Lung cancer, one of the most frequently diagnosed cancers worldwide has long relied on testing for the molecular biomarkers EGFR/ALK. However, achieving superior clinical outcomes for patients with lung cancer requires developing comprehensive techniques beyond contemporary EGFR/ALK testing. Current technologies are on par with molecular testing for EGFR/ALK in terms of efficacy, most of them failing to offer improvements perhaps primarily due to skepticism among clinicians, despite being recommended in the NCCN guidelines. The present study endeavored to minimize chemotherapy-dependence in EGFR/ALK-negative patient cohorts, and use evidence-based methods to identify ways to improve clinical outcomes. In total, 137 lung cancer cases obtained from 'PositiveSelect NGS data', comprising 91 males and 46 females, were investigated. EGFR-and ALK-positivity was used for data dichotomization to understand the therapeutic utility of rare gene alterations beyond just EGFR/ALK. Statistics obtained from PositiveSelect were collated with data from international studies to construct a meta-analysis intended to achieve better clinical outcomes. Upon dichotomization, 23% of cases harbored EGFR variants indicating that treating with EGFR TKIs would be beneficial; the remaining 77% exhibited no EGFR variants that would indicate favorable results using specific currently available chemotherapy practices. Similarly, 28% of cases had EGFR+ALK variants favoring EGFR/ALK-based targeted therapeutics; the remaining 72% harbored no EGFR/ALK variants with known beneficial chemotherapy routes. The present study aimed to overcome current inadequacies of targeted therapies in patients with a conventional EGFR/ALK-positive diagnosis and those in EGFR+ALK-negative cohorts. Upon analysis of the negative cohorts, significant and clinically relevant single nucleotide variants were identified in KRAS, ERBB2, MET and RET, with frequencies of 7, 1, 2 and 3% in patients who were EGFR-negative and 6, 1, 1, and 3% in patients who were EGFR and ALK-negative, respectively, enabling the use of targeted therapeutics aside from EGFR/ALK TKIs. From the results of the current study only 35% of the two negative arms (EGFR negative and EGFR+ALK negative) would be recommended NCCN or off-label chemotherapy; prior to the current study, the entire cohorts would have been recommended this treatment. The present study emphasizes the potential of comprehensive genomics in identifying hallmarks of lung cancer beyond EGFR/ALK, using broad-spectrum genetic testing and data-sharing among medical professionals to circumvent ineffective chemotherapy.
e21172 Background: Analysis of Real World Data (RWD) from Electronic Health Records (EHR) for applications such as Health Economics and Outcomes Research (HEOR) or regulatory submissions requires identification of the lines of therapy (LoT) patients have received. LoTs are typically not captured in EHR and must be manually abstracted. As the use of RWD increases, there is a growing need to create algorithms that can work on RWD to extract LoT information in an automated manner with high accuracy. We present here the results of such an algorithm created on NSCLC RWD. Methods: 10950 advanced NSCLC patients from the ConcertAI Oncology RWD database who had received anti-neoplastic treatment after advanced diagnosis were used to build and validate this algorithm. These data were further enriched by expert nurse curators to fill in missing oral drug information and identify progression events. We developed a progression-based LoT (pLoT) model that identified LoT changes in sync with tumor progressions. If patients received multiple regimens before progression they were captured as nested regimens within the LoT. The algorithm uses complex rules to define combination of drugs as regimens (combination rule), identify resumption of regimens (gap rule) or dropping of drugs from regimens as new lines and to handle noisiness in RWD etc. Results: The LoT model accurately captures line changes triggered by progression events as well as any nested regimen changes due to adverse events etc. Patient level validation of LoT was carried out by clinical experts using an in-house tool and found to be consistent with literature & individual drug data. Cohort level analysis of top 3 combinations of therapies used in 1st & 2nd line treatment between 2015-2020 (8200 patients) are shown in Table. Sensitivity analysis on the combination rule showed that this parameter can be changed between 28-33 days without significantly impacting the LoT output (<1% impact). We use a 30 day combination rule as the default. Similarly, the gap rule parameter is quite robust and does not show significant variation between 45 – 90 days (<2% impact). We use 63 days. Conclusions: We have developed a robust algorithm to derive pLoT on RWD at scale assuming availability of curated progression data which can be used to support use cases such as HEOR, clinical development and regulatory submissions. pLoT is better suited for outcomes analysis compared to regimen based LoT since it distinguishes changes in treatment due to progression events from changes due to toxicity, drug availability, etc., and allows analysis on a more homogeneous patient population relating to their past clinical experience. [Table: see text]
e21540 Background: Metastatic status is a crucial variable in most oncology studies but is not available in claims data. The objective of this study is to develop a machine learning model for Imputation of metastatic status from claims data with ground. Truth is derived from highly curated electronic medical record data. Methods: We used a set of 11389 melanoma patients from the ConcertAI real world database of intersecting claims and EMR data that includes data from CancerLinQ Discovery. Using features from claims and our gold standard labels from EMR we built an ML model using (XGBoost) extreme gradient boosting, an algorithm that iteratively combines a set of decision trees into a single model. We used 60% of the data for training, 20% for hyper-parameter tuning, and 20% for holdout testing. The model was built using 55 features. Results: The table below summarizes results. Metrics are on the final hold out set which was unseen by the model and entirely composed of highly curated EMR data. Conclusions: We are able to build a high precision model for the imputation of metastatic melanoma status using claims data. This could enable significantly better use of claims data stemming from the ability to find a metastatic cohort with very few false positives. Providing more precise cohort identification for comparative effectiveness studies. We found features such as secondary neoplasm diagnosis, anti-neoplastic meds, and radiation ranking highly in our analysis of model feature importances. Using techniques to analyze non-linear feature interactions in our AI model we found an interaction relationship between long term anti-neoplastic therapy, reported pain and metastatic status which we plan to further study. This work is preliminary and we are working to further improve model performance.[Table: see text]
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