The stromal compartment of the tumor microenvironment consists of a heterogeneous set of tissue-resident and tumor-infiltrating cells, which are profoundly moulded by cancer cells. An outstanding question is to what extent this heterogeneity is similar between cancers affecting different organs. Here, we profile 233,591 single cells from patients with lung, colorectal, ovary and breast cancer ( n = 36) and construct a pan-cancer blueprint of stromal cell heterogeneity using different single-cell RNA and protein-based technologies. We identify 68 stromal cell populations, of which 46 are shared between cancer types and 22 are unique. We also characterise each population phenotypically by highlighting its marker genes, transcription factors, metabolic activities and tissue-specific expression differences. Resident cell types are characterised by substantial tissue specificity, while tumor-infiltrating cell types are largely shared across cancer types. Finally, by applying the blueprint to melanoma tumors treated with checkpoint immunotherapy and identifying a naïve CD4 + T-cell phenotype predictive of response to checkpoint immunotherapy, we illustrate how it can serve as a guide to interpret scRNA-seq data. In conclusion, by providing a comprehensive blueprint through an interactive web server, we generate the first panoramic view on the shared complexity of stromal cells in different cancers.

Immune-checkpoint blockade (ICB) combined with neoadjuvant chemotherapy improves pathological complete response in breast cancer (BC). To understand why only a subset of tumors respond to ICB, patients with hormone receptor-positive or triple-negative BC were treated with anti-PD1 prior to surgery. Paired pre-versus on-treatment biopsies from treatment-naïve patients receiving anti-PD-1 (n=29) or patients receiving neoadjuvant chemotherapy prior to anti-PD1 (n=11) were subjected to single-cell transcriptome, T-cell receptor and proteome profiling. One-third of tumors contained PD1-expressing T-cells, which clonally expanded upon anti-PD1 treatment irrespective of tumor subtype. Expansion mainly involved CD8 + T-cells with pronounced expression of cytotoxic-activity (PRF1, GZMB), immune-cell homing (CXCL13) and exhaustion markers (HAVCR2, LAG3), and CD4 + T-cells characterized by expression of T-helper-1 (IFNG) and follicular-helper (BCL6, CXCR5) markers. In pre-treatment biopsies, the relative frequency of immunoregulatory dendritic cells (PD-L1), specific macrophage phenotypes (CCR2 or MMP9) and cancer cells exhibiting MHC class I/II expression correlated positively with T-cell expansion. Conversely, undifferentiated preeffector/memory T-cells (TCF7, GZMK) or inhibitory macrophages (CXCR3, C3) were inversely correlated. Collectively, our data identify various immunophenotypes and associated gene sets that are positively or negatively correlated with T-cell expansion following anti-PD1. We shed light on the heterogeneity in treatment response to anti-PD1 in breast cancer.

Objectives Aging is associated with altered immune function and chronic low‐grade inflammation, referred to as immunosenescence. As breast cancer is an age‐related disease, the impact of aging on tumor immune responses may have important consequences. However, effects of immunosenescence on breast tumor immune infiltration remain largely unknown. Methods This exploratory study investigated a broad panel of immune/senescence markers in peripheral blood and in the tumor microenvironment of young, middle‐aged and old patients diagnosed with early invasive luminal (hormone‐sensitive, HER2‐negative) breast cancer. In the old group, G8‐scores were computed as a correlate for clinical frailty. Results Significant age‐related changes in plasma levels of several inflammatory mediators (IL‐1α, IP‐10, IL‐8, MCP‐1, CRP), immune checkpoint markers (Gal‐9, sCD25, TIM‐3, PD‐L1), IGF‐1 and circulating miRs (miR‐18a, miR‐19b, miR‐20, miR‐155, miR‐195 and miR‐326) were observed. Shifts were observed in distinct peripheral blood mononuclear cell populations, particularly naive CD8+ T‐cells. At the tumor level, aging was associated with lower total lymphocytic infiltration, together with decreased abundance of several immune cell markers, especially CD8. The relative fractions of cell subsets in the immune infiltrate were also altered. Clinical frailty was associated with higher frequencies of exhausted/senescent (CD27−CD28− and/or CD57+) terminally differentiated CD8+ cells in the blood and with increased tumor infiltration by FOXP3+ cells. Conclusion Aging and frailty are associated with profound changes of the blood and tumor immune profile in luminal breast cancer, pointing to a different interplay between tumor cells, immune cells and inflammatory mediators at higher age.

40The stromal compartment of the tumour microenvironment consists of a heterogeneous 41 set of tissue-resident and tumour-infiltrating cells, which are profoundly moulded by 42 cancer cells. An outstanding question is to what extent this heterogeneity is similar 43 between cancers affecting different organs. Here, we profile 233,591 single cells from 44 patients with lung, colorectal, ovary and breast cancer (n=36) and construct a pan-45 cancer blueprint of stromal cell heterogeneity using different single-cell RNA and protein-46 based technologies. We identify 68 stromal cell populations, of which 46 are shared 47 58 59 60 61 62 63 64 65 66 67 68 KEYWORDS 69 Tumour microenvironment; stromal cell heterogeneity; single-cell RNA-seq; CITE-70 seq; therapeutic target; clinical response 71We therefore generated a comprehensive blueprint of stromal cell heterogeneity across 103 cancer types and provide a detailed view on the shared complexity and heterogeneity of 104 stromal cells in these cancers. We illustrate how this blueprint can serve as a guide to 105 interpret scRNA-seq data at individual patient level, even when comparing tumours 106 collected from different tissues or profiled using different scRNA-seq technologies. Our 107 single-cell blueprint can be visualised, analysed and downloaded from an interactive web 108 server (http://blueprint.lambrechtslab.org). 109 RESULTS 110 scRNA-seq and cell typing of tumour and normal tissue 111First, we performed scRNA-seq on tumours from 3 different organs (or cancer types): 112 colorectal cancer (CRC, n=7), lung cancer (LC, n=8) and ovarian cancer (OvC, n=5). 113Whenever possible, we retrieved both malignant (tumour) and matched non-malignant 114 (normal) tissue during surgical resection with curative intent. All tumours were treatment-115 naïve and reflected different disease stages (e.g. stage I-IV CRC) or histopathologies (e.g. 116 adenocarcinoma versus squamous LC), and whenever possible tissues were collected 117 from different anatomic sites (e.g. primary tumour from the ovary and omentum in OvC, 118 or from core versus border regions in CRC). Overall, 50 tumour tissues and 17 normal 119 tissues were profiled ( Fig. 1a). Clinical and tumour mutation data are summarised in 120 Tables S1-3. 121Following resection, tissues were rapidly digested to a single-cell suspension and 122 unbiasedly subjected to 3'-scRNA-seq. After quality filtering (Methods), we obtained ~1 123 billion unique transcripts from 183,373 cells with >200 genes detected. Of these, 71.7% 124 of cells originated from malignant tissue. Principle component analysis (PCA) using 125 variably expressed genes was used to generate t-SNEs at different resolutions 126 (Supplementary information, Fig. S1a,b). Marker genes were used to identify cell types 127 (Supplementary information, Fig. S1c). At low resolution, cells clustered based on cancer 128 type, whereas at high resolution they clustered based on patient identity (Supplementary 129 information, Fig. S1d). Also, when assessing how cell types p...

Obesity is associated with an increased risk of developing breast cancer (BC) and worse prognosis in BC patients, yet its impact on BC biology remains understudied in humans. This study investigates how the biology of untreated primary BC differs according to patients’ body mass index (BMI) using data from >2,000 patients. We identify several genomic alterations that are differentially prevalent in overweight or obese patients compared to lean patients. We report evidence supporting an ageing accelerating effect of obesity at the genetic level. We show that BMI-associated differences in bulk transcriptomic profile are subtle, while single cell profiling allows detection of more pronounced changes in different cell compartments. These analyses further reveal an elevated and unresolved inflammation of the BC tumor microenvironment associated with obesity, with distinct characteristics contingent on the estrogen receptor status. Collectively, our analyses imply that obesity is associated with an inflammaging-like phenotype. We conclude that patient adiposity may play a significant role in the heterogeneity of BC and should be considered for BC treatment tailoring.