• ovarian cancer
• tumor microenvironment

Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy characterized by considerable biological, histological and molecular heterogeneity. In most patients, ovarian cancer is diagnosed when the disease has progressed to an advanced stage, corresponding to stages IIB to IV of the International Federation of Gynecology and Obstetrics (FIGO) classification, with the involvement of the peritoneal cavity and other organs. Recent findings have already revealed that the tumor microenvironment (TME) plays an essential role in promoting cancer initiation, progression and metastasis. The TME consists of several different types of normal cells recruited and reprogrammed by the cancer cells to produce factors beneficial to tumor growth and spread. Cancer-associated fibroblasts (CAF)s are a major constituent of the TME and play a critical role in promoting many aspects of tumor function, but universal CAFs’ markers have not been identified. The biological importance of TME as a reactive platform orchestrating diverse aspects of tumor initiation, evolvement, metastatic progression, altered immune response, development of therapeutic resilience, and cancer recurrence is unquestionable and continuously reaffirmed, as highlighted in a multitude of studies. Given the unique ovarian cancer intraperitoneal TME and the emerging evidence of benefits from epigenetic-targeted therapeutics, I select new several biomarkers to evaluate the activation of CAFs: α-smooth muscle actin (α-SMA), CD90, Fibroblast activated protein (FAP). Furthermore, I select other biomarker (CD44, STAT3, Bcl2, p16) to correlate the progression and dissemination of disease with therapeutic response and rate of relapse.
Results
At the beginning, I investigated the association between the stromal expression of p16 and α-SMA: in our knowledge, there were no other studies about ovarian TME where authors describe the same correlation. This result supports the idea that an intracellular pathway could govern the CAFs activation through α-SMA expression in tumoral stroma. The statistical analysis carried out by the nonparametric test, the Spearman's rank-order correlation, showed a monotonic relationship between the stromal expression of α-SMA and p16 (r=-0,32, p-value=0,023).
A deeper insight into the roles of TME in supporting ovarian cancer cell growth, progression and metastatic outgrowth provide an unexploited, ushering in a new era of precise medicine. I investigate the role of α-SMA e FAP1 in the stromal remodelling and the correlation of the overexpression of CD44 e CD90 with a higher rate of metastasis or lymph node involvement. In a complex cancer landscape, I searched for a statistically significant association between BRCA mutation patients, different ovarian cancer histotypes (HGSC, HGEC, CCCO) and the expression of CD44.
I investigate a statistically significant correlation between the expression of our biomarkers and the different stage. Furthermore, this analysis allows to evaluate a possible correlation between the expression of p16, CD44, STAT3 and Bcl2 with the platinum free interval (PFI). The Spearman's rank-order correlation showed a positive association between the positive expression of STAT3 and Bcl-2 (rs=0,4977, p-value=0,0001 with α=0,05). Instead, there is a negative correlation between the CD44 and Bcl-2 (rs=-0,3128, p-value =0,0189). These biomarkers are linked with the chemotherapy resistance and CD44 seems to be, in my coorte, the only biomarker having a statistically significant association with rate of disease’s relapse and overall survival.
For several years, researchers focused their attention on tumor immune microenvironment (TIME), a complex ecosystem containing adaptive and innate immune cells with tumor-promoting and anti-tumoral roles: in ovarian cancer this complex ecosystem did not give promising results. My project and my results shifted the attention to a different constituent of tumor microenvironment (TME). The goal of my research is to improve the actual diagnostic and therapeutic strategies, offering new reliable biomarkers discovered through the analysis of pathogenic pathways of ovarian cancer identification of new targets and strategies for therapies. To achieve optimal clinical benefits, it is supposed to rationally design combination therapies by integrating individual information on the altered TME landscape, genomic analysis and molecular testing and identify additional predictive and prognostic biomarkers to select qualified candidates and to ensure appropriate and personalized cancer therapies.