Citations

Abstract Lung diseases, including lung cancer, are rising causes of global mortality. Despite novel imaging technologies and the development of biomarker assays, the detection of lung cancer remains a significant challenge. However, the lung communicates directly with the external environment and releases aerosolized droplets during normal tidal respiration, which can be collected, stored and analzsed as exhaled breath condensate (EBC). A few studies have suggested that EBC contains extracellular vesicles (EVs) whose microRNA (miRNA) cargos may be useful for evaluating different lung conditions, but the cellular origin of these EVs remains unknown. In this study, we used nanoparticle tracking, transmission electron microscopy, Western blot analyses and super resolution nanoimaging (ONi) to detect and validate the identity of exhaled EVs (exh‐EVs). Using our customizable antibody‐purification assay, EV‐CATCHER, we initially determined that exh‐EVs can be selectively enriched from EBC using antibodies against three tetraspanins (CD9, CD63 and CD81). Using ONi we also revealed that some exh‐EVs harbour lung‐specific proteins expressed in bronchiolar Clara cells (Clara Cell Secretory Protein [CCSP]) and Alveolar Type II cells (Surfactant protein C [SFTPC]). When conducting miRNA next generation sequencing (NGS) of airway samples collected at five different anatomic levels (i.e., mouth rinse, mouth wash, bronchial brush, bronchoalveolar lavage [BAL] and EBC) from 18 subjects, we determined that miRNA profiles of exh‐EVs clustered closely to those of BAL EVs but not to those of other airway samples. When comparing the miRNA profiles of EVs purified from matched BAL and EBC samples with our three tetraspanins EV‐CATCHER assay, we captured significant miRNA expression differences associated with smoking, asthma and lung tumor status of our subjects, which were also reproducibly detected in EVs selectively purified with our anti‐CCSP/SFTPC EV‐CATCHER assay from the same samples, but that confirmed their lung tissue origin. Our findings underscore that enriching exh‐EV subpopulations from EBC allows non‐invasive sampling of EVs produced by lung tissues.

Abstract Background: Non-small cell lung cancer (NSCLC) makes up ~85% of lung cancers, and its major subtypes are adenocarcinoma (AD) and squamous cell carcinoma (SC). The histology classification of NSCLC has significant therapeutic and prognostic implications. As liquid biopsies are rapid and non-invasive, computational methods to classify NSCLC subtypes using cell-free DNA (cfDNA) are desired. Methods: Using The Cancer Genome Atlas (TCGA) NSCLC methylation data, we developed a method to classify AD and SC subtypes from cfDNA samples. We mapped TCGA methylation data to Guardant Infinity’s promoter regions and transformed them to allow for the transfer of the TCGA trained model to Infinity cfDNA epigenomic data. An advantage of this approach is that TCGA training data originates from the primary tumor, while cfDNA samples have low tumor fraction (TF) and may also contain signals from metastatic tumors. We trained a penalized Logistic Regression model with LASSO. To better fit the model to Infinity cfDNA data, we performed region filtering. Results: Our model effectively classified AD and SC subtypes from TCGA samples (AUC 0.97) (10-fold cross validation; Table 1). When evaluating on cfDNA samples with epigenomic TF ≥ 1%, the final model shows reasonable performance (AUC 0.94) (Table 1); the performance decreases when reduced to TF > 0.05% (AUC 0.89) as expected (Table 1). When evaluating cfDNA from uncategorized lung samples, the predicted AD probabilities have significant association with KRAS mutations (p-value = 0.027). Conclusion: Our results show that the penalized Logistic Regression model trained with tissue TCGA methylation data through mapping, transformation, and region selection can classify NSCLC subtypes from cfDNA samples. This method may offer a fast, non-invasive tool to inform subtype-based clinical decisions, especially in the setting of poorly-differentiated carcinomas, and to identify subtype switches upon resistance to targeted therapies. Table 1: Model evaluation on tissue TCGA samples and cfDNA Infinity samples (different TF cutoffs) Sample type N samples AUC Accuracy Sensitivity Specificity Precision F1 score tissue TCGA 907 0.97 0.93 0.94 +/- 0.02 0.93 +/- 0.02 0.94 +/- 0.02 0.94 +/- 0.01 cfDNA Infinity 17 (TF ≥ 1%) 0.94 0.88 0.83 +/- 0.3 0.91 +/- 0.17 0.83 +/- 0.3 0.83 +/- 0.21 cfDNA Infinity 29 (TF > 0.05%) 0.89 0.83 0.88 +/- 0.23 0.81 +/- 0.17 0.64 +/- 0.28 0.74 +/- 0.2 Citation Format: Laura Tung, Denis Tolkunov, Catalin Barbacioru, Deepika Kale, Scott Shell, Jill Tsai, Leylah Drusbosky, Kaushal Parikh, Han-Yu Chuang. Non-small cell lung cancer (NSCLC) histology classification using DNA methylation data captured from liquid biopsies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3499.

Background Patients with paraneoplastic syndromes (PNS) are excluded from clinical trials involving immune checkpoint inhibitors (ICIs) due to safety concerns. Moreover, real-world data on efficacy and safety is scarce. Methods In this retrospective study, data were collected on patients with PNS and solid tumors receiving ICI between 2015 and 2022 at nine institutions. Patients were classified into: Cohort 1 (pre-existing PNS before ICI initiation), cohort 2 (PNS during ICI treatment), and cohort 3 (PNS after ICI discontinuation). Patients with metastatic non-small cell lung cancer (NSCLC) (mNSCLC) from cohort 1 were matched to patients who were PNS-free at each institution up to a 1:3 ratio for age, sex, type of ICI, use of concurrent chemotherapy, and number of lines of systemic therapy prior to ICI initiation. Kaplan-Meier method was used to assess overall survival (OS) and time-to-next treatment (TTNT). Results Among 109 patients with PNS treated with ICIs, median age at ICI initiation was 67 years (IQR: 58–74). The most represented cancer type was NSCLC (n=39, 36%). In cohort 1 (n=55), PNS exacerbations occurred in 16 (29%) patients with median time to exacerbation after ICI of 1.1 months (IQR: 0.7–3.3). Exacerbation or de novo PNS prompted temporary/permanent interruption of ICIs in 14 (13%) patients. For cohort 2 (n=16), median time between ICI initiation and de novo PNS was 1.2 months (IQR: 0.4–3.5). Treatment-related adverse events (trAEs) occurred in 43 (39%) patients. Grade ≥3 trAEs occurred in 18 (17%) patients. PNS-directed immunosuppressive therapy was required in 55 (50%) patients. We matched 18 patients with mNSCLC and PNS (cohort 1) to 40 without PNS, treated with ICIs. There was no significant difference in OS or TTNT between patients with mNSCLC with and without PNS, although a trend was seen towards worse outcomes in patients with PNS. TrAEs occurred in 6/18 (33%) and 14/40 (35%), respectively. Grade ≥3 trAEs occurred in 4 (22%) patients with PNS and 7 (18%) patients without PNS. Conclusions Exacerbations of pre-existing PNS occurred in 29% of patients treated with ICIs and both exacerbations and de novo PNS occur early in the ICI course. TrAE from ICIs were similar between patients with and without PNS. Our data suggest that pre-existing PNS should not preclude consideration of ICI therapy although patients may not derive the same clinical benefit compared with patients without PNS.

12051 Background: The combination of ipilimumab and nivolumab (Ipi-Nivo) is a standard of care regimen in many malignancies. However, immune-related adverse events (irAE) are common and are of great concern, especially when treating the elderly population. This subgroup of patients may be at greater risk for serious irAEs and be less able to tolerate the potential treatment side effects. We sought to retrospectively evaluate our experience treating elderly patients ≥ 80 years with Ipi-Nivo to characterize adverse events. Methods: A retrospective chart review of the electronic medical records of all patients treated with Ipi-Nivo at the Mayo Clinic Health System was performed. Patients with any solid tumor who received their first dose of Ipi-Nivo at the age of ≥ 80 were included. Records were analyzed for baseline characteristics, cancer type, toxicity, and response to therapy. Differences between continuous and categorical variables were assessed using the students’ t-test and the chi-squared test, respectively. Overall survival (OS) was estimated using the Kaplan-Meier method. Cox proportional hazard models assessed the relative contribution of factors to survival after treatment. P values < 0.05 were considered statistically significant. All analyses were performed using SAS V.9.4. Results: Forty-six patients met inclusion criteria, 13 females and 33 males. Median age of first dose was 83 (Range: 80 - 94). Sixty-seven percent had an ECOG performance status (PS) 0-1 and 33% had an ECOG PS 2-3. Thirteen (28.3%) patients received 4 cycles of Ipi-Nivo. Cancers included: 18 (40%) melanomas, 10 (21%) mesothelioma, 9 (19.5%) lung cancers, and 9 (19.5%) other types. Thirty-one patients (67%) developed at least one irAE. Of the irAEs, 8 (17.4%) were grade 1, 20 (43.5%) were grade 2, 14 (30.4%) were grade 3, and 3 (6.5%) were grade 4. Systemic steroids were used in 29 (63%) patients. Twenty-six (56.5%) patients were hospitalized for any cause, and 12 (26.1%) were hospitalized due to an irAE. Of the 26 patients hospitalized during treatment, 19 (73%) died within 1 year of discharge; 11 (42%) died within < 1 month of discharge and 3 (12%) died within 1-3 months of discharge. Median OS from the date of first Ipi-Nivo dose was 14.6 months in the ECOG PS 0-1 and 6.5 months in the ECOG PS 2-3. ECOG PS of 0-1 was associated with a significantly decreased risk of mortality (HR [hazard ratio] 0.33, 95 % CI [confidence interval]: 0.15-0.70; p=0.0025) compared to ECOG PS of 2-3. Conclusions: This is the first analysis to evaluate the outcomes of Ipi-Nivo in patients ≥ 80 years. Approximately 60% of the patients required systemic steroids and a quarter were hospitalized with grade 3-4 irAEs. As expected, patients with ECOG PS 2-3 did significantly worse than ECOG PS 0-1. Ipi-Nivo should be used with caution in patients ≥ 80 years.

9029 Background: Molecular biomarkers such as EML4-ALK fusion variant 3 (V3) and TP53 mutation are associated with poor prognosis in patients (pts) with anaplastic lymphoma kinase fusion positive ( ALK+) non–small cell lung cancer (NSCLC). We evaluated the effect of EML4-ALK fusion variants and TP53 mutation status on time to treatment discontinuation (TTD) of first-line (1L) treatment with a next-generation ALK tyrosine kinase inhibitor (TKI) in pts with ALK+ NSCLC in the real-world setting. Methods: Pts with advanced/metastatic NSCLC who were ALK+ in circulating tumor DNA by Guardant360 (G360) liquid biopsy test from Jan 1, 2018, to Dec 31, 2021, and received 1L monotherapy with a next-generation ALK TKI (alectinib, brigatinib, ceritinib, lorlatinib) were identified in the Guardant INFORM clinical-genomics database. Median TTD of 1L ALK TKI treatment was determined by Kaplan-Meier (KM) methods. The impact of EML4-ALK fusion variant and TP53 mutation status on TTD was evaluated. Results: Database search identified 495 pts with advanced/metastatic NSCLC who had ALK fusion by G360; of these, 214 pts received 1L treatment with a next-generation ALK TKI; 164 had G360 testing before treatment (alectinib, n=147; brigatinib, n=7; ceritinib, n=2; lorlatinib, n=8). Among these 164 pts, median age was 58.6 y, 53% were female, and 29% had baseline brain metastases. Median follow-up from start of 1L treatment was 14.8 mo. Median TTD was 19.6 mo (95% CI: 13.6–not reached [NR]). TP53 mutations were detected in 66 pts (40%). Median (95% CI) TTD in pts with TP53 mutation vs WT was 17.1 (9.5–28.5) vs 21.9 mo (14.3–NR; log-rank P=0.1712). Of 130 pts with EML4-ALK fusion variant type detected, 63 (48%) had V1, 54 (42%) V3, 8 (6%) V2, 4 (3%) V5, and 1 (1%) V8. In pts with EML4-ALK fusion V1 vs V3, median (95% CI) TTD was 19.0 (13.6–NR) vs 21.8 mo (8.7–NR; HR: 0.73; log-rank P=0.3039), with the KM plot showing numerically longer TTD in pts with V1 vs V3 during the first 18 mo and lines crossing at the tail end. Of 117 pts with both an EML4-ALK fusion variant (V1/V3) and TP53 data, 21 (18%) had both V3 and a TP53 mutation. Pts with co-occurring EML4-ALK V3 and TP53 mutation had the shortest TTD (median TTD 8.7 [3.9–NR; HR: 2.59; P=0.0219]; Table). Conclusions: Among pts with NSCLC and ALK fusion, co-occurrence of EML4-ALK V3 and TP53 mutation is common and associated with poor prognosis and high risk of early discontinuation of 1L treatment with a next-generation ALK TKI, most likely due to disease progression. Additional novel or combination therapies are warranted for this subpopulation. [Table: see text]

8567 Background: Adjuvant osimertinib (osi) improves disease-free survival (DFS) in patients (pts) with resected, early-stage, EGFR-mutant (EGFRmut) NSCLC, yet the benefit of osi after CRT in pts with unresectable locally advanced NSCLC is unknown. Post-hoc analysis of the PACIFIC trial showed a lack of survival benefit with consolidation durva versus placebo in pts with EGFRmut NSCLC. Comparisons between consolidation durva and EGFR TKIs in unresectable EGFRmut NSCLC following CRT are lacking. Methods: We conducted a multi-institutional retrospective analysis of pts with stage III unresectable EGFRmut NSCLC (exon19 deletion, exon21 L858R), who received EGFR TKI or durva after ≥ 2 cycles of platinum-based chemotherapy plus definitive radiation therapy between 2015-2022. Baseline characteristics including age, sex, smoking history, PD-L1 status, and outcomes on DFS, overall survival (OS), and safety were collected. Multivariable (MVA) cox regression analysis was used for statistical analysis. Treatment-related adverse events (trAE) were defined using CTCAE 5.0. Results: Seventeen pts from 12 institutions received an EGFR TKI (osi, n=15; erlotinib, n=2), and 13 pts received consolidation durva. Median follow-up was 23 months. Median age of all pts was 61 years (IQR:52-72) and 76.7% were female. Most pts in both groups had never-smoked and had adenocarcinoma. All pts received ≥ 60 Gy of radiation with concurrent chemotherapy. PD-L1 expression was ≥ 50% in 1/10 (10%) pts treated with durva vs 6/13 (46.2%) treated with TKI, p=0.09. Median duration on treatment for EGFR TKI and durva was 12.2 months and 4.8 months, respectively. Pts treated with EGFR TKI had significantly longer 24-month DFS versus pts treated with durva after adjusting for stage (3A vs 3B vs 3C) (Table). Any grade tRAE occurred in 58.8% (10/17) of pts treated with EGFR TKI vs 38.5% (5/13) with durva. Grade > 3 tRAE occurred in 15.4% (1 pneumonitis and 1 AST/ALT elevation) of pts treated with durva but in none of pts treated with EGFR TKI. Three pts within each arm came off treatment due to toxicity (EGFR TKI: 1 pneumonitis and 2 dermatitis; durva: 1 of each pneumonitis, Type 1 diabetes, AST/ALT elevation). Conclusions: EGFR TKI therapy after definitive CRT was associated with significantly longer DFS compared to durva in this retrospective study of pts with stage III unresectable EGFRmut NSCLC, without unanticipated safety signals. Follow-up is ongoing and OS outcomes will be evaluated subsequently. Further investigation is warranted to define the optimal therapy for locally advanced EGFRmut NSCLC. [Table: see text]

e13559 Background: Recent advances in targeted therapies have increased the survival and quality of life for non-small cell lung cancer (NSCLC) patients. Understanding clinical characteristics and predicting HER2 status may facilitate efficient use of next-generation sequencing (NGS) testing and promote precision medicine/targeted therapies. Methods: NSCLC patients ≤65 years old, diagnosed between 01/2011 and 06/2021 who have NGS test results were identified in from two National Cancer Institute designated cancer centers in the Northeastern region of the United States. Demographics, behavioral, and clinical characteristics were extracted from electronic health record (EHR) data and EHR-linked cancer registry. Prevalence prediction modelling of HER2/ERBB2 mutation was performed using machine learning (ML) methods including logistic regression (LR), random forest (RF), naïve Bayes (NB), extreme gradient boosting (XGBoost) and multi-layer perceptron (MLP). 10- fold cross validation and k-nearest neighbor method were used to impute missing values. Under-sampling, oversampling and feature selection methods were tried. Results: Among 761 NSCLC patients with NGS reports available, 353 (46%) were ≤65 years old. Among 353 (mean age 57, 58% female, 88% non-Hispanic, 69% white, 57% had metastasis, 58% prescribed chemotherapy) 25 (7%) had a HER2 mutation. Based on univariate significance tests with LR on the complete data: cerebrovascular disease, BMI between 27.5-29.9, KRAS, EGFR and MET were significant with odds ratios (ORs) ranging from 0.2-3.9. In our prediction models, BMI, cerebrovascular disease, weight loss, hepatic metastases, diabetes, congestive heart failure, and KRAS, EGFR, MET and RET alteration status were significant predictors in multivariate analyses, suggesting the potential usefulness of these features in predicting HER2 mutation among patients with NSCLC. Area under the ROC curve (AUC) varied across different models from 70.9% (RF), 70% (XGB and MLP), 69.5% (LR) to 68.2% (Naïve Bayes). The best performing model with RF yielding an AUC ~71% can provide relative high specificity and acceptable sensitivity depending on the cut off. Conclusions: Our prediction models yielded AUC around 70% with the best performing model’s AUC being 71% in a subgroup aged ≤65. After further validation, our prediction model may be used to identify patients appropriate for repeat NGS testing who did not have HER2 testing in first molecular profiling. Future work is needed to predict status in repeat testing with previously test negative patients who relapse or have progression of disease. Random forest (RF) Model performance across different cutoff for the predicted probability. [Table: see text]

The treatment metastatic non-small cell lung cancer (NSCLC) is largely influenced by the incorporation of immune checkpoint inhibitors (ICI) in the frontline setting. There are several ICI approved for the management of NSCLC based on the PD-L1 expression of the tumors. PD-L1 is a controversial biomarker with various inconsistencies in expression owing to temporal and spatial heterogeneity. Tumor mutational burden is another much studied biomarker associated with its own challenges and questionable concordance with tumor PD-L1 expression. In this article, we aim to discuss the challenges associated with the existing biomarkers, highlighting the need for emerging biomarkers that can help with decision making in the management of this there where several therapeutic options exist. There are emerging “me too” PD-1/PD-L1 drugs which may serve its purpose in many counties where there is limited access to current approved ICIs. What is increasingly apparent is the need to move the needle forward in the treatment of NSCLC and we will discuss the challenges associated with the current therapeutic landscape and the emerging checkpoints and the future directions that are being explored in the management of metastatic NSCLC.