7 research outputs found
Heterogeneity of glycan biomarker clusters as an indicator of recurrence in pancreatic cancer
IntroductionOutcomes following tumor resection vary dramatically among patients with pancreatic ductal adenocarcinoma (PDAC). A challenge in defining predictive biomarkers is to discern within the complex tumor tissue the specific subpopulations and relationships that drive recurrence. Multiplexed immunofluorescence is valuable for such studies when supplied with markers of relevant subpopulations and analysis methods to sort out the intra-tumor relationships that are informative of tumor behavior. We hypothesized that the glycan biomarkers CA19-9 and STRA, which detect separate subpopulations of cancer cells, define intra-tumoral features associated with recurrence.MethodsWe probed this question using automated signal thresholding and spatial cluster analysis applied to the immunofluorescence images of the STRA and CA19-9 glycan biomarkers in whole-block sections of PDAC tumors collected from curative resections.ResultsThe tumors (N = 22) displayed extreme diversity between them in the amounts of the glycans and in the levels of spatial clustering, but neither the amounts nor the clusters of the individual and combined glycans associated with recurrence. The combined glycans, however, marked divergent types of spatial clusters, alternatively only STRA, only CA19-9, or both. The co-occurrence of more than one cluster type within a tumor associated significantly with disease recurrence, in contrast to the independent occurrence of each type of cluster. In addition, intra-tumoral regions with heterogeneity in biomarker clusters spatially aligned with pathology-confirmed cancer cells, whereas regions with homogeneous biomarker clusters aligned with various non-cancer cells.ConclusionThus, the STRA and CA19-9 glycans are markers of distinct and co-occurring subpopulations of cancer cells that in combination are associated with recurrence. Furthermore, automated signal thresholding and spatial clustering provides a tool for quantifying intra-tumoral subpopulations that are informative of outcome
Mining High-Complexity Motifs in Glycans: A New Language To Uncover the Fine Specificities of Lectins and Glycosidases
DataSheet_2_Heterogeneity of glycan biomarker clusters as an indicator of recurrence in pancreatic cancer.xlsx
IntroductionOutcomes following tumor resection vary dramatically among patients with pancreatic ductal adenocarcinoma (PDAC). A challenge in defining predictive biomarkers is to discern within the complex tumor tissue the specific subpopulations and relationships that drive recurrence. Multiplexed immunofluorescence is valuable for such studies when supplied with markers of relevant subpopulations and analysis methods to sort out the intra-tumor relationships that are informative of tumor behavior. We hypothesized that the glycan biomarkers CA19-9 and STRA, which detect separate subpopulations of cancer cells, define intra-tumoral features associated with recurrence.MethodsWe probed this question using automated signal thresholding and spatial cluster analysis applied to the immunofluorescence images of the STRA and CA19-9 glycan biomarkers in whole-block sections of PDAC tumors collected from curative resections.ResultsThe tumors (N = 22) displayed extreme diversity between them in the amounts of the glycans and in the levels of spatial clustering, but neither the amounts nor the clusters of the individual and combined glycans associated with recurrence. The combined glycans, however, marked divergent types of spatial clusters, alternatively only STRA, only CA19-9, or both. The co-occurrence of more than one cluster type within a tumor associated significantly with disease recurrence, in contrast to the independent occurrence of each type of cluster. In addition, intra-tumoral regions with heterogeneity in biomarker clusters spatially aligned with pathology-confirmed cancer cells, whereas regions with homogeneous biomarker clusters aligned with various non-cancer cells.ConclusionThus, the STRA and CA19-9 glycans are markers of distinct and co-occurring subpopulations of cancer cells that in combination are associated with recurrence. Furthermore, automated signal thresholding and spatial clustering provides a tool for quantifying intra-tumoral subpopulations that are informative of outcome.</p
Mining High-Complexity Motifs in Glycans: A New Language To Uncover the Fine Specificities of Lectins and Glycosidases
Knowledge of lectin
and glycosidase specificities is fundamental
to the study of glycobiology. The primary specificities of such molecules
can be uncovered using well-established tools, but the complex details
of their specificities are difficult to determine and describe. Here
we present a language and algorithm for the analysis and description
of glycan motifs with high complexity. The language uses human-readable
notation and wildcards, modifiers, and logical operators to define
motifs of nearly any complexity. By applying the syntax to the analysis
of glycan-array data, we found that the lectin AAL had higher binding
where fucose groups are displayed on separate branches. The lectin
SNA showed gradations in binding based on the length of the extension
displaying sialic acid and on characteristics of the opposing branches.
A new algorithm to evaluate changes in lectin binding upon treatment
with exoglycosidases identified the primary specificities and potential
fine specificities of an α1–2-fucosidase and an α2–3,6,8-neuraminidase.
The fucosidase had significantly lower action where sialic acid neighbors
the fucose, and the neuraminidase showed statistically lower action
where α1–2 fucose neighbors the sialic acid or is on
the opposing branch. The complex features identified here would have
been inaccessible to analysis using previous methods. The new language
and algorithms promise to facilitate the precise determination and
description of lectin and glycosidase specificities
Mining High-Complexity Motifs in Glycans: A New Language To Uncover the Fine Specificities of Lectins and Glycosidases
Knowledge of lectin
and glycosidase specificities is fundamental
to the study of glycobiology. The primary specificities of such molecules
can be uncovered using well-established tools, but the complex details
of their specificities are difficult to determine and describe. Here
we present a language and algorithm for the analysis and description
of glycan motifs with high complexity. The language uses human-readable
notation and wildcards, modifiers, and logical operators to define
motifs of nearly any complexity. By applying the syntax to the analysis
of glycan-array data, we found that the lectin AAL had higher binding
where fucose groups are displayed on separate branches. The lectin
SNA showed gradations in binding based on the length of the extension
displaying sialic acid and on characteristics of the opposing branches.
A new algorithm to evaluate changes in lectin binding upon treatment
with exoglycosidases identified the primary specificities and potential
fine specificities of an α1–2-fucosidase and an α2–3,6,8-neuraminidase.
The fucosidase had significantly lower action where sialic acid neighbors
the fucose, and the neuraminidase showed statistically lower action
where α1–2 fucose neighbors the sialic acid or is on
the opposing branch. The complex features identified here would have
been inaccessible to analysis using previous methods. The new language
and algorithms promise to facilitate the precise determination and
description of lectin and glycosidase specificities