Abstract 2027: Spatial omics and multiplexed imaging to discover new biomarkers of response or resistance to immune checkpoint inhibitors (ICI) in advanced non-small cell lung cancer (NSCLC)

Abstract

Abstract Introduction: Despite the impressive outcomes with ICI in NSCLC, only a minority of the patients show long-term benefit from ICI. While PD-L1 immunohistochemistry is an approved companion diagnostic test, it is neither sensitive nor specific. Here, we describe the use of spatial transcriptomics using the GeoMx Digital Spatial Profiler (DSP) as a discovery platform to find biomarkers for ICI response or resistance. Methods: Pre-treatment samples from 56 patients with NSCLC treated with ICI were collected and analyzed in a tissue microarray (TMA) format in two-fold redundancy. The human whole transcriptome, represented by 18000 genes assessed by oligonucleotide-tagged in situ hybridization, was sequenced on the NovaSeq platform to quantify the mRNAs present in each region of interest. Three molecularly defined tissue compartments, defined by fluorescence co-localization (tumor [panCK+], leukocytes [CD45+/CD68-], macrophages [CD68+]) were generated to assess mRNA. All statistical testing was performed using a two-sided significance level of α=0.05. Result: 54000 gene variables were generated per case, from them about 27000 were analyzed after removing targets with expression lower than a prespecified frequency. Expression distribution of EPCAM in tumor, PTPRC in leukocytes and CD68 in macrophages confirmed that DSP successfully profiled the molecular compartments. Differential gene expression (DEG) analysis was performed for 6-month clinical benefit across two distinct tumor TMA spots per patient. This resulted in a DEG list of about 250, 125 and 122 genes in CK, CD45 and CD68 compartment respectively. When we used the intersection of significantly associated with benefit genes the candidate list was reduced dramatically to 5, 10 and 10 distinct genes in CK (CYBA, ESD, SH3BGRL3, TOP2B, UFM1), CD45 (ATRN, DGAT1, DNAAF9, GMPS, IFT20, MPHOSPH6, PKP4, SOCS2, TFAP4, TGFB3) and CD68 (ANKS3, ARFRP1, CTSD, DPYD, GTF3C5, HS2ST1, KIAA0895L, PIGR, SFTPA2, YEATS4) compartment respectively. From them, using the Cox Proportional-Hazards Model we further identified CYBA, ESD and UFM1 in CK, DGAT1 and IFT20 in CD45 and ANKS3 and DPYD in CD68 to predict survival as well. Conclusions: Using DSP technology allows rapid, patient-specific assessment of the transcriptome in TMAs by in situ hybridization in spatially defined molecular compartments. Here we show a small set of candidate genes that are associated with outcome in this ICI-treated cohort. By intersecting two non-adjacent cores of the same patient tissue sample, we have begun to dissect tumor heterogeneity and pilot biomarker candidate information with unique molecularly defined compartments for tumor cells, lymphocytes, and macrophages. Results from this cohort may lead to a novel, spatially defined, transcriptomic approach for developing new biomarkers for immunotherapy. Citation Format: Myrto Moutafi, Sandra Martinez-Morilla, Rolando Garcia-Milian, Thazin Nwe Aung, Ioannis Vathiotis, Niki Gavrielatou, Vasiliki Xirou, Leonidas Salichos, David L. Rimm. Spatial omics and multiplexed imaging to discover new biomarkers of response or resistance to immune checkpoint inhibitors (ICI) in advanced non-small cell lung cancer (NSCLC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2027.