Purpose: Response rates to immune checkpoint blockade (ICB; anti-PD-1/anti-CTLA-4) correlate with the extent of tumor immune infiltrate, but the mechanisms underlying the recruitment of T cells following therapy are poorly characterized. A greater understanding of these processes may see the development of therapeutic interventions that enhance T-cell recruitment and, consequently, improved patient outcomes. We therefore investigated the chemokines essential for immune cell recruitment and subsequent therapeutic efficacy of these immunotherapies. Experimental Design: The chemokines upregulated by dual PD-1/CTLA-4 blockade were assessed using NanoStringbased analysis with results confirmed at the protein level by flow cytometry and cytometric bead array. Blocking/neutralizing antibodies confirmed the requirement for key chemokines/cytokines and immune effector cells. Results were confirmed in patients treated with immune checkpoint inhibitors using single-cell RNA-sequencing (RNA-seq) and paired survival analyses. Results: The CXCR3 ligands, CXCL9 and CXCL10, were significantly upregulated following dual PD-1/CTLA-4 blockade and both CD8 þ T-cell infiltration and therapeutic efficacy were CXCR3 dependent. In both murine models and patients undergoing immunotherapy, macrophages were the predominant source of CXCL9 and their depletion abrogated CD8 þ T-cell infiltration and the therapeutic efficacy of dual ICB. Single-cell RNA-seq analysis of patient tumor-infiltrating lymphocytes (TIL) revealed that CXCL9/ 10/11 was predominantly expressed by macrophages following ICB and we identified a distinct macrophage signature that was associated with positive responses to ICB. Conclusions: These data underline the fundamental importance of macrophage-derived CXCR3 ligands for the therapeutic efficacy of ICB and highlight the potential of manipulating this axis to enhance patient responses.

Recent advances in cancer immunology have led to a better understanding of the role of the tumor microenvironment (TME) in tumor initiation, progression, and metastasis. Tumors can occur at many locations within the body and coevolution between malignant tumor cells and non-malignant cells sculpts the TME at these sites. It has become increasingly clear that there are specific differences of the TMEs at different anatomical locations, and these tissue-specific TMEs regulate tumor growth, determine metastatic progression, and impact on the outcome of therapy responses. Herein, we review the scientific advances in understanding tissue-specific TMEs, discuss their impact on immunotherapeutic response, and assess the current clinical knowledge in this emerging field. A deeper understanding of the tissue-specific TME will help to develop effective immunotherapies against tumors and their metastases and assist in predicting clinical outcomes.

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1+ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas.