Prognostic value of B cells in cutaneous melanoma

Abstract Background Measures of the adaptive immune response have prognostic and predictive associations in melanoma and other cancer types. Specifically, intratumoral T cell density and function have considerable prognostic and predictive value in skin cutaneous melanoma (SKCM). Less is known about...

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Main Authors: Sara R. Selitsky, Lisle E. Mose, Christof C. Smith, Shengjie Chai, Katherine A. Hoadley, Dirk P. Dittmer, Stergios J. Moschos, Joel S. Parker, Benjamin G. Vincent
Format: Article
Language:English
Published: BMC 2019-05-01
Series:Genome Medicine
Subjects:
Online Access:http://link.springer.com/article/10.1186/s13073-019-0647-5
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spelling doaj-b294449e664343b7b7d49abaff4d5f782020-11-25T03:34:08ZengBMCGenome Medicine1756-994X2019-05-0111111110.1186/s13073-019-0647-5Prognostic value of B cells in cutaneous melanomaSara R. Selitsky0Lisle E. Mose1Christof C. Smith2Shengjie Chai3Katherine A. Hoadley4Dirk P. Dittmer5Stergios J. Moschos6Joel S. Parker7Benjamin G. Vincent8Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel HillLineberger Comprehensive Cancer Center, The University of North Carolina at Chapel HillLineberger Comprehensive Cancer Center, The University of North Carolina at Chapel HillLineberger Comprehensive Cancer Center, The University of North Carolina at Chapel HillLineberger Comprehensive Cancer Center, The University of North Carolina at Chapel HillLineberger Comprehensive Cancer Center, The University of North Carolina at Chapel HillLineberger Comprehensive Cancer Center, The University of North Carolina at Chapel HillLineberger Comprehensive Cancer Center, The University of North Carolina at Chapel HillLineberger Comprehensive Cancer Center, The University of North Carolina at Chapel HillAbstract Background Measures of the adaptive immune response have prognostic and predictive associations in melanoma and other cancer types. Specifically, intratumoral T cell density and function have considerable prognostic and predictive value in skin cutaneous melanoma (SKCM). Less is known about the significance of tumor-infiltrating B cells in SKCM. Our goal was to understand the prognostic and predictive value of B cell phenotypic subsets in SKCM using RNA sequencing. Methods We used our previously published algorithm, V’DJer, to assemble B cell receptor (BCR) repertoires and estimate diversity from short-read RNA sequencing (RNA-seq). We applied machine learning-based cellular phenotype classifiers to measure relative similarity of bulk tumor sample gene expression profiles and different B cell phenotypes. We assessed these aspects of B cell biology in 473 SKCM from the Cancer Genome Atlas Project (TCGA) as well as in RNA-seq data corresponding to tumor samples procured from patients who received CTLA-4 and PD-1 inhibitors for metastatic SKCM. Results We found that the BCR repertoire was associated with different clinical factors, such as tumor tissue site and sex. However, increased clonality of the BCR repertoire was favorably prognostic in SKCM and was prognostic even after first conditioning on various clinical factors. Mutation burden was not correlated with any BCR measurement, and no specific mutation had an altered BCR repertoire. Lack of an assembled BCR in pre-treatment tumor tissues was associated with a lack of anti-tumor response to a CTLA-4 inhibitor in metastatic SKCM. Conclusions These findings suggest an important prognostic and predictive role for B cell characteristics in SKCM. This has implications for melanoma immunobiology and potential development of immunogenomics features to predict survival and response to immunotherapy.http://link.springer.com/article/10.1186/s13073-019-0647-5ImmunologyCancerB cellsMachine learningMelanoma
collection DOAJ
language English
format Article
sources DOAJ
author Sara R. Selitsky
Lisle E. Mose
Christof C. Smith
Shengjie Chai
Katherine A. Hoadley
Dirk P. Dittmer
Stergios J. Moschos
Joel S. Parker
Benjamin G. Vincent
spellingShingle Sara R. Selitsky
Lisle E. Mose
Christof C. Smith
Shengjie Chai
Katherine A. Hoadley
Dirk P. Dittmer
Stergios J. Moschos
Joel S. Parker
Benjamin G. Vincent
Prognostic value of B cells in cutaneous melanoma
Genome Medicine
Immunology
Cancer
B cells
Machine learning
Melanoma
author_facet Sara R. Selitsky
Lisle E. Mose
Christof C. Smith
Shengjie Chai
Katherine A. Hoadley
Dirk P. Dittmer
Stergios J. Moschos
Joel S. Parker
Benjamin G. Vincent
author_sort Sara R. Selitsky
title Prognostic value of B cells in cutaneous melanoma
title_short Prognostic value of B cells in cutaneous melanoma
title_full Prognostic value of B cells in cutaneous melanoma
title_fullStr Prognostic value of B cells in cutaneous melanoma
title_full_unstemmed Prognostic value of B cells in cutaneous melanoma
title_sort prognostic value of b cells in cutaneous melanoma
publisher BMC
series Genome Medicine
issn 1756-994X
publishDate 2019-05-01
description Abstract Background Measures of the adaptive immune response have prognostic and predictive associations in melanoma and other cancer types. Specifically, intratumoral T cell density and function have considerable prognostic and predictive value in skin cutaneous melanoma (SKCM). Less is known about the significance of tumor-infiltrating B cells in SKCM. Our goal was to understand the prognostic and predictive value of B cell phenotypic subsets in SKCM using RNA sequencing. Methods We used our previously published algorithm, V’DJer, to assemble B cell receptor (BCR) repertoires and estimate diversity from short-read RNA sequencing (RNA-seq). We applied machine learning-based cellular phenotype classifiers to measure relative similarity of bulk tumor sample gene expression profiles and different B cell phenotypes. We assessed these aspects of B cell biology in 473 SKCM from the Cancer Genome Atlas Project (TCGA) as well as in RNA-seq data corresponding to tumor samples procured from patients who received CTLA-4 and PD-1 inhibitors for metastatic SKCM. Results We found that the BCR repertoire was associated with different clinical factors, such as tumor tissue site and sex. However, increased clonality of the BCR repertoire was favorably prognostic in SKCM and was prognostic even after first conditioning on various clinical factors. Mutation burden was not correlated with any BCR measurement, and no specific mutation had an altered BCR repertoire. Lack of an assembled BCR in pre-treatment tumor tissues was associated with a lack of anti-tumor response to a CTLA-4 inhibitor in metastatic SKCM. Conclusions These findings suggest an important prognostic and predictive role for B cell characteristics in SKCM. This has implications for melanoma immunobiology and potential development of immunogenomics features to predict survival and response to immunotherapy.
topic Immunology
Cancer
B cells
Machine learning
Melanoma
url http://link.springer.com/article/10.1186/s13073-019-0647-5
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