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P01.02 HLA class-I and class-II restricted neoantigen loads predict overall survival in breast cancer
  1. YW Asmann1,
  2. Y Ren1,
  3. DP Wickland1,
  4. V Sarangi2,
  5. S Tian2,
  6. JM Carter2,
  7. AS Mansfield2,
  8. MS Block2,
  9. ME Sherman1,
  10. KL Knutson1 and
  11. Y Lin2
  1. 1Mayo Clinic, Jacksonville, FL, USA
  2. 2Mayo Clinic, Rochester, MN, USA


Background Tumors acquire numerous mutations during development and progression. These mutations give rise to neoantigens that can be recognized by T cells and generate antibodies. Tumor mutational burden (TMB) is correlated with, and has often been used as a surrogate of, neoantigen load, although that relationship is different depending on cancer types. Recent studies reported correlations between higher TMB and better overall survival after immune checkpoint blockade therapies in bladder, colorectal, head and neck, and lung cancers but not in breast cancer. On the other hand, the relationship between neoantigen load and survival has been controversial in literature. Higher neoantigen load has been linked to better overall survival in ovarian cancer and melanoma, but worse survival in multiple myeloma. Recently, no clear associations were found between neoantigen load and survival in 33 cancer types although only class-I restricted neoantigens were included.

Materials and Methods We developed a bioinformatics workflow, REAL-neo, for identification, quality control (QC), and prioritization of both class-I and class-II human leukocyte antigen (HLA) bound neoantigens that arise from tumor somatic single nucleotide mutations (SNM), small insertions and deletions (INDEL), and gene fusions. The correlations between TMB and neoantigen load per sample were calculated using Pearson Correlation Coefficient. TMB and neoantigen load comparisons between various groups were performed using Student’s t-test. The survival analyses were performed using the Cox proportional hazards models while correcting for covariates.

Results We applied REAL-neo to 835 primary breast tumors in the Cancer Genome Atlas (TCGA) and performed comprehensive profiling and characterization of the predicted neoantigens. SNMs contributed to only 6.25% of the total neoantigens (# of class-I vs. class-II neoantigens = 1: 3.5); INDELs accounted for 57.17% of the total (class-I : class-II= 1:2), and gene fusions were responsible for 36.58% of the total (class-I : class-II = 1:2.2). TMB were positively correlated with total and each sub-categories of neoantigen load (class I: SNM: r = 0.59, p < 2.2E-16; INDEL: r = 0.28, p < 2.2E-16; gene fusion: r = 0.26, p = 2.01E-11; class II: SNM: r = 0.47, p < 2.2E-16; INDEL: r = 0.16, p = 1.7E-05; gene fusion: r = 0.31, p = 4.37E-13). The vast majority (99.75%) of the predicted neoantigens occurred in ≤1% of the cases and 83.76% were patient-specific found in one patient only. Tumors with somatic and germline functional mutations in BRCA1 or BRCA2 genes had higher TMB (p = 2.76E-06) and overall neoantigen load (p = 0.009). Lower HLA class-I and class-II restricted neoantigen loads from SNM and INDEL were found to predict worse overall survival independent of TMB, breast cancer subtypes, tumor infiltrating lymphocyte (TIL) levels, tumor stage, and age at diagnosis (class-I: HR = 1.81, p = 0.04; class-II: HR = 1.89, p = 0.042).

Conclusions Our study highlighted the importance of accurate and comprehensive neoantigen profiling and QC, and is the first to report the predictive value of neoantigen load for overall survival in breast cancer. This work was support by the State of Florida Cancer Center Grant, the bioinformatics program of Mayo Clinic Center for Individualized Medicine, and the Mayo Clinic inter-SPORE development grant.

Disclosure Information Y.W. Asmann: None. Y. Ren: None. D.P. Wickland: None. V. Sarangi: None. S. Tian: None. J.M. Carter: None. A.S. Mansfield: None. M.S. Block: None. M.E. Sherman: None. K.L. Knutson: None. Y. Lin: None.

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