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Biology subjects

Pasam, A.

Publications and source records attributed to Pasam, A..

3 recordsLinked to original sources

Tumors Located in the Brain Impair the Frequency and Phenotype of Dendritic Cells in Blood and Tumor

BackgroundProfessional antigen-presenting dendritic cells (DC) are critical for anti-tumor immune responses, yet patients with glioblastoma, an aggressive primary brain tumor that responds poorly to current investigational immunotherapies, appear to be deficient in DC. The extent of this deficiency, the specific DC subsets affected, and the causative mechanisms remain undefined. Furthermore, DCs in other brain tumors have not been systematically investigated. MethodsHigh-parameter flow cytometry was used to profile circulating and intra-tumoral DCs in patients with glioblastoma, low-grade gliomas, and brain metastases, and non-CNS cancers. Plasma DC growth factors were quantified using ELISA. We also evaluated single-cell RNA sequencing (scRNAseq) datasets to compare intra-tumoral DCs in brain and lung tumors, and quantified DC number and phenotype in three intracranial mouse brain tumor models. ResultsOur studies reveal a profound systemic reduction of multiple DC subsets in the blood of patients with diverse brain tumors, coupled with reduced DC activation marker expression and lower plasma levels of FLT3L and G-CSF. Furthermore, scRNAseq analyses revealed reduced intra-tumoral DCs in glioblastoma compared to lung tumors. Circulating DC numbers inversely correlated with perioperative corticosteroid dose in patients with or without a brain tumor. However, brain tumor patients not receiving corticosteroids also had reduced DCs, suggesting a direct effect of the brain tumor. This was supported by our observation of systemic DC defects in mouse brain tumor models. ConclusionsWe reveal profound DC defects in patients with brain tumors, which may contribute to current difficulties in developing effective immunotherapies for glioblastoma. SummaryWe demonstrate multiple DC defects in patients with brain tumors. This includes a profound reduction in circulating DC number, diminished activation marker expression and growth factor levels in cancer patients with brain tumors compared to those without, and reduced intra-tumoral DCs in brain compared to lung tumors. This is the first time DC subsets have been fully characterized in a range of brain tumor patients. We show that corticosteroid usage is closely associated with DC defects, highlighting the adverse effects of a standard symptomatic treatment on these critical immune cells. However, tumors located within the brain also directly contribute to DC defects. We identified several mouse brain tumor models that can be used to further the understanding of this endogenous DC deficiency and to develop approaches to restore DCs, ultimately leading to new combination immunotherapies for the treatment of brain cancers. HighlightsO_LIDCs are reduced in brain tumor patients and mice with intracranial tumors. C_LIO_LIDCs are rare within glioblastoma tumor tissue. C_LIO_LIThe presence of a brain tumor and corticosteroid use are both associated with DC defects. C_LI

immunology↗

Treatment-mediated selection of lethal prostate cancer clones defined by copy number architectures

Despite initial responses to hormone treatment, metastatic prostate cancer invariably evolves to a lethal state. To characterize the intra-patient relationships of metastases that evade treatment, we performed genomewide copy number profiling and bespoke approaches targeting the androgen receptor (AR) on 142 metastatic regions from 10 organs harvested post-mortem from nine men who died from prostate cancer. We identified diverse and patient-unique alterations clustering around the AR in metastases from every patient with evidence of independent acquisition of related genomic changes within an individual and, in some patients, the co-existence of AR-neutral clones. Using the genomic boundaries of pan-autosome copy number change, we confirmed a common clone of origin across metastases and diagnostic biopsies; and identified in individual patients, clusters of metastases occupied by dominant clones with diverged autosomal copy number alterations. Autosome-defined clusters were characterized by cluster-specific AR gene architectures that in two index cases were topologically more congruent than by chance (p-values 0.03, 3.07x10-8). Integration with anatomical site suggested patterns of spread and points of genomic divergence. Copy number boundaries identified treatment-selected clones with putatively distinct lethal trajectories. Statement of significanceLethal prostate cancer evolves from a single clone of origin and upon a treatment-mediated selection, progresses to lethal disease via a limited number of related clones harboring patient-unique androgen receptor gene architectures.

cancer biology↗

SPIAT: An R package for the Spatial Image Analysis of Cells in Tissues

Spatial technologies that query the location of cells in tissues at single-cell resolution are gaining popularity and are likely to become commonplace. The resulting data includes the X, Y coordinates of millions of cells, cell phenotypes and marker or gene expression levels. However, to date, the tools for the analysis of this data are largely underdeveloped, making us severely underpowered in our ability to extract quantifiable information. We have developed SPIAT (Spatial Image Analysis of Tissues), an R package with a suite of data processing, quality control, visualization, data handling and data analysis tools. SPIAT includes our novel algorithms for the identification of cell clusters, cell margins and cell gradients, the calculation of neighbourhood proportions, and algorithms for the prediction of cell phenotypes. SPIAT also includes speedy implementations of the calculation of cell distances and detection of cell communities. This version of SPIAT is directly compatible with Opal multiplex immunohistochemistry images analysed through the HALO and InForm analysis software, but its intuitive implementation allows use with a diversity of platforms. We expect SPIAT to become a user-friendly and speedy go-to package for the spatial analysis of cells in tissues. SPIAT is available on Github: https://github.com/cancer-evolution/SPIAT

bioinformatics↗