Predictive biomarkers for immune checkpoint efficacy: is multi-omics breaking the deadlock?

Introduction

The progressive understanding of lung cancer biology led to the development of various biotherapies such as antiangiogenic agents, targeted therapies and immune checkpoint inhibitors (ICIs), revolutionizing lung cancer treatment and prognosis. ICIs targeting the programmed cell death protein 1 (PD-1)-programmed cell death ligand 1 (PD-L1) axis to restore anticancer immunity were first approved as single agents for pre-treated patients and then in the first line setting, for patients with a high PD-L1 expression (1,2). They were also approved in combination with chemotherapy regardless of PD-L1 expression (3). However, ICI objective response rate in real life remains disappointing and does not exceed 20% when given alone and 50% when combined with chemotherapy. The biological features underlying the benefit of a combination of systemic anticancer drugs are certainly various and complex. PD-L1 expression on tumor cells is to date the only approved routine biomarker for ICI in non-small cell lung cancer (NSCLC), although imperfect and insufficient because of operator variability and spatial heterogeneity (4,5). Several other predictive biomarkers were already studied, but none of them appeared to be reliable and reproducible enough to overcome PD-L1 expression. Tumor mutational burden (TMB), thought to reflect the amount of neoantigens by quantifying non-synonymous mutations in coding areas, failed to show a clear survival difference when used alone and remains difficult to standardize for routine use (6). In a same way, quantification of tumor infiltrating lymphocytes, interferon gamma pathway signatures, methylation profile, transcriptomic signature and microbiota assessment failed to confirm their predictive role (7-11). Considering existing literature, genomic, transcriptomic and proteomic data alone did not succeed to predict ICIs efficacy, thus justifying multi-omics strategy.

Herein, samples used for multi-omics analyses were provided from the Lung-MAP S1400I trial, a phase III study comparing nivolumab alone (N) to nivolumab and ipilimumab (NI) for advanced squamous NSCLC after a first line of platinum-based chemotherapy. Ipilimumab adjunction did not show any survival benefit and standard of care remains nowadays ICIs alone in this situation. Indeed, objective response rate was 18% in the NI arm and 17% in the N arm, and no overall survival difference was observed [hazard ratio (HR) =0.87, 95% confidence interval (CI): 0.66–1.16, P=0.34]. However, some patients actually derived long-term benefit from the ICI combination (12). In this paper, we will discuss ancillary analysis of the Lung-MAP S1400I study in which multiplex immunofluorescence (mIF), gene expression profiling, whole exome sequencing (WES) and circulating serum analyte measurements were performed to set up a multi-omics signature associated with ICIs benefit (13). Among the 252 patients included in the study, 160 longitudinal liquid biopsies were available for Olink analysis, 82 baseline tumor samples for mIF, 38 for trancriptomic analysis and 50 for WES.

Coherent data flow

Results of this ancillary study are concordant with the current scientific data emphasizing the importance of a “hot” immune tumor microenvironment (TME) to support ICI efficacy. Indeed, mIF on baseline samples showed that clinical benefit was associated to immune cell density in the stroma (14), especially PD-1 cytotoxic T-cells (CTc) (P=0.04) and Granzyme B CTc in the tumor compartment (P=0.01) in both arms. However, T regulatory lymphocytes presence was negatively associated with OS in the nivolumab and ipilimumab arm but surprisingly not in the nivolumab arm (15). This result may be explained by the small sample size but also highlight the lack of functional information of mIF analysis, knowing that some data showed an association between nivolumab benefit and receptor occupancy on effector regulatory T cells (16). Furthermore, transcriptomic analysis highlighted the association between ICI benefit and main cellular pathways, immune response as expected but also inflammation, apoptosis and proliferation gene expression, highlighting the complexity of underlying ICI pathways (Table 1). Existing literature already highlighted the overexpression of genes involved in immune response and proliferation but with various genes significantly associated with ICIs efficacy (10,17). Indeed, identification and validation of a unique and reproducible transcriptomic signature seems unlikely, standing for the multi-omics approach. After multivariate analysis adjusting on PDL1 expression and TMB, overexpression of CD163, BLNK, IRF1 and FCGR2A was still associated with better outcome (P<0.005), which support the fundamental role of immune response gene expression in ICI response after all and support their utilization for further predictive signatures.

Concerning WES analysis, high copy number variation was associated with a cold TME and immune evasion in this population, in line with previously published data (18,19), but this relation seems to be related to methylation profile specificities, unfortunately not explored in this multi-omics approach (20).

Innovative data flow

In this paper, the special attention paid to features of exceptional response (response >18 months and survival >24 months) compared to early progression (death <6 months) give originality to this subject already widely covered even considering the small sample-size. Despite this specificity, mIF data are consistent with overall population results with a higher density of CTc and memory T-cells in the total compartment in exceptional responders (respectively P=0.03 and P=0.04). Moreover, the focus on spatial cell organization adds a new interest to mIF data. Despite the small sample size (n=12), authors highlighted higher immune infiltration and CTc density among exceptional responder TME. Going further, spatial segregation analysis showed higher segregation between immune cells and tumor cells among early progressors confirmed by distance based hierarchical clustering, confirmed in the whole cohort analysis (Table 2). These data remain preliminary and seem difficult to transfer within daily care but artificial intelligence tools are currently developed to extrapolate segmentation and qualification of multiple histologic components with promising results (21).

Another original result in this study is the predictive role of LRP1B mutation, associated with less CTc infiltration, significantly negatively correlated with response and survival. LRP1B mutation are not described in the paper but previous studies in other tumor types rather highlighted a positive association between LRP1B mutation and ICI efficacy (22,23). This surprising association may result of the small sample size but we cannot exclude a particular role of LRP1B mutation in squamous NSCLC considering specific tobacco exposure in this population.

Finally, the adjunction of liquid-biopsies analysis in this multi-omics vision allows a non-invasive and dynamic approach to overcome the limits of baseline solid sample for biomarkers analysis. Despite the originality of the multi-sampling approach, results from the Olink proteomic analysis are consistent with data based on baseline solid sample. Indeed, immune activation and priming markers (ICOS-L, LAMP3/DC-LAMP, IL4, IL13, NRC1, CD5) appeared to be increased at baseline or early during the treatment among responders whereas macrophages, hyperinflammation and stromal markers (IL6, IL8, CXCL13, CSF-1, TNF-SF14, CCL23, VEGFA, HGF) appeared to be increased among non-responders.

Relevance of multi-omics analysis

Multi-omics analysis by recursive partitioning highlighted the ability of Olink analysis alone to predict ICI efficacy and the decision tree survival prediction model was able to identify two population with different survival relying on CTc mIF identification and IL6, LAG3 and MICA.B Olink identification. Both analyses did not find any relevant features in Nanostring and WES data, maybe because of small sample size but their relevance will have to be verified in larger validation cohorts considering these techniques are the most expensive and difficult to transpose in daily care. To confirm this viewpoint, a recent paper explored a multi-omic approach to overcome anti-PD-1 and anti-LAG3 resistance and this identified a negative regulation signature of TCR signal, but these data requested various cutting-edge methods and are not validated so far on a routine representative population (24).

Conclusions

This paper offers a multi-omics approach of immune features underlying ICI efficacy in a population of pretreated squamous NSCLC patients. Despite a good coherence with the existing literature on the topic considering CTc infiltration and immune TME as the main requirement for ICI efficacy, the focus on extreme responders and spatial cell repartition reinforces the results and give originality to the study. Some results remain unexpected such as the identification of LRP1B mutation as a negative predictive biomarker of ICI efficacy. In addition, mIF, spatial, trancriptomic, genomic and proteomic data all find almost the same biomarkers in the nivolumab arm and in the nivolumab plus ipilimumab arm. Indeed, this study does not provide innovative key to ICI intensification, especially since chemo-immunotherapy is the new first-line standard in NSCLC, making the study population rarer. To finish, this paper is reinforcing knowledge about various immune predictive biomarkers but failed to integrate a real and reproducible multi-omics approach, thus there is still a need for innovative and especially non-invasive and longitudinal technique, considering Olink proteomic analysis on liquid biopsies is already showing promising results.

Acknowledgments

Funding: None.

Footnote

Provenance and Peer Review: This article was commissioned by the editorial office, Translational Lung Cancer Research. The article has undergone external peer review.

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-24-594/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-24-594/coif). A.M. received honoraria from Amgen, Oseus, Viatris, and AstraZeneca; received support for attending meetings from BMS and Takeda; and took part in the advisory board of Takeda, AstraZeneca and Pfizer. L.G. received honoraria from Amgen, BMS, Takeda, Sanofi, Pfizer, MSD, and AstraZeneca; and took part in the advisory board of Amgen, AstraZeneca, Takeda, Sanofi, Roche, Pfizer, Novartis, MSD and BMS. P.T. received honoraria from Lilly, Amgen, Janssen, and AstraZeneca; received support for attending meetings from BMS and Daiichi Sankyo; and took part in the advisory board of Amgen, Janssen, and AstraZeneca. The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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