FGFR3 is a prognostic and predictive marker and is a validated therapeutic target in urothelial bladder cancer. Its utility as a marker and target in the context of immunotherapy is incompletely understood. We review the role of FGFR3 in bladder cancer and discuss preclinical and clinical clues of its effectiveness as a patient selection factor and therapeutic target in the era of immunotherapy.

Funding: This trial was funded and sponsored by the University of Chicago. The institution had no role in the design, execution, or analysis of this trial. There was no industry involvement in COVIDOSE.

Objectives To identify molecular characteristics of keratosis of unknown significance and to nominate pathways of molecular progression to oral cancer. Our work could provide a rationale for monitoring and treating these lesions definitively. Methods Patients with oral leukoplakia were eligible for our prospective observational study. We correlated alterations in cancer‐associated genes with clinical and histopathologic variables (keratosis of unknown significance vs. moderate‐to‐severe dysplasia) and compared these alterations to a previously molecularly characterized oral cancer population. Results Of 20 enrolled patients, 13 (65%) had evidence of keratosis of unknown significance, while seven (35%) had dysplasia. Nine patients (45%) developed oral cancer (4/13 with keratosis of unknown significance, 5/7 with dysplasia). At a median follow‐up of 67 (range 22–144) months, median overall survival was significantly shorter for patients with dysplasia (hazard ratio 0.11, p = .02). KMT2C and TP53 alterations were most frequent (75% and 35%, respectively). There were molecular similarities between keratosis of unknown significance and dysplasia patients, with no significant differences in mutational frequency among genes with ≥15% rate of alteration. Conclusions Among patients with leukoplakia, both patients with keratosis of unknown significance and patients with dysplasia developed oral cancer. Molecular alterations between these two groups were similar at this sample size.

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Rising cancer care costs impose financial burdens on health systems. Applying artificial intelligence to diagnostic algorithms may reduce testing costs and avoid wasteful therapy-related expenditures. To evaluate the financial and clinical impact of incorporating artificial intelligence-based determination of mismatch repair/microsatellite instability status into the first-line metastatic colorectal carcinoma setting, we developed a deterministic model to compare eight testing strategies: A) next-generation sequencing alone, B) high-sensitivity polymerase chain reaction or immunohistochemistry panel alone, C) high-specificity panel alone, D) high-specificity artificial intelligence alone, E) high-sensitivity artificial intelligence followed by next generation sequencing, F) high-specificity artificial intelligence followed by next-generation sequencing, G) high-sensitivity artificial intelligence and high-sensitivity panel, and H) high-sensitivity artificial intelligence and high-specificity panel. We used a hypothetical, nationally representative, population-based sample of individuals receiving first-line treatment for de novo metastatic colorectal cancer (N = 32,549) in the United States. Model inputs were derived from secondary research (peer-reviewed literature and Medicare data). We estimated the population-level diagnostic costs and clinical implications for each testing strategy. The testing strategy that resulted in the greatest project cost savings (including testing and first-line drug cost) compared to next-generation sequencing alone in newly-diagnosed metastatic colorectal cancer was using high-sensitivity artificial intelligence followed by confirmatory high-specificity polymerase chain reaction or immunohistochemistry panel for patients testing negative by artificial intelligence ($400 million, 12.9%). The high-specificity artificial intelligence-only strategy resulted in the most favorable clinical impact, with 97% diagnostic accuracy in guiding genotype-directed treatment and average time to treatment initiation of less than one day. Artificial intelligence has the potential to reduce both time to treatment initiation and costs in the metastatic colorectal cancer setting without meaningfully sacrificing diagnostic accuracy. We expect the artificial intelligence value proposition to improve in coming years, with increasing diagnostic accuracy and decreasing costs of processing power. To extract maximal value from the technology, health systems should evaluate integrating diagnostic histopathologic artificial intelligence into institutional protocols, perhaps in place of other genotyping methodologies.