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Ovarian cancer is one of the most frequent malignancies in women. The treatment landscape underwent significant changes as new agents were introduced in ovarian cancer management over the last decade. We present two cases of long responses to Olaparib in BRCA (BReast CAncer gene) mutant ovarian cancer patients. The first case belongs to a 42-year-old female diagnosed with advanced ovarian carcinoma with a rare germinal mutation (BRCA1 c.68_69delAG, commonly found in descendants of Ashkenazi Jewish populations, but also Arabic and Asian ones) and a significant family history of ovarian and breast cancers. After poorly tolerated neoadjuvant chemotherapy, the patient underwent total hysterectomy, bilateral adnexectomy, and intraperitoneal hyperthermic chemotherapy. After eight months, the disease progressed, and first-line platinum chemotherapy was administered. Although not well-tolerated (grade 3 anemia, allergic reactions), chemotherapy resulted in a partial response, and given the patient's characteristics, maintenance with Olaparib was recommended. Treatment is ongoing (total current duration 69 months) and tolerated well (grade 1 side effects). This case illustrates the long-term benefits that novel therapies like Olaparib may offer in patients with platinum-sensitive relapsed ovarian cancer harboring a rare BRCA mutation. The second case highlights a 55-year-old postmenopausal woman diagnosed with ovarian cancer, FIGO stage IVA. Initial treatment included six cycles of chemotherapy, which led to a partial response, followed by interval debulking surgery and another four cycles of chemotherapy. Subsequent Olaparib maintenance therapy post BRCA1 mutation identification contributed to a significant progression-free survival of 65 months until disease recurrence and secondary cytoreductive surgery, showcasing the effectiveness of PARP inhibitors in personalized oncology treatment of ovarian cancer. [ABSTRACT FROM AUTHOR]

Colorectal cancer is a successful model of genetic biomarker development in oncology. Currently, several predictive or prognostic genetic alterations have been identified and are used in clinical practice. The RAS gene family, which includes KRAS and NRAS act as predictors for anti-epithelial growth factor receptor treatment (anti-EGFR), and it has been suggested that NRAS mutations also play a role in prognosis: patients harboring NRAS alterations have a significantly shorter survival compared to those with wild type tumours. BRAF V600E mutations are rare and occur mostly in tumors located in the ascending colon in elderly female patients. BRAF is instrumental in establishing prognosis: survival is shorter by 10–16 months in BRAF-mutant patients, and BRAF may be a negative prognostic factor for patients who undergo hepatic or pulmonary metastasectomy. Moreover, this mutation is used as a negative predictive factor for anti-EGFR therapies. Two new biomarkers have recently been added to the metastatic colorectal cancer panel: HER2 and microsatellite instability. While HER2 is still being investigated in different prospective studies in order to validate its prognostic role, microsatellite instability already guides clinical decisions in substituted with advanced colorectal cancer.

Background: Machine learning models learn about general behavior from data by finding the relationships between features. Our purpose was to develop a predictive model to identify and predict which subset of colorectal cancer patients are more likely to experience chemotherapy-induced toxicity and to determine the specific attributes that influence the presence of treatment-related side effects., Methods: The predictor was general toxicity, and for the construction of our data training, we selected 95 characteristics that represent the health state of 74 patients prior to their first round of chemotherapy. After the data were processed, Random Forest models were trained to offer an optimal balance between accuracy and interpretability., Results: We constructed a machine learning predictor with an emphasis on assessing the importance of numerical and categorical variables in relation to toxicity., Conclusions: The incorporation of artificial intelligence in personalizing colorectal cancer management by anticipating and overseeing toxicities more effectively illustrates a pivotal shift towards more personalized and precise medical care.

Unlabelled: Hepatocellular carcinoma has an increasing incidence and an impressive mortality. At present, the only authorized systemic treatment is the multi-kinase inhibitor sorafenib. A multitude of clinical trials are aimed at improving outcomes, both in firstY- and in second-line therapy. In this multitude of clinical trials, the purpose of our article was to familiarize physicians with the mechanisms of action of new biological therapies and to offer an algorithm for optimal trial selection for each patient, based on clinical and biological indicators. The available data were structured as follows: antiangiogenic therapy, c -MET inhibitors, combinations of chemotherapy with sorafenib, immune response modulators, cellular metabolism modulators, mTOR inhibitors, other multi-kinase inhibitors., Conclusion: Treatment of advanced hepatocellular carcinoma remains a challenge for oncologists. Choosing the "right" trial may be the only chance of prolonging patient survival and improve his/her clinical status.