Nivolumab plus Cabozantinib versus Sunitinib for Advanced Renal-Cell Carcinoma

Cancer Vaccines for Genitourinary Tumors: Recent Progresses and Future Possibilities

“…The principal results have been obtained in the urological field among RCC and UC. In the mRCC, ICIs prolonged survival as single agents in pre-treated patients and combinations with other ICIs or TKIs in first line [1][2][3][4][5][6][7]. In the mUC, ICIs were superior to chemotherapy in the second-line setting; in the first line, pembrolizumab and atezolizumab showed superiority to chemotherapy for PD-L1 positive cisplatin-unfit patients, whereas the combination of ICIs and chemotherapy did not confer a significant survival advantage [8][9][10][11][12].…”

“…Also, the combination of vaccines and chemotherapy has been inadequately tested, as for many years the incompatibility of these two pharmacological classes has been postulated, and some trials were prematurely stopped without reaching the accrual or even due to an increased death rate [67,69,113,114]. Regarding TKIs, it is known that they exhibit immunomodulatory properties, such as increased tumor infiltration of T-cells and reduction of the production of anti-inflammatory mediators, explaining the success of combinations with ICIs also among GU tumors [1][2][3][4][5]115]. However, TKIs also exert immunosuppressive properties, such as reducing the production and function of T/NK cells and inducing the production of IL-10 [115].…”

“…Immunotherapy has represented a breakthrough therapy for many cancer subtypes in the last years. Among genitourinary (GU) neoplasms, the urothelial carcinoma (UC) and the renal cell carcinoma (RCC) have benefitted mainly from immune checkpoint inhibitors (ICIs) both as single agents and in combination with other ICIs or tyrosine kinase inhibitors (TKIs) [1][2][3][4][5][6][7][8][9][10][11][12][13]. However, in prostate cancer (PCa), ICIs have shown limited efficacy primarily due to an immunologically 'cold' and immunosuppressive tumor microenvironment (TME) [15][16][17][18].…”

Making Patient-Specific Treatment Decisions Using Prognostic Variables and Utilities of Clinical Outcomes

“…In actual RCT data, subgroups 1–4 ( Figure 2 ) are by far the most commonly encountered scenarios, whereas patterns such as that of subgroup 5, in which the subgroup HR estimate is incompatible with the overall treatment effect, are exceedingly rare. For example, when looking at first-line immunotherapy combinations for mccRCC, all the forest plots of subgroups from every major publication of phase 3 RCT trials published to date correspond to one of the scenarios represented by subgroups 1–4, and thus they do not provide any conclusive evidence of subgroup differences on the estimation scale [ 27 , 28 , 29 , 30 , 31 , 32 ]. The authors of the STAMPEDE phase 3 RCT, which established the addition of abiraterone to androgen deprivation therapy as a treatment option for locally advanced or metastatic prostate cancer, estimated effects in many implausible subgroups before finally identifying one that more closely resembled subgroup 5 than subgroups 1–4 [ 33 ].…”

“…As a result, a fundamental assumption behind most statistical models used in clinical drug development is that predictive subgroup effects can safely be ignored, whereas prognostic information should always be incorporated to increase the efficiency of inferences from the fitted model [ 10 ]. Indeed, the statistical models used for all primary endpoint analyses for the phase 3 RCTs of first-line immunotherapy combinations in mccRCC did not incorporate any predictive subgroup effects but accounted for prognostic subgroup information, such as the IMDC risk [ 27 , 28 , 29 , 30 , 31 , 32 ]. As we demonstrate below, prognostic subgroup information can be used similarly to make patient-specific inferences using strategies informed by the “risk-modeling” approach described by the Predictive Approaches to Treatment effect Heterogeneity (PATH) consensus statement [ 37 ].…”