Combining immunotherapy with an epidrug in squamous cell carcinomas of different locations: rationale and design of the PEVO basket trial.

Squamous cell carcinomas (SCCs) are among the most frequent solid tumors in humans. SCCs, related or not to the human papillomavirus, share common molecular features. Immunotherapies, and specifically immune checkpoint inhibitors, have been shown to improve overall survival in multiple cancer types, including SCCs. However, only a minority of patients experience a durable response with immunotherapy. Epigenetic modulation plays a major role in escaping tumor immunosurveillance and confers resistance to immune checkpoint inhibitors. Preclinical evidence suggests that modulating the epigenome might improve the efficacy of immunotherapy. We herein review the preclinical and the clinical rationale for combining immunotherapy with an epidrug, and detail the design of PEVOsq, a basket clinical trial combining pembrolizumab with vorinostat, a histone deacetylase inhibitor, in patients with SCCs of different locations. Sequential blood and tumor sampling will be collected in order to identify predictive and pharmacodynamics biomarkers of efficacy of the combination. We also present how clinical and biological data will be managed with the aim to enable the development of a prospective integrative platform to allow secure and controlled access to the project data as well as further exploitations.

Outcomes from second-line therapy in long-term responders to first-line tyrosine kinase inhibitor in clear-cell metastatic renal cell carcinoma.

BACKGROUND: Although sequential targeted therapy is standard in patients with metastatic clear-cell renal cell carcinoma (m-ccRCC), the choice of drugs and optimal administration sequence have yet to be established. The objective of this study was to explore whether it is preferable to rechallenge a long-term responder to a first-line tyrosine kinase inhibitor (TKI) with a TKI or whether to switch to a mammalian target of rapamycin inhibitor (mTORi); to determine whether second-line treatment response depends on duration of first-line response (TD1). PATIENTS AND METHODS: Retrospective multicenter study (2004-2011) of 241 consecutive mRCC patients (clear-cell histology) who received a first-line TKI for ≥6 months followed by a second-line TKI (n = 118) or mTORi (n = 123). END POINTS: Progression-free survival (PFS) and time-to-treatment failure (TTF) on second-line therapy. Multivariable full-model: second-line drug, TD1, ECOG-PS before first- and second-line, best objective response (first-line), Fuhrman grade, number of metastatic sites, and presence of bone metastases. Adjustment covariable: International mRCC Database Consortium (IMDC) risk score. Multiple propensity score and missing data methods were used. Any correlation between first-line and second-line PFS was investigated using censored quantile regression models (CQRM). RESULTS: Sequence effect in the overall cohort was in favor of the TKI-TKI sequence over the TKI-mTORi sequence on using TD1 as continuous covariable (HR ≈ 0.75 for PFS and TTF). TKI-TKI superiority was attributed in large part to the 11-22 month (TD1) subgroup of patients which displayed significantly better outcomes [HR ≈ 0.5; median PFS (months): 9.4 (5.9-12.2) versus 3.9 (3.0-5.5), P = 0.003; TTF(months): 8.0 (5.5-11.0) versus 3.6 (3.0-4.6), P = 0.009]. Upon full CQRM, long-term second-line responders were more likely to have received a second TKI than an mTORi and to have been long-term responders to first-line TKI. CONCLUSIONS: m-ccRCC patients who remained on first-line TKI between 11 and 22 months benefited from a TKI rechallenge rather than from second-line mTORi.

Development and validation of a prognostic model in patients with metastatic renal cell carcinoma treated with sunitinib: a European collaboration.

BACKGROUND: Accurate prediction of outcome for metastatic renal cell carcinoma (mRCC) patients receiving targeted therapy is essential. Most of the available models have been developed in patients treated with cytokines, while most of them are fairly complex, including at least five factors. We developed and externally validated a simple model for overall survival (OS) in mRCC. We also studied the recently validated International Database Consortium (IDC) model in our data sets. METHODS: The development cohort included 170 mRCC patients treated with sunitinib. The final prognostic model was selected by uni- and multivariate Cox regression analyses. Risk groups were defined by the number of risk factors and by the 25th and 75th percentiles of the model's prognostic index distribution. The model was validated using an independent data set of 266 mRCC patients (validation cohort) treated with the same agent. RESULTS: Eastern Co-operative Oncology Group (ECOG) performance status (PS), time from diagnosis of RCC and number of metastatic sites were included in the final model. Median OS of patients with 1, 2 and 3 risk factors were: 24.7, 12.8 and 5.9 months, respectively, whereas median OS was not reached for patients with 0 risk factors. Concordance (C) index for internal validation was 0.712, whereas C-index for external validation was 0.634, due to differences in survival especially in poor-risk populations between the two cohorts. Predictive performance of the model was improved after recalibration. Application of the mRCC International Database Consortium (IDC) model resulted in a C-index of 0.574 in the development and 0.576 in the validation cohorts (lower than those recently reported for this model). Predictive ability was also improved after recalibration in this analysis. Risk stratification according to IDC model showed more similar outcomes across the development and validation cohorts compared with our model. CONCLUSION: Our model provides a simple prognostic tool in mRCC patients treated with a targeted agent. It had similar performance with the IDC model, which, however, produced more consistent survival results across the development and validation cohorts. The predictive ability of both models was lower than that suggested by internal validation (our model) or recent published data (IDC model), due to differences between observed and predicted survival among intermediate and poor-risk patients. Our results highlight the importance of external validation and the need for further refinement of existing prognostic models.