We report the rational design of a 20-mer basic peptide, derived from the transcriptional antitermination protein N of bacteriophage P22, equipped with a luminescent DOTA[Tb³⁺] macrocyclic complex and a sensitizing tryptophan antenna. Folding of this peptide into an α helical conformation, which occurs upon binding to its target boxB RNA hairpin, results in a large increase in luminescence emission. Therefore, the peptide construct works as a highly sensitive and selective probe for this RNA hairpin.

This study set out to assess the performance of an artificial intelligence (AI) algorithm based on clinical data and dermatoscopic imaging for the early diagnosis of melanoma, and its capacity to define the metastatic progression of melanoma through serological and histopathological biomarkers, enabling dermatologists to make more informed decisions about patient management. Integrated analysis of demographic data, images of the skin lesions, and serum and histopathological markers were analyzed in a group of 196 patients with melanoma. The interleukins (ILs) IL-4, IL-6, IL-10, and IL-17A as well as IFNγ (interferon), GM-CSF (granulocyte and macrophage colony-stimulating factor), TGFβ (transforming growth factor), and the protein DCD (dermcidin) were quantified in the serum of melanoma patients at the time of diagnosis, and the expression of the RKIP, PIRIN, BCL2, BCL3, MITF, and ANXA5 proteins was detected by immunohistochemistry (IHC) in melanoma biopsies. An AI algorithm was used to improve the early diagnosis of melanoma and to predict the risk of metastasis and of disease-free survival. Two models were obtained to predict metastasis (including “all patients” or only patients “at early stages of melanoma”), and a series of attributes were seen to predict the progression of metastasis: Breslow thickness, infiltrating BCL-2 expressing lymphocytes, and IL-4 and IL-6 serum levels. Importantly, a decrease in serum GM-CSF seems to be a marker of poor prognosis in patients with early-stage melanomas.

Metastasis development represents an important threat for melanoma patients, even when diagnosed at early stages and upon removal of the primary tumor. In this scenario, determination of prognostic biomarkers would be of great interest. Serum contains information about the general status of the organism and therefore represents a valuable source for biomarkers. Thus, we aimed to define serological biomarkers that could be used along with clinical and histopathological features of the disease to predict metastatic events on the early‐stage population of patients. We previously demonstrated that in stage II melanoma patients, serum levels of dermcidin (DCD) were associated with metastatic progression. Based on the relevance of the immune response on the cancer progression and the recent association of DCD with local and systemic immune response against cancer cells, serum DCD was analyzed in a new cohort of patients along with interleukin 4 (IL‐4), IL‐6, IL‐10, IL‐17A, interferon γ (IFN‐γ), transforming growth factor‐β (TGF‐ β), and granulocyte–macrophage colony‐stimulating factor (GM‐CSF). We initially recruited 448 melanoma patients, 323 of whom were diagnosed as stages I‐II according to AJCC. Levels of selected cytokines were determined by ELISA and Luminex, and obtained data were analyzed employing machine learning and Kaplan–Meier techniques to define an algorithm capable of accurately classifying early‐stage melanoma patients with a high and low risk of developing metastasis. The results show that in early‐stage melanoma patients, serum levels of the cytokines IL‐4, GM‐CSF, and DCD together with the Breslow thickness are those that best predict melanoma metastasis. Moreover, resulting algorithm represents a new tool to discriminate subjects with good prognosis from those with high risk for a future metastasis.

Functional imaging has become an important tool in oncology because it not only provides information about the size and localization of the tumour, but also about the pathophysiological features of the tumoural cells. One of the characteristic features of some tumour types is that their fast growth leads to deficient intratumoral vascularization, which results in low oxygen availability. To overcome this lack of oxygen, tumoural cells activate the neoangiogenic program by upregulating the transcription factor HIF-1α. Herein we report a non-invasive in vitro detection method of hypoxia using designed fluorescent peptide probes based on the oxygen-dependent degradation domain of HIF-1α. The fluorescent probe retains the oxygen-sensing capability of HIF-1α, so that it is stabilized under hypoxia and readily degraded by the proteasome under normoxia, thus providing direct information of the cellular oxygen availability.

Background: Next‐generation sequencing (NGS) opens new options in clinical oncology, from therapy selection to genetic counseling. However, realization of this potential not only requires succeeding in the bioinformatics and interpretation of the results, but also in their integration into the clinical practice. We have developed a novel NGS diagnostic platform aimed at detecting (1) somatic genomic alterations associated with the response to approved targeted cancer therapies and (2) germline mutations predisposing to hereditary malignancies.Methods: Next‐generation sequencing libraries enriched in the exons of 215 cancer genes (97 for therapy selection and 148 for predisposition, with 30 informative for both applications), as well as selected introns from 17 genes involved in drug‐related rearrangements, were prepared from 39 tumors (paraffin‐embedded tissues/cytologies), 36 germline samples (blood) and 10 cell lines using hybrid capture. Analysis of NGS results was performed with specifically developed bioinformatics pipelines.Results: The platform detects single‐nucleotide variants (SNVs) and insertions/deletions (indels) with sensitivity and specificity >99.5% (allelic frequency ≥0.1), as well as copy‐number variants (CNVs) and rearrangements. Somatic testing identified tailored approved targeted drugs in 35/39 tumors (89.74%), showing a diagnostic yield comparable to that of leading commercial platforms. A somatic EGFR p.E746_S752delinsA mutation in a mediastinal metastasis from a breast cancer prompted its anatomopathologic reassessment, its definite reclassification as a lung cancer and its treatment with gefitinib (partial response sustained for 15 months). Testing of 36 germline samples identified two pathogenic mutations (in CDKN2A and BRCA2). We propose a strategy for interpretation and reporting of results adaptable to the aim of the request, the availability of tumor and/or normal samples and the scope of the informed consent.Conclusion: With an adequate methodology, it is possible to translate to the clinical practice the latest advances in precision oncology, integrating under the same platform the identification of somatic and germline genomic alterations.

Analyzing the mutational load of driver mutations in melanoma could provide valuable information regarding its progression. We aimed at analyzing the heterogeneity of mutational load of BRAF V600E in biopsies of melanoma patients of different stages, and investigating its potential as a prognosis factor. Mutational load of BRAF V600E was analyzed by digital PCR in 78 biopsies of melanoma patients of different stages and 10 nevi. The BRAF V600E load was compared among biopsies of different stages. Results showed a great variability in the load of V600E (0%-81%). Interestingly, we observed a significant difference in the load of V600E between the early and late melanoma stages, in the sense of an inverse correlation between BRAF V600E mutational load and melanoma progression. In addition, a machine learning approach showed that the mutational load of BRAF V600E could be a good predictor of metastasis in stage II patients. Our results suggest that BRAF V600E is a promising biomarker of prognosis in stage II patients.