In recent years, medicine has undergone profound changes, strongly entering a new phase defined as the “era of precision medicine”. In this context, patient clinical management involves various scientific approaches that allow for a comprehensive pathology evaluation: from preventive processes (where applicable) to genetic and diagnostic studies. In this scenario, biobanks play an important role and, over the years, have gained increasing prestige, moving from small deposits to large collections of samples of various natures. Disease-oriented biobanks are rapidly developing as they provide useful information for the management of complex diseases, such as melanoma. Indeed, melanoma, given its highly heterogeneous characteristics, is one of the oncologic diseases with the greatest clinical and therapeutic management complexity. So, the possibility of extrapolating tissue, genetic and imaging data from dedicated biobanks could result in more selective study approaches. In this review, we specifically analyze the several biobank types to evaluate their role in technology development, patient monitoring and research of new biomarkers, especially in the melanoma context.

Recurrent computed tomography (CT) examination has become a common diagnostic procedure for several diseases and injuries. Though each singular CT scan exposes individuals at low doses of low linear energy transfer (LET) radiation, the cumulative dose received from recurrent CT scans poses an increasing concern for potential health risks. Here, we evaluated the biological effects of recurrent CT scans on the DNA damage response (DDR) in human fibroblasts and retinal pigment epithelial cells maintained in culture for five months and subjected to four CT scans, one every four weeks. DDR kinetics and eventual accumulation of persistent-radiation-induced foci (P-RIF) were assessed by combined immunofluorescence for γH2AX and 53BP1, i.e., γH2AX/53BP1 foci. We found that CT scan repetitions significantly increased both the number and size of γH2AX/53BP1 foci. In particular, after the third CT scan, we observed the appearance of giant foci that might result from the overlapping of individual small foci and that do not associate with irreversible growth arrest, as shown by DNA replication in the foci-carrying cells. Whether these giant foci represent coalescence of unrepaired DNA damage as reported following single exposition to high doses of high LET radiation is still unclear. However, morphologically, these giant foci resemble the recently described compartmentalization of damaged DNA that should facilitate the repair of DNA double-strand breaks but also increase the risk of chromosomal translocations. Overall, these results indicate that for a correct evaluation of the damage following recurrent CT examinations, it is necessary to consider the size and composition of the foci in addition to their number.

Mechanistic relationships between heat shock protein 90 (HSP90) and human epidermal growth factor receptor 2 (HER2) are complex and clinical correlations in breast cancer remain inconsistent. We investigated the role of HSP90 expression in the response of breast cancer cells to HER2-targeted treatments, by measuring cell viability/proliferation and protein expression after genetic and pharmacologic HER2/HSP90 modulation. HSP90 expression was also assessed by immunohistochemistry in a series of 72 metastatic, HER2+ breast cancer patients. In HER2+ breast cancer models (AU565, BT474, MCF7-HER2), HER2 downregulation induced HSP90 upregulation and growth inhibitory synergism between trastuzumab and docetaxel. HSP90 downregulation blunted the response to trastuzumab and docetaxel and their synergistic interactions. The addition of pertuzumab caused little additional growth inhibition, but HSP90 silencing unmasked a synergistic growth inhibitory effect with the triple combination. Conversely, HSP90 downregulation blunted the therapeutic response to trastuzumab/pertuzumab/tamoxifen or trastuzumab–emtansine. In HER2+ breast cancer patients, high HSP90 expression was associated with significant progression-free survival benefit with the triple combination, as compared with trastuzumab and chemotherapy, although the interaction test was not statistically significant. Overall, our results highlight a mechanistic role for HSP90 in determining the response of breast cancer cells to HER2-targeted agents and suggest that trastuzumab/pertuzumab combinations may be particularly advantageous in HSP90-high, HER2+ breast cancer.

Background/Objective: The approach to the clinical management of metastatic melanoma patients is undergoing a significant transformation. The availability of a large amount of data from medical images has made Artificial Intelligence (AI) applications an innovative and cutting-edge solution that could revolutionize the surveillance and management of these patients. In this study, we develop and validate a machine-learning model based on radiomic data extracted from a computed tomography (CT) analysis of patients with metastatic melanoma (MM). This approach was designed to accurately predict prognosis and identify the potential key factors associated with prognosis. Methods: To achieve this goal, we used radiomic pipelines to extract the quantitative features related to lesion texture, morphology, and intensity from high-quality CT images. We retrospectively collected a cohort of 58 patients with metastatic melanoma, from which a total of 60 CT series were used for model training, and 70 independent CT series were employed for external testing. Model performance was evaluated using metrics such as sensitivity, specificity, and AUC (area under the curve), demonstrating particularly favorable results compared to traditional methods. Results: The model used in this study presented a ROC-AUC curve of 82% in the internal test and, in combination with AI, presented a good predictive ability regarding lesion outcome. Conclusions: Although the cohort size was limited and the data were collected retrospectively from a single institution, the findings provide a promising basis for further validation in larger and more diverse patient populations. This approach could directly support clinical decision-making by providing accurate and personalized prognostic information.

In recent years, the medical field has witnessed the rapid expansion and refinement of omics and imaging technologies, which have profoundly transformed patient surveillance and monitoring strategies, with stage-adapted protocols and cross-sectional imaging important in high-risk follow-up. In the melanoma context, diagnostic imaging plays a pivotal role in disease staging, follow-up and evaluation of therapeutic response. Moreover, the emergence of Artificial Intelligence (AI) has further driven the transition toward precision medicine, emphasizing the complexity and individuality of each patient: AI/Radiomics pipelines are increasingly supporting lesion characterization and response prediction within clinical workflows. Consequently, it is essential to emphasize the significant potential of quantitative imaging techniques and radiomic applications, as well as the role of AI in improving diagnostic accuracy and enabling personalized oncologic treatment. Early evidence demonstrates increased sensitivity and specificity, along with a reduction in unnecessary biopsies and imaging procedures, within selected care approaches. In this review, we will outline the current clinical guidelines for the management of melanoma patients and use them as a framework to explore and evaluate advanced imaging approaches and their potential contributions. Specifically, we compare the recommendations of major societies such as NCCN, which advocates more intensive imaging for stages IIB–IV; ESMO and AIOM, which recommend symptom-driven surveillance; and SIGN, which discourages routine imaging in the absence of clinical suspicion. Furthermore, we will describe the latest imaging technologies and the integration of AI-based tools for developing predictive models to actively support therapeutic decision-making and patient care. The conclusions will focus on the prospective role of novel imaging modalities in advancing precision oncology, improving patient outcomes and optimizing the allocation of clinical resources. Overall, the current evidences support a stage-adapted surveillance strategy (ultrasound ± elastography for lymph node regions, targeted brain MRI in high-risk patients, selective use of DECT or total-body MRI) combined with rigorously validated AI-based decision support systems to personalize follow-up, streamline workflows and optimize resource utilization.