Immunoprofiling of Breast Cancer Antigens Using Antibodies Derived from Local Lymph Nodes.

antibody secreting cell biomarker breast cancer immune profile immunotherapy lymph node microarray tumor antigen

Journal

Cancers
ISSN: 2072-6694
Titre abrégé: Cancers (Basel)
Pays: Switzerland
ID NLM: 101526829

Informations de publication

Date de publication:
16 May 2019
Historique:
received: 17 04 2019
revised: 08 05 2019
accepted: 13 05 2019
entrez: 19 5 2019
pubmed: 19 5 2019
medline: 19 5 2019
Statut: epublish

Résumé

Tumor antigens are responsible for initiating an immune response in cancer patients, and their identification may provide new biomarkers for cancer diagnosis and targets for immunotherapy. The general use of serum antibodies to identify tumor antigens has several drawbacks, including dilution, complex formation, and background reactivity. In this study, antibodies were generated from antibody-secreting cells (ASC) present in tumor-draining lymph nodes of 20 breast cancer patients (ASC-probes) and were used to screen breast cancer cell lines and protein microarrays. Half of the ASC-probes reacted strongly against extracts of the MCF-7 breast cancer cell line, but each with a distinct antigen recognition profile. Three of the positive ASC-probes reacted differentially with recombinant antigens on a microarray containing cancer-related proteins. The results of this study show that lymph node-derived ASC-probes provide a highly specific source of tumor-specific antibodies. Each breast cancer patient reacts with a different antibody profile which indicates that targeted immunotherapies may need to be personalized for individual patients. Focused microarrays in combination with ASC-probes may be useful in providing immune profiles and identifying tumor antigens of individual cancer patients.

Identifiants

pubmed: 31100936
pii: cancers11050682
doi: 10.3390/cancers11050682
pmc: PMC6562983
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : National Breast Cancer Foundation
ID : IN-16-014

Déclaration de conflit d'intérêts

The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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Auteurs

Anna Rachel Young (AR)

La Trobe Institute for Molecular Science (LIMS), La Trobe University, Melbourne 3086, Australia. a.young@latrobe.edu.au.

Jessica Da Gama Duarte (JDG)

Olivia Newton-John Cancer Research Institute, Level 5, ONJ Centre, Heidelberg Vic 3084, Australia. Jessica.Duarte@onjcri.org.au.
School of Cancer Medicine, La Trobe University, Melbourne 3086, Australia. Jessica.Duarte@onjcri.org.au.

Rhiannon Coulson (R)

Garvan Institute of Medical Research, St Vincent's Clinical School, Darlinghurst, NSW 2010, Australia. r.coulson@garvan.org.au.

Megan O'Brien (M)

Olivia Newton-John Cancer Research Institute, Level 5, ONJ Centre, Heidelberg Vic 3084, Australia. megan.obrien@onjcri.org.au.
School of Cancer Medicine, La Trobe University, Melbourne 3086, Australia. megan.obrien@onjcri.org.au.

Siddhartha Deb (S)

Consultant Pathologist, Anatpath. 120 Gardenvale Rd, Gardenvale Melbourne 3185, Australia. deb_siddhartha@hotmail.com.

Alex Lopata (A)

CancerProbe Pty Ltd, PO Box 2237, Prahran 3181, Australia. alex.lopata15@gmail.com.

Andreas Behren (A)

Olivia Newton-John Cancer Research Institute, Level 5, ONJ Centre, Heidelberg Vic 3084, Australia. Andreas.Behren@onjcri.org.au.
School of Cancer Medicine, La Trobe University, Melbourne 3086, Australia. Andreas.Behren@onjcri.org.au.

Suresh Mathivanan (S)

La Trobe Institute for Molecular Science (LIMS), La Trobe University, Melbourne 3086, Australia. s.mathivanan@latrobe.edu.au.

Elgene Lim (E)

Garvan Institute of Medical Research, St Vincent's Clinical School, Darlinghurst, NSW 2010, Australia. e.lim@garvan.org.au.

Els Meeusen (E)

La Trobe Institute for Molecular Science (LIMS), La Trobe University, Melbourne 3086, Australia. elza.meeusen@gmail.com.
CancerProbe Pty Ltd, PO Box 2237, Prahran 3181, Australia. elza.meeusen@gmail.com.

Classifications MeSH