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Purpose: Data from population-based studies have shown an increased incidence of certain types of neoplasms in patients younger than 50 years (early-onset cancer [EOC]); however, little information is derived from other real-world data sources. In a nonpopulation registry, we analyzed changes in the incidence of several neoplasms in successive generations., Methods: This cross-sectional study included all patients with a cancer diagnosis registered in one university hospital in Málaga, Spain, between 1998 and 2021, and 18 neoplasms were analyzed. For each neoplasm, the proportion of patients younger than 50 years and age 50 years and older (late-onset cancer [LOC]) of the total number of patients diagnosed each year was determined. In addition, the age limit was lowered to 45-40 years. Changes in these proportions between each year and the following year were assessed by calculating the annual percentage change (APC), and a final assessment of these changes was performed by determining the average APC (AAPC)., Results: Of the 24,596 patients, 5,466 (22.2%) had EOC, and 19,130 (77.8%) had LOC. The incidence of all tumors increased throughout the study period in both age groups. The AAPC increase was higher in patients with EOC than in those with LOC for the following neoplasms: head and neck (6.1% v 4.6%), colon (11.0% v 8.2%), testicular (16.3% v -13.1%), non-Hodgkin lymphoma (8.4% v 5.9%), rectum (16.1% v 6.8%), kidney (27.8% v 20.1%), and sarcoma (43.4% v 28.6%). This increase was confirmed in patients younger than 45 years and 40 years., Conclusion: Our results are consistent with the data published for most tumor sites analyzed. This global public health problem requires the utmost attention to decrease excess cancer in young patients.

Background: CDK4/6 inhibitors plus endocrine therapies are the current standard of care in the first-line treatment of HR+/HER2-negative metastatic breast cancer, but there are no well-established clinical or molecular predictive factors for patient response. In the era of personalised oncology, new approaches for developing predictive models of response are needed., Materials and Methods: Data derived from the electronic health records (EHRs) of real-world patients with HR+/HER2-negative advanced breast cancer were used to develop predictive models for early and late progression to first-line treatment. Two machine learning approaches were used: a classic approach using a data set of manually extracted features from reviewed (EHR) patients, and a second approach using natural language processing (NLP) of free-text clinical notes recorded during medical visits., Results: Of the 610 patients included, there were 473 (77.5%) progressions to first-line treatment, of which 126 (20.6%) occurred within the first 6 months. There were 152 patients (24.9%) who showed no disease progression before 28 months from the onset of first-line treatment. The best predictive model for early progression using the manually extracted dataset achieved an area under the curve (AUC) of 0.734 (95% CI 0.687-0.782). Using the NLP free-text processing approach, the best model obtained an AUC of 0.758 (95% CI 0.714-0.800). The best model to predict long responders using manually extracted data obtained an AUC of 0.669 (95% CI 0.608-0.730). With NLP free-text processing, the best model attained an AUC of 0.752 (95% CI 0.705-0.799)., Conclusions: Using machine learning methods, we developed predictive models for early and late progression to first-line treatment of HR+/HER2-negative metastatic breast cancer, also finding that NLP-based machine learning models are slightly better than predictive models based on manually obtained data., Competing Interests: Conflict of interest statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

In breast cancer (BC) the employment of sequencing technologies for metagenomic analyses has allowed not only the description of the overall metagenomic landscape but also the specific microbial changes and their functional implications. Most of the available data suggest that BC is related to bacterial dysbiosis in both the gut microenvironment and breast tissue. It is hypothesized that changes in the composition and functions of several breast and gut bacterial taxa may contribute to BC development and progression through several pathways. One of the most prominent roles of gut microbiota is the regulation of steroid-hormone metabolism, such as estrogens, a component playing an important role as risk factor in BC development, especially in postmenopausal women. On the other hand, breast and gut resident microbiota are the link in the reciprocal interactions between cancer cells and their local environment, since microbiota are capable of modulating mucosal and systemic immune responses. Several in vivo and in vitro studies show remarkable evidence that diet, probiotics and prebiotics could exert important anticarcinogenic effects in BC. Moreover, gut microbiota have an important role in the metabolism of chemotherapeutic drugs and in the activity of immunogenic chemotherapies since they are a potential dominant mediator in the response to cancer therapy. Then, the microbiome impact in BC is multi-factorial, and the gut and breast tissue bacteria population could be important in regulating the local immune system, in tumor formation and progression and in therapy response and/or resistance.

Background: The effect of anesthetic techniques on cancer recurrence has been the subject of intensive research in the past years, as it affects a large proportion of the population. The use of opioids and halogenated agents in cancer patients during the perioperative period may be related to higher rates of cancer recurrence and reduced disease-free survival., Methods: This was a prospective study. The sample was composed of 100 patients who underwent a radical cystectomy for infiltrating bladder cancer in a reference center. We compared disease-free survival associated with combined anesthesia versus opiate-based analgesia. The relationship between the administered hypnotic and disease-free survival was also investigated., Results: The median disease-free survival of the patients who received combined anesthesia was 585 (240-1,005) days versus 210 (90-645) days in the other group. A significant difference was observed between the two groups (p = 0.01). Combined analysis of all groups revealed significant differences in disease-free survival between patients who received combined anesthesia with propofol (510 [315-1,545] disease-free days) and those who received sevoflurane and opioids (150 [90-450] disease-free days) (p = 0.02)., Conclusions: Anesthesia may play a crucial role in tumor relapse, as it is administered at the moment of the greatest risk of dissemination: surgical handling of the tumor. Opioids and volatile agents have been related to an increased risk for cancer recurrence. We compared the use of propofol + local anesthesia versus sevoflurane + opioids and also found that disease-free survival was longer among patients who received propofol + local anesthesia. Disease-free survival increases with the use of propofol in combination with epidural anesthesia in patients who undergo surgery for infiltrating bladder cancer., (© 2020 S. Karger AG, Basel.)