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. 2024 Jul 1;12(7):e008974.
doi: 10.1136/jitc-2024-008974.

Extensive-stage small-cell lung cancer in patients receiving atezolizumab plus carboplatin-etoposide: stratification of outcome based on a composite score that combines gene expression profiling and immune characterization of microenvironment

Anna Tosi  1 , Martina Lorenzi  2 , Paola Del Bianco  3 , Anna Roma  4 , Alberto Pavan  5 , Antonio Scapinello  6 , Maria Vittoria Resi  7 , Laura Bonanno  8 , Stefano Frega  8 , Fiorella Calabrese  9 , Valentina Guarneri  7   8 , Antonio Rosato  1   7 , Giulia Pasello  10   8
Affiliations

Extensive-stage small-cell lung cancer in patients receiving atezolizumab plus carboplatin-etoposide: stratification of outcome based on a composite score that combines gene expression profiling and immune characterization of microenvironment

Anna Tosi et al. J Immunother Cancer. .

Abstract

Purpose: Small-cell lung cancer (SCLC) is an aggressive disease with a dismal prognosis. The addition of immune checkpoints inhibitors to standard platinum-based chemotherapy in first-line setting achieves a durable benefit only in a patient subgroup. Thus, the identification of predictive biomarkers is an urgent unmet medical need.

Experimental design: Tumor samples from naive extensive-stage (ES) SCLC patients receiving atezolizumab plus carboplatin-etoposide were analyzed by gene expression profiling and two 9-color multiplex immunofluorescence panels, to characterize the immune infiltrate and SCLC subtypes. Associations of tissue biomarkers with time-to-treatment failure (TTF), progression-free survival (PFS) and overall survival (OS), were assessed.

Results: 42 patients were included. Higher expression of exhausted CD8-related genes was independently associated with a longer TTF and PFS while increased density of B lymphocytes correlated with longer TTF and OS. Higher percentage of M2-like macrophages close to tumor cells and of CD8+T cells close to CD4+T lymphocytes correlated with increased risk of TF and longer survival, respectively. A lower risk of TF, disease progression and death was associated with a higher density of ASCL1+tumor cells while the expression of POU2F3 correlated with a shorter survival. A composite score combining the expression of exhausted CD8-related genes, B lymphocyte density, ASCL1 tumor expression and quantification of CD163+macrophages close to tumor cells, was able to stratify patients into high-risk and low-risk groups.

Conclusions: In conclusion, we identified tissue biomarkers and a combined score that can predict a higher benefit from chemoimmunotherapy in ES-SCLC patients.

Keywords: Biomarker; Immune Checkpoint Inhibitor; Lung Cancer; Tumor microenvironment - TME.

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Conflict of interest statement

Competing interests: GP reports Advisory Boards/Honoraria/Speakers’ fee/Consultant by Amgen, AstraZeneca, BMS, Eli Lilly, Jansenn, MSD, Novartis, Pfizer, Roche, and unconditioned research support by AstraZeneca, Roche, MSD. VG reports personal fees for advisory board membership for AstraZeneca, Daiichi Sankyo, Eisai, Eli Lilly, Exact Sciences, Gilead, Merck Serono, MSD, Novartis, Pfizer, Olema Oncology, Pierre Fabre; personal fees as an invited speaker for AstraZeneca, Daiichi Sankyo, Eli Lilly, Exact Sciences, Gilead, GSK, Novartis, Roche and Zentiva; personal fees for expert testimony for Eli Lilly.

Figures

Figure 1
Figure 1
Study design. Created with biorender.com. AUC, area under the curve; d, day; CT-IO, chemoimmunotherapy; EP, gene expression profile; IV, intravenous; TTF, time-to-treatment failure.
Figure 2
Figure 2
Forest plots of multivariate analyses illustrating independent factors associated with time-to-treatment failure (TTF), progression-free survival (PFS) and overall survival (OS) for extensive-stage small-cell lung cancer (ES-SCLC). The horizontal axes represent the OR with reference line, ORs (cube), and 95% CI (whiskers). ASCL1; achaete-scute family bHLH transcription factor; POU2F3, POU class 2 Homeobox 3; PD-L1, programmed death-ligand 1; PD-1, programmed cell death protein 1; HEY1, Hairy/enhancer-of-split related with YRPW motif protein 1; EIF5AL1; Eukaryotic Translation Initiation Factor 5A Like 1.
Figure 3
Figure 3
Small-cell lung cancer (SCLC) subtypes have different tumor immune microenvironmen (TME) composition. (A) Kaplan-Meier curves for time-to-treatment failure (TTF), progression-free survival (PFS) and overall survival (OS) according to the SCLC classification based on ASCL1, NEUROD1 and POU2F3 transcription factors expression. (B,C) Immune cells infiltrating the TME of SCLC subtypes. (D,E) Spatial metrics analysis in SCLC subtypes. (F) Representative images of spatial metrics analysis performed. In the left picture, CD163+macrophages within a 20 µm radius from CD8+cells are represented while the picture on the right shows PD-L1+tumor cells within a 20 µm radius from CD8+PD-1+ cells
Figure 4
Figure 4
Forest plots of multivariate analyses illustrating the overall impact of activated pathways, immune cell contextures and transcription factors expression on time-to-treatment failure (TTF), progression-free survival (PFS) and overall survival (OS) for extensive-stage small-cell lung cancer (ES-SCLC). The horizontal axes represent the OR with reference line, ORs (cube), and 95% CI (whiskers).
Figure 5
Figure 5
Stratification of patient risk based on the identified combined score. (A) Kaplan-Meier curves for time-to-treatment failure (TTF), progression-free survival (PFS) and overall survival (OS) according to the identified combined score. (B) The performance (Harrell’s C-index) of the combined score as compared with the single variables.
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