CD4 T cells have been implicated in cancer immunity for their helper functions. Moreover, their direct cytotoxic potential has been shown in some patients with cancer. Here, by mining single-cell RNA-seq datasets, we identified CD4 T cell clusters displaying cytotoxic phenotypes in different human cancers, resembling CD8 T cell profiles. Using the peptide-MHCII-multimer technology, we confirmed ex vivo the presence of cytolytic tumor-specific CD4 T cells. We performed an integrated phenotypic and functional characterization of these cells, down to the single-cell level, through a high-throughput nanobiochip consisting of massive arrays of picowells and machine learning. We demonstrated a direct, contact-, and granzyme-dependent cytotoxic activity against tumors, with delayed kinetics compared to classical cytotoxic lymphocytes. Last, we found that this cytotoxic activity was in part dependent on SLAMF7. Agonistic engagement of SLAMF7 enhanced cytotoxicity of tumor-specific CD4 T cells, suggesting that targeting these cells might prove synergistic with other cancer immunotherapies.

Brain metastases (BMs) are associated with poor prognosis in non-small cell lung cancer (NSCLC), but are only visible when large enough. Therapeutic decisions such as whole brain radiation therapy would benefit from patientspecific predictions of radiologically undetectable BMs. Here, we propose a mathematical modeling approach and use it to analyze clinical data of BM from NSCLC.Primary tumor growth was best described by a gompertzian model for the prediagnosis history, followed by a tumor growth inhibition model during treatment.Growth parameters were estimated only from the size at diagnosis and histology, but predicted plausible individual estimates of the tumor age (2.1-5.3 years). Multiple metastatic models were assessed from fitting either literature data of BM probability (n = 183 patients) or longitudinal measurements of visible BMs in two patients. Among the tested models, the one featuring dormancy was best able to describe the data. It predicted latency phases of 4.4 -5.7 months and onset of BMs 14 -19 months before diagnosis. This quantitative model paves the way for a computational tool of potential help during therapeutic management.

Recent studies have proposed that tumor-specific tumor-infiltrating CD8 + T lymphocytes (CD8 TIL) can be classified into two main groups: "exhausted" TILs, characterized by high expression of the inhibitory receptors PD-1 and TIM-3 and lack of transcription factor 1 (Tcf1); and "memory-like" TILs, with selfrenewal capacity and co-expressing Tcf1 and PD-1. However, a comprehensive definition of the heterogeneity existing within CD8 TILs has yet to be clearly established. To investigate this heterogeneity at the transcriptomic level, we performed paired single-cell RNA and TCR sequencing of CD8 T cells infiltrating B16 murine melanoma tumors, including cells of known tumor specificity. Unsupervised clustering and gene-signature analysis revealed four distinct CD8 TIL statesexhausted, memory-like, naïve and effector memory-like (EM-like)and predicted novel markers, including Ly6C for the EM-like cells, that were validated by flow cytometry. Tumor-specific PMEL T cells were predominantly found within the exhausted and memory-like states but also within the EM-like state. Further, T cell receptor sequencing revealed a large clonal expansion of exhausted, memory-like and EM-like cells with partial clonal relatedness between them. Finally, meta-analyses of public bulk and single-cell RNA-seq data suggested that anti-PD-1 treatment induces the expansion of EM-like cells. Our reference map of the transcriptomic landscape of murine CD8 TILs will help interpreting future bulk and single-cell transcriptomic studies and may guide the analysis of CD8IL subpopulations in response to therapeutic interventions.

Background Single-cell RNA sequencing (scRNA-seq) technologies offer unique opportunities for exploring heterogeneous cell populations. However, in-depth single-cell transcriptomic characterization of complex tissues often requires profiling tens to hundreds of thousands of cells. Such large numbers of cells represent an important hurdle for downstream analyses, interpretation and visualization. Results We develop a framework called SuperCell to merge highly similar cells into metacells and perform standard scRNA-seq data analyses at the metacell level. Our systematic benchmarking demonstrates that metacells not only preserve but often improve the results of downstream analyses including visualization, clustering, differential expression, cell type annotation, gene correlation, imputation, RNA velocity and data integration. By capitalizing on the redundancy inherent to scRNA-seq data, metacells significantly facilitate and accelerate the construction and interpretation of single-cell atlases, as demonstrated by the integration of 1.46 million cells from COVID-19 patients in less than two hours on a standard desktop. Conclusions SuperCell is a framework to build and analyze metacells in a way that efficiently preserves the results of scRNA-seq data analyses while significantly accelerating and facilitating them.

Brain metastases (BMs) are associated with poor prognosis in non-small cell lung cancer (NSCLC), but are only visible when large enough. Therapeutic decisions such as whole brain radiation therapy would benefit from patient-specific predictions of radiologically undetectable BMs. Here, we propose a mathematical modeling approach and use it to analyze clinical data of BM from NSCLC. Primary tumor growth was best described by a gompertzian model for the pre-diagnosis history, followed by a tumor growth inhibition model during treatment. Growth parameters were estimated only from the size at diagnosis and histology, but predicted plausible individual estimates of the tumor age (2.1–5.3 years). Multiple metastatic models were further assessed from fitting either literature data of BM probability (n = 183 patients) or longitudinal measurements of visible BMs in two patients. Among the tested models, the one featuring dormancy was best able to describe the data. It predicted latency phases of 4.4–5.7 months and onset of BMs 14–19 months before diagnosis. This quantitative model paves the way for a computational tool of potential help during therapeutic management.

Recent studies have proposed that tumor-specific tumor-infiltrating CD8 + T lymphocytes (CD8 TIL) can be classified into two main groups: "exhausted" TILs, characterized by high expression of the inhibitory receptors PD-1 and TIM-3 and lack of transcription factor 1 (Tcf1); and "memory-like" TILs, with self-renewal capacity and co-expressing Tcf1 and PD-1. However, a comprehensive definition of the heterogeneity existing within both tumor-specific and total CD8 TILs has yet to be clearly established.To investigate this heterogeneity at the transcriptomic level, we performed paired single-cell RNA and TCR sequencing of CD8 T cells infiltrating B16 murine melanoma tumors, including cells of known tumor specificity. Unsupervised clustering and gene signature analysis revealed four distinct CD8 TIL states -exhausted, memory-like, naïve and effector memory-like (EM-like) -and predicted novel markers, including Ly6C for the EM-like cells, that were validated by flow cytometry. Tumor-specific PMEL T cells were predominantly found within the exhausted and memory-like states but also within the EM-like state. Further, TCR repertoire sequencing revealed a large clonal expansion of exhausted, memory-like and EM-like cells with partial clonal relatedness between them. Finally, meta-analyses of public bulk and single-cell RNAseq data suggested that anti-PD-1 treatment induces expansion of EM-like cells.Our reference map of the transcriptomic landscape of murine CD8 TILs will help interpreting future bulk and single-cell transcriptomic studies and may guide the analysis of CD8 TIL subpopulations in response to therapeutic interventions.2

Single-cell RNA sequencing (scRNA-seq) technologies offer unique opportunities for exploring heterogeneous cell populations. However, in-depth single-cell transcriptomic characterization of complex tissues often requires profiling tens to hundreds of thousands of cells. Such large numbers of cells represent an important hurdle for downstream analyses, interpretation and visualization. Here we develop a network-based coarse-graining framework where highly similar cells are merged into super-cells. We demonstrate that super-cells not only preserve but often improve the results of downstream analyses including visualization, clustering, differential expression, cell type annotation, gene correlation, imputation, RNA velocity and data integration. By capitalizing on the redundancy inherent to scRNA-seq data, super-cells significantly facilitate and accelerate the construction and interpretation of single-cell atlases, as demonstrated by the integration of 1.46 million cells from COVID-19 patients in less than two hours on a standard desktop.

Three patients are described in whom primary gastric carcinoma of the linitis plastica type was initially diagnosed. All three patients had been treated for breast carcinoma; in two the disease had been bilateral, and in two the gastric tumour was the first manifestation of relapse of that patient's breast carcinoma. The three cases are examples of a pattern of spread particularly likely to occur in infiltrating lobular carcinoma of the breast. The importance of recognizing this is paramount if appropriate therapy is to be given. One of the patients has had an excellent remission on Tamoxifen, another may be showing some response. and the third has commenced treatment with Medroxyprogesterone acetate.