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The majority of oncogenic drivers are intracellular proteins, constraining their immunotherapeutic targeting to mutated peptides (neoantigens) presented by individual human leukocyte antigen (HLA) allotypes1. However, most cancers have a modest mutational burden that is insufficient for generating responses using neoantigen-based therapies2,3. Neuroblastoma is a paediatric cancer that harbours few mutations and is instead driven by epigenetically deregulated transcriptional networks4. Here we show that the neuroblastoma immunopeptidome is enriched with peptides derived from proteins essential for tumorigenesis. We focused on targeting the unmutated peptide QYNPIRTTF discovered on HLA-A*24:02, which is derived from the neuroblastoma-dependency gene and master transcriptional regulator PHOX2B. To target QYNPIRTTF, we developed peptide-centric chimeric antigen receptors (PC-CARs) through a counter panning strategy using predicted potentially cross-reactive peptides. We further proposed that PC-CARs can recognize peptides on additional HLA allotypes when presenting a similar overall molecular surface. Informed by our computational modelling results, we show that PHOX2B PC-CARs also recognize QYNPIRTTF presented by HLA-A*23:01, the most common non-A2 allele in people with African ancestry. Finally, we demonstrate potent and specific killing of neuroblastoma cells expressing these HLAs in vitro and complete tumour regression in mice. These data suggest that PC-CARs have the potential to expand the pool of immunotherapeutic targets to include non-immunogenic intracellular oncoproteins and allow targeting through additional HLA allotypes in a clinical setting.

Human activities are altering the structure of ecosystems, compromising the benefits they provide to nature and people. Effective conservation actions and management under ongoing global change rely on a better understanding of socio-ecological patterns and processes across broad spatiotemporal scales. Both macroecology and conservation science contribute to this improved understanding and, while they have different scopes, these disciplines have become increasingly interconnected over time. Here we describe examples of how macroecology has contributed to conservation science, and how conservation science can motivate further macroecological developments and applications. We identify challenges and untapped potential to further strengthen the links between these two disciplines. Major macroecological contributions include developing ecological theory, providing methodologies useful for biodiversity assessments and projections, making data more accessible and addressing knowledge gaps. These contributions have played a major role in the development of conservation science, and have supported outreach to policy makers, media, and the public. Nonetheless, a pure macroecological lens is limited to inform conservation decisions, particularly in local contexts, which frequently leads to the misuse of macroecological analyses for conservation applications, misunderstandings of research outputs, and skepticism among conservation practitioners and scientists. We propose possible solutions to overcome these challenges and strengthen links between macroecology and conservation science, including a stronger focus on ecological mechanisms and predictive approaches, and the creation of hybrid journals and meetings. Finally, we suggest new avenues for macroecological research that would further benefit conservation science.

Assessing the extinction risk of species based on the International Union for Conservation of Nature (IUCN) Red List (RL) is key to guiding conservation policies and reducing biodiversity loss. This process is resource demanding, however, and requires continuous updating, which becomes increasingly difficult as new species are added to the RL. Automatic methods, such as comparative analyses used to predict species RL category, can be an efficient alternative to keep assessments up to date. Using amphibians as a study group, we predicted which species are more likely to change their RL category and thus should be prioritized for reassessment. We used species biological traits, environmental variables, and proxies of climate and land‐use change as predictors of RL category. We produced an ensemble prediction of IUCN RL category for each species by combining 4 different model algorithms: cumulative link models, phylogenetic generalized least squares, random forests, and neural networks. By comparing RL categories with the ensemble prediction and accounting for uncertainty among model algorithms, we identified species that should be prioritized for future reassessment based on the mismatch between predicted and observed values. The most important predicting variables across models were species’ range size and spatial configuration of the range, biological traits, climate change, and land‐use change. We compared our proposed prioritization index and the predicted RL changes with independent IUCN RL reassessments and found high performance of both the prioritization and the predicted directionality of changes in RL categories. Ensemble modeling of RL category is a promising tool for prioritizing species for reassessment while accounting for models’ uncertainty. This approach is broadly applicable to all taxa on the IUCN RL and to regional and national assessments and may improve allocation of the limited human and economic resources available to maintain an up‐to‐date IUCN RL. [ABSTRACT FROM AUTHOR]

Copyright of Conservation Biology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The majority of oncogenic drivers are intracellular proteins, thus constraining their immunotherapeutic targeting to mutated peptides (neoantigens) presented by individual human leukocyte antigen (HLA) allotypes.sup.1. However, most cancers have a modest mutational burden that is insufficient to generate responses using neoantigen-based therapies.sup.2,3. Neuroblastoma is a paediatric cancer that harbours few mutations and is instead driven by epigenetically deregulated transcriptional networks.sup.4. Here we show that the neuroblastoma immunopeptidome is enriched with peptides derived from proteins that are essential for tumourigenesis and focus on targeting the unmutated peptide QYNPIRTTF, discovered on HLA-A*24:02, which is derived from the neuroblastoma dependency gene and master transcriptional regulator PHOX2B. To target QYNPIRTTF, we developed peptide-centric chimeric antigen receptors (CARs) using a counter-panning strategy with predicted potentially cross-reactive peptides. We further hypothesized that peptide-centric CARs could recognize peptides on additional HLA allotypes when presented in a similar manner. Informed by computational modelling, we showed that PHOX2B peptide-centric CARs also recognize QYNPIRTTF presented by HLA-A*23:01 and the highly divergent HLA-B*14:02. Finally, we demonstrated potent and specific killing of neuroblastoma cells expressing these HLAs in vitro and complete tumour regression in mice. These data suggest that peptide-centric CARs have the potential to vastly expand the pool of immunotherapeutic targets to include non-immunogenic intracellular oncoproteins and widen the population of patients who would benefit from such therapy by breaking conventional HLA restriction. Targeting peptides from unmutated cancer drivers that are expressed in tumours but not in normal tissues using peptide-centric chimeric antigen receptors shows potential as treatment for cancer., Author(s): Mark Yarmarkovich [sup.1] , Quinlen F. Marshall [sup.1] , John M. Warrington [sup.1] , Rasika Premaratne [sup.2] , Alvin Farrel [sup.1] [sup.3] , David Groff [sup.1] , Wei Li [...]

Reducing the rate of global biodiversity loss is a major challenge facing humanity.sup.1, as the consequences of biological annihilation would be irreversible for humankind.sup.2-4. Although the ongoing degradation of ecosystems.sup.5,6 and the extinction of species that comprise them.sup.7,8 are now well-documented, little is known about the role that remaining wilderness areas have in mitigating the global biodiversity crisis. Here we model the persistence probability of biodiversity, combining habitat condition with spatial variation in species composition, to show that retaining these remaining wilderness areas is essential for the international conservation agenda. Wilderness areas act as a buffer against species loss, as the extinction risk for species within wilderness communities is--on average--less than half that of species in non-wilderness communities. Although all wilderness areas have an intrinsic conservation value.sup.9,10, we identify the areas on every continent that make the highest relative contribution to the persistence of biodiversity. Alarmingly, these areas--in which habitat loss would have a more-marked effect on biodiversity--are poorly protected. Given globally high rates of wilderness loss.sup.10, these areas urgently require targeted protection to ensure the long-term persistence of biodiversity, alongside efforts to protect and restore more-degraded environments. Wilderness areas with minimal levels of human disturbance promote the persistence of biodiversity by acting as buffers against species loss, and therefore represent key targets for environmental protection., Author(s): Moreno Di Marco [sup.1] [sup.2] , Simon Ferrier [sup.3] , Tom D. Harwood [sup.3] , Andrew J. Hoskins [sup.4] , James E. M. Watson [sup.5] [sup.6] Author Affiliations: (1) [...]

Copyright of Conservation Biology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)