ALBERT

Description: Despite advances in the treatment of chronic lymphocytic leukemia (CLL), the transformation of CLL to an aggressive lymphoma, or Richter's transformation (RT), remains a clinical challenge, as it responds poorly to standard therapies and shortens survival. Recent studies demonstrate that RT, but not underlying CLL, responds to PD-1 checkpoint blockade (CPB) with an overall response rate of 43-65%. Given the central role of T cells in anti-tumor immunity, we hypothesized that differences in T cell populations underlie response and resistance to CPB in RT. We focused on a discovery cohort of 6 patients with RT (4 responders, 2 non-responders) and 2 patients with relapsed/refractory CLL enrolled on a study in which patients were initiated with anti-PD1 therapy (nivolumab 3 mg/kg every 2 weeks), with subsequent concurrent ibrutinib (420 mg daily)(NCT 02420912). We examined a total of 15 serial study marrow specimens collected at treatment initiation and 3 month response evaluation, as well as 2 healthy marrow donors. To systematically discover the T cell populations and states associated with CPB response in RT, we performed single-cell RNA-sequencing (scRNA-seq, 10x Genomics) of non-lymphoma (CD5-CD19-) cells isolated by flow cytometry from marrow samples. A total of 60,727 T and NK cells were captured with average detection of 1001 genes/cell. Using the novel joint clustering approach Conos, 11 transcriptionally distinct clusters of lymphocytes were identified. We first contrasted baseline RT/CLL with normal marrow and observed differences across T cell populations, which we confirmed through the examination of publicly available marrow scRNA-seq data from 28 healthy donors. Compared to normal marrow, RT/CLL marrow was enriched for cytotoxic populations, including both CD8 effector/effector memory (E/EM) (p=0.001, t-test) and cytotoxic CD4 (p=0.001) T cells as well as for cells expressing multiple exhaustion markers, including PDCD1, LAG3 and TIGIT (p=0.001). In contrast, normal marrow contained increased T cells with a naïve-like phenotype (p=0.06). When we focused on the pre-treatment samples from RT patients, RT responders had a larger CD8 E/EM population (p=0.04) and fewer T regulatory cells (p=0.006, t-test) than RT non-responders. Using DESeq2 to compare clusters from all samples, we evaluated if there were differences in gene expression between RT responders and non-responders. CD8 E/EM T cells of RT non-responders showed increased expression of TOX, a recently uncovered master regulator of cell exhaustion (padj =0.00016), while this cell subtype in RT responders upregulated a contrasting program of activating transcription factors as well as the co-stimulatory gene CD226 (padj =0.04). As for CD4 T cells, RT responders revealed an enriched cytotoxic gene program compared to RT non-responders (padjPRF1 5.9 x 10-10, GZMH 6.0 x 10-6, NKG7 6.4 x 10-19). To investigate whether response to CPB therapy for RT was associated with changes in the T cell receptor (TCR) repertoire, and to obtain protein-level validation of transcriptional signatures, we performed single-cell TCR sequencing with paired gene and protein expression (10x Genomics) on pre- and post-therapy samples from a RT responder and a non-responder. Indeed, we confirmed our gene expression findings, including validation of cytotoxic CD4 T cells and the enrichment of CD226 protein in E/EM CD8 T cells in the RT responder. TCR clonal expansion was observed in the RT responder at baseline with persistence of enriched clonotypes following CPB, suggesting the presence of tumor-reactive T cell clones. In contrast, the RT non-responder displayed higher TCR diversity with enriched clonotypes showing increased exhaustion post-CPB (p

Description: Hematopoietic cells and cell surface molecules have both been defined in the hundreds, and the cell-specific profiles arising from the presence of specific proteins on the surface of different cells or biological states (e.g. developmental stages, disease states, etc.) represent data of high combinatorial complexity. The dynamic surface marker profiles of cells have been extensively used for cell sorting and for therapeutics where specific surface markers are used to direct therapeutic agents to diseased cells, using either monoclonal antibodies or cell-based therapies. Immunophenotyping is commonly used to define and sort cells based on the proteins present on their surface. In order to efficiently separate similar cells, a large number of surface proteins are often used. Complete knowledge about unique, cell-specific profiles of surface protein expression are likely to reveal much simpler surface profiles than currently used, as well as the definition of surface profiles where non are currently defined. In cancer immunotherapy, adoptive transfer of chimeric antigen receptor (CAR) engineered T cells show promise as therapy modality. Currently, the main achievements utilizing this technique have been made targeting single malignancy-specific surface molecules, but progress is being made in requiring binding of several ligands before lymphocyte activation, which will increases the specificity of the therapy and thereby decrease off-target effects. Defining surface protein expression profiles for cell stratification and CAR therapy in silicorequires information about expression of a large number of surface proteins on a large number of cells. At present, no high-throughput technique for measuring surface protein expression exists, although efforts to increase throughput using mass spectrometry and computational prediction of protein expression from mRNA expression are being explored. However, surface molecule expression on individual cells has been characterized at low rates using immunohistochemistry or flow cytometry for decades, and vast amounts of cell-specific expression has been measured and published. This represents a rich, but unstructured source of data and information. To facilitate the definition of unique surface molecule profiles, we collected and organized large amounts of these data of human hematopoietic cells and the corresponding quantitative or qualitative presence (depending on availability) of known molecular surface molecules from the primary literature. To do so, we employed text mining techniques for article classification (as either containing information about surface protein expression or not) and subsequently extensive manual curation to assemble the data foundation for defining cell surface profiles for stratification and therapy. To analyze these data, we have developed algorithms for selection of cell surface protein for cell stratification and for target selection for CAR-based therapies. The resulting database contains expression of 305 surface proteins across 206 hematopoietic cells, totaling 6153 data points. We have applied our algorithm to define unique profiles for each of the 206 cells, thus characterizing the surface profiles of the majority of hematopoietic cells to increase efficiency and specificity of cell stratification and therapy targeting. Future efforts will include expanding the database to contain surface protein expression for cells in all human tissues, as well as experimental validation of discovered surface profiles. Disclosures No relevant conflicts of interest to declare.