The neoepitope landscape of breast cancer: Implications for immunotherapy 11 Medical and Health Sciences 1112 Oncology and Carcinogenesis

Pooja Narang, Meixuan Chen, Amit A. Sharma, Karen Anderson, Melissa A. Wilson

Research output: Contribution to journalArticle

Abstract

Background: Cancer immunotherapy with immune checkpoint blockade (CKB) is now standard of care for multiple cancers. The clinical response to CKB is associated with T cell immunity targeting cancer-induced mutations that generate novel HLA-binding epitopes (neoepitopes). Methods: Here, we developed a rapid bioinformatics pipeline and filtering strategy, EpitopeHunter, to identify and prioritize clinically relevant neoepitopes from the landscape of somatic mutations. We used the pipeline to determine the frequency of neoepitopes from the TCGA dataset of invasive breast cancers. We predicted HLA class I-binding neoepitopes for 870 breast cancer samples and filtered the neoepitopes based on tumor transcript abundance. Results: We found that the total mutational burden (TMB) was highest for triple-negative breast cancer, TNBC, (median = 63 mutations, range: 2-765); followed by HER-2(+) (median = 39 mutations, range: 1-1206); and lowest for ER/PR(+)HER-2(-) (median = 32 mutations, range: 1-2860). 40% of the nonsynonymous mutations led to the generation of predicted neoepitopes. The neoepitope load (NEL) is highly correlated with the mutational burden (R 2 = 0.86). Conclusions: Only half (51%) of the predicted neoepitopes are expressed at the RNA level (FPKM≥2), indicating the importance of assessing whether neoepitopes are transcribed. However, of all patients, 93% have at least one expressed predicted neoepitope, indicating that most breast cancer patients have the potential for neo-epitope targeted immunotherapy.

Original languageEnglish (US)
Article number200
JournalBMC Cancer
Volume19
Issue number1
DOIs
StatePublished - Mar 4 2019

Fingerprint

Immunotherapy
Carcinogenesis
Breast Neoplasms
Mutation
Health
Epitopes
Neoplasms
Triple Negative Breast Neoplasms
Standard of Care
Computational Biology
Immunity
RNA
T-Lymphocytes

Keywords

  • Breast cancer
  • Epitopes
  • Immunotherapy
  • Mutation burden
  • Neoepitope prediction
  • TNBC

ASJC Scopus subject areas

  • Oncology
  • Genetics
  • Cancer Research

Cite this

The neoepitope landscape of breast cancer : Implications for immunotherapy 11 Medical and Health Sciences 1112 Oncology and Carcinogenesis. / Narang, Pooja; Chen, Meixuan; Sharma, Amit A.; Anderson, Karen; Wilson, Melissa A.

In: BMC Cancer, Vol. 19, No. 1, 200, 04.03.2019.

Research output: Contribution to journalArticle

@article{746e45f4dda04a1ab27695e3d69ad9d1,
title = "The neoepitope landscape of breast cancer: Implications for immunotherapy 11 Medical and Health Sciences 1112 Oncology and Carcinogenesis",
abstract = "Background: Cancer immunotherapy with immune checkpoint blockade (CKB) is now standard of care for multiple cancers. The clinical response to CKB is associated with T cell immunity targeting cancer-induced mutations that generate novel HLA-binding epitopes (neoepitopes). Methods: Here, we developed a rapid bioinformatics pipeline and filtering strategy, EpitopeHunter, to identify and prioritize clinically relevant neoepitopes from the landscape of somatic mutations. We used the pipeline to determine the frequency of neoepitopes from the TCGA dataset of invasive breast cancers. We predicted HLA class I-binding neoepitopes for 870 breast cancer samples and filtered the neoepitopes based on tumor transcript abundance. Results: We found that the total mutational burden (TMB) was highest for triple-negative breast cancer, TNBC, (median = 63 mutations, range: 2-765); followed by HER-2(+) (median = 39 mutations, range: 1-1206); and lowest for ER/PR(+)HER-2(-) (median = 32 mutations, range: 1-2860). 40{\%} of the nonsynonymous mutations led to the generation of predicted neoepitopes. The neoepitope load (NEL) is highly correlated with the mutational burden (R 2 = 0.86). Conclusions: Only half (51{\%}) of the predicted neoepitopes are expressed at the RNA level (FPKM≥2), indicating the importance of assessing whether neoepitopes are transcribed. However, of all patients, 93{\%} have at least one expressed predicted neoepitope, indicating that most breast cancer patients have the potential for neo-epitope targeted immunotherapy.",
keywords = "Breast cancer, Epitopes, Immunotherapy, Mutation burden, Neoepitope prediction, TNBC",
author = "Pooja Narang and Meixuan Chen and Sharma, {Amit A.} and Karen Anderson and Wilson, {Melissa A.}",
year = "2019",
month = "3",
day = "4",
doi = "10.1186/s12885-019-5402-1",
language = "English (US)",
volume = "19",
journal = "BMC Cancer",
issn = "1471-2407",
publisher = "BioMed Central",
number = "1",

}

TY - JOUR

T1 - The neoepitope landscape of breast cancer

T2 - Implications for immunotherapy 11 Medical and Health Sciences 1112 Oncology and Carcinogenesis

AU - Narang, Pooja

AU - Chen, Meixuan

AU - Sharma, Amit A.

AU - Anderson, Karen

AU - Wilson, Melissa A.

PY - 2019/3/4

Y1 - 2019/3/4

N2 - Background: Cancer immunotherapy with immune checkpoint blockade (CKB) is now standard of care for multiple cancers. The clinical response to CKB is associated with T cell immunity targeting cancer-induced mutations that generate novel HLA-binding epitopes (neoepitopes). Methods: Here, we developed a rapid bioinformatics pipeline and filtering strategy, EpitopeHunter, to identify and prioritize clinically relevant neoepitopes from the landscape of somatic mutations. We used the pipeline to determine the frequency of neoepitopes from the TCGA dataset of invasive breast cancers. We predicted HLA class I-binding neoepitopes for 870 breast cancer samples and filtered the neoepitopes based on tumor transcript abundance. Results: We found that the total mutational burden (TMB) was highest for triple-negative breast cancer, TNBC, (median = 63 mutations, range: 2-765); followed by HER-2(+) (median = 39 mutations, range: 1-1206); and lowest for ER/PR(+)HER-2(-) (median = 32 mutations, range: 1-2860). 40% of the nonsynonymous mutations led to the generation of predicted neoepitopes. The neoepitope load (NEL) is highly correlated with the mutational burden (R 2 = 0.86). Conclusions: Only half (51%) of the predicted neoepitopes are expressed at the RNA level (FPKM≥2), indicating the importance of assessing whether neoepitopes are transcribed. However, of all patients, 93% have at least one expressed predicted neoepitope, indicating that most breast cancer patients have the potential for neo-epitope targeted immunotherapy.

AB - Background: Cancer immunotherapy with immune checkpoint blockade (CKB) is now standard of care for multiple cancers. The clinical response to CKB is associated with T cell immunity targeting cancer-induced mutations that generate novel HLA-binding epitopes (neoepitopes). Methods: Here, we developed a rapid bioinformatics pipeline and filtering strategy, EpitopeHunter, to identify and prioritize clinically relevant neoepitopes from the landscape of somatic mutations. We used the pipeline to determine the frequency of neoepitopes from the TCGA dataset of invasive breast cancers. We predicted HLA class I-binding neoepitopes for 870 breast cancer samples and filtered the neoepitopes based on tumor transcript abundance. Results: We found that the total mutational burden (TMB) was highest for triple-negative breast cancer, TNBC, (median = 63 mutations, range: 2-765); followed by HER-2(+) (median = 39 mutations, range: 1-1206); and lowest for ER/PR(+)HER-2(-) (median = 32 mutations, range: 1-2860). 40% of the nonsynonymous mutations led to the generation of predicted neoepitopes. The neoepitope load (NEL) is highly correlated with the mutational burden (R 2 = 0.86). Conclusions: Only half (51%) of the predicted neoepitopes are expressed at the RNA level (FPKM≥2), indicating the importance of assessing whether neoepitopes are transcribed. However, of all patients, 93% have at least one expressed predicted neoepitope, indicating that most breast cancer patients have the potential for neo-epitope targeted immunotherapy.

KW - Breast cancer

KW - Epitopes

KW - Immunotherapy

KW - Mutation burden

KW - Neoepitope prediction

KW - TNBC

UR - http://www.scopus.com/inward/record.url?scp=85062420316&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85062420316&partnerID=8YFLogxK

U2 - 10.1186/s12885-019-5402-1

DO - 10.1186/s12885-019-5402-1

M3 - Article

VL - 19

JO - BMC Cancer

JF - BMC Cancer

SN - 1471-2407

IS - 1

M1 - 200

ER -