ASNEO: Identification of personalized alternative splicing based neoantigens with RNA-seq

Zhang, Zhanbing; Zhou, Chi; Tang, Lihua; Gong, Yukang; Wei, Zhiting; Zhang, Gongchen; Wang, Feng; Liu, Qi; Yu, Jing

doi:10.18632/aging.103516

Cited by 32 publications

(22 citation statements)

References 38 publications

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“…Both RNA‐seq and WES data were available in Van Allen's melanoma cohort [23]. The processing results (including HLA, TMB, CYT) of the two melanoma cohorts were retrieved from our previous studies [25–27]. Chowell's melanoma cohort [9] contains 164 patients under anti‐CTLA4 treatment.…”

Section: Methodsmentioning

confidence: 99%

Benchmarking HLA genotyping and clarifying HLA impact on survival in tumor immunotherapy

Zhou

Chen

et al. 2021

Molecular Oncology

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Human Leucocyte Antigen (HLA) genotyping gains intensive attention due to its critical role in cancer immunotherapy. It is still a challenging issue to generate reliable HLA genotyping results through in silico tools. In addition, the survival impact of HLA alleles in tumor prognosis and immunotherapy remains controversial. In this study, the benchmarking of HLA genotyping on TCGA is performed and a "Gun-Bullet" model which helps to clarify the survival impact of HLA allele is presented. The performance of HLA class I genotyping is generally better than class II. POLYSOLVER, OptiType and xHLA perform generally better at HLA class I calling with an accuracy of 0.954, 0.949 and 0.937, respectively. HLA-HD obtained the highest accuracy of 0.904 on HLA class II alleles calling. Each HLA-genotyping tool displayed specific error patterns. The ensemble HLA calling from the top-3 tools is superior to any individual one. HLA alleles show distinct survival impact among cancers. Cytolytic activity (CYT) was proposed as the underlying mechanism to interpret the survival impact of HLA alleles in the "Gun-Bullet" model for fighting cancer. A strong HLA allele plus a high tumor mutation burden (TMB) could stimulate intensive immune CYT, leading to extended survival. We established an up-to-now most reliable TCGA HLA benchmark dataset, composing of concordance alleles generated from eight prevalently used HLA genotyping tools. Our findings indicate that reliable HLA genotyping should be performed based on concordance alleles integrating multiple tools and incorporating TMB background with HLA genotype, which helps to improve the survival prediction compared to HLA genotyping alone.

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Section: Methodsmentioning

confidence: 99%

Benchmarking HLA genotyping and clarifying HLA impact on survival in tumor immunotherapy

Zhou

Chen

et al. 2021

Molecular Oncology

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“…Therefore, further studies are needed to ensure a full understanding of their mechanisms of action. Alternative splicing widely occurs in tumor samples and it has been proven to contribute to the generation of candidate neoantigens [ 15 ]. Here, we found that KIRC has shown a variety of specificities in alternative splicing.…”

Section: Resultsmentioning

confidence: 99%

“…Alternative splicing is an important mechanism in post-transcriptional regulation, and increasing evidences noted that alternative splicing is tightly associated with invasion and metastasis of cancer cells [ 14 ]. Alternative splicing is a rich source of tumor-specific neoantigen targets for immunotherapy [ 15 , 16 ]. It is worth noting that global changes in alternative splicing patterns also occur during the in vitro derivation of embryonic stem cells from the inner cell mass of blastocysts, suggesting that alternative splicing is not only progressing during the differentiation of stem cells, but also during the acquisition of stemness features in cancer stem cells [ 17 ].…”

Section: Backgroudmentioning

confidence: 99%

Alternative splicing associated with cancer stemness in kidney renal clear cell carcinoma

et al. 2021

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Backgroud Cancer stemness is associated with metastases in kidney renal clear cell carcinoma (KIRC) and negatively correlates with immune infiltrates. Recent stemness evaluation methods based on the absolute expression have been proposed to reveal the relationship between stemness and cancer. However, we found that existing methods do not perform well in assessing the stemness of KIRC patients, and they overlooked the impact of alternative splicing. Alternative splicing not only progresses during the differentiation of stem cells, but also changes during the acquisition of the stemness features of cancer stem cells. There is an urgent need for a new method to predict KIRC-specific stemness more accurately, so as to provide help in selecting treatment options. Methods The corresponding RNA-Seq data were obtained from the The Cancer Genome Atlas (TCGA) data portal. We also downloaded stem cell RNA sequence data from the Progenitor Cell Biology Consortium (PCBC) Synapse Portal. Independent validation sets with large sample size and common clinic pathological characteristics were obtained from the Gene Expression Omnibus (GEO) database. we constructed a KIRC-specific stemness prediction model using an algorithm called one-class logistic regression based on the expression and alternative splicing data to predict stemness indices of KIRC patients, and the model was externally validated. We identify stemness-associated alternative splicing events (SASEs) by analyzing different alternative splicing event between high- and low- stemness groups. Univariate Cox and multivariable logistic regression analysisw as carried out to detect the prognosis-related SASEs respectively. The area under curve (AUC) of receiver operating characteristic (ROC) was performed to evaluate the predictive values of our model. Results Here, we constructed a KIRC-specific stemness prediction model with an AUC of 0.968,and to provide a user-friendly interface of our model for KIRC stemness analysis, we have developed KIRC Stemness Calculator and Visualization (KSCV), hosted on the Shiny server, can most easily be accessed via web browser and the url https://jiang-lab.shinyapps.io/kscv/. When applied to 605 KIRC patients, our stemness indices had a higher correlation with the gender, smoking history and metastasis of the patients than the previous stemness indices, and revealed intratumor heterogeneity at the stemness level. We identified 77 novel SASEs by dividing patients into high- and low- stemness groups with significantly different outcome and they had significant correlations with expression of 17 experimentally validated splicing factors. Both univariate and multivariate survival analysis demonstrated that SASEs closely correlated with the overall survival of patients. Conclusions Basing on the stemness indices, we found that not only immune infiltration but also alternative splicing events showed significant different at the stemness level. More importantly, we highlight the critical role of these differential alternative splicing events in poor prognosis, and we believe in the potential for their further translation into targets for immunotherapy.

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“…The Cm has potential clinical applications, including vaccine target detection and prediction of therapeutic effects of immune checkpoint inhibitors that have been reported to be associated with the degree of tumor mutation burdens. 7,8 Although we focused on SNV in the present study, several software programs for predicting neoantigens derived from fusion genes 61 and alternative splice events 62,63 have been developed using RNA-seq data. These variants are known to be an important source of potentially immunogenic neoantigens; [64][65][66] therefore, further utilization of RNA-seq data in addition to Cm may provide a comprehensive landscape of immunogenic neoantigens.…”

Section: Discussionmentioning

confidence: 99%

Neoantigen prediction in human breast cancer using RNA sequencing data

et al. 2020

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ASNEO: Identification of personalized alternative splicing based neoantigens with RNA-seq

Cited by 32 publications

References 38 publications

Benchmarking HLA genotyping and clarifying HLA impact on survival in tumor immunotherapy

Benchmarking HLA genotyping and clarifying HLA impact on survival in tumor immunotherapy

Alternative splicing associated with cancer stemness in kidney renal clear cell carcinoma

Neoantigen prediction in human breast cancer using RNA sequencing data

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