Search bioRxiv⌕ Search

Biology subjects

Goldin, R. D.

Publications and source records attributed to Goldin, R. D..

2 recordsLinked to original sources

Therapeutic inhibition of monocyte recruitment prevents checkpoint inhibitor-induced hepatitis

Checkpoint inhibitor-induced hepatitis (CPI-hepatitis) is an emerging problem with the widening use of CPIs in cancer immunotherapy. Here, we developed a mouse model to characterise the mechanism of CPI-hepatitis and to therapeutically target key pathways driving this pathology. C57BL/6 wild-type (WT) mice were dosed with TLR9-agonist (TLR9-L) for hepatic priming combined with anti-CTLA-4 plus anti-PD-1 (CPI) or control (PBS) for up to 7 days. Co-administration of CPIs with TLR9-L induced liver pathology closely resembling human disease, with increased infiltration and clustering of granzyme B+perforin+CD8+ T cells and CCR2+ monocytes, 7 days post treatment. This was accompanied by apoptotic hepatocytes surrounding these clusters and elevated cytokeratin-18 and alanine transaminase plasma levels. Liver RNA sequencing identified key signalling pathways (JAK-STAT, NF-{kappa}B) and cytokine/chemokine networks (Ifn{gamma}, Cxcl9, Ccl2/Ccr2) as drivers of CPI-hepatitis. Using this model, we show that CD8+ T cells mediate hepatocyte damage in experimental CPI-hepatitis. However, their liver recruitment, clustering, and cytotoxic activity is dependent the presence of CCR2+ monocytes. Absence of hepatic monocyte recruitment in Ccr2rfp/rfp mice and CCR2 therapeutic inhibition by cenicriciroc (CVC) in WT mice prevented CPI-hepatitis. In conclusion, using this newly established mouse model, we demonstrate a central role of liver infiltrating CCR2+ monocyte interaction with cytotoxic CD8+ T cells in the pathogenesis of CPI-hepatitis and highlight novel therapeutic targets.

immunology↗

Network analysis of mass spectrometry imaging data from colorectal cancer identifies key metabolites common to metastatic development.

A deeper understanding of inter-tumor and intra-tumor heterogeneity is a critical factor for the advancement of next generation strategies against cancer. The heterogeneous morphology exhibited by solid tumors is mirrored by their metabolic heterogeneity. Defining the basic biological mechanisms that underlie tumor cell variability will be fundamental to the development of personalized cancer treatments. Variability in the molecular signatures found in local regions of cancer tissues can be captured through an untargeted analysis of their metabolic constituents. Here we demonstrate that DESI mass spectrometry imaging (MSI) combined with network analysis can provide detailed insight into the metabolic heterogeneity of colorectal cancer (CRC). We show that network modules capture signatures which differentiate tumor metabolism in the core and in the surrounding region. Moreover, module preservation analysis of network modules between patients with and without metastatic recurrence explains the inter-subject metabolic differences associated with diverse clinical outcomes such as metastatic recurrence.\n\nSignificanceNetwork analysis of DESI-MSI data from CRC human tissue reveals clinically relevant co-expression ion patterns associated with metastatic susceptibility. This delineates a more complex picture of tumor heterogeneity than conventional hard segmentation algorithms. Using tissue sections from central regions and at a distance from the tumor center, ion co-expression patterns reveal common features among patients who developed metastases (up of > 5 years) not preserved in patients who did not develop metastases. This offers insight into the nature of the complex molecular interactions associated with cancer recurrence. Presently, predicting CRC relapse is challenging, and histopathologically like-for-like cancers frequently manifest widely varying metastatic tendencies. Thus, the methodology introduced here more robustly defines the risk of metastases based on tumor biochemical heterogeneity.\n\nAuthor contributionsP.I., Z.T., R.C.G.: designed the study, developed the workflow, analyzed the data, interpreted the results, wrote the paper; N.S. collected the MS, performed the H...E staining, wrote the paper; L.D.: interpreted the results, wrote the paper; A.M.: collected the MS; A.S.: histological assessment; L.P.: collected the tissue specimens and clinical metadata; A.D.: collected the MS; H.K.: performed the H...E staining; R.M.: collected the tissue specimens and clinical metadata. R.G.: histological assessment; J.K.N: designed the study, edited the paper.

cancer biology↗