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Validation of genetic classifiers derived from mouse and human tumors to identify molecular subtypes of colorectal cancer.

ABSTRACT: Colorectal cancer (CRC) is a leading cause of cancer death in the United States. Standard treatment for advanced-stage CRC for decades has included 5-fluorouracil-based chemotherapy. More recently, targeted therapies for metastatic CRC are being used based on the individual cancer's molecular profile. In the past few years, several different molecular subtype schemes for human CRC have been developed. The molecular subtypes can be distinguished by gene expression signatures and have the potential to be used to guide treatment decisions. However, many subtyping classification methods were developed using mRNA expression levels of hundreds to thousands of genes, making them impractical for clinical use. In this study, we assessed whether an immunohistochemical approach could be used for molecular subtyping of CRCs. We validated two previously published, independent sets of immunohistochemistry classifiers and modified the published methods to improve the accuracy of the scoring methods. In addition, we evaluated whether protein and genetic signatures identified originally in the mouse were linked to clinical outcomes of patients with CRC. We found that low DDAH1 or low GAL3ST2 protein levels in human CRCs correlate with poor patient outcomes. The results of this study have the potential to impact methods for determining the prognosis and therapy selection for patients with CRC.

SUBMITTER: Snow SM

PROVIDER: S-EPMC9936405 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Validation of genetic classifiers derived from mouse and human tumors to identify molecular subtypes of colorectal cancer.

Snow Santina M SM Matkowskyj Kristina A KA Maresh Morgan M Clipson Linda L Vo Tien N TN Johnson Katherine A KA Deming Dustin A DA Newton Michael A MA Grady William M WM Pickhardt Perry J PJ Halberg Richard B RB

Human pathology 20211014

Colorectal cancer (CRC) is a leading cause of cancer death in the United States. Standard treatment for advanced-stage CRC for decades has included 5-fluorouracil-based chemotherapy. More recently, targeted therapies for metastatic CRC are being used based on the individual cancer's molecular profile. In the past few years, several different molecular subtype schemes for human CRC have been developed. The molecular subtypes can be distinguished by gene expression signatures and have the potentia ...[more]

PMID: 34655611

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Project description:BackgroundBreast cancer is a complex and heterogeneous disease that is usually characterized by histological parameters such as tumor size, cellular arrangements/rearrangments, necrosis, nuclear grade and the mitotic index, leading to a set of around twenty subtypes. Together with clinical markers such as hormone receptor status, this classification has considerable prognostic value but there is a large variation in patient response to therapy. Gene expression profiling has provided molecular profiles characteristic of distinct subtypes of breast cancer that reflect the divergent cellular origins and degree of progression.MethodsHere we present a large-scale proteomic and transcriptomic profiling study of 477 sporadic and hereditary breast cancer tumors with matching mRNA expression analysis. Unsupervised hierarchal clustering was performed and selected proteins from large-scale tandem mass spectrometry (MS/MS) analysis were transferred into a highly multiplexed targeted selected reaction monitoring assay to classify tumors using a hierarchal cluster and support vector machine with leave one out cross-validation.ResultsThe subgroups formed upon unsupervised clustering agree very well with groups found at transcriptional level; however, the classifiers (genes or their respective protein products) differ almost entirely between the two datasets. In-depth analysis shows clear differences in pathways unique to each type, which may lie behind their different clinical outcomes. Targeted mass spectrometry analysis and supervised clustering correlate very well with subgroups determined by RNA classification and show convincing agreement with clinical parameters.ConclusionsThis work demonstrates the merits of protein expression profiling for breast cancer stratification. These findings have important implications for the use of genomics and expression analysis for the prediction of protein expression, such as receptor status and drug target expression. The highly multiplexed MS assay is easily implemented in standard clinical chemistry practice, allowing rapid and cheap characterization of tumor tissue suitable for directing the choice of treatment.

Dataset Information

Validation of genetic classifiers derived from mouse and human tumors to identify molecular subtypes of colorectal cancer.

Publications

Validation of genetic classifiers derived from mouse and human tumors to identify molecular subtypes of colorectal cancer.

Similar Datasets

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