Genomics and Epigenomics in the Molecular Biology of Melanoma-A Prerequisite for Biomarkers Studies.

Melanoma is a common and aggressive tumor originating from melanocytes. The increasing incidence of cutaneous melanoma in recent last decades highlights the need for predictive biomarkers studies. Melanoma development is a complex process, involving the interplay of genetic, epigenetic, and environmental factors. Genetic aberrations include BRAF, NRAS, NF1, MAP2K1/MAP2K2, KIT, GNAQ, GNA11, CDKN2A, TERT mutations, and translocations of kinases. Epigenetic alterations involve microRNAs, non-coding RNAs, histones modifications, and abnormal DNA methylations. Genetic aberrations and epigenetic marks are important as biomarkers for the diagnosis, prognosis, and prediction of disease recurrence, and for therapeutic targets. This review summarizes our current knowledge of the genomic and epigenetic changes in melanoma and discusses the latest scientific information.

A Complete Response in a Metastatic Melanoma Patient After a Single Dose of Dual Checkpoint Inhibitors Blockade Could Be Predictable: A Case Report.

Cutaneous malignant melanoma is one of the most aggressive forms of skin cancer and thus, a high mortality has been reported over decades. The prognosis for melanoma varies widely based on several factors, including the stage at which it is diagnosed, the location and thickness of the tumor, the patient's age and overall health, and specific genetic factors associated with melanoma. Therapeutic options include checkpoint inhibitors, regardless of V-Raf Murine Sarcoma Viral Oncogene Homolog B status (BRAF), and targeted therapy (anti-BRAF) in the adjuvant or metastatic setting. Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment but predicting which patients will benefit from these therapies remains challenging. Biomarkers like leukocytes, neutrophils, eosinophils, basophils, platelets, and other peripheral blood biomarkers have been investigated for their potential to predict responses to ICIs. Tumor mutational burden (TMB), circulating tumor DNA (ctDNA), and soluble PD-L1 (sPD-L1) have emerged as potential biomarkers for predicting responses to ICIs. Elevated baseline levels of ctDNA and elevated sPD-L1 levels have been associated with worse prognosis in melanoma patients. High TMB is often associated with better responses to ICIs in melanoma. Here we present a case from our department, of a 57-year-old patient, diagnosed in 2019 with stage IV - pT4cNx cM1 (lymph nodes metastases) and suspicion of lung metastases, BRAF wild-type right hallux malignant melanoma. Due to impressive results, first-line treatment with ICIs nivolumab and ipilimumab was the preferred treatment of choice, which showed a favorable response, with regression of oncological disease after the first cycle, and achieving complete response afterward. Unfortunately, the treatment was discontinued due to severe hepatic and pancreatic toxicity, but the favorable response to immunotherapy has been maintained for four years and is ongoing. Identifying predictive biomarkers is important to achieve the best response for the patient, with minimal adverse events, especially if long-term clinical benefit can be reached.

The Importance of Predictive Biomarkers and Their Correlation with the Response to Immunotherapy in Solid Tumors-Impact on Clinical Practice.

Background/Objectives: Immunotherapy has changed the therapeutic approach for various solid tumors, especially lung tumors, malignant melanoma, renal and urogenital carcinomas, demonstrating significant antitumor activity, with tolerable safety profiles and durable responses. However, not all patients benefit from immunotherapy, underscoring the need for predictive biomarkers that can identify those most likely to respond to treatment. Methods: The integration of predictive biomarkers into clinical practice for immune checkpoint inhibitors (ICI) holds great promise for personalized cancer treatment. Programmed death ligand 1 (PD-L1) expression, tumor mutational burden (TMB), microsatellite instability (MSI), gene expression profiles and circulating tumor DNA (ctDNA) have shown potential in predicting ICI responses across various cancers. Results: Challenges such as standardization, validation, regulatory approval, and cost-effectiveness must be addressed to realize their full potential. Predictive biomarkers are crucial for optimizing the clinical use of ICIs in cancer therapy. Conclusions: While significant progress has been made, further research and collaboration among clinicians, researchers, and regulatory institutes are essential to overcome the challenges of clinical implementation. However, little is known about the relationship between local and systemic immune responses and the correlation with response to oncological therapies and patient survival.

Pelvic Exenteration in Advanced, Recurrent or Synchronous Cancers-Last Resort or Therapeutic Option?

First described some 80 years ago, pelvic exenteration remain controversial interventions with variable results and ever-changing indications. The previous studies are not homogenous and have different inclusion criteria (different populations and different disease characteristics) and methodologies (including evaluation of results), making it extremely difficult to properly assess the role of pelvic exenteration in cancer treatment. This study aims to describe the indications of pelvic exenterations, the main prognostic factors of oncologic results, and the possible complications of the intervention. Methods: For this purpose, we conducted a retrospective study of 132 patients who underwent various forms of pelvic exenterations in the Institute of Oncology "Prof. Dr. Al. Trestioreanu" in Bucharest, Romania, between 2013 and 2022, collecting sociodemographic data, characteristics of patients, information on the disease treated, data about the surgical procedure, complications, additional cancer treatments, and oncologic results. Results: The study cohort consists of gynecological, colorectal, and urinary bladder malignancies (one hundred twenty-seven patients) and five patients with complex fistulas between pelvic organs. An R0 resection was possible in 76.38% of cases, while on the rest, positive margins on resection specimens were observed. The early morbidity was 40.63% and the mortality was 2.72%. Long-term outcomes included an overall survival of 43.7 months and a median recurrence-free survival of 24.3 months. The most important determinants of OS are completeness of resection, the colorectal origin of tumor, and the presence/absence of lymphovascular invasion. Conclusions: Although still associated with high morbidity rates, pelvic exenterations can deliver important improvements in oncological outcomes in the long-term and should be considered on a case-by-case basis. A good selection of patients and an experienced surgical team can facilitate optimal risks/benefits.

Diagnostic Accuracy of Machine-Learning Models on Predicting Chemo-Brain in Breast Cancer Survivors Previously Treated with Chemotherapy: A Meta-Analysis.

We performed a meta-analysis of chemo-brain diagnostic, pooling sensitivities, and specificities in order to assess the accuracy of a machine-learning (ML) algorithm in breast cancer survivors previously treated with chemotherapy. We searched PubMed, Web of Science, and Scopus for eligible articles before 30 September 2022. We identified three eligible studies from which we extracted seven ML algorithms. For our data, the χ2 tests demonstrated the homogeneity of the sensitivity's models (χ2 = 7.6987, df = 6, p-value = 0.261) and the specificities of the ML models (χ2 = 3.0151, df = 6, p-value = 0.807). The pooled area under the curve (AUC) for the overall ML models in this study was 0.914 (95%CI: 0.891-0.939) and partial AUC (restricted to observed false positive rates and normalized) was 0.844 (95%CI: 0.80-0.889). Additionally, the pooled sensitivity and pooled specificity values were 0.81 (95% CI: 0.75-0.86) and 0.82 (95% CI: 0.76-0.86), respectively. From all included ML models, support vector machine demonstrated the best test performance. ML models represent a promising, reliable modality for chemo-brain prediction in breast cancer survivors previously treated with chemotherapy, demonstrating high accuracy.

Adenocarcinoma versus pancreatic neuroendocrine tumor - case report.

Pancreatic cancer represents one of the most aggressive types of cancer, resulting in a late diagnosis and rapid death (poor overall survival). After adenocarcinoma (counting almost 80% of cases of pancreatic cancer), the second category, as frequency, is represented by the family of gastroenteropancreatic neuroendocrine tumors (GEP-NETs). Pancreatic cancer is characterized by genetic heterogeneity and may results in different evolution among metastases, which may acquire driver mutations with the ability to transform under the action of several cancer treatments. Here we report a case of a 64-year-old patient diagnosed with pancreatic tumor localized on the body and tail, invasive in the splenic and portal vein, pT3pN0M0 (adenocarcinoma pancreatic cancer), treated with a multimodal approach: surgery (splenectomy and distal pancreatectomy, with suture of the portal vein), chemotherapy, in 2010, that relapsed in 2015, with local recurrence that was resected and distant liver metastases. Immunohistochemistry of the recurrence tumor showed a neuroendocrine transformation of the tumor, with major implications in treatment and prognosis. Computed tomography examination, as well as histopathological and immunohistochemically testing, sustained positive and differential diagnosis.