Interaction between epidermal growth factor receptor (EGFR) signaling with GM3 ganglioside expression has been previously described. However, little is known about EGFR and NeuGcGM3 co-expression in cancer patients and their therapeutic implications. In this paper, we evaluate the co-expression of EGFR and NeuGcGM3 ganglioside in tumors from 92 patients and in two spontaneous lung metastasis models of mice (Lewis lung carcinoma (3LL-D122) in C57BL/6 and mammary carcinoma (4T1) in BALB/c). As results, co-expression of EGFR and NeuGcGM3 ganglioside was frequently observed in 63 of 92 patients (68 %), independently of histological subtype. Moreover, EGFR is co-expressed with NeuGcGM3 ganglioside in the metastasis of 3LL-D122 and 4T1 murine models. Such dual expression appears to be therapeutically relevant, since combined therapy with mAbs against these two molecules synergistically increase the survival of mice treated. Overall, our results suggest that NeuGcGM3 and EGFR may coordinately contribute to the tumor cell biology and that therapeutic combinations against these two targets might be a valid strategy to explore.
Cancer is the second cause of death in Cuba and the first cause of potential years of life lost, and therefore the cause most affecting increase in life expectancy of Cubans at birth. Among Cuban women, breast cancer has the highest incidence (excluding skin tumors) and is the second leading cause of cancer mortality. Given that approximately 2500 new cases are diagnosed annually, and more than 1000 women die of breast cancer each year,[1] malignant breast tumors are considered a major health problem in the Cuban population.With cancer, knowledge of the prognosis plays a fundamental role in medical decision-making and disease management.[2] Prognostic factors have been defined as variables that help explain heterogeneity in the course and development of a disease, regardless of the treatment administered, and that are associated with event-free and overall survival.[3] Once prognostic factors are identified, patients with similar characteristics can be classified for the purpose of selecting and administering therapeutic regimens of greater or lesser intensity. Analysis of factors predicting response to treatment likewise enables administration of specific therapies only in those patients expected to respond, thus avoiding harm with no benefit to patients lacking these factors. Treatments are becoming increasingly more personalized, significantly improving patients' quality of life. [4]
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