McGregor, Kimberly

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.

4009 Background: IDH1/2 genetic aberrations (GA) occur in 20% of IHCC cases and may be specifically targeted by IDH inhibitors. The genomic and immunologic profile of IHCC with IDH1/2 GA remains undefined. IDH1 mutations impair DNA damage repair (DDR), loss of heterozygosity (LOH) and may represent a biomarker for DDR in these patients. Methods: Comprehensive genomic profiling (CGP) was performed in 3,067 cases of advanced stage IHCC using a hybrid capture-based FDA-approved assay. Tumor mutational burden (TMB) was determined on 0.8 Mbp of sequenced DNA and microsatellite instability (MSI) was determined on 95 loci. PD-L1 expression in tumor cells (Dako 22C3) was measured by immunohistochemistry (IHC). Genomic LOH (gLOH) was assessed for samples meeting quality criteria. Densities of tumor associated immune cells and immune-checkpoint markers expressed in epithelial malignant cells and in the tumor microenvironment of 100 surgical samples from 96 patients were evaluated by IHC using digital image analysis of 14 markers (CD3, CD4, CD8, CD68, PD1,PD-L1, B7-H4, B7-H3, IDO1, ICOS, VISTA, OX40, TIM3, LAG3). Tissue microarrays were generated for multiplex immune panel analysis. A p value < 0.05 was considered statistically significant. Results: 426 (14%) of IHCC were IDH1+ and 125 (4%) were IDH2+ (Table) and were mutually exclusive. All IDH1 GA occurred at the R132 locus and included R132C (69%) and R132L/G/S/H/F (16%/7%/4%/3%/ < 1%) and 119Q ( < 1%) and IDH2 GA at R172 (94.4%) and R140 (6.6%). IDH1+ and IDH2+ IHCC had fewer co-occurring targetable GA than IDH1/2 wildtype (WT, IDHwt) cases including FGFR2 rearrangements (RE) (P <.0001), ERBB2 (P =.0009) and BRAF (P =.04). Median gLOH were not significantly different between IDH1+ IDH2+ IHCC vs. IDHwt IHCC (p = 0.37). Potential biomarkers of immune checkpoint inhibition (ICI) response including MSI High, TMB > 10 mut/Mb, and PD-L1 positivity were more frequent in IDHwt IHCC than IDH1+ IHCC. 29/96 (30%) surgical patients were positive for IDH1/2 mutation though mutational status did not confer a statistically significant difference in the 14 immune biomarker panel expression. Conclusions: CGP reveals significant differences in GA between IDH1+, IDH2+ and IDHwt IHCC consistent with IDH1 and IDH2 being driver oncogenes for IHCC. Immune biomarker expression and gLOH did not differ significantly between IDH mutated and WT cases.[Table: see text]