Predictive biomarkers are essential components to this effort to get the right drug to the right patient at the right time. biomarkers are essential to distinguish between responders and nonresponders to optimize treatment across the population. In this problem of the Journal, Bandini et al. (3) used data from 105 individuals to construct a model to predict pT0N0 in response to pembrolizumb. pT0N0 has been validated like a surrogate marker for overall survival in the case of cisplatin-based chemotherapy (4); however, it is not known whether pT0N0 has the same association with overall survival after neoadjuvant immunotherapy. Longer follow-up and additional clinical tests in the neoadjuvant space will hopefully elucidate the association between pT0N0 and overall survival for individuals treated with neoadjuvant immune checkpoint inhibitor therapy prior to cystectomy. The predictive model that was developed in the current article incorporates pretreatment medical T stage and 2 biomarkers that had been prespecified candidates at study inception: programmed cell-death ligand (PD-L1) protein manifestation, in both tumor and infiltrating immune cells, measured as a continuous variable from the combined positive score with the DAKO 22C3 antibody and tumor mutational burden (TMB) measured as a continuous variable. Predictive biomarkers in malignancy medicine are often targets of the restorative agent: HER2 for trastuzumab in breast and gastric malignancy (5), Mebhydrolin napadisylate mutated estimated glomerular filtration rate in non-small cell lung malignancy for erlotinib and additional small molecule inhibitors of this kinase (4), Mebhydrolin napadisylate and fibroblast growth element receptors 2 and 3 mutations or fusionsfor the inhibitors of those receptor kinases. In some cases, the predictive marker is not the direct target of the drug but a component of the same pathway [BRAF + MEK inhibitors for BRAF-mutated melanoma (6)] Mebhydrolin napadisylate or a component of a pathway having a synthetic lethal relationship with the prospective [poly(ADP-ribose) polymerase inhibitors for tumors with loss of function of homologous recombination DNA restoration components such as and (7)]. Biomarkers can be tumor intrinsic or derived from the microenvironment. It is noteworthy that the 2 2 molecular biomarkers, PD-L1 and TMB, that form the basis of the PURE-01 predictive model are linked to the proposed mechanism of action for pembrolizumab. TMB is usually tumor intrinsic, whereas the combined positive score for PD-L1 is derived from both tumor and infiltrating cell expression. The PURE-01 investigators also used broad-based screening to identify novel candidate predictive biomarkers and signatures. More than 400 genes known to be mutated or rearranged in cancer were sequenced in tumor specimens using the commercially available FoundationOne platform (8). None of these selected genes were predictive of pT0N0. In a separate publication, the PURE-01 investigators showed that immune gene expression signatures were correlated with pT0N0 (9). Of interest, this association was not seen in a separate cohort of patients treated with neoadjuvant platinum-based chemotherapy. Study of the genes contained within the immune signature panels may lead to target discovery for future immunotherapeutic approaches. The FoundationOne genomic mutation and the Rabbit polyclonal to ACSS3 gene expression panels each contain a limited number of genes. Whole-exome and whole-genome sequencing could identify additional genes whose expression or mutation might be incorporated into predictive models of checkpoint inhibitor response and could lead to target discovery. High TMB is thought to facilitate immune checkpoint inhibitor response via the generation of neoantigen peptides presented to T lymphocytes (10). TMB predicted response to immune checkpoint inhibitors in PURE-01 as well as in other studies and tumor types. However, total TMB may not be the most accurate measure of neoantigen load. There are data that frameshift mutations generate more plentiful and potent neoantigens than point mutations (11). A more qualitative assessment of TMB and neoantigen content could one day surpass the predictive power of the total TMB in predicting response to checkpoint inhibitor therapy. The predictive model presented by Bandini et al. (3) performed well, with a concordance statistic (C index) of 0.77 (95% confidence interval = 0.68 to 0.86). The authors have helpfully included an Excel spreadsheet tool for modeling pT0N0. This calculator is usually freely available as an online web resource at https://marco-bandini-md-sanraffaele.shinyapps.io/real01/. The neoadjuvant ABACUS study of the PD-L1 monoclonal antibody atezolizumab, with a design similar to PURE-01, observed a comparable pT0N0 rate of 31% (12). However, there was no statistically significant.