Tumor extracellular matrix has been associated with drug resistance and immune suppression. Here, proteomic and RNA profiling reveal increased collagen levels in lung tumors resistant to PD-1/PD-L1 blockade. Additionally, elevated collagen correlates with decreased total CD8+ T cells and increased exhausted CD8+ T cell subpopulations in murine and human lung tumors. Collagen-induced T cell exhaustion occurs through the receptor LAIR1, which is upregulated following CD18 interaction with collagen, and induces T cell exhaustion through SHP-1. Reduction in tumor collagen deposition through LOXL2 suppression increases T cell infiltration, diminishes exhausted T cells, and abrogates resistance to anti-PD-L1. Abrogating LAIR1 immunosuppression through LAIR2 overexpression or SHP-1 inhibition sensitizes resistant lung tumors to anti-PD-1. Clinically, increased collagen, LAIR1, and TIM-3 expression in melanoma patients treated with PD-1 blockade predict poorer survival and response. Our study identifies collagen and LAIR1 as potential markers for immunotherapy resistance and validates multiple promising therapeutic combinations.

Purpose The requirement of frozen tissues for microarray experiments limits the clinical usage of genome-wide expression profiling using microarray technology. The goal of this study is to test the feasibility of developing lung cancer prognosis gene signatures using genome-wide expression profiling of formalin-fixed paraffin-embedded (FFPE) samples, which are widely available and provide a valuable rich source for studying the association of molecular changes in cancer and associated clinical outcomes. Experimental Design We randomly selected 100 Non-Small-Cell lung cancer (NSCLC) FFPE samples with annotated clinical information from the UT-Lung SPORE Tissue Bank. We micro dissected tumor area from FFPE specimens, and used Affymetrix U133 plus 2.0 arrays to attain gene expression data. After strict quality control and analysis procedures, a supervised principal component analysis was used to develop a robust prognosis signature for NSCLC. Three independent published microarray data sets were used to validate the prognosis model. Results This study demonstrated that the robust gene signature derived from genome-wide expression profiling of FFPE samples is strongly associated with lung cancer clinical outcomes, can be used to refine the prognosis for stage I lung cancer patients and the prognostic signature is independent of clinical variables. This signature was validated in several independent studies and was refined to a 59-gene lung cancer prognosis signature. Conclusions We conclude that genome-wide profiling of FFPE lung cancer samples can identify a set of genes whose expression level provides prognostic information across different platforms and studies, which will allow its application in clinical settings.

No abstract

Lung cancer is the leading cause of cancer-related death. Despite a number of studies that have provided prognostic biomarkers for lung cancer, a paucity of reliable markers and therapeutic targets exist to diagnose and treat this aggressive disease. In this study we investigated the potential of nuclear receptors (NRs), many of which are well-established drug targets, as therapeutic markers in lung cancer. Using quantitative real-time PCR, we analyzed the expression of the 48 members of the NR superfamily in a human panel of 55 normal and lung cancer cell lines. Unsupervised cluster analysis of the NR expression profile segregated normal from tumor cell lines and grouped lung cancers according to type (i.e. small vs. non-small cell lung cancers). Moreover, we found that the NR signature was 79% accurate in diagnosing lung cancer incidence in smokers (n = 129). Finally, the evaluation of a subset of NRs (androgen receptor, estrogen receptor, vitamin D receptor, and peroxisome proliferator-activated receptor-γ) demonstrated the therapeutic potential of using NR expression to predict ligand-dependent growth responses in individual lung cancer cells. Preclinical evaluation of one of these receptors (peroxisome proliferator activated receptor-γ) in mouse xenografts confirmed that ligand-dependent inhibitory growth responses in lung cancer can be predicted based on a tumor's receptor expression status. Taken together, this study establishes NRs as theragnostic markers for predicting lung cancer incidence and further strengthens their potential as therapeutic targets for individualized treatment.