Reinmuth, Niels

Abstract Introduction We describe the safety of sotorasib monotherapy in patients with KRAS G12C-mutated advanced non-small cell lung cancer (NSCLC) and discuss practical recommendations for managing key risks. Methods Incidence rates of treatment-related adverse events (TRAEs) were pooled from 4 clinical trials: CodeBreaK 100 (NCT03600883), CodeBreaK 101 (NCT04185883), CodeBreaK 105 (NCT04380753), and CodeBreaK 200 (NCT04303780) and graded according to CTCAE v5.0. Adverse events were deemed sotorasib-related per investigator causality assessment. Results In the pooled population (n = 549), TRAEs were reported in 388 (70.7%) patients (grade 1: 124 [22.6%]; grade 2: 117 [21.3%]; grade ≥ 3: 147 [26.8%]). Gastrointestinal and hepatic TRAEs, including diarrhea (171 [31.1%]), nausea (80 [14.6%]), elevated alanine aminotransferase (ALT; 68 [12.4%]), and elevated aspartate aminotransferase (AST; 67 [12.2%]) were the most common (≥10%). Dose interruption and dose reduction of sotorasib resulted in the resolution of >90% of diarrhea events; median time to resolution were 18.0 days and 22.0 days, respectively. Similar trends were observed for elevated ALT and AST events. Patients who stopped immunotherapy <3 months before initiating sotorasib had a higher incidence of treatment-related hepatotoxicity (80/240 [33.3%]) than those who stopped immunotherapy ≥3 months before initiating sotorasib (26/188 [13.8%]). Treatment-related pneumonitis/interstitial lung disease (ILD) and corrected QT (QTc) prolongation were observed in 9 (1.6%) and 4 (0.7%) patients, respectively. Two (0.4%) patients died with TRAEs, 1 with ILD whose ultimate cause of death was disease progression, and the other with an unknown cause. Conclusions Sotorasib has a well-characterized safety profile in patients with KRAS G12C-mutated advanced NSCLC, and key risks are manageable with dose modification.

Abstract Background: Immune checkpoint inhibitors (ICIs) have significantly improved outcomes for patients with metastatic non-small cell lung cancer (NSCLC). However, clinical management is challenging. The PD-L1 biomarker does not optimally guide the choice between ICI monotherapy and combination ICI-chemotherapy, and there are currently no biomarkers to predict immune-related adverse events (irAEs). Here, we describe a versatile platform based on plasma proteomics and machine learning for generating predictive tools to support treatment decisions. Methods: Pre-treatment plasma samples were collected from 616 NSCLC patients receiving ICI-based therapy (NCT04056247). Clinical benefit (CB) data and irAEs were recorded. Proteomic profiling of plasma samples was performed using the SomaScan® assay, covering ~7000 proteins per sample. Proteins associated with Clinical Benefit (progression-free survival at 12 months) were classified as resistance-associated proteins (RAPs). Proteins associated with grade ≥3 irAEs occurring within the first 100 days of treatment were classified as toxicity-associated proteins (TAPs). RAPs and TAPs were integrated into two separate machine learning-based models, yielding CB and irAE probabilities, respectively. The CB model used a CB probability threshold to assign patients a PROphet-positive or PROphet-negative result. Bioinformatic analysis was performed on RAPs and TAPs. Results: CB and irAE models displayed strong predictive performance with a high correlation between predicted probability and observed CB or irAE rate (CB model: R2=0.98; p-value<0.0001; irAE model: R2= 0.92; p-value <0.0001). In the CB model, patients classified as PROphet-positive achieved significantly longer overall survival (OS) than PROphet-negative patients, with a median OS of 25.9 vs. 10.8 months (hazard ratio, HR = 0.51; p-value<0.001). Furthermore, patients with PD-L1 ≥50% tumors and a PROphet-negative result had significantly longer OS with ICI-chemotherapy in comparison to ICI monotherapy (HR=0.23, p-value = 0.0003), suggesting that combination therapy is preferable for such patients despite high PD-L1 levels. Bioinformatic analysis showed that RAPs related to immune regulation, angiogenesis, chemo-resistance, and other potential resistance mechanisms displayed higher expression levels in patients who did not benefit from treatment. Neutrophil- and inflammation-related proteins were significantly enriched in patients who experienced irAEs. Conclusions: We describe two machine-learning based predictive tools that may be used separately or in combination to inform treatment decisions based on the patient’s likelihood to benefit from ICI therapy and to develop serious irAEs. Our findings also provide biological insights related to treatment resistance and immune-related toxicity. Citation Format: Michal Harel, Petros Christopoulos, Igor Puzanov, Jair Bar, Iris Kamer, Niels Reinmuth, Ina Koch, Mor Moskovitz, Adva Levy-Barda, Alona Zer, Michal Lotem, David Farrugia, Rivka Katzenelson, Gillian Price, Abed Agbarya, Helen Cheley, Adam Hassani, Mahmoud Abu-Amna, Tom Geldart, Anirban Chatterjee, Andreas Polychronis, Maya Gottfried, Yanyan Lou, Tatiana Harkovsky, Alison Brewster, Ido Wolf, Ella Tepper, Ben Yellin, Itamar Sela, Coren Lahav, Yehonatan Elon, Raya Leibowitz, Adam P. Dicker, Young Kwang Chae, Ryan J. Sullivan, David Carbone, David Gandara, Jarushka Naidoo. Plasma proteomics-based models for predicting therapeutic benefit and immune-related adverse events in non-small cell lung cancer patients treated with immunotherapy [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 1208.

PURPOSE Current guidelines for the management of metastatic non–small cell lung cancer (NSCLC) without driver mutations recommend checkpoint immunotherapy with PD-1/PD-L1 inhibitors, either alone or in combination with chemotherapy. This approach fails to account for individual patient variability and host immune factors and often results in less-than-ideal outcomes. To address the limitations of the current guidelines, we developed and subsequently blindly validated a machine learning algorithm using pretreatment plasma proteomic profiles for personalized treatment decisions. PATIENTS AND METHODS We conducted a multicenter observational trial (ClinicalTrials.gov identifier: NCT04056247 ) of patients undergoing PD-1/PD-L1 inhibitor–based therapy (n = 540) and an additional patient cohort receiving chemotherapy (n = 85) who consented to pretreatment plasma and clinical data collection. Plasma proteome profiling was performed using SomaScan Assay v4.1. RESULTS Our test demonstrates a strong association between model output and clinical benefit (CB) from PD-1/PD-L1 inhibitor–based treatments, evidenced by high concordance between predicted and observed CB ( R 2 = 0.98, P < .001). The test categorizes patients as either PROphet-positive or PROphet-negative and further stratifies patient outcomes beyond PD-L1 expression levels. The test successfully differentiates between PROphet-negative patients exhibiting high tumor PD-L1 levels (≥50%) who have enhanced overall survival when treated with a combination of immunotherapy and chemotherapy compared with immunotherapy alone (hazard ratio [HR], 0.23 [95% CI, 0.1 to 0.51], P = .0003). By contrast, PROphet-positive patients show comparable outcomes when treated with immunotherapy alone or in combination with chemotherapy (HR, 0.78 [95% CI, 0.42 to 1.44], P = .424). CONCLUSION Plasma proteome–based testing of individual patients, in combination with standard PD-L1 testing, distinguishes patient subsets with distinct differences in outcomes from PD-1/PD-L1 inhibitor–based therapies. These data suggest that this approach can improve the precision of first-line treatment for metastatic NSCLC.

9122 Background: Initial treatment selection for advanced NSCLC patients without driver mutations is mainly based on evaluating PD-L1 expression levels in the tumor tissue. However, PD-L1 assays are only moderately predictive. In addition, the guidelines for the PD-L1≥50% subpopulation are not definitive, enabling the usage of immune checkpoint inhibitors (ICI) either as a monotherapy or combined with chemotherapy. Here we aim to provide a decision-making tool for the first-line treatment of advanced NSCLC patients based on plasma derived biomarkers obtained before treatment initiation. Methods: Pre-treatment plasma samples were collected from 545 NSCLC patients receiving ICI-based therapy. Clinical benefit was evaluated based on progression-free survival (PFS) at 12 months as a threshold. Deep plasma proteomic profiling was performed using aptamer technology. Based on the proteomic profiles, a computational model, termed PROphet, was developed and evaluated in a blinded manner on the validation subset (n = 272). The model output, clinical benefit probability, was used to stratify the patients into two groups, PROphet-positive or -negative. Results: The model displayed good performance with a high correlation between the predicted clinical benefit and the observed clinical benefit rate (R2= 0.97), outperforming a PD-L1-based prediction model (R2= 0.35). Patients classified as PROphet-positive achieved significantly longer overall survival (OS) than PROphet-negative patients, with a median OS of 25.9 versus 10.8 months (hazard ratio, HR = 0.51; 95% confidence interval, CI = 0.37-0.70; p-value < 0.001). Next, we examined the clinical utility of combining the PROphet output with PD-L1 levels by comparing different treatment modalities. Focusing on PD-L1≥50%, patients with PROphet-positive results displayed similar OS and PFS when receiving ICI monotherapy or ICI-chemotherapy (OS HR = 0.77; CI = 0.42-1.43; p-value = 0.4096), suggesting that these patients may consider monotherapy, thus avoiding chemotherapy-related toxicity. Conversely, PD-L1≥50% patients with a PROphet-negative result displayed a significantly longer OS and PFS when receiving ICI-chemotherapy in comparison to ICI monotherapy, with a median OS that was not reached versus 11.10 months in the combination therapy and monotherapy groups, respectively (HR = 0.29; CI = 0.14-0.59; p-value < 0.001). These patients should consider combination therapy, avoiding a treatment that is suboptimal for them. Results for PD-L1 1-49% and < 1% provide additional clinical information for each subgroup. Conclusions: Plasma proteomic profiling can provide biomarkers that accurately predict the benefit of ICI-based treatment. The model output complements tissue PD-L1 expression levels as a tool to assist therapeutic decisions.

Abstract Introduction: Treatment modality selection for metastatic NSCLC patients (immunotherapy alone vs. combination of immunotherapy with chemotherapy) relies mainly on determining the programmed cell death 1 (PD-L1) expression levels in the tumor. However, available assays are only moderately predictive. Here we set to develop PROphet®, a plasma proteomics-based predictive model for informing treatment decisions for NSCLC patients receiving immune checkpoint inhibitor (ICI)-based therapy. Methods: Pre-ICI plasma samples were collected in 12 centers in a clinical trial (NCT04056247) from 367 advanced-stage NSCLC patients. Clinical benefit (CB) was assessed at 12 months based on the occurrence of progression-free survival (PFS) until this time point. Deep proteomic profiling of the plasma samples was performed using aptamer-based technology. A novel machine learning model was developed to determine the CB probability for each patient, and the performance was successfully evaluated on an independent validation set, followed by training and prediction over the entire cohort using cross-validation. The resulting PROphet® score (positive or negative) was determined by setting the median CB rate probability as a threshold. The patients were divided into four subgroups based on their PD-L1 expression level combined with their PROphet® score prediction, and the overall survival (OS) was examined for each subgroup. Results: The PROphet® computational model was evaluated in a blinded manner on a subset of 85 patients and displayed strong predictive capability with area under the curve (AUC) of the receiver operating characteristics (ROC) plot of 0.78 (p-value = 5.00e-05), outperforming a PD-L1-based predictive model (AUC = 0.62; p-value 2.76e-01). When combining PROphet® score with PD-L1 expression levels, four different outcome patterns were identified: (i) Patients with PD-L1 ≥50% and PROphet® negative score, who displayed significantly longer OS when treated with ICI-chemotherapy combination therapy compared to ICI monotherapy and may consider combination therapy. (ii) Patients with PD-L1 ≥50% and PROphet® positive score, who benefit similarly from either treatment modalities, and may consider monotherapy to avoid the potential toxicity of the combination therapy. (iii) Patients with PD-L1&lt;50% and PROphet® negative score, who do not benefit from either treatment modalities and may consider chemotherapy alone or treatment beyond standard of care. (iv) Patients with PD-L1&lt;50% and PROphet® positive score who benefit from combination therapy. Conclusions: Altogether, the PROphet® model, when combined with PD-L1 test, stratifies the patients into four subgroups, providing additional resolution to the PD-L1 biomarker currently used to guide treatment selection. Furthermore, the model accurately predicts CB at 12 months based on proteomic analysis of a pre-treatment plasma sample. Citation Format: Michal Harel, Petros Christopoulos, Coren Lahav, Itamar Sela, Nili Dahan, Niels Reinmuth, Ina Koch, Alona Zer, Mor Moskovitz, Adva Levy-Barda, Michal Lotem, Hovav Nechushtan, Rivka Katzenelson, Abed Agbarya, Mahmoud Abu-Amna, Maya Gottfried, Ido Wolf, Ella Tepper, Yanyan Lou, Raya Leibowitz, Adam P. Dicker, David Gandara, David P. Carbone. A pretreatment blood-based proteomic biomarker for enhanced decision-making in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2159.

PURPOSE: IMpower133 (ClinicalTrials.gov identifier: NCT02763579 ), a randomized, double-blind, phase I/III study, demonstrated that adding atezolizumab (anti-programmed death-ligand 1 [PD-L1]) to carboplatin plus etoposide (CP/ET) for first-line (1L) treatment of extensive-stage small-cell lung cancer (ES-SCLC) resulted in significant improvement in overall survival (OS) and progression-free survival (PFS) versus placebo plus CP/ET. Updated OS, disease progression patterns, safety, and exploratory biomarkers (PD-L1, blood-based tumor mutational burden [bTMB]) are reported. PATIENTS AND METHODS: Patients with untreated ES-SCLC were randomly assigned 1:1 to receive four 21-day cycles of CP (area under the curve 5 mg per mL/min intravenously [IV], day 1) plus ET (100 mg/m 2 IV, days 1-3) with atezolizumab (1,200 mg IV, day 1) or placebo, and then maintenance atezolizumab or placebo until unacceptable toxicity, disease progression, or loss of clinical benefit. Tumor specimens were collected; PD-L1 testing was not required for enrollment. The two primary end points, investigator-assessed PFS and OS, were statistically significant at the interim analysis. Updated OS and PFS and exploratory biomarker analyses were conducted. RESULTS: Patients received atezolizumab plus CP/ET (n = 201) or placebo plus CP/ET (n = 202). At the updated analysis, median follow-up for OS was 22.9 months; 302 deaths had occurred. Median OS was 12.3 and 10.3 months with atezolizumab plus CP/ET and placebo plus CP/ET, respectively (hazard ratio, 0.76; 95% CI, 0.60 to 0.95; descriptive P = .0154). At 18 months, 34.0% and 21.0% of patients were alive in atezolizumab plus CP/ET and placebo plus CP/ET arms, respectively. Patients derived benefit from the addition of atezolizumab, regardless of PD-L1 immunohistochemistry or bTMB status. CONCLUSION: Adding atezolizumab to CP/ET as 1L treatment for ES-SCLC continued to demonstrate improved OS and a tolerable safety profile at the updated analysis, confirming the regimen as a new standard of care. Exploratory analyses demonstrated treatment benefit independent of biomarker status.

Abstract Background: IMpower133 (NCT02763579), a global Phase I/III, randomized, double-blind, placebo (PBO)-controlled trial, showed that the addition of atezo (anti-PD-L1) to CE for 1L ES-SCLC led to statistically and clinically significant OS and PFS improvement vs CE alone. Here we report updated OS and exploratory analyses. Methods: Pts with untreated ES-SCLC were randomized 1:1 to receive four 21-day cycles of E (100 mg/m2 IV, days 1-3) + C (AUC 5 mg/mL/min IV, day 1) with atezo (1200 mg IV, day 1) or PBO, followed by maintenance therapy with atezo or PBO until intolerable toxicity, progression or loss of clinical benefit. PD-L1 testing was not required for enrollment. Coprimary endpoints were investigator-assessed PFS (RECIST 1.1) and OS. Interim and final OS analyses were planned for ≈240 and ≈306 events, respectively. OS was significant at the interim analysis. Updated OS, exploratory biomarkers and patterns of disease progression were analyzed. Results: 201 and 202 pts were randomized to receive atezo+CE and PBO+CE, respectively. The median follow-up was 22.9 mo and 302 deaths had occurred. Median OS for the atezo and PBO arms was 12.3 and 10.3 mo, respectively (HR, 0.76 [95% CI: 0.60, 0.95]; descriptive P = 0.0154). At the 18-mo landmark, the OS rate was 13% higher with atezo+CE than with PBO+CE (Table). Exploratory analyses showed treatment benefit with atezo+CE regardless of biomarker status. 181 (90.0%) And 194 (96.0%) pts in the atezo+CE and PBO+CE arms, respectively, had RECIST-defined disease progression. Progression at existing, new or existing and new lesions was numerically lower with atezo+CE than with PBO+CE. Common sites of new lesions included the CNS, lung, lymph node and liver, with similar incidences between arms. Conclusion: Adding atezo to CE continued to provide OS improvement for 1L ES-SCLC in an all-comer population. The updated results of IMpower133 further support this regimen for untreated ES-SCLC. Landmark OSAtezo + CE, n = 201PBO + CE, n = 20212 Mo, n (%)93 (51.9)74 (39.0)18 Mo, n (%)61 (34.0)39(21.0)Median OS in biomarker subgroupsAtezo + CEPBO + CEITT (N = 403), mo12.310.3HR (95% CI)0.76 (0.61, 0.96)aITT-BEP (n = 137), mo9.98.9HR (95% CI)0.70 (0.48, 1.02)Non-BEP (n = 266), mo14.611.2HR (95% CI)0.81 (0.61, 1.08)PD-L1 expression, 1% TC or IC&lt; 1% (n = 65), mo10.28.3HR (95% CI)0.51 (0.30, 0.89)≥ 1% (n = 72), mo9.710.6HR (95% CI)0.87 (0.51, 1.49)PD-L1 expression, 5% TC or IC&lt; 5% (n = 108), mo9.28.9HR (95% CI)0.77 (0.51, 1.17)≥ 5% (n = 29), mo21.69.2HR (95% CI)0.60 (0.25, 1.46)Disease progression at sites, n (%)Atezo + CEPBO + CEExisting116 (57.7)131 (64.9)New86 (42.8)99 (49.0)Existing and new42 (20.9)57 (28.2)BEP, biomarker-evaluable population; ITT, intention-to-treat population.a Stratified HR. BEP included pts evaluable by PD-L1 IHC using the VENTANA SP263 assay. Citation Format: Leora Horn, Stephen V. Liu, Aaron S. Mansfield, Tony Mok, Arnaud Scherpereel, Niels Reinmuth, Marina Chiara Garassino, Javier De Castro Carpeno, Raffaele Califano, Makoto Nishio, Francisco Orlandi, Jorge Arturo Alatorre Alexander, Ticiana Leal, Ying Cheng, Jong-Seok Lee, Sivuonthanh Lam, Mark McCleland, Yu Deng, See Phan, Martin Reck. IMpower133: Updated OS and exploratory analyses of first-line (1L) atezolizumab (atezo) + carboplatin (C) + etoposide (E) in extensive-stage SCLC (ES-SCLC) [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr CT220.