Biologically Aggressive Phenotype and Anti-cancer Immunity Counterbalance in Breast Cancer with High Mutation Rate

Abstract

While cancer cells gain aggressiveness by mutations, abundant mutations release neoantigens, attracting anti-cancer immune cells. We hypothesized that in breast cancer (BC), where mutation is less common, tumors with high mutation rates demonstrate aggressive phenotypes and attract immune cells simultaneously. High mutation rates were defined as the top 10% of the mutation rate, utilizing TCGA and METABRIC transcriptomic data. Mutation rate did not impact survival although high mutation BCs were associated with aggressive clinical features, such as more frequent in ER-negative tumors (p < 0.01), in triple-negative subtype (p = 0.03), and increased MKI-67 mRnA expression (p < 0.01) in both cohorts. Tumors with high mutation rates were associated with APOBEC3B and homologous recombination deficiency, increasing neoantigen loads (all p < 0.01). Cell proliferation and immune activity pathways were enriched in BCs with high mutation rates. Furthermore, there were higher lymphocytes and M1 macrophage infiltration in high mutation BCs. Additionally, T-cell receptor diversity, cytolytic activity score (CYT), and T-cell exhaustion marker expression were significantly elevated in BCs with high mutation rates (all p < 0.01), indicating strong immunogenicity. In conclusion, enhanced immunity due to neoantigens can be one of possible forces to counterbalance aggressiveness of a high mutation rate, resulting in similar survival rates to low mutation BCs. Accumulation of somatic mutations, or somatic genome instability, has been the principle of carcinogenesis; cancer cells stem from a clone that has gained the somatically acquired genetic abnormalities, leading to malignant transformation and further progression 1. With advances in next-generation sequencing technologies, clinical interest in assessing mutation burden and identifying specific mutations in certain cancer types has been growing to utilize targeted therapies or to assess tumor biology, such as FoundationOne genomic panel testing 2-4. Mutation rates are variable among cancer types and outliers with significantly high mutation burdens, hypermutation, do exist in many cancer types 5. Interestingly, the definition of hypermutation has been variable in the literature 5-7 , although the common definition is usually greater than 10-100 mutations per Mega base pairs (Mbps). Campbell and colleagues recently performed comprehensive analysis of hypermutation in various tumor types, providing more insight into tumor evolution and identifying possible clinically actionable mutation signatures 6. The etiology of hypermutation varies between cancer types; ultraviolet (UV) light is the significant cause of mutations in melanoma, while smoking causes the mutations in non-small cell lung cancer (NSCLC) and head and neck squamous cell carcinoma (SCC) 7,8. Whereas UV light and smoking are examples of exogenous mutagens, there are endogenous processes for mutation, such as microsatellite instability (MSI), and Apolipoprotein

High G2M Pathway Score Pancreatic Cancer is Associated with Worse Survival, Particularly after Margin-Positive (R1 or R2) Resection

“…Previously, we reported that the high G2M score for breast cancer is associated with a characteristic tumor-immune microenvironment [ 17 ]. Since cancer cell proliferation and aggressiveness can be attenuated by immune cell infiltration in the tumor microenvironment [ 22 ], we expected that high G2M score tumors may have a characteristic immune cell infiltration profile. Utilizing the xCell algorithm, we found that pancreatic tumors with high G2M score were significantly associated with lower fraction of CD8 T cell, and M2 macrophage, and higher fraction of T helper type 1 (Th1) cells and T helper type 2 (Th2) cells in the TCGA cohort ( Figure 4 A).…”

G2M Cell Cycle Pathway Score as a Prognostic Biomarker of Metastasis in Estrogen Receptor (ER)-Positive Breast Cancer

“…Within each cohort, tumor samples were categorized into high and low G2M activity score groups using the median GSVA score as cut-off. For gene set enrichment analysis (GSEA) [54], GSEA software (Java version 4.0) and the Hallmark gene set collection were used, as we described previously [55][56][57][58][59][60]. As recommended for GSEA, a false discovery rate threshold of 0.25 was used to deem significance.…”

Abundance of Regulatory T Cell (Treg) as a Predictive Biomarker for Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer

“…Although IHC is the gold standard to identify and quantify immune cells, it is examiner-dependent and therefore results may vary and be inaccurate. Our group and others have reported the clinical relevance of the tumor immune microenvironment (TIME) using computational algorithms from bulk tumor transcriptome of the patient cohorts [ 20 , 21 , 22 , 23 , 24 ]. Utilizing xCell algorithm on transcriptomic data, we found that adipocytes in TIME of breast cancer are associated with metastasis and inflammation-related pathways particularly in ER-positive/human epidermal growth factor receptor 2 (HER2)-negative breast cancer [ 25 ].…”

“…Utilizing xCell algorithm on transcriptomic data, we found that adipocytes in TIME of breast cancer are associated with metastasis and inflammation-related pathways particularly in ER-positive/human epidermal growth factor receptor 2 (HER2)-negative breast cancer [ 25 ]. We also found that, in breast cancer, a biologically aggressive phenotype and anti-cancerous immunity is associated with high mutation rate [ 22 ]. An advantage of this approach is that it allows the analyses of actual patient cohorts that are linked with transcriptomic data but were collected for completely unrelated motives.…”