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BACKGROUND: Clinical trials are controlled patient studies aiming to objectively assess the effectiveness of treatment interventions. However, the average effectiveness observed at the group level does not directly apply to individual patients. Cancer clinical trials that include molecular profiling on the baseline tumor samples enable the discovery of treatment response-associated molecular features and the development of prediction models that help discriminate responders from non-responders in each treatment arm. Despite potential opportunities, there are many challenges associated with the predictive modeling of such data, as exemplified by the small sample size and large feature space. Advances in machine learning and artificial intelligence (AI) may provide new solutions to address these challenges. However, for machine learning and AI to have an impact, data needs to be carefully curated, high-quality, standardized, and easily accessible and understood by data scientists, who may not have the domain knowledge. METHODS: Here, we created a python package “ClinicalOmicsDB” to address the challenges of data accessibility and promote development and application of machine learning methods to omics data from clinical trial samples with treatment response information. The package makes data readily analyzable by data scientists so that they can develop, utilize, and optimize their algorithms for predicting treatment responses and discovering novel biomarkers. To promote a two-way dialogue, we have also developed several Jupyter Notebook tutorials for biologists or clinicians who wish to gain expertise in machine learning. Omics data from clinical studies are downloaded from Gene Expression Omnibus (GEO) and responses were determined based on clinical trial primary endpoints. Currently, the package has datasets from 22 breast cancer clinical trials, including a total of 5050 patients (Table 1). It will be continuously expanded to include additional trials for breast cancer and other cancer types. RESULTS: To evaluate package utility, we built machine learning models to predict neoadjuvant chemotherapy with four cycles of 5-fluorouracil/epirubicin/cyclophosphamide (FEC) followed by four cycles of docetaxel/capecitabine on US Oncology clinical trial 02-103 [GSE42822]. The best performing model was the Random Forest Classifier model, which had an AUC of 0.817. To determine the generalizability of machine learning models established from the package, we trained a Random Forest Classifier model using the GSE25055 breast cancer dataset and apply the model to a different breast cancer dataset, GSE20194, which yielded an AUC of 0.648. These results suggest utilizing machine learning on clinical omics datasets can provide predictive and generalizable models that could be implemented in clinical settings for future breast cancer patients. CONCLUSION: We are expanding the database for data scientists, biologists, and clinicians to practice novel biotechnology-derived therapies to facilitate the implementation of precision medicine approaches for future patients. As more people add new data to the package, we will work towards improving pharmaceutical and private companies’ clinical trial data sharing policies and practices to promote data sharing. Table 1. Available breast cancer datasets in ClinicalOmicsDB The current database has 22 breast cancer clinical trials with 5050 total patients. Therapy shows various cytotoxic and/or targeted treatments utilized in a clinical trial. The database will be continuously expanded to include additional trials for breast cancer and other cancer types. Citation Format: Chang In Moon, Byron Jia, Bing Zhang. ClinicalomicsDB - Bridging the gap between clinical omics data and machine learning [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-12-02.

Background: Few omics data-based prediction models have made a clinical impact due to lack of access to real-world, clinically relevant datasets for method development and evaluation. The expanding integration of omics profiling into cancer clinical trials opens a new opportunity to address this problem. Clinical trials ensure high quality of the treatment response information and clinical relevance of the identified molecular features and constructed prediction models. Here we present a python package ClinicalOmicsDB that provides a framework for systematic benchmarking of machine learning algorithms using a compendium of carefully collected and curated clinical omics datasets. The package further leverages interpretable AI to gain biological insights from high-performing models. Approach: We downloaded omics datasets from 28 clinical trials across 7 cancer types, totaling 6127 patients and 60 treatment arms. For each treatment arm, we performed random stratified splitting of the data into 80% for training and the remaining 20% for testing, and this was repeated 30 times to create 30 simulated train-test pairs. Based on all 60 treatment arms, we created 1800 train-test pairs for prediction model development and evaluation. To illustrate the utility of this package, we benchmarked six supervised machine learning algorithms for their ability to distinguish responders from non-responders. For each training dataset, 5-fold cross-validation was used to select the top 20 non-redundant features and to optimize hyperparameters. The optimized pipeline was used to train a full model based on the complete training dataset, which was then applied to the paired testing dataset for performance evaluation using AUROC. Shapley plots were created for models with satisfactory AUROC scores to facilitate biological interpretation. Results: Among the 6 algorithms tested, random forests had the best overall performance. It achieved an AUROC>0.75 for 11 treatment arms. Shapley plot identified LPIN1 as a top predictive marker for the combination treatment of fluorouracil, doxorubicin, and cyclophosphamide (AC) in unselected breast cancers. The cancer testis antigen MAGEA1 was among the top predictive markers for combination treatment with pembrolizumab, taxane, and AC in HER2- breast cancers. Shapley plot of a protein-based model identified SRC_Y527 and ERBB3 as the top biomarkers for predicting whether HER2+ breast cancer would respond to the combination of trastuzumab emtansine, pertuzumab, and AC. Conclusion: We created a computational framework that uses real-world omics data to benchmark machine learning algorithms and to identify predictive markers based on best-performing models. More datasets will be continuously added, and new algorithms can be easily benchmarked against existing ones. ClinicalOmicsDB unifies the efforts from the clinical and machine learning communities to improve cancer treatment. Citation Format: Chang In Moon, Byron Jia, Bing Zhang. Bridging the gap between clinical-omics and machine learning to improve cancer treatment. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5717.