spatialGE: A user-friendly web application to democratize spatial transcriptomics analysis.

Spatial transcriptomics (ST) is a powerful tool for understanding tissue biology and disease mechanisms. However, its potential is often underutilized due to the advanced data analysis and programming skills required. To address this, we present spatialGE, a web application that simplifies the analysis of ST data. The application spatialGE provides a user-friendly interface that guides users without programming expertise through various analysis pipelines, including quality control, normalization, domain detection, phenotyping, and multiple spatial analyses. It also enables comparative analysis among samples and supports various ST technologies. We demonstrate the utility of spatialGE through its application in studying the tumor microenvironment of melanoma brain metastasis and Merkel cell carcinoma. Our results highlight the ability of spatialGE to identify spatial gene expression patterns and enrichments, providing valuable insights into the tumor microenvironment and its utility in democratizing ST data analysis for the wider scientific community.

Spatial transcriptomics analysis identifies a tumor-promoting function of the meningeal stroma in melanoma leptomeningeal disease.

Leptomeningeal disease (LMD) remains a rapidly lethal complication for late-stage melanoma patients. Here, we characterize the tumor microenvironment of LMD and patient-matched extra-cranial metastases using spatial transcriptomics in a small number of clinical specimens (nine tissues from two patients) with extensive in vitro and in vivo validation. The spatial landscape of melanoma LMD is characterized by a lack of immune infiltration and instead exhibits a higher level of stromal involvement. The tumor-stroma interactions at the leptomeninges activate tumor-promoting signaling, mediated through upregulation of SERPINA3. The meningeal stroma is required for melanoma cells to survive in the cerebrospinal fluid (CSF) and promotes MAPK inhibitor resistance. Knocking down SERPINA3 or inhibiting the downstream IGR1R/PI3K/AKT axis results in tumor cell death and re-sensitization to MAPK-targeting therapy. Our data provide a spatial atlas of melanoma LMD, identify the tumor-promoting role of meningeal stroma, and demonstrate a mechanism for overcoming microenvironment-mediated drug resistance in LMD.

WITHDRAWN: Impact of spatial clustering of cytotoxic and tumor infiltrating lymphocytes on overall survival in women with high grade serous ovarian cancer.

The authors have withdrawn their manuscript owing to incorrect handling of multiple measures in the survival analyses. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

Branched-chain keto acids promote an immune-suppressive and neurodegenerative microenvironment in leptomeningeal disease.

Leptomeningeal disease (LMD) occurs when tumors seed into the leptomeningeal space and cerebrospinal fluid (CSF), leading to severe neurological deterioration and poor survival outcomes. We utilized comprehensive multi-omics analyses of CSF from patients with lymphoma LMD to demonstrate an immunosuppressive cellular microenvironment and identified dysregulations in proteins and lipids indicating neurodegenerative processes. Strikingly, we found a significant accumulation of toxic branched-chain keto acids (BCKA) in the CSF of patients with LMD. The BCKA accumulation was found to be a pan-cancer occurrence, evident in lymphoma, breast cancer, and melanoma LMD patients. Functionally, BCKA disrupted the viability and function of endogenous T lymphocytes, chimeric antigen receptor (CAR) T cells, neurons, and meningeal cells. Treatment of LMD mice with BCKA-reducing sodium phenylbutyrate significantly improved neurological function, survival outcomes, and efficacy of anti-CD19 CAR T cell therapy. This is the first report of BCKA accumulation in LMD and provides preclinical evidence that targeting these toxic metabolites improves outcomes.

Spatial transcriptomics analysis identifies a unique tumor-promoting function of the meningeal stroma in melanoma leptomeningeal disease.

Leptomeningeal disease (LMD) remains a rapidly lethal complication for late-stage melanoma patients. The inaccessible nature of the disease site and lack of understanding of the biology of this unique metastatic site are major barriers to developing efficacious therapies for patients with melanoma LMD. Here, we characterize the tumor microenvironment of the leptomeningeal tissues and patient-matched extra-cranial metastatic sites using spatial transcriptomic analyses with in vitro and in vivo validation. We show the spatial landscape of melanoma LMD to be characterized by a lack of immune infiltration and instead exhibit a higher level of stromal involvement. We show that the tumor-stroma interactions at the leptomeninges activate pathways implicated in tumor-promoting signaling, mediated through upregulation of SERPINA3 at the tumor-stroma interface. Our functional experiments establish that the meningeal stroma is required for melanoma cells to survive in the CSF environment and that these interactions lead to a lack of MAPK inhibitor sensitivity in the tumor. We show that knocking down SERPINA3 or inhibiting the downstream IGR1R/PI3K/AKT axis results in re-sensitization of the tumor to MAPK-targeting therapy and tumor cell death in the leptomeningeal environment. Our data provides a spatial atlas of melanoma LMD, identifies the tumor-promoting role of meningeal stroma, and demonstrates a mechanism for overcoming microenvironment-mediated drug resistance unique to this metastatic site.

spatialGE: quantification and visualization of the tumor microenvironment heterogeneity using spatial transcriptomics.

SUMMARY: Spatially resolved transcriptomics promises to increase our understanding of the tumor microenvironment and improve cancer prognosis and therapies. Nonetheless, analytical methods to explore associations between the spatial heterogeneity of the tumor and clinical data are not available. Hence, we have developed spatialGE, a software that provides visualizations and quantification of the tumor microenvironment heterogeneity through gene expression surfaces, spatial heterogeneity statistics that can be compared against clinical information, spot-level cell deconvolution and spatially informed clustering, all using a new data object to store data and resulting analyses simultaneously. AVAILABILITY AND IMPLEMENTATION: The R package and tutorial/vignette are available at https://github.com/FridleyLab/spatialGE. A script to reproduce the analyses in this manuscript is available in Supplementary information. The Thrane study data included in spatialGE was made available from the public available from the website https://www.spatialresearch.org/resources-published-datasets/doi-10-1158-0008-5472-can-18-0747/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

spatialTIME and iTIME: R package and Shiny application for visualization and analysis of immunofluorescence data.

SUMMARY: Multiplex immunofluorescence (mIF) staining combined with quantitative digital image analysis is a novel and increasingly used technique that allows for the characterization of the tumor immune microenvironment (TIME). Generally, mIF data is used to examine the abundance of immune cells in the TIME; however, this does not capture spatial patterns of immune cells throughout the TIME, a metric increasingly recognized as important for prognosis. To address this gap, we developed an R package spatialTIME that enables spatial analysis of mIF data, as well as the iTIME web application that provides a robust but simplified user interface for describing both abundance and spatial architecture of the TIME. The spatialTIME package calculates univariate and bivariate spatial statistics (e.g. Ripley's K, Besag's L, Macron's M and G or nearest neighbor distance) and creates publication quality plots for spatial organization of the cells in each tissue sample. The iTIME web application allows users to statistically compare the abundance measures with patient clinical features along with visualization of the TIME for one tissue sample at a time. AVAILABILITY AND IMPLEMENTATION: spatialTIME is implemented in R and can be downloaded from GitHub (https://github.com/FridleyLab/spatialTIME) or CRAN. An extensive vignette for using spatialTIME can also be found at https://cran.r-project.org/web/packages/spatialTIME/index.html. iTIME is implemented within a R Shiny application and can be accessed online (http://itime.moffitt.org/), with code available on GitHub (https://github.com/FridleyLab/iTIME). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Challenges and Opportunities in the Statistical Analysis of Multiplex Immunofluorescence Data.

Immune modulation is considered a hallmark of cancer initiation and progression. The recent development of immunotherapies has ushered in a new era of cancer treatment. These therapeutics have led to revolutionary breakthroughs; however, the efficacy of immunotherapy has been modest and is often restricted to a subset of patients. Hence, identification of which cancer patients will benefit from immunotherapy is essential. Multiplex immunofluorescence (mIF) microscopy allows for the assessment and visualization of the tumor immune microenvironment (TIME). The data output following image and machine learning analyses for cell segmenting and phenotyping consists of the following information for each tumor sample: the number of positive cells for each marker and phenotype(s) of interest, number of total cells, percent of positive cells for each marker, and spatial locations for all measured cells. There are many challenges in the analysis of mIF data, including many tissue samples with zero positive cells or "zero-inflated" data, repeated measurements from multiple TMA cores or tissue slides per subject, and spatial analyses to determine the level of clustering and co-localization between the cell types in the TIME. In this review paper, we will discuss the challenges in the statistical analysis of mIF data and opportunities for further research.

Elevated levels of adaption in Helicobacter pylori genomes from Japan; a link to higher incidences of gastric cancer?

Helicobacter pylori is a bacterium that lives in the human stomach and is a major risk factor for gastric cancer and ulcers. H.pylori is host dependent and has been carried with human populations around the world after their departure from Africa. We wished to investigate how H.pylori has coevolved with its host during that time, focusing on strains from Japanese and European populations, given that gastric cancer incidence is high in Japanese populations, while low in European. A positive selection analysis of eight H.pylori genomes was conducted, using maximum likelihood based pairwise comparisons in order to maximize the number of strain-specific genes included in the study. Using the genic Ka/Ks ratio, comparisons of four Japanese H.pylori genomes suggests 25-34 genes under positive selection, while four European H.pylori genomes suggests 16-21 genes; few of the genes identified were in common between lineages. Of the identified genes which were annotated, 38% possessed homologs associated with pathogenicity and / or host adaptation, consistent with their involvement in a coevolutionary 'arms race' with the host. Given the efficacy of identifying host interaction factors de novo, in the absence of functionally annotated homologs our evolutionary approach may have value in identifying novel genes which H.pylori employs to interact with the human gut environment. In addition, the larger number of genes inferred as being under positive selection in Japanese strains compared to European implies a stronger overall adaptive pressure, potentially resulting from an elevated immune response which may be linked to increased inflammation, an initial stage in the development of gastric cancer.