"Sufficient life expectancy": an amazing inclusion criterion in cancer phase II-III trials

Three-month life expectancy as inclusion criterion for clinical trials in advanced pancreatic cancer: is it really a valid tool for patient selection?

PURPOSE

To analyze the 3-month life expectancy rate in pancreatic cancer (PC) patients treated within prospective trials from the German AIO study group.

PATIENTS AND METHODS

A pooled analysis was conducted for patients with advanced PC that were treated within five phase II/III studies conducted between 1997 and 2017 (Gem/Cis, Ro96, RC57, ACCEPT, RASH). The primary goal for the current report was to identify the actual 3-month survival rate, a standard inclusion criterion in oncology trials.

RESULTS

Overall, 912 patients were included, 83% had metastatic and 17% locally advanced PC; the estimated median overall survival (OS) was 7.1 months. Twenty-one percent of the participants survived < 3 months, with a range from 26% in RC57 to 15% in RASH. Significant predictors for not reaching 3-month OS were > 1 previous treatment line (p < 0.001) and performance status (p < 0.001).

CONCLUSIONS

Despite the definition of a life expectancy of > 3 months as a standard inclusion criterion in clinical trials for advanced PC, a significant proportion of study patients does not survive > 3 months.

TRIAL REGISTRATION NUMBERS

NCT00440167 (AIO-PK0104), NCT01729481 (RASH), NCT01728818 (ACCEPT).

Re-Evaluating Eligibility Criteria for Oncology Clinical Trials: Analysis of Investigational New Drug Applications in 2015

Clinical trial eligibility criteria are necessary to define the patient population under study and improve trial safety. However, there are concerns that eligibility criteria for cancer clinical trials are too restrictive and limit patient enrollment in clinical trials. Recently, there have been initiatives to re-examine and modernize eligibility criteria for oncology clinical trials. To assess current eligibility requirements for cancer clinical trials, we have conducted a comprehensive review of eligibility criteria for commercial investigational new drug clinical trial applications submitted to the US Food and Drug Administration Office of Hematology and Oncology Products in 2015. Our findings suggest that eligibility criteria for current cancer clinical trials tend to narrowly define the study population and limit the study to lower-risk patients, which may not be reflective of the greater patient population outside of the study. We discuss potential areas for expanding eligibility criteria to include more patients in clinical trials and design options for clinical trials incorporating expanded eligibility criteria. The broadening of clinical trial eligibility criteria can be considered to better reflect the real-world patient population, improve clinical trial participation, and increase patient access to new investigational treatments.

PRognostic factor of Early Death In phase II Trials or the end of 'sufficient life expectancy' as an inclusion criterion? (PREDIT model)

Background

Optimizing patient selection is a necessary step to design better clinical trials. ‘Life expectancy’ is a frequent inclusion criterion in phase II trial protocols, a measure that is subjective and often difficult to estimate. The aim of this study was to identify factors associated with early death in patients included in phase II studies.

Methods

We retrospectively collected medical records of patients with advanced solid tumors included in phase II trials in two French Comprehensive Cancer Centers (Bordeaux, Center 1 set; Lille, Center 2 set). We analyzed patients’ baseline characteristics. Predictive factors associated with early death (mortality at 3 months) were identified by logistic regression. We built a model (PREDIT, PRognostic factor of Early Death In phase II Trials) based on prognostic factors isolated from the final multivariate model.

Results

Center 1 and 2 sets included 303 and 227 patients, respectively. Patients from Center 1 and 2 sets differed in tumor site, urological (26 % vs 15 %) and gastrointestinal (18 % vs 28 %) and in lung metastasis incidence (10 % vs 49 %). Overall survival (OS) at 3 months was 88 % (95 % CI [83.5; 91.0], Center 1 set) and 91 % (95 % CI [86.7; 94.2], Center 2 set). Presence of a ‘life expectancy’ inclusion criterion did not improve the 3-month OS (HR 0.6, 95 % CI [0.2; 1.2], p = 0.2325). Independent factors of early death were an ECOG score of 2 (OR 13.3, 95%CI [4.1; 43.4]), hyperleukocytosis (OR 5.5, 95 % CI [1.9; 16.3]) and anemia (OR 2.8, 95 % CI [1.1; 7.1]). Same predictive factors but with different association levels were found in the Center 2 set. Using the Center 1 set, ROC analysis shows a good discrimination to predict early death (AUC: 0.89 at 3 months and 0.86 at 6 months).

Conclusions

Risk modeling in two independent cancer populations based on simple clinical parameters showed that baseline ECOG of 2, hyperleukocytosis and anemia are strong early-death predictive factors. This model allows identifying patients who may not benefit from a phase II trial investigational drug and may, therefore, represent a helpful tool to select patients for phase II trial entry.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-016-2819-7) contains supplementary material, which is available to authorized users.

An inflammation based score can optimize the selection of patients with advanced cancer considered for early phase clinical trials

BACKGROUND

Adequate organ function and good performance status (PS) are common eligibility criteria for phase I trials. As inflammation is pathogenic and prognostic in cancer we investigated the prognostic performance of inflammation-based indices including the neutrophil (NLR) and platelet to lymphocyte ratio (PLR).

METHODS

We studied inflammatory scores in 118 unselected referrals. NLR normalization was recalculated at disease reassessment. Each variable was assessed for progression-free (PFS) and overall survival (OS) on uni- and multivariate analyses and tested for 90 days survival (90DS) prediction using receiving operator curves (ROC).

RESULTS

We included 118 patients with median OS 4.4 months, 23% PS>1. LDH≥450 and NLR≥5 were multivariate predictors of OS (p<0.001). NLR normalization predicted for longer OS (p<0.001) and PFS (p<0.05). PS and NLR ranked as most accurate predictors of both 90DS with area under ROC values of 0.66 and 0.64, and OS with c-score of 0.69 and 0.60. The combination of NLR+PS increased prognostic accuracy to 0.72. The NLR was externally validated in a cohort of 126 subjects.

CONCLUSIONS

We identified the NLR as a validated and objective index to improve patient selection for experimental therapies, with its normalization following treatment predicting for a survival benefit of 7 months. Prospective validation of the NLR is warranted.

Prediction of early death among patients enrolled in phase I trials: development and validation of a new model based on platelet count and albumin

Background:

Selecting patients with ‘sufficient life expectancy’ for Phase I oncology trials remains challenging. The Royal Marsden Hospital Score (RMS) previously identified high-risk patients as those with ⩾2 of the following: albumin <35 g l⁻¹; LDH > upper limit of normal; >2 metastatic sites. This study developed an alternative prognostic model, and compared its performance with that of the RMS.

Methods:

The primary end point was the 90-day mortality rate. The new model was developed from the same database as RMS, but it used Chi-squared Automatic Interaction Detection (CHAID). The ROC characteristics of both methods were then validated in an independent database of 324 patients enrolled in European Organization on Research and Treatment of Cancer Phase I trials of cytotoxic agents between 2000 and 2009.

Results:

The CHAID method identified high-risk patients as those with albumin <33 g l⁻¹ or ⩾33 g l⁻¹, but platelet counts ⩾400.000 mm⁻³. In the validation data set, the rates of correctly classified patients were 0.79 vs 0.67 for the CHAID model and RMS, respectively. The negative predictive values (NPV) were similar for the CHAID model and RMS.

Conclusion:

The CHAID model and RMS provided a similarly high level of NPV, but the CHAID model gave a better accuracy in the validation set. Both CHAID model and RMS may improve the screening process in phase I trials.