Karyometry in the estimation of nuclear population in pulmonary carcinomas

Modeling Resistance and Recurrence Patterns of Combined Targeted-Chemoradiotherapy Predicts Benefit of Shorter Induction Period

Optimal integration of molecularly targeted therapies, such as tyrosine kinase inhibitors (TKI), with concurrent chemotherapy and radiation (CRT) to improve outcomes in genotype-defined cancers remains a current challenge in clinical settings. Important questions regarding optimal scheduling and length of induction period for neoadjuvant use of targeted agents remain unsolved and vary among clinical trial protocols. Here, we develop and validate a biomathematical framework encompassing drug resistance and radiobiology to simulate patterns of local versus distant recurrences in a non-small cell lung cancer (NSCLC) population with mutated EGFR receiving TKIs and CRT. Our model predicted that targeted induction before CRT, an approach currently being tested in clinical trials, may render adjuvant targeted therapy less effective due to proliferation of drug-resistant cancer cells when using very long induction periods. Furthermore, simulations not only demonstrated the competing effects of drug-resistant cell expansion versus overall tumor regression as a function of induction length, but also directly estimated the probability of observing an improvement in progression-free survival at a given cohort size. We thus demonstrate that such stochastic biological simulations have the potential to quantitatively inform the design of multimodality clinical trials in genotype-defined cancers. SIGNIFICANCE: A biomathematical framework based on fundamental principles of evolution and radiobiology for in silico clinical trial design allows clinicians to optimize administration of TKIs before chemoradiotherapy in oncogene-driven NSCLC.

Prediction of Treatment Response for Combined Chemo- and Radiation Therapy for Non-Small Cell Lung Cancer Patients Using a Bio-Mathematical Model

The goal of this work was to develop a mathematical model to predict Kaplan-Meier survival curves for chemotherapy combined with radiation in Non-Small Cell Lung Cancer patients for use in clinical trial design. The Gompertz model was used to describe tumor growth, radiation effect was simulated by the linear-quadratic model with an α/β-ratio of 10, and chemotherapy effect was based on the log-cell kill model. To account for repopulation during treatment, we considered two independent methods: 1) kickoff-repopulation using exponential growth with a decreased volume doubling time, or 2) Gompertz-repopulation using the gradually accelerating growth rate with tumor shrinkage. The input parameters were independently estimated by fitting to the SEER database for untreated tumors, RTOG-8808 for radiation only, and RTOG-9410 for sequential chemo-radiation. Applying the model, the benefit from concurrent chemo-radiation comparing to sequential for stage III patients was predicted to be a 6.6% and 6.2% improvement in overall survival for 3 and 5-years respectively, comparing well to the 5.3% and 4.5% observed in RTOG-9410. In summary, a mathematical model was developed to model tumor growth over extended periods of time, and can be used for the optimization of combined chemo-radiation scheduling and sequencing.

Quantitative nuclear texture features analysis confirms WHO classification 2004 for lung carcinomas

The purpose of this study was to discriminate the main subsets of lung carcinomas of the WHO classification of 2004 by nuclear chromatin texture feature analysis. Our collective comprised 56 typical and 19 atypical carcinoids, 37 small-cell carcinomas, 15 large-cell neuroendocrine carcinomas, 42 adenocarcinomas, and 26 squamous cell carcinomas. After Feulgen staining, cell nuclei were automatically measured using a high-resolution image analyser (CytoSavant Oncometrics, Vancouver, BC, Canada). Texture features describing the granularity and the compactness of the nuclear chromatin were extracted for calculation of classification rules, which allowed the discrimination of different tumor groups. By applying the classification rule that described the granularity of the nuclear chromatin (defined by four different parameters) small-cell and non-small-cell lung carcinoma could correctly be discriminated in 93%. No significant discrimination was possible between the different subtypes of large-cell carcinomas, including large-cell neuroendocrine carcinoma. When using compactness of chromatin (defined by four texture parameters) as a means of discrimination, carcinoids and non-small-cell lung carcinomas were correctly distinguished in 92%. No significant discrimination between neuroendocrine tumors was achieved though. Our findings are in accordance with the new WHO classification of 2004: neuroendocrine tumors of the lung are now classified according only to their mitotic counts and presence of necrosis but not by their morphology; their discrimination by the means of nuclear image analysis is not sufficient and therefore not appropriate any longer.