Immune checkpoint inhibitors (ICIs) are commonly used to treat patients with advanced urothelial cancer. However, a significant number of patients do not respond to ICI, and the lack of validated predictive biomarkers impedes the success of the ICI strategy alone or in combination with chemotherapy or targeted therapies. In addition, some patients experience potentially severe adverse events with limited clinical benefit. Therefore, identifying biomarkers of response to ICI is crucial to guide treatment decisions. The most evaluated biomarkers to date are programmed death ligand 1 expression, microsatellite instability/defective mismatch repair phenotype, and tumor mutational burden. Other emerging biomarkers, such as circulating tumor DNA and microbiota, require evaluation in clinical trials. This review aims to examine these biomarkers for ICI response in urothelial cancer and assess their analytical and clinical validation.

Few prognostic factors have been identified in patients with metastatic urothelial carcinoma (mUC) treated with immune checkpoint inhibitors (ICIs). The Lung Immune Prognostic Index (LIPI) was associated with clinical outcomes for ICIs in several tumor types. We aim to assess the value of the LIPI in patients with mUC treated with ICIs. A retrospective ICI cohort and a validation cohort (SAUL cohort) included, respectively, patients with mUC treated with ICI in 8 European centers (any line) and patients treated with atezolizumab in a second or further line. A chemotherapy-only cohort was also analyzed. The LIPI score was based on 2 factors, derived neutrophils/(leukocytes minus neutrophils) ratio (dNLR) > 3 and lactate dehydrogenase > upper limit of normal, and defined 3 prognostic groups. The association of LIPI with progression-free survival (PFS) and overall survival (OS) was assessed. In the ICI and SAUL cohorts, 137 and 541 patients were respectively analyzed. In the ICI cohort, mPFS and mOS were 3.6 mo (95% CI; 2.6–6.0) and 13.8 mo (95% CI; 11.5–23.2) whereas in the SAUL cohort the mPFS and mOS were 2.2 mo (95% CI; 2.1–2.3) and 8.7 mo (95% CI; 7.8–9.9) respectively. The LIPI classified the population of these cohorts in good (56%; 52%), intermediate (35%; 36%) and poor (9%; 12%) prognostic groups (values for the ICI and SAUL cohorts respectively). Poor LIPI was associated with a poorer OS in both cohorts: hazard ratio (HR) for the ICI cohort = 2.69 (95% CI; 1.24–5.84, p = 0.035); HR = 2. 89 for the SAUL cohort (CI 95%: 1.93–4.32, p < 0.0001). Similar results were found in the chemo cohort. The LIPI score allows to identify different subgroups in patients with good prognostis according to the Bellmunt score criteria, with a subset of patients with poorer outcomes having an mOS of 3.7 mo compared to the good and intermediate LIPI subgroups with mOS of 17.9 and 7.4 mo, respectively. The LIPI score was associated with survival in mUC patients treated by ICIs. Future prospective studies will be required to test the combination of Bellmunt score and the LIPI score as a more accurate prognosis tool.

Background: Advanced pancreatic ductal adenocarcinoma (aPDAC) is often accompanied by significant muscle mass loss, contributing to poor prognosis. SarcAPACaP, an ancillary study of the GERCOR‐APACaP phase III trial, evaluated the role of adapted physical activity (APA) in aPDAC Western patients receiving first‐line chemotherapy. The study aimed to assess (1) the potential impact of computed tomography (CT)–quantified muscle mass before and during treatments on health‐related quality of life (HRQoL) and overall survival (OS) and (2) the role of APA in mitigating muscle mass loss. Methods: In the APACaP trial, aPDAC patients with ECOG performance status (PS) 0–2 were randomized 1:1 to usual care including first‐line chemotherapy or usual care plus a 16‐week home‐based APA program. In the SarcAPACaP study, the surface muscular index (SMI) was determined from L3 CT scan slices. Two patient populations were analysed: those with CT scan available at baseline (modified[m] intent‐to‐treat [ITT]1‐W0) and those with CT scans available at both W0 and W16 (mITT2 W0–W16). Low muscle mass was defined by low SMI with SMI < 41 cm ² /m ² for women and < 43 and < 53 cm ² /m ² for men with body max index < 25.0 and ≥ 25.0 kg/m ² , respectively. Muscle loss was defined by the relative difference of SMI between W0 and W16 (100*[SMI W16–SMI W0]/SMI W0). In mITT2 W0–W16, patients were stratified into three groups based on the severity of muscle loss: none, moderate (0%–10%) and high (≥ 10%). Associations between muscle mass loss and OS, time until definitive deterioration (TUDD) of HRQoL and the effect of APA on loss of muscle mass were assessed. Results: Between October 2014 and May 2020, 313 patients were prospectively enrolled, with 225 in mITT1 W0 and 128 in mITT2 W0–W16, with 65 assigned to the APA arm. Both groups had similar baseline characteristics with comparable OS and TUDD. A low SMI at W0 was not associated with OS and TUDD of HRQoL in either group. Among mITT2 W0–W16 patients, high muscle mass loss ( n = 27) independently predicted OS ( p = 0.012) and showed a trend toward negatively affecting TUDD of HRQoL. Notably, APA did not mitigate muscle loss in our study population. Conclusions: Longitudinal muscle mass loss emerged as a predictive factor for both OS and HRQoL in aPDAC patients undergoing chemotherapy, while a low SMI at diagnosis did not provide prognostic value. APA did not impact muscle mass loss in this population.

Background: Immune checkpoint inhibitors (ICIs) improve overall survival (OS) in advanced/metastatic urothelial cancer (a/mUC) patients. Preliminary evidence suggests a prognostic role of inflammatory biomarkers in this setting. We aimed to develop a disease-specific prognostic inflammatory index for a/mUC patients on ICIs. Methods: Fifteen variables were retrospectively correlated with OS and progression-free survival (PFS) in a development (D, n = 264) and a validation (V, n = 132) cohort of platinum-pretreated a/mUC pts receiving ICIs at L2 or further line. A nomogram and inflammatory prognostic index (U-IPI) were developed. The index was also tested in a control cohort of patients treated with chemotherapy only (C, n = 114). Results: The strongest predictors of OS were baseline platelet/lymphocyte (PLR) and neutrophil/lymphocyte (NLR) ratios, and lactate dehydrogenase (LDH), NLR, and albumin changes at 4 weeks. These were used to build the U-IPI, which can distinctly classify patients into good or poor response groups. The nomogram scoring is significant for PFS and OS (p < 0.001 in the D, V, and combined cohorts) for the immunotherapy (IO) cohort, but not for the control cohort. Conclusions: The lack of a baseline systemic inflammatory profile and the absence of early serum inflammatory biomarker changes are associated with significantly better outcomes on ICIs in a/mUC pts. The U-IPI is an easily applicable dynamic prognostic tool for PFS and OS, allowing for the early identification of a sub-group with dismal outcomes that would not benefit from ICIs, while distinguishing another that draws an important benefit.