Image: Immunohistochemistry of estrogen-receptor-positive (ER (+)) breast cancer (Photo courtesy of Biocare Medical).
Treatment failure, due to drug resistance, still remains a major obstacle for more effective anti-cancer therapy and personalized medicine. In estrogen-receptor-positive (ER (+)) breast cancer, approximately 40% to 50% of patients eventually develop tamoxifen-resistance.
Mitochondrial genes can be routinely checked in biopsies of patients diagnosed with many different cancer types, including breast, lung, ovarian or gastric cancers and they prove more accurate than current methods of predicting a patient's response to treatment.
Collaborating scientists at the University of Salford (Greater Manchester, UK) and the University of Calabria (Cosenza, Italy) identified new measures by looking at the expression levels of mitochondrial genes in samples from post-treatment cancer patients. The team used multiple Kaplan-Meier curves to extrapolate how mitochondrial gene levels correlated with recurrence in hundreds of cancer patients. Certain genes predicted up to five times higher rates of recurrence or metastasis. One particularly useful biomarker, namely Heat Shock Protein Family D (Hsp60) Member 1(HSPD1), is associated with mitochondrial biogenesis, the process of making of new mitochondria.
The scientists combined four mitochondrial proteins to generate a compact mitochondrial gene signature, and this signature also successfully predicted distant metastasis and was effective in larger groups of 2,447 ER(+), 540 basal and 193 HER2(+)breast cancers. It was also effective in all 3,180 breast cancers, if considered together as a single group. The scientists noted that using mitochondria biomarkers would enable clinicians to predict with far greater accuracy, which patients will respond poorly to drug treatments, such as Tamoxifen, which is commonly administered to prevent disease progression in a sub-set of breast cancer patients.
Federica Sotgia, PhD, the lead investigator of the study, said, “In practical terms, a person in remission could be predicted to be 80% likely to fail treatment. If doctors can predict that a treatment will likely fail, it gives them more positive options; either they can monitor the patient more closely or offer an alternative course of treatment.” The study was published on July 27, 2017, in the journal Oncotarget.
University of Salford
University of Calabria