardioprotective properties have been introduced and compete with metformin for its place in the treatment of T2DM. In this review we will discuss actual insights in the various working mechanisms of metformin and the evidence for its beneficial effects on (the prevention of) cardiovascular disease, cancer and dementia. In addition to observational evidence, emphasis is placed on randomized trials and recent meta-analyses to obtain an up-to-date overview of the use of metformin in clinical practice.

Risk prediction for delayed clearance of high-dose methotrexate in pediatric hematological malignancies by machine learning

This study aimed to establish a predictive model to identify children with hematologic malignancy at high risk for delayed clearance of high-dose methotrexate (HD-MTX) based on machine learning. A total of 205 patients were recruited. Five variables (hematocrit, risk classification, dose, SLC19A1 rs2838958, sex) and three variables (SLC19A1 rs2838958, sex, dose) were statistically significant in univariable analysis and, separately, multivariate logistic regression. The data was randomly split into a "training cohort" and a "validation cohort". A nomogram for prediction of delayed HD-MTX clearance was constructed using the three variables in the training dataset and validated in the validation dataset. Five machine learning algorithms (cart classification and regression trees, naïve Bayes, support vector machine, random forest, C5.0 decision tree) combined with different resampling methods were used for model building with five or three variables. When developed machine learning models were evaluated in the validation dataset, the C5.0 decision tree combined with the synthetic minority oversampling technique (SMOTE) using five variables had the highest area under the receiver operating characteristic curve (AUC 0.807 [95% CI 0.724-0.889]), a better performance than the nomogram (AUC 0.69 [95% CI 0.594-0.787]). The results support potential clinical application of machine learning for patient risk classification.