Blood memory CD8 T cell phenotypes in lung cancer patients predict immune checkpoint treatment responses

Kanxing Wu^1*Florian Schmidt^1*Ke Xin Bok²Yovita Ida Purwanti¹Nicholas Tan¹Daniel Carbajo¹Andreas Wilm¹Michael Fehlings¹Daniel MacLeod¹Alessandra Nardin¹Daniel Tan²Katja Fink¹

• ¹ImmunoSCAPE Pte. Ltd., Singapore, Singapore
• ²National Cancer Centre Singapore, Singapore, Singapore

Immune checkpoint inhibition (ICI) has become a standard treatment to re-invigorate tumor-attacking T~cell responses in multiple cancer indications. Yet, a patient’s response is unpredictable even with confirmed expression of the relevant targets such as PD-1 or PD-L1.Previously identified biomarkers of response have relatively low accuracy, making it difficult to reliably employ them as predictors of clinical response.We comprehensively phenotyped peripheral blood CD8+ T cells from patients with non-small cell lung cancer by analyzing surface marker expression, transcriptome and TCR repertoire with single cell sequencing technology. The cohorts were comprised of patients that a) responded to anti-PD(L)1 treatment for a prolonged period of time, b) were new-on-treatment responders and c) were new-on-treatment non-responders. Using various bioinformatics analyses, we defined signatures of ICI response and evaluated their performance on external scRNA-seq data sets.We identified response specific signals in cell type and cell state proportions, as well as in TCR repertoire diversity and TCR inter donor similarity.Enrichment analysis revealed several pathways and regulatory modules enriched in different response groups. Using machine learning, we identified cell type specific signatures that predicted the ICI response with an accuracy between $66\%$ and $93\%$ at single cell and up to $94\%$ at patient level. Effector memory CD8+ T cells in long term-responders were most predictive of response and the inferred effector memory signature could be successfully applied to two related scRNA-seq data set. CD44, GIMAP4, CD69 and CCL4L2 were among the most relevant contributing markers defining the predictive ML signatures on lung cancer samples.%Our findings suggest that CD8+ T cell subset-specific models reach an accuracy that have the potential to inform treatment decisions in a clinical setting.Our findings suggest that CD8+ T cell subset-specific models reach an accuracy that possesses the potential to inform treatment decisions in a clinical setting.