Prognostic significance of tumour-infiltrating T lymphocytes in ovarian cancer: a protocol for systematic review and meta-analysis

Abstract

Introduction

Ovarian cancer remains a significant clinical challenge due to its aggressive nature and high mortality rate. Tumour-infiltrating lymphocytes (TILs) play a critical role in the tumour microenvironment, influencing treatment response and patient survival across various cancer types, including ovarian cancer. A systematic review is warranted to consolidate evidence on TILs as prognostic biomarkers in ovarian cancer, with the goals of integrating them into clinical practice to enhance patient outcomes. This study aims to assess the prognostic significance of TILs in ovarian cancer.

Methods and analysis

A comprehensive literature search will be conducted across multiple databases, including PubMed, Embase, Web of Science, Scopus, Cochrane Library, CINAHL, ScienceDirect and LILACS. No restrictions regarding publication date or language will be applied. Original studies evaluating the role of TILs in women with ovarian cancer will be considered for inclusion. Two independent authors will screen titles and abstracts, and any discrepancies will be resolved through discussion with a third author. The risk of bias in included studies will be assessed using the Quality in Prognosis Studies (QUIPS) tool. Data synthesis will be performed using R software (V.4.3.1).

Ethics and dissemination

This study reviews the published data; thus, obtaining ethical approval is unnecessary. The findings of this systematic review will be published in a peer-reviewed journal.

PROSPERO registration number

CRD42024543955.

STRENGTHS AND LIMITATIONS OF THIS STUDY.

• Comprehensive search strategy across multiple databases, with no language or time restrictions.

• Independent and duplicate screening, data extraction and risk of bias assessment to minimise author bias.

• The QUIPS tool provides a robust assessment of study bias risk.

• Application of robust statistical methods (meta-analysis, meta-regression, subgroup/sensitivity analyses).

Introduction

Ovarian cancer is one of the deadliest gynaecological neoplasms, representing a significant clinical challenge due to its often late diagnosis and the aggressive nature of the disease.¹ In 2020, the global estimate was over 313 000 new cases of ovarian cancer and approximately 207 000 deaths worldwide, making it the eighth most common cause of cancer-related death among women. The identification of reliable prognostic biomarkers is essential to enhance disease management and guide therapeutic strategies for affected patients.^{2 3}

Tumour-infiltrating lymphocytes (TILs) have emerged as key components of the tumour microenvironment, playing a central role in the immune response against tumour cells.⁴ Recent studies have demonstrated that the presence and activity of TILs are associated with therapeutic response and survival in patients with various malignancies, including ovarian cancer. These infiltrating lymphocytes contribute to antitumour immunity by recognising and attacking malignant cells, and their abundance and functional characteristics may reflect the balance between immune surveillance and immunosuppression within the tumour microenvironment.^{5 6}

Evidence suggests that increased infiltration of cytotoxic T lymphocytes (CD8+) is frequently associated with better prognosis in several malignancies, including ovarian cancer.⁷ In contrast, the presence of regulatory TILs has been associated with poorer prognosis due to their immunosuppressive effects within the tumour microenvironment.^{8 9} However, significant heterogeneity across studies and methodological variations in the quantification and characterisation of TILs hinder the ability to draw definitive conclusions regarding their prognostic significance.¹⁰

Given the clinical relevance of TILs, a quantitative synthesis of existing data on their prognostic value in ovarian cancer is essential. Systematic reviews and meta-analyses are powerful tools for consolidating this evidence, providing a clearer insight into the impact of TILs on disease progression and contributing to the identification of potential prognostic biomarkers that can be integrated into clinical practice.^{11 12}

Objective

To evaluate the prognostic significance of TILs in ovarian cancer.

Methods and analysis

This systematic review and meta-analysis has been registered in PROSPERO (CRD42024543955) and follows the reporting guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA) checklist. The study is scheduled to begin in August 2025 and conclude in January 2026.

Inclusion and exclusion criteria

Articles will be included that encompass cohort studies, case–control studies, cross-sectional studies and clinical trials that assess the prognostic role of TILs in patients with ovarian cancer. There will be no age restrictions. Case reports and studies that do not evaluate the predefined outcomes will be excluded, as will articles focusing solely on other types of cancer.

Patient, intervention, comparison, outcome and study type

• Patients: Women with ovarian cancer.

• Intervention: Tumour-infiltrating lymphocytes (TILs).

• Comparison: Not applicable.

• Outcome: Overall survival (OS), recurrence rate (RR), cancer-specific survival (CSS), disease-free survival (DFS), progression-free survival (PFS) and disease-specific survival (DSS).

• Study types: Original articles.

Search strategy

A literature search was conducted in the following databases: PubMed/MEDLINE, Embase, Web of Science, Scopus, Cochrane Library, ScienceDirect, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Literatura Latino-Americana e do Caribe em Ciências da Saúde (LILACS). The search strategy includes all terms and their synonyms related to: “tumor-infiltrating lymphocytes,” “T lymphocytes,” “T cells,” “regulatory T cells,” “ovarian cancer,” “ovarian tumor,” “ovarian carcinoma” and “ovarian neoplasms.” Reference lists associated with all studies found in the search were used to identify other relevant publications. There will be no language or date restrictions. The search strategy used for all databases is shown in table 1.

Table 1. Detailed search strategy for each database (September 2024).

Databases	Strategy*	Number of studies located
PubMed/ MEDLINE	((“Ovarian Neoplasms”(Mesh)) OR (Neoplasm*, Ovarian) OR (Ovarian Neoplasm) OR (Neoplasm*, Ovar*) OR (Ovar* Neoplasm*) OR (Ovar* Cancer*) OR (Cancer*, Ovar*) OR (Cancer of Ovar*) OR (Cancer of the Ovar*) OR (Carcinoma*, Ovar* Epithelial) OR (Epithelial Carcinoma*, Ovar*) OR (Ovar* Epithelial Carcinoma*) OR (Epithelial Ovar* Carcinoma*) OR (Carcinoma*, Epithelial Ovar*) OR (Ovar* Carcinoma*, Epithelial) (Ovar* Epithelial Cancer*) OR (Cancer*, Ovar* Epithelial) OR (Epithelial Cancer*, Ovar*) OR (Ovar* Cancer*, Epithelial) OR (Cancer*, Epithelial Ovar*) OR (Epithelial Ovar* Cancer*)) AND ((“Lymphocytes, Tumor-Infiltrating”(Mesh)) OR (Tumor-Infiltrating Lymphocyte*) OR (Lymphocyte, Tumor-Infiltrating) OR (Tumor Infiltrating Lymphocyte*) OR (Infiltrating Lymphocyte*, Tumor) OR (Lymphocyte*, Tumor Infiltrating) OR (Activated Cell*, Tumor-Derived) OR (Tumor-Derived Activated Cell*) OR (Tumor Derived Activated Cell*))	1.060

Study selection

Following the database search, all retrieved articles will be imported into Rayyan software for reference management, and duplicates will be removed. The initial selection of titles and abstracts will be performed independently by two authors (GFdV and FBdM), and disagreements will be resolved by the third author (SCSO). The study selection process will be documented and summarised using the PRISMA flow diagram (figure 1).

Figure 1. PRISMA flow diagram for systematic review and meta-analysis. *Number of records identified from each database. **Records excluded by Rayyan. PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

Data extraction

Data from each included study will be extracted independently by three authors (GFdV, FBdM and LSdSJ). Any discrepancies will be resolved through discussion with a fourth author (EMS). Extracted data will include information on the authors, year of publication, study design, country, sample size, follow-up duration, participant characteristics, tumour size, TIL phenotype, outcomes and estimated HRs. All data will be collected using a custom-designed extraction table developed by the authors.

Assessment of methodological quality

Two authors (ACAS and KSM) will independently assess the risk of bias using the Quality in Prognosis Studies (QUIPS) tool. This tool evaluates six potential sources of bias in prognostic studies: (1) study participation, (2) study attrition, (3) prognostic factor measurement, (4) confounding measurement and control, (5) outcome measurement and (6) statistical analysis and reporting.

Data synthesis

The results of clinically similar studies will be pooled in a meta-analysis using the ‘meta’ package in R software (V.4.3.1). Effect measures, including relative risks and estimated hazard ratios with 95% CIs, will be calculated. Heterogeneity among studies will be assessed using I² statistics. Given the anticipated variability across studies, a random-effects model will be employed. A two-tailed p<0.05 will be considered statistically significant.

If more than 10 eligible studies are available, publication bias will be visually examined through funnel plots and statistically using Egger’s or Begg’s regression models. Potential sources of statistical heterogeneity will be further investigated through sensitivity analyses.

Subgroup analysis

Two subgroup analyses will be conducted to investigate the impact of lymphocyte subsets and the location of lymphocyte infiltration on prognostic outcomes.

Patient and public involvement

Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Discussion

The role of TILs as prognostic biomarkers in ovarian cancer, one of the most aggressive among gynaecological malignancies, has gained increasing attention in recent years. The association between the exact location, density and subpopulation of TILs within the tumour microenvironment and clinical outcomes is still being elucidated.¹³ Higher concentrations of intraepithelial CD3+, CD8+ and CD103+ cells have been associated with improved prognosis.¹²¹⁴,¹⁶ However, there are still discrepancies regarding the influence of CD3+cells on the prognosis of patients with ovarian cancer.

Furthermore, the recognised immunogenicity of ovarian tumours and the potential influence of TILs have prompted investigations into therapeutic strategies involving chemotherapeutic and cytotoxic agents.¹⁷,¹⁹ A recent review article assessed the predictive and prognostic roles of TILs in ovarian cancer and stated that their addition as a prognostic factor in patients with this type of neoplasm is positive. However, the authors emphasised the need for further studies and systematic reviews to better define the stratification potential and therapeutic implications of these immune cells.²⁰

The heterogeneity of the tumour microenvironment and the complex interaction between tumour and immune cells highlight the need for personalised therapeutic approaches. Recent studies indicate that the expression of immunosuppressive molecules, such as programmed cell death protein 1 (PD-1) and cytotoxic T-lymphocyte antigen 4 (CTLA-4), in TILs may modulate their antitumour activity, allowing tumour cells to escape immune detection.²¹ Clinical trials with immune checkpoint inhibitors (ICIs) have shown promising results by restoring the effector function of TILs, leading to durable tumour responses in subgroups of patients.²² However, the efficacy of ICIs can vary significantly among patients, suggesting that combining ICIs with other therapeutic modalities, such as chemotherapy and poly (ADP-ribose) polymerase inhibitors (PARPi), may optimise clinical outcomes.²³

In parallel, the molecular characterisation of ovarian tumours has revealed a spectrum of actionable mutations. Notably, alterations in genes involved in the RAS-RAF-MEK-ERK signalling pathway have been detected in several histological subtypes.²⁴ For example, low-grade serous ovarian carcinoma (LGSOC) exhibits a distinct molecular profile when compared with high-grade serous carcinoma (HGSC), frequently harbouring KRAS and BRAF mutations.^{25 26} Although BRAF inhibitors have been approved as monotherapy for tumours with BRAF mutations, many patients ultimately experience disease progression. Preclinical studies suggest that dual inhibition of BRAF and MEK may delay resistance, prompting the ongoing clinical investigation of combination therapies in this setting.²⁷,²⁹

Additionally, approximately 20% of patients with epithelial ovarian cancer (EOC) carry germline or somatic BRCA1/2 mutations, which compromise homologous recombination repair (HRR) and confer increased sensitivity to platinum-based chemotherapy and PARPi.³⁰ Furthermore, up to 50% of HGSC exhibit homologous recombination deficiency (HRD) even in the absence of BRCA mutations. Other genes, such as RAD51, PALB2 and RAD51C/D, also contribute to the HRD phenotype and may predict benefit from PARPi therapy.³¹ Additionally, mutations in mismatch repair (MMR) genes, often associated with Lynch syndrome, are more commonly found in non-serous ovarian tumours and have implications for immunotherapy, including the use of pembrolizumab in tumours exhibiting microsatellite instability (MSI) or MMR deficiency.³²

Despite these advances, a universally accepted therapeutic algorithm for advanced ovarian cancer remains elusive. Treatment decisions should be guided by molecular characteristics, prior therapeutic responses and the risk of developing resistance. Ongoing clinical trials are exploring combinatory regimens that include PARPi, immunotherapy and other targeted agents to improve outcomes and expand treatment options.^{33 34}

In this evolving landscape, biomarker testing—especially for BRCA and HRD status—has become indispensable in guiding treatment selection and implementing personalised medicine in ovarian cancer care. However, several challenges remain, including resistance to PARPi, the uncertain benefit of PARPi in HRD-negative patients and the integration of new therapeutic strategies into clinical practice.

Additionally, the quantitative and qualitative assessment of TILs as prognostic biomarkers holds promise for improving risk stratification and guiding therapeutic decision-making. Advanced imaging modalities and genomic analysis have been employed to characterise the immune profile of tumours, allowing for the identification of patients who are most likely to benefit from immunotherapy.³⁵ Integrating these insights into the clinical management of ovarian cancer may promote a more precise and effective treatment approach, ultimately improving patient outcomes and quality of life. Future research should prioritise the validation of these biomarkers in larger, well-defined cohorts and explore novel therapeutic targets aimed at enhancing the antitumour immune response.³⁶,³⁹

Ethics and dissemination

The results of this systematic review will be shared through publication in a peer reviewed open-access journal, as well as through scientific publications and reports. Ethics committee review is not required, as only publicly available literature will be evaluated.