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Biology subjects

Goodyear, T.

Publications and source records attributed to Goodyear, T..

2 recordsLinked to original sources

The spatially resolved tumor microenvironment predicts treatment outcome in relapsed/refractory Hodgkin lymphoma

PURPOSEAbout a third of relapsed or refractory classic Hodgkin lymphoma (r/r CHL) patients succumb to their disease after high-dose chemotherapy followed by autologous stem cell transplantation (HDC/ASCT). Here, we aimed to describe spatially resolved tumor microenvironment (TME) ecosystems to establish novel biomarkers associated with treatment failure in r/r CHL. METHODSWe performed imaging mass cytometry (IMC) on 169 paired primary diagnostic and relapse biopsies using a marker panel specific for CHL biology. For each cell type in the TME, we calculated a spatial score measuring the distance of nearest neighbor cells to the malignant Hodgkin Reed Sternberg cells within close interaction range. Spatial scores were used as features in prognostic model development for post-ASCT outcomes. RESULTSHighly multiplexed IMC data revealed shared TME patterns in paired diagnostic and early relapse/refractory CHL samples, whereas TME patterns were more divergent in pairs of diagnostic and late relapse samples. Integrated analysis of IMC and single cell RNA sequencing data identified unique architecture defined by CXCR5+ HRS cells and their strong spatial relationship with CXCL13+ macrophages in the TME. We developed a prognostic assay ( RHL4S) using four spatially resolved parameters, CXCR5+ HRS cells, PD1+CD4+ T cells, tumor-associated macrophages, and CXCR5+ B cells, which effectively separated patients into high-risk vs low-risk groups with significantly different post-ASCT outcomes. The RHL4S assay was validated in an independent r/r CHL cohort using a multicolor immunofluorescence assay. CONCLUSIONSWe identified the interaction of CXCR5+ HRS cells with ligand-expressing CXCL13+ macrophages as a prominent crosstalk axis in relapsed CHL. Harnessing this TME biology, we developed a novel prognostic model applicable to r/r CHL biopsies, RHL4S, opening new avenues for spatial biomarker development.

cancer biology↗

Integrated single cell analysis reveals co-evolution of malignant B cells and the tumor microenvironment in transformed follicular lymphoma

Follicular lymphoma (FL) is the most common indolent form of non-Hodgkin lymphoma. Histological transformation of FL to a more aggressive form of lymphoma occurs with a linear incidence of 2-3% per year and is associated with poor outcome. Divergent clonal evolution and an altered tumour microenvironment (TME) have both been implicated in the transformation process. However, the phenotypic consequences of this evolution and its implication in reshaping the TME remain unknown. To address this knowledge gap we performed single cell whole genome (scWGS) and single cell whole transcriptome sequencing (scWTS) of paired pre/post transformation samples of 11 FL patients. We further performed scWTS analysis of additional 11 FL samples from patients that had not undergone transformation within 7 years. Our comprehensive single cell analysis revealed the evolutionary dynamics of transformation at unprecedented resolution. Computational integration of scWGS and scWTS allowed us to identify gene programs upregulated and positively selected during evolution. Furthermore, our scWTS analysis revealed a shifting TME landscape, with an exhausted CD8 T cell signature emerging during transformation. Using multi-color immunofluorescence we transferred these findings to a novel TME based biomarker of transformation, subsequently validated in 2 independent cohorts of pretreatment FL samples. Taken together, our results provide a comprehensive view of the combined genomic and phenotypic evolution of malignant cells during transformation, and the shifting cross-talk between malignant cells and the TME. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=98 SRC="FIGDIR/small/516951v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@92abf7org.highwire.dtl.DTLVardef@157f2c2org.highwire.dtl.DTLVardef@102a879org.highwire.dtl.DTLVardef@182daec_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗