The unmet need – better predictive biomarkers for immunotherapies
Immunotherapies, namely immune checkpoint inhibitors (ICI) such as Keytruda and Opdivo, have revolutionized cancer treatment, providing durable clinical benefits for patients with metastatic solid tumors. While ICIs are nowadays considered a backbone treatment in many cancer types, only a small subset of patients (20-30%) benefit and respond to those drugs, highlighting the need for better predictive biomarkers for patient selection.Compared with targeted therapies, which are guided by the identification of the target mutation in next-generation sequencing (NGS), checkpoint inhibitors are most commonly selected using the quantification of PD-L1 expression on biopsy slides. While NGS provides a clear binary result, PD-L1 scoring is a continuous and subjective measurement. The difference between these two biomarkers, as well as the distinct mechanisms of actions of targeted therapies and immunotherapies, create different response dynamics of the two drugs. Patients positive to NGS-biomarkers have high response rates to targeted therapies, but only for a short time. Patients positive to PD-L1, on the other hand, tend to have low response rates to immunotherapies, but see benefits from the drug for a long time.
How can we identify better predictive biomarkers for immunotherapy that will allow patients to benefit from high response rates that are also durable over time?
The new generation of biomarkers – tumor-microenvironment spatial analysis
It is clear today that the analysis of the immune system and the tumor microenvironment (TME) are critical for predicting response to ICI and that the tumor-centric approach that makes targeted therapies successful is not sufficient for Immunotherapies. Several papers published in August have provided interesting observations on the importance and potential clinical benefit of biomarkers that integrate immune cell and TME analysis. In the Journal of Thoracic Oncology, M Shiwasawa et al. presented a score that combines PD-L1 and tumor-infiltrating lymphocyte (TIL) density and showed it was better correlated with ICI response than PD-L1 scoring alone. The score was retrospectively analyzed on 228 Non-Small Cell Lung Cancer patients treated with ICI. The researchers divided the patients as follows: Type I: PD-L1 High (TPS≥50%) / TIL High (≥85/mm2); Type II: PD-L1 Low (TPS<50%)/TIL Low (<85/mm2); Type III: PD-L1 High/TIL Low; and Type IV: PD-L1 Low/TIL High. ORR and PFS clearly differed according to the different TME types: Type I: 64%, 14.5 months; Type II: 12%, 2.1 months; Type III: 24%, 3.6 months; Type IV; 41%, 10.8 months. In Nature Cancer, L Vanhersecke et al. showed the association between the presence of mature tertiary lymphoid structures (TLS) and ICI efficacy cross-cancer. In a retrospective study of 328 patients treated with IO for different cancer types, the presence of mature TLSs was associated with improved objective response rates, progression-free survival, and overall survival, independent of PD-L1 expression status and CD8+ T cell density. Lastly, Eli Lilly presented outcome data from the phase III ORIENT-11 study on their PD-1 drug, Sintilimab, in NSCLC patients. While the efficacy of the drug, when combined with chemo, was high, the researchers also tested the predictive role of TILs (analyzed by RNAseq) in predicting response in the study. What they found was that high or medium immune cell infiltration was strongly associated with improved PFS in the combo group, in contrast to absent or low immune cell infiltration.
Nucleai Solution – Spatial analysis of the TME
In addition to these important observations, a strong body of evidence suggests that the spatial analysis of those TME and immune parameters are equally important. Our mission at Nucleai is to utilize our AI spatial analysis platform to identify novel predictive biomarkers for cancer therapies. We were very pleased to present the interim results of our biomarker discovery study in ASCO2021, showing the predictive power of spatial analysis of TILs in NSCLC. In a retrospective analysis of 90 NSCLC patients treated with first-line single-agent pembrolizumab, we identified a spatial immune signature in H&E slides that could identify patients with durable clinical benefit. The novel predictive biomarker, composed of different histological features such as the proximity between tumor cells and tumor-infiltrating lymphocytes (TILs), was validated on a cohort of 43 patients. Patients with positive Nucleai scores had a significantly higher median Overall Survival (OS) (Not reached vs.17.8m) and 2-year OS (70.8% vs. 33%, p=0.02) than patients with negative Nucleai score.
Powered by strategic data partnerships with leading medical centers, we strive to keep unlocking the potential of combining AI, real-world data, and spatial biology to drive novel insights to support the development of the new generation of immuno-oncology drugs
Learn more about how Nucleai’s AI-powered spatial biology platform
- Differential Immune-Related Microenvironment Determines Programmed Cell Death Protein-1/Programmed Death-Ligand 1 Blockade Efficacy in Patients With Advanced NSCLC
- Mature tertiary lymphoid structures predict immune checkpoint inhibitor efficacy in solid tumors independently of PD-L1 expression
- Updated Overall Survival Data and Predictive Biomarkers of Sintilimab Plus Pemetrexed and Platinum as First-Line Treatment for Locally Advanced or Metastatic Nonsquamous NSCLC in the Phase 3 ORIENT-11 Study