
==== Front
Cancer Immunol Immunother
Cancer Immunol Immunother
Cancer Immunology, Immunotherapy : CII
0340-7004
1432-0851
Springer Berlin Heidelberg Berlin/Heidelberg

39235488
3798
10.1007/s00262-024-03798-z
Research
Impaired TGF-β signaling via AHNAK family mutations elicits an esophageal cancer subtype with sensitivities to genotoxic therapy and immunotherapy
Mai Zihang 123
Kongjia Luo 123
Wang Xinye 123
Xie Xiuying 123
Pang Lanlan 2
Yang Hong yanghong@sysucc.org.cn

123
Wen Jing wenjing@sysucc.org.cn

23
Fu Jianhua fujh@mail.sysu.edu.cn

123
1 grid.488530.2 0000 0004 1803 6191 Department of Thoracic Surgery, Sun Yat-Sen University Cancer Center, Guangzhou, 510060 Guangdong Province China
2 grid.12981.33 0000 0001 2360 039X State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060 Guangdong Province China
3 https://ror.org/0493m8x04 grid.459579.3 Guangdong Esophageal Cancer Institute, Guangzhou, 510060 Guangdong Province China
5 9 2024
5 9 2024
11 2024
73 11 22522 3 2024
1 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Background

Genome instability (GI) is a hallmark of esophageal squamous cell carcinoma (ESCC) while factors affecting GI remain unclear.

Methods

Here, we aimed to characterize genomic events representing specific mechanisms of GI based on 201 ESCC samples and validated our findings at the patient, single-cell and cancer cell-line levels, including a newly generated multi-omics dataset of the trial NCT04006041.

Results

A two-gene (AHNAK and AHNAK2) mutation signature was identified to define the “AHNAK1/2-mutant” cancer subtype. Single-cell-assisted multi-omics analysis showed that this subtype had a higher neoantigen load, active antigen presentation, and proficient CD8 + T cell infiltrations, which were validated at pan-cancer levels. Mechanistically, AHNAK1/2-mutant ESCC was characterized by impaired response of TGF-β and the inefficient alternative end-join repair (Alt-EJ) that might promote GI. Knockdown of AHNAK in ESCC cell lines resulted in more Alt-EJ events and increased sensitivities to cisplatin. Furthermore, this two-gene signature accurately predicted better responses to DNA-damaging therapy in various clinical settings (HR ≈ 0.25). The two-gene signature predicted higher pCR rates in ESCCs receiving neoadjuvant immunotherapy-involved treatment. Finally, a molecular classification scheme was built and outperformed established molecular typing models in the prognosis stratification of ESCC patients.

Conclusion

Our study extended our understanding of the AHNAK family in promoting GI and selecting treatment responders of ESCC.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00262-024-03798-z.

Keywords

Esophageal cancer
Multi-omics
Molecular classification
Chemoradiotherapy
Immunotherapy
National Natural Science Foundation of China82272881/82273032/82072607 82272881/82273032/82072607 82272881/82273032/82072607 Yang Hong Wen Jing Fu Jianhua issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2024
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pmcIntroduction

Esophageal cancer, the sixth leading cause of cancer-related deaths, is a major health threat worldwide, with approximately 500,000 cases diagnosed in China annually. Esophageal squamous cell carcinoma (ESCC), the predominant pathological subtype in China, is a heterogeneous disease with various prognosis and treatment responses [1]. Recent studies have elucidated the molecular determinants behind clinical phenomenon across various cancer types [2], such as genome instability, but progress in ESCC is scarce [3]. This malignancy type also had an unstable genome, which was characterized by complicated genetic alterations [4–9]. However, the accelerators of the ESCC genome instability during cancer progression and their correlations with patient outcomes remain mysterious. Moreover, previous studies mostly focused on the influence of genomic alterations on cancer cell itself [4–9]. Because these alterations might increase cancer’s immunogenicity or endow cancer cells with immune escape, how these genomic features shaped the tumor microenvironment (TME) are essential as immune checkpoint inhibitors (ICIs) are becoming a pillar of ESCC treatment. Few information regarding ESCC is available, and more efforts are needed to address this unmet clinical demand.

Among the molecular indicators of genome instability, tumor mutation burden (TMB) ranks one of the most important biomarkers because of its promising capacity on predicting prognosis and treatment responses [10]. However, the lack of a unified threshold and costs have prevented the broad implementation of TMB assessment. Rather than direct quantification of TMB, some genomic features linked to the over-generation of somatic mutations have been proposed as surrogates of TMB, including alterations in the DNA damage repair pathway [2]. Unfortunately, these genomic features are not prevalent in squamous cell carcinoma (SCC), especially ESCC, limiting their utility in ESCC [3]. Further studies are required to broaden our knowledge of the ESCC mutagenesis.

Based on public sequencing cohorts and multi-omics data we generated, we discovered that AHNAK family mutations in ESCC were associated with higher TMB, suppression of TGF-β pathway, higher activity of alternative end joining (Alt-EJ) that may increase genome instability and introduce massive mutations. Translation of these relationships identified a two-gene signature for robust prediction of responses to DNA-damaging therapy and immunotherapy. Our results proposed a practical tool for selecting responders of cancer therapy and a rationale for TGF-β inhibitors to sensitize recalcitrant ESCC to genotoxic therapies.

Methods

Collection of exome data from ESCC

In total, 832 patients with exome data from previous studies were included [4–7, 9, 11, 12], as summarized in Table S1. We excluded the WGS data as TMB calculated from WGS data could not directly compared with those in the WES data. For the discovery analysis, 201 ESCC patients with exome data from a 548-gene panel were obtained from our previous study [5]. Validation cohort 1 included 390 ESCC patients representing Chinese populations [4, 6, 7, 12], 70 of whom were derived from our in-house database [13]. Validation cohort 2 contained 165 ESCC patients from regions other than China [9, 11]. Another dataset containing single cell RNA sequencing (scRNAseq) and paired WES data from 46 ESCC patients was also included for TME characterization [14]. We also generated a multi-omics dataset for validations of our findings using samples from our clinical trial (NEO1901 cohort), the first phase II trial to evaluate the efficacy of PD-1 antibody combined with chemoradiotherapy in ESCC. Thirty tumor samples with paired leukocyte DNA were subjected to whole-exome sequencing, among which RNA-seq of 28 samples were also performed [15].

Identification of prognostic biomarkers positively correlated with TMB

The TMB was defined as the number of non-synonymous mutations per megabase. Patients were dichotomized into TMB-H and TMB-L groups based on the upper quantile of TMB [16], and Fisher’s test was employed to screen mutated genes enriched in TMB-H populations that correlated with both the prognosis and TMB in the discovery cohort. Moreover, univariate Cox analyses on overall survival (OS, mutant vs. wild-type) were conducted to identify potential biomarkers of survival. A permutation-based algorithm [17, 18] was used to further examine the specificity of the relationships between biomarkers and TMB as well as prognosis. Other methodological details are described in the supplementary note.

Statistical analysis

All analyses were performed with R 4.0.2. Student’s t test, the Wilcoxon rank-sum test and Fisher’s exact test were used to assess associations between two groups of continuous or discrete variables as appropriate. The drug sensitivity curves and IC50 were estimated using GraphPad 8. The survival rate of molecular subtypes followed a certain trend in the discovery cohort (i.e., FAT/FRY mutant < both negative < AHNAK1/2 mutant), and we employed the Cox trend test to validate the survival of patients with molecular events in other datasets [2]. All datasets fulfilled the proportional hazards assumption (P > 0.1). The P value threshold for statistical significance was set at 0.05 unless specified otherwise.

Result

Somatic mutations in AHNAK family correlated with a high TMB

In our study, data from 201 patients in our previous study were used as the discovery set [5], and data from 555 patients from published studies were used for validations [4, 6–9], including cohorts from the TCGA and ICGC project. The exome panel used in the discovery set covered 548 gene loci, so we first measured the correlation between TMB of 548 loci and the WES-based TMB. As shown in Fig. 1A, the TMB calculated from the panel was strongly correlated with the WES-based TMB in the WES dataset (Rpearson = 0.90, P < 0.0001), suggesting that the panel-based TMB was a good surrogate for WES-based TMB.Fig. 1 Overview of the TMB in ESCC and TMB-related genes. A Spearman correlation analysis of TMB calculated based on the 548 frequently mutated gene loci and the whole exome. B Identification of genes that correlated with both TMB and OS. The size of the dots represents the mutation frequencies of the genes in the discovery set. C, D Lollipop plot displaying the mutation spectra of genes in the AHNAK family, AHNAK (C) and AHNAK2 (D). E AHNAK1/2 mutations correlated with a higher TMB in the discovery cohort and WES datasets. F AHNAK1/2 mutation correlated with higher TMB independent of mutations in DNA mismatch repair genes. G The AHNAK1/2-mutant group had a higher neoantigen load in both our discovery cohort and the validation datasets. TMB: tumor mutation burden; WES: whole-exome sequencing

To identify TMB-related prognostic biomarkers, we screened mutations at gene level that were enriched in TMB-H (≥ 8 mutations/Mb) in our discovery cohort (Fig. 1B). Using the thresholds of FDRTMB-H ≤ 0.05 and POS ≤ 0.05, we selected eight genes, among which AHNAK ranked first in the TMB-H associations. The nucleoprotein AHNAK, as well as its homologous protein AHNAK2, shared similar functional domain that were involved in the P53 complex [19–21]. In TMB-L ESCC, we observed mutations that were dispersed across the whole AHNAK and AHNAK2 genes, while mutations in the TMB-H group were concentrated in functional domains of both AHNAK (OR: 17) and AHNAK2 (Fig. 1C, D, OR:3, P < 0.05, Fisher’s exact test) [19–23], suggesting that distribution of mutations in AHNAK family were not merely random hits. Interestingly, we observed a trend of co-mutation between AHNAK and AHNAK2 (OR: 4.93, Fisher’s exact test). Therefore, we defined an ESCC subtype named “AHNAK1/2-mutant” for patients harboring mutations in the AHNAK family.

In our discovery set, AHNAK1/2-mutant ESCC had a higher TMB (P = 4.5 × 10−6), and this phenomenon was also validated in the WES datasets (Fig. 1E). Recent studies [17, 18] have noted that some genes, such as MUC16 [18] and TTN [24], have a higher probability of being randomly mutated in tumors with a high background mutation rate, suggesting that these mutations are the results of rather than potential contributors to genome instability. To eliminate this potential bias, we repeated association analysis controlling for TMB [25] (see the supplementary methods), and found the correlations between AHNAK1/2 mutations and the TMB remained (Pempirical = 0.013 in the discovery set, only 13 of 1000 simulations had P values < 4.5 × 10−6, indicating that these associations were unlikely caused by random mutations (Pempirical = 0.01 in the WES datasets). Note that the association between AHNAK family mutation and TMB was independent of the mutation types and mutation locations (Fig. S1A–C). We also compared the mutation frequencies of other genes in both molecular subgroups. In both datasets, we did not observe other common driver mutations enriched in the AHNAK1/2 mutant group, suggesting the alterations of AHNAK family were critical genomic features of this molecular subtype (Tables S6 and S7).

To rule out factors that would confound the associations between AHNAK1/2 mutations and TMB, we built a multivariate logistic regression model incorporating the prevalent factors. As shown in Fig. S2A, B, associations between AHNAK1/2 mutations and TMB-H remained statistically significant after adjusting for TMB-H-related factors (OR = 5.55 (2.44–12.59), P < 0.0001). We also validated the relationship between AHNAK1/2 mutations and TMB across multiple squamous cancers and digestive malignancies in TCGA. The co-mutation of AHNAK1/2 and MMR-related genes tended to indicate a higher TMB than other groups, consistent with our findings in ESCC (Figs. 1F and S3A, D).

Active antitumor immunity in AHNAK1/2 mutant ESCC

In our discovery and the WES validation set, we found that ESCCs with AHNAK1/2 mutations had a remarkably higher neo-antigen load (NAL) than those with wild-type alleles, and consistent findings were observed in other cancer types in TCGA (Figs. 1G and S3B, E).

For high-resolution TME characterization of ESCC, we investigated a sing-cell RNAseq dataset with WES data from 46 surgically removed ESCCs [14]. In the scRNA dataset [14], AHNAK1/2-mutant ESCC also had a high TMB phenotype (Fig. S4B). After quality control and Louvain-based clustering, we annotated 134,024 cells as 23 cell types using canonical markers [26] (Fig. 2A). From a single-cell perspective, the TME of AHNAK1/2-mutant ESCC showed higher infiltrations of CD8 + T cell lineages (Fig. 2B), the naïve, effector and exhausted subsets of CD8 + T cells were enriched in AHNAK1/2-mutant ESCC (Fig. S4A). They showed higher expression of tumor-reactive scores [27] (e.g., ENTPD1 and CXCL13) (Fig. 2C and Table S2). Exposed in higher neo-antigen context, these T cells underwent greater clonal expansion and differentiated into more clonotypes (Fig. S4C, D). We also performed the differential NicheNet analysis [28] to identify subtype-specific cellular interactions. Several genes (CXCL13, CD6, FASLG and GZMB) mainly expressed by activated and cytotoxic CD8 + T cells were ranked as the most reactive ligands targeting malignant cells in AHNAK1/2 mutant ESCC, indicating that CD8 + T cells play an antitumor role rather than a bystander role (Table S5) [27]. We also found that conventional dendritic cells in AHNAK1/2-mutant TME exhibited higher levels of MHC molecules and maturity scores, presenting a mature and activated phenotype capable of antigen presentation [26] (Fig. 2C).Fig. 2 AHNAK1/2-mutant ESCC features a “hot” immune microenvironment. A Global view of the UMAP plot of 134,224 cells from 46 patients in GSE160269 [14]. B Composition of immune cells in 46 ESCC patients using scRNA-seq. C, D Specific gene expression in immune (C) and tumor cells (D) in the scRNA dataset. E CIBERSORT-based calculation of infiltrating immune cells in the TCGA-ESCC cohort. F Gene sets enriched in AHNAK1/2-mutant ESCC according to TCGA dataset analysis (FDR < 0.05). G, H Representative images of IHC staining (G) and quantifications of CD8 + TIL infiltration (H) in the discovery cohort. I Expression of AHNAK family in samples from NEO1901 trial. TIL: tumor infiltrated lymphocytes

Next, we analyzed the RNA-seq data from the TCGA-ESCC cohort. The CIBERSORT analysis [29] also revealed enrichment of CD8 + T cells in the AHNAK1/2-mutant ESCC (P = 0.014, Fig. 2E). Gene set enrichment analysis (GSEA) showed that HLA genes involved in antigen presentation were upregulated (Fig. S3G). Consistently, higher expression of antigen presentation-related genes and increased infiltrations of CD8 + T cells were also observed across multiple cancer types in TCGA (Fig. S3G, H). In addition to in-silico estimates, we also analyzed the immunohistochemistry data (IHC) from our discovery cohort [13]; the results further verified the higher infiltration of CD8 + T cells in AHNAK1/2-mutant ESCC (Fig. 2G, H).

We next focused on epithelial cells and identified 29,277 malignant cells by inferring the CNV burden with inferCNV [30]. Interestingly, we found that AHNAK family genes were downregulated in AHNAK1/2-mutant group (Fig. 2D). This phenomenon was also observed in TCGA-ESCC and NEO1901 cohorts, our previous trial evaluating the efficacy of toripalimab-based neoadjuvant therapy [15] (Fig. 2I and Table S4). Furthermore, quantitative PCR analysis on samples with tumor content ≧ 80% in our discovery cohort also showed that AHNAK1/2 mutant ESCCs exhibited lower expression of AHNAK family genes (Fig. 3A), indicating that mutations in AHNAK family may lead to the loss-of-function of AHNAK1/2 due to decreased expression [22]. Interestingly, the ESCCs with low AHNAK1/2 expression score also had higher abundance of CD8 + T cells in both the TCGA-ESCC and NEO1901 datasets (Fig. S8M).Fig. 3 Impaired TGF-β response and enhanced Alt-EJ repair in AHNAK1/2-mutant ESCC. A Expression level of AHNAK family in our discovery cohort detected using qPCR. B Cell communication analysis of the TME in both ESCC subtypes in GSE160269. C Assessments of the activities of the TGF-β and Alt-EJ pathways in malignant cells. D Activities of the TGF-β and Alt-EJ pathways in tumors from TCGA-ESCC cohort. E Distinct truncating mutation burdens in two genomic subgroups across two datasets. F, G Knock-down efficiency and drug sensitivity assay in KYSE180 and KYSE410 cells transfected with 2 siRNAs against AHNAK or a control siRNA (siNC). H Representative results from the DNA repair reporter assay measured using flow cytometry. I, J Total NHEJ and Alt-EJ repair activities in siNC and si-AHNAK cells. NHEJ: non-homologous end joining; Alt-EJ: alternative end joining

Impaired TGF-β response in AHNAK1/2-mutant ESCC

AHNAK family genes might be regulators of response to TGF-β in TME [22, 23], so we sought to investigate whether AHNAK1/2 mutations dysregulated TGF-β signaling. First, we applied differential NicheNet analysis [28] on single-cell data to investigate to what extent TGF-β in TME stimulated transcriptional changes of cancer cell in different genomic subgroups. We found that intensity of TGF-β-mediated communications between stromal and tumor cells in AHNAK1/2-mutant tumors was weaker compared to the wild-type tumors (Fig. 3B). Specifically, expression levels of TGF-β analogs in major cell types were similar across both genomic subtypes (Fig. S3I), while AHNAK1/2-mutant tumor cells exhibited downregulation of TGF-β target genes (Fig. 3C), suggesting that the weakened TGF-β response of AHNAK1/2-mutant ESCC was tumor cell-intrinsic deficiency instead of reduced stimulation from other cells in TME. We also analyzed the RNA-seq dataset SRP140279 of KYSE-150 cells perturbed by RNA interference of AHNAK2. Compared with untreated cells, TGF-β signaling was suppressed in cells with si-AHNAK2, verifying the critical role of AHNAK family genes in the TGF-β signal transduction (Fig. S4H).

Enhanced Alt-EJ and therapeutic vulnerability in AHNAK1/2 mutant ESCC

As already known, TGF-β served as a multi-functional cytokine via autocrine or paracrine. Other than its cell-growth regulation and immunosuppression role, TGF-β might also suppress the error-prone Alt-EJ pathway against double strand breaks (DSB) and maintain regular DNA repair [31, 32]. In the scRNA dataset, genes responsible for Alt-EJ were upregulated in tumor cells in AHNAK1/2-mutant patients [14] (Fig. 3C). We also reviewed the bulk RNA-seq data from TCGA-ESCC and Cancer Cell Line Encyclopedia (CCLE) and found that TGF-β signaling exhibited a strong inverse correlation with Alt-EJ activities across ESCC samples and cell lines (R = − 0.7, P < 0.001, Fig. S4E). Moreover, AHNAK1/2-mutant ESCCs showed lower activity of TGF-β signaling and higher Alt-EJ activity (Fig. 3D and S4F). Although Alt-EJ repairs broken DNA, this error-prone mechanism produces more mutations. Among patient samples, and cell lines from CCLE, we found that AHNAK1/2-mutant ESCCs bore a heavier insertion and deletion (indel) burden (Fig. 3E), as well as an increased frequency of indels at the micro-homology junctions (Fig. S4G), which is considered as specific genomic “scar” of Alt-EJ repair [33]. These findings were also observed in head and neck cancer, cervical cancer and gastrointestinal malignancies including gastric and colorectal cancers (Figs. S3C, F and S4G).

Next, we chose AHNAK as a representative because of similar function of the two homologous proteins to investigate the function of AHNAK family in ESCC. Knockdown of endogenous wild-type AHNAK level in KYSE410 and KYSE180 cells led to increased sensitivity of cisplatin (Fig. 3F, G). Ablation of mutant AHNAK in TE-1 and KYSE150 cells did not affect the drug sensitivity (Fig. S4L, M), indicating that mutant AHNAK might be loss-of-function in cancer cells [22]. Consistently, in cell lines from CCLE with available omics data, we observed a linear correlation between AHNAK expression and the IC50 of platinum agents at both RNA and protein levels (Fig. S4N, O). To further examine the effect of AHNAK on Alt-EJ repair, we measured the Alt-EJ activity using flow cytometry with specific reporter cassettes (EJ2 for Alt-EJ and EJ5 for overall DSB repair proficiency [34], Fig. 3H). In both ESCC cell lines, AHNAK knockdown caused an approximately twofold increase in Alt-EJ (EJ2), while the overall DSB repair proficiency (EJ5) showed little changes [34] (Fig. 3I, J), suggesting AHNAK family might specifically affect Alt-EJ repair efficiency.

A previous report demonstrated loss of TGF-β signaling sensitized cancer cell’s response to PARP inhibitors [35]. Consistently, we also observed that AHNAK1/2-mutant ESCC cell lines showed higher sensitivity to olaparib than the wild-type alleles using two in vivo drug sensitivity databases [36], as evidenced by increased sensitivity of AHNAK knockdown cells to olaparib (Fig. S4I, J). Moreover, we also observed lower expression of genes responsible for the epithelial-mesenchymal transition (EMT) in AHNAK1/2-mutant ESCCs (Figs. 2F and 3C) across both the scRNA and bulk RNA-seq datasets, which was a metastasis-predisposed phenotype regulated by TGF-β [37].

Patients with AHNAK1/2-mutant ESCC exhibit long-term survival

In our discovery cohort [5], the AHNAK1/2 mutation signature was a significant protective factor for longer disease-free survival (DFS, Fig. 4A) and OS (HR: 0.44, 95% CI: 0.29–0.65, P < 0.001, Fig. 4B). The OS association was validated in both Chinese [4, 6, 7, 12] and non-Chinese validation cohorts [9, 11] (HR: 0.43 (0.26–0.72), P < 0.001 across the whole validation set, Figs. 4C and S5A, B). Notably, most of the validation samples lacked DFS data, preventing us from examining the association between AHNAK1/2 mutations and DFS. This two-gene signature was further verified as an independent prognosticator in both datasets, as supported by the permutation tests (Pempirical < 0.001 in the discovery set, and Pempirical = 0.019 in WES datasets, Fig. S2C, D and Table S3). These results indicated that AHNAK1/2 mutation signature was a robust prognostic indicator not simply due to the statistical bias toward its high mutation frequencies [17].Fig. 4 AHNAK1/2 mutation signature predicts better prognosis and responses to immunotherapy. A In the discovery set with disease-free survival (DFS) information, the AHNAK1/2 mutation signature predicted a prolonged DFS. B, C The AHNAK1/2-mutant group experienced a longer OS in both the discovery and validation datasets. D, E Association between AHNAK1/2-mutation status, expression level and therapy responses in ICI-involved therapy trial NEO1901. pCR: pathological complete response

A previous study summarized three core determinants regarding to immunotherapy response: preexisting T cell immunity, high TMB and loss of TGF-β signaling [38]. These factors were concomitantly presented in AHNAK1/2-mutant tumors, inspiring us to further explore whether this subtype could gain more benefits from ICIs. In NEO1901 trial that evaluated the efficacy of ICI-based neoadjuvant therapy [15], AHNAK1/2-mutant ESCCs showed higher rates of pCR than the wild-type tumors (OR:7.32, P = 0.049, Fig. 4D), so did the AHNAK1/2-low expression groups (defined as the geometric mean of AHNAK1/2, Fig. 4E), suggesting that AHNAK1/2-mutant tumors were responders of ICI. Similarly, the AHNAK1/2 mutation signature was associated with prolonged survival in ICI cohorts of other cancer types [39–41] (Fig. S5C).

AHNAK1/2-mutant ESCC patients benefit from genotoxic therapy

Inspired by previous studies [10, 33], we probed whether AHNAK1/2-mutant patients with TGF-β loss and hyperactive Alt-EJ repair might be vulnerable to DNA-damaging therapy (e.g., platinum and radiotherapy).

In the discovery and our in-house validation datasets with 271 patients undergoing postsurgery adjuvant therapy (ADT, chemotherapy or chemoradiotherapy) or not, we explored the capacity of the AHNAK1/2-mutation signature to predict efficacy of ADT. To mitigate the effect of factors affecting treatment decisions, we performed 1:1 PSM between patients treated with and without ADT. In the PSM dataset, ADT indeed prolonged patient DFS and OS, consistent with our previous study [42] (Fig. S6A–D). In both two cohorts, patients with AHNAK1/2 mutations benefited from ADT (Plogrank < 0.05, Fig. 5A, B). In contrast, ADT did not provide survival benefits for patients lacking AHNAK1/2 mutation (Plogrank > 0.1, Fig. 5C, D). We subsequently pooled all patients to test the interaction between receipt of ADT and the AHNAK1/2-mutant status. A statistically significant treatment-by-biomarker interaction confirmed the AHNAK1/2 mutation signature as a predictor of response to genotoxic therapy (Pinteraction < 0.05 for both DFS and OS, Fig. 5E, F). Among patients with ADT, ROC analysis revealed that the AHNAK1/2-mutation signature accurately predicted prognosis of patients, especially when combined with pathological stages (Fig. 5G, H). Repeated analyses in datasets without PSM yielded similar results (Fig. S7A–D). We further verified this predictive biomarker in other cohorts with different clinical background [43]. In an ESCC cohort about radical chemoradiotherapy (CRT), AHNAK1/2-mutant ESCC patients showed significantly lower risks of tumor recurrence and cancer-related death after chemoradiotherapy than their wild-type counterparts (HRDFS: 0.18, HROS: 0.20, P < 0.05, Fig. S8A, B). A similar significance was also observed in neoadjuvant chemoradiotherapy (neoCRT) settings, but this phenomenon should be further confirmed by longer follow-up (Fig. S7F, G).Fig. 5 Survival of ESCC patients stratified by AHNAK1/2 mutation status and receipt of adjuvant treatment in the PSM-adjusted populations. A–D Kaplan–Meier curves showing, DFS (A, C) and OS (B, D) of pN + ESCC patients in our discovery set (A, C) and the GECI dataset (B, D) after stratification by AHNAK1/2 mutation status and treatment group. E, F The quantitative analysis of treatment-biomarker interactions for DFS (E) and OS (F) indicated that AHNAK1/2 mutation status was a predictive biomarker of adjuvant therapy efficacy. G, H Time-dependent ROC curve analysis of the AHNAK1/2 mutation signature. DFS: disease-free survival; OS: overall survival; ROC: receptor operative characteristics

As the expression of AHNAK1/2 was downregulated in AHNAK1/2-mutant ESCC cells (Figs. 2I and 3A), we investigated whether low AHNAK1/2 expression was responsible for better responses of genotoxic therapy. In a cohort with 65 pN + patients [44], adjuvant chemotherapy only prolonged patient OS in those with low AHNAK1/2 expression (Pinteraction < 0.01, Fig. S8C, D). We also evaluated the associations between AHNAK1/2 expression signature and the efficacy of neoCRT. As shown in Fig. S8, low AHNAK1/2 expression in pre-neoCRT biopsies was a powerful predictor of short-term pathological responses and long-term survival in our published dataset [45] (HRDFS: 0.09, HROS: 0.14, Fig. S8E–H), and the microarray data (HRDFS: 0.27, HROS: 0.26, Fig. S8I–L) from a clinical trial [1, 46], improving the prognostic accuracy of ypTNM stage. Overall, low AHNAK1/2 expression (AHNAK1/2-mutant-like) are promising predictors of efficacy of genotoxic therapy, verifying the critical roles of AHNAK1/2-mediated TGF-β pathway inactivation in sensitizing genotoxic therapy.

Molecular classification of ESCC

Furthermore, we sought to integrate genetic events we discussed here and previously into decision-tree-based molecular classifications of ESCC. Other than AHNAK family alterations, we recently reported a Hippo pathway-related mutation signature to recognize a FAT/FRY-mutant ESCC subtype with an adverse prognosis [13]. As shown in Fig. 6A, we classified tumors into three subtypes: the AHNAK1/2-mutant subtype consisting of ESCCs with AHNAK or AHNAK2 mutations regardless of the mutation status of the FAT1/FAT3/FRY, the FAT/FRY-mutated subtype including tumors with FAT1/FAT3/FRY mutations but without AHNAK1/2 mutations, and the double-negative subtype including all the remaining tumors.Fig. 6 Genomic classification of ESCC and survival association. A Molecular classifications of ESCC. B–D In the discovery dataset (B), non-Chinese (C) and Chinese cohorts (D), three genomic subtypes were associated with OS, and the Cox trend test showed an overall P < 0.0001. E Across the whole dataset, three molecular subtypes were strongly correlated with OS. F Schematic of integration of our genomic subtype and lymph node metastasis status for better stratification of cancer-related death risk. G, H Death risk stratification of the integrated molecular subtypes in all pN + datasets (G) and the pN0-3 validation datasets (H). I, J Validation of the prognostic value of the integrated molecular subtypes stratified in the Chinese (I) and non-Chinese populations (J). K The predictive capacity of the models for OS was determined by the concordance index with 1000 bootstrap resamplings for models, as shown in the box plot

In our discovery set, the three groups presented separate OS curves (Pcox-trend < 0.001, Fig. 6B), and the survival trend of three molecular subtypes still persisted in both non-Chinese (Fig. 6C) and Chinese populations (Pcox-trend < 0.001, Fig. 6D). Despite distinct clinicopathological backgrounds, the three subtypes had different prognoses across the whole dataset (Fig. 6E). When stratified by clinical factors (age, sex and TNM stage), the genomic subtypes still provided a robust prognostic stratification, especially for patients within stage IIIB (Fig. S9C).

Intratumoral heterogeneity (ITH) is a hallmark of ESCC, so we used a dataset consisting of multiple samples per patient to investigate whether ITH biased our molecular classification scheme [47]. We observed that the molecular subtypes of most samples (49/51) were consistent with each other within the same patient, indicating the truncal location of these genetic events and limited ITH-induced bias in our model (Fig. S10).

We next compared our molecular classification scheme with the clusters generated by TCGA study [9] and the genomic classification scheme described by Cui [3]. Based on 437 samples with copy number profiling, we found that NFE2L2-mutant subtype and RAS-MYC-pathway amplification subtype were distributed equally across the three subgroups we identified, suggesting the independency of two molecular classification schemes (Fig. S11A). In the TCGA cohort, cluster 1, which was characterized by YAP1 amplifications, was enriched in the FAT/FRY-mutant subtype characterized by inactivation of the Hippo pathway (Fig. S11B). In terms of prognostic stratification, both the TCGA clusters [9] and genomic subtypes proposed by Cui [3] showed weaker associations with survival, than our model (Fig. S11C–F).

As our molecular classification was developed independent of the pTNM stage, we further sought to integrate pTNM information with molecular subtypes for better prognostic stratification. Consistent with previous study reporting the distinct prognostic value of mutations across different stages [48], the prognostic value of the FAT/FRY mutation signature exhibited distinct prognostic values in pN + and pN0 patients (Pinteraction < 0.05). Therefore, we devised our model (dMS) to subdivide AHNAK1/2 wild-type patients based on the lymph node metastasis status, as displayed in Fig. 6F. In cohorts of all pN + patients in the absence of pN + /FAT_FRY wild-type subtype, our dMS achieved a more powerful risk stratification for ESCC patients than the pathological stage (Figs. 6G and S12A–C). The dMS model also provided powerful risk stratifications of both the Chinese and non-Chinese populations in validation sets, which include patients that spanned across pN0-3 stages (Fig. 6I, J); this stratification was more significant than the pathological stage alone in terms of prognosis stratification (Figs. 6H and S12D–F). Thus, the bootstrap validation analysis showed that the ability of our dMS to predict OS was superior to that of pathological stages (Figs. 6K and S12G, H).

Discussion

Alterations in the AHNAK family have been detected in multiple cancers, and previous studies have described them as significantly mutated genes because of high mutation frequencies, mutation bias toward functional domains and effects on reshaping tumor transcriptome [49–51]. In our study focusing on ESCC, we found that AHNAK1/2-mutant patients had higher TMB and longer survival. Yosef reported that all genes have a higher probability of being mutated on a unstable genome, and genes with higher mutation frequencies are prone to being prognostic factors, suggesting these observations as byproducts of genome instability [17]. To examine whether this bias existed, we performed association analyses with TMB controlled, following a statistical framework the researchers proposed [17, 18]. The results suggested that the high TMB and good prognosis in AHNAK1/2-mutant tumors were unlikely the random mutation caused by genome instability.

The nucleoprotein family members, AHNAK and AHNAK2 have similar functional domains [52]. They are critical enhancers of the TGF-β signaling cascade [22, 23, 37], while mutations in AHNAK resulted in loss of function (Fig. 3A–C) [22]. Our data also supported that AHNAK family mutations as critical drivers of TGF-β loss during ESCC progression. The TGF-β loss in AHNAK1/2 mutant ESCC was associated with genome instability induced by the inefficient and error-prone Alt-EJ repair, that we theorized would be reflected in ESCC genome by upregulation of Alt-EJ genes, high TMB and indels at micro-homology [22, 23, 31]. Coincidentally, a study focusing on gastroesophageal adenocarcinoma also reported that other TGF-β pathway alterations (AVCR2A and TGFBR2) were enriched in samples with higher TMB [53].

As usually the first step of cancer-immune-system crosstalk [54], neo-antigens in cancers encoded by genome alterations stimulate antigen-presentation cells and thus initiate the anti-tumor immune responses. The AHNAK1/2-mutant signature defined a TGF-β-inactive subtype with higher mutation burden, specially truncating mutations which generated more neo-antigens than missense mutations did [55]. The high antigen exposure of AHNAK1/2 mutant tumors strikingly induced an inflamed TME with active antigen presentations and CD8 + T cell infiltrations, suggesting this cancer subtype might be beneficiaries of ICIs [15]. These better responses to PD-1 antibody-based therapies were further validated at our data from clinical trial [15] and at the pan-cancer cohorts [39–41], suggesting the robustness of our findings.

Previous studies have examined some gene signatures to predict responses of genotoxic therapy and immunotherapy [46, 56]. Most of them had limited sample size and failed to illustrate the biological background under the gene signatures. Several molecular features favoring therapy responses discussed above converged on such an ESCC subpopulation inspired us to propose a biologically sound signature for predicting efficacy of genotoxic therapy. With validations in multiple cohorts, this signature exerted as an indicator of genotoxic therapy response specifically, whatever in cohorts receiving neoadjuvant, adjuvant and radical treatments. Our findings were consistent with previous studies which demonstrated that TGF-β blockades could enhance the chemoradiotherapy responses and anti-tumor immunity [57, 58]. As neoadjuvant chemoradiotherapy serves as a standard treatment for locally advanced ESCC, the AHNAK1/2 mutation signature show great potential to become a companion diagnostic tool to select responders of CRT once passed the prospective validation.

Our study was limited because of the potential bias of retrospective analyses, so we applied PSM to minimize the confounding effects. In addition, the biomarker discovery of our study was mainly based on genomic data, further integration of transcriptome might achieve more comprehensive stratifications of therapy responses. Furthermore, our data supported the correlations between AHNAK1/2 mutations with ICIs responses, it remains unclear whether the AHNAK family has interfered with the regulation of the PD-L1 pathway. It remains undetermined whether the AHNAK1/2-mutant ESCC subtype is responders of the chemoimmunotherapy. Finally, it seems the AHNAK family alterations and FAT/FRY mutations represent two patient subgroups with distinct prognoses, the biological connections and interactions between these two types of alterations deserves further investigations to better refine the molecular classifications.

In summary, we developed the AHNAK1/2 mutation signature that allowed us to discover a molecular subtype of ESCC with loss of TGF-β signaling and enhanced antitumor responses. Clinically, AHNAK1/2-mutant ESCC patients had long-term survival and gained more benefits from genotoxic therapies. The molecular classifications we proposed could be well integrated with pathological variables to refine death risk stratifications. Our study will not be the last analysis but one of upcoming efforts to improve outcomes of ESCC patients.

Supplementary Information

Below is the link to the electronic supplementary material.Supplementary file1 (DOCX 4234 KB)

Supplementary file2 (XLSX 942 KB)

Abbreviations

AUC Area under curve

DFS Disease-free survival

ESCC Esophageal squamous cell carcinoma

GI Genome instability

ICI Immune checkpoint inhibitors

LN Lymph node

OR Odd ratio

OS Overall survival

pN Pathological lymph node stage

pT Pathological tumor stage

ROC curve Receiver operation characteristic curve

scRNA Single-cell RNA

SNV Single nucleotide variation

TCGA The Cancer Genome Atlas

TMB Tumor mutation burden

TME Tumor microenvironment

TNM Tumor-lymph node-metastasis stage

Acknowledgements

We faithfully thank the International Cancer Genome Consortium (ICGC) and professor Weihua Jia for kindly providing the raw data for validations of our results.

Author contributions

Jianhua Fu, Jing Wen and Hong Yang conceived and designed the study. Zihang Mai, Kongjia Luo, Xiuying Xie, Hong Yang collected the data. Zihang Mai, Jing Wen, Hong Yang and Zihang Mai analyzed and interpreted the data. Zihang Mai, Jing Wen and Kongjia Luo did the statistical analysis. Zihang Mai, Kongjia Luo, Jing Wen, Xinye Wang and Jianhua Fu wrote the manuscript. Hong Yang and Lanlan Pang read and provided critical revision of the manuscript for intellectual contents. All authors have read and approved the final manuscript.

Funding

This work was granted by the National Natural Science Foundation of China (No. 82272881/82273032/82072607).

Data availability

The discovery dataset reported in the current study are available in the Genome Sequence Archive in the BIG Data Center (https://ngdc.cncb.ac.cn/gsa), Beijing Institute of Genomics, Chinese Academy of Sciences, with accession code HRA000777.

Declarations

Conflict of interests

The authors declare no competing interests.

Ethical approval

The Ethics Committee approved the study protocol and waived the requirement for informed consent given the retrospective nature of the study (SZR2019-109).

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Zihang Mai, Kongjia Luo and Xinye Wang have contributed equally to this work.
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