Correlation of variant status (v3 vs non-v3) with somatic co-mutation in liquid biopsy cohort

Clonal and non-clonal ALK resistance mutations across patients with multiple resistance mutations. Each ALK mutation is plotted by sample and colored by putative clonal classification based on the maximum difference between resistance mutation mutant allele frequencies (MAFs). Inset table provides the clonal and non-clonal patient counts for those with v1/v3 variant types. Chi-squared statistic indicates there is no significant association with resistance clonality and fusion type.

Association of specific co-alterations with presence of ALK resistance mutations and variant type in EML-4-ALK samples (n=1118). Co-alterations of interest were (A) TP53 mutation, (B) PIK3CA mutation, (C) Wnt/B-catenin/PIK3CA pathway mutations [APC, CTNNB1, PIK3CA], (D) MET or MYC amplifications, and (E) cell cycle loss of function alterations [CDK4/6 or CDK2NA/B loss or loss of function mutations]. P-value is derived from Fisher's test. Significance at the level of p < 0.05 is indicated by the asterisk (*). Abbreviations: WT, wild-type.

Distribution of tissue NGS platforms utilized in the clinical cohort. Includes 10 patients who underwent testing with more than one tissue NGS platform. Other commercial NGS platform includes other commercially available platforms. Other institutional NGS platform includes all other academic in house NGS platforms.

Introduction: Up to 20% of EGFR-mutated NSCLC cases harbor uncommon EGFR mutations, including atypical exon 19 and compound mutations. Relatively little is known about the ef ficacy of osimertinib in these cases. Methods: Patients treated with first-line osimertinib for NSCLC with rare EGFR exon 19 (non E746_A750del) or compound mutations were included. Response assessment and time to progression were determined using Response Evaluation Criteria in Solid Tumors version 1.1 criteria. Kaplan -Meier analyses were used to estimate progressionfree survival (PFS), time to treatment discontinuation (TTD), and overall survival (OS). Results: Thirty-seven patients with NSCLC harboring an atypical EGFR exon 19 mutation or compound mutation were treated with first-line osimertinib at Johns Hopkins from 2016 to 2021. Overall response rate (ORR) was 76% and median PFS, TTD, and OS were 13 months (95% con fidence interval [CI]: 10 -15), 22 months (95% CI: 17 -32) and 36 months (95% CI, 29 -48), respectively. Among atypical exon 19 mutations (n = 25), ORR was 80%, median PFS was 12 months (95% CI: 10 -15), median TTD was 19 months (95% CI: 17 -38), and median OS was 48 months (95% CI: 25 -not reached). Compound mutations (n = 12) had an ORR of 67%, median PFS of 14 months (95% CI: 5 -22), median TTD of 26 months (95% CI: 5 -36), and median OS of 36 months (95% CI: 20 -46). Twelve patients (32%) continued first-line osimertinib after local therapy for oligoprogression. Conclusions: Osimertinib exhibited favorable outcomes for rare EGFR exon 19 and compound mutations. The heterogeneity in outcomes among these groups of tumors with similar mutations underscores the need for continued reporting and further study of outcomes among rare variants to optimize management for each patient. (c) 2024 The Authors. Published by Elsevier Inc. on behalf of the International Association for the Study of Lung Cancer. This is an open access article under the CC BY -NC -ND license (http://creativecommons.org/licenses/by-nc-nd/ 4.0/).

CONSORT diagram depicting inclusion and exclusion criteria for clinical cohort

Kaplan-Meier curve for (A) Overall survival (OS), (B) first-line TKI progression-free survival (PFS) and (C) PFS on first-line alectinib or brigatinib by PD-L1 high (TPS > 50%) status

Kaplan–Meier curve for OS and PFS on first-line TKI by EML4-ALK v3 status (A). B, OS and PFS subgroup analysis by Cox regression. HR, unadjusted hazard ratio; CI, confidence interval.

Abstract While tyrosine kinase inhibitors (TKI) have shown remarkable efficacy in anaplastic lymphoma kinase (ALK) fusion-positive advanced non–small cell lung cancer (NSCLC), clinical outcomes vary and acquired resistance remains a significant challenge. We conducted a retrospective study of patients with ALK-positive NSCLC who had clinico-genomic data independently collected from two academic institutions (n = 309). This was paired with a large-scale genomic cohort of patients with ALK-positive NSCLC who underwent liquid biopsies (n = 1,118). Somatic co-mutations in TP53 and loss-of-function alterations in CDKN2A/B were most commonly identified (24.1% and 22.5%, respectively in the clinical cohort), each of which was independently associated with inferior overall survival (HR: 2.58; 95% confidence interval, CI: 1.62–4.09 and HR: 1.93; 95% CI: 1.17–3.17, respectively). Tumors harboring EML4-ALK variant 3 (v3) were not associated with specific co-alterations but were more likely to develop ALK resistance mutations, particularly G1202R and I1171N (OR: 4.11; P < 0.001 and OR: 2.94; P = 0.026, respectively), and had inferior progression-free survival on first-line TKI (HR: 1.52; 95% CI: 1.03–2.25). Non-v3 tumors were associated with L1196M resistance mutation (OR: 4.63; P < 0.001). EML4-ALK v3 and somatic co-alterations in TP53 and CDKN2A/B are associated with inferior clinical outcomes. v3 status is also associated with specific patterns of clinically important ALK resistance mutations. These tumor-intrinsic features may inform rational selection and optimization of first-line and consolidative therapy. Significance: In a large-scale, contemporary cohort of patients with advanced ALK-positive NSCLC, we evaluated molecular characteristics and their impact on acquired resistance mutations and clinical outcomes. Our findings that certain ALK variants and co-mutations are associated with differential survival and specific TKI-relevant resistance patterns highlight potential molecular underpinnings of the heterogenous response to ALK TKIs and nominate biomarkers that may inform patient selection for first-line and consolidative therapies.

A,EML4-ALK variant type observed in clinical and liquid biopsy cohorts. B, Clinically relevant somatic co-mutations observed in clinical and liquid biopsy (LB) cohorts. Copy-number deletion/loss not reported in the liquid biopsy cohort; loss-of-function mutations in CDKN2A/B, PTEN, and CTNNB1 are included.

Baseline characteristics of patients with ALK-positive NSCLC in clinical cohort by EML4-ALK v1 and v3 subgroups

Distribution of resistance ALK mutations by EML4-ALK variant subtype in (A) clinical and (B) liquid biopsy cohorts. P-value is derived from Fisher's test of v3 vs non-v3. Significance at the level of p < 0.05 is indicated by the asterisk (*).

ALK resistance mutations observed in clinical (n = 26; A) and liquid biopsy (n = 202; B) cohorts, by patient (columns) and grouped by EML4-ALK variant type. Filled regions of the oncoprint indicate presence of the ALK resistance mutation designated by the row name for a given patient. Resistance mutation percentages indicate share of each among total patients, by cohort. Both G1202R and I1171N are associated with EML4-ALK v3 and L1196M is associated with EML4-ALK non-v3.

(A) Samples with single versus multiple ALK resistance mutations in liquid biopsy cohort. (B) Distribution of patients with multiple ALK resistance mutations across the ALK resistance cohort by fusion variant type, with percentages above each variant type column indicate the proportion of patients with multiple ALK resistance mutations. The inset chi-squared statistic demonstrates no significant association between multiple resistance mutations and variant type. (C) Distribution of most common ALK resistance co-mutation pairs

Kaplan–Meier curve for OS (A) and PFS (B) on first-line TKI by TP53 mutation and CDKN2A/B loss.

Kaplan-Meier curve for progression-free survival (PFS) on first-line alectinib or brigatinib by (A) variant type, (B) TP53 mutation, and (C) CDKN2A/B mutation status

Summary of adjusted and unadjusted hazard ratios (HR) with respective confidence intervals as generated by Cox proportional-hazards survival models. Clinically relevant variables and their relative effect sizes on (A) overall survival (OS), (B) progression-free survival (PFS) on first-line tyrosine kinase inhibitor (TKI), and (C) PFS on first-line alectinib or brigatinib. Significance at the level of p < 0.05 is indicated by the asterisk (*).

138 Background: Biomarker testing is necessary for optimal 1 st line treatment selection in advanced NSCLC. Patient understanding of timing and purpose of biomarker testing is imperative for patient engagement in care but is challenging to achieve (1). The 4R Oncology model of patient self-management and timely care delivery has been shown to improve patient knowledge of timing/sequence of care in breast cancer (2) but has not been studied in NCLSC in relation to biomarker testing. 4R is being implemented at 5 centers (3 community and 2 academic) in NSCLC. We report baseline assessment of patient awareness of biomarker testing timing and purpose to inform 4R implementation. Methods: Pre-implementation surveys of patients with advanced NSCLC at 5 centers Aug – Dec 2022. Metrics are listed in Table. Results: Survey response rate: 46% (65/142). Respondents were 65% White, 42% high school educated or less, 46% with annual income ≤$30K. While all patients were aware of receiving non-biomarker tests, >40% were not aware of receiving biomarker testing (Table). About half knew why or how long they need to wait for results, but only 29% received this information from providers. Of those tested, the majority reported provider discussions of biomarkers or therapy selection and were clear about results or therapy selection. Awareness (eg, knowing to wait for results before therapy) was associated with receiving printed NSCLC materials (45% vs 7%, p<.01), education above high school (69% vs 41%, p=.03) and income >$30K (67% vs 32%, p=.01). Willingness to wait for test results was associated with knowing how to prepare for treatment while waiting (89% vs 46%, p<.01). Race did not impact these metrics. Conclusions: Considerable gaps exist in patient awareness of timing and purpose of biomarker testing. We refined the 4R model to emphasize biomarker timing in printed visual material, facilitate patient-provider discussions and engage patients in health maintenance while waiting for results. We revised 4R to a lower literacy level. Results will be reported when available. (1) Martin, Oncol Issues, 2022. (2) Trosman JCOOP 2021.[Table: see text]

e21059 Background: Biomarker testing is necessary for optimal 1 st line treatment selection in advanced NSCLC. Patient understanding of timing and purpose of biomarker testing is imperative for patient engagement in care, but is challenging to achieve (Martin Oncol Issues 2022). The 4R Oncology model of patient self-management and timely care delivery has been shown to improve patient knowledge of timing/sequence of care in breast cancer (Trosman JCOOP 2021) but has not been studied in NCLSC in relation to biomarker testing. 4R is being implemented at 5 centers (3 community and 2 academic) in NSCLC. We report baseline assessment of patient awareness of biomarker testing timing and purpose to inform 4R implementation. Methods: Pre-implementation surveys of patients with advanced NSCLC at 5 centers Aug – Dec 2022. Metrics are listed in Table. Results: Survey response rate: 46% (65/142). Respondents were 65% white, 42% high school educated or less, 46% with annual income ≤$30K. While all patients were aware of receiving non-biomarker tests, > 40% were not aware of receiving biomarker testing (Table). About half knew why or how long they needed to wait for results, but only 29% received this information from providers. Of those tested, the majority reported provider discussions of biomarkers or therapy selection and were clear about results or therapy selection. Awareness (eg knowing to wait for results before therapy) was associated with receiving printed NSCLC materials (45% vs 7%, p < .01), education above high school (69% vs 41%, p = .03) and income > $30K (67% vs 32%, p = .01). Willingness to wait for test results was associated with knowing how to prepare for treatment while waiting (89% vs 46%, p < .01). Race did not impact these metrics. Conclusions: Considerable gaps exist in patient awareness of timing and purpose of biomarker testing. We refined the 4R model to emphasize biomarker timing in printed visual material, facilitate patient-provider discussions and engage patients in health maintenance while waiting for results. We revised 4R to a lower literacy level. Results will be reported when available. [Table: see text]

Cancer costs in the United States continue to escalate at an alarming and unsustainable rate. These costs are not driven exclusively by a higher demand for services or by an aging population; rather, a number of systemic failures, highlighted by the Institute of Medicine (IOM) continue to plague our cancer care delivery systems and need to be rectified. Drug costs, plus expensive diagnostic tests, hospital admissions/readmissions, and unreasonable end-of-life care, combine to inflate the total cost of care. Cancer, particularly lung cancer, is one of the most expensive diseases in the United States. While individual oncologists are unlikely to influence costs in the short term, they can become more proficient at evaluating the value derived from new treatment options and maximizing the clinical benefit for their patients. Discussions of cost and patient values need not hinder patient-physician relationships, and, in fact, can strengthen them. This article discusses ways in which the oncologist can incorporate value into the management of patients with lung cancer and comply with the underlying principles of the Choose Wisely Campaign, as well as recent American Society of Clinical Oncology and European Society for Medical Oncology initiatives, to bend the cost curve downwards while maintaining efficacy.