OptiType: precision HLA typing from next-generation sequencing data

Background: Patients with highly mutated tumors, such as melanoma or smoking-related lung cancer, have higher rates of response to immune checkpoint blockade therapy, perhaps due to increased neoantigen expression. Many chemotherapies including platinum compounds are known to be mutagenic, but the impact of standard treatment protocols on mutational burden and resulting neoantigen expression in most human cancers is unknown. Methods: We sought to quantify the effect of chemotherapy treatment on computationally predicted neoantigen expression for high grade serous ovarian carcinoma patients enrolled in the Australian Ovarian Cancer Study. In this series, 35 of 114 samples were collected after exposure to chemotherapy; 14 are matched with an untreated sample from the same patient. Our approach integrates whole genome and RNA sequencing of bulk tumor samples with class I MHC binding prediction and mutational signatures extracted from studies of chemotherapy-exposed Caenorhabditis elegans and Gallus gallus cells. We additionally investigated the relationship between neoantigens, tumor infiltrating immune cells estimated from RNA-seq with CIBERSORT, and patient survival. Results: Greater neoantigen burden and CD8+ T cell infiltration in primary, pre-treatment samples were independently associated with improved survival. Relapse samples collected after chemotherapy harbored a median of 78% more expressed neoantigens than untreated primary samples, a figure that combines the effects of chemotherapy and other processes operative during relapse. The contribution from chemotherapy-associated signatures was small, accounting for a mean of 5% (range 0-16) of the expressed neoantigen burden in relapse samples. In both treated and untreated samples, most neoantigens were attributed to COSMIC Signature (3), associated with BRCA disruption, Signature (1), associated with a slow mutagenic process active in healthy tissue, and Signature (8), of unknown etiology. Conclusion: Relapsed ovarian cancers harbor more predicted neoantigens than primary tumors, but the increase is due to pre-existing mutational processes, not mutagenesis from chemotherapy.

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“…HLA typing was performed using a consensus of seq2HLA [17] and OptiType [18] across the samples for each patient (Additional file 3).…”

Section: Variant Annotation Hla Typing and Mhc Binding Predictionmentioning

confidence: 99%

Chemotherapy weakly contributes to predicted neoantigen expression in ovarian cancer

O’Donnell

Christie

Buros

et al. 2016

Preprint

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“…Many of the existing algorithms try to reduce the noise level by filtering out less common HLA alleles from their candidate allele set. For example, OptiType ignores any allele with no reported allele frequency in AlleleFrequency.net (9,13), whereas HLA-VBSeq only considers ∼100 HLA alleles as candidates (10). Second, despite the extensive polymorphisms within each HLA gene, alleles from different HLA genes can share regions that are extremely similar…”

Section: Resultsmentioning

confidence: 99%

“…After aligning reads to the HLA reference sequences, the next task is to infer the most likely HLA allele combination that best explains the alignments. Rather than treating each gene separately, and by using alleles with the largest number of aligned reads, OptiType uses integer linear programing to find the complete allele set as a single optimization problem (9). This method is a better approach than the other methods because it explicitly considers short reads that map to multiple HLA allele candidates.…”

Section: Significancementioning

confidence: 99%

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Fast and accurate HLA typing from short-read next-generation sequence data with xHLA

Xie¹,

Yeo²,

Wong³

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

130

100

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The HLA gene complex on human chromosome 6 is one of the most polymorphic regions in the human genome and contributes in large part to the diversity of the immune system. Accurate typing of HLA genes with short-read sequencing data has historically been difficult due to the sequence similarity between the polymorphic alleles. Here, we introduce an algorithm, xHLA, that iteratively refines the mapping results at the amino acid level to achieve 99-100% four-digit typing accuracy for both class I and II HLA genes, taking only ∼3 min to process a 30× whole-genome BAM file on a desktop computer.MHC | autoimmune diseases | transplantation

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“…We typed the human leukocyte antigen locus of each patient to determine their MHC-I alleles. [8][9][10] Finally, we calculated the PHBR scores for each patient-mutation combination, creating a matrix (9,176 patients x 1018 mutations) representing the ability of each patient to present each mutation in the driver space to the immune system. We found extensive patient heterogeneity of MHC-I presentation across the driver space.…”

Section: Introductionmentioning

confidence: 99%