Patient-Specific Mutation-Derived Tumor Antigens As Targets for Cancer Immunotherapy in Multiple Myeloma

Multiple myeloma (MM) is an incurable plasma cell malignancy accounting for more than 10,000 deaths in the US each year. Novel therapeutic approaches for relapsed MM are urgently needed. Tumor-specific mutations are ideal targets for cancer immunotherapy as they can be potentially recognized as neo-antigens by mature T-cells. Targeting tumor-specific antigens harboring somatic mutations presented on major histocompatibility complex class I molecules (MHC-I) with peptides could personalize the therapeutic approach for relapsed patients. To test this possibility, we examined 6 relapsed MM tumor samples from Mount Sinai, NY to predict in silico patient-specific tumor mutations that may activate the patient’s immune systems. This is the first study to utilize Whole-Exome Sequence data (WES) from relapsed MM patients to show the feasibility of using exome sequencing to identify mutation derived neo-antigens that are patient-specific.

DNA and RNA from six MM patients were extracted from sorted CD138+ cells from bone marrow aspirates. At the time of sample collection all patients had relapsed following at least five lines of therapy including Autologous Stem Cell Transplantation. The exome capture for DNA sequencing was carried out using the Agilent human whole-exome SureSelect assay. RNA-seq libraries were prepared using Illumina mRNA-seq protocol. All libraries were sequenced on an Illumina HiSeq2500 to generate 100 nucleotide reads. RNA reads were aligned to human reference genome (hg19) and assembled into transcripts using Bowtie-TopHat-Cufflinks. WES data was mapped to reference genome by BWA and then processed by MuTect to detect somatic mutations. Patient-specific alleles were determined using Seq2HLA. The identified mutations lead to candidate antigenic peptides that were filtered by tumor expression level (FPKM >2) using RNA sequence data. Candidate peptides of 8-11 character long were then ranked based on peptide-MHC binding affinity prediction (IC₅₀nM) performed in silico using NetMHCpan.

We identified a total of 340 tumor-specific nonsynonymous somatic mutations expressed in the context of patient specific HLA type. 263 (77%) genes were strong binders (IC₅₀<150nM) and 77 (23%) genes were moderate/weak binders (IC₅₀150-500nM). The number of mutated genes that were immunogenic per patient ranged from a minimum of 6 genes to 147 genes.  Further, Database for Annotation, Visualization and Integrated Discovery (DAVID) tool was used to identify potentially enriched biological processes among the 340 genes using Gene Ontology (GO) terms. Enrichment analysis of 263 genes showed that they are mainly involved in myeloid cell activation during immune response (eg.LAT2, MYO1F), cell cycle process (CDK1, CHEK2, DNM2, EP300, SETD8), cellular response to stress (RAD21, HDAC2, MAT2B), chromatin silencing (SIRT2, SMARCA4), cell apoptosis and signal transduction (KRAS, NLRP1, ING4, IGF2R). Similarly enrichment analysis of 77 genes revealed their involvement mainly in B cell activation and leukocyte differentiation (LRRC8A, CD3E, PRKDC). Examples of some of these significantly mutated genes with binding affinity and predicted peptides are shown in Table 1.

In this study, we show for the first time a correlation between tumor mutations and the epitope landscape by in silico data, suggesting that somatic mutations in MM are immunogenic and could potentially confer antitumor vaccine activity. Our results support an approach in creating cancer vaccines that use tumor-specific immunogenic mutations for the development of personalized vaccines for MM patients.

Table 1: Immunogenic mutations in Multiple Myeloma

#	Patient Specific alleles	Peptides	IC50	Mutated Genes	Effect
	Patient#1
1	HLA-C*14:02	CYGHTMVAF	57.75	LZTR1	p.R284C
2	HLA-C*14:02	LYFFGMHVQEY	29.75	EP300	p.C1372Y
3	HLA-C*14:02	TFNEPSSEYF	114.21	SMARCA4	p.G1146S
	Patient#2
4	HLA-B*15:01	MSLHNLGTVF	26.2	BCR	p.A1160G
5	HLA-A*31:01	SIISDSPR	149.38	FcRL5	p.V269I
6	HLA-A*31:01	HYFMHLLK	37.16	SIRT2	p.R116H
	Patient#3
7	HLA-C*05:01	ITDFGHSEIL	25.18	CHEK2	p.K344E
	Patient#4
8	HLA-A*11:01	VVGARGVGK	121.47	KRAS	p.G12R
9	HLA-B*41:01	LEIDQLFRI	132.84	CDK1	p.S208L
	Patient#5
10	HLA-A*31:01	AVGCGFRRARR	106.68	MAT2B	p.P65R
	Patient#6
11	HLA-A*30:01	HQRVLYIEI	93.61	HDAC2	p.D145E
12	HLA-A*30:01	FTRCLTPLL	63.19	RAD21	p.V397L