Abstract
In invasive lung adenocarcinoma (LUAD), patients with micropapillary (MIP) or solid (SOL) components had a significantly poorer prognosis than those with only lepidic (LEP), acinar (ACI) or papillary (PAP) components. It is interesting to explore the genetic features of different histologic subtypes, especially the highly aggressive components.
Based on a cohort of 5,933 patients, this study observed that in different tumor size groups, LUAD with MIP/SOL components showed a different prevalence, and patients with ALK alteration or TP53 mutations had a higher probability of developing MIP/SOL components. To control individual differences, this research used spatial whole-exome sequencing (WES) via laser-capture microdissection of five patients harboring these five coexistent components and identified genetic features among different histologic components of the same tumor. In tracing the evolution of components, we found that titin (TTN) mutation might serve as a crucial intratumor potential driver for MIP/SOL components, which was validated by a cohort of 146 LUAD patients undergoing bulk WES. Functional analysis revealed that TTN mutations enriched the complement and coagulation cascades, which correlated with the pathway of cell adhesion, migration, and proliferation.
Collectively, the histologic subtypes of invasive LUAD were genetically different, and certain trunk genotypes might synergize with branching TTN mutation to develop highly aggressive components.
Keywords: Invasive lung adenocarcinoma, Histological grades, Histological subtypes, Driver mutation, TTN mutation, Laser-capture microdissection, Whole-exome sequencing, Genetic features
List of abbreviations
| ACI | acinar |
| AIS | lung adenocarcinoma in situ |
| ALK | Anaplastic lymphoma kinase |
| CCFs | cancer cell fractions |
| COSIMC | catalogue of somatic mutation in cancer |
| CSMD3 | CUB and Sushi Multiple Domains 3 |
| DEGs | differentially expressed genes |
| EGFR | Epidermal Growth Factor Receptor |
| ERBB2 | erb-b2 receptor tyrosine kinase 2 |
| FAT1 | FAT Atypical Cadherin 1 |
| FLG | Filaggrin |
| GO | gene ontology |
| GSEA | Gene set enrichment analysis |
| IA | invasive adenocarcinoma |
| IASLC/ATS/ERS | the International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society |
| KEGG | Kyoto Gene, and Genome Database |
| KRAS | kirsten rat sarcoma viral oncogene |
| LEP | lepidic |
| LUAD | lung adenocarcinoma |
| MET | MET proto-oncogene |
| MIA | micro-invasive lung adenocarcinoma |
| MIP | micropapillary |
| MUC16 | Mucin 16 |
| MUC17 | Mucin 17 |
| MUC19 | Mucin 19 |
| OR | odds ratio |
| PAP | papillary |
| PBL | peripheral blood lymphocyte |
| PC | Pearson-correlation |
| RET | Rearranged during transfection proto-oncogene |
| RYR2 | Ryanodine Receptor 2 |
| SOL | solid |
| sSNVs | somatic single nucleotide variants |
| TCGA | the cancer genome atlas |
| TIDE | Tumor Immune Dysfunction and Exclusion |
| TMB | tumor mutational burden |
| TP53 | tumor protein p53 |
| TTN | Titin |
| USH2A | Usherin |
| VAFs | variant allele frequencies |
| WES | whole-exome sequencing |
| ZFHX4 | Zinc Finger Homeobox 4 |
Novelty & Impact Statements.
This study employed laser-capture microdissection and spatial whole-exome sequencing (WES) to uncover the genetic characteristics of LUAD pathological components individually. This approach enables the investigation of interactions among drivers of histological subtypes that promote malignant progression.
Alt-text: Unlabelled box
Introduction
According to the pathological classification proposed by the International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society (IASLC/ATS/ERS) in 2011, Invasive lung adenocarcinoma (LUAD) are classified by predominant patterns with lepidic (LEP), acinar (ACI), papillary (PAP), micropapillary (MIP) and solid (SOL) [1]. Subsequently, the 2015 World Health Organization (WHO) classification proposed an architectural histologic grading system for resectable LUAD, in which LEP patterns were classified as low grade, ACI/PAP patterns as intermediate grade, and MIP/SOL patterns as high grade [2]. To more accurately evaluating prognosis, the IASLC pathology committee proposed a grading system for invasive LUAD in 2020. This system separated histological subtypes into three prognostic groups: low-grade LEP patterns with no or less than 20 % of high-grade patterns, intermediate-grade ACI/PAP patterns with no or less than 20 % of high-grade patterns, and high-grade MIP/SOL patterns comprising 20 % or more of the sample [3]. Accumulating evidences have revealed that different histological subtypes of invasive LUAD affect postoperative patients' recurrence and long-term survival. Patients harboring MIP or SOL components have the poorest prognosis, followed by those with ACI/PAP; patients with only LEP show the most favorable prognosis [3,4].
There is significant heterogeneity with regard to the prognosis and morphology of components in different grades of LUAD; the genetic relationship between different histologic subtypes has rarely been studied. Caso et al. conducted a study using next-generation sequencing (NGS) and the MSK-IMPACT platform to uncover genetic characteristics of several common components in lung adenocarcinoma. Their findings shed light on the molecular landscape of these histological subtypes [5]. Additionally, Tavernari et al. employed multiple spatial-omics technologies to investigate the underlying driver genes of different histological subtypes in LUAD [6]. Previous studies have attempted to explore the molecular features of these histological subtypes and uncover their associated common driver mutations [[7], [8], [9], [10]]. However, these studies were based on a limited-genotype panel and were unable to adjust individual differences due to the population-level study design.
Our study firstly combines the laser-capture microdissection plus spatial whole-exome sequencing (WES) to reveal the genetic features underlying LUAD pathological components individually. It is important to note that conducting sequencing without laser-capture microdissection can solely capture inter-individual variations. However, adopting the research approach of sequencing after laser-capture microdissection provides a distinct advantage in effectively elucidating the genetic characteristics specific to various pathological components. WES is more comprehensive than panel genotyping with a relatively high depth sequencing to detect lower frequency mutations, which is an optimal tool to study intratumor heterogeneity [[11], [12], [13], [14], [15], [16]]. In addition, we used a large NGS-genotyping cohort to identify the genetic risk factors for aggressive components. This study helps to gain a deeper understanding of the genetic features of invasive LUAD and provides new insight into the genes associated with the highly aggressive components.
Methods
Patient cohort
The patients who underwent complete surgical resection for LUAD in the First Affiliated Hospital of Guangzhou Medical University from March 2011 to July 2019 were included in this study. The records of each patient's medical history were retrieved. The data of histological subtypes and tumor size were collected from the pathological records which were independently reviewed by three pathologists in the First Affiliated Hospital of Guangzhou Medical University. The analysis will only focus on the five common histological subtypes of LUAD, which are LEP, ACI, PAP, MIP and SOL components. Patients with mucinous patterns or other histological patterns will be excluded from this study. Besides invasive LUAD, lung adenocarcinoma in situ (AIS) or micro-invasive lung adenocarcinoma (MIA), which were deemed to LEP predominant pattern of LUAD were also included [1]. To reveal the genetic features the histological subtypes of LUAD, a total of 4 cohorts were established, which involve populational and individual level (Figure S1). Informed consent form was signed by each enrolled patient, and the study was approved by the Institutional Review Board of the First Affiliated Hospital of Guangzhou Medical University.
Pathological classification of LUAD
According to the pathological classification, LUAD can be divided into five histological subtypes according to the highest percentage of histological subtypes: lepidic predominant pattern (LEP), acinar predominant pattern (ACI), papillary predominant pattern (PAP), micropapillary predominant pattern (MIP) and solid predominant pattern (SOL) [1]. Cribriform components were considered as a special subtype of ACI components [17,18]. In architectural grading system established by the 2015 WHO classification, invasive lung adenocarcinoma is grouped as follows to better explore the genetic information behind these histological subtypes, low-grade: lepidic (LEP), intermediate-grade: acinar and papillary (ACI/PAP), and high-grade: micropapillary and solid (MIP/SOL) [2]. The grade systems mentioned in this paper are all based on this classification.
The distribution of histological subtype in different tumor size groups analysis
To investigate the distribution of different histological subtypes in lung adenocarcinomas of varying tumor sizes and the distribution of different component combination patterns. The records of tumor size and histological subtypes were retrieved from the pathological records of 5,933 patients enrolled in cohort 1 (Figure S1). According to the tumor size, cohort 1 was divided into 8 groups as follows: ≤1cm, 1.1-1.5cm,1.6-2cm, 2.1-2.5cm, 2.6-3cm, 3.1-3.5cm, 3.6-4cm, >4cm. The distribution of different histological grades or subtypes in each group was summarized as a percentage. As mentioned above, there are two pathological classifications of LUAD. We respectively calculated the percentage of each grade or subtype in the corresponding group. We also displayed the proportions of each grade or subtype in different tumor sizes and all the combinations of histological subtypes of our samples.
The association between driver mutation and histological subtype analysis
To investigate the relationship between driver mutations and different histological subtypes of LUAD, 2,499 patients had performed next generation sequencing-based genomic profiling to detect the common driver mutations of LUAD were selected to establish cohort 2 (Figure S1). The target DNA in next-generation sequencing was extracted from Formalin-fixed paraffin-embedded (FFPE) tissue of resection specimens for library establishment, to capture of 13 introns and 436 exons from 145 cancer-related genes. The hybrid capture libraries were then sequenced to >5009 average unique coverage using Ion Proton Sequencers (Thermo Fisher) [19]. Besides, sections of Formalin-fixed paraffin-embedded (FFPE) tissue were the continuous sections immediately following the HE sections. A case-control study was carried out to compare the driver mutations in different histological grades or subtypes. After adjusting the tumor size, logistics regression analysis was conducted to find the difference between driver mutations and different histological grades. The statistical difference between driver mutations and different histological subtypes in the same grade was further explored using the same statistical analysis.
Phylogenetic reconstruction analysis based on laser-capture microdissection and spatial WES
To explore the phylogenetic relationship between different histological grades, five patients were selected as cohort 3, each of whom were reported with five histological subtypes (Figure S1). Their samples with different histological subtypes were independently confirmed by three pathologist and separated via laser-capture microdissection by two clinicians who under the guidance of the pathologist, and collected to perform the whole-exome sequencing (WES). The corresponding peripheral blood lymphocyte (PBL) sample was also collected as the paired normal control for somatic variant detection for each patient. Hierarchical clustering was performed on samples using R packages hclust and APE [20,21], and visualized with reconstructed trees [22]. PyClone was used to measure the cancer cell fractions (CCFs) of selected mutations, using variant allele frequencies (VAFs) and copy number profiles obtained from WES, and adjusted for tumor purity generated by ABSOLUTE [23,24]. Truncal mutations that occurred before malignant transformation were identified as clusters of somatic single nucleotide variants (sSNVs) centered around CCF =1 in all tumor samples for each patient [25]. ClonEvol was used to infer a consensus clonal evolution model using mutational clusters containing at least 4 mutations [22]. Each histological subtype is conducted for hierarchical clustering and clonal structure analysis. Meanwhile, from the results of WES, the gene mutations in corresponding clusters were shown if they were the top 20 gene mutations of lung cancer in the cancer genome atlas (TCGA) [26]/the catalogue of somatic mutation in cancer (COSIMC) database [27] and had significant expression in each cluster. Detailed description for the methods of WES and the phylogenetic reconstruction analysis are showed in supplemental materials (Text 1) and the sequencing coverage and quality statistics for each sample are summarized in Supplementary Table S7.
Further verification of the association between genetic mutation and histological subtype
From the results of WES and phylogenetic reconstruction analysis, some significant gene mutations were enriched in specific histological grades and subtypes. To further verify such association between gene mutations and histological subtype, 146 consecutive patients with early-stage lung adenocarcinoma dissected in our center during May 2014 to June 2014 were selected as cohort 4, and their tissue samples were sent for WES (Figure S1). The detailed methods description can be found in the supplementary materials (Text 3) and the sequencing coverage and quality statistics for each sample are summarized in Supplementary Table S8. The landscapes of gene mutations in these patients showed the distribution of the mutation in different histological grades or subtypes. We used the corresponding histological subtypes from the pathological records to verify the association between different histological subtypes and the significant gene mutation using the Pearson correlation analysis. To study the interaction between these mutations, whether it is mutually exclusive or co-occurring, the pair-wise fisher's exact test was performed based on cohort 4 and the cBioportal cohort. Our study not only explored the impact of these significant mutations on survival in the cBioPortal cohort but also their activation of the pathway from the gene ontology (GO), Kyoto Encyclopedia of Genes and Genome Database (KEGG), and Gene set enrichment analysis (GSEA) based on TCGA database. In addition, the HE sections and genetic information of 398 LUAD patients with stage I and II from TCGA database as well as 146 LUAD patients from cohort 4 were reviewed to compare the proportion of MIP/SOL in patients with different drivers.
Statistical analysis
The relative effect of the Pearson-correlation analysis was presented as Pearson-correlation (PC), the relative effect of the logistics regression analysis was presented as odds ratio (OR). Analyses were executed using SPSS 23.0 and R 3.5.2. All statistical tests were two-sided and p <0.05 was considered statistically significant.
Results
Patient characteristic and study design
Four cohorts were designed in this study to reveal the genetic characteristics of lung adenocarcinoma with different histological subtypes and the patient selection algorithm was showed in Fig. 1 and Figure S1. A total of 5,933 patients were included in cohort 1 (median age at resection, 58±10.9); 399 (6.72 %) were adenocarcinoma in situ (AIS), 1,094 (18.44 %) were minimally invasive adenocarcinoma (MIA), and 4,440 (74.84 %) were invasive adenocarcinoma (IA), respectively. Most tumors were at pathological stage I (n = 5,580, 94.05 %), with a small proportion at stage II (n = 315, 5.30 %) and III (n = 38, 0.64 %). The subtypes in each histological grade were LEP, (n = 3,961, 37 %), ACI/PAP, (n = 4,417, 42 %) and MIP/SOL, (n = 2,249, 21 %), respectively. The components were LEP, (n = 3,961, 29 %), ACI, (n = 4,237, 31 %), PAP, (n = 2,841, 21 %), MIP, (n = 1,772, 13 %) and SOL, (n = 860, 6 %), respectively (Table S1).
Fig. 1.
Flow chart of the study design.
Cohort 2 was established to explore the relationship between the common driver mutations and histological subtypes. In cohort 2, 2,499 patients (median age at resection, 58.43±10.77) from cohort 1 were tested with the next generation sequencing of LUAD driver genes after diagnosis. The top 5 common mutations of lung adenocarcinoma were as follows, EGFR (57.20 %), KRAS (7.89 %), TP53(3.15 %), ALK alteration (2.6 %), RET (1.28 %), ERBB2 (1.28 %). The subtype in each histological grade was LEP, (n = 1,772, 38.51 %), ACI/PAP, (n = 1,867, 41.76 %) and MIP/SOL, (n = 882, 19.73 %), respectively. The components were LEP, (n = 1,772, 29.95 %), ACI, (n = 1,801, 31.33 %), PAP, (n = 1,185, 20.61 %), MIP, (n = 714, 12.42 %) and SOL, (n = 327, 5.69 %), respectively (Table S2).
This study selected 5 specimens that each contained 5 histological subtypes with more than 10 % in each subtype to establish cohort 3, which were sent to reveal the genetic landscape by WES. (Table S3) To further revealed the drivers underlying each histological subtype, 146 patients (median age at resection, 58.51±10.49) were filtered as cohort 4, and the top five genes with high detected mutation frequency were EGFR (31.78%), FLG (17.78 %), TP53 (15.45 %), MUC16 (8.75 %), TTN (8.16 %). The subtype in each histological grade was LEP, (n = 110, 36.3 %), ACI/PAP, (n = 145, 47.85 %) and MIP/SOL, (n = 48, 15.84 %), respectively. The component of each histological subtype was LEP, (n = 110, 28.35 %), ACI, (n = 143, 36.86 %), PAP, (n = 81, 20.88 %), MIP, (n = 40, 10.31 %) and SOL, (n = 14, 3.61 %), respectively (Table S4).
Ethological features of each histological subtype
There were 25 histological subtype combinations in total. The top 5 most common combinations were LEP+ACI+PAP (n = 873, 19.88 %), LEP+ACI (n = 849, 19.34 %), ACI+PAP+MIP (n = 680, 15.49 %), ACI+PAP (n = 405, 9.22 %) and LEP+ACI+PAP+MIP (338, 7.7 %). The proportion of LEP+MIP+SOL, LEP+SOL, LEP+ACI+SOL, and LEP+ACI+MIP+SOL was 0.05 % or less, respectively (Figure S2a). Under the microscope, we observed a tendency that the LEP (low-grade) adjoined the ACI/PAP (intermediate-grade), the ACI/PAP adjoined the MIP/SOL (high-grade). Furthermore, a cross-grade combination such as LEP and MIP/SOL was rare (Figure S2b). The cohort 1 was divided into different groups according to the greatest diameter of tumor: ≤1cm, 1.1-1.5cm, 1.6-2cm, 2.1-2.5cm, 2.6-3cm, 3.1-3.5cm, 3.6-4cm and >4cm (Table S1). The various proportions of histological subtype in different tumor size groups were shown in Figure S2c and the LEP decreases and MIP/SOL increases gradually in different tumor size groups.
ALK alteration and TP53 mutation are positively associated with MIP/SOL components
In cohort 2, 2,499 patients who postoperatively performed the genotyping of multiple driver mutations detection for LUAD were included. EGFR mutation, ALK alteration, KRAS, andTP53 mutation were the most common (Table S2). The result of the logistic regression analysis showed a positive association between LEP components and MET mutation (OR=9.651, p =0.010). Compared to other driver mutations, ALK alteration (OR=6.464, p <0.001) and TP53 mutation (OR=4.435, p <0.001) were significantly correlated with increased MIP/SOL components. In each subtype, the positive associations between ALK alteration (OR=5.334, p <0.001)/TP53 mutation (OR=3.200, p <0.001) and micropapillary components, as well as ALK alteration (OR=5.196, p <0.001)/TP53 mutation (OR=4.172, p <0.001) and solid components, were also observed. Notably, a significant association was observed between ROS1 alteration and MIP components (OR=3.089, p = 0.025) rather than SOL components (OR=2.989, p = 0.051). Additionally, we observed a positive association between ALK alteration (OR=4.208, p = 0.026) and KRAS mutation (OR=2.439, p = 0.040) with ACI/PAP components in LUAD, while no significant associations were found with either ACI components or PAP components. Furthermore, we identified a positive association between EGFR L858R mutations and both ACI (OR=2.335, p = 0.005) and PAP components (OR=1.540, p = 0.045) in lung adenocarcinoma. However, EGFR 19del mutations were specifically associated with ACI components (OR=2.319, p = 0.006) rather than PAP components (OR=1.471, p = 0.078) (Fig. 2b, Table S5).
Fig. 2.
(a) The proportion of different common mutations in different histological grades (right) and the proportion of different common mutations in different histological subtypes (left). Each color represents a kind of mutation, and the width of the histogram represents the size of proportion. (b) the forest plots of the impact of different driver mutations on MIP/SOL, SOL components, and MIP components, respectively. The orange dots represent the mutations that have a significantly positive association with the histological grade/subtypes. The odds ratio (OR) >1 represents that this driver mutation positively correlates with the histological grade/subtypes. 95 % confidence interval (95 %CI) represents that the true values of the population parameter have a 95 % probability occur within the interval. It is statistically significant if the interval does not contain the value of 1.
Genetic heterogeneity in Histological grades/subtypes of LUAD
Each subtype from the five specimens of cohort 3 was separated to perform the WES, but some subtypes had insufficient samples to generate results. In these five cases that underwent spatial WES for each component, it found common mutations shared by different components. Heterogeneity in genetic mutations was revealed in different histological subtypes; USH2A, TP53, and FLG mutations were specifically enriched in the LEP components, MUC16, CSMD3, TTN, ZFHX4, and RYR2 mutation were enriched in ACI/PAP. Besides, FAT1, MUC16, CSMD3, TTN, RYR2, and USH2A mutations were positively associated with the histological subtype of MIP/SOL (Fig. 3). In the histological subtype level, the MUC16, CSMD3, TTN, RYR2, and USH2A mutations were correlated with SOL, and the MUC16 and TTN mutation were correlated with MIP (Figure S3). More detailed mutation sites of differentiated genetic mutation from the phylogenetic reconstruction were shown in supplementary materials.
Fig. 3.
The development of intratumor heterogeneity of five patients with LUAD base on phylogenetic reconstruction analysis. In each phylogenetic reconstruction, the clock diagram shows a section at a simulated time point and the proportion of each histological grade at that time point in attempt to reproduce the evolutionary process of each histological grade. The evolutionary model of each grade was deduced according to the change of mutations. In each cluster, mutations were showed if them were the top 20 mutations of TCGA/COSIMC database.
TTN mutation is a potential driver for the generation of MIP/SOL components
From 146 cases with results of bulk WES, the most frequent mutations were MUC17 (77 %), EGFR (75 %), MUC19 (69 %) and FLG2 (50 %). The most common mutations in different grades or subtypes identified: MUC17 (67 %), EGFR (65 %), MUC19 (65 %), FLG2 (48 %), TTN (29 %) in MIP/SOL; MUC17 (77 %), EGFR (75 %), MUC19 (69 %), FLG2 (50 %), TTN (19 %) in ACI/PAP; EGFR (78 %), MUC17 (76 %), MUC19 (70 %) and FLG2 (49 %) in LEP (Fig. 4a, Figure S4, Table S4). In the correlation analysis, the TP53 (PC= 0.169, P =0.041) and TTN mutations (PC =0.178, P =0.032) were positively associated with MIP/SOL (Fig. 4b, Table S6). And the SOL component was significantly correlated with EGFR (PC =-0.185, P =0.026), MUC16 (PC =0.180, P =0.030), TTN (PC =0.196, P =0.018), RYR2 (PC =0.184, P =0.027) and TP53 (PC =0.286, p <0.001) mutations, in which EGFR mutation(Figure S4b, Table S6) was negatively correlated. Compared to other mutations found from phylogenetic reconstruction, the proportions of TTN mutation (50 % vs. 28.8 %, P =0.032) and TP53 mutation (43.4 % vs. 26.9 %, P =0.041) were significant higher in patients with MIP/SOL components (Fig. 4c). In the cBioPortal cohort including 2,734 LUAD patients from 7 studies, patients with TTN mutations were associated with unfavorable prognosis (Fig. 4d).
Fig. 4.
(a) The landscapes of the potential drivers from WES in different histological grades. (b) the correlation between the potential drivers from bulk WES and different histological grades. The size of the circle represents the Pearson correlation coefficient. Red represents positive correlation, and purple represents negative correlation. The ★ represents the p-value < 0.01 and the • represents p-value <0.05. (c) the significant proportion of patients with MIP/SOL components in TTN (+) vs TTN (-) and TP53 (+) vs TP53(-). (d) the Over Survival curve of the patients with TTN mutation based on the cBioPortal cohort. (e) different drivers are mutually exclusive or co-occurring based on the cohort 4 (left) and the cBioPortal cohort (right). The bule represents co-occurring, and the pink represents mutually exclusive. The ★ represents the p-value <0.01 and the • represents p-value <0.05.
The activation of malignant pathways by TTN mutation
Based on the results of the GO, KEGG, GESA analysis from TCGA database, TTN mutation as the differentially expressed genes (DEGs) were related to cell proliferation (e.g., organelle fission, nuclear division, mitotic nuclear division). TTN mutations were enriched in the Complement and Coagulation Cascades, which was related to the pathway of cell adhesion, migration, proliferation in the biological process (Figure S5a-c). In addition, the proportions of some tumor immune cell types (e.g., T cell CD4 memory activated, macrophages.M0, macrophages.M1 and others) differed in patients with or without TTN mutation by the immune correlation analysis from Tumor Immune Dysfunction and Exclusion (TIDE) (Figure S5d).
TTN mutation might synergize with ALK alteration or TP53 to generate aggressive components
To explore the correlation between tumor drivers and component drivers mentioned above, we used cohort 4 and the cBioPortal cohort to study the mutual exclusivity of these drivers in lung adenocarcinoma, respectively. In cohort 4, TTN mutation was preferred to co-occur with TP53 mutation. 2,791 lung adenocarcinoma samples were combined to show the mutual exclusivity of genes in the cBioPortal cohort, and ALK alteration frequently co-occurs with TTN mutation. TP53 mutation frequently co-occurs with TTN mutations (p <0.01) (Fig. 4e). In the above analysis of cohort 2, cohort 4, and the cBioPortal cohort we observed that ALK alteration, TP53, and TTN mutations had positive associations with MIP/SOL. Taken together, it indicated that TTN mutation might synergize with ALK alteration or TP53 to generate the aggressive components in LUAD (Fig. 5 right). This hypothesis has been further supported that the proportion of MIP/SOL in patients with both positive mutations (TP53(+) and TTN (+)) was the highest than that in patients with TP53 mutation or TTN mutation positive and patients with both negative mutation in cohort 4. Same result was also obtained from the TCGA cohort by reviewed 398 patients‘ HE sections to diagnosed MIP/SOL components (Fig. 5 left).
Fig. 5.
The proportion of the patients with or without TTN mutation and TP53 mutation in cohort 4 (above) and TCGA cohort (bottom) (left). The schematic diagram of TTN mutation synergize with ALK alteration or TP53 to generate aggressive components. Each arrow represents the way to generate aggressive components and the width of arrow presents the possibility to generate aggressive components (right).
Discussion
The histological subtype profile of invasive LUAD has been proved to be a crucial determinant of prognosis after surgery by many studies. Compared to LEP, ACI, and PAP components, the existence of MIP and SOL components indicated a poorer prognosis, and patients with such components were advised on adjuvant therapy to decrease the risk of recurrence and metastasis [[28], [29], [30], [31]]. The main purpose of this study is to explore the genetic information underlying different histological grades, especially MIP/SOL components. Therefore, we lean towards the pathological classification of the 2015 WHO classification. It is noteworthy that, according to the classification criteria of the histologic grading system, the frequency of ACI/PAP may differ from the sum of frequencies based on the predominant patterns classification criteria of ACI and PAP. This is because when both ACI components and PAP components coexist within the same case, the sum of frequencies based on the latter criteria is counted one additional time. Consequently, inconsistencies arise in the frequencies of the two classifications in the baseline information table. Currently, the genetic map for different histological subtypes of LUAD and their impact on the aggressiveness disparity, especially the landmark molecular events, were rarely studied. We herein explored the genetic heterogeneity and phylogenetic relationship in different histological subtypes/grades, by the populational-based correlation analysis and the individual-based WES via laser-capture microdissection.
Growing evidences have revealed that some driver mutations (e.g., EGFR, ALK alteration, KRAS, TP53 mutations, and so on) are related to the prognosis of LUAD with different histological subtypes [[32], [33], [34]]. Therefore, we also retrospectively analyzed the association between driver mutations and each histological grade or subtype to explore the drivers underlying the development of different histological subtypes in invasive LUAD. Our results showed that MET mutations were mostly found in patients with LEP components, EGFR mutation may prefer to ACI/PAP components while ALK alteration and TP53 mutations frequently exist in MIP and SOL components. Our findings revealed that MET mutations were predominantly observed in patients with LEP components in lung adenocarcinoma. Furthermore, EGFR mutations exhibited a preference for ACI/PAP components, while ALK alteration and TP53 mutations were frequently identified in MIP and SOL components. Briefly, this finding was consistent with the previously published results that ALK alteration and TP53 mutations may promote the MIP/SOL components in LUAD growth and indicate a poor prognosis [[35], [36], [37], [38], [39], [40], [41], [42]].
The above results were population-based. To account for inter-individual variations and gain a deeper understanding of the genomic characteristics underlying different histological grades or subtypes, the laser-capture microdissection was used to subdivide the pathological components within each specimen in the present study. To filter the interference caused by germline mutations and obtain the more accurate genetic information, the peripheral blood lymphocyte samples were collected as the paired control for somatic variant detection. Based on the mutations showed in the phylogenetic tree, MUC16 and TTN mutations were enriched in MIP/SOL components, indicating MUC16 and TTN that might play an essential role in the occurrence and progression of these malignant components.
Further verification from cohort 4 confirmed the association between the histological subtypes and mutations discovered from phylogenetic reconstruction analysis. The MUC16 mutation has been reported to cause a poorer prognosis in human carcinomas [41]. In cohort 4, MUC16 mutations were positively associated with SOL components and conferred a poorer prognosis. Furthermore, TP53 and TTN mutation were positively associated with MIP/SOL components and the TTN mutations is frequently existing in MIP/SOL components. This study further validated the relationship in the mutations mentioned above and found that TTN mutations co-occurred with TP53 and ALK alteration in cohort 4 and the cBioPortal cohort. In addition, we also revealed the activation of malignant pathways with TTN mutation from the TCGA databases, while Bailey et al. reported that TTN mutations do not act as driver genes in lung adenocarcinoma [43]. Interesting, a study from Oh JH et al showed that TTN mutations can serve as a marker for high tumor mutational burden (TMB) [44]. This finding indirectly indicates that TTN mutations have a negative impact on patient prognosis, which aligns with our observation of decreased survival in patients harboring TTN mutations in the cBioportal cohort. Based on the results of the genetic functional analysis, it is noteworthy that TTN mutations are associated with cellular processes such as cell adhesion and migration. Despite TTN not being currently acknowledged as a driver gene in lung adenocarcinoma, our findings suggest that TTN mutations warrant close attention due to their potential implications in tumor biology and progression. Further investigation is warranted to elucidate the precise mechanisms through which TTN mutations contribute to lung adenocarcinoma and their relevance for therapeutic strategies.
Taking together we put forward a hypothesis that during the growth of the lung adenocarcinoma, the ALK alteration and TP53 mutations might increase the TTN mutation rate in the tumor cell, or they act synergistically to prompt the tumor to be more malignant and aggressive. This may genetically explain the poorer prognosis in lung adenocarcinoma with MIP/SOL components. The higher proportion of MIP/SOL in LUAD patients with positive TTN mutation and TP53 mutation makes this hypothesis reliable. Although Caso et al. conducted an in-depth assessment of tumor genomic profiling in predominant LUAD histologic subtypes, their focus was primarily on parameters such as TMB, fraction of genome altered, copy number amplifications, and number of oncogenic pathways altered. The study did not specifically investigate driver genes in lung adenocarcinoma [5]. Subsequently, a published study showed a molecular map of intra-tumor heterogeneity of LUAD by multiple spatial-omics technologies [6], indicated certain epigenetic and transcriptional reprogramming underlying the component divergence, but suggested no genetic evolution. However, the genetic results may be false-negative because the analysis were limited to only several common mutations. This study analyzed genes across all exons supported genetic evolution of histological subtypes. Thereby, integrating these complementary results could help to complete the progression landscape of LUAD.
This is the first study that focuses on the populational and intratumorally genetic heterogeneity in different histological grades or subtypes. In addition, it is the largest study to explore the relationship between genetic drivers and histological subtypes, which was well validated in the population-based correlation analysis and the subtypes-based spatial WES via microdissection. However, the single-sample specimens with well-proportioned histological subtypes are rare. The samples were collected for WES contained 5 subtypes, but some subtypes were insufficient for sequencing after separated by laser-capture microdissection. Thus, it was unable to observe the comprehensive genetic map in a patient with all five histological subtypes. In addition, the causality between the mutations and the histological subtypes could not be directly determined, and it remained unclear how the mutations drive different histological subtypes. Further studies using the spontaneous tumor model to trace the process and verify the gene function are warranted in the future.
Conclusion
Our study indicates that different mutations were enriched in specific histological subtypes. LUAD with ALK alteration or TP53 mutation is enriched in MIP/SOL components. It also identifies TTN mutation is the potential driver for the occurrence of MIP/SOL components, which implies TTN mutations might synergize with ALK alteration and TP53 mutations during the tumor growth to confer aggressive components. More works are encouraged to study how these drivers behind the histological subtype interact to facilitate the malignant progression.
Ethical approval and consent to participate
This study obtained the ethical approval from the Ethics committee of the First Affiliated Hospital of Guangzhou Medical University.
Consent for publication
All authors of this study consent to publication.
Availability of supporting data
The datasets used and analyzed in this study are available from the corresponding author upon reasonable request.
The raw sequence data of WES generated from 146 and 5 patients reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics & Bioinformatics 2021) in National Genomics Data Center (Nucleic Acids Res 2022), China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA-Human: HRA005908 and HRA005952) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa-human. Other data that support the findings of this study are available from the corresponding author upon request.
Funding
This study is supported by National Natural Science Foundation of China (NSFC) (Grant No. 81871893; Grant No. 82022048) and Key Project of Guangzhou Scientific Research Project (Grant No. 201804020030).
CRediT authorship contribution statement
Jianfu Li: Writing – review & editing, Writing – original draft, Validation, Software, Methodology, Formal analysis, Data curation, Conceptualization. Shan Xiong: Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Data curation. Ping He: Supervision, Methodology, Formal analysis, Data curation. Peng Liang: Writing – original draft, Methodology, Formal analysis. Caichen Li: Formal analysis, Data curation. Ran Zhong: Writing – review & editing, Writing – original draft. Xiuyu Cai: Data curation. Zhanhong Xie: Formal analysis, Data curation. Jun Liu: Data curation. Bo Cheng: Writing – original draft, Formal analysis. Zhuxing Chen: Formal analysis. Hengrui Liang: Formal analysis. Shen Lao: Formal analysis. Zisheng Chen: Formal analysis. Jiang Shi: Formal analysis. Feng Li: Writing – review & editing. Yi Feng: Writing – review & editing. Zhenyu Huo: Writing – review & editing. Hongsheng Deng: Writing – review & editing. Ziwen Yu: Writing – review & editing. Haixuan Wang: Writing – review & editing. Shuting Zhan: Writing – review & editing. Yang Xiang: Writing – review & editing. Huiting Wang: Writing – review & editing. Yongmin Zheng: Data curation. Xiaodong Lin: Data curation. Jianxing He: Writing – review & editing, Supervision, Formal analysis, Conceptualization. Wenhua Liang: Writing – review & editing, Supervision, Formal analysis, Conceptualization.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
Thanking the website that BioRender.com for helping to create Fig. 1 and some parts of Fig. 5 in our study.
Footnotes
Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.neo.2024.101013.
Contributor Information
Jianxing He, Email: drjianxing.he@gmail.com.
Wenhua Liang, Email: liangwh1987@163.com.
Appendix. Supplementary materials
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets used and analyzed in this study are available from the corresponding author upon reasonable request.
The raw sequence data of WES generated from 146 and 5 patients reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics & Bioinformatics 2021) in National Genomics Data Center (Nucleic Acids Res 2022), China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA-Human: HRA005908 and HRA005952) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa-human. Other data that support the findings of this study are available from the corresponding author upon request.