Ajit Johnson Nirmal

Diseases such as cancer involve alterations in cell proportions, states and interactions, as well as complex changes in tissue morphology and architecture. Histopathological diagnosis of disease and most multiplexed spatial profiling relies on inspecting thin (4–5 µm) specimens. Here we describe a high-plex cyclic immunofluorescence method for three-dimensional tissue imaging and use it to show that few, if any, cells are intact in conventional thin tissue sections, reducing the accuracy of cell phenotyping and interaction analysis. However, three-dimensional cyclic immunofluorescence of sections eightfold to tenfold thicker enables accurate morphological assessment of diverse protein markers in intact tumor, immune and stromal cells. Moreover, the high resolution of this confocal approach generates images of cells in a preserved tissue environment at a level of detail previously limited to cell culture. Precise imaging of cell membranes also makes it possible to detect and map cell–cell contacts and juxtracrine signaling complexes in immune cell niches.

Localized cutaneous melanoma can be cured by excision but success critically depends on early detection and risk assessment of primary lesions. However, their initiation, progression, and immunology remain poorly understood, partly due to high intra- and inter-tumor heterogeneity. We studied this heterogeneity using spatial profiling in over 300 histological domains, each representing a single progression stage, and found that 200-600 cell neighborhoods from a single melanoma can be as different in RNA and protein expression as neighborhoods from different tumors. These differences are not stochastic, however, and disease progression can be mapped at the neighborhood level onto a cell state landscape defined by the activity of the melanocyte master regulator MITF and genes associated with a dedifferentiated neural crest phenotype. Position in this landscape is influenced by proximity to immune cells, perivascular environments, and other tissue features, but no single association is absolute, giving rise to complex spatial patterns.

PI3K-δ inhibitors have shown impressive activity in lymphoid malignancies but have been hampered by autoimmune and infectious toxicities, leading to market withdrawals. We previously demonstrated activity of the PI3K-δγ inhibitor duvelisib in T cell lymphomas (TCLs) that was associated with inflammatory adverse events. As reported here, we conducted a phase 1b/2a study of duvelisib in combination with either romidepsin (n = 66) or bortezomib (n = 32) in patients with relapsed/refractory TCL and found that the addition of romidepsin, but not bortezomib, appeared to increase efficacy while attenuating PI3K inhibitor-driven toxicity. The primary endpoint of the study was to determine the safety and maximum tolerated dose of duvelisib, which was 75 mg twice daily when combined with romidepsin versus 25 mg twice daily when combined with bortezomib. The most common adverse events were neutropenia (42%, 25/59) and fatigue (37%, 22/59) in patients treated with duvelisib and romidepsin and diarrhea (48%, 11/23) and neutropenia (30%, 7/23) in patients treated with duvelisib and bortezomib. Duvelisib and romidepsin resulted in less grade 3/4 hepatotoxicity (14%, 8/59) compared to 40% (14/35) in our previous study with duvelisib monotherapy. This was associated with reductions in circulating inflammatory mediators and myeloid cell inflammatory gene expression. Secondary endpoints of overall and complete response rates were 55% (35/64) and 34% (22/64) for patients treated with duvelisib and romidepsin and 34% (11/32) and 13% (4/32) for patients treated with duvelisib and bortezomib. Among patients with peripheral T cell lymphomas (PTCLs), overall and complete response rates of duvelisib and romidepsin were 56% (27/48) and 44% (21/48), respectively, with exploratory analyses showing increased response rates in patients with a follicular helper T cell subtype. These findings support further development of combined PI3K and histone deacetylase (HDAC) inhibition in TCLs and suggest a unique strategy to enable PI3K inhibitor-based combinations for additional patient populations. ClinicalTrials.gov identifier: NCT02783625.

Multiplexed imaging data are revolutionizing our understanding of the composition and organization of tissues and tumors ("Catching up with Multiplexed Tissue Imaging," 2022). A critical aspect of such "tissue profiling" is quantifying the spatial relationships among cells at different scales from the interaction of neighboring cells to recurrent communities of cells of multiple types. This often involves statistical analysis of 10⁷ or more cells in which up to 100 biomolecules (commonly proteins) have been measured. While software tools currently cater to the analysis of spatial transcriptomics data (Liu et al., 2022), there remains a need for toolkits explicitly tailored to the complexities of multiplexed imaging data including the need to seamlessly integrate image visualization with data analysis and exploration. We introduce SCIMAP, a Python package specifically crafted to address these challenges. With SCIMAP, users can efficiently preprocess, analyze, and visualize large datasets, facilitating the exploration of spatial relationships and their statistical significance. SCIMAP's modular design enables the integration of new algorithms, enhancing its capabilities for spatial analysis.

Summary: To uncover the intricate, chemotherapy-induced spatiotemporal remodeling of the tumor microenvironment, we conducted integrative spatial and molecular characterization of 97 high-grade serous ovarian cancer (HGSC) samples collected before and after chemotherapy. Using single-cell and spatial analyses, we identify increasingly versatile immune cell states, which form spatiotemporally dynamic microcommunities at the tumor-stroma interface. We demonstrate that chemotherapy triggers spatial redistribution and exhaustion of CD8+ T cells due to prolonged antigen presentation by macrophages, both within interconnected myeloid networks termed “Myelonets” and at the tumor stroma interface. Single-cell and spatial transcriptomics identifies prominent TIGIT-NECTIN2 ligand-receptor interactions induced by chemotherapy. Using a functional patient-derived immuno-oncology platform, we show that CD8+T-cell activity can be boosted by combining immune checkpoint blockade with chemotherapy. Our discovery of chemotherapy-induced myeloid-driven spatial T-cell exhaustion paves the way for novel immunotherapeutic strategies to unleash CD8+ T-cell-mediated anti-tumor immunity in HGSC.

Highly multiplexed tissue imaging and in situ spatial profiling aim to extract single-cell data from specimens containing closely packed cells of diverse morphology. This is challenging due to the difficulty of accurately assigning boundaries between cells (segmentation) and then generating per-cell staining intensities. Existing methods use gating to convert per-cell intensity data to positive and negative scores; this is a common approach in flow cytometry, but one that is problematic in imaging. In contrast, human experts identify cells in crowded environments using morphological, neighborhood, and intensity information. Here we describe a computational approach (Cell Spotter or CSPOT) that uses supervised machine learning in combination with classical segmentation to perform automated cell type calling. CSPOT is robust to artifacts that commonly afflict tissue imaging and can replace conventional gating. The end-to-end Python implementation of CSPOT can be integrated into cloud-based image processing pipelines to substantially improve the speed, accuracy, and reproducibility of single-cell spatial data.

Homeostasis of normal tissues and the emergence of diseases such as cancer are controlled by changes in the proportions and states of diverse cell types, cell-cell interactions, and acellular components of the tissue microenvironment¹. Spatial omics using highly multiplexed tissue profiling² makes it possible to study these processes in situ, usually on thin, 4-5 micron thick sections (the standard histopathology format)³. Microscopy-based tissue imaging is commonly performed at a resolution sufficient to determine cell types but not detect the subtle morphological features associated with cytoskeletal reorganisation, juxtracrine signalling, or membrane trafficking⁴. Here we introduce a 3D imaging approach using existing instruments and reagents that is able to characterize a wide variety of organelles and structures at sub-micron scale while simultaneously quantifying millimetre-scale spatial features. We perform high-resolution 54-plex cyclic immunofluorescence (CyCIF) imaging³ on sections of primary human melanoma thick enough (30-40 microns) to fully encompass two or more layers of intact cells. In pre-invasive melanoma in situ⁵, 3D imaging of entire cell volumes showed that transformed melanocytic cells are plastic in state and participate in tightly localised niches of interferon signalling near sites of initial invasion into the underlying dermis. Below this layer, immune cells engaged in an unexpectedly diverse array of membrane-membrane interactions as well as looser “neighbourhood” associations⁶. These data provide new insight into the transitions occurring during early tumour formation and demonstrate the potential for phenotyping tissues at a level of detail previously restricted to cultured cells and organoids.

Gene fusions involving tumor protein p63 gene (TP63) occur in multiple T and B cell lymphomas and portend a dismal prognosis for patients. The function and mechanisms of TP63 fusions remain unclear, and there is no target therapy for patients with lymphoma harboring TP63 fusions. Here, we show that TP63 fusions act as bona fide oncogenes and are essential for fusion-positive lymphomas. Transgenic mice expressing TBL1XR1::TP63, the most common TP63 fusion, develop diverse lymphomas that recapitulate multiple human T and B cell lymphomas. Here, we identify that TP63 fusions coordinate the recruitment of two epigenetic modifying complexes, the nuclear receptor corepressor (NCoR)—histone deacetylase 3 (HDAC3) by the N-terminal TP63 fusion partner and the lysine methyltransferase 2D (KMT2D) by the C-terminal TP63 component, which are both required for fusion-dependent survival. TBL1XR1::TP63 localization at enhancers drives a unique cell state that involves up-regulation of MYC and the polycomb repressor complex 2 (PRC2) components EED and EZH2. Inhibiting EZH2 with the therapeutic agent valemetostat is highly effective at treating transgenic lymphoma murine models, xenografts, and patient-derived xenografts harboring TP63 fusions. One patient with TP63 -rearranged lymphoma showed a rapid response to valemetostat treatment. In summary, TP63 fusions link partner components that, together, coordinate multiple epigenetic complexes, resulting in therapeutic vulnerability to EZH2 inhibition.