IMPACT: A web server for exploring immunotherapeutic predictive and cancer prognostic biomarkers

Immune checkpoint inhibitors (ICIs) are a breakthrough in oncology treatment, and studies of screening predictive biomarkers of ICIs are emerging. We developed a web server named IMPACT (http://impact.brbiotech.com/) to thoroughly explore immunotherapeutic predictive or prognostic biomarkers. IMPACT contains a large dataset of 6,276 patients treated with ICIs and integrates 11 well-designed function modules, enabling an in-depth solution for biomarkers exploration. Compared with the existing tools, IMPACT was implemented with one exclusive module for interaction analysis and several optimized conventional functions for discovering novel biomarkers. Specifically, the interaction analysis of biomarker-treatment effect is essential to determine whether a biomarker is predictive and/or prognostic for ICIs. Moreover, several optimized functions allow complicated biomarker exploration, including customized selections of variant types in more detail, automatically screening meaningful co-mutations among multiple genes, and selecting cut-off values for gene expression biomarkers. In summary, IMPACT is a comprehensive analysis resource to facilitate biomarker research of ICIs.

of KRAS/TP53/STK11 with IMPACT showed that predictive effects of KRAS depended on the presence of other co-mutation genes, which was consistent with previous results (Figures 2C-F and S1C,D and Table S3). 5o further explore the biomarker of interest, users can verify the finding with a detailed analysis in each dataset.Compared with similar functions of other tools, the survival analysis module allows users to freely select variant types to define meaningful mutation status, screen cutpoints for continuous variables, and perform customised multivariable and subgroup analyses to verify independence of a biomarker.Here, PBRM1, bTMB and ATM are used as examples to illustrate these functions: (1) mutation types.Since different types of mutations usually influence gene functions (e.g., gain or loss of function), it is necessary to consider mutation types when defining gene alterations.As reported in kidney cancer, patients with PBRM1truncating mutations had significantly longer overall survival (OS) than those with PBRM1 non-truncating mutations but not non-synonymous mutations. 6The same result was obtained using IMPACT (Figure 3A,B).(2) Continuous biomarkers.For continuous biomarkers, such as gene expression and tumor mutational burden (TMB), it is critical to explore the effects of selected cut-off values on the associations between biomarkers and survival.Previously, Gandara et al. failed to demonstrate the association of blood-based TMB and OS with a pre-defined cut-off. 7sing the cutpoint analysis sub-module on IMPACT, the forest plot of hazard ratio based on various bTMB cutoffs showed that bTMB tended to be negatively associated with OS (Figure 3C,D), which inspired a valuable work of using maximum somatic allele frequency modified bTMB to predict ICI outcome. 7(3) Verify the independence of biomarkers.To determine whether a biomarker is independent of other factors, multivariable and subgroup analyses can be done in the Cox regression and subgroup analysis sub-modules.Conveniently, users can freely select confounders and stratified factors according to their prior knowledge rather than a fixed factor (Figures 3E and S2A).Moreover, users can implement interaction analysis module, which is uniquely available on IMPACT, to determine whether a potential biomarker is predictive of immunotherapeutic efficacy by analysing the interaction effects between biomarkers and treatment groups.
Here, we use ATM and STK11 to illustrate this function.
ATM mutation was reported as a predictive biomarker by analysing the ICI databases of lung adenocarcinoma. 8owever, it was undefined whether it is an ICI-specific predictive biomarker.The result of interaction analysis module shows that ATM mutation was significantly associated with prolonged survival only in patients treated with ICIs, instead of patients with other treatments and the p-value for interaction was significant (p for interaction = .009,Figure 3F), indicating that the ATM mutation is a predictive biomarker specifically for ICI treatment.In contrast, STK11 mutation was associated with worse survival in both treatment and control arms (p for interaction = .96,Figure S2B), which suggests that the STK11 mutation is a prognostic biomarker independent of treatment.This result was absent in the previous study. 9fter a potential biomarker has been identified by the above analysis, immunogenicity and tumor microenvironment (TME) modules provide users with a simple way to analyse immunogenicity or immune signatures, aiding to understand the biological mechanisms.In the immunogenicity module, users can analyse the correlation between gene expression/mutation and TMB, neoantigen and genome instability scores (Figure 4A-C).Moreover, TME analysis module allows users to analyse the correlation between gene expression and oncogenic/immune signatures (Figure 4D-F).
Although IMPACT provides comprehensive functions for biomarker exploration (Table S4), further biological or clinical validation is needed to validate the identified biomarkers.Previously, using IMPACT, we noticed a negative association between TGFBR2 mutation and survival after immunotherapy (Figure S2C).Subsequently, we also reported a case in which a lung cancer patient with a TGFBR2 mutation experienced hyper-progression after receiving ICI monotherapy. 10These findings suggest that the biomarkers discovered by IMPACT may be validated in the clinic, which shows the potential value of IMPACT in biomarker exploration.
In summary, IMPACT is a user-friendly platform, conveying a comprehensive resource with more datasets and functions for sophisticated exploration of predictive and/or prognostic biomarkers, interaction effects and potential biological mechanisms, which eases bioinformatic analyses for researchers.With long-term support, continuous upgrading and optimisation, we believe IMPACT will be a popular tool to facilitate immunotherapy research.

C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare they have no conflicts of interest.

D ATA AVA I L A B I L I T Y S TAT E M E N T
All the data used in IMPACT can be downloaded from our server at https://impact.brbiotech.com(or http://www.brimpact.cn/).All the code can be accessed at https:// github.com/wenchuanxie/IMPACT.All the descriptions of methods can be found in the Supporting Information.

F I G U R E 2
Examples of pathway alteration and co-mutation analyses.(A) Waterfall plot of DNA damage response (DDR) pathway genes according to ATM mutation.(B) Meta-analysis of associations between DDR pathway alterations and progression-free survival.(C) Waterfall plot of STK11 and TP53 gene mutations according to KRAS mutation status.(D) Meta-analysis of associations between KRAS gene mutation and progression-free survival.(E) Meta-analysis of associations between KRAS/TP53 co-mutation and progression-free survival.(F) Meta-analysis of associations between KRAS/STK11 co-mutation and progression-free survival.

F I G U R E 3
Examples of survival analysis and interaction analysis modules.(A) The association of PBRM1 truncating mutation with overall survival (OS) in the kidney cancer dataset of Checkmate_025.(B) The association of PBRM1 nonsynonymous mutation with OS in the kidney cancer dataset of Checkmate_025.(C) The hazard ratios of bTMB and OS across various cutoffs in the Gandara_2018 dataset.(D) The hazard ratios of bTMB and progression-free survival (PFS) across various cutoffs in the Gandara_2018 dataset.(E) Univariable and multivariable Cox regression of ATM mutation with PFS in the Gandara_2018 dataset.(F) Kaplan-Meier curve of ATM mutation for overall survival in patients treated with atezolizumab and docetaxel.F I G U R E 4 Examples of immunogenicity and tumor microenvironment (TME) analysis modules.(A) The association of ATM nonsynonymous mutation with tumor mutational burden (TMB), neoantigen and programmed cell death protein ligand 1 (PD-L1) expression in the lung cancer dataset of Hellmann_2018.(B) The association of ATM nonsynonymous mutation with genome instability indicators and immune infiltration signatures in the TCGA lung adenocarcinoma (LUAD) dataset.(C) The association of ATM nonsynonymous mutations with oncogenic and immune signatures in the TCGA LUAD dataset.(D) The correlation between NOTCH pathway expression and immune signatures in the TCGA LUAD dataset.(E) The correlation between NOTCH pathway expression and oncogenic signatures in the TCGA LUAD dataset.(F) The heatmap of immune pathway expression between high or low CD8A expression groups in the TCGA LUAD dataset.