A pan-cancer clinical platform to predict immunotherapy outcomes and prioritize immuno-oncology combinations in early-phase trials.

BACKGROUND: Immunotherapy is effective, but current biomarkers for patient selection have proven modest sensitivity. Here, we developed VIGex, an optimized gene signature based on the expression level of 12 genes involved in immune response with RNA sequencing. METHODS: We implemented VIGex using the nCounter platform (Nanostring) on a large clinical cohort encompassing 909 tumor samples across 45 tumor types. VIGex was developed as a continuous variable, with cutoffs selected to detect three main categories (hot, intermediate-cold and cold) based on the different inflammatory status of the tumor microenvironment. FINDINGS: Hot tumors had the highest VIGex scores and exhibited an increased abundance of tumor-infiltrating lymphocytes as compared with the intermediate-cold and cold. VIGex scores varied depending on tumor origin and anatomic site of metastases, with liver metastases showing an immunosuppressive tumor microenvironment. The predictive power of VIGex-Hot was observed in a cohort of 98 refractory solid tumor from patients treated in early-phase immunotherapy trials and its clinical performance was confirmed through an extensive metanalysis across 13 clinically annotated gene expression datasets from 877 patients treated with immunotherapy agents. Last, we generated a pan-cancer biomarker platform that integrates VIGex categories with the expression levels of immunotherapy targets under development in early-phase clinical trials. CONCLUSIONS: Our results support the clinical utility of VIGex as a tool to aid clinicians for patient selection and personalized immunotherapy interventions. FUNDING: BBVA Foundation; 202-2021 Division of Medical Oncology and Hematology Fellowship award; Princess Margaret Cancer Center.

Phase I prognostic online (PIPO): A web tool to improve patient selection for oncology early phase clinical trials.

PURPOSE: Patient selection in phase 1 clinical trials (Ph1t) continues to be a challenge. The aim of this study was to develop a user-friendly prognostic calculator for predicting overall survival (OS) outcomes in patients to be included in Ph1t with immune checkpoint inhibitors (ICIs) or targeted agents (TAs) based on clinical parameters assessed at baseline. METHODS: Using a training cohort with consecutive patients from the VHIO phase 1 unit, we constructed a prognostic model to predict median OS (mOS) as a primary endpoint and 3-month (3m) OS rate as a secondary endpoint. The model was validated in an internal cohort after temporal data splitting and represented as a web application. RESULTS: We recruited 799 patients (training and validation sets, 558 and 241, respectively). Median follow-up was 21.2 months (m), mOS was 10.2 m (95% CI, 9.3-12.7) for ICIs cohort and 7.7 m (95% CI, 6.6-8.6) for TAs cohort. In the multivariable analysis, six prognostic variables were independently associated with OS - ECOG, number of metastatic sites, presence of liver metastases, derived neutrophils/(leukocytes minus neutrophils) ratio [dNLR], albumin and lactate dehydrogenase (LDH) levels. The phase 1 prognostic online (PIPO) calculator showed adequate discrimination and calibration performance for OS, with C-statistics of 0.71 (95% CI 0.64-0.78) in the validation set. The overall accuracy of the model for 3m OS prediction was 87.2% (95% CI 85%-90%). CONCLUSIONS: PIPO is a user-friendly objective and interactive tool to calculate specific survival probabilities for each patient before enrolment in a Ph1t. The tool is available at https://pipo.vhio.net/.

Capturing Hyperprogressive Disease with Immune-Checkpoint Inhibitors Using RECIST 1.1 Criteria.

PURPOSE: Most hyperprogression disease (HPD) definitions are based on tumor growth rate (TGR). However, there is still no consensus on how to evaluate this phenomenon. PATIENTS AND METHODS: We investigated two independent cohorts of patients with advanced solid tumors treated in phase I trials with (i) programmed cell death 1 (PD-1)/PD-L1 antibodies in monotherapy or combination and (ii) targeted agents (TA) in unapproved indications. A Response Evaluation Criteria in Solid Tumors (RECIST) 1.1-based definition of hyperprogression was developed. The primary endpoint was the assessment of the rate of HPD in patients treated with ICIs or TAs using both criteria (RECIST and TGR) and the impact on overall survival (OS) in patients who achieved PD as best response. RESULTS: Among 270 evaluable patients treated with PD-1/PD-L1 inhibitors, 29 PD-1/PD-L1-treated patients (10.7%) had HPD by RECIST definition. This group had a significantly lower OS (median of 5.23 months; 95% CI, 3.97-6.45) when compared with the non-HPD progressor group (median, 7.33 months; 95% CI, 4.53-10.12; HR = 1.73, 95% CI, 1.05-2.85; P = 0.04). In a subset of 221 evaluable patients, 14 (6.3%) were categorized as HPD using TGR criteria, differences in median OS (mOS) between this group (mOS 4.2 months; 95% IC, 2.07-6.33) and non-HPD progressors (n = 44) by TGR criteria (mOS 6.27 months; 95% CI, 3.88-8.67) were not statistically significant (HR 1.4, 95% IC, 0.70-2.77; P = 0.346). Among 239 evaluable patients treated with TAs, 26 (10.9%) were classified as having HPD by RECIST and 14 using TGR criteria in a subset of patients. No differences in OS were observed between HPD and non-HPD progressors treated with TAs. CONCLUSIONS: HPD measured by TGR or by RECIST was observed in both cohorts of patients; however, in our series, there was an impact on survival only in the immune-checkpoint inhibitor cohort when evaluated by RECIST. We propose a new way to capture HPD using RECIST criteria that is intuitive and easy to use in daily clinical practice.