SpatialCells: automated profiling of tumor microenvironments with spatially resolved multiplexed single-cell data.

Cancer is a complex cellular ecosystem where malignant cells coexist and interact with immune, stromal and other cells within the tumor microenvironment (TME). Recent technological advancements in spatially resolved multiplexed imaging at single-cell resolution have led to the generation of large-scale and high-dimensional datasets from biological specimens. This underscores the necessity for automated methodologies that can effectively characterize molecular, cellular and spatial properties of TMEs for various malignancies. This study introduces SpatialCells, an open-source software package designed for region-based exploratory analysis and comprehensive characterization of TMEs using multiplexed single-cell data. The source code and tutorials are available at https://semenovlab.github.io/SpatialCells. SpatialCells efficiently streamlines the automated extraction of features from multiplexed single-cell data and can process samples containing millions of cells. Thus, SpatialCells facilitates subsequent association analyses and machine learning predictions, making it an essential tool in advancing our understanding of tumor growth, invasion and metastasis.

Multi-organ immune-related adverse events from immune checkpoint inhibitors and their downstream implications: a retrospective multicohort study.

BACKGROUND: Understanding co-occurrence patterns and prognostic implications of immune-related adverse events is crucial for immunotherapy management. However, previous studies have been limited by sample size and generalisability. In this study, we leveraged a multi-institutional cohort and a population-level database to investigate co-occurrence patterns of and survival outcomes after multi-organ immune-related adverse events among recipients of immune checkpoint inhibitors. METHODS: In this retrospective study, we identified individuals who received immune checkpoint inhibitors between May 31, 2015, and June 29, 2022, from the Massachusetts General Hospital, Brigham and Women's Hospital, and Dana-Farber Cancer Institute (Boston, MA, USA; MGBD cohort), and between April 30, 2010, and Oct 11, 2021, from the independent US population-based TriNetX network. We identified recipients from all datasets using medication codes and names of seven common immune checkpoint inhibitors, and patients were excluded from our analysis if they had incomplete information (eg, diagnosis and medication records) or if they initiated immune checkpoint inhibitor therapy after Oct 11, 2021. Eligible patients from the MGBD cohort were then propensity score matched with recipients of immune checkpoint inhibitors from the TriNetX database (1:2) based on demographic, cancer, and immune checkpoint inhibitor characteristics to facilitate cohort comparability. We applied immune-related adverse event identification rules to identify patients who did and did not have immune-related adverse events in the matched cohorts. To reduce the likelihood of false positives, patients diagnosed with suspected immune-related adverse events within 3 months after chemotherapy were excluded. We performed pairwise correlation analyses, non-negative matrix factorisation, and hierarchical clustering to identify co-occurrence patterns in the MGBD cohort. We conducted landmark overall survival analyses for patient clusters based on predominant immune-related adverse event factors and calculated accompanying hazard ratios (HRs) and 95% CIs, focusing on the 6-month landmark time for primary analyses. We validated our findings using the TriNetX cohort. FINDINGS: We identified 15 246 recipients of immune checkpoint inhibitors from MGBD and 50 503 from TriNetX, of whom 13 086 from MGBD and 26 172 from TriNetX were included in our propensity score-matched cohort. Median follow-up durations were 317 days (IQR 113-712) in patients from MGBD and 249 days (91-616) in patients from TriNetX. After applying immune-related adverse event identification rules, 8704 recipients of immune checkpoint inhibitors were retained from MGBD, of whom 3284 (37·7%) had and 5420 (62·3%) did not have immune-related adverse events, and 18 162 recipients were retained from TriNetX, of whom 5538 (30·5%) had and 12 624 (69·5%) did not have immune-related adverse events. In both cohorts, positive pairwise correlations of immune-related adverse events were commonly observed. Co-occurring immune-related adverse events were decomposed into seven factors across organs, revealing seven distinct patient clusters (endocrine, cutaneous, respiratory, gastrointestinal, hepatic, musculoskeletal, and neurological). In the MGBD cohort, the patient clusters that predominantly had endocrine (HR 0·53 [95% CI 0·40-0·70], p<0·0001) and cutaneous (0·61 [0·46-0·81], p=0·0007) immune-related adverse events had favourable overall survival outcomes at the 6-month landmark timepoint, while the other clusters either had unfavourable (respiratory: 1·60 [1·25-2·03], p=0·0001) or neutral survival outcomes (gastrointestinal: 0·86 [0·67-1·10], p=0·23; musculoskeletal: 0·97 [0·78-1·21], p=0·78; hepatic: 1·20 [0·91-1·59], p=0·19; and neurological: 1·30 [0·97-1·74], p=0·074). Similar results were found in the TriNetX cohort (endocrine: HR 0·75 [95% CI 0·60-0·93], p=0·0078; cutaneous: 0·62 [0·48-0·82], p=0·0007; respiratory: 1·21 [1·00-1·46], p=0·044), except for the neurological cluster having unfavourable (rather than neutral) survival outcomes (1·30 [1·06-1·59], p=0·013). INTERPRETATION: Reliably identifying the immune-related adverse event cluster to which a patient belongs can provide valuable clinical information for prognosticating outcomes of immunotherapy. These insights can be leveraged to counsel patients on the clinical impact of their individual constellation of immune-related adverse events and ultimately develop more personalised surveillance and mitigation strategies. FUNDING: US National Institutes of Health.

The role of intracranial pressure variability as a predictor of intracranial hypertension and mortality in critically ill patients.

OBJECTIVE: Intracranial pressure (ICP) monitoring is a widely utilized and essential tool for tracking neurosurgical patients, but there are limitations to the use of a solely ICP-based paradigm for guiding management. It has been suggested that ICP variability (ICPV), in addition to mean ICP, may be a useful predictor of neurological outcomes, as it represents an indirect measure of intact cerebral pressure autoregulation. However, the current literature regarding the applicability of ICPV shows conflicting associations between ICPV and mortality. Thus, the authors aimed to investigate the effect of ICPV on intracranial hypertensive episodes and mortality using the eICU Collaborative Research Database version 2.0. METHODS: The authors extracted from the eICU database 1,815,676 ICP readings from 868 patients with neurosurgical conditions. ICPV was computed using two methods: the rolling standard deviation (RSD) and the absolute deviation from the rolling mean (DRM). An episode of intracranial hypertension was defined as at least 25 minutes of ICP > 22 mm Hg in any 30-minute window. The effects of mean ICPV on intracranial hypertension and mortality were computed using multivariate logistic regression. A recurrent neural network with long short-term memory was used for time-series predictions of ICP and ICPV to prognosticate future episodes of intracranial hypertension. RESULTS: A higher mean ICPV was significantly associated with intracranial hypertension using both ICPV definitions (RSD: aOR 2.82, 95% CI 2.07-3.90, p < 0.001; DRM: aOR 3.93, 95% CI 2.77-5.69, p < 0.001). ICPV was significantly associated with mortality in patients with intracranial hypertension (RSD: aOR 1.28, 95% CI 1.04-1.61, p = 0.026, DRM: aOR 1.39, 95% CI 1.10-1.79, p = 0.007). In the machine learning models, both definitions of ICPV achieved similarly good results, with the best F1 score of 0.685 ± 0.026 and an area under the curve of 0.980 ± 0.003 achieved with the DRM definition over 20 minutes. CONCLUSIONS: ICPV may be useful as an adjunct for the prognostication of intracranial hypertensive episodes and mortality in neurosurgical critical care as part of neuromonitoring. Further research on predicting future intracranial hypertensive episodes with ICPV may help clinicians react expediently to ICP changes in patients.

SpatialCells: Automated Profiling of Tumor Microenvironments with Spatially Resolved Multiplexed Single-Cell Data.

Background: Cancer is a complex cellular ecosystem where malignant cells coexist and interact with immune, stromal, and other cells within the tumor microenvironment. Recent technological advancements in spatially resolved multiplexed imaging at single-cell resolution have led to the generation of large-scale and high-dimensional datasets from biological specimens. This underscores the necessity for automated methodologies that can effectively characterize the molecular, cellular, and spatial properties of tumor microenvironments for various malignancies. Results: This study introduces SpatialCells, an open-source software package designed for region-based exploratory analysis and comprehensive characterization of tumor microenvironments using multiplexed single-cell data. Conclusions: SpatialCells efficiently streamlines the automated extraction of features from multiplexed single-cell data and can process samples containing millions of cells. Thus, SpatialCells facilitates subsequent association analyses and machine learning predictions, making it an essential tool in advancing our understanding of tumor growth, invasion, and metastasis.