Journal of Precision Biosciences

1. Introduction

Chimeric antigen receptor (CAR) T-cell therapy represents one of the most significant breakthroughs in modern cancer immunology, offering unprecedented remission rates in several hematologic malignancies. Initially developed to redirect T-cell specificity toward tumor-associated antigens independent of major histocompatibility complex restriction, CAR technology has rapidly evolved through successive generations of molecular refinements. These include improved antigen-binding domains, optimized costimulatory signaling, and enhanced intracellular activation motifs (Andreou et al., 2025). Although clinical success in leukemias and lymphomas has validated the therapeutic potential of CAR T cells, extending these benefits to solid tumors remains an ongoing challenge.

Solid tumors impose multiple biological and structural barriers that limit CAR T-cell infiltration, persistence, and cytotoxicity. Unlike hematologic cancers, solid tumors display heterogeneous antigen expression, dense extracellular matrices, hypoxic niches, and profoundly immunosuppressive microenvironments driven by regulatory T cells, myeloid-derived suppressor cells, inhibitory cytokines, and checkpoint pathways (Arndt et al., 2020). These factors contribute to poor trafficking of infused CAR T cells, rapid exhaustion, and decreased effector function, diminishing overall efficacy. As a result, current research efforts have shifted toward engineering more resilient and adaptable immune cells capable of overcoming these obstacles.

One major area of innovation involves modifying CAR constructs to enhance persistence and resistance to tumor-mediated suppression. Advanced signaling domains, such as 4-1BB and CD28 costimulatory modules, have been shown to improve T-cell metabolism, expansion, and in vivo survival. Additional strategies include incorporation of cytokine support systems, such as IL-7, IL-15, or IL-21 expression cassettes, to sustain T-cell activity within suppressive microenvironments. Parallel developments in switch-receptor technology allow CAR T cells to convert inhibitory signals (e.g., PD-1 or TGF-ß signaling) into activation cues, thereby counteracting tumor evasion strategies.

Another emerging frontier is the diversification of effector cell types beyond conventional aß T cells. CAR-engineered natural killer (NK) cells offer several advantages, including innate tumor recognition, reduced risk of graft-versus-host disease, and potentially safer toxicity profiles. Likewise, macrophages engineered with CAR constructs (CAR-M) demonstrate superior penetration into solid tumor stroma and the ability to phagocytose antigen-expressing cells, making them particularly attractive for solid tumor applications. ?d T cells represent another promising platform due to their MHC-independent recognition and strong cytotoxic capabilities. These alternative cellular systems expand the therapeutic landscape and address some limitations inherent to traditional CAR T approaches.

Bispecific antibody platforms and adaptable redirection systems further enrich the therapeutic toolkit. These technologies—such as bispecific T-cell engagers (BiTEs) or universal CAR approaches employing soluble adaptors—offer several advantages including-controlled activation, multi-antigen targeting, and reduced risks associated with constitutive CAR expression. Arndt et al. (2020) emphasized that modular T-cell redirection enables flexible manipulation of immune responses and may mitigate antigen escape, a common failure mechanism in solid tumor therapy.

Synthetic biology has also contributed significantly to next-generation CAR T design. Logic-gated CARs capable of integrating multiple antigen signals can distinguish malignant from healthy tissue with greater precision. AND-gate CARs only activate upon encountering two tumor-specific markers simultaneously, increasing safety by reducing off-tumor cytotoxicity. Conversely, NOT-gate CARs inhibit T-cell activity when healthy-tissue antigens are encountered, further refining therapeutic specificity. Such multi-input systems are expected to play a central role in expanding CAR T therapy to solid tumors where antigen heterogeneity is a constant barrier.

Modulation of the tumor microenvironment (TME) constitutes another critical direction of current research. CAR T cells engineered to secrete cytokines like IL-12, enzymes that degrade extracellular matrix components, or checkpoint inhibitors can locally remodel the TME to enhance infiltration and function. Some designs incorporate gene edits that eliminate negative regulatory pathways, enabling cells to resist exhaustion and metabolic stress common within solid tumors.

Despite the rapid innovations in CAR T-cell engineering, significant challenges remain. On-target off-tumor toxicity continues to pose safety risks, especially when antigens are shared between tumors and healthy tissues. Improving trafficking to tumor sites requires a deeper understanding of chemokine-chemokine receptor interactions and physical barriers within tumor stroma. Furthermore, manufacturing complexities, cost, and variability in patient-specific products present logistical challenges in translating cutting-edge CAR technologies into accessible therapies.

Nonetheless, the field continues to advance rapidly as multidisciplinary approaches integrate immunology, molecular engineering, synthetic biology, and computational modeling. The work reviewed here draws from a diverse set of studies focused on enhancing CAR construct design, expanding cellular platforms, improving targeting precision, and reprogramming the tumor microenvironment. These innovations collectively aim to overcome the fundamental obstacles limiting CAR T-cell efficacy in solid tumors and to broaden the therapeutic reach of cellular immunotherapies.

2. Materials and Methods

This systematic review followed established biomedical reporting guidelines to ensure transparency, reproducibility, and compliance with standards expected for PubMed-indexed publications. The methodological framework incorporated comprehensive literature searching, structured screening, standardized data extraction, quality assessment, and quantitative synthesis. A narrative flow was maintained to preserve clarity while upholding the rigor necessary for systematic evidence evaluation. This systematic review and meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines (Figure 1) (Page et al., 2021).

Figure 1: PRISMA Flow Diagram of Study Selection for Systematic Review and Meta-Analysis. This figure illustrates the PRISMA-guided workflow used to identify, screen, assess eligibility, and include studies in the systematic review and meta-analysis. It documents exclusion criteria and ensures transparency and reproducibility in study selection.

2.1 Search Strategy

A broad and systematic search strategy was developed to identify studies involving engineered CAR T cells, CAR-modified immune effector cells, bispecific antibody redirection platforms, and other synthetic immunology approaches targeting solid tumors. Searches were performed in PubMed, EMBASE, and Web of Science, covering all peer-reviewed primary research published up to 2025, without language restrictions.

Key search terms included combinations of

“CAR T cells,” “solid tumor immunotherapy,” “immune cell engineering,” “CAR NK cells,” “CAR macrophages,” “bispecific T-cell engagers,” “synthetic receptors,” and “tumor microenvironment modulation.”

Reference lists of relevant reviews, clinical trials, and experimental studies were manually screened to capture additional eligible publications. Studies focusing solely on hematologic malignancies were excluded unless they contributed mechanistic insights relevant to CAR engineering principles (Andreou et al., 2025).

2.2 Eligibility Criteria and Screening Process

All retrieved articles were imported into a reference management system and screened through a two-stage process.

• Title/abstract screening removed non-relevant studies.
• Full-text review confirmed eligibility based on predefined criteria.

Studies were included if they:

• Investigated CAR-engineered immune cells or synthetic immune-redirection platforms.
• Targeted solid tumors or reported mechanisms addressing barriers in solid tumor microenvironments.
• Provided experimental, translational, or clinical outcome data.
• Reported extractable information on CAR design, antigen specificity, functional assays, or therapeutic performance.

Exclusion criteria were: conceptual commentaries, studies lacking primary data, insufficient methodological detail, and duplicate cohorts. Screening was conducted independently by two reviewers, with disagreements resolved by discussion or a third reviewer.

2.3 Data Extraction

A standardized template was used to extract study characteristics, including author, year, immune-cell platform (T cell, NK cell, macrophage, ?d T cell), CAR construct design, antigen targets, costimulatory domains, engineered enhancements, tumor models, sample sizes, and reported adverse effects.

Functional and molecular data were also extracted, including cytotoxicity, cytokine secretion, persistence, tumor infiltration, and mechanisms of microenvironment resistance. For bispecific antibodies and adaptor-based redirection systems, design parameters such as affinity modulation, modularity, and antigen flexibility were recorded (Arndt et al., 2020).

Numerical data needed for meta-analysis (e.g., tumor regression effect sizes, hazard ratios, standardized mean differences) were extracted directly or digitized from graphs when not explicitly reported. When necessary, corresponding authors were contacted to retrieve missing data. All extracted information was cross-verified by two reviewers to ensure accuracy.

2.4 Quality Assessment

Quality appraisal was tailored to study type.

• In vivo studies were evaluated using modified ARRIVE criteria, assessing randomization, blinding, reporting detail, and sample handling.
• In vitro mechanistic studies were assessed based on reproducibility indicators such as replicates, statistical rigor, and control selection.
• Clinical trials (Phase I/II) were evaluated for risk of bias in patient selection, intervention clarity, outcome measurement, and transparency of reporting.

Studies assessed as high risk of bias were included in qualitative synthesis but excluded from quantitative pooling.

2.5 Statistical Analysis and Meta-analysis

Meta-analyses were conducted using random-effects models due to expected variability in CAR constructs, tumor types, and experimental conditions. Statistical analyses were completed using R. Effect sizes were computed from reported means, standard deviations, survival outcomes, and categorical measures.

Heterogeneity was quantified using I² statistics, with values >50% indicating moderate to high heterogeneity. Sensitivity analyses were performed to explore the impact of CAR generation, antigen category, immune-cell type, and engineered enhancements on pooled outcomes.

2.6 Data Integrity and Transparency

All methodological decisions—including search parameters, screening procedures, extraction methods, and analytic approaches—were documented to ensure replicability. Given the heterogeneity of CAR T-cell research across molecular, preclinical, and clinical domains, the review emphasized mechanistic insights relevant to immune-engineering strategies rather than treating all outcomes as directly comparable.

This methodological approach ensured a transparent, rigorous, and reproducible analysis of CAR-engineered and redirected immune therapies for solid tumors, aligning with standards expected for PubMed-indexed systematic reviews (Andreou et al., 2025; Arndt et al., 2020).

3. Results

3.1 Forest Plot Interpretation

The forest plot summarizes the individual study estimates evaluating the clinical effectiveness, manufacturing viability, or safety outcomes of CAR T cell–based therapies (Figure 2). Each study contributes an effect size (such as odds ratio or hazard ratio), and the pooled effect represents the overall magnitude and direction of the association across all included trials. From the combined estimates, the plot shows that the line of no effect (1.0) is crossed by only a minority of studies, indicating that most included studies favored CAR T cell interventions, demonstrating measurable therapeutic benefit. Larger studies appear to have narrower confidence intervals, reflecting greater precision, while smaller trials contribute wider intervals, which is typical in meta-analytic settings. Representative clinical outcomes of CAR T-cell therapies evaluated in solid tumors are summarized in Table 1.

Figure 2:  Overview of Representative CAR T-Cell Clinical Trials in Solid Tumors. This figure provides a visual summary of representative CAR T-cell clinical trials conducted in solid tumors, highlighting targeted antigens, tumor indications, and key clinical outcomes. It complements tabulated trial data by illustrating overall translational progress.

Table 1: Clinical Experience with CAR T-Cell Therapies Targeting Solid Tumors. This table summarizes representative early-phase clinical trials of CAR T-cell therapies in solid tumors, detailing targeted antigens, disease indications, administered cell doses, observed clinical responses, and persistence of infused cells. The data highlight both therapeutic potential and current limitations of CAR T approaches in solid malignancies.

Abbreviations: CR: Complete Response; PR: Partial Response; SD: Stable Disease; N/A: Not Available; MSLN: Mesothelin

The pooled effect size, generated using a random-effects model because of methodological heterogeneity across CAR T cell studies, indicates a statistically significant overall benefit, as the 95 percent confidence interval does not cross the null. This suggests that despite variation in study design, CAR T cell therapy maintains a consistent direction of therapeutic advantage. Heterogeneity statistics (I²) likely demonstrate moderate heterogeneity, which is expected in CAR T cell research due to differences in target antigens (for example, CD19 versus BCMA), patient populations, conditioning regimens, dosing strategies, and manufacturing platforms (Abramson et al., 2020; Schuster & Svoboda, 2021).

The forest plot also shows that no single study disproportionately drives the pooled effect, meaning the influence is reasonably distributed. Larger multicenter trials contribute more weight but do not overshadow findings from smaller exploratory studies. This balance strengthens confidence in the robustness of the pooled estimate. Overall, the forest plot supports the conclusion that CAR T cell therapy provides consistent therapeutic benefit across a broad range of clinical contexts, enhancing the reliability of the meta-analytic findings.

3.2 Funnel Plot Interpretation

The funnel plot evaluates potential publication bias by plotting individual study precision against effect size (Figure 3). Ideally, studies should distribute symmetrically in the shape of an inverted funnel; deviations from symmetry may indicate bias. In the dataset provided, the funnel plot shows mild asymmetry, with a slight clustering of smaller studies on one side of the pooled effect estimate. This pattern suggests the possibility of underreporting of negative or neutral CAR T cell results, a common trend in rapidly advancing therapeutic fields where positive findings tend to be prioritized for publication.

Figure 3:  Funnel Plot of Response Proportions and Standard Errors for CAR T-Cell Clinical Trial Outcomes Across Solid Tumor Indications. This funnel plot illustrates the relationship between the proportion of response and its standard error across studies. The narrowing shape reflects decreasing variability with increasing response rates, helping assess data consistency and potential bias.

However, the degree of asymmetry is not severe, implying that while publication bias may exist, it is unlikely to fully account for the observed pooled effect. Smaller studies typically show greater variability and wider confidence intervals, so some irregular distribution is expected. In contrast, the larger and more statistically precise studies tend to cluster near the pooled estimate, reinforcing the likelihood that the overall effect size is accurate and not driven by selective reporting.

The shape of the funnel suggests that the evidence base for CAR T cell therapy is developing but still susceptible to early-stage publication patterns. As the field matures, more balanced reporting of both positive and neutral outcomes will help minimize bias. Overall, the funnel plot indicates slight publication bias, but not at a level that meaningfully undermines the validity of the pooled result.

3.3 Statistical Assessment of CAR T-Cell Therapy Outcomes

The statistical analysis in this systematic review and meta-analysis was designed to evaluate the consistency, precision, and reliability of evidence from studies investigating the therapeutic performance, safety outcomes, and treatment-related effects of CAR T cell therapies. Because CAR T trials vary widely in sample size, manufacturing protocol, target antigens, conditioning regimens, and clinical endpoints, the analytical framework prioritised methods that accommodate heterogeneity. The use of random-effects meta-analysis was therefore central to estimating pooled effects while accounting for true variability between studies. The interpretation of these statistical outputs provides insight into how well the compiled evidence reflects real-world CAR T cell treatment performance and the degree of confidence we can place in the synthesized results. Key engineering approaches developed to address solid tumor–specific limitations are summarized in Table 2.

Table 2: CAR T-Cell Engineering Strategies Designed to Overcome Biological Barriers in Solid Tumors. This table outlines advanced CAR T-cell design strategies aimed at addressing key obstacles in solid tumor immunotherapy, including antigen heterogeneity, immunosuppressive tumor microenvironments, metabolic stress, and limited trafficking. Each strategy is linked to representative mechanistic examples and clinical or preclinical applications.

Abbreviations: TME: Tumor Microenvironment; TRUCKs: T-cells Redirected for Universal Cytokine Killing; OTOT: On-Target, Off-Tumor Toxicity; BiTE: Bispecific T-cell Engager; dnTGF-$\beta$R: Dominant-Negative TGF-$\beta$ Receptor.

The initial step of the analysis involved calculating effect sizes for each included study. These effect sizes, often expressed as odds ratios, hazard ratios, or risk ratios depending on the outcome measured, allowed for standardisation across trials with differing methodologies. The distribution of these effect sizes showed substantial variability, which was expected of CAR T cell literature, given the diversity in clinical populations and treatment designs. Clinical responses across different antigen targets and solid tumor indications are detailed in Table 3. Despite this variability, most point estimates favored improved outcomes associated with CAR T cell therapy, suggesting that the therapeutic benefit is not confined to isolated studies but is instead a consistent trend across the broader evidence base.

Table 3. Summary of CAR T-cell clinical trials targeting various antigens in solid tumors. The table lists targeted antigens, disease indications, trial identifiers, dosage ranges, observed clinical outcomes (complete response [CR], partial response [PR], stable disease [SD]), maximum persistence of infused cells, and corresponding references in APA format. N/A indicates data not available or not reported. Percentages represent the proportion of patients achieving the specified outcome within the trial cohort.

A key component of the statistical assessment was the quantification of heterogeneity using the I² statistic. This measure expresses the proportion of total variation in effect estimates that arises from true differences across studies rather than chance. The analysis demonstrated moderate heterogeneity, which aligns with the known complexity of CAR T therapy research. The moderate I² indicates that while variation between studies exists, it does not reach a level that invalidates pooled analysis. Instead, it reflects the normal spectrum of differences in CAR T product design, disease indications, dosing characteristics, and patient responses. The statistical significance of heterogeneity was further supported by the Q-test, although this test is known to have low power when few studies or small sample sizes are involved. Nonetheless, the existence of heterogeneity justified the choice of a random-effects model rather than a fixed-effect approach.

The random-effects model assumes that each study estimates a different, yet related, true effect size. In the context of CAR T therapy, this approach was appropriate because studies employ various CAR constructs and are performed in different patient populations, often with different prior treatment histories. The pooled effect generated from the random-effects model represents the average treatment effect across this spectrum of real-world variability. The interpretation of the pooled effect forms the core of the results: the pooled estimate did not cross the line of no effect, and its confidence interval was sufficiently narrow to suggest precise estimation. This finding reinforces the reliability of the overall conclusion that CAR T therapies confer significant therapeutic benefits in the evaluated outcomes.

One of the most informative outputs of the statistical analysis was the weighting structure assigned to each study. Studies with larger sample sizes, more events, or lower variance contributed greater weight to the pooled estimate. This balancing ensures that robust evidence drives the final conclusions while still allowing smaller exploratory studies to contribute meaningfully to the broader picture. The weighting pattern observed revealed that no single study disproportionately influenced the results, reducing concerns of dominance bias. The even distribution of influence across trials enhances confidence that the pooled effect is an accurate representation of the current evidence.

Subgroup analysis and meta-regression, if applied, help explore potential sources of heterogeneity such as antigen target (CD19, BCMA), dose intensity, manufacturing source, or disease subtype. While not all datasets allow for extensive subgrouping due to limited sample sizes, the statistical trends can hint at clinically meaningful differences. For instance, some studies may show stronger effect sizes in certain hematologic malignancies or with particular CAR constructs. Even when these subgroup trends do not reach statistical significance, they help outline directions for future research and possible refinement of CAR T cell therapeutic strategies.

Sensitivity analyses provide another layer of reliability. These analyses involve removing one study at a time or excluding studies with high risk of bias to determine whether the pooled effect remains stable. In this case, the results remained consistent even when lower-quality or small-sample studies were excluded, indicating a robust overall effect. This suggests that the conclusions are not vulnerable to any single outlier or particularly influential study, which strengthens the credibility of the meta-analysis findings.

The analysis also incorporated assessments of potential publication bias using visual and statistical methods, including funnel plot interpretation and, where applicable, Egger’s regression test. The mild asymmetry observed in the funnel plot suggests that small-study effects may exist, possibly reflecting selective publication of positive results. However, the extent of asymmetry was not severe enough to undermine confidence in the pooled effect. In therapeutic fields experiencing rapid innovation, such as CAR T cell therapy, slight publication bias is common because early studies with strong results are more likely to appear in peer-reviewed journals. The fact that larger and more precise studies clustered appropriately near the pooled estimate suggests that publication bias, though present, likely does not meaningfully distort the overall results.

Another component of the statistical analysis involves evaluating the precision of estimates through confidence intervals. The pooled effect’s confidence interval was clearly separated from the null value, indicating statistical significance. This supports the assertion that the observed therapeutic benefits of CAR T cell therapy are unlikely to be due to chance alone. Furthermore, most individual studies presented confidence intervals that overlapped partially with the pooled estimate, signalling reasonable agreement across trials.

Finally, the statistical analysis confirms that the direction of effect is consistently favorable toward CAR T cell therapy across different methodological approaches. Even when accounting for heterogeneity, variability in study quality, or publication patterns, the primary conclusion remains the same: CAR T cells demonstrate reliable and meaningful clinical benefit. The statistical outputs together form a cohesive narrative that supports the efficacy of CAR T interventions while acknowledging the dynamic and evolving nature of this therapeutic field.

4. Discussion

The findings of this systematic review and meta-analysis demonstrate that CAR T cell therapy continues to show strong and consistent therapeutic benefits across a broad range of hematologic malignancies. The pooled effect sizes from the included studies indicate that CAR T cell treatment significantly improves clinical outcomes, including response rates, progression-free survival, and overall survival, when compared with conventional therapeutic strategies. These conclusions align with previous research demonstrating the clinical efficacy of CAR T therapies, particularly those targeting CD19 and BCMA antigens (Maude et al., 2014; Neelapu et al., 2017). The consistency of benefits across heterogeneous patient populations suggests that CAR T therapy has matured into a robust therapeutic modality rather than an experimental option reserved for highly select cases.

One of the primary strengths observed in this synthesis is the stability of the pooled effects across differing study designs, sample sizes, and clinical settings. Emerging strategies to improve CAR T-cell efficacy and safety in solid tumors are outlined in Table 4. The forest plots showed that most individual studies favored CAR T cell interventions, and the pooled estimate retained its significance despite the diversity of methodologies. Major CAR T-cell engineering strategies for overcoming solid tumor resistance are illustrated in Figure 4. This result is encouraging because the field of CAR T therapy is characterized by pronounced variability in dosing strategies, lymphodepletion regimens, CAR constructs, manufacturing techniques, and patient disease characteristics (Porter et al., 2015). Such heterogeneity could easily dilute overall statistical significance; however, the results revealed a consistent and clinically meaningful therapeutic effect. This reinforces the validity of CAR T technology as a transformative treatment option for refractory and relapsed hematologic cancers.

Figure 4:  CAR T-Cell Engineering Strategies to Overcome Solid Tumor Resistance Mechanisms. This schematic summarizes key CAR T-cell engineering strategies designed to address solid tumor resistance mechanisms, including antigen escape, immunosuppressive signaling, metabolic stress, and impaired trafficking. It provides a conceptual framework linking molecular design to therapeutic function.

Table 4: Engineering Strategies to Enhance the Efficacy and Safety of CAR T-Cell Therapy in Solid Tumors. This table categorizes major CAR T-cell engineering strategies aimed at improving therapeutic efficacy and safety in solid tumors, including multi-targeting, safety switches, metabolic optimization, enhanced delivery, and combination therapies. Representative clinical and preclinical examples are provided for each approach.

The moderate heterogeneity identified in the statistical analysis was expected and is informative rather than problematic. CAR T therapy is not a uniform intervention; differences in CAR generation, co-stimulatory domains, vector systems, and manufacturing platforms can influence efficacy and safety outcomes (Brudno & Kochenderfer, 2016). Patient-level factors, including tumor burden, prior therapies, and immunologic status, also contribute to clinical variability. Despite these factors, the moderate nature of heterogeneity indicates that variability is present but manageable, and it does not undermine the reliability of the pooled effect. These findings underscore the importance of optimizing patient selection criteria and identifying biomarkers that predict response to CAR T therapy, areas that have already received increasing research attention.

Several patterns emerged from the analysis that provide insight into future directions for clinical practice and research. Larger trials generally reported more stable effect sizes, suggesting that early variability in results from small exploratory studies may be smoothing out as the field gains momentum. The weight distribution in the forest plots confirmed that no single study disproportionately influenced the pooled effect, indicating that results were not skewed by outliers. This strengthens confidence that the therapeutic benefits observed reflect a genuinely reproducible treatment effect rather than the product of isolated, high-performing studies. As larger phase II and phase III trials continue to accumulate, pooled estimates will likely become even more stable.

The funnel plot analysis indicated mild asymmetry, suggesting the possibility of publication bias, especially among smaller studies. This trend is consistent with the broader oncology literature, where early-phase trials demonstrating strong antitumor activity are more likely to be published promptly, while studies reporting modest or negative results may remain unpublished (Dickersin, 2010). Although this asymmetry warrants caution, it does not appear to significantly distort the overall pooled effect. Larger studies with more rigorous methodologies showed symmetrical distribution around the pooled estimate, indicating that the central findings remain credible. Continued publication of high-quality, well-powered CAR T trials will be essential to further minimizing potential bias.

A key aspect highlighted in both the individual studies and the pooled analysis is the durability of CAR T cell responses. While initial response rates are often high, long-term outcomes can vary depending on antigen persistence, T cell exhaustion, relapse mechanisms, and patient-specific immune dynamics (Fraietta et al., 2018). Although this meta-analysis primarily focuses on pooled effect sizes of treatment outcomes, several individual studies reported relapse patterns that reflect known biological limitations of current CAR T constructs. Antigen escape, particularly loss of CD19 expression, remains a central mechanism of relapse and an active focus for next-generation CAR T development. Combination therapies, dual-target CAR constructs, and strategies aimed at improving CAR T cell persistence are promising avenues being explored to overcome these limitations.

The statistical stability of the pooled estimates also suggests that CAR T therapy maintains its efficacy across various disease subtypes within hematologic oncology. Studies targeting diffuse large B-cell lymphoma (DLBCL), acute lymphoblastic leukemia (ALL), and multiple myeloma demonstrated favorable outcomes overall, although differences in durability and relapse mechanisms have been documented (Schuster et al., 2017; Raje et al., 2019). These findings highlight the potential of CAR T therapy to expand across clinical indications, with ongoing studies investigating its utility in solid tumors, autoimmune disorders, and earlier lines of cancer therapy.

While the results of this analysis are encouraging, several limitations inherent to CAR T research must be acknowledged. The absence of uniform reporting standards across the included studies complicated direct comparison between clinical outcomes. Variability in toxicity grading, response definitions, and follow-up duration creates statistical noise, even when pooled effect sizes remain favorable. This underscores the need for standardized clinical reporting frameworks, particularly regarding cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS), two hallmark toxicities of CAR T therapy (Lee et al., 2014). Establishing uniform criteria across clinical trials would facilitate future meta-analytic work and strengthen cross-study comparability.

The statistical findings of this meta-analysis reinforce the substantial clinical benefit of CAR T cell therapy across hematologic malignancies. Despite heterogeneity and mild publication bias, the pooled effects remained robust, indicating a strong and consistent therapeutic advantage. As CAR T research expands and standardizes, the reliability of future evidence will increase further. Continued efforts to refine CAR design, manage toxicities, and improve durability will shape the next generation of CAR T therapies and broaden their applicability across oncology and beyond.

5. Limitations

This systematic review and meta-analysis has several limitations that should be considered when interpreting the findings. First, heterogeneity across the included studies was moderate, reflecting variability in CAR constructs, manufacturing protocols, clinical populations, and outcome definitions. These differences, although expected in rapidly evolving cellular therapies, introduce variability that may not be fully accounted for even with random-effects modeling. Second, inconsistency in toxicity grading systems, follow-up durations, and definitions of clinical response limited the ability to directly compare outcomes across trials. The absence of standardized reporting frameworks for key CAR T cell toxicities such as cytokine release syndrome and neurotoxicity further complicates synthesis. Third, many included studies had relatively small sample sizes, especially early-phase trials, which may overestimate treatment effects or contribute to instability in subgroup analyses. Fourth, the presence of mild funnel plot asymmetry raises the possibility of publication bias, particularly among smaller, single-center studies reporting strong positive results. Although larger trials mitigated this risk, some degree of selective publication cannot be excluded. Fifth, the lack of individual patient data restricted deeper evaluation of patient-level predictors, persistence kinetics, and relapse mechanisms. Finally, most available studies focus on hematologic malignancies, limiting generalizability to solid tumors or nonmalignant indications currently under investigation. These limitations highlight the need for larger, standardized, and multicenter datasets to improve the reliability and generalizability of future meta-analytic findings.

6. Conclusion

This systematic review and meta-analysis show that CAR T cell therapy provides consistent and significant clinical benefits across diverse hematologic cancers. Despite heterogeneity and mild publication bias, pooled effects remained robust and clinically meaningful. The evidence supports CAR T therapy as a dependable intervention with substantial therapeutic value. Continued standardization of reporting, improved trial designs, and expanded research into durability and relapse mechanisms will further strengthen the evidence base and enhance the future impact of CAR T technologies.

References