Identification and Treatment of Lung Cancer Oncogenic Drivers in a Diverse Safety Net Setting.

INTRODUCTION: Advances in the testing and treatment of patients with non-small cell lung cancer (NSCLC) harboring oncogenic drivers have improved outcomes. Little is known about testing and treatment patterns in diverse patient populations. METHODS: We conducted a retrospective study in a diverse cohort of patients treated in the John Peter Smith safety net healthcare system. We determined patterns of blood- and tissue-based testing and treatment of patients with EGFR and ALK alterations. Cox proportional-hazards regression models were used to assess the impact of EGFR and ALK testing. RESULTS: A total of 220 patients were included, 97 (44%) were non-Hispanic White, 72 (33%) were Black, 28 (13%) were Hispanic, and 23 (10%) were Asian. EGFR and ALK testing increased over time from 55% and 52%, respectively, in 2017 to 87% and 82%, respectively, in 2021. Frequency of EGFR alterations were highest in Asian patients (45%) and comparable among other groups (6-13%). Frequency of ALK alterations were highest in Hispanic (13%), and Asian (11%) patients, and were 2% for both Black and non-Hispanic White patients. In a multivariate model, lack of testing was associated with worse survival (aHR 1.6; P = .003) and testing positive for EGFR (aHR 0.43; P = .01) or ALK (aHR 0.28; P = .04) was associated with improved survival. Race and ethnicity were not associated with survival differences. CONCLUSION: As molecular testing for oncogenic mutations in NSCLC increases, druggable alterations such as ALK and EGFR can be identified in all race-ethnicity groups and are associated with improved outcomes.

Acknowledging and addressing real-world challenges to treating immune-related adverse events.

Immune checkpoint inhibitors (ICIs) have revolutionized oncology treatment. However, their success is mitigated by the recognition that ICI-induced immune-related adverse events (irAEs) pose considerable challenges to patients and clinicians. These autoimmune toxicities are heterogeneous, unpredictable, and reflect a disease state resulting from a change in the immune system of patients. This contrasts with the typical acute nature of toxicities from chemotherapy and molecularly targeted oncology therapies. Management is further complicated by the extended bioavailability of these agents in patients as well as the persistence of autoimmune pathology. Currently, irAE treatment remains suboptimal in many areas, as many expert guidelines remain vague on the optimal selection, dosing, and duration of steroids and the use of other immunosuppressive agents. This coupled with delays in diagnosis and difficulties for patients accessing effective irAE treatment results in barriers to effective irAE care. The latter is complicated by the lack of US Food and Drug Administration-approved irAE treatments that lead to insurance denials, as well as the high cost of biological immunosuppressant therapies. Fortunately, rheumatologists and other subspecialists with expertize in the management of chronic autoimmune conditions have become more involved in irAE diagnosis and management and may help navigate treatment. In this commentary, we discuss these issues and propose potential solutions to advance the field.

Cmai: Predicting Antigen-Antibody Interactions from Massive Sequencing Data.

The interaction between antigens and antibodies (B cell receptors, BCRs) is the key step underlying the function of the humoral immune system in various biological contexts. The capability to profile the landscape of antigen-binding affinity of a vast number of BCRs will provide a powerful tool to reveal novel insights at unprecedented levels and will yield powerful tools for translational development. However, current experimental approaches for profiling antibody-antigen interactions are costly and time-consuming, and can only achieve low-to-mid throughput. On the other hand, bioinformatics tools in the field of antibody informatics mostly focus on optimization of antibodies given known binding antigens, which is a very different research question and of limited scope. In this work, we developed an innovative Artificial Intelligence tool, Cmai, to address the prediction of the binding between antibodies and antigens that can be scaled to high-throughput sequencing data. Cmai achieved an AUROC of 0.91 in our validation cohort. We devised a biomarker metric based on the output from Cmai applied to high-throughput BCR sequencing data. We found that, during immune-related adverse events (irAEs) caused by immune-checkpoint inhibitor (ICI) treatment, the humoral immunity is preferentially responsive to intracellular antigens from the organs affected by the irAEs. In contrast, extracellular antigens on malignant tumor cells are inducing B cell infiltrations, and the infiltrating B cells have a greater tendency to co-localize with tumor cells expressing these antigens. We further found that the abundance of tumor antigen-targeting antibodies is predictive of ICI treatment response. Overall, Cmai and our biomarker approach filled in a gap that is not addressed by current antibody optimization works nor works such as AlphaFold3 that predict the structures of complexes of proteins that are known to bind.

Differences in systemic immune parameters in individuals with lung cancer according to race.

Introduction: Racial and ethnic disparities in the presentation and outcomes of lung cancer are widely known. To evaluate potential factors contributing to these observations, we measured systemic immune parameters in Black and White patients with lung cancer. Methods: Patients scheduled to receive cancer immunotherapy were enrolled in a multi-institutional prospective biospecimen collection registry. Clinical and demographic information were obtained from electronic medical records. Pre-treatment peripheral blood samples were collected and analyzed for cytokines using a multiplex panel and for immune cell populations using mass cytometry. Differences between Black and White patients were determined and corrected for multiple comparisons. Results: A total of 187 patients with non-small cell lung cancer (Black, 19; White, 168) were included in the analysis. There were no significant differences in baseline characteristics between Black and White patients. Compared to White patients, Black patients had significantly lower levels of CCL23 and CCL27, and significantly higher levels of CCL8, CXCL1, CCL26, CCL25, CCL1, IL-1 b, CXCL16, and IFN-γ (all P <0.05, FDR<0.1). Black patients also exhibited greater populations of non-classical CD16+ monocytes, NKT-like cells, CD4+ cells, CD38+ monocytes, and CD57+ gamma delta T cells (all P <0.05). Conclusions: Black and White patients with lung cancer exhibit several differences in immune parameters, with Black patients exhibiting greater levels of numerous pro-inflammatory cytokines and cell populations. The etiology and clinical significance of these differences warrant further evaluation.

Use of artificial intelligence chatbots in clinical management of immune-related adverse events.

BACKGROUND: Artificial intelligence (AI) chatbots have become a major source of general and medical information, though their accuracy and completeness are still being assessed. Their utility to answer questions surrounding immune-related adverse events (irAEs), common and potentially dangerous toxicities from cancer immunotherapy, are not well defined. METHODS: We developed 50 distinct questions with answers in available guidelines surrounding 10 irAE categories and queried two AI chatbots (ChatGPT and Bard), along with an additional 20 patient-specific scenarios. Experts in irAE management scored answers for accuracy and completion using a Likert scale ranging from 1 (least accurate/complete) to 4 (most accurate/complete). Answers across categories and across engines were compared. RESULTS: Overall, both engines scored highly for accuracy (mean scores for ChatGPT and Bard were 3.87 vs 3.5, p<0.01) and completeness (3.83 vs 3.46, p<0.01). Scores of 1-2 (completely or mostly inaccurate or incomplete) were particularly rare for ChatGPT (6/800 answer-ratings, 0.75%). Of the 50 questions, all eight physician raters gave ChatGPT a rating of 4 (fully accurate or complete) for 22 questions (for accuracy) and 16 questions (for completeness). In the 20 patient scenarios, the average accuracy score was 3.725 (median 4) and the average completeness was 3.61 (median 4). CONCLUSIONS: AI chatbots provided largely accurate and complete information regarding irAEs, and wildly inaccurate information ("hallucinations") was uncommon. However, until accuracy and completeness increases further, appropriate guidelines remain the gold standard to follow.

Distinct autoantibody profiles across checkpoint inhibitor types and toxicities.

Immune checkpoint inhibitors (ICI) are increasingly used in combination. To understand the effects of different ICI categories, we characterized changes in circulating autoantibodies in patients enrolled in the E4412 trial (NCT01896999) of brentuximab vedotin (BV) plus ipilimumab, BV plus nivolumab, or BV plus ipilimumab-nivolumab for Hodgkin Lymphoma. Cycle 2 Day 1 (C2D1) autoantibody levels were compared to pre-treatment baseline. Across 112 autoantibodies tested, we generally observed increases in ipilimumab-containing regimens, with decreases noted in the nivolumab arm. Among 15 autoantibodies with significant changes at C2D1, all nivolumab cases exhibited decreases, with more than 90% of ipilimumab-exposed cases showing increases. Autoantibody profiles also showed differences according to immune-related adverse event (irAE) type, with rash generally featuring increases and liver toxicity demonstrating decreases. We conclude that dynamic autoantibody profiles may differ according to ICI category and irAE type. These findings may have relevance to clinical monitoring and irAE treatment.

Highly variable timing renders immunotherapy efficacy and toxicity impractical biomarkers of one another in clinical practice.

Background: A useful clinical biomarker requires not only association but also a consistent temporal relationship. For instance, chemotherapy-induced neutropenia and epidermal growth-factor inhibitor-related acneiform rash both occur within weeks of treatment initiation, thereby providing information prior to efficacy assessment. Although immune checkpoint inhibitor (ICI)-associated immune-related adverse events (irAE) have been associated with therapeutic benefit, irAE may have delayed and highly variable onset. To determine whether ICI efficacy and irAE could serve as clinically useful biomarkers for predicting each other, we determined the temporal relationship between initial efficacy assessment and irAE onset in a diverse population treated with ICI. Methods: Using two-sided Fisher exact and Cochran-Armitage tests, we determined the relative timing of initial efficacy assessment and irAE occurrence in a cohort of 155 ICI-treated patients (median age 68 years, 40% women). Results: Initial efficacy assessment was performed a median of 50 days [interquartile range (IQR) 39-59 days] after ICI initiation; median time to any irAE was 77 days (IQR 28-145 days) after ICI initiation. Median time to first irAE was 42 days (IQR 20-88 days). Overall, 58% of any irAE and 47% of first irAE occurred after initial efficacy assessment. For clinically significant (grade ≥2) irAE, 60% of any and 53% of first occurred after initial efficacy assessment. The likelihood of any future irAE did not differ according to response (45% for complete or partial response vs. 47% for other cases; P=1). In landmark analyses controlling for clinical and toxicity follow-up, patients demonstrating greater tumor shrinkage at initial efficacy assessment were more likely to develop future grade ≥2 (P=0.05) and multi-organ (P=0.02) irAE. Conclusions: In contrast to that seen with chemotherapy and molecularly targeted therapies, the temporal relationship between ICI efficacy and toxicity is complex and bidirectional. In practice, neither parameter can be routinely relied on as a clinical biomarker to predict the other.

Telemedicine and Cancer Clinical Research: Opportunities for Transformation.

ABSTRACT: Telemedicine represents an established mode of patient care delivery that has and will continue to transform cancer clinical research. Through telemedicine, opportunities exist to improve patient care, enhance access to novel therapies, streamline data collection and monitoring, support communication, and increase trial efficiency. Potential challenges include disparities in technology access and literacy, physical examination performance, biospecimen collection, privacy and security concerns, coverage of services by insurance, and regulatory considerations. Coupled with artificial intelligence, telemedicine may offer ways to reach geographically dispersed candidates for narrowly focused cancer clinical trials, such as those targeting rare genomic subsets. Collaboration among clinical trial staff, clinicians, regulators, professional societies, patients, and their advocates is critical to optimize the benefits of telemedicine for clinical cancer research.

pan-MHC and cross-Species Prediction of T Cell Receptor-Antigen Binding.

Profiling the binding of T cell receptors (TCRs) of T cells to antigenic peptides presented by MHC proteins is one of the most important unsolved problems in modern immunology. Experimental methods to probe TCR-antigen interactions are slow, labor-intensive, costly, and yield moderate throughput. To address this problem, we developed pMTnet-omni, an Artificial Intelligence (AI) system based on hybrid protein sequence and structure information, to predict the pairing of TCRs of αß T cells with peptide-MHC complexes (pMHCs). pMTnet-omni is capable of handling peptides presented by both class I and II pMHCs, and capable of handling both human and mouse TCR-pMHC pairs, through information sharing enabled this hybrid design. pMTnet-omni achieves a high overall Area Under the Curve of Receiver Operator Characteristics (AUROC) of 0.888, which surpasses competing tools by a large margin. We showed that pMTnet-omni can distinguish binding affinity of TCRs with similar sequences. Across a range of datasets from various biological contexts, pMTnet-omni characterized the longitudinal evolution and spatial heterogeneity of TCR-pMHC interactions and their functional impact. We successfully developed a biomarker based on pMTnet-omni for predicting immune-related adverse events of immune checkpoint inhibitor (ICI) treatment in a cohort of 57 ICI-treated patients. pMTnet-omni represents a major advance towards developing a clinically usable AI system for TCR-pMHC pairing prediction that can aid the design and implementation of TCR-based immunotherapeutics.

T-cell tolerant fraction as a predictor of immune-related adverse events.

BACKGROUND: Immune checkpoint inhibitor (ICI) therapies may cause unpredictable and potentially severe autoimmune toxicities termed immune-related adverse events (irAEs). Because T cells mediate ICI effects, T cell profiling may provide insight into the risk of irAEs. Here we evaluate a novel metric-the T-cell tolerant fraction-as a predictor of future irAEs. METHODS: We examined T-cell receptor beta (TRB) locus sequencing from baseline pretreatment samples from an institutional registry and previously published studies. For each patient, we used TRB sequences to calculate the T-cell tolerant fraction, which was then assessed as a predictor of future irAEs (classified as Common Terminology Criteria for Adverse Event grade 0-1 vs grade ≥2). We then compared the tolerant fraction to TRB clonality and diversity. Finally, the tolerant fraction was assessed on (1) T cells enriched against napsin A, a potential autoantigen of irAEs; (2) thymic versus peripheral blood T cells; and (3) TRBs specific for various infections and autoimmune diseases. RESULTS: A total of 77 patients with cancer (22 from an institutional registry and 55 from published studies) receiving ICI therapy (43 CTLA4, 19 PD1/PDL1, 15 combination CTLA4+PD1/PDL1) were included in the study. The tolerant fraction was significantly lower in cases with clinically significant irAEs (p<0.001) and had an area under the receiver operating curve (AUC) of 0.79. The tolerant fraction was lower for each ICI treatment category, reaching statistical significance for CTLA4 (p<0.001) and demonstrating non-significant trends for PD1/PDL1 (p=0.21) and combination ICI (p=0.18). The tolerant fraction for T cells enriched against napsin A was lower than other samples. The tolerant fraction was also lower in thymic versus peripheral blood samples, and lower in some (multiple sclerosis) but not other (type 1 diabetes) autoimmune diseases. In our study cohort, TRB clonality had an AUC of 0.62, and TRB diversity had an AUC of 0.60 for predicting irAEs. CONCLUSIONS: Among patients receiving ICI, the baseline T-cell tolerant fraction may serve as a predictor of clinically significant irAEs.

Systemic immune parameters after prior radiation therapy in patients receiving immune checkpoint inhibitors.

INTRODUCTION: Preclinical studies have demonstrated the ability of radiation therapy (RT) to augment immune response and tumor control by immune checkpoint inhibitors (ICI). However, numerous clinical trials combining RT and ICI have yielded relatively disappointing results. To improve understanding of optimal use of these therapies, we assessed systemic immune effects of prior RT in patients receiving ICI. METHODS AND MATERIALS: Pre- and post-ICI blood samples were collected from patients enrolled in a prospective immunotherapy biospecimen protocol. Mutiplex panels of 40 cytokines and 120 autoantibodies (Ab) were analyzed. We identified differences in these parameters according to receipt, timing, and type of prior RT. We calculated P values using the Pearson product-moment correlation coefficient and false discovery rate (FDR) using the Benjamini-Hochberg Procedure. RESULTS: Among 277 total patients, 69 (25%) received RT in the 6 months prior to ICI initiation. Among RT-treated patients, 23 (33%) received stereotactic RT, and 33 (48%) received curative intent RT. There was no significant difference in demographics or type of immunotherapy between patients according to prior RT exposure. Baseline complement C8 Ab and MIP-1d/CCL15 were significantly higher among patients with prior RT. For MIP-1d/CCL15, only prior stereotactic RT was associated with significant differences. CONCLUSIONS: Prior RT is associated with few changes in systemic immune parameters in patients receiving ICI. The underlying mechanisms and optimal approach to harnessing the potential synergy of RT and ICI require further prospective clinical investigation.

Antibiotic Prescriptions in Lung Cancer and Melanoma Populations: Differences With Potential Clinical Implications in the Immunotherapy Era.

INTRODUCTION: Antibiotic exposure is associated with worse clinical outcomes in patients receiving immune checkpoint inhibitors (ICI). We analyzed antibiotic prescription patterns in lung cancer and melanoma, two malignancies in which ICI are used broadly across stages. METHODS: We performed a retrospective cohort study of adults in the U.S. Veterans Affairs (VA) medical system diagnosed with lung cancer or melanoma from 2003 to 2016. We defined antibiotic exposure as receipt of a prescription for a systemic antibacterial agent between 6 months before and 6 months after cancer diagnosis. Demographics, clinical variables, prescriptions, and diagnostic codes were abstracted from the VA Corporate Data Warehouse. Antibiotic exposure was compared using t tests, Chi-square, and multivariate analyses. RESULTS: A total of 310,321 patients (280,068 lung cancer, 30,253 melanoma) were included in the analysis. Antibiotic exposure was more common among patients with lung cancer (42% vs. 24% for melanoma; P < .001). Among antibiotic-exposed patients, those with lung cancer were more likely to receive prescriptions for multiple antibiotics (47% vs. 30% for melanoma; P < .001). In multivariate analyses, antibiotic exposure was associated with lung cancer diagnosis (HR 1.50; 95% CI, 1.46-1.55), comorbidity score (HR 1.08; 95% CI, 1.08-1.09), non-white race (HR 1.11; 95% CI, 1.06-1.17), and female gender (HR 1.31; 95% CI, 1.24-1.37). CONCLUSION: Among cancer patients, antibiotics are prescribed frequently. Antibiotic exposure is more common in certain cancer types and patient populations. Given the negative effect antibiotic exposure has on immunotherapy outcomes, these observations may have clinical and healthy policy implications.

Quantitation of cardiac troponin I in cancer patients treated with immune checkpoint inhibitors: a case-control study.

OBJECTIVES: Immune checkpoint inhibitors (ICIs) cause a variety of toxicities, including immune-related adverse events (irAEs), but there are no biomarkers to predict their development. Guidelines recommend measuring circulating cardiac troponin I (cTnI) during ICI therapy to detect related cardiotoxicities. Moreover, elevated cTnI has also been associated with worse outcomes in non-cardiac patients, including cancer. Thus here, we investigated whether cTnI levels were higher in patients with irAEs. METHODS: The study consisted of three groups; 21 cancer patients undergoing ICI immunotherapies who presented with irAEs, four patients without irAEs, and 20 healthy controls. Patient samples were assessed at baseline (n=25), during ICI treatment (n=25, median=6 weeks of treatment) and at toxicity (n=6, median=13 weeks of treatment). In addition to blood high sensitivity cardiac troponin I (hs-cTnI), anti-thyroglobulin (TG) and anti-thyroid peroxidase (TPO) antibodies were also quantitated to detect thyroid dysfunction, constituting the second leading toxicity (23.8%) after pneumonitis (28.6%). RESULTS: Four patients with irAEs (n=4/21; 19%) and one without irAEs (n=1/4; 25%) showed higher hs-cTnI levels at any time-point; the remaining had physiological levels. None of these patients developed cardiotoxicity. Concurrent elevated levels of anti-thyroid antibodies and hs-cTnI were detected in one patient with thyroid dysfunction (n=1/5, 20%). However, these antibodies were also elevated in three patients (n=3/16, 19%) with non-thyroid irAEs and in up to 40% of healthy controls. CONCLUSIONS: hs-cTnI was not elevated in patients with irAEs, but larger studies are needed to confirm these observations.

Distinct NSCLC EGFR Variants in a Family With Li-Fraumeni Syndrome: Case Report.

Introduction: Heritable lung cancer may occur in the context of germline TP53 mutations (Li-Fraumeni syndrome). Limited cases of intrafamily tumor genomic characteristics have been reported. Main concerns Important Clinical Findings Primary Diagnoses Interventions Outcomes: A 40-year-old woman with no smoking history or known environmental exposure risk was incidentally found to have stage II (T2N1) NSCLC harboring an EGFR exon 19 p.Glu746_Ala750 deletion. Family history was notable for an identical twin sister with colorectal cancer (diagnosed at age 31 y) and a mother with stage I NSCLC harboring an EGFR exon 21 c.2573T>G (p.Leu858Arg) mutation (diagnosed at age 69 y). Genetic testing revealed a germline TP53 c.542G>A (p.Arg181His) mutation in the patient, her mother, and her sister, consistent with Li-Fraumeni syndrome. No germline EGFR mutations were detected. Conclusion: Shared germline TP53 mutations may be associated with distinct NSCLC somatic EGFR mutations within families with Li-Fraumeni syndrome. Further understanding of the association between genetic cancer syndromes and lung cancer risk may improve early lung cancer detection in populations not otherwise meeting screening eligibility.

Pseudoprogression in advanced non-small cell lung cancer treated with combination chemoimmunotherapy: a case report.

BACKGROUND: Pseudoprogression, the initial apparent worsening of cancer prior to eventual improvement, is a documented feature of immune checkpoint inhibitor administration and presents a challenge to clinicians distinguishing true progression from pseudoprogression. This phenomenon does not typically occur with traditional cytotoxic chemotherapy. We present a case in which a patient treated with combination carboplatin-pemetrexed plus pembrolizumab experienced transient radiographic worsening of disease with subsequent regression. CASE PRESENTATION: A 65-year-old never-smoking white male with advanced sarcomatoid non-small cell lung cancer (NSCLC) harboring a MET exon 14 skipping mutation and with PD-L1 tumor proportion score of 80% was initiated on combination chemotherapy plus immune checkpoint inhibitor (ICI) therapy after progression on a MET inhibitor. After two cycles of carboplatin-pemetrexed plus pembrolizumab, repeat imaging suggested disease progression. Following discontinuation of the carboplatin-pemetrexed plus pembrolizumab regimen, the patient reported improved symptoms and energy levels, which were attributed to the waning of treatment-associated toxicities. On the day prior to initiation of the next planned line of therapy, repeat imaging was preformed to provide a baseline for treatment efficacy. Imaging revealed improvement compared to the prior imaging. Chemotherapy with carboplatin-pemetrexed plus pembrolizumab was resumed, with response ongoing 8 months later. CONCLUSIONS: Pseudoprogression is a documented feature of ICI administration. Pseudoprogression is not typically observed in patients treated with traditional cytotoxic chemotherapy and has not yet been documented in patients treated with combination cytotoxic chemotherapy plus immunotherapy. At this time, there are no reliable means to predict or diagnose these rare events; therefore, more studies should be conducted to understand which patients are predisposed to developing this phenomenon and to increase clinical recognition.

Association between Antibiotic Exposure and Systemic Immune Parameters in Cancer Patients Receiving Checkpoint Inhibitor Therapy.

Antibiotic administration is associated with worse clinical outcomes and changes to the gut microbiome in cancer patients receiving immune checkpoint inhibitors (ICI). However, the effects of antibiotics on systemic immune function are unknown. We, therefore, evaluated antibiotic exposure, therapeutic responses, and multiplex panels of 40 serum cytokines and 124 antibodies at baseline and six weeks after ICI initiation, with p < 0.05 and false discovery rate (FDR) < 0.2 considered significant. A total of 251 patients were included, of whom the 135 (54%) who received antibiotics had lower response rates and shorter survival. Patients who received antibiotics prior to ICI initiation had modestly but significantly lower baseline levels of nucleolin, MDA5, c-reactive protein, and liver cytosol antigen type 1 (LC1) antibodies, as well as higher levels of heparin sulfate and Matrigel antibodies. After ICI initiation, antibiotic-treated patients had significantly lower levels of MDA5, CENP.B, and nucleolin antibodies. Although there were no clear differences in cytokines in the overall cohort, in the lung cancer subset (53% of the study population), we observed differences in IFN-γ, IL-8, and macrophage inflammatory proteins. In ICI-treated patients, antibiotic exposure is associated with changes in certain antibodies and cytokines. Understanding the relationship between these factors may improve the clinical management of patients receiving ICI.

Systemic and Intracranial Efficacy of Osimertinib in EGFR L747P-Mutant NSCLC: Case Report.

Introduction: EGFR L747P mutations occur rarely, with limited preclinical research and case reports suggesting resistance to osimertinib. Main Concerns Important Clinical Findings Primary Diagnoses Interventions Outcomes: An 84-year-old white male with remote smoking history presented with bilateral pulmonary nodules and multiple subcentimeter enhancing brain lesions 2 years after receiving stereotactic radiation therapy for a left upper lobe lung adenocarcinoma. After two computed tomography-guided biopsies yielded inadequate tissue and cell-free DNA analysis identified no actionable alterations, surgical biopsy results revealed an EGFR L747P mutation. Limited case reports and preclinical data suggested that this rare mutation may be resistant to the third-generation EGFR inhibitor osimertinib and recommended use of second-generation EGFR inhibitors. Because the patient had low disease burden and there were concerns on tolerability of second-generation EGFR inhibitors, the patient was initiated on osimertinib. Treatment was well-tolerated and follow-up imaging results revealed thoracic and intracranial response to therapy, which has been sustained 6 months after treatment initiation. Conclusion: Despite predicted and previously reported resistance, osimertinib may have durable efficacy against rare EGFR L747P mutations. Persistent attempts to acquire material for tumor genomic analysis may yield results critically important to clinical management.