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Forecasting how climate change will impact biological systems represents a grand challenge for biologists. However, climate change biology lacks an effective framework for anticipating and resolving uncertainty. Here, we introduce the concept of climate change wildcards: biological or bioclimatic processes with a high degree of uncertainty and a large impact on our ability to address the biotic consequences of climate change. Wildcards may occur at multiple points in the progression of research—from understanding, to predicting, to forecasting biological responses. Our understanding of biological responses is limited by the components and processes we exclude to make research tractable. Our ability to predict biological responses often requires integration between biological levels of organization, across multiple stressors, and from specific cases to general systems. However, these types of integration can be dramatically affected by, respectively, differences between biological levels in their critical points, nonadditivity of the effects of different stressors, and historical and geographic contingency. Finally, our ability to forecast biological responses to climate change requires incorporating climatic projections in bioclimatic models. Such forecasts are vulnerable to the compounding of biological and climatic uncertainty, especially when biological responses occur in novel areas of bioclimatic parameter space. Both biological responses and climate change are dynamic processes; the potential of biological systems to be buffered against or rescued from the effects of climate change depends on the relative timing of biological and climatic effects—one of the least predictable aspects of both systems. In sum, our framework identifies stress points in the research process where we should anticipate and forestall wildcards. Focusing on universal currencies, like energy and elements, and universal structures, like functional traits and ecological networks, will improve our ability to generalize results. Most importantly, by modeling and communicating uncertainty, climate change biology can identify critical foci for future research. [ABSTRACT FROM AUTHOR]

Circulating tumor DNA (ctDNA) analysis may improve early-stage breast cancer treatment via non-invasive tumor burden assessment. To investigate subtype-specific differences in the clinical significance and biology of ctDNA shedding, we perform serial personalized ctDNA analysis in hormone receptor (HR)-positive/HER2-negative breast cancer and triple-negative breast cancer (TNBC) patients receiving neoadjuvant chemotherapy (NAC) in the I-SPY2 trial. ctDNA positivity rates before, during, and after NAC are higher in TNBC than in HR-positive/HER2-negative breast cancer patients. Early clearance of ctDNA 3 weeks after treatment initiation predicts a favorable response to NAC in TNBC only. Whereas ctDNA positivity associates with reduced distant recurrence-free survival in both subtypes. Conversely, ctDNA negativity after NAC correlates with improved outcomes, even in patients with extensive residual cancer. Pretreatment tumor mRNA profiling reveals associations between ctDNA shedding and cell cycle and immune-associated signaling. On the basis of these findings, the I-SPY2 trial will prospectively test ctDNA for utility in redirecting therapy to improve response and prognosis. [Display omitted] • Early clearance of ctDNA in triple-negative patients associates with good response • ctDNA dynamics during neoadjuvant chemotherapy predict clinical outcomes • ctDNA negativity associates with improved survival despite having residual cancer • Expression analysis reveals pathways associated with ctDNA shedding Magbanua et al. examine the dynamics of ctDNA in plasma of high-risk early-stage breast cancer patients receiving neoadjuvant chemotherapy. Understanding the predictive and prognostic value of ctDNA and biology of ctDNA shedding in different breast cancer subtypes can inform the use of ctDNA for treatment selection to improve patient outcomes. [ABSTRACT FROM AUTHOR]