Exploring the Neoantigen burden in Breast Carcinoma Patients
Animesh, S.; Ren, X.; An, O.; Chen, K.; Lee, S. C.; Yang, H.; Fullwood, M.
Show abstract
In this study we performed a multi-omics analysis comprising whole-exome sequencing (WES) and RNA sequencing (RNA-Seq) on seven breast cancer patients, consisting of three Estrogen receptor (ER) positive and four Triple negative breast cancer (TNBC) subtypes to understand the neoantigen burden in breast cancer tumor samples. We predicted both class-I and class-II human leukocyte antigen (HLA) bound neoantigens by analyzing matched tumor-normal pair of exomes. Across all the patients, we predicted 434 unique neoantigens (NeoFil) in total, affecting 237 different genes and 87% of them (n = 378) are expressed at RNA level (Neoexp). The missense mutations (87%) are the major contributor in neoantigen (Neoexp) generation, followed by frameshift (11%) and indels (2%). The neoantigens (NeoFil) were found to be positively correlated with the somatic mutations (R2 = 0.89). We also noted that the vast majority (99.98%) of the predicted neoantigens are patient specific. Overall, the current study offers significant insight into the neoantigen profile in tumor types with intermediate/low mutation burdens like breast cancer.
Matching journals
The top 12 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Proteogenomic Characterization of Triple-Negative Apocrine Carcinoma Reveals Molecular Features of Progression and Chemotherapy Response 95%
- Transcriptome Profiling and Characterization of Peritoneal Metastasis Ovarian Cancer Xenografts in Humanized Mice 93%
- Detection of genomic alterations in breast cancer with circulating tumour DNA sequencing 93%
Similar papers in this journal
- Prelude to Malignancy: A Gene Expression Signature in Normal Mammary Gland from Breast Cancer Patients Suggests Pre-tumorous Alterations and Is Associated with Adverse Outcomes 95%
- SPARC in cancer-associated fibroblasts is an independent poor prognostic factor in non-metastatic triple-negative breast cancer and exhibits pro-tumor activity 93%
- A Novel Melatonergic Signature Predicts Reccurence Risk and Therapeutic Response in Breast Cancer Patients 91%
Similar papers in this journal
- Macrophage Infiltration and ITGB2 Expression in ESCC: A Novel Correlation 93%
- Restriction site associated DNA sequencing for tumour mutation burden estimation and mutation signature analysis 92%
- Combination of hotspot mutations with methylation and fragmentomic profiles to enhance Multi-Cancer Early Detection 91%
Similar papers in this journal
- Evaluation of tumor antigen-specific antibody responses in patients with metastatic triple negative breast cancer treated with cyclophosphamide and pembrolizumab 95%
- HERV-derived epitopes represent new targets for T-cell based immunotherapies in ovarian cancer 94%
- Tumor-agnostic transcriptome-based classifier identifies spatial infiltration patterns of CD8+ T cells in the tumor microenvironment and predicts clinical outcome in early- and late-phase clinical trials 93%
Similar papers in this journal
- Protein profiling of breast carcinomas reveals expression of immune-suppressive factors and signatures relevant to patient outcome 94%
- NDRG1 expression is an independent prognostic factor in inflammatory breast cancer 94%
- Use of high-plex data reveals novel insights into the tumour microenvironment of clear cell renal cell carcinoma 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.