TP53 or CDKN2A/B covariation in ALK/RET/ROS1-rearranged NSCLC is associated with a high TMB, tumor immunosuppressive microenvironment and poor prognosis

ALK-rearranged lung adenocarcinomas with TP53 mutations have more unstable genomic features, poorer ALK-TKI efficacy and a worse prognosis than ALK-rearranged lung adenocarcinomas with wild-type TP53. Here, we examine the gene variations that co-occur with ALK/RET/ROS1 rearrangements in NSCLC and the corresponding tumor immune microenvironment, as well as their association with prognosis. A total of 155 patients with ALK/RET/ROS1 fusions were included retrospectively. Tumor genome mutation analysis was performed by next-generation sequencing. PD-L1 expression and tumor-infiltrating lymphocytes were assessed by multiplex immunohistochemistry. The correlations among gene covariation, the tumor immune microenvironment, and clinicopathological characteristics were analyzed. Among the 155 patients, concomitant TP53 mutation appeared most frequently (31%), followed by CDKN2A/B copy number loss (15%). The ALK/RET/ROS1 fusion and TP53 or CDKN2A/B covariation group had more males and patients with stage IV disease (p < 0.001, p = 0.0066). Patients with TP53 or CDKN2A/B co-occurrence had higher tumor mutation burdens and more neoantigens (p < 0.001, p = 0.0032). PD-L1 expression was higher in the tumor areas of the TP53 or CDKN2A/B co-occurring group (p = 0.00038). However, the levels of CD8+, CD8+PD1−, and CD8+PD-L1− TILs were lower in the tumor areas of this group (p = 0.043, p = 0.029, p = 0.025). In the TCGA NSCLC cohorts, the top 2 mutated genes were CDKN2A/B (24%) and TP53 (16%). The TP53 or CDKN2A/B co-occurring group had higher tumor mutation burdens and shorter OS (p < 0.001, p < 0.001). Patients with co-occurring TP53/CDKN2A/B variations and ALK/RET/ROS1 rearrangements are associated with high TMB, more neoantigens, an immunosuppressive microenvironment and a worse prognosis.


Background
With the development of next-generation sequencing technologies, ALK/RET/ROS1 rearrangements are being detected more frequently than before, and more clinically significant comutations have been identified in lung cancer. ALK-positive lung cancer accounts for approximately 3-7% of non-small cell lung cancer (NSCLC) cases (Caccese et al. 2016;Zhang et al. 2021). ROS1 and RET gene rearrangements account for approximately 1-2% of all NSCLC cases (Rikova et al. 2007; Gainor and Shaw 2013;Bergethon et al. 2012). Patients with ALK-rearranged NSCLC have similar clinical features to patients with ROS1-rearranged NSCLC; for example, they are often young and nonsmokers and have adenocarcinoma, which suggests that ALK and ROS1 rearrangements may have a similar pathogenesis (Rikova et al. 2007). ALK-specific tyrosine kinase inhibitors (TKIs) have consistently shown superior efficacy and tolerability in patients with ALK-positive lung cancer (Caccese et al. 2016;Zhang et al. 2021). However, the degree of benefit of ALK-TKIs varied among different ALK-positive patients. Although such heterogeneity lacks accepted reasons, genomic co-occurrence is thought to affect the efficacy of ALK-TKIs (Costa 2018). Several studies have demonstrated that the co-occurrence of TP53 mutation and ALK rearrangement is a poor prognostic factor, resulting in shorter progression-free survival (PFS) and overall survival (OS) (Shaw et al. 2014).
Some studies on gene fusions and comutations in lung cancer have analyzed the relationship between PD-L1 expression and comutation, trying to explore the poor prognosis of comutated patients from the perspective of the tumor immune microenvironment. PD-L1 is highly expressed in NSCLC with both gene fusion and mutation, and previous studies have attempted to explain this phenomenon. It could be that ALK upregulates PD-L1 via HIF-1α and/or STAT3-derived pADC or that ALK and mutant EGFR upregulate the expression by activating the PI3K-AKT and MEK-ERK signaling pathways in NSCLC (Koh et al. 2016;Ota et al. 2015). However, in NSCLC with cooccurring EGFR mutation and ALK rearrangement, tumors were rarely accompanied by CD8 + tumor-infiltrating lymphocyte (TIL) infiltration and PD-L1 expression. This may explain the low response rate to PD-1/PD-L1 inhibitors in this group of patients (Gainor et al. 2016). However, the sample size of these studies was small, and the types of TILs in the tumor microenvironment studied were limited to one or two, so a larger sample size and more types of TILs are needed for further study.
This study focuses on describing the co-occurring gene mutations and the corresponding TIL infiltration in ALK/ RET/ROS1-rearranged NSCLC to provide a clear subclassification and a complete picture of the overall tumor immunogenicity and immune microenvironment. The tumor immune microenvironment (TIME) was assessed by the levels of multiple markers, including PD-1, PD-L1, CD8, and CD68. A deeper exploration of co-occurring genes in NSCLC can strengthen the selection of patients for more precise treatment and guide the development of new treatment strategies.

Patients and samples
A total of 155 tumor tissue samples from NSCLC patients harboring ALK/RET/ROS1 rearrangements were retrospectively collected from Zhongnan Hospital of Wuhan University and Guiqian International General Hospital, all of which were either surgical specimens or puncture specimens at the time of primary treatment. Paired samples were taken from the same patient. Tumor tissue was collected along with para-carcinoma tissue or blood samples as a control. The study design was approved by the Ethics Committees of Zhongnan Hospital of Wuhan University and Guiqian International General Hospital. The validation cohort contained 340 samples with both RNA transcriptome and DNA mutation data, which were selected from the lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) categories in The Cancer Genome Atlas (TCGA, https:// portal. gdc. cancer. gov/).

DNA extraction and capture-based targeted DNA sequencing
DNA was extracted from 5 to 10 µm formalin-fixed paraffin-embedded (FFPE) sections. Hybrid capture-based comprehensive genomic profiling analysis identified genetic rearrangement events and co-occurring genomic alterations (GAs). DNA libraries were captured with a personalized customization panel of 769 genes (Genecast, Beijing, China), which comprehensively covered sensitive and resistant sites and immunotherapy-related molecular markers. After library construction, the target region was captured by probe hybridization, and the enriched target region was paired-end sequenced using an Illumina NovaSeq 6000 sequencing platform.

Bioinformatics analysis pipeline
Once the raw sequencing data were obtained, the data were subjected to a comprehensive series of bioinformatic analyses. Quality control was first performed using Trimmomatic (v0.36), and data that passed quality control were analyzed downstream. For data mapping, reference alignment was performed via BWA aligner (v0.7.17) and Picard (v2.23.0) for sorting and duplication masking. Then, Genome Analysis Toolkit (GATK, version 3.7) was used for realignment. VarDict (version 1.5.1) was used for the variant calling step to call single-nucleotide variant (SNV) mutations, and gene functions were annotated with ANNOVAR. SNVs were filtered with the following rules: (i) The loci with a sequencing depth of less than 30× were filtered out. (ii) Mutations in the blacklist were filtered out, while those in the whitelist were retained. (iii) SNV mutations in the intronic or intergenic regions and nonsense mutations were filtered out. Copy number variations (CNVs) were called via Cnvkit (v0.9.2) from the tumor samples, and the Genecast normal database was used as a paired control. The copy number threshold for CNV gain was four, and that for CNV loss was 1.2. The tumor mutational burden (TMB) test used a standard protocol, while the analysis method involved a proprietary module from the Genecast company. To determine the TMB value, the numbers of somatic nonsynonymous SNVs detected by next-generation sequencing (NGS) were quantified, and the values were extrapolated to the whole exome using a validated algorithm. First, somatic SNV mutations were screened to identify candidate loci for calculating the TMB value. The standards for filtering nonsynonymous mutations in the coding region were as follows. (i) Mutations in driver genes and hotspot mutations included in the Exome Aggregation Consortium (ExAC)/COSMIC database were filtered out. (ii) Mutation loci that met certain sequencing depth and mutation frequency thresholds were identified. Second, TMB was calculated based on the candidate mutations for the tumor mutation load screened above, equal to the absolute number of mutation counts divided by the panel's base number of exonic regions. It was measured in mutations per Mb. ALK/RET/ROS1 rearrangements status were also identified by analysis of NGS.

Fluorescent multiplex immunohistochemistry (mIHC) analysis
Tissue samples were processed into paraffin-embedded blocks and then cut into 3-5 µm FFPE sections. The immune markers, including CD68, CD8, PD-L1, and PD-1, were identified and quantified by mIHC. Then, the infiltration of CD8 + TILs, CD68 + macrophages, CD8 + PD-1 + TILs, and CD68 + PD-L1 + macrophages was evaluated. Samples of immunohistochemical markers were evaluated with antibodies. In this case, the main antibodies used were CD8 antibody (clone 144B, ab17147, Abcam; dilution 1:25), CD68 antibody (clone KP1, ZM-0060, Zsbio; dilution 1:400), CD163 antibody (clone 10D6, ZM0428, Zsbio; dilution 1:200), PD-L1 antibody (clone SP142, Ventana; dilution 1:25), and PD-1 antibody (clone CAL20, ab237728, Abcam; dilution 1:100). Each antigen was subjected to a complete labeling process, including primary antibody incubation, secondary antibody incubation, and TSA visualization, before the next antibody was labeled. To better assess the expression of immune markers quantitatively, the percentage of positive cells was used to describe the fluorescent staining result, and it was interpreted by capturing images of the tumor area at 200× magnification and using image analysis software for quantitative statistical analysis. The fluorescent result is the percentage of positive staining of total nucleated cells. All the mIHC slides were scanned by the Perki-nElmer Vectra (Vectra 3.0.5; PerkinElmer, Massachusetts, USA) platform. In addition, PD-L1 was also stained with traditional diaminobenzidine (DAB), and the staining results were used as a reference and for quality control.

Statistical analysis
Statistical analysis was performed using the R software program (version 3.6.1, https:// www.r-proje ct. org/). The Wilcoxon test was used to compare whether there was a significant difference in the TIME between the co-occurring and control groups. Wilcoxon rank-sum or Kruskal-Wallis tests were used for continuous variables, and Student's t tests were used for categorical variables to analyze group comparisons. A p value < 0.05 was considered statistically significant if not explicitly stated. OS was defined as the time from the start of randomization to death (from any cause). Survival analyses were conducted using hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs) estimated from a Cox proportional hazards model.

Patient clinical characteristics
The tumor tissue samples of 155 NSCLC patients were collected retrospectively. The detailed baseline characteristics are shown in Table 1. Among all NSCLC patients, 128 patients (82.6%) had LUAD, two patients (1.3%) had LUSC, and 25 patients (16.1%) had other types of NSCLC. All patients had gene translocation events in this study. There were 84 patients (54.2%) with ALK rearrangement, 38 patients (24.5%) with RET rearrangement, and 33 patients (21.3%) with ROS1 rearrangement. The median age of this cohort was 55.0 years, ranging from 29.0 to 86.0 years. A total of 104 patients (67.1%) were females, and 51 patients (32.9%) were males. A total of 122 patients (78.7%) had no smoking history, these proportions were higher in the RET fusion-positive subgroup, which were 81.6% (Table 1). The patients in this cohort were characterized by being younger, being female, having adenocarcinoma, and having no history of smoking, consistent with the classic features of patients with ALK fusions. These data were all consistent with previous studies (Rikova et al. 2007). The clinical features of RET and ROS1 rearrangements are largely consistent with those of ALK rearrangement, so those three fusion types were subsequently combined into one category for further analysis.
Other clinical characteristics, including smoking, age, and cancer type, differed between the groups, as shown in Supplementary Fig. 1. These data showed that the clinical characteristics of NSCLC patients with covariation were predominantly male sex and stage IV compared with wildtype NSCLC patients. For the following reason, we combined ALK/RET/ROS1 fusions in the next analysis. The reason was that although the fusions occurred in different genes, such as RET, and ROS1, the clinical characteristics of the included patients were well distributed and consistent with the typical clinical features of ALK fusions.
TMB and the number of neoantigens were higher in samples with TP53 or CDKN2A/B covariations As shown in Fig. 2A, the samples in the 3 co-occurring groups had higher TMB than the samples in their corresponding WT groups (p < 0.001, p = 0.017, and p < 0.001). Further analysis revealed that the number of neoantigens was higher in the TP53 co-occurring group, as well as in the TP53/CDKN2A/B co-occurring group (p = 0.014 and p = 0.0032). The CDKN2A/B co-occurring group also had more neoantigens, although the difference was not statistically significant (p = 0.31) (Fig. 2B). Even in the HLA-A and HLA-B subgroups, there were no differences between the co-occurring and WT groups, and HLA-A*03 was a prevalent HLA type present in those groups.

TP53 or CDKN2A/B covariations were associated with an immune microenvironment characterized by high PD-L1 expression and low CD8 + TIL infiltration
A previous study reported that TP53/ALK comutation led to a poorer prognosis (Koh et al. 2016). To investigate the possible reasons for this phenomenon, fluorescent mIHC with classic immune markers (CD8, CD68, PD-1, and PD-L1) was performed on 50 samples from 155 NSCLC patients harboring ALK/RET/ROS1 rearrangements, and then the differences in TIL infiltration levels were analyzed. Representative images of classic markers on tumor tissues from patient A (co-occurring group) and patient B (WT group) are shown in Fig. 3A. The lower image shows the fluorescence map of the double marker (CD8&PD-1 and CD8&PD-L1) field of view on the mIHC technology platform. The percentage of all markers was calculated separately from the tumor area, stromal area and all areas of tumor tissue. The results of these three kinds of areas were obtained separately, and the p values are presented in Supplementary Table 1.
We compared the expression levels of classic markers of the immune microenvironment in 3 regions (tumor, stroma, and all regions) in the TP53/CDKN2A/B co-occurring and WT groups. The co-occurring group had significantly less infiltration of CD8 + (p = 0.043), CD8 + PD-1 − (p = 0.029), and CD8 + PD-L1 − (p = 0.025) TILs than the WT group (Fig. 3B). In stromal areas, the level of CD8 + PD-L1 + cells was significantly increased in the covariation group compared with the WT group (p = 0.021). There were slightly higher trends of CD8 + , CD68 + , CD8 + PD1 − , and CD68 + PD-L1 − infiltration in the covariation group (Fig. 3C). In the total areas, none of the markers except PD-L1 showed statistically significant differences between the two groups, and only some markers, such as CD8 + and CD8 + PD-L1 − , showed a decreasing trend in the covariation group (Fig. 3D). For the comparison of the TP53 and CDKN2A/B co-occurring groups with the WT group, similar trends were also found ( Supplementary  Fig. 2).
Notably, the level of PD-L1 + cells was higher in the covariation group than in the WT group in tumor areas (p = 0.00038), stromal areas (p = 0.0016), and total areas (p = 0.00064) (Fig. 3E). To reverse verify PD-L1 + expression in the two groups, the expression level of PD-L1 in three different regions of the two groups was divided into high expression and low expression groups according to a 5% threshold. The results confirmed that the percentage of samples with TP53/CDKN2A/B covariations was significantly higher in the PD-L1 high expression group than in the PD-L1 low expression group in tumor areas (p < 0.001), stromal areas (p = 0.04), and total areas (p = 0.003) (Fig. 3F).

Validation of poor prognosis in patients with covariation in the TCGA NSCLC cohorts
In NSCLC patients harboring ALK/RET/ROS1 rearrangements, the above results provide important insights into the differences in prognosis between the co-occurring groups and the WT groups. Since the covariation group had higher TMB and PD-L1 expression but less CD8 + TIL infiltration, we predicted a poorer prognosis in this group of patients. To validate our hypothesis, we explored the genomic co-occurring status in the LUSC and LUAD cohorts from the TCGA database. The clinical baseline characteristics included the baseline age, sex, type of cancer, stage, and smoking history. Among all 340 patients, 64.6% (n = 219) were female, and 35.6% (n = 121) were male. The median age at baseline was 60.9 years, and the age range was 16-90 years (Supplementary Table 2 adenocarcinoma and no history of smoking, which is consistent with the characteristics of our study cohort. In NSCLC patients with ALK/RET/ROS1 rearrangements from the TCGA cohort (Fig. 4A), the top 5 mutated genes were CDKN2A/B (24%), TP53 (16%), MDM2 (8%), MYC (7%), and SETD2 (7%). As with the results of our cohort, in patients with covariation, the TMBs were significantly higher than in patients without covariation (p < 0.001, p = 0.0018, and p < 0.001) (Fig. 4B). The prognostic analysis showed that, regardless of whether it was the TP53 comutation group, the CDKN2A/B covariation group, or the TP53/ CDKN2A/B covariation group, the OS of these three groups was shorter than that of their control groups (WT groups) (p < 0.001, HR = 2.53, 95% CI = 1.59-4.04; p = 0.0625, HR = 1.54, 95% CI = 0.97-2.42; p < 0.001, HR = 2.21, 95% CI = 1.47-3.33). In summary, the trends of the validation cohort were generally consistent with our datasets in terms of clinical characteristics, the genomic alteration landscape, and TMB. Furthermore, the findings in the validation cohort suggested that the group of patients harboring the co-occurring TP53/ CDKN2A/B covariations and ALK/RET/ROS1 rearrangements had a worse prognosis than the group of patients without the co-occurring TP53/CDKN2A/B covariations and ALK/RET/ROS1 rearrangements.

Discussion
The frequency of covariation in NSCLC in the presence of ALK/RET/ROS1 fusions has been reported in several studies. TP53 is the most common comutated gene with EGFR, ALK, ROS1, and other genes in lung cancer, and the presence of TP53 comutation is predictive of shorter survival (Christopoulos et al. 2019;Lin et al. 2017). We obtained similar results. In our cohort, the top 2 covariation genes were TP53 and CDKN2A/B (Fig. 1). Nguyen et al. found that the most frequently observed significant alteration was TP53 mutation, which was more frequent in metastases in several tumor types. A possible explanation is that TP53 mutation is a later event in some of these tumor types; in others, it may simply be a hallmark of more aggressive disease. Another genomic alteration that was most often enriched in metastases included CDKN2A deletion (Jiang et al. 2016;Nguyen et al. 2021). Metastases have higher chromosomal instability across many tumor types. Therefore, we speculate that tumors with enriched mutations in genes such as TP53 or CDKN2A/B may have higher TMBs and neoantigen loads (Belinsky et al. 1998;Deneka et al. 2022;Alidousty et al. 2018). Our study validated this hypothesis. The ALK/ RET/ROS1-rearranged samples with co-occurring TP53 or CDKN2A/B in our cohort had higher TMBs and more neoantigens (Fig. 2).
The covariation leads to an increase in TMB and neoantigens, and antigen-presenting cells (APCs) present more tumor antigens to CD8 + cells. After CD8 + cells are activated, some cytokines, such as interferon-gamma (IFN-γ), are released to regulate immune responses (Tumeh et al. 2014;Xu-Monette et al. 2017). With the activation of overall immune cells in the microenvironment (mainly CD8 + cells), tumor cells and the immune system start to perform immune braking actions, manifested by tumor cells expressing PD-L1 against the killing caused by immune cell activation and immune cells in the immune system (mainly cells such as CD68) expressing PD-L1 against normal tissue damage (physiological direction), hence the increase in PD-L1 + tumor cells and PD-L1 + immune cells in the microenvironment (Liu et al. 2020;Dong et al. 2017). We believe that these events occurred in the tumors of the comutation group in our cohort. Our results showed that the level of PD-L1 + cells in the tumors in the TP53 or CDKN2A/B covariation group was higher than that of tumors in the WT group (Fig. 3).
Studies have found that in EGFR-mutated or ALK-rearranged lung cancers, PD-L1-positive tumors have a poorer prognosis (Liu et al. 2018). In stage III NSCLC patients treated with chemotherapy, PD-L1-positive tumors with low CD8 + cell infiltration had the worst prognosis (Tokito et al. 2016). Our study found that covariation samples had not only higher PD-L1 + cells but also lower infiltration of CD8 + TILs. Except for PD-L1, the expression of markers in the total region did not differ significantly between groups. CD8 + PD-L1 + in the stroma in the covariation group was higher, which indicated that CD8 + T cells were exhausted in the covariation stromal area. The expression levels of CD8 + , CD8 + PD1 − , and CD8 + PD-L1 − in the tumor areas were lower in the covariation group, further suggesting that there were fewer non-exhausted CD8 + T cells in the covariation group. Together, these results indicated that the immune microenvironment was suppressed in the covariation group. Therefore, we hypothesized that patients with covariation have a worse prognosis than WT patients. To test this hypothesis, we analyzed data from the TCGA NSCLC cohorts, and the results were as expected. The prognostic analysis showed that, regardless of whether it was the TP53 comutation group, the CDKN2A/B covariation group, or the TP53/CDKN2A/B covariation group, the overall survival of these three groups was shorter than that of their control groups (WT groups) (Fig. 4). Several studies have demonstrated that the co-occurrence of mutation and rearrangement is a poor prognostic factor, resulting in shorter PFS and OS (Christopoulos et al. 2019;Kron et al. 2018).
Our observations can have a meaningful impact on clinical treatment decisions. Regarding the poor prognosis of patients with co-occurrence, this observation has also been explained in the previous section in terms of genetic Fig. 3 Comparison of PD-L1 expression and TIL infiltration in all samples without/with TP53 or CDKN2A alterations. A Representative images of mIHC staining for PD-L1, CD8, CD68, and PD-1. Patient A: co-occurring group; patient B: wild-type group. The lower plots show the fluorescence maps of CD8 + PD-1 − and CD8 + PD-L1 − cells. Heatmaps showing the differences in the expression level of each type of immune cell between the co-occurring groups and WT groups in tumor areas (B), stromal areas (C), and all areas (D). The values are scaled across each marker (rows) and are represented by the Z score, with red and green representing high and low relative percentages of expression, respectively. E The level of PD-L1 + cells between the co-occurring groups and WT groups in tumor areas, stromal areas, and all areas. F Histogram showing the reverse validation of PD-L1 expression in tumor areas, stromal areas, and all areas in the two groups ◂ heterogeneity. A. Kron conducted a molecular analysis of 216 patients with ALK-positive advanced NSCLC and hypothesized that concurrent mutations might underlie differences in prognosis. The correlation between the PD-L1 expression score and TP53 mutation status was assessed in 34 ALK-positive patients in the study, and it was found that in 8 patients (23.5%), PD-L1 was expressed in more than 50% of tumor cells. PD-L1 positivity was significantly correlated with TP53 mutation. It is tempting to speculate Fig. 4 Landscape of genomic variation signatures and prognostic analysis in patients from the TCGA NSCLC cohorts. A Landscape of the genomic alterations of the TCGA cohorts. Different colors represent different variation types. B The differences in TMB between the co-occurring mutation groups and the WT groups. C Kaplan-Meier survival curves of overall survival between the groups with and without covariation that a higher TMB may lead to a better effect of immune checkpoint inhibitors (Kron et al. 2018). In a case published in 2021 by Takamasa Nakasuka of PD-L1-positive lung adenocarcinoma patient with the coexistence of RET fusions and NF1/TP53 mutations, this with poor fitness status, multiple cancer-associated thrombosis and respiratory failure eventually achieved better outcomes with pembrolizumab monotherapy (Nakasuka et al. 2021). This finding may provide additional insight into the efficacy of pembrolizumab immunotherapy in patients with RET fusions accompanied by comutations in TP53.
In addition, there is a special case where a comutation can occur at the time of drug resistance progression, thus giving the patient more treatment options. RET fusion has been shown to be an acquired resistance mechanism against EGFR-TKIs in approximately 5% of cases in which osimertinib resistance biopsies were performed. Piotrowska and colleagues reported two patients in whom an EGFR T790Mresistant clone was successfully suppressed by osimertinib, and a RET fusion was detected when osimertinib resistance progression was again continued genetically. In this case, the combination of osimertinib and BLU-667 was well tolerated, and a rapid radiological response was seen. RET fusion may be a relevant factor in treatment selection in the event of EGFR resistance progression (Rich et al. 2019;Klempner et al. 2015;Piotrowska et al. 2018).
The limitations of this research are primarily related to the limitations of the public datasets used. The conclusions of the TME were not validated accordingly when we used the data from the TCGA database. In future work, more comprehensive cohort data should be used to overcome these limitations.
Co-occurring genomics are extremely important not only for ALK-rearranged NSCLC but also for other oncogenedriven NSCLC. For example, NSCLCs with epidermal growth factor receptor (EGFR) mutations were often found to have many co-occurring genomic events, including TP53, phosphatidylinositol-4, PIK3CA, PTEN, MET, etc (Costa 2018;Aisner et al. 2018). Some of these comutated genomic events, such as high levels of ERBB2 or MET amplification, can eliminate the ability to induce a response to EGFR-TKI monotherapy (Carney et al. 2017). Future research could be extended to investigate the mechanisms of the immune microenvironment in other oncogene-driven NSCLCs.

Conclusion
Overall, the TP53 or CDKN2A/B covariation in ALKrearranged NSCLC is associated with a high TMB, high PD-L1 expression level characterizing the TIME and poor prognosis. The differences in prognosis and the extent of benefit from therapy between patients with and without cooccurrence may be due to differences in clinical characteristics, genomic alteration characteristics, tumor mutation loads, neoantigen levels and the immune microenvironment. Anti-PD-L1 therapy for patients with such co-occurring tumors may provide better curative effects.