Modeling Tumor Dynamics and Overall Survival in Advanced Non–Small-Cell Lung Cancer Treated with Erlotinib

C-Reactive Protein as an Early Predictor of Efficacy in Advanced Non-Small-Cell Lung Cancer Patients: A Tumor Dynamics-Biomarker Modeling Framework

In oncology, longitudinal biomarkers reflecting the patient's status and disease evolution can offer reliable predictions of the patient's response to treatment and prognosis. By leveraging clinical data in patients with advanced non-small-cell lung cancer receiving first-line chemotherapy, we aimed to develop a framework combining anticancer drug exposure, tumor dynamics (RECIST criteria), and C-reactive protein (CRP) concentrations, using nonlinear mixed-effects models, to evaluate and quantify by means of parametric time-to-event models the significance of early longitudinal predictors of progression-free survival (PFS) and overall survival (OS). Tumor dynamics was characterized by a tumor size (TS) model accounting for anticancer drug exposure and development of drug resistance. CRP concentrations over time were characterized by a turnover model. An x-fold change in TS from baseline linearly affected CRP production. CRP concentration at treatment cycle 3 (day 42) and the difference between CRP concentration at treatment cycles 3 and 2 were the strongest predictors of PFS and OS. Measuring longitudinal CRP allows for the monitoring of inflammatory levels and, along with its reduction across treatment cycles, presents a promising prognostic marker. This framework could be applied to other treatment modalities such as immunotherapies or targeted therapies allowing the timely identification of patients at risk of early progression and/or short survival to spare them unnecessary toxicities and provide alternative treatment decisions.

A comprehensive regulatory and industry review of modeling and simulation practices in oncology clinical drug development

Exposure-response (E-R) analyses are an integral component in the development of oncology products. Characterizing the relationship between drug exposure metrics and response allows the sponsor to use modeling and simulation to address both internal and external drug development questions (e.g., optimal dose, frequency of administration, dose adjustments for special populations). This white paper is the output of an industry-government collaboration among scientists with broad experience in E-R modeling as part of regulatory submissions. The goal of this white paper is to provide guidance on what the preferred methods for E-R analysis in oncology clinical drug development are and what metrics of exposure should be considered.

Radiomics Based Bayesian Inversion Method for Prediction of Cancer and Pathological Stage

OBJECTIVE

To develop a Bayesian inversion framework on longitudinal chest CT scans which can perform efficient multi-class classification of lung cancer.

METHODS

While the unavailability of large number of training medical images impedes the performance of lung cancer classifiers, the purpose built deep networks have not performed well in multi-class classification. The presented framework employs particle filtering approach to address the non-linear behaviour of radiomic features towards benign and cancerous (stages I, II, III, IV) nodules and performs efficient multi-class classification (benign, early stage cancer, advanced stage cancer) in terms of posterior probability function. A joint likelihood function incorporating diagnostic radiomic features is formulated which can compute likelihood of cancer and its pathological stage. The proposed research study also investigates and validates diagnostic features to discriminate accurately between early stage (I, II) and advanced stage (III, IV) cancer.

RESULTS

The proposed stochastic framework achieved 86% accuracy on the benchmark database which is better than the other prominent cancer detection methods.

CONCLUSION

The presented classification framework can aid radiologists in accurate interpretation of lung CT images at an early stage and can lead to timely medical treatment of cancer patients.

PET/CT-Based Response Evaluation in Cancer-a Systematic Review of Design Issues

Positron emission tomography/x-ray computed tomography (PET/CT) has long been discussed as a promising modality for response evaluation in cancer. When designing respective clinical trials, several design issues have to be addressed, especially the number/timing of PET/CT scans, the approach for quantifying metabolic activity, and the final translation of measurements into a rule. It is unclear how well these issues have been tackled in quest of an optimised use of PET/CT in response evaluation. Medline via Ovid and Science Citation Index via Web of Science were systematically searched for articles from 2015 on cancer patients scanned with PET/CT before and during/after treatment. Reports were categorised as being either developmental or evaluative, i.e. focusing on either the establishment or the evaluation of a rule discriminating responders from non-responders. Of 124 included papers, 112 (90 %) were accuracy and/or prognostic studies; the remainder were response-curve studies. No randomised controlled trials were found. Most studies were prospective (62 %) and from single centres (85 %); median number of patients was 38.5 (range 5-354). Most (69 %) of the studies employed only one post-baseline scan. Quantification was mainly based on SUVmax (91 %), while change over time was most frequently used to combine measurements into a rule (79 %). Half of the reports were categorised as developmental, the other half evaluative. Most development studies assessed only one element (35/62, 56 %), most frequently the choice of cut-off points (25/62, 40 %). In summary, the majority of studies did not address the essential open issues in establishing PET/CT for response evaluation. Reasonably sized multicentre studies are needed to systematically compare the many different options when using PET/CT for response evaluation.

Correlations of 18F-FDG and 18F-FLT uptake on PET with Ki-67 expression in patients with lung cancer: a meta-analysis

Background Positron emission tomography (PET) imaging using the radiotracers 18F-fluorodeoxyglucose (FDG) or 18F-fluorothymidine (FLT) has been proposed as imaging biomarkers of cell proliferation. Purpose To explore the correlations of FDG and FLT uptake with the Ki-67 labeling index in patients with lung cancer. Material and Methods Major databases were systematically searched for all relevant literature published in English. The correlation coefficient (rho) and its 95% confidence interval (CI) of individual studies were meta-analyzed using a random-effects model. The sources of heterogeneity were explored by subgroup analyses. Results Twenty-seven articles involving 1213 patients were included in this meta-analysis, comprising 22 studies for FDG uptake/Ki-67 expression correlation and eight for FLT uptake/Ki-67 expression correlation. The pooled rho values for 18F-FDG/Ki-67 correlation and 18F-FLT/Ki-67 correlation were 0.45 (95% CI, 0.41-0.50) and 0.65 (95% CI, 0.56-0.73), respectively, which indicated a moderate correlation for the former and a significant one for the latter. Although the subgroup analyses based on study design, scanner, sample method, and Ki-67 labeling method did not significantly explain the heterogeneity, these factors were potential sources of heterogeneity. In lung cancer, the pooled SUVmax of FDG uptake was significantly higher than that of FLT uptake (7.59 versus 3.86, P < 0.05). In addition, compared to FDG, FLT showed higher specificity yet lower sensitivity for the diagnosis of pulmonary lesions. Conclusion Both 18F-FDG and 18F-FLT correlate significantly with the Ki-67 labeling index in pulmonary lesions, and the latter, with a stronger correlation, may be more reliable for assessing tumor cell proliferation in lung cancer.

Investigation of transrenal KRAS mutation in late stage NSCLC patients correlates to disease progression

PURPOSE

Using transrenal DNA to detect KRAS mutations in non-small cell lung cancer (NSCLC), the study addressed the clinical impact for longitudinal monitoring and prognostic value for disease outcome.

METHODS

Digital droplet PCR was used to detect the mutant DNA. A total of 200 NSCLC patients were recruited with varying molecular profiles. To ascertain the specificity of transrenal DNA to accurately profile the disease, primary tissues were compared. Subsequently, serial samplings were performed at different treatment cycles to gauge the predictive value.

RESULTS

Transrenal DNA was successfully detected in all 200 patients. Overall concordance rate for mutant KRAS DNA within urine specimens and primary tissue biopsies was 95% (k = 0.87; 95% CI: 0.82-0.95). Patients with positive results at baseline had lower median overall survival (OS) than the wildtype group. More importantly, longitudinal monitoring of urine specimens showed an increase in the quantity of transrenal DNA, which were highly associated with disease progression and outcome.

CONCLUSIONS

Our study showed a highly associative link to the patient's tumor KRAS profile. Monitoring its variations aided in stratifying patients with worse outcome. Urinary specimens that can be extracted non-invasively presents new opportunities to track patients with KRAS mutation undergoing therapy.

TLG-S criteria are superior to both EORTC and PERCIST for predicting outcomes in patients with metastatic lung adenocarcinoma treated with erlotinib

PURPOSE

In this retrospective review of prospectively collected data, we sought to investigate whether early FDG-PET assessment of treatment response based on total lesion glycolysis measured using a systemic approach (TLG-S) would be superior to either local assessment with EORTC (European Organization for Research and Treatment of Cancer) criteria or single-lesion assessment with PERCIST (PET Response Criteria in Solid Tumors) for predicting clinical outcomes in patients with metastatic lung adenocarcinoma treated with erlotinib. We also examined the effect of bone flares on tumor response evaluation by single-lesion assessment with PERCIST in patients with metastatic bone lesions.

METHODS

We performed a retrospective review of prospectively collected data from 23 patients with metastatic lung adenocarcinoma treated with erlotinib. All participants underwent FDG-PET imaging at baseline and on days 14 and 56 after completion of erlotinib treatment. In addition, diagnostic CT scans were performed at baseline and on day 56. FDG-PET response was assessed with TLG-S, EORTC, and PERCIST criteria. Response assessment based on RECIST 1.1 (Response Evaluation Criteria in Solid Tumors) from diagnostic CT imaging was used as the reference standard. Two-year progression-free survival (PFS) and overall survival (OS) served as the main outcome measures.

RESULTS

We identified 13 patients with bone metastases. Of these, four (31 %) with persistent bone uptake due to bone flares on day 14 were erroneously classified as non-responders according to the PERCIST criteria, but they were correctly classified as responders according to both the EORTC and TLG-S criteria. Patients who were classified as responders on day 14 based on TLG-S criteria had higher rates of 2-year PFS (26.7 % vs. 0 %, P = 0.007) and OS (40.0 % vs. 7.7 %, P = 0.018). Similar rates were observed in patients who showed a response on day 56 based on CT imaging according to the RECIST criteria. Patients classified as responders on day 14 according to the EORTC criteria on FDG-PET imaging had better rates of 2-year OS than did non-responders (36.4 % vs. 8.3 %, P = 0.015).

CONCLUSIONS

TLG-S criteria may be of greater help in predicting survival outcomes than other forms of assessment. Bone flares, which can interfere with the interpretation of treatment response based on PERCIST criteria, are not uncommon in patients with metastatic lung adenocarcinoma treated with erlotinib.

PK-PD modeling of individual lesion FDG-PET response to predict overall survival in patients with sunitinib-treated gastrointestinal stromal tumor

Pharmacometric models were developed to characterize the relationships between lesion-level tumor metabolic activity, as assessed by the maximum standardized uptake value (SUVmax) obtained on [(18)F]-fluorodeoxyglucose (FDG) positron emission tomography (PET), tumor size, and overall survival (OS) in 66 patients with gastrointestinal stromal tumor (GIST) treated with intermittent sunitinib. An indirect response model in which sunitinib stimulates tumor loss best described the typically rapid decrease in SUVmax during on-treatment periods and the recovery during off-treatment periods. Substantial interindividual and interlesion variability were identified in SUVmax baseline and drug sensitivity. A parametric time-to-event model identified the relative change in SUVmax at one week for the lesion with the most pronounced response as a better predictor of OS than tumor size. Based on the proposed modeling framework, early changes in FDG-PET response may serve as predictor for long-term outcome in sunitinib-treated GIST.

Bringing Model-Based Prediction to Oncology Clinical Practice: A Review of Pharmacometrics Principles and Applications

Despite much investment and progress, oncology is still an area with significant unmet medical needs, with new therapies and more effective use of current therapies needed. The emergent field of pharmacometrics combines principles from pharmacology (pharmacokinetics [PK] and pharmacodynamics [PD]), statistics, and computational modeling to support drug development and optimize the use of already marketed drugs. Although it has gained a role within drug development, its use in clinical practice remains scarce. The aim of the present study was to review the principal pharmacometric concepts and provide some examples of its use in oncology. Integrated population PK/PD/disease progression models as part of the pharmacometrics platform provide a powerful tool to predict outcomes so that the right dose can be given to the right patient to maximize drug efficacy and reduce drug toxicity. Population models often can be developed with routinely collected medical record data; therefore, we encourage the application of such models in the clinical setting by generating close collaborations between physicians and pharmacometricians.

IMPLICATIONS FOR PRACTICE

The present review details how the emerging field of pharmacometrics can integrate medical record data with predictive pharmacological and statistical models of drug response to optimize and individualize therapies. In order to make this routine practice in the clinic, greater awareness of the potential benefits of the field is required among clinicians, together with closer collaboration between pharmacometricians and clinicians to ensure the requisite data are collected in a suitable format for pharmacometrics analysis.

A Modeling and Simulation Framework for Adverse Events in Erlotinib-Treated Non-Small-Cell Lung Cancer Patients

Treatment with erlotinib, an epidermal growth factor receptor tyrosine kinase inhibitor used for treating non-small-cell lung cancer (NSCLC) and other cancers, is frequently associated with adverse events (AE). We present a modeling and simulation framework for the most common erlotinib-induced AE, rash, and diarrhea, providing insights into erlotinib toxicity. We used the framework to investigate the safety of high-dose erlotinib pulses proposed to limit acquired resistance while treating NSCLC. Continuous-time Markov models were developed using rash and diarrhea AE data from 39 NSCLC patients treated with erlotinib (150 mg/day). Exposure and different covariates were investigated as predictors of variability. Rash was also tested as a survival predictor. Models developed were used in a simulation analysis to compare the toxicities of different regimens, including the previously mentioned pulsed strategy. Probabilities of experiencing rash or diarrhea were found to be highest early during treatment. Rash, but not diarrhea, was positively correlated with erlotinib exposure. In contrast with some common understandings, radiotherapy decreased transitioning to higher rash grades by 81% (p < 0.01), and experiencing rash was not correlated with positive survival outcomes. Model simulations predicted that the proposed pulsed regimen (1600 mg/week + 50 mg/day remaining week days) results in a maximum of 20% of the patients suffering from severe rash throughout the treatment course in comparison to 12% when treated with standard dosing (150 mg/day). In conclusion, the framework demonstrated that radiotherapy attenuates erlotinib-induced rash, providing an opportunity to use radiotherapy and erlotinib together, and demonstrated the tolerability of high-dose pulses intended to address acquired resistance to erlotinib.