Tumor Mutation Burden and Tumor Microenvironment as Biomarkers of Glioma Treatment Outcome and Prognosis: A Systematic Review and Meta-Analysis Protocol

Background: Gliomas are the most common solid malignant tumors of the brain; diffuse gliomas pose a remarkable conundrum on treatment strategy. WHO Grade IV (Glioblastomas) delineate a refractory resistance to treatment even with standard combination regimen therapy of surgery, chemotherapy and radiation therapy causing increased recurrence rate with a median survival of less than one year. Management of gliomas is precluded by several factors including intra and inter tumoral heterogeneity, genomic landscape and microenvironment immunosuppression ability, which spell the inflicted pathways that counteract the therapeutic interventions. This is systematic review aiming to evaluate the effect of Tumor Mutation Burden (TMB) and Tumor Microenvironment (TME) as biomarkers of treatment outcome and prognosis of gliomas.

Methods and Analysis: This systematic Review and Meta-analysis will consider the PRISMA 2020 guideline correspondence. For source of literature evidence several electronic databases including EMBASE, PubMed, Cochrane Library, SCOPUS, Web of Science, Semantic Scholar and Google scholar will be searched. All non-RCTs peer reviewed original research articles addressing the prognostic role of tumor mutation burden, tumor mutation pathways, microenvironment will be included, and data will be extracted using the Ms Excel Sheets. Studies with homogeneity and low risk of bias according to NOS 4-9 will constitute a Meta-analysis for evaluating the effect of TMB, Mutation pathways and Microenvironment for treatment outcome and prognosis of glioma.

Ethical Consideration and Dissemination: Ethical approval does not apply for there is no human participant involvement in this review study .The findings of this study will be disseminated at scientific conferences and the manuscript for publication will be submitted for publication to a reputable peer reviewed journal of Neurosurgery. This review was registered at PROSPERO CRD42022377829.

Gliomas are the most common solid malignant tumors of the brain; diffuse gliomas pose a remarkable conundrum on treatment strategy. WHO Grade IV (Glioblastomas) delineate a refractory resistance to treatment even with standard combination regimen therapy of surgery, chemotherapy and radiation therapy causing increased recurrence rate with a median survival of less than one year [1]. Management of gliomas is precluded by several factors including intra and inter tumoral heterogeneity, genomic landscape and microenvironment immunosuppression ability which spell inflicted pathways that counteract several therapeutic interventions [2].

Tumor mutation burden is normally equivalent to 1 mutation per Megabase (Mb) of DNA, however some pathways can induce the hypermutation of more than 20 mutations/Mb. Tumor Mutation Burden (TMB) interplay the effect of Immune checkpoint Inhibitors treatment response predicated through immunosensitisation by elaboration of many mutation-derived PD-1 proteins and PD-L1 on tumor cell surface [3]. Recent literatures have elucidated two major hypermutation pathways Mismatch Repair (MMR) and resistance derived MMR attributable to Tomozolamide (TMZ) therapy. MMR deficient tumors accumulate more TMBs, and lack T lymphocytes infiltration surrounding their microenvironment rendering resistance to Immune checkpoint inhibitors.

Hypermutated exomes produce higher amount of neoantigens on which the CD8+ Lymphocytes may recognize their putative ligands and target malignant cells. Immunotherapy treatment achievement in non-CNS cancer has instigated an outstanding strategy for use of ICIs in treatment of high-grade diffuse gliomas. Given that the standard treatment of gliomas is maximum surgical excision , grade III and IV are less promising with radiation and chemotherapy adjuvant therapy, diffuse infiltrating glioma lose sensitivity with adjuvant therapy portending a significance resistance and recurrence [2].

Tumor Mutation Burden (TMB) is defined as number of mutation per Millions base of DNA harbored by a tumor cell, it usually measured using the exome sequencing or targeted sequencing technique. TMB corresponds proportionally with neoantigen expression and immunogenicity such that it predicts the treatment response and prognostic outcome of patients with gliomas. Neoantigen recognition by T-lymphocytes spells a key hallmark for immunotherapy response in gliomas. The TMB magnitude corresponds with anti -PD1 therapy. Literatures have elucidated that Tumor with high TMB exomes express more neoanigens with promising response on Immune checkpoint inhibitors and favorable prognosis compared to tumors with low TMB [4-6].

Tumor Microenvironment (TME) comprises of tumor associated epithelial cells, stromal cells, bone marrow derived cells, vascular cells and extracellular matrix. Most of stromal and bone marrow derived cells promote tumor growth and invasion, the Tumor Associated Macrophage (TAM) is a major component of TME which have a distinct role for tumor growth and invasion its phenotypes and recruitment is regulated by tumor secreted factors. Epithelial and stromal cells contribute to tumor growth, invasion and treatment resistance [7-9].

The disparity of tumoral genomic heterogeneity and immune infiltration status influence immunotherapy response , however the resistance and recurrence candidacy of high grade gliomas has not been vividly described such that comprehensive understanding of tumor mutation load , neoantigens and tumor microenvironment would set a critical point for conducting more ICIs clinical trials in high grade gliomas [2,6,10].

High-grade gliomas such as Glioblastoma are attributable to poor therapeutic response and recurrence therefore a comprehensive understanding of mutation genomic load and immune microenvironment characteristics of these tumors is of importance paramount to stimulate the end point for appropriate tumor classification, prognosis and therapeutic strategy.

Remarkable mutation occurs in IDH glioma accounting for more than 75%, studies have delineated a remarkable relationship between the mutation load and patient survival and recurrence. The mutation burden is more expressed in genes for Mismatch Repair (MMR) and cell cycle associated genes. Tumor mutation burden ascribes an independent predictor of treatment outcome and prognosis [6].

The interaction between tumor cells and surrounding environment is a complex stemmed by genomic landscape of specific tumor subtype such that the association niche between tumor cells, endothelial cells and immune cells articulates to imperative tumorigenesis and progression pathways which are important target for therapeutic strategy [7,8].

Tumor Microenvironment (TME) expounds a key regulator of cancer progression, effector cells of immune infiltration keep malignant cells under check for control of tumor invasion and metastasis, however cancer cells my circumvent the TME by developing multiple dynamics of biochemical and physiological alteration which may escape and overdue the effector cells regulation pathways. The existing linearity between the cancer cells and TME expounds that development of TME targeted immunotherapy creates immunogenic TME potentiating immune response and increased drug delivery which increased the therapeutic efficacy and prognosis [7-9].

Tumor mutation burden and tumor microenvironment spell the outstanding impact on treatment response and prognosis of glioma. Adequate elaboration of genomic landscape with its influence on Tumor microenvironment architecture such as tumor associated immune cells, endothelia cells, stromal cells and ECM would invigorate the effect dynamics of immune checkpoint inhibitors .Henceforth a systematic review to summarize the evidence on this topic will expound more and underpin the treatment strategy outcome and prognosis in gliomas.

This review will address the research question according to PICOS.

Participants

This review will recruit all articles reporting the effect of TMB, Mutation Pathways and on prognosis and treatment outcome in human adult for both male and female sex; only non-RCTs will be included.

Intervention/Exposure

Tumor mutation burden, mutation pathways, neoantigenic expression and microenvironments status.

Comparators

Low tumor mutation burden versus high tumor mutation burden, type of mutation pathways, tumor microenvironment status.

Outcome

Disease Free Survival (DFS) defined as time since from detection of TMB and TME in glioma and immunotherapy initiation to time of tumor disease recurrence, progression or death from any cause. Overall Survival (OS) defined as time from detection of TMB and TME in gliomas and initiation of immunotherapy or chemotherapy to time of death from any cause.

Study design

This is a systematic review and Meta-analysis.

Broad objective

To evaluate the effect of Tumor Mutation Burden (TMB) and Tumor Microenvironment (TME) as biomarkers of treatment outcome and prognosis of gliomas.

Specific objectives

• To assess the tumor mutation burden magnitude and its effect on treatment outcome and prognosis in gliomas.
• To assess the tumor mutation pathways in gliomas and their effect on immunotherapy treatment response.
• To evaluate the epigenetic status on treatment outcome and prognosis in gliomas.
• To determine the neoantigenic expression and its effect on treatment outcome and prognosis of gliomas.
• To evaluate the tumor microenvironment status and its effect on treatment response and prognosis in gliomas.

Protocol and registration

The protocol was developed with correspondence consideration to Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020 guideline and a review of this study was registered at PROSPERO ID CRD42022377829.

Eligibility criteria

Inclusion criteria: All original articles reporting on the effect of Tumor mutation burden, mutation pathways, neoantigenic expression, epigenetic status and tumor microenvironment status in human adult will be included, all cross sectional, cohort and case control studies published from January, 2018 to December, 2022 without language limitation will be included.

Exclusion criteria: All animal studies and pediatric population will be excluded, all RCTs will be excluded, all articles with missing data will be excluded.

Information sources and search strategy

The following databases EMBASE, PubMed, Cochrane Library, SCOPUS, Web of Science, Semantic Scholar and Google scholar will be searched using the strategy constructed by MeSH terms, Boolean Operators and Filters.

Study selection

Three review authors (VK , EM, AA ) will independently screen the titles and abstracts of all references identified by search strategy .The selection procedure will be blinded and all three reviewers will evaluate the full text of each record deemed potentially eligible to determine the study eligibility and dissension will be resolved by consensus or adjudication from a fourth author. The selection process will be recorded in PRISMA flow diagram using Rayyan or Covidence and RevMan softwares. The reasons for exclusion of key full text articles will be listed in the characteristics of excluded studies table (Figure 1).

Figure 1: PRISMA Study selection flow diagram.

Data extraction

Three review authors (VK, EM, and AA) will independently screen the titles and abstracts of all titles identified by search strategy. All three-review authors will further evaluate the full text of each article deemed potential for study quality and eligibility. Any discrepancies encountered will be resolved by consensus or adjunction from another reviewer (BT). The data will be extracted using piloted Microsoft excel checklist.

Data items

The following data extracted will include study details such as study design, number of study centers, locations, study setting, date of study, duration of study, funding source, year of publication, type of data analysis, baseline characteristics of study participants, inclusion and exclusion criteria, sample size, age, sex, histology, grade and stage.

Characteristics of exposure including tumor mutation burden status and magnitude, mutation pathways, tumor microenvironment status, type of neoantigens and Characteristics of outcome including Disease Free Survival (DFS) and Overall survival.

Risk of Bias (ROB) in individual studies

Three review authors (VK, EM, AA) will independently evaluate the quality of studies for inclusion using the Newcastle Ottawa Scale (NOS). Studies with score of 4-9 will be considered as low risk of bias and 1-3 will be will be considered as high risk for bias .Only studies with score from 4-9 will be included for review and meta-analysis.

Measurement of effect

The time to event of outcome will be analyzed using the Hazards Ratio (HR), corresponding confidence intervals .Hazard Ratio and associated variance will be extracted from published studies, and estimated log (HR) and its standard error for unreported HR will be calculated. Dichotomous outcomes will be expressed using Relative Risk and CI and continuous outcomes will be expressed using mean difference and CI.

Data synthesis

Meta-analysis will be performed for data based on PP analysis when possible. The Tumor mutation burden and Tumor microenvironment will be synthesized separately, meta-analysis will be conducted considering the REM .For dichotomous outcomes the pooled RR and 95% CI will be estimated and for continuous outcomes, the MD will be estimated .For time to even the pooled HR will be estimated using the inverse variance method. Meta-analysis will be performed using RevMan software .If outcome data cannot be synthesized using meta-analysis, we will perform analysis based on Synthesis without meta-analysis (SWiM) and a scoping analysis with table presentation will be performed.

Generally clinical heterogeneity, methodological heterogeneity, statistical heterogeneity and publication bias will be evaluated. The inconsistency will be determined using a blobbogram, Chi2-test and I2-test the possible source of inconsistency will be explored across subgroup analysis, if a significant clinical heterogeneity is detected a meta-analysis will not be performed rather a narrative summary will be conducted .

Risk of bias across studies

If at least 10 studies will be included in a meta-analysis, the publication bias will be evaluated using the funnel plot

Subgroup analysis and evaluation of heterogeneity

If adequate consistency is found the subgroup analysis will be performed considering the following factors:

• Tumor mutation burden low or high.
• Tumor mutation pathways i.e IDH mutant or IDH wild type.
• Tumor microenvironment status.
• Neoantigens expression.
• Low grade glioma or high grade glioma.

Sensitivity analysis

If adequate data will be found, we will conduct the sensitivity analysis to assess the robustness of results. The sensitivity will be tested by excluding the studies at high risk of bias, with unpublished data, with missing data, where there are different definitions of DFS and OS among the studies.

Strength of body of evidence

Three review authors (VK , EM ,AA) will rate the certainty for outcomes using the five GRADE domains risk of bias , inconsistency , imprecision, indirectness and publication bias .The overall certainty of evidence will be rated as high , moderate , low and very low.

Ethical approval does not apply for there is no human participant involvement in this review study. The findings of this study will be disseminated at scientific conferences and the manuscript for publication will be submitted for publication to a reputable peer reviewed journal of Neurosurgery.

VK, EM and AA designed and drafted the protocol. The final protocol was reviewed and endorsed by all other authors. BT will mitigate disagreement resolution.

Authors declare that they have no competing interests.

1. Ahmad H, Fadul CE, Schiff D, Purow B. Checkpoint inhibitor failure in hypermutated and mismatch repair-mutated recurrent high-grade gliomas. Neurooncol Pract. 2019 Dec;6(6):424-427. doi: 10.1093/nop/npz016. Epub 2019 Apr 7. PMID: 31832212; PMCID: PMC6899050.
2. Yu G, Pang Y, Merchant M, Kesserwan C, Gangalapudi V, Abdelmaksoud A, Ranjan A, Kim O, Wei JS, Chou HC, Wen X, Sindiri S, Song YK, Xi L, Kaplan RN, Armstrong TS, Gilbert MR, Aldape K, Khan J, Wu J. Tumor Mutation Burden, Expressed Neoantigens and the Immune Microenvironment in Diffuse Gliomas. Cancers (Basel). 2021 Dec 3;13(23):6092. doi: 10.3390/cancers13236092. PMID: 34885201; PMCID: PMC8657099.
3. Kang K, Xie F, Wu Y, Wang Z, Wang L, Long J, Lian X, Zhang F. Comprehensive exploration of tumor mutational burden and immune infiltration in diffuse glioma. Int Immunopharmacol. 2021;96:107610. doi: 10.1016/j.intimp.2021.107610.
4. Lee M, Samstein RM, Valero C, Chan TA, Morris LGT. Tumor mutational burden as a predictive biomarker for checkpoint inhibitor immunotherapy. Hum Vaccines Immunother. 2020;16(1):112-115. doi: 10.1080/21645515.2019.1631136.
5. Strickler JH, Hanks BA, Khasraw M. Tumor Mutational Burden as a Predictor of Immunotherapy Response: Is More Always Better? Clin Cancer Res. 2021 Mar 1;27(5):1236-1241. doi: 10.1158/1078-0432.CCR-20-3054. Epub 2020 Nov 16. PMID: 33199494.
6. Alghamri MS, Thalla R, Avvari RP, Dabaja A, Taher A, Zhao L, Ulintz PJ, Castro MG, Lowenstein PR. Tumor mutational burden predicts survival in patients with low-grade gliomas expressing mutated IDH1. Neurooncol Adv. 2020 Mar 27;2(1):vdaa042. doi: 10.1093/noajnl/vdaa042. PMID: 32642696; PMCID: PMC7212865.
7. Su J, Long W, Ma Q, Xiao K, Li Y, Xiao Q, Peng G, Yuan J, Liu Q. Identification of a Tumor Microenvironment-Related Eight-Gene Signature for Predicting Prognosis in Lower-Grade Gliomas. Front Genet. 2019 Nov 15;10:1143. doi: 10.3389/fgene.2019.01143. PMID: 31803233; PMCID: PMC6872675.
8. Cooper LAD, Gutman DA, Chisolm C, Appin C, Kong J, Rong Y, Kurc T, Van Meir EG, Saltz JH, Moreno CS, Brat DJ. The tumor microenvironment strongly impacts master transcriptional regulators and gene expression class of glioblastoma. Am J Pathol. 2012;180(5):2108-2119. doi: 10.1016/j.ajpath.2012.01.040.
9. Shen M, Kang Y. Complex interplay between tumor microenvironment and cancer therapy. Front Med. 2018 Aug;12(4):426-439. doi: 10.1007/s11684-018-0663-7. Epub 2018 Aug 10. PMID: 30097962.
10. Wang L, Ge J, Lan Y, Shi Y, Luo Y, Tan Y, Liang M, Deng S, Zhang X, Wang W, Tan Y, Xu Y, Luo T. Tumor mutational burden is associated with poor outcomes in diffuse glioma. BMC Cancer. 2020 Mar 12;20(1):213. doi: 10.1186/s12885-020-6658-1. PMID: 32164609; PMCID: PMC7069200.