is the most common oncogenic driver in lung adenocarcinoma (LUAC). We previously reported that (KL) or (KP) comutations define distinct subgroups of -mutant LUAC. Here, we examine the efficacy of PD-1 inhibitors in these subgroups. Objective response rates to PD-1 blockade differed significantly among KL (7.4%), KP (35.7%), and K-only (28.6%) subgroups ( < 0.001) in the Stand Up To Cancer (SU2C) cohort (174 patients) with -mutant LUAC and in patients treated with nivolumab in the CheckMate-057 phase III trial (0% vs. 57.1% vs. 18.2%; = 0.047). In the SU2C cohort, KL LUAC exhibited shorter progression-free ( < 0.001) and overall ( = 0.0015) survival compared with ; LUAC. Among 924 LUACs, alterations were the only marker significantly associated with PD-L1 negativity in TMB LUAC. The impact of alterations on clinical outcomes with PD-1/PD-L1 inhibitors extended to PD-L1-positive non-small cell lung cancer. In-mutant murine LUAC models, loss promoted PD-1/PD-L1 inhibitor resistance, suggesting a causal role. Our results identify alterations as a major driver of primary resistance to PD-1 blockade in -mutant LUAC. This work identifies alterations as the most prevalent genomic driver of primary resistance to PD-1 axis inhibitors in-mutant lung adenocarcinoma. Genomic profiling may enhance the predictive utility of PD-L1 expression and tumor mutation burden and facilitate establishment of personalized combination immunotherapy approaches for genomically defined LUAC subsets. .

No abstract

The treatment of low-risk primary prostate cancer entails active surveillance only, while high-risk disease requires multimodal treatment including surgery, radiation therapy, and hormonal therapy. Recurrence and development of metastatic disease remains a clinical problem, without a clear understanding of what drives immune escape and tumor progression. Here, we comprehensively describe the tumor microenvironment of localized prostate cancer in comparison with adjacent normal samples and healthy controls. Single-cell RNA sequencing and high-resolution spatial transcriptomic analyses reveal tumor context dependent changes in gene expression. Our data indicate that an immune suppressive tumor microenvironment associates with suppressive myeloid populations and exhausted T-cells, in addition to high stromal angiogenic activity. We infer cell-to-cell relationships from high throughput ligand-receptor interaction measurements within undissociated tissue sections. Our work thus provides a highly detailed and comprehensive resource of the prostate tumor microenvironment as well as tumor-stromal cell interactions.