A joint physics and radiobiology DREAM team vision - Towards better response prediction models to advance radiotherapy.

Radiotherapy developed empirically through experience balancing tumour control and normal tissue toxicities. Early simple mathematical models formalized this practical knowledge and enabled effective cancer treatment to date. Remarkable advances in technology, computing, and experimental biology now create opportunities to incorporate this knowledge into enhanced computational models. The ESTRO DREAM (Dose Response, Experiment, Analysis, Modelling) workshop brought together experts across disciplines to pursue the vision of personalized radiotherapy for optimal outcomes through advanced modelling. The ultimate vision is leveraging quantitative models dynamically during therapy to ultimately achieve truly adaptive and biologically guided radiotherapy at the population as well as individual patient-based levels. This requires the generation of models that inform response-based adaptations, individually optimized delivery and enable biological monitoring to provide decision support to clinicians. The goal is expanding to models that can drive the realization of personalized therapy for optimal outcomes. This position paper provides their propositions that describe how innovations in biology, physics, mathematics, and data science including AI could inform models and improve predictions. It consolidates the DREAM team's consensus on scientific priorities and organizational requirements. Scientifically, it stresses the need for rigorous, multifaceted model development, comprehensive validation and clinical applicability and significance. Organizationally, it reinforces the prerequisites of interdisciplinary research and collaboration between physicians, medical physicists, radiobiologists, and computational scientists throughout model development. Solely by a shared understanding of clinical needs, biological mechanisms, and computational methods, more informed models can be created. Future research environment and support must facilitate this integrative method of operation across multiple disciplines

The global biogeography of lizard functional groups

Aim: Understanding the mechanisms determining species richness is a primary goal of biogeography. Richness patterns of sub-groups within a taxon are usually assumed to be driven by similar processes. However, if richness of distinct ecological strategies respond differently to the same processes, inferences made for an entire taxon may be misleading. We deconstruct the global lizard assemblage into functional groups and examine the congruence among richness patterns between them. We further examine the species richness – functional richness relationship to elucidate the way functional diversity contributes to the overall species richness patterns. Location: Global. Methods: Using comprehensive biological trait databases we classified the global lizard assemblage into ecological strategies based on body size, diet, activity times and microhabitat preferences, using Archetypal Analysis. We then examined spatial gradients in the richness of each strategy at the one-degree grid cell, biomes and realm scales. Results: We found that lizards can best be characterized by seven 'ecological strategies': scansorial, terrestrial, nocturnal, herbivorous, fossorial, large and semiaquatic. There are large differences among the global richness patterns of these strategies. While the major richness hotspot for lizards in general is in Australia, several strategies exhibit highest richness in the Amazon Basin. Importantly, the global maximum in lizard species richness is achieved at intermediate values of functional diversity and increasing functional diversity further result in a shallow decline of species richness. Main conclusions: The deconstruction of the global lizard assemblage along multiple ecological axes offers a new way to conceive lizard diversity patterns. It suggests that local lizard richness mostly increases when species belonging to particular ecological strategies become hyper-diverse there, and not because more ecological types are present in the most species rich localities. Thus maximum richness and maximum ecological diversity do not overlap. These results shed light on the global richness pattern of lizards, and highlight previously unidentified spatial patterns in understudied functional groups

Real-world effectiveness of caplacizumab vs the standard of care in immune thrombotic thrombocytopenic purpura

Immune thrombotic thrombocytopenic purpura (iTTP) is a thrombotic microangiopathy caused by anti-ADAMTS13 antibodies. Caplacizumab is approved for adults with an acute episode of iTTP in conjunction with plasma exchange (PEX) and immunosuppression. The objective of this study was to analyze and compare the safety and efficacy of caplacizumab vs the standard of care and assess the effect of the concomitant use of rituximab. A retrospective study from the Spanish TTP Registry of patients treated with caplacizumab vs those who did not receive it was conducted. A total of 155 patients with iTTP (77 caplacizumab, 78 no caplacizumab) were included. Patients initially treated with caplacizumab had fewer exacerbations (4.5% vs 20.5%; P <.05) and less refractoriness (4.5% vs 14.1%; P <.05) than those who were not treated. Time to clinical response was shorter when caplacizumab was used as initial treatment vs caplacizumab used after refractoriness or exacerbation. The multivariate analysis showed that its use in the first 3 days after PEX was associated with a lower number of PEX (odds ratio, 7.5; CI, 2.3-12.7; P <.05) and days of hospitalization (odds ratio, 11.2; CI, 5.6-16.9; P <.001) compared with standard therapy. There was no difference in time to clinical remission in patients treated with caplacizumab compared with the use of rituximab. No severe adverse event was described in the caplacizumab group. In summary, caplacizumab reduced exacerbations and refractoriness compared with standard of care regimens. When administered within the first 3 days after PEX, it also provided a faster clinical response, reducing hospitalization time and the need for PEX

Environmental and Genetic Determinants of Colony Morphology in Yeast

Nutrient stresses trigger a variety of developmental switches in the budding yeast Saccharomyces cerevisiae. One of the least understood of such responses is the development of complex colony morphology, characterized by intricate, organized, and strain-specific patterns of colony growth and architecture. The genetic bases of this phenotype and the key environmental signals involved in its induction have heretofore remained poorly understood. By surveying multiple strain backgrounds and a large number of growth conditions, we show that limitation for fermentable carbon sources coupled with a rich nitrogen source is the primary trigger for the colony morphology response in budding yeast. Using knockout mutants and transposon-mediated mutagenesis, we demonstrate that two key signaling networks regulating this response are the filamentous growth MAP kinase cascade and the Ras-cAMP-PKA pathway. We further show synergistic epistasis between Rim15, a kinase involved in integration of nutrient signals, and other genes in these pathways. Ploidy, mating-type, and genotype-by-environment interactions also appear to play a role in the controlling colony morphology. Our study highlights the high degree of network reuse in this model eukaryote; yeast use the same core signaling pathways in multiple contexts to integrate information about environmental and physiological states and generate diverse developmental outputs

A joint physics and radiobiology DREAM team vision - towards better response prediction models to advance radiotherapy

Radiotherapy developed empirically through experience balancing tumour control and normal tissue toxicities. Early simple mathematical models formalized this practical knowledge and enabled effective cancer treatment to date. Remarkable advances in technology, computing, and experimental biology now create opportunities to incorporate this knowledge into enhanced computational models. The ESTRO DREAM (Dose Response, Experiment, Analysis, Modelling) workshop brought together experts across disciplines to pursue the vision of personalized radiotherapy for optimal outcomes through advanced modelling. The ultimate vision is leveraging quantitative models dynamically during therapy to ultimately achieve truly adaptive and biologically guided radiotherapy at the population as well as individual patient-based levels. This requires the generation of models that inform response-based adaptations, individually optimized delivery and enable biological monitoring to provide decision support to clinicians. The goal is expanding to models that can drive the realization of personalized therapy for optimal outcomes. This position paper provides their propositions that describe how innovations in biology, physics, mathematics, and data science including AI could inform models and improve predictions. It consolidates the DREAM team's consensus on scientific priorities and organizational requirements. Scientifically, it stresses the need for rigorous, multifaceted model development, comprehensive validation and clinical applicability and significance. Organizationally, it reinforces the prerequisites of interdisciplinary research and collaboration between physicians, medical physicists, radiobiologists, and computational scientists throughout model development. Solely by a shared understanding of clinical needs, biological mechanisms, and computational methods, more informed models can be created. Future research environment and support must facilitate this integrative method of operation across multiple disciplines. [Abstract copyright: Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.

A new HLA-C*05 allele, HLA-C*05:156, characterized by full-length hemizygous sequencing

HLA-C*05:156 allele differs from C*05:01:01:02 by a nucleotide change in exon 2 at codon 9