Discrepancies in metabolomic biomarker identification from patient-derived lung cancer revealed by combined variation in data pre-treatment and imputation methods

Evaluation of Lung Cancer Patient Response to First-Line Chemotherapy by Integration of Tumor Core Biopsy Metabolomics with Multiscale Modeling

The standard of care for intermediate (Stage II) and advanced (Stages III and IV) non-small cell lung cancer (NSCLC) involves chemotherapy with taxane/platinum derivatives, with or without radiation. Ideally, patients would be screened a priori to allow non-responders to be initially treated with second-line therapies. This evaluation is non-trivial, however, since tumors behave as complex multiscale systems. To address this need, this study employs a multiscale modeling approach to evaluate first-line chemotherapy response of individual patient tumors based on metabolomic analysis of tumor core biopsies obtained during routine clinical evaluation. Model parameters were calculated for a patient cohort as a function of these metabolomic profiles, previously obtained from high-resolution 2DLC-MS/MS analysis. Evaluation metrics were defined to classify patients as Disease-Control (DC) [encompassing complete-response (CR), partial-response (PR), and stable-disease (SD)] and Progressive-Disease (PD) following first-line chemotherapy. Response was simulated for each patient and compared to actual response. The results show that patient classifications were significantly separated from each other, and also when grouped as DC vs. PD and as CR/PR vs. SD/PD, by fraction of initial tumor radius metric at 6 days post simulated bolus drug injection. This study shows that patient first-line chemotherapy response can in principle be evaluated from multiscale modeling integrated with tumor tissue metabolomic data, offering a first step towards individualized lung cancer treatment prognosis.

Precise prediction of multiple anticancer drug efficacy using multi target regression and support vector regression analysis

BACKGROUND AND OBJECTIVES

The prediction of multiple drug efficacies using machine learning prediction techniques based on clinical and molecular attributes of tumors is a new approach in the field of precision medicine of oncology. The selection of suitable and effective therapeutic drugs among the potential drugs is performed computationally considering the tumor features. In this study, we developed and validated machine learning models to predict the efficacy of five anti-cancer drugs according to the clinical and molecular attributes of 30 oral squamous cell carcinoma (OSCC) cohorts. This sounds a bit odd - consider: Ranking of the drugs was achieved using their apoptotic priming.

METHODS

We developed multiple drug efficacy prediction models based on three types of tumor characteristics by applying machine learning methods, including multi-target regression (MTR) and support vector regression (SVR). The prediction accuracy of existing machine learning methods was enhanced by introducing novel pre-processing techniques to develop Enhanced MTR (E_MTR), Enhanced Log-based MTR (EL_MTR), Enhanced Multi-target SVR (EM_SVR), and Enhanced Log-based Multi-target SVR (ELM_SVR). As a unique capability, ELM_SVR and EL_MTR rank the drugs based on their predicted efficacy. All the drug efficacy prediction models were built using OSCC real samples and theoretical samples. The best model was selected was based on dataset size and evaluation metrics, such as error terms, residuals and parameter tuning, and cross-validated (CV) using 30 real samples and 340 theoretical samples.

RESULTS

When 30 real tumor samples were used for the train-test and CV methods, MTR models predicted the efficacy with less error than SVR models. Comparatively, using 340 theoretical samples for the train-test and CV methods, though MTR improved the performance, SVR predicted the efficacy with zero error. We found that, for small samples, the proposed MTR provided a 0.01 difference between actual apoptotic priming and predicted priming of five drugs. For large samples, the predicted values by the proposed SVR had a difference of 0.00001. The error terms (Actual vs. Predicted) also reveal that the enhanced log model is suitable when MTR is applied. Meanwhile, the enhanced model is suitable for SVR learning for multiple drug efficacy prediction. It was found that the predicted ranks of the drugs based on the multi-targeted efficacy prediction exactly match the actual rankings.

CONCLUSION

We developed efficient statistical and machine learning models using MTR and SVR analysis for anticancer drug efficacy, which will be useful in the field of precision medicine to choose the most suitable drugs in personalized manner. The performance results of the proposed enhanced ranking techniques are described as follows: i) EL_MTR is the best to predict multiple anticancer drug efficacies and improve the accuracy of ranking drugs, irrespective of sample size; and ii) ELM_SVR performs better than other MTR models with a large sample size and precise ranking process.

Evaluation of disease staging and chemotherapeutic response in non-small cell lung cancer from patient tumor-derived metabolomic data

OBJECTIVES

Despite extensive effort, the search for clinically-relevant metabolite biomarkers for early detection, disease monitoring, and outcome prediction in lung cancer remains unfulfilled. Although biofluid evaluation has been explored, the complexity inherent in metabolite data and the dynamic discrepancy between metabolites in biofluids vs. tumor tissue have prevented conclusive results. This proof-of-concept study explored models predictive of staging and chemotherapy response based on metabolomic analysis of fresh, patient-derived non-small cell lung cancer (NSCLC) core biopsies.

MATERIALS AND METHODS

Samples (n = 36) were evaluated with high-resolution 2DLC-MS/MS and ¹³C-glucose enrichment, and the data were comprehensively analyzed with machine learning techniques. Patients were categorized as Disease-Control (DC) [encompassing complete-response (CR), partial-response (PR), and stable-disease (SD)] and Progressive-Disease (PD) in terms of first-line chemotherapy. Four major types of learning methods (partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), artificial neural networks, and random forests (RF)) were applied to differentiate between positive (DC and CR/PR) and poor (PD and SD/PD) responses, and between stage I/II/III and stage IV disease. Models were trained with forward feature selection based on variable importance and tested on validation subsets.

RESULTS

The models predicted patient classifications in the validation subsets with AUC (95 % CI): DC vs. PD (SVM), 0.970(0.961-0.979); CR/PR vs. SD/PD (PLS-DA), 0.880(0.865-0.895); stage I/II/III vs. IV (SVM), 0.902(0.880-0.924). Highest performing model was SVM for DC vs. PD (balanced accuracy = 0.92; kappa = 0.74).

CONCLUSION

This study illustrates a comprehensive evaluation of patient tumor-specific metabolic profiles, with the potential to identify disease stage and predict response to first-line chemotherapy.