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Lung cancer remains the leading cause of cancer‐related deaths worldwide. However, significant progress has been made individualizing therapy based on molecular aberrations (e.g., EGFR, ALK) and pathologic subtype. KRAS is one of the most frequently mutated genes in non‐small cell lung cancer (NSCLC), found in approximately 30% of lung adenocarcinomas, and is thus an appealing target for new therapies. Although no targeted therapy has yet been approved for the treatment of KRAS‐mutant NSCLC, there are multiple potential therapeutic approaches. These may include direct inhibition of KRAS protein, inhibition of KRAS regulators, alteration of KRAS membrane localization, and inhibition of effector molecules downstream of mutant KRAS. This article provides an overview of the KRAS pathway in lung cancer and related therapeutic strategies under investigation. Implications for Practice: The identification of oncogene‐addicted cancers and specific inhibitors has revolutionized non‐small cell lung cancer (NSCLC) treatment and outcomes. One of the most commonly mutated genes in adenocarcinoma is KRAS, found in approximately 30% of lung adenocarcinomas, and thus it is an appealing target for new therapies. This review provides an overview of the KRAS pathway and related targeted therapies under investigation in NSCLC. Some of these agents may play a key role in KRAS‐mutant NSCLC treatment in the future.

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Background: The widespread uptake of electronic health records (EHRs) has made the creation of custom, real-world datasets for research more feasible. As a result, multiple research datasets with overlapping populations are often generated, using different methodologies, and frequently siloed within and between research groups, limiting the scope of the data’s use. Currently, there is no standard for collating and evaluating such data. Using existing lung oncology datasets, we developed an approach to determine optimal methods of combining and curating clinical data from different sources. Methods: Two separate study datasets containing data for lung cancer patients diagnosed and/or treated within Princess Margaret Cancer Centre (PM, Toronto) were investigated. Study 1 manually abstracted clinical data for 1,990 patients, first seen at PM between 2014-2016; Study 2 leveraged the artificial intelligence engine, DARWEN™, to extract clinical data directly from EHRs for 4,466 patients, diagnosed between 2014-2018. Each dataset was individually assessed for internal consistency before comparing the overlapping population (Test Group, n=1892) to identify, investigate, and resolve differences. Patterns of data extraction performance were evaluated to define optimal methods for combining datasets and informing future data collection. Herein, epidermal growth factor receptor (EGFR) mutation status is used as an illustrative example. Results: Study 1 and 2 had similar distributions of clinicodemographic data and frequency of EGFR mutations. The Test Group had 100% agreement for date of birth, and >99% agreement for sex, with all discrepancies resulting from human error in Study 1. The Test Group had a 98% agreement for EGFR positivity and 98-99% agreement for specific exon mutations. Of the 106 disagreements for specific mutations, 50% (n=53) were due to Study 1 human error. Study 2 prioritized specificity over sensitivity for biomarker extraction, resulting in more false negatives (25% of errors, n=26). As DARWEN™ only extracted EGFR data from pathology reports, 18% (n=19) of discrepancies were due to lack of access to relevant information captured elsewhere in patients’ EHRs. Adjudicators could not resolve the remaining 7% of disagreements (n=8). Conclusions: By comparing overlapping datasets, the strengths and weaknesses of each study design and extraction methodology were identified. This process demonstrated the effectiveness of artificial intelligence for extracting accurate patient-level clinicodemographic and mutation status data from EHRs, and the value of targeted manual chart review. Our approach provides a roadmap for leveraging existing clinical datasets to their fullest potential, which is relevant across diverse data extraction methods and study designs. Citation Format: Benjamin M. Grant, Aein Zarrin, Luna Zhan, Rami Ajaj, Lina Darwish, Khaleeq Khan, Devalben Patel, Kaitlyn Chiasson, Karmugi Balaratnam, Maisha T. Chowdhury, Amir-Arsalan Sabouhanian, Joshua Herman, Preet Walia, Evan Strom, Catherine Brown, Miguel Garcia-Pardo, Sabine Schmid, Christopher Pettengell, Erin L. Stewart, Geoffrey Liu. Developing a standardized framework for curating oncology datasets generated by manual abstraction and artificial intelligence [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4087.

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Background: Around 5% of EGFR gene alterations in non-small cell lung cancer (NSCLC) involve exon 18. Limited prospective data on the therapeutic actionability of individual exon 18 alterations with specific EGFR tyrosine kinase inhibitors (TKIs) are available to guide clinical decision making for these patients. Methods:We conducted a literature search on PubMed using search terms "exon 18" OR "uncommon" OR "non-common" OR "rare" AND "EGFR" AND "NSCLC" OR "lung" for studies published in English on/before 6 February, 2021, that reported patientlevel outcomes from NSCLC patients with defined exon 18 alterations treated with a single named EGFR TKI, with reported progression free survival (PFS) data. We extracted and analysed demographic and clinical data.Results: Forty-five publications, containing 143 patient-level reports, met the eligibility criteria. 64 patients had a single EGFR exon 18 alteration (37 being G719X). 79 patients had compound EGFR alterations (75 doublet and 4 triplet). For the whole cohort, treatment with afatinib was associated with the highest reported median(m) PFS (17 months; n¼15), followed by erlotinib (8.4 months; n¼49), gefitinib (7.0 months; n¼70), osimertinib (7.0 months; n¼6) and icotinib (2.3 months; n¼3). Pairwise analysis suggested afatinib was associated with higher mPFS compared with gefitinib or erlotinib (both: p¼0.01), but should be interpreted on the light of the selection/publication bias. Reported PFS exceeded 2 years in 13 cases, these being G719X (N¼6 and 16.2% of all G719X cases), compound (N¼6) or L692F (N¼1) alterations, and all treated with first/second generation TKIs. This individual patient data and mutation level resolution presented suggest limited data supporting the actionability of certain rare alterations, heterogeneity in reported outcomes for patients with more prevalent alterations, and potential differences in alteration-specific drug sensitivity.Conclusions: Reported patient level outcomes for distinct exon 18 mutations strongly imply distinct actionability with EGFR TKIs. In concert with reported cohort/trial and preclinical data, our data provide an additional resource to help clinical decision making for patients with these rare EGFR alterations.Legal entity responsible for the study: The authors.