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Key Points Prostate cancer pathogenesis is dependent on signalling through the steroid nuclear hormone androgen receptor (AR), which is activated after binding of the androgen ligand testosterone or dihydrotestosterone. Ligand-bound AR translocates to the nucleus, where it serves to induce or repress gene expression through binding to chromatin at cis androgen response elements. Medical castration to substantially deplete serum testosterone is the mainstay of therapy for advanced prostate cancer that recurs following surgical removal of the prostate (prostatectomy) or radiotherapy. However, castration therapy is not curative, and patients will eventually progress to lethal castration-resistant prostate cancer (CRPC). Despite a castrate level of testosterone, CRPC almost uniformly remains dependent on AR signalling. Next-generation hormonal therapies for prostate cancer, abiraterone and enzalutamide, are now in widespread clinical use; abiraterone attacks AR signalling through inhibition of extra-gonadal androgen biosynthesis and enzalutamide interferes directly with androgen binding to AR. Resistance mechanisms to these drugs have been identified that result in restoration of AR signalling through gain-of-function AR mutations, upregulation of constitutively active AR splice variants or increased intratumoural androgen biosynthesis. Another resistance mechanism bypasses AR by switching to the related glucocorticoid receptor (GR) to maintain transcriptional regulation of a subset of the same genes. At resistance, a subset of patients are now presenting with low or no AR in their tumours, suggesting that evolution to complex genomic states completely independently of AR could increasingly become a cause for concern. Comprehensive analyses of late-stage CRPC are uncovering multiple genetic lesions in this patient cohort that indicate that it may eventually be possible to stratify patients based on the genomic profile of their cancer. These efforts will aid in clinical trial design and facilitate the use of rationally designed combination strategies to improve patient outcomes. This Review highlights emerging mechanisms of acquired resistance to agents targeting the androgen receptor in castration-resistant prostate cancer, which fall into the three broad categories of restored androgen receptor (AR) signalling, AR bypass signalling and complete AR independence. During the past 10 years, preclinical studies implicating sustained androgen receptor (AR) signalling as the primary driver of castration-resistant prostate cancer (CRPC) have led to the development of novel agents targeting the AR pathway that are now in widespread clinical use. These drugs prolong the survival of patients with late-stage prostate cancer but are not curative. In this Review, we highlight emerging mechanisms of acquired resistance to these contemporary therapies, which fall into the three broad categories of restored AR signalling, AR bypass signalling and complete AR independence. This diverse range of resistance mechanisms presents new challenges for long-term disease control, which may be addressable through early use of combination therapies guided by recent insights from genomic landscape studies of CRPC.

Results from the fully and biogeochemically coupled simulations in which CO2 increases at a rate of 1 % yr−1 (1pctCO2) from its preindustrial value are analyzed to quantify the magnitude of carbon–concentration and carbon–climate feedback parameters which measure the response of ocean and terrestrial carbon pools to changes in atmospheric CO2 concentration and the resulting change in global climate, respectively. The results are based on 11 comprehensive Earth system models from the most recent (sixth) Coupled Model Intercomparison Project (CMIP6) and compared with eight models from the fifth CMIP (CMIP5). The strength of the carbon–concentration feedback is of comparable magnitudes over land (mean ± standard deviation = 0.97 ± 0.40 PgC ppm−1) and ocean (0.79 ± 0.07 PgC ppm−1), while the carbon–climate feedback over land (−45.1 ± 50.6 PgC ∘C−1) is about 3 times larger than over ocean (−17.2 ± 5.0 PgC ∘C−1). The strength of both feedbacks is an order of magnitude more uncertain over land than over ocean as has been seen in existing studies. These values and their spread from 11 CMIP6 models have not changed significantly compared to CMIP5 models. The absolute values of feedback parameters are lower for land with models that include a representation of nitrogen cycle. The transient climate response to cumulative emissions (TCRE) from the 11 CMIP6 models considered here is 1.77 ± 0.37 ∘C EgC−1 and is similar to that found in CMIP5 models (1.63 ± 0.48 ∘C EgC−1) but with somewhat reduced model spread. The expressions for feedback parameters based on the fully and biogeochemically coupled configurations of the 1pctCO2 simulation are simplified when the small temperature change in the biogeochemically coupled simulation is ignored. Decomposition of the terms of these simplified expressions for the feedback parameters is used to gain insight into the reasons for differing responses among ocean and land carbon cycle models.

The terrestrial biosphere currently absorbs about 30% of anthropogenic CO 2 emissions. This carbon uptake over land results primarily from vegetation’s response to increasing atmospheric CO 2 but other factors also play a role. Here we show that since the 1930s increasing population densities and cropland area have decreased global area burned, consistent with the charcoal record and recent satellite-based observations. The associated reduced wildfire emissions from increase in cropland area do not enhance carbon uptake since natural vegetation that is spared burning was deforested anyway. However, reduction in fire CO 2 emissions due to fire suppression and landscape fragmentation associated with increases in population density is calculated to enhance land carbon uptake by 0.13 Pg C yr −1 , or ~19% of the global land carbon uptake (0.7 ± 0.6 Pg C yr −1 ), for the 1960–2009 period. These results identify reduction in global wildfire CO 2 emissions as yet another mechanism that is currently enhancing carbon uptake over land. Anthropogenic influences alter natural fire regimes in multiple ways but the resulting effect on the land carbon budget has not been quantified. Here the authors show that the reduction in global area burned and wildfire emissions due to anthropogenic influences is currently enhancing carbon uptake over land.

The magnitude and evolution of parameters that characterize feedbacks in the coupled carbon–climate system are compared across nine Earth system models (ESMs). The analysis is based on results from biogeochemically, radiatively, and fully coupled simulations in which CO₂ increases at a rate of 1% yr−1. These simulations are part of phase 5 of the Coupled Model Intercomparison Project (CMIP5). The CO₂ fluxes between the atmosphere and underlying land and ocean respond to changes in atmospheric CO₂ concentration and to changes in temperature and other climate variables. The carbon–concentration and carbon–climate feedback parameters characterize the response of the CO₂ flux between the atmosphere and the underlying surface to these changes. Feedback parameters are calculated using two different approaches. The two approaches are equivalent and either may be used to calculate the contribution of the feedback terms to diagnosed cumulative emissions. The contribution of carbon–concentration feedback to diagnosed cumulative emissions that are consistent with the 1% increasing CO₂ concentration scenario is about 4.5 times larger than the carbon–climate feedback. Differences in the modeled responses of the carbon budget to changes in CO₂ and temperature are seen to be 3–4 times larger for the land components compared to the ocean components of participating models. The feedback parameters depend on the state of the system as well the forcing scenario but nevertheless provide insight into the behavior of the coupled carbon–climate system and a useful common framework for comparing models.

In the context of phase 5 of the Coupled Model Intercomparison Project, most climate simulations use prescribed atmospheric CO₂ concentration and therefore do not interactively include the effect of carbon cycle feedbacks. However, the representative concentration pathway 8.5 (RCP8.5) scenario has additionally been run by earth system models with prescribed CO₂ emissions. This paper analyzes the climate projections of 11 earth system models (ESMs) that performed both emission-driven and concentration-driven RCP8.5 simulations. When forced by RCP8.5 CO₂ emissions, models simulate a large spread in atmospheric CO₂; the simulated 2100 concentrations range between 795 and 1145 ppm. Seven out of the 11 ESMs simulate a larger CO₂ (on average by 44 ppm, 985 ± 97 ppm by 2100) and hence higher radiative forcing (by 0.25 W m−2) when driven by CO₂ emissions than for the concentration-driven scenarios (941 ppm). However, most of these models already overestimate the present-day CO₂, with the present-day biases reasonably well correlated with future atmospheric concentrations’ departure from the prescribed concentration. The uncertainty in CO₂ projections is mainly attributable to uncertainties in the response of the land carbon cycle. As a result of simulated higher CO₂ concentrations than in the concentration-driven simulations, temperature projections are generally higher when ESMs are driven with CO₂ emissions. Global surface temperature change by 2100 (relative to present day) increased by 3.9° ± 0.9°C for the emission-driven simulations compared to 3.7° ± 0.7°C in the concentration-driven simulations. Although the lower ends are comparable in both sets of simulations, the highest climate projections are significantly warmer in the emission-driven simulations because of stronger carbon cycle feedbacks.

Afforestation, the conversion of croplands or marginal lands into forests, is considered one of the key climate-change mitigation strategies available to governments. Model simulations suggest that the temperature benefits of realistic afforestation efforts are marginal. Afforestation, the conversion of croplands or marginal lands into forests, results in the sequestration of carbon. As a result, afforestation is considered one of the key climate-change mitigation strategies available to governments by the United Nations 1 . However, forests are also less reflective than croplands, and the absorption of incoming solar radiation is greater over afforested areas. Afforestation can therefore result in net climate warming, particularly at high latitudes 2 , 3 , 4 , 5 . Here, we use a comprehensive Earth system model to assess the climate-change mitigation potential of five afforestation scenarios, with afforestation carried out gradually over a 50-year period. Complete (100%) and partial (50%) afforestation of the area occupied at present by crops leads to a reduced warming of around 0.45 and 0.25 °C respectively, during the period 2081–2100. Temperature benefits associated with more realistic global afforestation efforts, where less than 50% of cropland is converted, are expected to be even smaller, indicating that afforestation is not a substitute for reduced greenhouse-gas emissions. We also show that warming reductions per unit afforested area are around three times higher in the tropics than in the boreal and northern temperate regions, suggesting that avoided deforestation and continued afforestation in the tropics are effective forest-management strategies from a climate perspective.

Evapotranspiration (ET) is critical in linking global water, carbon and energy cycles. However, direct measurement of global terrestrial ET is not feasible. Here, we first reviewed the basic theory and state-of-the-art approaches for estimating global terrestrial ET, including remote-sensing-based physical models, machine-learning algorithms and land surface models (LSMs). We then utilized 4 remote-sensing-based physical models, 2 machine-learning algorithms and 14 LSMs to analyze the spatial and temporal variations in global terrestrial ET. The results showed that the ensemble means of annual global terrestrial ET estimated by these three categories of approaches agreed well, with values ranging from 589.6 mm/yr (6.56×10^4 cu.km/yr) to 617.1 mm/yr (6.87×10^4 cu.km/yr). For the period from 1982 to 2011, both the ensembles of remote-sensing-based physical models and machine-learning algorithms suggested increasing trends in global terrestrial ET (0.62 mm/sq.yr with a significance level of p<0.05 and 0.38 mm yr−2 with a significance level of p<0.05, respectively). In contrast, the ensemble mean of the LSMs showed no statistically significant change (0.23 mm/sq.yr, p>0.05), although many of the individual LSMs reproduced an increasing trend. Nevertheless, all 20 models used in this study showed that anthropogenic Earth greening had a positive role in increasing terrestrial ET. The concurrent small interannual variability, i.e., relative stability, found in all estimates of global terrestrial ET, suggests that a potential planetary boundary exists in regulating global terrestrial ET, with the value of this boundary being around 600 mm/yr. Uncertainties among approaches were identified in specific regions, particularly in the Amazon Basin and arid/semiarid regions. Improvements in parameterizing water stress and canopy dynamics, the utilization of new available satellite retrievals and deep-learning methods, and model–data fusion will advance our predictive understanding of global terrestrial ET.

Global net land carbon uptake or net biome production (NBP) has increased during recent decades 1 . Whether its temporal variability and autocorrelation have changed during this period, however, remains elusive, even though an increase in both could indicate an increased potential for a destabilized carbon sink 2 , 3 . Here, we investigate the trends and controls of net terrestrial carbon uptake and its temporal variability and autocorrelation from 1981 to 2018 using two atmospheric-inversion models, the amplitude of the seasonal cycle of atmospheric CO 2 concentration derived from nine monitoring stations distributed across the Pacific Ocean and dynamic global vegetation models. We find that annual NBP and its interdecadal variability increased globally whereas temporal autocorrelation decreased. We observe a separation of regions characterized by increasingly variable NBP, associated with warm regions and increasingly variable temperatures, lower and weaker positive trends in NBP and regions where NBP became stronger and less variable. Plant species richness presented a concave-down parabolic spatial relationship with NBP and its variability at the global scale whereas nitrogen deposition generally increased NBP. Increasing temperature and its increasing variability appear as the most important drivers of declining and increasingly variable NBP. Our results show increasing variability of NBP regionally that can be mostly attributed to climate change and that may point to destabilization of the coupled carbon–climate system. Increasing variability of net biome production over recent decades may be due to climate change and points to destabilization of the carbon–climate system.

The observed global net land carbon sink is captured by current land models. All models agree that atmospheric CO2 and nitrogen deposition driven gains in carbon stocks are partially offset by climate and land-use and land-cover change (LULCC) losses. However, there is a lack of consensus in the partitioning of the sink between vegetation and soil, where models do not even agree on the direction of change in carbon stocks over the past 60 years. This uncertainty is driven by plant productivity, allocation, and turnover response to atmospheric CO2 (and to a smaller extent to LULCC), and the response of soil to LULCC (and to a lesser extent climate). Overall, differences in turnover explain ~70% of model spread in both vegetation and soil carbon changes. Further analysis of internal plant and soil (individual pools) cycling is needed to reduce uncertainty in the controlling processes behind the global land carbon sink.

The standard of care treatment for muscle-invasive bladder cancer (MIBC) is radical cystectomy, which is typically preceded by neoadjuvant chemotherapy. However, the inability to assess minimal residual disease (MRD) noninvasively limits our ability to offer bladder-sparing treatment. Here, we sought to develop a liquid biopsy solution via urine tumor DNA (utDNA) analysis. We applied urine Cancer Personalized Profiling by Deep Sequencing (uCAPP-Seq), a targeted next-generation sequencing (NGS) method for detecting utDNA, to urine cell-free DNA (cfDNA) samples acquired between April 2019 and November 2020 on the day of curative-intent radical cystectomy from 42 patients with localized bladder cancer. The average age of patients was 69 years (range: 50 to 86), of whom 76% (32/42) were male, 64% (27/42) were smokers, and 76% (32/42) had a confirmed diagnosis of MIBC. Among MIBC patients, 59% (19/32) received neoadjuvant chemotherapy. utDNA variant calling was performed noninvasively without prior sequencing of tumor tissue. The overall utDNA level for each patient was represented by the non-silent mutation with the highest variant allele fraction after removing germline variants. Urine was similarly analyzed from 15 healthy adults. utDNA analysis revealed a median utDNA level of 0% in healthy adults and 2.4% in bladder cancer patients. When patients were classified as those who had residual disease detected in their surgical sample (n = 16) compared to those who achieved a pathologic complete response (pCR; n = 26), median utDNA levels were 4.3% vs. 0%, respectively (p = 0.002). Using an optimal utDNA threshold to define MRD detection, positive utDNA MRD detection was highly correlated with the absence of pCR (p < 0.001) with a sensitivity of 81% and specificity of 81%. Leave-one-out cross-validation applied to the prediction of pathologic response based on utDNA MRD detection in our cohort yielded a highly significant accuracy of 81% (p = 0.007). Moreover, utDNA MRD-positive patients exhibited significantly worse progression-free survival (PFS; HR = 7.4; 95% CI: 1.4-38.9; p = 0.02) compared to utDNA MRD-negative patients. Concordance between urine- and tumor-derived mutations, determined in 5 MIBC patients, was 85%. Tumor mutational burden (TMB) in utDNA MRD-positive patients was inferred from the number of non-silent mutations detected in urine cfDNA by applying a linear relationship derived from The Cancer Genome Atlas (TCGA) whole exome sequencing of 409 MIBC tumors. We suggest that about 58% of these patients with high inferred TMB might have been candidates for treatment with early immune checkpoint blockade. Study limitations included an analysis restricted only to single-nucleotide variants (SNVs), survival differences diminished by surgery, and a low number of DNA damage response (DRR) mutations detected after neoadjuvant chemotherapy at the MRD time point. utDNA MRD detection prior to curative-intent radical cystectomy for bladder cancer correlated significantly with pathologic response, which may help select patients for bladder-sparing treatment. utDNA MRD detection also correlated significantly with PFS. Furthermore, utDNA can be used to noninvasively infer TMB, which could facilitate personalized immunotherapy for bladder cancer in the future.