Back

Transcriptomics and mutational analysis to screen immunogenic neoantigen peptides and Patient stratification based on immune subtypes for TNBC

Vasudevan, K.; T, D.; Kumar Selvam, P.; Krishnan, A.; B G, S.; Mudipalli Elavarasu, S.; Mohan, S.; Karunakaran, R.

2026-02-19 cancer biology
10.64898/2026.02.18.706559 bioRxiv
Show abstract

Triple-negative breast cancer (TNBC) is a highly aggressive and heterogeneous subtype with limited therapeutic options. In this study, we performed an integrative analysis of TNBC genomics data, including gene expression, somatic mutations, copy number alterations, survival outcomes, immune profiling, and clustering, to identify potential neoantigens, patient populations suitable for vaccination, and biomarkers for evaluating vaccine efficacy. This Integrated analysis identified POSTN and CAP1 as tumor-specific antigens. Incorporation of TNBC-specific mutations into the screened wild-type antigens led to the identification of three neoantigenic peptides with high potential for vaccine development. Immune subtyping stratified TNBC patients into four distinct subtypes, among which IS1 and IS3 were characterized by poor immune infiltration, lower mutation burden, and unfavorable prognosis, whereas IS2 and IS4 exhibited enhanced immune activity and better clinical outcomes. A vaccine incorporating the identified neoantigen peptides may potentially remodel the immune landscape of immune-cold subtypes (IS1 and IS3), converting them into immune-enriched phenotypes through vaccine-induced immune stimulation. Furthermore, weighted gene co-expression network analysis identified ten immune-related biomarkers from the blue and gray modules that were significantly associated with improved survival in IS2 and IS4. Functional enrichment and protein-protein interaction analyses revealed that hub genes primarily involved in immunoglobulin kappa chains and cytokine/TNF signaling pathways may serve as valuable immune biomarkers for prognostic assessment and monitoring vaccine efficacy.

Matching journals

The top 13 journals account for 50% of the predicted probability mass.

1
Genomics, Proteomics & Bioinformatics
171 papers in training set
Top 0.5%
10.7%
2
Genome Medicine
154 papers in training set
Top 0.9%
6.5%
3
Cell Reports Medicine
140 papers in training set
Top 0.6%
5.0%
4
Signal Transduction and Targeted Therapy
29 papers in training set
Top 0.2%
4.4%
5
eLife
5422 papers in training set
Top 24%
3.7%
6
iScience
1063 papers in training set
Top 6%
3.3%
7
Frontiers in Immunology
586 papers in training set
Top 2%
3.2%
8
Frontiers in Oncology
95 papers in training set
Top 1%
2.8%
9
PLOS ONE
4510 papers in training set
Top 44%
2.7%
10
Cell Discovery
54 papers in training set
Top 2%
2.5%
11
Scientific Reports
3102 papers in training set
Top 49%
2.2%
12
Nature Communications
4913 papers in training set
Top 46%
2.1%
13
Journal for ImmunoTherapy of Cancer
64 papers in training set
Top 0.5%
1.9%
50% of probability mass above
14
Advanced Science
249 papers in training set
Top 9%
1.9%
15
Cell Reports
1338 papers in training set
Top 22%
1.8%
16
Journal of Translational Medicine
46 papers in training set
Top 0.8%
1.7%
17
Cancer Research
116 papers in training set
Top 2%
1.7%
18
Cancer Cell
38 papers in training set
Top 1%
1.5%
19
eBioMedicine
130 papers in training set
Top 2%
1.4%
20
Cell Genomics
162 papers in training set
Top 4%
1.4%
21
Annals of Oncology
13 papers in training set
Top 0.6%
1.3%
22
Nucleic Acids Research
1128 papers in training set
Top 15%
1.0%
23
Briefings in Bioinformatics
326 papers in training set
Top 5%
1.0%
24
PLOS Computational Biology
1633 papers in training set
Top 21%
1.0%
25
Communications Biology
886 papers in training set
Top 16%
1.0%
26
International Journal of Biological Macromolecules
65 papers in training set
Top 3%
0.9%
27
Clinical and Translational Medicine
30 papers in training set
Top 0.7%
0.9%
28
Cancer Medicine
24 papers in training set
Top 1%
0.8%
29
Theranostics
33 papers in training set
Top 1%
0.8%
30
National Science Review
22 papers in training set
Top 2%
0.8%