Summary Nomograms are commonly used tools to estimate prognosis in oncology and medicine. With the ability to generate an individual numerical probability of a clinical event by integrating diverse prognostic and determinant variables, nomograms fulfill our desire for biologically and clinically integrated models and our drive towards personalized medicine. Rapid computation through user friendly digital interfaces, together with increased accuracy, and more easily understood prognoses compared to conventional staging, allow for seamless incorporation of nomogram derived prognosis to aid in clinical decision making. This has lead to the ubiquitous appearance of nomograms on the internet and in medical journals, and increasing nomogram use by patients and physicians alike. However, the statistical foundations of nomogram construction, their precise interpretation, and evidence supporting their use is commonly misunderstood, leading to an under appreciation of the inherent uncertainties regarding nomogram use. We provide a systematic, practical approach to evaluating and comprehending nomogram derived prognoses, with particular emphasis on clarifying common misconceptions and highlighting limitations.

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Highlights d Proteomic profiles of extracellular vesicles and particles (EVPs) from 426 human samples d Identification of pan-EVP markers d Characterization of tumor-derived EVP markers in human tissues and plasma d EVP proteins can be useful for cancer detection and determining cancer type

Checkpoint blockade immunotherapies enable the host immune system to recognize and destroy tumor cells1. Their clinical activity has been correlated with activated T-cell recognition of neoantigens, which are tumor-specific, mutated peptides presented on the surface of cancer cells2,3. Here, we present a fitness model for tumors based on immune interactions of neoantigens that predicts response to immunotherapy. Two main factors determine neoantigen fitness: its likelihood of presentation by the major histocompatibility complex (MHC) and its subsequent T-cell recognition. We estimate these two components using a neoantigen’s relative MHC binding affinity and a non-linear dependence on its sequence similarity to known antigens. To describe the evolution of a heterogeneous tumor, we evaluate its fitness as a weighted effect of dominant neoantigens in the tumor’s subclones. Our model predicts survival in anti- CTLA-4 treated melanoma patients4,5 and anti-PD-1 treated lung cancer patients6. Importantly, low-fitness neoantigens identified by our method may be leveraged for developing novel immunotherapies. By using an immune fitness model to study immunotherapy, we reveal broad similarities between the evolution of tumors and rapidly evolving pathogens7–9.