Trastuzumab Resistance in Patients With HER2-Positive Advanced Breast Cancer:Results From the SONABRE Registry

BACKGROUND: This study aims to explore whether first-line pertuzumab use modifies the effect of prior use of (neo-) adjuvant trastuzumab on the PFS of first-line HER2-targeted therapy in patients with human epidermal growth factor receptor 2 (HER2)-positive advanced breast cancer (ABC). METHODS: Patients diagnosed with HER2-positive ABC in 2008 to 2018 in 9 Dutch hospitals were derived from the SONABRE Registry (NCT03577197). Patients diagnosed with de novo metastatic breast cancer were excluded. Patients receiving first-line trastuzumab-based therapy for ABC were selected and divided into trastuzumab naïve (n = 113) and trastuzumab pretreated (n = 112). Progression-free survival (PFS) was compared using multivariable Cox proportional hazard models. The interaction effect of first-line pertuzumab was tested using the likelihood-ratio test. RESULTS: The median follow-up time was 47 months (95% confidence interval [CI]: 42-52). When comparing trastuzumab pretreated with trastuzumab naïve patients, the hazard ratio for first-line progression was 2.07 (CI:1.47-2.92). For trastuzumab pretreated patients who received first-line trastuzumab without pertuzumab, the hazard ratio for progression was 2.60 (95% CI:1.72-3.93), whereas for those who received first-line trastuzumab with pertuzumab the hazard ratio was 1.43 (95% CI: 0.81-2.52) (P interaction = .10). CONCLUSIONS: Prior use of trastuzumab as (neo-)adjuvant treatment had a negative impact on PFS of first-line HER2-targeted therapy outcomes. Adding pertuzumab to first-line trastuzumab-based therapy decreased the negative impact of prior (neo-)adjuvant trastuzumab use on first-line PFS. Further studies are needed to assess the effect of prior (neo-)adjuvant pertuzumab use on the outcomes of first-line pertuzumab-based therapy

Adaptive radiotherapy : The Elekta Unity MR-linac concept

Background and purpose: The promise of the MR-linac is that one can visualize all anatomical changes during the course of radiotherapy and hence adapt the treatment plan in order to always have the optimal treatment. Yet, there is a trade-off to be made between the time spent for adapting the treatment plan against the dosimetric gain. In this work, the various daily plan adaptation methods will be presented and applied on a variety of tumour sites. The aim is to provide an insight in the behavior of the state-of-the-art 1.5 T MRI guided on-line adaptive radiotherapy methods. Materials and methods: To explore the different available plan adaptation workflows and methods, we have simulated online plan adaptation for five cases with varying levels of inter-fraction motion, regions of interest and target sizes: prostate, rectum, esophagus and lymph node oligometastases (single and multiple target). The plans were evaluated based on the clinical dose constraints and the optimization time was measured. Results: The time needed for plan adaptation ranged between 17 and 485 s. More advanced plan adaptation methods generally resulted in more plans that met the clinical dose criteria. Violations were often caused by insufficient PTV coverage or, for the multiple lymph node case, a too high dose to OAR in the vicinity of the PTV. With full online replanning it was possible to create plans that met all clinical dose constraints for all cases. Conclusion: Daily full online replanning is the most robust adaptive planning method for Unity. It is feasible for specific sites in clinically acceptable times. Faster methods are available, but before applying these, the specific use cases should be explored dosimetrically

Associations Between Daily Affective Instability and Connectomics in Functional Subnetworks in Remitted Patients with Recurrent Major Depressive Disorder

Remitted patients with major depressive disorder (rMDD) often report more fluctuations in mood as residual symptomatology. It is unclear how this affective instability is associated with information processing related to the default mode (DMS), salience/reward (SRS), and frontoparietal (FPS) subnetworks in rMDD patients at high risk of recurrence (rrMDD). Sixty-two unipolar, drug-free rrMDD patients (>= 2 MDD episodes) and 41 healthy controls (HCs) were recruited. We used experience sampling methodology to monitor mood/cognitions (10 times a day for 6 days) and calculated affective instability using the mean adjusted absolute successive difference. Subsequently, we collected resting-state functional magnetic resonance imaging data and performed graph theory to obtain network metrics of integration within (local efficiency) the DMS, SRS, and FPS, and between (participation coefficient) these subnetworks and others. In rrMDD patients compared with HCs, we found that affective instability was increased in most negative mood/cognition variables and that the DMS had less connections with other subnetworks. Furthermore, we found that rrMDD patients, who showed more instability in feeling down and irritated, had less connections between the SRS and other subnetworks and higher local efficiency coefficients in the FPS, respectively. In conclusion, rrMDD patients, compared with HCs, are less stable in their negative mood and these dynamics are related to differences in information processing within-and between-specific functional subnetworks. These results are a first step to gain a better understanding of how mood fluctuations in real life are represented in the brain and provide insights into the vulnerability profile of MDD

The evolution of living beings started with prokaryotes and in interaction with prokaryotes

In natural world, no organism exists in absolute isolation, and thus every organism must interact with the environment and other organisms. Next-generation sequencing technologies are increasingly revealing that most of the cells in the environment resist cultivation in the laboratory and several prokaryotic divisions have no known cultivated representatives. Based on this, we hypothesize that species that live together in the same ecosystem are more or less dependent upon each other and are very large in diversity and number, outnumbering those that can be isolated in single-strain laboratory culture. In natural environments, bacteria and archaea interact with other organisms (viruses, protists, fungi, animals, plants, and human) in complex ecological networks, resulting in positive, negative, or no effect on one or another of the interacting partners. These interactions are sources of ecological forces such as competitive exclusion, niche partitioning, ecological adaptation, or horizontal gene transfers, which shape the biological evolution. In this chapter, we review the biological interactions involving prokaryotes in natural ecosystems, including plant, animal, and human microbiota, and give an overview of the insights into the evolution of living beings. We conclude that studies of biological interactions, including multipartite interactions, are sources of novel knowledge related to the biodiversity of living things, the functioning of ecosystems, the evolution of the cellular world, and the ecosystem services to the living beings