Impact of different TP53 mutation subtypes on response and outcome in advanced lung adenocarcinoma receiving first-line immunotherapy or chemo-immunotherapy

Introduction

Lung cancer remains the leading cause of cancer-related mortality worldwide (1), with non-small cell lung cancer (NSCLC) accounting for approximately 80–85% of all cases (2-4). Among NSCLC cases, adenocarcinoma is the most common subtype (50–60%) (4). More than half of patients are diagnosed at an advanced or metastatic stage, rendering them ineligible for curative surgery (4,5). For patients without targetable driver mutations (e.g., EGFR, ALK, or ROS1), immune checkpoint inhibitors (ICIs), either as a monotherapy or in combination with chemotherapy, have become the standard first-line therapy (5-7). However, only a minority of patients derive clinical benefit from ICIs (8,9). While several biomarkers—including programmed cell death ligand 1 (PD-L1) expression, tumor mutation burden (TMB), microsatellite instability (MSI), and tumor-infiltrating lymphocytes (TILs)—are currently utilized to predict the efficacy of immunotherapy, their predictive accuracy remains limited by intratumoral heterogeneity and assay variability (8,10). Consequently, identifying more robust and precise molecular predictors continues to be a major clinical challenge in the management of advanced NSCLC.

TP53 is the most frequently mutated tumor suppressor gene across human cancers (11,12) and is altered in approximately 60% of NSCLC cases (13,14). TP53 mutations are highly heterogeneous, encompassing missense, nonsense, frameshift, splice-site, and in-frame mutations (15,16), with missense variants predominating and frequently affecting the DNA-binding domain (12,17). Notably, approximately 30% of all TP53 mutations cluster at specific ‘hotspot’ residues, including R175H, R248Q/W, R273H/C, R282W, G245S, R249S, Y220C, and V157F (11,18-20). Given the high frequency of alterations at certain positions, codons 175, 245, 248, 249, 273, and 282 are recognized as key hotspot codons (11,20,21).

Despite extensive investigation into the association between TP53 mutation status and tumor prognosis, the intrinsic complexity of TP53 alterations in NSCLC has often been underestimated. In contrast to oncogenic drivers like KRAS and EGFR, whose mutations are concentrated in a few recurrent variants (6,22), the mutational landscape of TP53 is highly dispersed across multiple codons (Figure S1). Consequently, most retrospective studies in the context of NSCLC have assessed TP53 status as mutant vs. wild-type (23-28), potentially overlooking subtype-specific prognostic effects. Although some investigators have further stratified TP53 alterations into disruptive vs. non-disruptive (16,29,30) or truncating vs. non-truncating mutations (16,30), comprehensive analyses at the levels of TP53 mutation subtypes, individual codons, and recurrent hotspot variants remain limited.

Emerging evidence suggests that TP53 mutations may influence the tumor immune microenvironment and response to immunotherapy. TP53-mutant lung adenocarcinoma (LUAD) has been associated with increased PD-L1 expression, elevated TMB, and enhanced immune infiltration (23,31,32). Moreover, differential immunotherapy responses have been reported among distinct TP53 mutation categories (14,25,31), indicating that not all TP53 alterations exert equivalent biological or clinical effects.

Nevertheless, the impact of specific TP53 mutation subtypes on the efficacy of first-line immunotherapy in LUAD remains poorly defined. Therefore, the present study was designed to investigate the association between TP53 mutation subtypes and clinical outcomes in patients with locally advanced or metastatic LUAD receiving first-line immunotherapy or chemo-immunotherapy, excluding tumors harboring targetable driver alterations such as EGFR, ALK, or ROS1. We present this article in accordance with the REMARK reporting checklist (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-0824/rc).

Methods

Patients

This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This research was approved by the Ethics Committee of Shanghai Chest Hospital (No. KS1973), and written informed consent was obtained from all patients. In this retrospective cohort study, we enrolled 129 patients who were diagnosed with locally advanced or metastatic LUAD and underwent first-line immunotherapy or chemo-immunotherapy between January 2018 and November 2022 at Shanghai Chest Hospital (Figure S2). Clinical follow-up ended on December 31, 2023. The inclusion criteria were as follows:

• A clinical and pathological diagnosis of LUAD (stage IIIB–IV) based on the 8th edition of the tumor-node-metastasis (TNM) staging system (33).
• Confirmed TP53 mutation by next-generation sequencing (NGS), without the presence of sensitizing epidermal growth factor receptor (EGFR) mutations, anaplastic lymphoma kinase (ALK) rearrangements, or ROS proto-oncogene 1 (ROS1) rearrangements.
• Receiving first-line immunotherapy or chemo-immunotherapy: ICI monotherapy or ICI combined with chemotherapy.

Patients harboring two or more different TP53 mutation subtypes were excluded.

Clinicopathological variables

Clinicopathologic variables in our cohort included age at diagnosis, gender, smoking status, Eastern Cooperative Oncology Group (ECOG) performance status (PS), PD-L1 expression, and brain metastasis. PD-L1 expression was presented as Tumor Proportion Score (TPS) using the PD-L1 IHC 22C3 pharmDx kit. Tumor burden was monitored and treatment responses were assessed throughout the immunotherapy or chemo-immunotherapy course in accordance with the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, categorized as complete response (CR), partial response (PR), stable disease (SD), or progressive disease (PD) (34).

Targeted NGS

Tumor tissue specimens obtained from diagnostic biopsies were formalin-fixed and paraffin-embedded (FFPE) according to standard clinical practice. DNA was extracted from archived FFPE samples and subjected to NGS for genomic profiling. No separate biological control samples were included in this retrospective study. DNA was extracted using the QIAamp DNA FFPE tissue kit (Qiagen, Hilden, Germany). DNA fragments within 200–400 base pairs (bp) size were selected with a bead (Agencourt AMPure XP Kit; Beckman Coulter, Brea, CA, USA), followed by hybridization with capture probe baits. Then the hybrid was selected with magnetic beads and amplified by polymerase chain reaction (PCR). The concentration and quality of DNA fragments were assessed using Qubit 2.0 Fluorimeter with the dsDNA high-sensitivity assay kit (Life Technologies, Carlsbad, CA, USA). Indexed samples were sequenced on the Nextseq500 sequencer (Illumina, Inc., CA, USA) with paired-end reads and a target sequencing depth of 1,000×. Genomic profiling was performed using a panel consisting of 68 lung cancer-related genes (Table S1) spanning 245 kilobases of the human genome (Lung Core, Burning Rock Biotech, Guangzhou, China).

Sequence data analysis

Sequence data were mapped to the reference human genome (hg19) using Burrows-Wheeler Aligner (v.0.7.10, Heng Li, Cambridge, UK). Local alignment optimization, variant calling, and annotation were performed by Genome Analysis Toolkit (v.3.2, Aaron McKenna, MA, USA) and VarScan (v.2, Daniel C Koboldt, MO, USA). Variants with a depth of less than 100 were filtered out using the VarScan. A minimum of five supporting reads were needed for INDELs and eight supporting reads were needed for SNV calling. Single nucleotide polymorphisms (SNPs), defined as variants with population frequency over 0. 1% in ExAC, 1000 Genomes, database single nucleotide polymorphism (dbSNP), or ESP6500SI-V2 database, were excluded from further analysis. The remaining variants were annotated with ANNOVAR (v.3, Kai Wang, PA, USA) and SnpEff (v3.6, Pablo Cingolani, MI, USA). DNA translocation analysis was performed using Tophat2 (Daehwan Kim, MD, USA) and Factera (v.1.4.3, Aaron M. Newman, CA, USA).

TP53 mutation subtype classification

In this study, two complementary classification systems were applied to define TP53 mutation subtypes. First, TP53 mutations were classified into four categories according to their functional annotation: missense, nonsense, frameshift, and other mutations. Variants annotated as stop-gain mutations were categorized as nonsense mutations. The “other” category consisted of splice-site mutations and in-frame insertion/deletion variants. Second, TP53 mutations were further categorized as hotspot vs. non-hotspot mutations. Hotspot codons were defined as codons 175, 245, 248, 249, 273, and 282, and hotspot variants included R175H, R248Q, R248W, R273H, R273C, R282W, G245S, R249S, Y220C, and V157F, whereas all remaining TP53 mutations were classified as non-hotspot mutations.

Statistical analysis

Data processing and statistical analysis were conducted using IBM SPSS Statistics (Windows, version 26.0), GraphPad Prism version 9.0, and R software (Windows, version 4.3.2). Categorical variables across groups were compared using the Chi-squared (χ²) test or Fisher’s exact test. The endpoints were progression-free survival (PFS) and overall survival (OS). PFS was defined as the time from treatment initiation to disease progression or death from any cause. Patients alive without disease progression on the last follow-up date (December 31, 2023) were censored. OS was defined as the period of time between the onset of treatment and any cause of death. Patients alive on the last follow-up date (December 31, 2023) were censored. The median follow-up time was calculated by the reverse Kaplan-Meier method. The Kaplan-Meier method was used to depict the survival analysis of PFS and OS across groups, and the log-rank test was used to compare the results. Univariate and multivariate Cox proportional hazards models were employed to identify potential survival predictors. Independent variables with a P<0.05 in the univariate models were enrolled in the multivariate models.

Results

Clinicopathological characteristics of patients and molecular analysis (Table 1, Figure 1)

A total of 129 locally advanced or metastatic LUAD (stage IIIB–IV) patients who received first-line immunotherapy or chemo-immunotherapy between January 2018 and November 2022 at Shanghai Chest Hospital, who carried TP53 mutations validated by NGS, were enrolled in this study. The baseline clinicopathological characteristics of these patients are summarized in Table 1. Patients were stratified by TP53 mutation subtypes (missense, nonsense, frameshift, and others), and no significant differences in baseline characteristics were observed among the four groups. Of all patients, the median age at diagnosis was 64 years (range from 37 to 83 years). Most patients were males (104/129, 80.6%), smokers (69/129, 53.5%), without brain metastasis at diagnosis (97/129, 75.2%), and had an ECOG PS ≥1 (71/129, 55.0%). PD-L1 expression was categorized into three groups based on TPS: <1% (36/129, 27.9%), 1–49% (43/129, 33.3%), ≥50% (41/129, 31.8%), and PD-L1 expression was not detected in 9 patients (9/129, 7.0%). Most patients (123/129, 95.3%) received first-line ICI combined with chemotherapy, while the remaining 6 patients (6/129, 4.7%) received first-line ICI monotherapy. Molecular profiles of all patients are shown in Figure 1A. As for TP53 mutation subtypes (Figure 1C), the majority of patients carried missense mutations (78/129, 60.5%), followed by frameshift (23/129, 17.8%), nonsense (18/129, 14.0%), and other mutations (10/129, 7.8%). The other mutation category consisted of 6 splice-site mutations and 4 in-frame insertion/deletion variants. For “hotspot” categories (Figure 1B,1D,1E), most were non-hotspot mutations: hotspot codons (14/129, 10.9%) and hotspot variants (6/129, 4.7%) vs. non-hotspot codons (115/129, 89.1%) and non-hotspot variants (123/129, 95.3%).

Figure 1 The genomic landscape of 129 LUAD patients in our cohort. (A) Mutational spectrum of 129 LUAD patients stratified by TP53 mutation subtypes (missense, nonsense, frameshift, and others). Other stratification factors include age, gender, smoking status, PD-L1 expression, and clinical stage. (B) Distribution of all TP53 mutations along the TP53 gene. (C) The proportion of four TP53 mutation subtypes in this cohort. (D,E) Proportions of missense mutations occurring at codon 158 and six hotspot codons in this cohort. CN_amp, copy number amplification;CN_del, copy number deletion;LGR, large genomic rearrangement; LUAD, lung adenocarcinoma; PD-L1, programmed cell death ligand-1; TAD, transactivation domain

TP53 frameshift mutations were associated with shorter PFS and OS

The median follow-up duration was 26.9 months (range from 25.5 to 28.4 months). At the data cutoff, 100 patients (100/129, 77.5%) had experienced disease progression, and 54 patients (54/129, 41.9%) had reached the OS endpoint. For the entire cohort, the median PFS (mPFS) was 11.5 months [95% confidence interval (CI): 9.2–13.8; Figure 2A], and the median OS (mOS) was 36.8 months (95% CI: 31.6–41.9; Figure 2B). When categorizing TP53 mutation subtypes as missense, nonsense, frameshift, and other mutations, the mPFS was 11.5 months (95% CI: 8.8–14.2) for missense, 16.5 months (95% CI: 12.8–20.1) for nonsense, 6.0 months (95% CI: 2.6–9.4) for frameshift, and 16.3 months (95% CI: 12.2–20.4) for other mutations (Figure 2C). The mOS was 36.8 months (95% CI: 30.2–43.4) in the missense group, 13.3 months (95% CI: 9.9–16.7) in the frameshift group, 35.2 months [95% CI: not available (NA)] in others, while the nonsense group had not yet reached the mOS (Figure 2D). Notably, patients with frameshift mutations exhibited the poorest survival outcomes among the four TP53 mutation categories. Because the “other mutations” subgroup was small and biologically heterogeneous (n=10), we performed a sensitivity analysis excluding these patients. The survival analysis was repeated among patients with TP53 missense, nonsense, and frameshift mutations. The frameshift subgroup continued to exhibit the least favorable survival outcomes, indicating that the observed association between TP53 frameshift mutations and poor prognosis was not driven by the heterogeneous “other mutations” subgroup (Figure S3). To investigate the impact of TP53 frameshift mutations on the efficacy of first-line immunotherapy or chemo-immunotherapy, we further categorized TP53 mutations into frameshift group and non-frameshift group. Baseline clinicopathological characteristics were comparable between the two groups as well (Table S2). We observed that frameshift mutations significantly shortened PFS [mPFS, 6.0 vs. 13.5 months; hazard ratio (HR) =2.128; 95% CI: 1.309–3.458; P=0.002] and OS (mOS, 13.3 vs. 39.9 months; HR =2.793; 95% CI: 1.564–4.989; P<0.001) (Figure 3A,3B). To further exclude the potential confounding effect of nonsense mutations—which showed a favorable survival trend—we performed a sensitivity analysis directly comparing the frameshift group (n=23) with the missense group (n=78). This analysis confirmed that patients with TP53 frameshift mutations still experienced significantly shorter PFS (mPFS, 6.0 vs. 11.5 months; HR =1.843; 95% CI: 1.117–3.040; P=0.02) and OS (mOS, 13.3 vs. 36.8 months; HR =2.568; 95% CI: 1.401–4.708; P=0.002) compared to patients with missense mutations (Figure S4), reinforcing the specific adverse prognostic impact of the frameshift subtype.

Figure 2 Kaplan-Meier curves for PFS and OS among our cohort. (A) PFS of all 129 LUAD patients carrying TP53 mutations. (B) OS of all 129 LUAD patients carrying TP53 mutations. (C) Kaplan-Meier estimates of PFS among four TP53 mutation subtypes. (D) Kaplan-Meier estimates of OS among four TP53 mutation subtypes. CI, confidence interval; LUAD, lung adenocarcinoma; NA, not available; OS, overall survival; PFS, progression-free survival.

Figure 3 Impact of TP53 frameshift mutations on the first-line immunotherapy or chemo-immunotherapy in locally advanced or metastatic LUAD in our cohort. (A) PFS of TP53 frameshift mutation group vs. non-frameshift mutation group. (B) OS of TP53 frameshift mutation group vs. non-frameshift mutation group. (C,D) Distribution of best treatment response in TP53 frameshift mutation group and non-frameshift mutation group. CI, confidence interval; HR, hazard ratio; LUAD, lung adenocarcinoma; OS, overall survival; PD, disease progression; PFS, progression-free survival; PR, partial response; SD, stable disease.

The PFS and OS were also analyzed between the missense and non-missense groups (Figure S5A,S5B) and the nonsense and non-nonsense groups (Figure S5C,S5D). However, neither missense nor nonsense mutations were associated with improved PFS or OS. Taking into account that nonsense and frameshift mutations were combined as truncating mutations in earlier studies (16,30), the PFS and OS were also compared between truncating and non-truncating mutation groups; however, no differences were observed (Figure S5E,S5F). When categorizing TP53 mutations as hotspot and non-hotspot mutations, no survival differences were observed between the hotspot codon/variant group and the non-hotspot codon/variant group (Figure S6A-S6D). Among individual TP53 variants, R158L was the most frequent in our cohort (7/129, 5.4%), but it was not significantly associated with PFS or OS (Figure S6E,S6F). The univariate analysis of different TP53 mutation subtypes showed that only frameshift mutations exhibited prognostic value (Figure S7).

Next, we compared the best overall response between frameshift and non-frameshift mutation groups based on the RECIST v1.1 criteria (34). Patients carrying frameshift mutations showed worse treatment response, with 21.7% achieving PR, 47.8% having SD, and 30.4% experiencing PD. Only 7.6% of patients in the non-frameshift mutation group experienced PD as their best overall response, which was lower than the 30.4% observed in the frameshift group (P=0.006). The percentages of patients reaching PR and SD were comparable between the two groups. In the non-frameshift group, 38.7% of patients achieved PR, and 53.8% exhibited SD (Figure 3C,3D).

Distribution of key co-occurring mutations across TP53 mutation subtypes

To assess whether the adverse survival associated with TP53 frameshift mutations could be explained by imbalances in key co-occurring genomic alterations, we compared the distribution of recurrent alterations with a mutation frequency of >8% in the overall cohort between the TP53 frameshift and non-frameshift groups (Table 2). The frequency of STK11 mutations—a well-known marker of primary resistance to immunotherapy in LUAD (32,35-39)—was comparable between the frameshift and non-frameshift groups (P>0.99). Among all sequenced genes, KRAS was the only gene that exhibited a significant difference in mutation frequency (P=0.047), with KRAS mutations being more prevalent in the non-frameshift group. Building on these distribution findings and given the established roles of KRAS (14,23) and STK11 in modulating LUAD biology and immunotherapy response, both variables were subsequently included in the multivariable Cox regression model to account for potential confounding effects. Additionally, the mutation frequencies of all genes included in the sequencing panel across the TP53 frameshift and non-frameshift groups are provided in Table S3. Notably, KEAP1 was not included in the sequencing panel used in this study, and its potential impact on the association between TP53 frameshift mutations and clinical outcomes could therefore not be assessed.

Univariate and multivariate analyses of clinicopathologic and molecular variables associated with survival outcomes

Univariate Cox regression analysis identified TP53 frameshift mutations as significantly associated with shorter PFS, whereas PR as the best overall response was significantly associated with prolonged PFS. For OS, ECOG PS ≥1 and TP53 frameshift mutations were significantly associated with worse outcomes (Table 3). KRAS and STK11 mutation status were not significantly associated with OS in univariate analysis. In the multivariate Cox proportional hazards model, after adjustment for clinicopathologic and molecular covariates, including KRAS and STK11 mutation status, TP53 frameshift mutations remained independently associated with both shorter PFS (HR =2.229, 95% CI: 1.337–3.715, P=0.002) and OS (HR =3.278, 95% CI: 1.730–6.210, P<0.001). KRAS mutations were also independently associated with worse OS (HR =1.946, 95% CI: 1.055–3.588, P=0.03). PR as the best overall response was independently associated with prolonged PFS (HR =0.565, 95% CI: 0.365–0.876, P=0.01). STK11 mutation status did not show significant association with either PFS or OS in this cohort. These findings suggest that the adverse survival observed in patients with TP53 frameshift mutations may not be entirely explained by KRAS or STK11 co-mutations, indicating a potential independent prognostic association of TP53 frameshift alterations in patients with advanced LUAD receiving first-line immunotherapy.

Discussion

ICIs, alone or in combination with chemotherapy, are the standard first-line therapy for advanced NSCLC without actionable driver mutations, yet patient responses remain highly variable. TP53, a well-established tumor suppressor gene (11), encodes p53, which regulates multiple cellular processes, such as proliferation, apoptosis, DNA repair, metabolism, and autophagy (12,15,17). TP53 alterations represent one of the most frequent molecular events in NSCLC, but their biological consequences may vary substantially according to mutation type. Although previous studies have highlighted the clinical relevance of TP53 mutations in NSCLC, most investigations have evaluated TP53 mutations as a single category or according to broad functional classifications, potentially overlooking the heterogeneity among individual mutation subtypes. Notably, Sun et al. reported differences in immunologic characteristics and clinical outcomes between TP53 missense and nonsense mutations in LUAD. However, the limited number of frameshift mutations precluded a separate analysis of this subgroup, leaving their clinical significance unclear (31).

In this study, we systematically characterized TP53 alterations at the subtype level in LUAD patients receiving first-line immunotherapy or chemo-immunotherapy after exclusion of actionable oncogenic drivers, including EGFR, ALK, and ROS1. Notably, patients harboring TP53 frameshift mutations exhibited the poorest PFS and OS among different TP53 mutation subtypes. This unfavorable association remained consistent after excluding rare and heterogeneous variants in the “other” subgroup and was further supported by the comparison between frameshift and non-frameshift mutations. Moreover, the trend toward a less favorable best overall response in patients with TP53 frameshift mutations further suggests that specific TP53 mutation subtypes may have distinct biological and clinical implications. To further examine the specific impact of TP53 frameshift mutations, we performed a sensitivity analysis comparing frameshift and missense subtypes, thereby minimizing potential confounding by nonsense mutations, which showed a favorable survival trend. Within this comparison, frameshift mutations remained significantly associated with shorter PFS and OS compared with missense mutations, further supporting their potential adverse clinical impact.

Interestingly, our results also indicate that conventional functional classifications may not fully capture the clinical heterogeneity of TP53 alterations. Frameshift and nonsense mutations are categorized as truncating alterations in some studies (16,30,40,41). However, although previous studies reported that TP53 truncating mutations were associated with poorer prognosis in localized NSCLC (16) and in a mixed-stage Brazilian LUAD cohort (30), combining nonsense and frameshift mutations into a single truncating group did not significantly stratify survival outcomes in our cohort. This discrepancy may be attributable to differences in study design and patient selection. Above two studies lacked detailed treatment information and did not exclude patients with actionable oncogenic driver alterations, whereas our cohort comprised patients with advanced LUAD receiving first-line immunotherapy or chemo-immunotherapy after excluding such alterations, including EGFR, ALK, ROS1. These differences suggest that grouping nonsense and frameshift mutations together may obscure potentially distinct prognostic effects, particularly the adverse prognostic impact associated with frameshift mutations. Our results therefore support a more refined, subtype-level characterization of TP53 alterations rather than relying solely on the conventional truncating vs. non-truncating classification.

The biological mechanisms underlying the unfavorable outcomes associated with TP53 frameshift mutations remain to be elucidated. Most TP53 frameshift alterations generate premature termination codons, triggering nonsense-mediated messenger RNA decay (NMD) and potentially resulting in a p53-null state characterized by loss of p53 protein expression (12,42-44), which may ultimately lead to complete loss of its tumor-suppressive function. This profound loss of tumor-suppressive function may contribute to the poorer clinical outcomes observed in patients with TP53 frameshift mutations in our cohort by promoting genomic instability, aggressive tumor behavior, and impaired antitumor immune surveillance (17,45). However, the functional consequences of TP53 frameshift alterations may extend beyond simple loss of function. Unlike missense mutations, which predominantly cluster within the DNA-binding domain, frameshift alterations are more broadly distributed across the gene (15,46), a pattern also observed in our cohort (Figure 1B). This broader positional distribution may contribute to greater functional heterogeneity. Indeed, one study reported that certain TP53 frameshift variants may promote chemoresistance and aggressive tumor growth, further underscoring the complex biological consequences of these alterations (47). Moreover, compensatory frameshifting indels have been reported to restore the reading frame and partially rescue protein functionality following an initial frameshift. Although not yet established for TP53, such mechanisms raise the possibility that some frameshift variants may generate altered proteins rather than simply truncated or nonfunctional products (48-50). These proteins may have distinct, yet incompletely characterized functions, potentially including gain-of-function (GOF) or dominant-negative (DN) effects (21), which could contribute to heterogeneity in treatment response and clinical outcomes. Accordingly, the inferior survival observed in our cohort may primarily reflect an NMD-induced p53-null status, with GOF-like properties of selected frameshift variants may act as secondary modifier of tumor aggressiveness. Nevertheless, our mechanistic interpretations remain speculative in the absence of transcriptomic, protein-level, or functional validation. Future studies integrating RNA sequencing, p53 protein assessment, and functional assays are warranted to further substantiate these hypotheses.

Beyond these tumor-intrinsic effects, the potential impact of TP53 frameshift mutations on tumor immunogenicity and the immune microenvironment warrants particular consideration, given the previously reported association between frameshift alterations and immunotherapy outcomes. Previous studies have suggested that frameshift alterations may have increased neoantigenic potential by generating novel peptide sequences through disruption of the reading frame and may be associated with immunotherapy outcomes (51,52). Chae et al. found (53) that a higher frameshift insertion/deletion burden was associated with increased CD4⁺ and CD8⁺ T-cell infiltration, higher TMB, and greater predicted neoantigen load in The Cancer Genome Atlas (TCGA) lung cancer cohorts; importantly, a higher frameshift burden was also associated with more favorable clinical outcomes in patients with NSCLC receiving immune checkpoint blockade. In a subsequent pan-cancer analysis, the same group further showed that a higher frameshift burden was associated with improved response to ICI in patients with low-TMB tumors (54). Notably, these studies evaluated frameshift alterations as an overall genomic feature and did not consider the functional context of individual mutated genes. In our study, TP53 frameshift mutations were associated with inferior outcomes in LUAD patients receiving first-line ICI alone or in combination with chemotherapy. Therefore, the favorable association observed for genome-wide frameshift burden may not necessarily apply to frameshift alterations affecting a critical tumor suppressor gene such as TP53. Moreover, this difference may reflect the distinct functional consequences of TP53 frameshift alterations, whereby the potential immunogenic benefit of frameshift-derived neoantigens may be counterbalanced by the adverse effects of complete p53 loss on tumor biology and antitumor immunity. In addition, the NSCLC cohort reported by Chae et al. included patients treated with ICI monotherapy across different lines of therapy, and exclusion based on actionable driver alterations was not specifically reported (53). In contrast, all patients in our cohort received first-line ICI either alone or in combination with chemotherapy after excluding tumors harboring actionable drivers such as EGFR, ALK, or ROS1, with the vast majority receiving chemo-immunotherapy. Therefore, the contribution of chemotherapy cannot be excluded, and whether p53 loss influences response to chemotherapy in this setting warrants further investigation.

Furthermore, the presence of a frameshift mutation alone may be insufficient to elicit an effective tumor-specific immune response. Kanemura et al. suggested that not all frameshift alterations necessarily give rise to potentially immunogenic neoantigens, and even when neoantigens are generated, their antitumor effects may further depend on effective antigen presentation and a permissive, inflamed tumor microenvironment (55). Consistent with this concept, durable clinical benefit from ICI therapy in their study was observed primarily in tumors characterized by both a high frameshift neoantigen load and an inflamed tumor microenvironment (55). These findings suggest that the genomic potential to generate neoantigens does not necessarily translate into effective antitumor immunity. In this context, our findings suggest that the potential immunogenicity conferred by frameshift alterations may not be sufficient to offset the adverse biological consequences of TP53 loss. The unfavorable outcomes observed in patients with TP53 frameshift mutations may therefore reflect the combined effects of impaired p53 tumor-suppressive function and ineffective antitumor immunity. These observations underscore the importance of evaluating frameshift alterations in a gene-specific and treatment-specific context rather than considering frameshift burden solely as a global genomic feature.

Accordingly, our findings should not be interpreted as establishing TP53 frameshift mutations as a specific biomarker of resistance to immune checkpoint blockade. Rather, they suggest that the clinical significance of frameshift alterations may vary according to the affected gene, its functional consequences, and the treatment setting. Future studies integrating gene-specific frameshift alterations with functional neoantigen generation, antigen-presentation capacity, immune microenvironment features, and treatment context are warranted to better define their clinical and immunologic significance.

Beyond the direct biological and immunologic effects of TP53 frameshift alterations, co-occurring genomic alterations may also influence clinical outcomes. In particular, STK11 and KEAP1 mutations have been associated with poor clinical response and therapeutic resistance to immunotherapy in LUAD (36,37,39,56-58). We therefore compared the distribution of recurrent co-mutations between the TP53 frameshift and non-frameshift groups, focusing on genes mutated in more than 8% of the overall cohort. Among these alterations, STK11 mutations were similarly distributed between the two groups, whereas KRAS mutations were significantly more frequent in the non-frameshift group. In univariate analyses, neither STK11 nor KRAS mutations were significantly associated with PFS or OS. After adjustment for STK11 and KRAS status in multivariable Cox models, TP53 frameshift mutations remained independently associated with shorter PFS and OS, suggesting that the unfavorable survival outcomes observed in the frameshift group cannot be fully explained by differences in these co-mutations.

The higher frequency of KRAS mutations in the non-frameshift group is nevertheless noteworthy. Previous studies have suggested that concurrent KRAS and TP53 mutations may be associated with distinct immune characteristics and clinical outcomes following immune checkpoint blockade compared with either alteration alone (14,23,32,59-61). Bischoff et al. further showed that (14) the clinical significance of KRAS/TP53 co-mutation may vary according to specific KRAS mutation subtypes, with KRAS G12C/TP53 co-mutations identifying a subgroup with particularly favorable long-term outcomes following ICI monotherapy, while TP53 alterations were still considered as a single mutational category. Our findings raise the complementary possibility that the clinical impact of KRAS/TP53 co-mutation may also vary according to the subtype of TP53 alteration. Although our sample size was insufficient for robust analyses of individual KRAS/TP53 subtype combinations, this observation further supports the importance of considering TP53 mutation heterogeneity when evaluating co-mutation patterns. Notably, KRAS mutation status showed a modest association with OS, but not PFS, in multivariate analysis, whereas STK11 mutations were not significantly associated with survival in our cohort. Given the predominance of chemo-immunotherapy and the limited sample size, these findings should be interpreted cautiously. Overall, our results support TP53 frameshift mutation as a distinct molecular subtype independently associated with unfavorable survival in LUAD patients receiving first-line ICI alone or in combination with chemotherapy. However, KEAP1 was not included in the targeted sequencing panel used in this study, and its potential contribution to the prognostic association of TP53 frameshift mutations warrants further investigation.

We further investigated whether specific TP53 hotspot mutations—which often exhibit distinct GOF properties or varying oncogenic potentials depending on the amino acid substitution and tumor type (62-66)—could influence immunotherapy outcomes. However, our analysis found no significant survival differences when comparing patients based on hotspot codons or specific amino acid substitutions. Notably, the frequency of TP53 mutations in lung cancer differs from the pan-cancer spectrum (67). In alignment with the International Agency for Research (IARC) TP53 database (R20, July 2019, https://TP53.isb-cgc.org), we identified R158 as a high-frequency residue in LUAD (Figure S1D), with R158L being the most prevalent variant in our cohort. Nevertheless, R158L did not correlate with distinct PFS or OS. These null findings, while possibly constrained by the limited sample size of individual hotspot variants, do not negate the potential significance of site-specific heterogeneity. Therefore, larger cohorts or multi-center cohorts are warranted in future studies to definitively determine the prognostic impact of specific TP53 hotspot mutations on immunotherapy outcomes.

In our cohort, the majority of patients (95.3%) received first-line immunotherapy combined with chemotherapy. Subgroup analysis within this combination treatment group confirmed that TP53 frameshift mutations continued to be associated with poorer PFS (Figure S8A,S8B) and OS (Figure S8C,S8D), suggesting that the adverse impact of the frameshift subtype persists even in the context of chemo-immunotherapy. However, the limited number of patients receiving ICI monotherapy in our cohort precluded a similar analysis, highlighting the need for future studies to evaluate the impact of TP53 mutation subtypes in the setting of first-line ICI monotherapy.

Several limitations of this study should be acknowledged. First, a TP53 wild-type cohort was not included. Given that the prognostic disparity between TP53-mutant and wild-type LUAD is well-established, our study deliberately focused on the intrinsic heterogeneity among TP53-mutant subtypes. Furthermore, achieving a rigorously matched wild-type control within a retrospective single-center framework was methodologically challenging and could have introduced significant selection bias. Future multi-center studies with larger, well-balanced cohorts are needed to further validate these findings against a wild-type baseline. Second, our targeted sequencing panel did not include KEAP1, a co-mutation associated with poor immunotherapy response, preventing assessment of its interaction with TP53 frameshift mutations. Third, most patients received first-line immunotherapy combined with chemotherapy, and the small number receiving ICI monotherapy precluded analysis of single-agent outcomes. Finally, the modest sample size of this single-center cohort may limit generalizability, highlighting the need for larger, multi-center studies.

From a clinical perspective, our findings may provide additional information for molecular risk stratification in TP53-mutant LUAD. Rather than considering TP53 alterations as a single mutational category, identification of frameshift mutations may help distinguish a subgroup of patients at increased risk of unfavorable survival during first-line ICI-based therapy. Because TP53 mutation type is readily available from routine targeted sequencing, these findings highlight the potential clinical value of retaining mutation-type information in genomic interpretation rather than simplifying TP53 alterations into a binary mutant vs. wild-type classification. Patients with TP53 frameshift mutations may warrant closer clinical surveillance and represent a population of interest for future trials evaluating alternative or intensified therapeutic strategies. Nevertheless, prospective validation is required before TP53 frameshift status can be used to guide treatment selection in clinical practice.

Conclusions

Our study highlights the clinical heterogeneity of TP53-mutated LUAD, showing that TP53 frameshift mutations—rather than pooled truncating alterations—may be associated with poorer outcomes in patients receiving first-line immunotherapy, predominantly chemo-immunotherapy. Our findings emphasize the necessity of refined molecular annotation for TP53 in clinical practice. Future large-scale studies should focus on granular mutation subtyping across diverse treatment settings to further validate these prognostic differences.

Acknowledgments

We would like to thank Pei Gu and Jing Wang from Burning Rock Biotech for their valuable suggestions and great support.

Footnote

Reporting Checklist: The authors have completed the REMARK reporting checklist. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-0824/rc

Data Sharing Statement: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-0824/dss

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-0824/prf

Funding: This work was supported by National Natural Science Foundation of China (Nos. 82030045, 82241227, and 82202924), National Multidisciplinary Treatment Project for Major Diseases (No. 2020 NMDTP), and Collaborative Innovation Center for Clinical and Translational Science by Ministry of Education & Shanghai (Nos. CCTS-202204 and CCTS-202304).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-0824/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This research was approved by the Ethics Committee of Shanghai Chest Hospital (No. KS1973), and written informed consent was obtained from all patients.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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