Genomic and Transcriptomic Dynamics in the Stepwise Progression of Lung Adenocarcinoma

Genomic and Transcriptomic Dynamics in the Stepwise Progression of Lung Adenocarcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Genomic and Transcriptomic Dynamics in the Stepwise Progression of Lung Adenocarcinoma Haiquan Chen, Yue Zhao, Jun Shang, Yueren Yan, He Jiang, Han Han, and 15 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4977481/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Lung adenocarcinoma (LUAD) progresses from pre-invasive to invasive stages, as well as from ground-glass opacities (GGOs) to solid nodules. However, the dynamic genomic and transcriptomic changes underlying LUAD progression remain incompletely understood. Here we analysed genomic and transcriptomic profiles of 1,008 LUAD samples, encompassing all developmental stages in 954 patients, with comprehensive follow-up data. Frequently mutated driver genes were identified, with more mutations in tumor suppressor genes and higher levels of genomic instability observed as the disease progressed. Notably, MAP2K1 E102-I103 deletion was frequently identified in pre-invasive samples, which endowed Alveolar Type II (ATII) cells with increased growth potential and initiated tumor formation, suggesting it a potential driver mutation of LUAD. In summary, our study highlights key molecular changes in the stepwise progression of lung adenocarcinoma, provides insights into identifying novel therapeutic targets, and helps define the curative time window for this disease. Health sciences/Diseases/Cancer/Lung cancer/Non-small-cell lung cancer Health sciences/Diseases/Cancer/Oncogenes Health sciences/Medical research/Genetics research Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Lung cancer is the leading cause of cancer-related death worldwide, presenting a multifaceted challenge in cancer research 1 . Among the various pathological subtypes, lung adenocarcinoma (LUAD) is the most common, accounting for more than 45% of all lung cancer cases, and its incidence is still rising 2 . It is generally believed that lung adenocarcinoma progresses through a sequence of atypical adenomatous hyperplasia (AAH) to adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and eventually to invasive adenocarcinoma (ranging from Stage I to stage IV disease). On CT imaging, some lung adenocarcinomas progress from ground-glass opaciites (GGOs) to part-solid nodules containing both GGO and solid components, and finally to solid nodules 3 – 5 . Studies have demonstrated an association between radiological manifestations and pathological stages: most AIS/MIA lesions tend to present as pure GGOs, while the presence of solid components on CT images correlates with increased histological invasiveness 6 . Pre-invasive stages of lung adenocarcinoma, such as AIS and MIA, as well as LUADs presenting as pure GGOs, have a nearly 100% survival rate following complete surgical resection, leading to the characterization of GGO-like LUADs as an inert subtype due to their slow growth and favourable prognosis 6 – 9 . Nonetheless, the 5-year survival rate for invasive adenocarcinoma drops dramatically as the disease develops from Stage I to Stages II, III and IV, and from pure GGOs to solid nodules 10 . Therefore, it is crucial to uncover the dynamic changes in genomic and transcriptomic profiles as LUAD progresses, and to find out evidence of the optimal curative window for surgical intervention that offers definitive cure while avoiding overtreatment 11 . Metastasis plays a crucial role in cancer-related death, with tumors at more advanced stages showing higher metastatic rates and poorer survival outcoms 12 , 13 . understanding how the genetic profile of LUAD may influence the pattern of metastatic spread is pivotal for refining treatment, predicting disease outcomes and planning follow-up strategies, this prompts us to delve into the intricate landscape of site-specific metastasis in lung adenocarcinoma 14 – 17 . In this study, we perform whole-genome sequencing (WGS) and RNA sequencing (RNA-seq) on 1,008 lung adenocarcinoma samples encompassing all pathological and radiological features, along with matched adjacent normal lung tissues from 954 patients with complete follow-up data. Our aim is to provide an extensive exploration of the dynamically changing genomic and transcriptomic profiles associated with the progression of LUAD. Results Clinical and Pathological characteristics of patients A total of 1,008 tumors from 954 patients were included in this study. The median recurrence-free survival (RFS) was 56.0 months, and the median overall survival (OS) was 60.8 months. Among the patients, 548 (57.4%) were female and 406 (42.6%) were male. Additionally, 731 patients (76.6%) were self-reported never smokers. The median age was 60 years (range: 23–89). Of all the lesions, 141 (14.0%) appeared as pure ground-glass opacities (GGOs), 287 (28.5%) appeared as mixed GGOs, and 580 (57.5%) appeared as solid nodules on CT scans. The pathological classifications included 1 case of atypical adenomatous hyperplasia (AAH, 0.1%), 42 cases of adenocarcinoma in situ (AIS, 4.2%), 116 cases of minimally invasive adenocarcinoma (MIA, 11.5%), and 849 cases of invasive adenocarcinoma (LUAD, 84.2%, Fig. 1a-b). During follow-up, 200 patients experienced disease recurrence, with 83 (41.5%) at Stage I, 25 (12.5%) at Stage II, 87 (43.5%) at stage III, and 7 (3.5%) at stage IV (Fig. 1c). Pre-invasive lesions demonstrated perfect survival after complete surgical resection, as evidenced by 100% recurrence-free survival (RFS) in patients with AAH/AIS/MIA throughout the follow-up period (Fig. 1d). Furthermore, RFS for lesions presenting as pure GGOs on CT scans was also 100%, indicating a subset of invasive adenocarcinomas (mainly the lepidic predominant subtype) had excellent outcomes after complete surgical resection (Fig. 1e). Notably, there were more female patients with early-stage lung adenocarcinomas, and the proportion of patients with a smoking history increased as the disease progressed to higher stages (Fig. 1f-h). Subtypes considered low-grade and associated with poor prognosis, such as the micropapillary and solid subtypes 18 , were more commonly seen in the later stages of the disease (Fig. 1i). For patients who experienced disease recurrence after surgery, the sites of recurrence were recorded. Apart from recurrences in the lungs (31.5%), the most common metastatic sites were the brain (28.0%), bone (20.0%), lymph nodes (10.0%) and liver (6.0%, Fig. 1j). Additionally, 65 of 200 (32.5%) patients had disease recurrence in less than 12 months after surgery, while 135 of 200 (67.5%) patients had disease recurrence in more than 12 months post-surgery (Fig. 1k). Landscape of somatic mutations in lung adenocarcinoma at different pathological stages Somatic mutations of major driver genes and tumor suppressor genes in our study cohort were identified. We first divided the samples into pre-invasive (AAH/AIS/MIA) and invasive (LUAD) ones, and found that in pre-invasive (AAH/AIS/MIA) samples, the most frequently mutated gene was EGFR (50%), followed by RBM10 (13%), ERBB2 (11%), KMT2D (11%), MAP2K1 (10%) and BRAF (5%). In contrast, in LUAD samples, the most frequently mutated gene was EGFR (67%), followed by TP53 (32%), RBM10 (11%), KRAS (7%), KMT2D (7%) and ARID1A (4%, Fig. 2a). We observed a significantly higher mutation frequency in EGFR and KRAS mutations and ALK fusions in invasive stages compared to pre-invasive stages of lung adenocarcinoma (supplementary Fig. 1a). However, a significantly lower mutation frequency in ERBB2 and MAP2K1 was seen in invasive adenocarcinoma compared with pre-invasive lesions (supplementary Fig. 1b). Notably, we identified a mutation hotspot in MAP2K1 , a potential candidate driver mutation of lung adenocarcinoma in the Ras/Raf/RTK pathway, which was previously reported to have a mutation in 2 out of 230 (0.9%) patients with invasive lung adenocarcinoma 19 . In our study, 13 cases with MAP2K1 p.E102-I103 deletion were identified, of which 11 were AIS/MIA samples (Fig. 2a-b). This mutation hotspot was also identified in our previous study, which utilized whole-exome sequencing to decipher the genomic profiles of pre-invasive and invasive lung adenocarcinoma 20 . In that study, 4 out of 197 (2.0%) lung adenocarcinoma patients harboured MAP2K1 p.E102-I103 deletion, with all the 4 samples being pre-invasive (Fig. 2c). Being part of the RTK/RAS/RAF pathway and mutually exclusive to known driver mutations of lung adenocarcinoma, MAP2K1 p.E102-I103 deletion might be considered a potential driver mutation of lung adenocarcinoma. The frequency of mutations in tumor suppressor genes tended to increase with tumor stage progressionn, including mutations in TP53, RB1, MGA, KEAP1 and STK11 (supplementary Fig. 1c). The median tumor mutation burden (TMB) across the entire study was 1.73 mutations per megabase (Mb), with 1.20/Mb for AAH/AIS/MIA and 1.81/Mb for LUAD (Fig. 2d, supplementary Fig. 2a). TMB increased as tumors progressed to higher radiological and pathological stages (supplementary Fig. 2b-c). Landscape of somatic copy number alterations at different pathological stages Somatic copy number alterations (SCNAs) were identified and categorized as gains or losses in both pre-invasive and invasive samples. In pre-invasive samples, the most frequently observed SCNA event was a gain in chromosome 5p, followed by gains in chromosomes 8p, 8q, 17q, 7p and 16p (Fig. 2a, 2e). Copy number losses in chromosomes 19p and 19q were also identified in pre-invasive samples (Fig. 2a, 2e). In invasive samples, the most frequent gains were observed in chromosomes 7p, 5p, 1q, 8q and 7q, while the most frequent losses were in chromosomes 8p and 21p (Fig. 2a, 2e). Co-mutation status and its survival indications Driver events in tumors can be mutually exclusive or tend to co-occur, and their co-mutation status has been reported to have prognostic value 21 , 22 . Known oncogenic mutations in lung adenocarcinoma, such as mutations in KRAS, EGFR, ERBB2 and BRAF , were mutually exclusive in our cohort (Fig. 2f). Notably, MAP2K1 mutations were mutually exclusive to these major oncogenic mutations (Fig. 2f). Mutual exclusivity was observed in certain oncogene-tumor suppressor gene pairs as well - STK11 and KEAP1 were mutually exclusive with EGFR mutations, and TP53 was mutually exclusive with BRAF mutations (Fig. 2f). In total, 66 co-mutation pairs involving major oncogenes and tumor suppressor genes were identified. These included pairs consisting of one oncogene and one tumor suppressor gene, such as EGFR co-mutated with TP53/RBM10 , and pairs consisting of two tumor suppressor genes, such as TP53 co-mutated with KEAP1/ARID1A/RB1/FAT1 (Fig. 2f). Among co-mutation pairs of one oncogene and one tumor suppressor gene, with a mutation frequency greater than 3% in the invasive group, 24 pairs were associated with worse recurrence free survival (RFS) and 24 were associated with worse overall survival (OS, supplementary Fig. 3a-b). For pairs consisting of two tumor suppressor genes with a mutation frequency greater than 3% in the invasive group, 27 pairs were associated with worse RFS and 24 pairs were associated with worse OS (supplementary Fig. 3a-b). Interestingly, co-mutation with TP53 was the most common factor associated with worse survival outcomes, impacting both RFS and OS in patients. This was followed by STK11 , which was also associated with poorer survival outcomes in several co-mutation pairs (supplementary Fig. 3c-d). In summary, co-mutation pairs involving oncogenes and tumor suppressor genes, as well as pairs involving two tumor suppressor genes, hold significant clinical importance and prognostic value in lung adenocarcinoma. MAP2K1 p.E101-I102 deletion drives lung adenocarcinoma formation via enhanced Ras/Raf/RTK signaling in alveolar type II organoids Given the oncogenic potential of MAP2K1 within the Ras/Raf/RTK pathway, and its hotspot mutation p.E102-I103 deletion being mutually exclusive with other oncogenic mutations and significantly enriched in pre-invasive lung adenocarcinomas (Fig. 2f), we conducted in vitro and in vivo experiments to assess its oncogenicity. We generated mouse lung ATII organoid culturing from C57BL/6J Trp53 L/L ; LSL-Cas9 tdTomato mice, where the floxed Trp53 gene could be conditionally inactivated and tdTomato-Cas9 could be conditionally activated by Cre recombinase. These Trp53 L/L ; Cas9 ATII organoids formed spheres after 5 days of culture (Fig. 3a-c). Following expansion, these organoids were infected with adenovirus-Cre (Ad-Cre). The tdTomato + cells were then sorted using flow cytometry, generating Trp53 −/− ATII organoids (Fig. 3a). To investigate the oncogenic potential of the MAP2K1 ΔE101−I102 mutation, we overexpressed the MAP2K1 ΔE101−I102 mutant (MT) and wild-type (WT) MAP2K1 in the Trp53 −/− organoids, alongside a negative control lentivirus. The mutant and wild-type MAP2K1 cDNA sequences were verified by genomic sequencing (supplementary Fig. 4). Western blot analysis showed that overexpression of MAP2K1 ΔE101−I102 significantly upregulated phosphorylated MEK1 (pMEK1) and markedly increased the expression of phosphorylated ERK (pERK), which is the direct downstream molecule of MEK in the MAPK signalling pathway (Fig. 3b). We then cultured the organoids in vitro for five days and performed viability assays, finding the proliferation capability of the MAP2K1 ΔE101−I102 group was significantly higher than that of the WT and NC groups (Fig. 3d). Morphologically, the spheroids formed in the MAP2K1 ΔE101−I102 group were also significantly larger than those of the other two groups (Fig. 3e). All three groups showed strong positive for Ttf1 and EpCAM through IHC assays (Fig. 3c). These data demonstrated that the MAP2K1 mutant could promote proliferation and adeno-differentiation of ATII organoids in vitro . To assess the tumorigenic capability of genetically engineered organoids in vivo , we implanted the Trp53 −/− ; MAP2K1 MT organoids, Trp53 −/− ; MAP2K1 WT organoids and control Trp53 −/− organoids into the lower flanks of NSG mice (Fig. 3a). Strikingly, 10 days after injection, tumors formed in mice injected with Trp53 −/− ; MAP2K1 MT organoids, but not in those injected with the Trp53 −/− organoids or Trp53 −/− ; MAP2K1 WT organoids (Fig. 3f-h). Importantly, this suggests that loss of Trp53 alone or combined with overexpression of MAP2K1 WT in ATII organoids is not sufficient to generate LUAD in vivo . Histologic analysis of Trp53 −/− ; MAP2K1 MT tumors revealed strong expression of Ttf1 and EpCAM , consistent with LUAD markers (Fig. 3i). In summary, these findings support that MAP2K1 ΔE101−I102 , in the absence of Trp53 , can drive LUAD formation in vivo . Comparison of genomic features of lung adenocarcinoma at different developmental stages It is generally believed that before developing into lung adenocarcinoma, cells progress through stages of AAH, AIS and MIA (Fig. 4a). EGFR, KRAS and ALK had higher mutation frequencies as disease progressed from pre-invasive (AAH/AIS/MIA) to invasive stages (supplementary Fig. 1a). Mutations in genes within the RTK-RAS pathway were also more commonly observed in invasive stages (supplementary Fig. 1a). Regarding tumor suppressor genes, there was a significant increase in the frequencies of TP53, KEAP1 and STK11 mutations from pre-invasive to invasive stages. Notably, the mutation frequency of TP53 was 0 in AAH/AIS/MIA samples but rose sharply as the disease progressed to stage I LUAD and continued to increase in higher stages (supplementary Fig. 1c). Interestingly, KEAP1 mutation frequency remained was relatively low until the disease reached stage IV LUAD, suggesting it a late event in tumor evolution 23 . A combination of all tumor suppressor genes was found significantly increased in mutation frequencies as the disease progressed (supplementary Fig. 1c). Genomic instability was higher in LUAD compared with AAH/AIS/MIA samples We observed a higher level of genomic instability in LUAD compared to AAH/AIS/MIA, characterized by an increased TMB and a higher level of SCNA burden as tumors progressed to advanced stages. A sharp increase was seen between AAH/AIS/MIA and stage I LUAD samples (Fig. 4b, supplementary Fig. 5a). Whole genome doubling (WGD) events were also more frequent in LUAD compared to AAH/AIS/MIA (Fig. 4b). Additionally, structural variances were more common as the disease progressed, with a notable increase in the number of structural variants between AAH/AIS/MIA and stage I LUAD samples (Fig. 4b, supplementary Fig. 5b). To gain a deeper understanding of the progressive and prognostic value of SCNA events, we compared the frequency of focal copy number alterations. We found that 14 copy number amplifications and 3 copy number deletions were significantly more frequent in LUAD compared to AAH/AIS/MIA samples (Fig. 4c). Among the 17 significantly different focal SCNA events, the amplification of 14q13.3, where NKX2-1 is located, and the loss of 9p21.3, where CDKN2A/2B were located, were of significant prognostic value (Fig. 4d). Patients harbouring either 14q13.3 amplifications or 9p21.3 losses had significantly worse RFS and OS (Fig. 4e, 4f). APOBEC signature activity was higher in LUAD compared with AAH/AIS/MIA samples Mutation signatures reflect normal cell biology, environmental exposures, and neoplastic progression. They are associated with survival outcomes and have therapeutic implications for patients of various cancer types 24 – 28 . In this study cohort, signatures of single base substitutions (SBS) were identified and compared across groups. We found that the activity of APOBEC signature (SBS2/13) tended to increase as the disease progressed to higher stages (Fig. 4b). Additionally, patients with higher APOBEC signature activities had significantly worse RFS and OS (Fig. 4g, 4h). Discussion Cancer is a systemic disease, with its initiation and progression a multistep process 32 . In this study, we analysed genomic and transcriptomic profiles across different pathological and radiological stages. Our aim was to provide comprehensive knowledge of the genomic and transcriptomic changes that occur as lung adenocarcinomas progress from pre-invasive stages to late-stage metastatic tumors (Fig. 1). Driver mutations in major oncogenes in LUADs, such as EGFR, KRAS, ERBB2 , etc., were identified in as early as pre-invasive stages, namely AAH, AIS and MIA, indicating that cells had already acquired key genetic changes and undergone malignant transformation in these very early stages (Fig. 2a). From a clinical perspective, these pre-invasive lung adenocarcinomas, along with LUADs presented as pure GGOs on CT imaging, had nearly 100% 5-year or even 10-year survival rates after complete surgical resection 6 – 9 . As the survival rate drops dramatically when tumors progress to invasive stages, this indicates a curative time window for complete surgical resection in patients with early-stage lung adenocarcinoma. Previous studies have depicted mutational landscapes and identified major driver genetic events in LUADs 14 , 19 , 33 , 34 . In this study, EGFR mutations were detected in 64.5% sequenced samples, reflecting the East Asian population and predominance of never smokers in the study cohort. Compared with invasive lung adenocarcinomas, pre-invasive stages – AAH, AIS and MIA – had lower mutation frequencies in genes in the RTK-RAS pathway. Moreover, mutations in tumor suppressor genes were more frequently detected in invasive lung adenocarcinomas, suggesting the key role of loss of function of these genes in the progression of lung adenocarcinoma. Specifically, TP53 was not detected in AAH/AIS/MIA but rose to 32% in LUADs, highlighting its critical role in disease progression from precursors to invasive tumors 35 , 36 . In line with a previous study which involves 98 pre-invasive and 99 invasive lung adenocarcinomas, we found that tumor mutation burden (TMB), APOBEC signature activity, WGD events, and SCNA burden all increased as tumor progressed from pre-invasive to invasive stages 20 . By utilizing whole-genome sequencing data, we were able to detect structural variants (SVs). As a result, SVs were more prevalent as the disease progressed to higher stages, with a sharp increase in SV burden from MIA to stage I LUAD, suggesting a sudden change in genomic instability as disease progressed from pre-invasive to invasive stages. A similar trend was observed radiologically, offering insights into the genomic changes of lung adenocarcinomas manifesting as pure and mixed GGOs on CT scans 6 , 7 . It has been reported that patients with pure GGOs had excellent survival rate after surgery 8 , 9 . As lung adenocarcinomas may develop from pure GGOs to solid nodules on CT scans, our study provides insights into the genomic and transcriptomic changes during this process, offering evidence for the curative window of lung adenocarcinoma. While oncogenes are usually considered mutually exclusive, their co-occurrence with tumor suppressor genes has been reported to be associated with poor prognosis 19 , 21 , 37 . In this study, we analysed the prognostic value of co-mutations involving one oncogene and one tumor suppressor gene, as well as co-mutations of two tumor suppressor genes. Among them, co-mutations involving TP53 were the most common and had the most significant negative impact on survival, suggesting a pivotal role of TP53 in lung adenocarcinoma. Interestingly, mutual exclusivity was also observed between oncogenes and tumor suppressor genes; for example, EGFR was mutually exclusive with STK11 and KEAP1 , and BRAF was mutually exclusive with TP53 (Fig. 2f). T findings could shed light on individualized treatment strategies for lung adenocarcinoma 38 , 39 . Driver mutations are the ones that play critical roles in cancer initiation and progression 40 , 41 . Identifying driver mutations is significant in discovering novel drug targets, although defining a driver mutation can often be challenging 42 , 43 . In this study, we identified a hotspot mutation (p.E102-I103 deletion) in MAP2K1 , which encodes a protein kinase essential to the MAP kinase signal transduction pathway 44 . This mutation was previously found in 2 out of 230 (0.9%) patients with LUAD 19 . Interestingly, in our study cohort, 13 patients harboured this mutation, with 11 (6.9%) of them being pre-invasive samples. This mutation hotspot was also detected in our previous study, where 4 out of 98 (2.0%) pre-invasive adenocarcinoma patients harboured it 20 . In vitro and in vivo experiments found that ATII organoids with overexpressed MAP2K1 ΔE101−I102 significantly outgrew ATII organoids with overexpressed wild-type MAP2K1 and the control group. Moreover, in vivo experiments suggested that MAP2K1 ΔE101−I102 in the absence of Trp53 could drive LUAD formation in NSG mice. These results suggest that the MAP2K1 p.E102-I103 deletion could be a novel driver mutation of lung adenocarcinoma. Though mainly enriched in pre-invasive stages, this finding provides new insights into the initiation and evolution of lung adenocarcinoma. Metastasis is the leading cause of cancer-related death, and lung adenocarcinomas harbouring different genomic alterations exhibit organotropism 17 , 45 . In this study, we compared the genomic and transcriptomic profiles of patients with site-specific metastasis and those without metastasis after surgery. We found that TP53 mutations were significantly associated with brain metastasis, while mutations in the TP53 pathway were significantly associated with brain metastasis and lung recurrence. High activity of APOBEC signature was observed in patients with brain and lymph node metastasis. Up-regulation of SPP1 and down-regulation of SFTPC were also found in patients with metastasis both in general and specifically in brain and bone metastasis, while SPP1 was also up-regulated in patients with lung recurrences. Moreover, up-regulation of SPP1 was associated with worse prognosis, and it was found to be up-regulated in a stepwise manner in comparing tumors with different radiological manifestations. Its association with CD8 + T cell and Treg infiltrations also implied changes in the tumor microenvironment. The up-regulation of SPP1 in patients with bone, brain and lung recurrences suggests that SPP1 could be a potential biomarker for monitoring the progression of lung adenocarcinoma. In summary, this integrated genomic and transcriptomic study provides a comprehensive understanding of the progression of lung adenocarcinoma, shedding light on defining the therapeutic window for surgical resection, guiding individualized treatment strategies, and offering evidence for drug discovery in lung adenocarcinoma. Methods Study cohort A total of 1,008 surgically resected samples from 954 patients with LUAD who underwent surgery between August 2011 and March 2019 at the Department of Thoracic Surgery, Fudan University Shanghai Cancer Center, were retrospectively included in this study. None of the patients received neoadjuvant therapy. Informed consents of all patients for donating their samples to the tissue bank of Fudan University Shanghai Cancer Center were obtained from patients themselves or their relatives. This study was approved by the Committee for Ethical Review of Research (Fudan University Shanghai Cancer Center Institutional Review Board, No. 090977-1). Radiological and histological evaluation Each patient received whole-lung CT scanning before surgery. Tumor size on CT images was defined as the maximum diameter of the lesion, and solid component on the single largest axial dimension was recorded on lung window. Based on the CT images, those pulmonary nodules were further categorized into 3 groups: pure GGOs, where there was no solid component in one pulmonary nodule; mixed GGOs, where both solid and GGO components existed in the same pulmonary nodule; and solid nodules, where the nodule contained only solid components (Fig. 5a). CT images were reviewed by two independent radiologists, with inter-observer and intra-observer agreements measured to quantify the reproducibility and accuracy as previously described 6 . In terms of histological diagnosis, intraoperative frozen section diagnosis was first made after the tumor was resected, and final postoperative pathological diagnosis was made after surgery by two independent pathologists. Following IASLC/ATS/ERS guidelines, tumors were classified as AIS, MIA or invasive LUAD based on their histological presentations 46 . Invasive LUADs were further subclassified into different subtypes, namely lepidic, acinar, papillary, micropapillary, solid and invasive mucinous subtypes. The subtypes were determined depending on the cell morphology under microscopes and recorded in 5% increments. The predominant subtype was defined as the one with the largest percentage in one sample 46 . Pathological stage of the disease was determined according to the eighth TNM staging system 10 . Follow-up protocol Patients were followed up regularly after surgery as we previously described 6 . Briefly, patients were followed up every 3 months for the first 2 years after surgery, where physical examination, chest CT scans and abdominal ultrasonography were performed every 3–6 months. The follow-up interval was changed to every 6 months for the third year and once a year from the fourth year onwards. Brain CT or magnetic resonance imaging (MRI) and bone scintigraphy were performed every 6 months for patients with invasive LUADs in the first 3 years. In addition, positron emission tomography (PET)-CT scans were performed if necessary. Recurrence-free survival (RFS) was defined as the time between surgery and first recurrence or last follow-up. Patients with no recurrence but died from other causes were censored on that date. Overall survival (OS) was defined as the time between surgery and death or the last follow-up date. Whole-genome sequencing (WGS) and RNA sequencing (RNA-seq) Genomic DNA from tumors and paired adjacent normal lung tissues were extracted and prepared using the QIAamp DNA Mini Kit (Qiagen, Germany) following the manufacturer’s instructions. A total amount of 2µg genomic DNA for each sample was fragmented into an average size of ~ 350 bp. Libraries were constructed using and sequenced on Illumina NovaSeq 6000 platform and 150-bp paired end reads were generated. Total RNA from tumors and paired adjacent normal lung tissues was extracted and prepared using NucleoZOL (Macherey-Nagel, Germany) and NucleoSpin RNA Set for NucleoZOL (Macherey-Nagel, Germany) following the manufacturer’s instructions. A total amount of RNA sample was used as initial material for RNA sample preparations. Ribosomal RNA was removed using KAPA Stranded RNA-Seq Kit with RiboErase (KK8481, Roche, Switzerland). Libraries were generated and sequenced on Illumina NovaSqe 6000 platform, and 150-bp paired end reads were generated. Quality control Ensuring the quality control of the entire sequencing data generation process relies on the utilization of Quartet reference material 47 , 48 . In our experimental design, we performed DNA and RNA library construction with a maximum of 96 samples, including 94 lung cancer samples and 2 Quartet reference samples. This design guarantees that each lung cancer sample is accompanied by Quartet reference samples throughout the processes of library construction, sequencing, and data analysis. Our prior studies have established quality control metrics based on Quartet reference material, encompassing Mendelian consistent rate (MCR) and F1 score at the DNA level 49 , as well as signal-to-noise ratio (SNR) at the RNA-seq level 50 . Once all batches of lung cancer samples and their corresponding Quartet samples are generated, we analyze the Quartet samples and calculate the MCR, F-score, and SNR. These metrics are then used to assess the quality of each batch of data, ensuring qualification. Simultaneously, we monitor the consistency of experimental conditions across batches, thereby guaranteeing the generation of high-quality sequencing data. The quality and data size of genomic and transcriptomic sequencing data were assessed using FastQC (v0.11.9). The Fastq Screen (v0.15.1) software was employed to analyze the ratio of sequenced reads across species, determining whether the target DNA and RNA were contaminated with genetic material from other species. NGSCheckMate (v1.0.0) 51 was employed to calculate the allele fractions of known single-nucleotide polymorphisms (SNPs) in each sample and to assess whether the samples originated from the same individual. This was done by comparing the correlation of allele fractions of these SNPs between samples. In cases where Tumor and Normal samples were unpaired, they were excluded before somatic variant calling. The WGS data size for this cohort exceeded 150 GB, while the RNA-seq data size surpassed 15 GB (Figures S9 A and S9B). NGScheckmate confirmed that both the tumor and paired normal samples originated from the same individual (Figure S9 C). All samples underwent base quality assessment, including Q30 scores (Figures S9 A and S9B), with Quartet DNA and RNA standards incorporated into each batch for quality control (Figure S9 D). Each batch included 94 lung adenocarcinoma samples along with 2 Quartet standard material samples. The quality of Quartet DNA and RNA data was evaluated using specific metrics: F-score and Mendelian Concordance Rate (MCR) for DNA sequencing, and Signal-to-Noise Ratio (SNR) for RNA sequencing. The results of these evaluations—F-score, MCR, and SNR—demonstrate that the WGS and RNA-seq data generation for all tumor samples in this cohort is consistently reliable throughout the entire process (Figures S9 E and S9F). Somatic variant analysis We harnessed the capabilities of the BWA algorithm, integrated into Sentieon (version sentieon-genomics-202112.04) 52 , 53 , to align reads originating from tumor and normal samples to the human reference genome (GRCh38.d1.vd1.fa). Following this alignment, Sentieon's Dedup function was applied to tag and eliminate duplicated reads, ensuring the integrity of downstream analyses. For subsequent analyses, encompassing somatic variations and Indel assessments, we leveraged the aligned data from both tumor and normal samples (Dedup Bam). The comprehensive Mutation Variants Calling process involved the utilization of TNseq 52 , TNScope 53 (sentieon-genomics-202112.04), and strelka2 (v2.9.10) 54 . Variants that received concurrent calls from at least two software tools were retained. Additionally, we conducted manual verification using the Integrative Genomics Viewer (IGV) for common mutation sites in lung adenocarcinoma. Somatic varians were annotated utilizing ANNOVAR (v.2019-10-24) 55 and Variant Effect Predictor (VEP, v104.0) 56 . Tumor mutation burden (TMB) was characterized as the total count of non-synonymous single nucleotide variants per megabase (mut/Mb), with the capture size aligning with the TCGA cohort. Somatic variant signature Mutational signature underwent analysis using the SigProfiler computational framework 57 , with SigProfilerExtractor (v1.1.21) employed to profile Single Base Substitutions (SBS). Single Base Substitutions (SBS), also known as single nucleotide variants, involve the replacement of specific nucleotide bases. In the context of pyrimidines within the Watson-Crick base pair, six possible substitutions exist: C > A, C > G, C > T, T > A, T > C, and T > G. A total of 96 COSMIC SBS mutations have been identified. The mutations in each sample were decomposed based on the COSMIC SBS mutation signature (v3.3) using the Analyzer function in SigProfilerExtractor. Subsequently, the mutation probability or number of mutations for each SBS signature in each sample was generated, enabling diverse genetic characterizations based on clinical phenotype. APOBEC-induced mutations are primarily linked to C > T transition events. Except for SBS2 and SBS13, we also used trinucleotideMatrix function in maftools to evaluate APOBEC Enrichment Score. Copy number variant analysis The parsing of copy number variants was executed using ascatNgs (v4.3.3) 58 with the ASCAT (v2.5.1) 59 wrapper. Subsequent copy number variant analyses were performed on tumor and normal BAM files derived from Sentieon-BWA aligment. The reference genome employed for these copy number variant analyses corresponded to the human reference genome used in the Sentieon-BWA alignment (GRCh38.d1.vd1.fa). In the clinical phenotype linkage analysis, samples lacking ASCAT solutions or demonstrating 100% purity were excluded from both the copy number and clinical phenotype linkage analyses. GISTIC2 (v2.0.23) 60 is utilized for analyzing Copy Number Variation (CNV) amplification or deletion changes at the arm, focal, and gene levels. Each sample's copy number variation segment file was obtained using ascatNGS, and these segment results were employed as input for GISTIC. GISTIC applied parameters, including ta 0.25, td 0.25, qvt 0.25, cap 1.5, brlen 0.5, conf 0.95, armpeel 1, broad 1, and savegene1. In the cohort, significant changes in arm events were identified using a threshold of q < 0.01, while deletions less than − 1 and amplifications greater than or equal to 2 are displayed in the oncoplot. At the gene level, Copy Number Alteration (CNA) amplification and deletion were identified using thresholds of -2 and + 2. The TCGA CNV ASCAT2 data were analyzed using the same methods and parameters. GISTIC amplification and deletion profiles were independently analyzed using the R package cngpld (v0.1), which employed a gaussian process latent difference model. This approach allowed for the identification of specific genomic regions within the invasive LUAD cohort that exhibited significantly higher G scores compared to those in the preinvasive LUAD cohort. HLALOH HLALOH identification was carried out using the LOHHLA algorithm 61 . Initially, the HLA typing for each patient was determined by POLYSOLVER (v1.0.0) 62 , based on the WGS sequencing data from paired normal samples. Subsequently, the HLA fasta file was compared with the WGS data from tumors and paired normal tissues for each patient to evaluate the LOH status of their HLA typing. Structural variation analysis Structural variation (SV) analysis was conducted on aligned tumor and normal bam file using GRIDSS2 (v2.13.2) 63 . Following the retrieval of SV data from all tumor samples, the events were filtered using the panel-of-normal file provided by GRIDSS2. The SV events from each sample were then categorized into five types based on variant characteristics: CTX, Inversion, Insertion, Deletion, and Duplication. RNA-seq data analysis Expression profiling from raw FASTA data was accomplished through the utilization of the Hisat2-StringTie pipeline 64 . Initial preprocessing steps involved the application of FastP (v0.36) 65 to remove adapters from the raw RNA-seq reads. The alignment of reads to the human reference genome (GRCh38, release-84) was executed using Hisat2 (v2.2.1), with data sourced from Genomic Data Commons (GDC). Following alignment, the reads were assembled into transcripts or genes by StringTie (v2.2.1), utilizing the genome annotation file (gencode.v36.annotation.gtf). To quantify gene expression, Fragments Per Kilobase of exon model per Million mapped fragments (FPKM) was employed. Gene fusion detection In the analysis of gene fusions, STAR-Fusion 66 was utilized to identify and profile potential fusion events within RNA-seq data. The workflow involved aligning RNA-seq reads to a reference genome, systematically examining the aligned reads for abnormal gene connections indicative of fusion events, and implementing rigorous filtering criteria to preserve only validated fusion events for subsequent analysis. The confirmed fusion events then underwent detailed annotation, furnishing comprehensive insights into the implicated genes, their genomic coordinates, and potential functional implications. Differential gene expression, mutation and copy number alterations analysis The limma 67 package (v3.50.0) in R was employed for differential expression analysis comparing clinical phenotype. Differentially expressed genes were identified using standard cutoffs (p = 1) 68 . Fisher's exact test (p < 0.05) was employed to identify differentially mutated genes between GGOs and solid cohorts, as well as between the no-relapse and relapse cohorts. ssGSEA analysis To estimate gene-set enrichment scores, the R package GSVA (v1.42.0) 69 with the default GSVA method was employed. Following the analytical approach of the previous study 70 , the hallmark gene set (h.all.v7.5.symbols.gmt) was downloaded from the GSEA website 71 . Enrichment scoring for each sample per hallmark gene set was conducted using GSVA, with the method parameters set to ssgsea. Identification of hallmarks significantly enriched with genes from the input set was based on an adjusted P-value (P < 0.05). The differences among the pure GGO, mixed GGO, and Solid groups were assessed using the ANOVA statistical test. The p-values were corrected for false discovery rate (FDR), and Hallmarks with an FDR-adjusted p-value of less than 0.05 were selected for further analysis. Analysis of molecular events related to recurrence and site of recurrence Patients who did not experience recurrence for over 5 years were defined as non-recurrent, while those who experienced recurrence during follow-up were classified as the recurrent. For patients with recurrence, the site of recurrence was documented in as much detail as possible based on CT. We conducted a comprehensive analysis of recurrence, along with gene mutations and copy number variants associated with the site of recurrence, using Fisher's exact test (P < 0.05). Gene mutations and copy number variants were analyzed for their association with time to relapse or site-specific relapse using COX regression (P < 0.05). q-value represents the p-value after FDR correction. Since multiple genes with copy number variations on a chromosome cytoband may be relevant to relapse, relapse site, or relapse timing, we present only the chromosome cytoband where these genes are located in the final presentation. Statistical analysis All statistical analyses were conducted using R (v4.1.2). Various statistical tests, including t-tests, Fisher's exact tests, and Pearson correlation, were applied. Kaplan-Meier survival analysis and Cox regression hazard modelling were performed using the R packages survival (v3.2-13) and survminer (0.4.9). Kaplan-Meier survival analysis, coupled with the log-rank test, was employed for both overall survival (OS) and relapse-free survival (RFS) assessments. Heatmaps were generated using the R package ComplexHeatmap (v2.15.4) 72 . Principal component analysis (PCA) was executed with the R package stats (v4.1.2). Oncoplot and lollipop plots were created using maftools (v2.10.05) 73 . Boxplots and scatter plots were crafted using R packages ggpubr (v0.4.0) and ggplot2 (v3.4.0). This comprehensive suite of analyses and visualizations ensures a thorough exploration and presentation of the dataset. Mice studies All mouse work was approved by the Animal Ethics committee of School of Basic Medical Sciences at Fudan University and performed in compliance with the NIH Guide for the Care and Use of Laboratory Animals. Trp53 L/L ;Cas9 tdTomato mice ( Trp53 homogenous and LSL-Cas9 tdTomato heterozygous) were obtained to generate Trp53 L/L ;Cas9 tdTomato ATII lung organoids by crossing Trp53 conditional knock-out mice with Cas9 tdTomato conditional knock-in mice. Briefly, LoxP sites were inserted flanking the exon 5–7 region of Trp53 via homologous recombination. Trp53 was knockout by Cre recombinase-mediated removal of Trp53 exons 5–7 using Ad-Cre system. NOD/ShiLtGpt-Prkdc em26cd52 Il2rg em26cd22 /cpt mice (NSG, procured from Gem Pharmatech Co., Ltd., Nanjing, China) were used for the human ATII organoid allografts. Organoid culture and manipulation Trp53 L/L ;Cas9 tdTomato ATII lung organoids were generated from 6–8 weeks Trp53 L/L ;Cas9 tdTomato mice of the C57BL/6J background. In brief, the lungs were dissected and rinsed twice with phosphate-buffered saline (PBS). The tissues were finely chopped using scissors and then digested in a solution of collagenase D and DNase I in Hank’s Balanced Salt Solution (HBSS) at 37°C for 30 minutes. Following incubation, the digested tissue was filtered through a 70 µm cell strainer to obtain single-cell suspensions. The cells were pelleted by centrifugation at 200g for 5 minutes, resuspended in Advanced-DMEM media, and plated in a 6 cm dish. After 36 hours of culture, the cells were washed 5–6 times with PBS and treated with 1 ml of Trypsin-EDTA (Meilunbio, Cat#: MA0233) to digest epithelial cells. The ATII lung organoids were maintained for successive passages using a 1:2 mixture of PneumaCult™ Alveolar Organoid Expansion Medium (Stemcell, Cat#:) and growth factor-reduced Matrigel (Corning, Cat#: 354230). Trp53 −/− ;Cas9 tdTomato organoids were generated from Trp53 L/L ;Cas9 tdTomato ATII lung organoids by Ad-Cre virus infection, followed by flow cytometry sorting of tdTomato + cells. Lentiviral plasmids expressing MAP2K1 (NM_002755) wild-type (WT) and MAP2K1 (NM_002755) mutant (MT, p.E102_I103del) were constructed by ligating the corresponding PCR products into MCS-3FLAG-SV40-Cherry-IRES-Blasticidin vector. Lentiviral particles were packaged using ViraPower Lentiviral Expression Systems (Thermo Fisher Scientific) following the manufacturer's manual. To generate Trp53 −/− ; MAP2K1 WT or Trp53 −/− ; MAP2K1 MT AT Organoids were isolated by digesting the Matrigel with 0.25% Trypsin-EDTA (Meilunbio, Cat#: MA0233) in culture plates for 10 minutes at 37°C and washed with PBS. Once organoids were dissociated, cells were pelleted and resuspended in 50 µL lentiviral solution. Spinoculation was performed by transferring the suspension into a 24-well plate and centrifuging the plate at 600g for 1 hour at 32°C. Plates were then incubated at 37°C for 6 hours before washing the suspension with fresh media and pelleting the cells to be embedded in fresh Matrigel media mixture. Antibiotics (blasticidin, 5 µg/mL) were added to the media to select the infected organoids. Organoid viability was assessed using the bioGenous LivingCell-Fluo™ Organoid Vitality Assay Kit (bioGenous, Catalog: E238004). Morphological images were captured 5 days after passaging under various experimental conditions and analyzed with ImageJ to evaluate the organoid area. To investigate whether Trp53 −/− ; MAP2K1 MT organoids could form tumors in vivo. 1×10 6 Trp53 −/− ;NC or overexpressing MAP2K1 WT/MT ATII cells were resuspended in ice-cold 100 µL PBS buffer and implanted subcutaneously into the lower flanks of NSG mice. After tumor formation, tumor volume was estimated every 2–3 days with the following formula: (L * W ^ 2) / 2. Immunohistochemistry staining and analysis For immunohistochemistry (IHC) analysis, paraffin-embedded tissue or organoid sections were first deparaffinized using xylene and then rehydrated through a series of graded alcohols into water. Antigen retrieval was performed by heating the sections in citrate buffer (10 mM sodium citrate buffer, pH 6.0) at sub-boiling temperature for 15 minutes. The sections were then permeabilized with 0.5% Triton-100 in PBS for 20 minutes. To block endogenous peroxidase activity, a 3% H 2 O 2 solution was applied for 10 minutes, followed by three PBS washes. The sections were then incubated with blocking buffer (3% BSA in PBS) for 30 minutes. Primary antibodies were applied, and the sections were incubated overnight at 4℃. After three washes, the sections were incubated with the appropriate secondary antibodies for 30 minutes at room temperature. Signals were visualized using freshly prepared DAB substrate solution (ZSGB-BIO Company, Beijing, China). Finally, the sections were counterstained with hematoxylin, dehydrated, and mounted with coverslips. The following primary antibodies were used: EpCam (Abcam, Cat#: ab71916), Ttf1 (Abcam, Cat#: ab76013), Krt7 (Proteintech, Cat#: 17513-1-AP). Western blotting and analysis Western blotting was conducted following previously established protocols 74 . In brief, cells were lysed in RIPA buffer (Meilunbio) containing 50 mM Tris (pH 7.4), 150 mM NaCl, 1% Triton X-100, 1% sodium deoxycholate, 0.1% SDS, and EDTA, supplemented with protease and phosphatase inhibitors (Cat. No. HY-K0010, MCE Chemicals). A total of 30 µg of protein was loaded onto SDS–polyacrylamide gels, transferred to PVDF membranes (Millipore), probed with specific antibodies, and visualized. The following primary antibodies were used: ERK1/2 (1:2000 dilution; Cell Signaling Technology Cat, Cat#: 4695), pERK1/2 (1:2000 dilution; Cell Signaling Technology, Cat#: 9101), MEK1(1:2000 dilution; Abcam. Cat#: ab32091), pMEK1 (1:2000 dilution; Abcam, Cat#: ab96379) and GAPDH (1:5000 dilution; Proteintech, Cat#: CL594-60004) Declarations This work was supported by the National Natural Science Foundation of China (81930073, 82203037), Shanghai Science and Technology Innovation Action Project (20JC1417200), Shanghai Municipal Science and Technology Major Project (2017SHZDZX01, VBH1323001/026), Shanghai Municipal Key Clinical Specialty Project (SHSLCZDZK02104), Pilot Project of Fudan University (IDF159045), Shanghai Sailing Program (22YF1408900) and State Sponsored Postdoctoral Fellowship Programme (GZC20230501). Competing Financial Interest Declarations The authors declare no competing financial interests. Acknowledgements This work was supported by the National Natural Science Foundation of China (81930073, 82203037), Shanghai Science and Technology Innovation Action Project (20JC1417200), Shanghai Municipal Science and Technology Major Project (2017SHZDZX01, VBH1323001/026), Shanghai Municipal Key Clinical Specialty Project (SHSLCZDZK02104), Pilot Project of Fudan University (IDF159045), Shanghai Sailing Program (22YF1408900) and State Sponsored Postdoctoral Fellowship Programme (GZC20230501). Data Availability The raw data from whole-genome sequencing (WGS) and RNA-seq have been deposited in the National Omics Data Encyclopedia (NODE) (Accession number: OEP002580) and Genome Sequence Archive (GSA) (DNA accession number: HRA002624, RNA accession number: HRA002983). Data on copy number variation, gene expression, and survival prognosis for the TCGA-LUAD cohort were retrieved from the GDC database ( https://portal.gdc.cancer.gov/ ). 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Additional Declarations There is NO Competing Interest. Supplementary Files FigureS1.pdf Supplementary figure 1. Comparison of mutation frequencies in major oncogenes and tumor suppressor genes in samples at different pathological stages. A) Comparison of EGFR and KRAS mutations, ALK fusions and mutations in the RTK-RAS pathway in samples at different pathological stages. B) Comparison of ERBB2 and MAP2K1 mutations in samples at different pathological stages. C) Comparison of TP53 , KEAP1 and STK11 and mutations in the RTK-RAS pathway in samples at different pathological stages. FigureS2.pdf Supplementary figure 2. Comparison of tumor mutation burden between samples in our study cohort divided as A) preinvasive and invasive ones, B) preinvasive and stage I-IV invasive ones, C) those with different radiological manifestations and TCGA cohorts. FigureS3.pdf Supplementary figure 3. Survival impacts of co-mutation pairs between oncogenes and tumor suppressor genes. A) Mutation pairs that have impacts on RFS. Color bars indicate normalized hazard ratio. *, 0.01 < p £ 0.05; **, 0.001 < p £ 0.01; ***, p £ 0.001. B) Mutation pairs that have impacts on OS. Color bars indicate normalized hazard ratio. *, 0.01 < p £ 0.05; **, 0.001 < p £ 0.01; ***, p £ 0.001. C) Number of genes that have significant impacts on RFS when co-mutating with specific tumor suppressor genes. D) Number of genes that have significant impacts on OS when co-mutating with specific tumor suppressor genes. FigureS4.pdf Supplementary figure 4. Sanger sequencing showing the mutational status in MAP2K1 of the 3 groups. NC, the control group; WT, organoids with overexpressed wild-type MAP2K1; MT, organoids with overexpressed MAP2K1 ΔE101-I102 . FigureS5.pdf Supplementary figure 5. Overview of somatic copy number alterations (SCNAs) and structural variances (SVs) across different pathological stages of lung adenocarcinoma. A) Overview of SCNAs across different pathological stages of lung adenocarcinoma. B) Overview of SVs across different pathological stages of lung adenocarcinoma. FigureS6.pdf Supplementary figure 6. Stepwise transcriptomic changes in the progression of lung adenocarcinoma. A) Differentially expressed genes between each two adjacent pathological stages. From left to right: differentially expressed genes between stage I and pre-invasive samples; between stage II and stage I samples; between stage III and stage II samples; and between stage IV and stage III samples. B) Expression of LAG3 , HAVCR2 , PDCD1 , TIGIT , ENTPD1 and CTLA4 between samples with high and low SPP1 expression. FigureS7.pdf Figure 7. Genomic alterations with site-specific metastasis. A) Overview of number of patients with specific metastatic sites. B) Driver mutations with significantly different frequencies between patients with disease relapse and those without relapse in at least 5 years’ follow up period. C) Mutations in specific genes and pathways associated with site-specific metastasis. D) SCNAs associated with specific metastatic sites. E) Mutation signatures associated with disease relapse and site-specific metastasis. F) Mutations in genes and pathways that were associated with shorter time to relapse. G) Mutations in specific genes and pathways associated with shorter time site-specific metastasis. H) SCNAs associated with shorter time site-specific metastasis. FigureS8thisone.pdf Supplementary figure 8. Comparison of genomic and transcriptomic changes between patients with metastasis and those without metastasis in at least 5 years follow-up time. A) Landscape of somatic mutations and SCNAs in patients with metastasis and those without metastasis in at least 5 years follow-up time. B) Differentially expressed genes between patients with metastasis and those without metastasis in at least 5 years follow-up time. The first panel compares patients with vs without metastasis, while the other panels compare patients with site-specific metastasis vs without metastasis. FigureS9.pdf Supplementary figure 9. WGS and RNA-seq data quality control. A) Data size and Q30 scores for all WGS samples. B) Data size and Q30 scores for all RNA-seq samples. C) Pairwise detection of tumor and normal samples in WGS data. D) Quality control processes during data generation, monitored using Quartet's DNA and RNA Reference Standard Material. E) Distribution of F-score and Mendelian Concordance Rate for WGS data, evaluated using Quartet DNA standard material throughout the entire WGS data generation process for tumor samples. F) PCA and SNR results for four sample types in Quartet RNA Reference Standard Material RNA-seq data, encompassing the full RNA-seq data generation process for tumor samples. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4977481","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":373969718,"identity":"58d835fe-a595-42cf-81a3-2785f2c2ff8e","order_by":0,"name":"Haiquan Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYBACAwYGNiAlIcfAwEOaFgtjkrVUJDYQrcWcPfnZg487JNI3HD978MEHBjs53QYCWix7npkbzjwjkbvhTF6y4QyGZGOzA4QcdiOHTZq3DajlQI6ZNA/DgcRtRGn52yaRbnD+DSlaGNskEoAMYrWceWYm2dsmYTjzxhtjwxkGxPjlePIziZ9tdfJ853MMH3yosJMjqIWBIQFCKYBVGhBUjqRFvoEo1aNgFIyCUTASAQCwLEFVnx8p0wAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-7846-257X","institution":"Fudan University Shanghai Cancer Center","correspondingAuthor":true,"prefix":"","firstName":"Haiquan","middleName":"","lastName":"Chen","suffix":""},{"id":373969719,"identity":"1bf422f4-d7a1-4d3d-ad84-01cde1330ad6","order_by":1,"name":"Yue Zhao","email":"","orcid":"https://orcid.org/0000-0003-3645-2634","institution":"Department of Thoracic 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Center","correspondingAuthor":false,"prefix":"","firstName":"Yuanting","middleName":"","lastName":"Zheng","suffix":""},{"id":373969735,"identity":"8298cc31-c2d1-455c-92d4-b44bae660e70","order_by":17,"name":"Yuan Li","email":"","orcid":"https://orcid.org/0000-0001-9338-2463","institution":"Fudan University Shanghai Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Li","suffix":""},{"id":373969736,"identity":"f5ce5a43-31de-47aa-8114-675e77c4bfd1","order_by":18,"name":"Yang Zhang","email":"","orcid":"","institution":"Fudan University Shanghai Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Zhang","suffix":""},{"id":373969737,"identity":"c7bd68ca-68b5-45c0-9f84-93d2754a001b","order_by":19,"name":"Li Jin","email":"","orcid":"https://orcid.org/0000-0001-9201-2321","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Jin","suffix":""},{"id":373969738,"identity":"2c3666d8-6fa5-486a-a8df-739af96652e8","order_by":20,"name":"Leming Shi","email":"","orcid":"https://orcid.org/0000-0002-2981-4150","institution":"State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai 200438, China","correspondingAuthor":false,"prefix":"","firstName":"Leming","middleName":"","lastName":"Shi","suffix":""}],"badges":[],"createdAt":"2024-08-26 11:05:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4977481/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4977481/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68354912,"identity":"83463084-deec-4716-b567-9c58ac6d9cc2","added_by":"auto","created_at":"2024-11-06 11:11:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":266559,"visible":true,"origin":"","legend":"\u003cp\u003eStudy design, and clinicopathological characteristics of patients in the study cohort.\u003c/p\u003e\n\u003cp\u003eA) 1,008 tumors from 954 patients who underwent surgery between August 2011 and March 2019 at the Department of Thoracic Surgery, Fudan University Shanghai Cancer Center, were retrospectively included in this study. Whole-genome sequencing (WGS) and RNA sequencing (RNA-seq) were performed on all tumors and their paired adjacent normal lung tissues. After quality control, 986 samples with WGS and 968 samples with RNA-seq data were satisfactory for downstream analyses. The number of samples at different radiological and pathological stages was recorded. B) Sankey plot demonstrating the connection between samples of different radiological and pathological stages. C) Sankey plot demonstrating the connection of between samples of different radiological and pathological stages for patients with disease recurrence. D) Kaplan-Meier curves demonstrating the recurrence-free survival (RFS, left) and overall survival (OS, right) for patients with different pathological stages. E) Kaplan-Meier curves demonstrating the recurrence-free survival (RFS, left) and overall survival (OS, right) for patients with different radiological manifestations. F) Frequency of patients with different pathological (left) and radiological (right) stages in each age group. G) Frequency of patients with different smoking statuses at different pathological (left) and radiological (right) stages. H) Frequency of patients with different sex at different pathological (left) and radiological (right) stages. I) Frequency of patients with different LUAD subtypes at different pathological (left) and radiological (right) stages. J) Number of patients with disease metastasis at specific sites. K) Number of patients with disease metastasis at different length of time after surgery. GGO, ground-glass opacity.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4977481/v1/87b1bff1834aea1aefca0759.png"},{"id":68354916,"identity":"612fa84c-93dc-4101-9679-5bb075d8e6cc","added_by":"auto","created_at":"2024-11-06 11:11:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":183353,"visible":true,"origin":"","legend":"\u003cp\u003eGenomic changes in the progression of lung adenocarcinoma. A) Mutation landscape demonstrating pre-invasive (AAH/AIS/MIA, left) and invasive LUAD (right) samples. B) \u003cem\u003eMAP2K1\u003c/em\u003e mutations identified in this study cohort. C) \u003cem\u003eMAP2K1\u003c/em\u003e mutations identified in our previous study involving 98 pre-invasive and 99 invasive LUAD samples. D) Comparison of tumor mutation burden between samples in this cohort and TCGA cohorts. E) Landscape of somatic copy number alterations (SCNAs) in this study cohort, with pre-invasive samples at the top and invasive samples at the bottom. F) Co-mutational statuses demonstrating the mutual exclusivity and co-occurrence between major oncogenes and tumor suppressor genes.\u003c/p\u003e","description":"","filename":"figure2thisone.png","url":"https://assets-eu.researchsquare.com/files/rs-4977481/v1/9444a00f1238c47366f2e700.png"},{"id":68354914,"identity":"e0d1b87e-05c2-4cac-992b-1449e7044916","added_by":"auto","created_at":"2024-11-06 11:11:40","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1399027,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eIn vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e experiments to test the oncogenicity of \u003cem\u003eMAP2K1\u003c/em\u003e\u003csup\u003eΔE101-I102\u003c/sup\u003e. A) Study design. B) Western blot analysis of phosphorylated and total Mek1 and Erk in \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003e-/-\u003c/sup\u003e-ATII organoids. NC, negative control group; WT, overexpression of wild-type \u003cem\u003eMAP2K1\u003c/em\u003e group; MT, overexpression of \u003cem\u003eMAP2K1\u003c/em\u003e\u003csup\u003eΔE101-I102\u003c/sup\u003e group. C) Morphological changes (top), H\u0026amp;E staining (second row) and immunohistochemistry of \u003cem\u003eEpCAM\u003c/em\u003e (third row) and \u003cem\u003eTtf1\u003c/em\u003e (bottom) after 5 days of culturing the \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003e-/-\u003c/sup\u003e-ATII organoids. D) Relative viability of organoids on Day 1, 3 and 5. N = 3 biological replicates. E) Relative organoid area of organoids on Day 5. F-H) Growth curve (F), end point illusion (G) and tumor weight (H) of \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003e−/−\u003c/sup\u003e(n=4), \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003e−/−\u003c/sup\u003e; \u003cem\u003eMAP2K1\u003c/em\u003e\u003csup\u003e\u003cem\u003eWT\u003c/em\u003e\u003c/sup\u003e(n=4), and \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003e−/−\u003c/sup\u003e;\u0026nbsp;\u003cem\u003eMAP2K1\u003c/em\u003e\u003csup\u003e\u003cem\u003eMT\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e \u003c/em\u003e(n=4) allografts. I) H\u0026amp;E staining and immunohistochemistry of \u003cem\u003eTtf1, Krt7\u003c/em\u003e and \u003cem\u003eEpCAM\u003c/em\u003e of the \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003e−/−\u003c/sup\u003e;\u0026nbsp;\u003cem\u003eMAP2K1\u003c/em\u003e\u003csup\u003e\u003cem\u003eMT\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e \u003c/em\u003eorganoid allograft at end point (day 48).\u003c/p\u003e","description":"","filename":"figure3thisone.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4977481/v1/514ef4a880c6b32e18e1a0c0.jpg"},{"id":68355317,"identity":"6a3e2e43-0b35-499f-a3ab-64f1ae08c53c","added_by":"auto","created_at":"2024-11-06 11:19:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":794268,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of genomic features of lung adenocarcinomas at different developmental stages. A) Pathological presentations of AIS, MIA and LUAD. B) Left to right: comparison of tumor mutation burden (TMB), APOBEC signature (SBS2/13) activities, whole-genome doubling (WGD) events and structural variations (SVs) between pre-invasive and invasive LUADs. C) Comparison of somatic copy number alteration (SCNA) events between pre-invasive and invasive LUADs. D) Comparison of G-scores of 14q13.3 gain (left) and 9p21.3 loss (right) between pre-invasive and invasive LUADs. E) Kaplan-Meier curves demonstrating OS (left) and RFS (right) for patients having chromosome 14q13.3 amplifications. F) Kaplan-Meier curves demonstrating OS (left) and RFS (right) for patients having chromosome 9p21.3 losses. G) Kaplan-Meier curves demonstrating OS (left) and RFS (right) for patients with different SBS2 signature activities. H) Kaplan-Meier curves demonstrating OS (left) and RFS (right) for patients with different SBS13 signature activities.\u003c/p\u003e","description":"","filename":"figure4thisone.png","url":"https://assets-eu.researchsquare.com/files/rs-4977481/v1/cdf96bf3bdbabdc8107175cb.png"},{"id":68356626,"identity":"39e7bfdc-a7a0-45c4-9b58-e6f07b31d778","added_by":"auto","created_at":"2024-11-06 11:27:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":237289,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of genomic and transcriptomic profiles for lung adenocarcinoma at different radiological stages. A) Radiological manifestations of lung adenocarcinomas at different developmental stages. B) Comparison of TMB among different radiological groups. C) Comparison of WGD among different radiological groups. D) comparison of SBS2/13 activities among different radiological groups. E) Comparison of mutation frequencies in driver genes between solid and non-solid samples. F) Comparison of mutation frequencies in major oncogenes in samples with different radiological manifestations. G) Comparison of mutation frequencies in major tumor suppressor genes in samples with different radiological manifestations. H) Expression of different pathways of samples with different radiological manifestations. I) Differential expressed genes between mixed and pure GGOs (left); and between solid nodules and mixed GGOs (right).\u003c/p\u003e","description":"","filename":"figure5thisone.png","url":"https://assets-eu.researchsquare.com/files/rs-4977481/v1/e89da6bf6a776f9e4e1a23ee.png"},{"id":68355312,"identity":"4e06ac8c-e0a2-419c-b563-808aed185d5d","added_by":"auto","created_at":"2024-11-06 11:19:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":194313,"visible":true,"origin":"","legend":"\u003cp\u003eKey transcriptomic changes that were associated with LUAD progression. A) Differential expressed genes between samples having chromosome 14q13.3 amplifications or not (left), samples manifesting as solid nodules vs GGOs (middle), and invasive vs pre-invasive samples (right). B) Venn diagram demonstrating significantly up-regulated genes in all the 3 comparisons. C) Comparison of \u003cem\u003eSPP1\u003c/em\u003e expression in samples having chromosome 14q13.3 amplifications or not (left), samples manifesting as solid nodules vs GGOs (middle), and invasive vs pre-invasive samples (right). D) Venn diagram demonstrating significantly down-regulated genes in all the 3 comparisons. E) Comparison of \u003cem\u003eSFTPC\u003c/em\u003e expression in samples having chromosome 14q13.3 amplifications or not (left), samples manifesting as solid nodules vs GGOs (middle), and invasive vs pre-invasive samples (right). F) Kaplan-Meier curves showing the OS (left) and RFS (right) of patients having high or low expression of \u003cem\u003eSPP1\u003c/em\u003e. G) Kaplan-Meier curves showing the OS (left) and RFS (right) of patients having high or low expression of \u003cem\u003eSFTPC. \u003c/em\u003eH) Comparison of CD8+ T cell and Treg infiltration between samples with high and low \u003cem\u003eSPP1\u003c/em\u003e expression. I) Comparison of \u003cem\u003eSFTPC\u003c/em\u003e and \u003cem\u003eSPP1\u003c/em\u003eexpression between samples having chromosome 14q13.3 amplification or not in the TCGA-LUAD cohort. J) Kaplan-Meier curves showing the OS of patients having high or low expression of \u003cem\u003eSPP1 \u003c/em\u003e(left) and \u003cem\u003eSFTPC \u003c/em\u003e(right) in the TCGA-LUAD cohort\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"figure6thisone.png","url":"https://assets-eu.researchsquare.com/files/rs-4977481/v1/15538c5ef13f9c3f9398700c.png"},{"id":71112687,"identity":"3bb6b39b-db90-4f24-aebd-58f71a9a208b","added_by":"auto","created_at":"2024-12-11 09:08:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10936608,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4977481/v1/1a438525-a111-4420-b1e0-df9f955d9c23.pdf"},{"id":68354915,"identity":"d0f86a78-298f-4226-8507-eff47699e6e9","added_by":"auto","created_at":"2024-11-06 11:11:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":481782,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary figure 1. Comparison of mutation frequencies in major oncogenes and tumor suppressor genes in samples at different pathological stages. A) Comparison of \u003cem\u003eEGFR\u003c/em\u003e and \u003cem\u003eKRAS\u003c/em\u003emutations, \u003cem\u003eALK\u003c/em\u003e fusions and mutations in the RTK-RAS pathway in samples at different pathological stages. B) Comparison of \u003cem\u003eERBB2\u003c/em\u003e and \u003cem\u003eMAP2K1\u003c/em\u003emutations in samples at different pathological stages. C) Comparison of \u003cem\u003eTP53\u003c/em\u003e, \u003cem\u003eKEAP1\u003c/em\u003e and \u003cem\u003eSTK11\u003c/em\u003e and mutations in the RTK-RAS pathway in samples at different pathological stages.\u003c/p\u003e","description":"","filename":"FigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4977481/v1/b6d99f37d730d23ab7114307.pdf"},{"id":68355318,"identity":"dbd6f855-fef6-45b9-9531-11b20897dd4e","added_by":"auto","created_at":"2024-11-06 11:19:40","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2701779,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary figure 2. Comparison of tumor mutation burden between samples in our study cohort divided as A) preinvasive and invasive ones, B) preinvasive and stage I-IV invasive ones, C) those with different radiological manifestations and TCGA cohorts.\u003c/p\u003e","description":"","filename":"FigureS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4977481/v1/0b5fb7e3e0863a20e01b9032.pdf"},{"id":68354919,"identity":"a31e80de-d0b8-46ab-925c-7dbf74526400","added_by":"auto","created_at":"2024-11-06 11:11:40","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":588951,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary figure 3. Survival impacts of co-mutation pairs between oncogenes and tumor suppressor genes. A) Mutation pairs that have impacts on RFS. Color bars indicate normalized hazard ratio. *, 0.01 \u0026lt; p £ 0.05; **, 0.001 \u0026lt; p £ 0.01; ***, p £ 0.001. B) Mutation pairs that have impacts on OS. Color bars indicate normalized hazard ratio. *, 0.01 \u0026lt; p £ 0.05; **, 0.001 \u0026lt; p £ 0.01; ***, p £ 0.001. C) Number of genes that have significant impacts on RFS when co-mutating with specific tumor suppressor genes. D) Number of genes that have significant impacts on OS when co-mutating with specific tumor suppressor genes.\u003c/p\u003e","description":"","filename":"FigureS3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4977481/v1/9e0f8befda566f6c04274268.pdf"},{"id":68355314,"identity":"24af28a4-7c91-486f-a13d-6449efa133dd","added_by":"auto","created_at":"2024-11-06 11:19:40","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":409510,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary figure 4. Sanger sequencing showing the mutational status in MAP2K1 of the 3 groups. NC, the control group; WT, organoids with overexpressed wild-type MAP2K1; MT, organoids with overexpressed MAP2K1\u003csup\u003eΔE101-I102\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"FigureS4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4977481/v1/2515d3a5588ebac52f525496.pdf"},{"id":68356627,"identity":"858caf49-7745-4bd1-8c86-6b18745c15c2","added_by":"auto","created_at":"2024-11-06 11:27:40","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":6617437,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary figure 5. Overview of somatic copy number alterations (SCNAs) and structural variances (SVs) across different pathological stages of lung adenocarcinoma. A) Overview of SCNAs across different pathological stages of lung adenocarcinoma. B) Overview of SVs across different pathological stages of lung adenocarcinoma.\u003c/p\u003e","description":"","filename":"FigureS5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4977481/v1/d15d1afea479147897e2cc0a.pdf"},{"id":68355315,"identity":"ef9b52f2-7fb7-4a2a-bbb5-720c574c7a3d","added_by":"auto","created_at":"2024-11-06 11:19:40","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":2121927,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary figure 6. Stepwise transcriptomic changes in the progression of lung adenocarcinoma. A) Differentially expressed genes between each two adjacent pathological stages. From left to right: differentially expressed genes between stage I and pre-invasive samples; between stage II and stage I samples; between stage III and stage II samples; and between stage IV and stage III samples. B) Expression of \u003cem\u003eLAG3\u003c/em\u003e, \u003cem\u003eHAVCR2\u003c/em\u003e, \u003cem\u003ePDCD1\u003c/em\u003e, \u003cem\u003eTIGIT\u003c/em\u003e, \u003cem\u003eENTPD1\u003c/em\u003e and \u003cem\u003eCTLA4\u003c/em\u003e between samples with high and low \u003cem\u003eSPP1\u003c/em\u003e expression.\u003c/p\u003e","description":"","filename":"FigureS6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4977481/v1/2aab6eb9f58660dda4a7752e.pdf"},{"id":68354926,"identity":"b42f84ca-8991-485b-955b-c375e98d71ba","added_by":"auto","created_at":"2024-11-06 11:11:40","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":2027164,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 7. Genomic alterations with site-specific metastasis. A) Overview of number of patients with specific metastatic sites. B) Driver mutations with significantly different frequencies between patients with disease relapse and those without relapse in at least 5 years’ follow up period. C) Mutations in specific genes and pathways associated with site-specific metastasis. D) SCNAs associated with specific metastatic sites. E) Mutation signatures associated with disease relapse and site-specific metastasis. F) Mutations in genes and pathways that were associated with shorter time to relapse. G) Mutations in specific genes and pathways associated with shorter time site-specific metastasis. H) SCNAs associated with shorter time site-specific metastasis.\u003c/p\u003e","description":"","filename":"FigureS7.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4977481/v1/26965fcc3f5416db053f072b.pdf"},{"id":68354923,"identity":"4c2aed72-8bb2-48ef-bf89-ecd8b5078e7d","added_by":"auto","created_at":"2024-11-06 11:11:40","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":2185060,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary figure 8. Comparison of genomic and transcriptomic changes between patients with metastasis and those without metastasis in at least 5 years follow-up time. A) Landscape of somatic mutations and SCNAs in patients with metastasis and those without metastasis in at least 5 years follow-up time. B) Differentially expressed genes between patients with metastasis and those without metastasis in at least 5 years follow-up time. The first panel compares patients with vs without metastasis, while the other panels compare patients with site-specific metastasis vs without metastasis.\u003c/p\u003e","description":"","filename":"FigureS8thisone.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4977481/v1/ff463add11aacaf4e5800984.pdf"},{"id":68354920,"identity":"852e99a6-a4f9-472a-b715-61e6253a2c4f","added_by":"auto","created_at":"2024-11-06 11:11:40","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":2072754,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary figure 9. WGS and RNA-seq data quality control. A) Data size and Q30 scores for all WGS samples. B) Data size and Q30 scores for all RNA-seq samples. C) Pairwise detection of tumor and normal samples in WGS data. D) Quality control processes during data generation, monitored using Quartet's DNA and RNA Reference Standard Material. E) Distribution of F-score and Mendelian Concordance Rate for WGS data, evaluated using Quartet DNA standard material throughout the entire WGS data generation process for tumor samples. F) PCA and SNR results for four sample types in Quartet RNA Reference Standard Material RNA-seq data, encompassing the full RNA-seq data generation process for tumor samples.\u003c/p\u003e","description":"","filename":"FigureS9.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4977481/v1/19e761567b26ae0efc754ab3.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Genomic and Transcriptomic Dynamics in the Stepwise Progression of Lung Adenocarcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer is the leading cause of cancer-related death worldwide, presenting a\u003c/p\u003e \u003cp\u003emultifaceted challenge in cancer research\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Among the various pathological subtypes, lung adenocarcinoma (LUAD) is the most common, accounting for more than 45% of all lung cancer cases, and its incidence is still rising\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. It is generally believed that lung adenocarcinoma progresses through a sequence of atypical adenomatous hyperplasia (AAH) to adenocarcinoma \u003cem\u003ein situ\u003c/em\u003e (AIS), minimally invasive adenocarcinoma (MIA), and eventually to invasive adenocarcinoma (ranging from Stage I to stage IV disease). On CT imaging, some lung adenocarcinomas progress from ground-glass opaciites (GGOs) to part-solid nodules containing both GGO and solid components, and finally to solid nodules\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Studies have demonstrated an association between radiological manifestations and pathological stages: most AIS/MIA lesions tend to present as pure GGOs, while the presence of solid components on CT images correlates with increased histological invasiveness\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Pre-invasive stages of lung adenocarcinoma, such as AIS and MIA, as well as LUADs presenting as pure GGOs, have a nearly 100% survival rate following complete surgical resection, leading to the characterization of GGO-like LUADs as an inert subtype due to their slow growth and favourable prognosis\u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Nonetheless, the 5-year survival rate for invasive adenocarcinoma drops dramatically as the disease develops from Stage I to Stages II, III and IV, and from pure GGOs to solid nodules\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Therefore, it is crucial to uncover the dynamic changes in genomic and transcriptomic profiles as LUAD progresses, and to find out evidence of the optimal curative window for surgical intervention that offers definitive cure while avoiding overtreatment\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMetastasis plays a crucial role in cancer-related death, with tumors at more advanced stages showing higher metastatic rates and poorer survival outcoms\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. understanding how the genetic profile of LUAD may influence the pattern of metastatic spread is pivotal for refining treatment, predicting disease outcomes and planning follow-up strategies, this prompts us to delve into the intricate landscape of site-specific metastasis in lung adenocarcinoma\u003csup\u003e\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, we perform whole-genome sequencing (WGS) and RNA sequencing (RNA-seq) on 1,008 lung adenocarcinoma samples encompassing all pathological and radiological features, along with matched adjacent normal lung tissues from 954 patients with complete follow-up data. Our aim is to provide an extensive exploration of the dynamically changing genomic and transcriptomic profiles associated with the progression of LUAD.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eClinical and Pathological characteristics of patients\u003c/h2\u003e \u003cp\u003eA total of 1,008 tumors from 954 patients were included in this study. The median recurrence-free survival (RFS) was 56.0 months, and the median overall survival (OS) was 60.8 months. Among the patients, 548 (57.4%) were female and 406 (42.6%) were male. Additionally, 731 patients (76.6%) were self-reported never smokers. The median age was 60 years (range: 23\u0026ndash;89). Of all the lesions, 141 (14.0%) appeared as pure ground-glass opacities (GGOs), 287 (28.5%) appeared as mixed GGOs, and 580 (57.5%) appeared as solid nodules on CT scans. The pathological classifications included 1 case of atypical adenomatous hyperplasia (AAH, 0.1%), 42 cases of adenocarcinoma \u003cem\u003ein situ\u003c/em\u003e (AIS, 4.2%), 116 cases of minimally invasive adenocarcinoma (MIA, 11.5%), and 849 cases of invasive adenocarcinoma (LUAD, 84.2%, Fig.\u0026nbsp;1a-b).\u003c/p\u003e \u003cp\u003eDuring follow-up, 200 patients experienced disease recurrence, with 83 (41.5%) at Stage I, 25 (12.5%) at Stage II, 87 (43.5%) at stage III, and 7 (3.5%) at stage IV (Fig.\u0026nbsp;1c). Pre-invasive lesions demonstrated perfect survival after complete surgical resection, as evidenced by 100% recurrence-free survival (RFS) in patients with AAH/AIS/MIA throughout the follow-up period (Fig.\u0026nbsp;1d). Furthermore, RFS for lesions presenting as pure GGOs on CT scans was also 100%, indicating a subset of invasive adenocarcinomas (mainly the lepidic predominant subtype) had excellent outcomes after complete surgical resection (Fig.\u0026nbsp;1e). Notably, there were more female patients with early-stage lung adenocarcinomas, and the proportion of patients with a smoking history increased as the disease progressed to higher stages (Fig.\u0026nbsp;1f-h). Subtypes considered low-grade and associated with poor prognosis, such as the micropapillary and solid subtypes\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, were more commonly seen in the later stages of the disease (Fig.\u0026nbsp;1i).\u003c/p\u003e \u003cp\u003eFor patients who experienced disease recurrence after surgery, the sites of recurrence were recorded. Apart from recurrences in the lungs (31.5%), the most common metastatic sites were the brain (28.0%), bone (20.0%), lymph nodes (10.0%) and liver (6.0%, Fig.\u0026nbsp;1j). Additionally, 65 of 200 (32.5%) patients had disease recurrence in less than 12 months after surgery, while 135 of 200 (67.5%) patients had disease recurrence in more than 12 months post-surgery (Fig.\u0026nbsp;1k).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLandscape of somatic mutations in lung adenocarcinoma at different pathological stages\u003c/h3\u003e\n\u003cp\u003eSomatic mutations of major driver genes and tumor suppressor genes in our study cohort were identified. We first divided the samples into pre-invasive (AAH/AIS/MIA) and invasive (LUAD) ones, and found that in pre-invasive (AAH/AIS/MIA) samples, the most frequently mutated gene was \u003cem\u003eEGFR\u003c/em\u003e (50%), followed by \u003cem\u003eRBM10\u003c/em\u003e (13%), \u003cem\u003eERBB2\u003c/em\u003e (11%), \u003cem\u003eKMT2D\u003c/em\u003e (11%), \u003cem\u003eMAP2K1\u003c/em\u003e (10%) and \u003cem\u003eBRAF\u003c/em\u003e (5%). In contrast, in LUAD samples, the most frequently mutated gene was \u003cem\u003eEGFR\u003c/em\u003e (67%), followed by \u003cem\u003eTP53\u003c/em\u003e (32%), \u003cem\u003eRBM10\u003c/em\u003e (11%), \u003cem\u003eKRAS\u003c/em\u003e (7%), \u003cem\u003eKMT2D\u003c/em\u003e (7%) and \u003cem\u003eARID1A\u003c/em\u003e (4%, Fig.\u0026nbsp;2a).\u003c/p\u003e \u003cp\u003eWe observed a significantly higher mutation frequency in \u003cem\u003eEGFR\u003c/em\u003e and \u003cem\u003eKRAS\u003c/em\u003e mutations and \u003cem\u003eALK\u003c/em\u003e fusions in invasive stages compared to pre-invasive stages of lung adenocarcinoma (supplementary Fig.\u0026nbsp;1a). However, a significantly lower mutation frequency in \u003cem\u003eERBB2\u003c/em\u003e and \u003cem\u003eMAP2K1\u003c/em\u003e was seen in invasive adenocarcinoma compared with pre-invasive lesions (supplementary Fig.\u0026nbsp;1b).\u003c/p\u003e \u003cp\u003eNotably, we identified a mutation hotspot in \u003cem\u003eMAP2K1\u003c/em\u003e, a potential candidate driver mutation of lung adenocarcinoma in the Ras/Raf/RTK pathway, which was previously reported to have a mutation in 2 out of 230 (0.9%) patients with invasive lung adenocarcinoma\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. In our study, 13 cases with \u003cem\u003eMAP2K1\u003c/em\u003e p.E102-I103 deletion were identified, of which 11 were AIS/MIA samples (Fig.\u0026nbsp;2a-b). This mutation hotspot was also identified in our previous study, which utilized whole-exome sequencing to decipher the genomic profiles of pre-invasive and invasive lung adenocarcinoma\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. In that study, 4 out of 197 (2.0%) lung adenocarcinoma patients harboured \u003cem\u003eMAP2K1\u003c/em\u003e p.E102-I103 deletion, with all the 4 samples being pre-invasive (Fig.\u0026nbsp;2c). Being part of the RTK/RAS/RAF pathway and mutually exclusive to known driver mutations of lung adenocarcinoma, \u003cem\u003eMAP2K1\u003c/em\u003e p.E102-I103 deletion might be considered a potential driver mutation of lung adenocarcinoma.\u003c/p\u003e \u003cp\u003eThe frequency of mutations in tumor suppressor genes tended to increase with tumor stage progressionn, including mutations in \u003cem\u003eTP53, RB1, MGA, KEAP1\u003c/em\u003e and \u003cem\u003eSTK11\u003c/em\u003e (supplementary Fig.\u0026nbsp;1c). The median tumor mutation burden (TMB) across the entire study was 1.73 mutations per megabase (Mb), with 1.20/Mb for AAH/AIS/MIA and 1.81/Mb for LUAD (Fig.\u0026nbsp;2d, supplementary Fig.\u0026nbsp;2a). TMB increased as tumors progressed to higher radiological and pathological stages (supplementary Fig.\u0026nbsp;2b-c).\u003c/p\u003e\n\u003ch3\u003eLandscape of somatic copy number alterations at different pathological stages\u003c/h3\u003e\n\u003cp\u003eSomatic copy number alterations (SCNAs) were identified and categorized as gains or losses in both pre-invasive and invasive samples. In pre-invasive samples, the most frequently observed SCNA event was a gain in chromosome 5p, followed by gains in chromosomes 8p, 8q, 17q, 7p and 16p (Fig.\u0026nbsp;2a, 2e). Copy number losses in chromosomes 19p and 19q were also identified in pre-invasive samples (Fig.\u0026nbsp;2a, 2e). In invasive samples, the most frequent gains were observed in chromosomes 7p, 5p, 1q, 8q and 7q, while the most frequent losses were in chromosomes 8p and 21p (Fig.\u0026nbsp;2a, 2e).\u003c/p\u003e\n\u003ch3\u003eCo-mutation status and its survival indications\u003c/h3\u003e\n\u003cp\u003eDriver events in tumors can be mutually exclusive or tend to co-occur, and their co-mutation status has been reported to have prognostic value\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Known oncogenic mutations in lung adenocarcinoma, such as mutations in \u003cem\u003eKRAS, EGFR, ERBB2\u003c/em\u003e and \u003cem\u003eBRAF\u003c/em\u003e, were mutually exclusive in our cohort (Fig.\u0026nbsp;2f). Notably, \u003cem\u003eMAP2K1\u003c/em\u003e mutations were mutually exclusive to these major oncogenic mutations (Fig.\u0026nbsp;2f). Mutual exclusivity was observed in certain oncogene-tumor suppressor gene pairs as well - \u003cem\u003eSTK11\u003c/em\u003e and \u003cem\u003eKEAP1\u003c/em\u003e were mutually exclusive with \u003cem\u003eEGFR\u003c/em\u003e mutations, and \u003cem\u003eTP53\u003c/em\u003e was mutually exclusive with \u003cem\u003eBRAF\u003c/em\u003e mutations (Fig.\u0026nbsp;2f). In total, 66 co-mutation pairs involving major oncogenes and tumor suppressor genes were identified. These included pairs consisting of one oncogene and one tumor suppressor gene, such as \u003cem\u003eEGFR\u003c/em\u003e co-mutated with \u003cem\u003eTP53/RBM10\u003c/em\u003e, and pairs consisting of two tumor suppressor genes, such as \u003cem\u003eTP53\u003c/em\u003e co-mutated with \u003cem\u003eKEAP1/ARID1A/RB1/FAT1\u003c/em\u003e (Fig.\u0026nbsp;2f). Among co-mutation pairs of one oncogene and one tumor suppressor gene, with a mutation frequency greater than 3% in the invasive group, 24 pairs were associated with worse recurrence free survival (RFS) and 24 were associated with worse overall survival (OS, supplementary Fig.\u0026nbsp;3a-b). For pairs consisting of two tumor suppressor genes with a mutation frequency greater than 3% in the invasive group, 27 pairs were associated with worse RFS and 24 pairs were associated with worse OS (supplementary Fig.\u0026nbsp;3a-b).\u003c/p\u003e \u003cp\u003eInterestingly, co-mutation with \u003cem\u003eTP53\u003c/em\u003e was the most common factor associated with worse survival outcomes, impacting both RFS and OS in patients. This was followed by \u003cem\u003eSTK11\u003c/em\u003e, which was also associated with poorer survival outcomes in several co-mutation pairs (supplementary Fig.\u0026nbsp;3c-d). In summary, co-mutation pairs involving oncogenes and tumor suppressor genes, as well as pairs involving two tumor suppressor genes, hold significant clinical importance and prognostic value in lung adenocarcinoma.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMAP2K1\u003c/b\u003e \u003cb\u003ep.E101-I102 deletion drives lung adenocarcinoma formation via enhanced Ras/Raf/RTK signaling in alveolar type II organoids\u003c/b\u003e\u003c/p\u003e \u003cp\u003eGiven the oncogenic potential of \u003cem\u003eMAP2K1\u003c/em\u003e within the Ras/Raf/RTK pathway, and its hotspot mutation p.E102-I103 deletion being mutually exclusive with other oncogenic mutations and significantly enriched in pre-invasive lung adenocarcinomas (Fig.\u0026nbsp;2f), we conducted \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e experiments to assess its oncogenicity. We generated mouse lung ATII organoid culturing from C57BL/6J \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003e\u003cem\u003eL/L\u003c/em\u003e\u003c/sup\u003e; LSL-Cas9\u003csup\u003etdTomato\u003c/sup\u003e mice, where the floxed \u003cem\u003eTrp53\u003c/em\u003e gene could be conditionally inactivated and tdTomato-Cas9 could be conditionally activated by Cre recombinase. These \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003e\u003cem\u003eL/L\u003c/em\u003e\u003c/sup\u003e; Cas9 ATII organoids formed spheres after 5 days of culture (Fig.\u0026nbsp;3a-c). Following expansion, these organoids were infected with adenovirus-Cre (Ad-Cre). The tdTomato\u003csup\u003e+\u003c/sup\u003e cells were then sorted using flow cytometry, generating \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;/\u0026minus;\u003c/em\u003e\u003c/sup\u003e ATII organoids (Fig.\u0026nbsp;3a).\u003c/p\u003e \u003cp\u003eTo investigate the oncogenic potential of the \u003cem\u003eMAP2K1\u003c/em\u003e\u003csup\u003eΔE101\u0026minus;I102\u003c/sup\u003e mutation, we overexpressed the \u003cem\u003eMAP2K1\u003c/em\u003e\u003csup\u003eΔE101\u0026minus;I102\u003c/sup\u003e mutant (MT) and wild-type (WT) \u003cem\u003eMAP2K1\u003c/em\u003e in the \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;/\u0026minus;\u003c/em\u003e\u003c/sup\u003e organoids, alongside a negative control lentivirus. The mutant and wild-type \u003cem\u003eMAP2K1\u003c/em\u003e cDNA sequences were verified by genomic sequencing (supplementary Fig.\u0026nbsp;4). Western blot analysis showed that overexpression of \u003cem\u003eMAP2K1\u003c/em\u003e\u003csup\u003eΔE101\u0026minus;I102\u003c/sup\u003e significantly upregulated phosphorylated MEK1 (pMEK1) and markedly increased the expression of phosphorylated ERK (pERK), which is the direct downstream molecule of MEK in the MAPK signalling pathway (Fig.\u0026nbsp;3b). We then cultured the organoids \u003cem\u003ein vitro\u003c/em\u003e for five days and performed viability assays, finding the proliferation capability of the \u003cem\u003eMAP2K1\u003c/em\u003e\u003csup\u003eΔE101\u0026minus;I102\u003c/sup\u003e group was significantly higher than that of the WT and NC groups (Fig.\u0026nbsp;3d). Morphologically, the spheroids formed in the \u003cem\u003eMAP2K1\u003c/em\u003e\u003csup\u003eΔE101\u0026minus;I102\u003c/sup\u003e group were also significantly larger than those of the other two groups (Fig.\u0026nbsp;3e). All three groups showed strong positive for \u003cem\u003eTtf1\u003c/em\u003e and \u003cem\u003eEpCAM\u003c/em\u003e through IHC assays (Fig.\u0026nbsp;3c). These data demonstrated that the \u003cem\u003eMAP2K1\u003c/em\u003e mutant could promote proliferation and adeno-differentiation of ATII organoids \u003cem\u003ein vitro\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eTo assess the tumorigenic capability of genetically engineered organoids \u003cem\u003ein vivo\u003c/em\u003e, we implanted the \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e; \u003cem\u003eMAP2K1\u003c/em\u003e\u003csup\u003e\u003cem\u003eMT\u003c/em\u003e\u003c/sup\u003e organoids, \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e; \u003cem\u003eMAP2K1\u003c/em\u003e\u003csup\u003e\u003cem\u003eWT\u003c/em\u003e\u003c/sup\u003e organoids and control \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e organoids into the lower flanks of NSG mice (Fig.\u0026nbsp;3a). Strikingly, 10 days after injection, tumors formed in mice injected with \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e; \u003cem\u003eMAP2K1\u003c/em\u003e\u003csup\u003e\u003cem\u003eMT\u003c/em\u003e\u003c/sup\u003e organoids, but not in those injected with the \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e organoids or \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e; \u003cem\u003eMAP2K1\u003c/em\u003e\u003csup\u003e\u003cem\u003eWT\u003c/em\u003e\u003c/sup\u003e organoids (Fig.\u0026nbsp;3f-h). Importantly, this suggests that loss of \u003cem\u003eTrp53\u003c/em\u003e alone or combined with overexpression of \u003cem\u003eMAP2K1\u003c/em\u003e\u003csup\u003e\u003cem\u003eWT\u003c/em\u003e\u003c/sup\u003e in ATII organoids is not sufficient to generate LUAD \u003cem\u003ein vivo\u003c/em\u003e. Histologic analysis of \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e; \u003cem\u003eMAP2K1\u003c/em\u003e\u003csup\u003e\u003cem\u003eMT\u003c/em\u003e\u003c/sup\u003e tumors revealed strong expression of \u003cem\u003eTtf1\u003c/em\u003e and \u003cem\u003eEpCAM\u003c/em\u003e, consistent with LUAD markers (Fig.\u0026nbsp;3i). In summary, these findings support that \u003cem\u003eMAP2K1\u003c/em\u003e\u003csup\u003e\u003cem\u003eΔE101\u0026minus;I102\u003c/em\u003e\u003c/sup\u003e, in the absence of \u003cem\u003eTrp53\u003c/em\u003e, can drive LUAD formation \u003cem\u003ein vivo\u003c/em\u003e.\u003c/p\u003e\n\u003ch3\u003eComparison of genomic features of lung adenocarcinoma at different developmental stages\u003c/h3\u003e\n\u003cp\u003eIt is generally believed that before developing into lung adenocarcinoma, cells progress through stages of AAH, AIS and MIA (Fig.\u0026nbsp;4a). \u003cem\u003eEGFR, KRAS\u003c/em\u003e and \u003cem\u003eALK\u003c/em\u003e had higher mutation frequencies as disease progressed from pre-invasive (AAH/AIS/MIA) to invasive stages (supplementary Fig.\u0026nbsp;1a). Mutations in genes within the RTK-RAS pathway were also more commonly observed in invasive stages (supplementary Fig.\u0026nbsp;1a). Regarding tumor suppressor genes, there was a significant increase in the frequencies of \u003cem\u003eTP53, KEAP1\u003c/em\u003e and \u003cem\u003eSTK11\u003c/em\u003e mutations from pre-invasive to invasive stages. Notably, the mutation frequency of \u003cem\u003eTP53\u003c/em\u003e was 0 in AAH/AIS/MIA samples but rose sharply as the disease progressed to stage I LUAD and continued to increase in higher stages (supplementary Fig.\u0026nbsp;1c). Interestingly, \u003cem\u003eKEAP1\u003c/em\u003e mutation frequency remained was relatively low until the disease reached stage IV LUAD, suggesting it a late event in tumor evolution\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. A combination of all tumor suppressor genes was found significantly increased in mutation frequencies as the disease progressed (supplementary Fig.\u0026nbsp;1c).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGenomic instability was higher in LUAD compared with AAH/AIS/MIA samples\u003c/h2\u003e \u003cp\u003eWe observed a higher level of genomic instability in LUAD compared to AAH/AIS/MIA, characterized by an increased TMB and a higher level of SCNA burden as tumors progressed to advanced stages. A sharp increase was seen between AAH/AIS/MIA and stage I LUAD samples (Fig.\u0026nbsp;4b, supplementary Fig.\u0026nbsp;5a). Whole genome doubling (WGD) events were also more frequent in LUAD compared to AAH/AIS/MIA (Fig.\u0026nbsp;4b). Additionally, structural variances were more common as the disease progressed, with a notable increase in the number of structural variants between AAH/AIS/MIA and stage I LUAD samples (Fig.\u0026nbsp;4b, supplementary Fig.\u0026nbsp;5b).\u003c/p\u003e \u003cp\u003eTo gain a deeper understanding of the progressive and prognostic value of SCNA events, we compared the frequency of focal copy number alterations. We found that 14 copy number amplifications and 3 copy number deletions were significantly more frequent in LUAD compared to AAH/AIS/MIA samples (Fig.\u0026nbsp;4c). Among the 17 significantly different focal SCNA events, the amplification of 14q13.3, where \u003cem\u003eNKX2-1\u003c/em\u003e is located, and the loss of 9p21.3, where \u003cem\u003eCDKN2A/2B\u003c/em\u003e were located, were of significant prognostic value (Fig.\u0026nbsp;4d). Patients harbouring either 14q13.3 amplifications or 9p21.3 losses had significantly worse RFS and OS (Fig.\u0026nbsp;4e, 4f).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAPOBEC signature activity was higher in LUAD compared with AAH/AIS/MIA samples\u003c/h3\u003e\n\u003cp\u003eMutation signatures reflect normal cell biology, environmental exposures, and neoplastic progression. They are associated with survival outcomes and have therapeutic implications for patients of various cancer types\u003csup\u003e\u003cspan additionalcitationids=\"CR25 CR26 CR27\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. In this study cohort, signatures of single base substitutions (SBS) were identified and compared across groups. We found that the activity of APOBEC signature (SBS2/13) tended to increase as the disease progressed to higher stages (Fig.\u0026nbsp;4b). Additionally, patients with higher APOBEC signature activities had significantly worse RFS and OS (Fig.\u0026nbsp;4g, 4h).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCancer is a systemic disease, with its initiation and progression a multistep process\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. In this study, we analysed genomic and transcriptomic profiles across different pathological and radiological stages. Our aim was to provide comprehensive knowledge of the genomic and transcriptomic changes that occur as lung adenocarcinomas progress from pre-invasive stages to late-stage metastatic tumors (Fig.\u0026nbsp;1). Driver mutations in major oncogenes in LUADs, such as \u003cem\u003eEGFR, KRAS, ERBB2\u003c/em\u003e, etc., were identified in as early as pre-invasive stages, namely AAH, AIS and MIA, indicating that cells had already acquired key genetic changes and undergone malignant transformation in these very early stages (Fig.\u0026nbsp;2a). From a clinical perspective, these pre-invasive lung adenocarcinomas, along with LUADs presented as pure GGOs on CT imaging, had nearly 100% 5-year or even 10-year survival rates after complete surgical resection\u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. As the survival rate drops dramatically when tumors progress to invasive stages, this indicates a curative time window for complete surgical resection in patients with early-stage lung adenocarcinoma.\u003c/p\u003e \u003cp\u003ePrevious studies have depicted mutational landscapes and identified major driver genetic events in LUADs\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. In this study, \u003cem\u003eEGFR\u003c/em\u003e mutations were detected in 64.5% sequenced samples, reflecting the East Asian population and predominance of never smokers in the study cohort. Compared with invasive lung adenocarcinomas, pre-invasive stages \u0026ndash; AAH, AIS and MIA \u0026ndash; had lower mutation frequencies in genes in the RTK-RAS pathway. Moreover, mutations in tumor suppressor genes were more frequently detected in invasive lung adenocarcinomas, suggesting the key role of loss of function of these genes in the progression of lung adenocarcinoma. Specifically, \u003cem\u003eTP53\u003c/em\u003e was not detected in AAH/AIS/MIA but rose to 32% in LUADs, highlighting its critical role in disease progression from precursors to invasive tumors\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. In line with a previous study which involves 98 pre-invasive and 99 invasive lung adenocarcinomas, we found that tumor mutation burden (TMB), APOBEC signature activity, WGD events, and SCNA burden all increased as tumor progressed from pre-invasive to invasive stages\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. By utilizing whole-genome sequencing data, we were able to detect structural variants (SVs). As a result, SVs were more prevalent as the disease progressed to higher stages, with a sharp increase in SV burden from MIA to stage I LUAD, suggesting a sudden change in genomic instability as disease progressed from pre-invasive to invasive stages.\u003c/p\u003e \u003cp\u003eA similar trend was observed radiologically, offering insights into the genomic changes of lung adenocarcinomas manifesting as pure and mixed GGOs on CT scans\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. It has been reported that patients with pure GGOs had excellent survival rate after surgery\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. As lung adenocarcinomas may develop from pure GGOs to solid nodules on CT scans, our study provides insights into the genomic and transcriptomic changes during this process, offering evidence for the curative window of lung adenocarcinoma.\u003c/p\u003e \u003cp\u003eWhile oncogenes are usually considered mutually exclusive, their co-occurrence with tumor suppressor genes has been reported to be associated with poor prognosis\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. In this study, we analysed the prognostic value of co-mutations involving one oncogene and one tumor suppressor gene, as well as co-mutations of two tumor suppressor genes. Among them, co-mutations involving \u003cem\u003eTP53\u003c/em\u003e were the most common and had the most significant negative impact on survival, suggesting a pivotal role of \u003cem\u003eTP53\u003c/em\u003e in lung adenocarcinoma. Interestingly, mutual exclusivity was also observed between oncogenes and tumor suppressor genes; for example, \u003cem\u003eEGFR\u003c/em\u003e was mutually exclusive with \u003cem\u003eSTK11\u003c/em\u003e and \u003cem\u003eKEAP1\u003c/em\u003e, and \u003cem\u003eBRAF\u003c/em\u003e was mutually exclusive with \u003cem\u003eTP53\u003c/em\u003e (Fig.\u0026nbsp;2f). T findings could shed light on individualized treatment strategies for lung adenocarcinoma\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDriver mutations are the ones that play critical roles in cancer initiation and progression\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Identifying driver mutations is significant in discovering novel drug targets, although defining a driver mutation can often be challenging\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. In this study, we identified a hotspot mutation (p.E102-I103 deletion) in \u003cem\u003eMAP2K1\u003c/em\u003e, which encodes a protein kinase essential to the MAP kinase signal transduction pathway\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. This mutation was previously found in 2 out of 230 (0.9%) patients with LUAD\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Interestingly, in our study cohort, 13 patients harboured this mutation, with 11 (6.9%) of them being pre-invasive samples. This mutation hotspot was also detected in our previous study, where 4 out of 98 (2.0%) pre-invasive adenocarcinoma patients harboured it\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eIn vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e experiments found that ATII organoids with overexpressed \u003cem\u003eMAP2K1\u003c/em\u003e\u003csup\u003eΔE101\u0026minus;I102\u003c/sup\u003e significantly outgrew ATII organoids with overexpressed wild-type \u003cem\u003eMAP2K1\u003c/em\u003e and the control group. Moreover, \u003cem\u003ein vivo\u003c/em\u003e experiments suggested that \u003cem\u003eMAP2K1\u003c/em\u003e\u003csup\u003eΔE101\u0026minus;I102\u003c/sup\u003e in the absence of \u003cem\u003eTrp53\u003c/em\u003e could drive LUAD formation in NSG mice. These results suggest that the \u003cem\u003eMAP2K1\u003c/em\u003e p.E102-I103 deletion could be a novel driver mutation of lung adenocarcinoma. Though mainly enriched in pre-invasive stages, this finding provides new insights into the initiation and evolution of lung adenocarcinoma.\u003c/p\u003e \u003cp\u003eMetastasis is the leading cause of cancer-related death, and lung adenocarcinomas harbouring different genomic alterations exhibit organotropism\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. In this study, we compared the genomic and transcriptomic profiles of patients with site-specific metastasis and those without metastasis after surgery. We found that \u003cem\u003eTP53\u003c/em\u003e mutations were significantly associated with brain metastasis, while mutations in the \u003cem\u003eTP53\u003c/em\u003e pathway were significantly associated with brain metastasis and lung recurrence. High activity of APOBEC signature was observed in patients with brain and lymph node metastasis. Up-regulation of \u003cem\u003eSPP1\u003c/em\u003e and down-regulation of \u003cem\u003eSFTPC\u003c/em\u003e were also found in patients with metastasis both in general and specifically in brain and bone metastasis, while \u003cem\u003eSPP1\u003c/em\u003e was also up-regulated in patients with lung recurrences. Moreover, up-regulation of \u003cem\u003eSPP1\u003c/em\u003e was associated with worse prognosis, and it was found to be up-regulated in a stepwise manner in comparing tumors with different radiological manifestations. Its association with CD8\u0026thinsp;+\u0026thinsp;T cell and Treg infiltrations also implied changes in the tumor microenvironment. The up-regulation of \u003cem\u003eSPP1\u003c/em\u003e in patients with bone, brain and lung recurrences suggests that \u003cem\u003eSPP1\u003c/em\u003e could be a potential biomarker for monitoring the progression of lung adenocarcinoma.\u003c/p\u003e \u003cp\u003eIn summary, this integrated genomic and transcriptomic study provides a comprehensive understanding of the progression of lung adenocarcinoma, shedding light on defining the therapeutic window for surgical resection, guiding individualized treatment strategies, and offering evidence for drug discovery in lung adenocarcinoma.\u003c/p\u003e "},{"header":"Methods","content":" \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eStudy cohort\u003c/h2\u003e \u003cp\u003eA total of 1,008 surgically resected samples from 954 patients with LUAD who underwent surgery between August 2011 and March 2019 at the Department of Thoracic Surgery, Fudan University Shanghai Cancer Center, were retrospectively included in this study. None of the patients received neoadjuvant therapy. Informed consents of all patients for donating their samples to the tissue bank of Fudan University Shanghai Cancer Center were obtained from patients themselves or their relatives. This study was approved by the Committee for Ethical Review of Research (Fudan University Shanghai Cancer Center Institutional Review Board, No. 090977-1).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eRadiological and histological evaluation\u003c/h2\u003e \u003cp\u003eEach patient received whole-lung CT scanning before surgery. Tumor size on CT images was defined as the maximum diameter of the lesion, and solid component on the single largest axial dimension was recorded on lung window. Based on the CT images, those pulmonary nodules were further categorized into 3 groups: pure GGOs, where there was no solid component in one pulmonary nodule; mixed GGOs, where both solid and GGO components existed in the same pulmonary nodule; and solid nodules, where the nodule contained only solid components (Fig.\u0026nbsp;5a). CT images were reviewed by two independent radiologists, with inter-observer and intra-observer agreements measured to quantify the reproducibility and accuracy as previously described\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn terms of histological diagnosis, intraoperative frozen section diagnosis was first made after the tumor was resected, and final postoperative pathological diagnosis was made after surgery by two independent pathologists. Following IASLC/ATS/ERS guidelines, tumors were classified as AIS, MIA or invasive LUAD based on their histological presentations\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Invasive LUADs were further subclassified into different subtypes, namely lepidic, acinar, papillary, micropapillary, solid and invasive mucinous subtypes. The subtypes were determined depending on the cell morphology under microscopes and recorded in 5% increments. The predominant subtype was defined as the one with the largest percentage in one sample\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Pathological stage of the disease was determined according to the eighth TNM staging system\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eFollow-up protocol\u003c/h2\u003e \u003cp\u003ePatients were followed up regularly after surgery as we previously described\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Briefly, patients were followed up every 3 months for the first 2 years after surgery, where physical examination, chest CT scans and abdominal ultrasonography were performed every 3\u0026ndash;6 months. The follow-up interval was changed to every 6 months for the third year and once a year from the fourth year onwards. Brain CT or magnetic resonance imaging (MRI) and bone scintigraphy were performed every 6 months for patients with invasive LUADs in the first 3 years. In addition, positron emission tomography (PET)-CT scans were performed if necessary. Recurrence-free survival (RFS) was defined as the time between surgery and first recurrence or last follow-up. Patients with no recurrence but died from other causes were censored on that date. Overall survival (OS) was defined as the time between surgery and death or the last follow-up date.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eWhole-genome sequencing (WGS) and RNA sequencing (RNA-seq)\u003c/h2\u003e \u003cp\u003eGenomic DNA from tumors and paired adjacent normal lung tissues were extracted and prepared using the QIAamp DNA Mini Kit (Qiagen, Germany) following the manufacturer\u0026rsquo;s instructions. A total amount of 2\u0026micro;g genomic DNA for each sample was fragmented into an average size of ~\u0026thinsp;350 bp. Libraries were constructed using and sequenced on Illumina NovaSeq 6000 platform and 150-bp paired end reads were generated. Total RNA from tumors and paired adjacent normal lung tissues was extracted and prepared using NucleoZOL (Macherey-Nagel, Germany) and NucleoSpin RNA Set for NucleoZOL (Macherey-Nagel, Germany) following the manufacturer\u0026rsquo;s instructions. A total amount of RNA sample was used as initial material for RNA sample preparations. Ribosomal RNA was removed using KAPA Stranded RNA-Seq Kit with RiboErase (KK8481, Roche, Switzerland). Libraries were generated and sequenced on Illumina NovaSqe 6000 platform, and 150-bp paired end reads were generated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eQuality control\u003c/h2\u003e \u003cp\u003eEnsuring the quality control of the entire sequencing data generation process relies on the utilization of Quartet reference material\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. In our experimental design, we performed DNA and RNA library construction with a maximum of 96 samples, including 94 lung cancer samples and 2 Quartet reference samples. This design guarantees that each lung cancer sample is accompanied by Quartet reference samples throughout the processes of library construction, sequencing, and data analysis.\u003c/p\u003e \u003cp\u003eOur prior studies have established quality control metrics based on Quartet reference material, encompassing Mendelian consistent rate (MCR) and F1 score at the DNA level\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, as well as signal-to-noise ratio (SNR) at the RNA-seq level\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Once all batches of lung cancer samples and their corresponding Quartet samples are generated, we analyze the Quartet samples and calculate the MCR, F-score, and SNR. These metrics are then used to assess the quality of each batch of data, ensuring qualification. Simultaneously, we monitor the consistency of experimental conditions across batches, thereby guaranteeing the generation of high-quality sequencing data.\u003c/p\u003e \u003cp\u003eThe quality and data size of genomic and transcriptomic sequencing data were assessed using FastQC (v0.11.9). The Fastq Screen (v0.15.1) software was employed to analyze the ratio of sequenced reads across species, determining whether the target DNA and RNA were contaminated with genetic material from other species. NGSCheckMate (v1.0.0)\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e was employed to calculate the allele fractions of known single-nucleotide polymorphisms (SNPs) in each sample and to assess whether the samples originated from the same individual. This was done by comparing the correlation of allele fractions of these SNPs between samples. In cases where Tumor and Normal samples were unpaired, they were excluded before somatic variant calling.\u003c/p\u003e \u003cp\u003eThe WGS data size for this cohort exceeded 150 GB, while the RNA-seq data size surpassed 15 GB (Figures \u003cspan refid=\"MOESM9\" class=\"InternalRef\"\u003eS9\u003c/span\u003eA and S9B). NGScheckmate confirmed that both the tumor and paired normal samples originated from the same individual (Figure \u003cspan refid=\"MOESM9\" class=\"InternalRef\"\u003eS9\u003c/span\u003eC). All samples underwent base quality assessment, including Q30 scores (Figures \u003cspan refid=\"MOESM9\" class=\"InternalRef\"\u003eS9\u003c/span\u003eA and S9B), with Quartet DNA and RNA standards incorporated into each batch for quality control (Figure \u003cspan refid=\"MOESM9\" class=\"InternalRef\"\u003eS9\u003c/span\u003eD). Each batch included 94 lung adenocarcinoma samples along with 2 Quartet standard material samples. The quality of Quartet DNA and RNA data was evaluated using specific metrics: F-score and Mendelian Concordance Rate (MCR) for DNA sequencing, and Signal-to-Noise Ratio (SNR) for RNA sequencing. The results of these evaluations\u0026mdash;F-score, MCR, and SNR\u0026mdash;demonstrate that the WGS and RNA-seq data generation for all tumor samples in this cohort is consistently reliable throughout the entire process (Figures \u003cspan refid=\"MOESM9\" class=\"InternalRef\"\u003eS9\u003c/span\u003eE and S9F).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eSomatic variant analysis\u003c/h2\u003e \u003cp\u003eWe harnessed the capabilities of the BWA algorithm, integrated into Sentieon (version sentieon-genomics-202112.04)\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, to align reads originating from tumor and normal samples to the human reference genome (GRCh38.d1.vd1.fa). Following this alignment, Sentieon's Dedup function was applied to tag and eliminate duplicated reads, ensuring the integrity of downstream analyses. For subsequent analyses, encompassing somatic variations and Indel assessments, we leveraged the aligned data from both tumor and normal samples (Dedup Bam). The comprehensive Mutation Variants Calling process involved the utilization of TNseq\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e, TNScope\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e (sentieon-genomics-202112.04), and strelka2 (v2.9.10)\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Variants that received concurrent calls from at least two software tools were retained. Additionally, we conducted manual verification using the Integrative Genomics Viewer (IGV) for common mutation sites in lung adenocarcinoma. Somatic varians were annotated utilizing ANNOVAR (v.2019-10-24)\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e and Variant Effect Predictor (VEP, v104.0)\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Tumor mutation burden (TMB) was characterized as the total count of non-synonymous single nucleotide variants per megabase (mut/Mb), with the capture size aligning with the TCGA cohort.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eSomatic variant signature\u003c/h2\u003e \u003cp\u003eMutational signature underwent analysis using the SigProfiler computational framework\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e, with SigProfilerExtractor (v1.1.21) employed to profile Single Base Substitutions (SBS). Single Base Substitutions (SBS), also known as single nucleotide variants, involve the replacement of specific nucleotide bases. In the context of pyrimidines within the Watson-Crick base pair, six possible substitutions exist: C\u0026thinsp;\u0026gt;\u0026thinsp;A, C\u0026thinsp;\u0026gt;\u0026thinsp;G, C\u0026thinsp;\u0026gt;\u0026thinsp;T, T\u0026thinsp;\u0026gt;\u0026thinsp;A, T\u0026thinsp;\u0026gt;\u0026thinsp;C, and T\u0026thinsp;\u0026gt;\u0026thinsp;G. A total of 96 COSMIC SBS mutations have been identified. The mutations in each sample were decomposed based on the COSMIC SBS mutation signature (v3.3) using the Analyzer function in SigProfilerExtractor. Subsequently, the mutation probability or number of mutations for each SBS signature in each sample was generated, enabling diverse genetic characterizations based on clinical phenotype. APOBEC-induced mutations are primarily linked to C\u0026thinsp;\u0026gt;\u0026thinsp;T transition events. Except for SBS2 and SBS13, we also used trinucleotideMatrix function in maftools to evaluate APOBEC Enrichment Score.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eCopy number variant analysis\u003c/h2\u003e \u003cp\u003eThe parsing of copy number variants was executed using ascatNgs (v4.3.3)\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e with the ASCAT (v2.5.1)\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e wrapper. Subsequent copy number variant analyses were performed on tumor and normal BAM files derived from Sentieon-BWA aligment. The reference genome employed for these copy number variant analyses corresponded to the human reference genome used in the Sentieon-BWA alignment (GRCh38.d1.vd1.fa). In the clinical phenotype linkage analysis, samples lacking ASCAT solutions or demonstrating 100% purity were excluded from both the copy number and clinical phenotype linkage analyses.\u003c/p\u003e \u003cp\u003eGISTIC2 (v2.0.23)\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e is utilized for analyzing Copy Number Variation (CNV) amplification or deletion changes at the arm, focal, and gene levels. Each sample's copy number variation segment file was obtained using ascatNGS, and these segment results were employed as input for GISTIC. GISTIC applied parameters, including ta 0.25, td 0.25, qvt 0.25, cap 1.5, brlen 0.5, conf 0.95, armpeel 1, broad 1, and savegene1. In the cohort, significant changes in arm events were identified using a threshold of q\u0026thinsp;\u0026lt;\u0026thinsp;0.01, while deletions less than \u0026minus;\u0026thinsp;1 and amplifications greater than or equal to 2 are displayed in the oncoplot. At the gene level, Copy Number Alteration (CNA) amplification and deletion were identified using thresholds of -2 and +\u0026thinsp;2. The TCGA CNV ASCAT2 data were analyzed using the same methods and parameters.\u003c/p\u003e \u003cp\u003eGISTIC amplification and deletion profiles were independently analyzed using the R package cngpld (v0.1), which employed a gaussian process latent difference model. This approach allowed for the identification of specific genomic regions within the invasive LUAD cohort that exhibited significantly higher G scores compared to those in the preinvasive LUAD cohort.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eHLALOH\u003c/h2\u003e \u003cp\u003eHLALOH identification was carried out using the LOHHLA algorithm\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Initially, the HLA typing for each patient was determined by POLYSOLVER (v1.0.0)\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e, based on the WGS sequencing data from paired normal samples. Subsequently, the HLA fasta file was compared with the WGS data from tumors and paired normal tissues for each patient to evaluate the LOH status of their HLA typing.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eStructural variation analysis\u003c/h2\u003e \u003cp\u003eStructural variation (SV) analysis was conducted on aligned tumor and normal bam file using GRIDSS2 (v2.13.2)\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Following the retrieval of SV data from all tumor samples, the events were filtered using the panel-of-normal file provided by GRIDSS2. The SV events from each sample were then categorized into five types based on variant characteristics: CTX, Inversion, Insertion, Deletion, and Duplication.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eRNA-seq data analysis\u003c/h2\u003e \u003cp\u003eExpression profiling from raw FASTA data was accomplished through the utilization of the Hisat2-StringTie pipeline\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. Initial preprocessing steps involved the application of FastP (v0.36)\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e to remove adapters from the raw RNA-seq reads. The alignment of reads to the human reference genome (GRCh38, release-84) was executed using Hisat2 (v2.2.1), with data sourced from Genomic Data Commons (GDC). Following alignment, the reads were assembled into transcripts or genes by StringTie (v2.2.1), utilizing the genome annotation file (gencode.v36.annotation.gtf). To quantify gene expression, Fragments Per Kilobase of exon model per Million mapped fragments (FPKM) was employed.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eGene fusion detection\u003c/h2\u003e \u003cp\u003eIn the analysis of gene fusions, STAR-Fusion\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e was utilized to identify and profile potential fusion events within RNA-seq data. The workflow involved aligning RNA-seq reads to a reference genome, systematically examining the aligned reads for abnormal gene connections indicative of fusion events, and implementing rigorous filtering criteria to preserve only validated fusion events for subsequent analysis. The confirmed fusion events then underwent detailed annotation, furnishing comprehensive insights into the implicated genes, their genomic coordinates, and potential functional implications.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eDifferential gene expression, mutation and copy number alterations analysis\u003c/h2\u003e \u003cp\u003eThe limma\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e package (v3.50.0) in R was employed for differential expression analysis comparing clinical phenotype. Differentially expressed genes were identified using standard cutoffs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |log2(fold change)| \u0026gt;= 1) \u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. Fisher's exact test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was employed to identify differentially mutated genes between GGOs and solid cohorts, as well as between the no-relapse and relapse cohorts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003essGSEA analysis\u003c/h2\u003e \u003cp\u003eTo estimate gene-set enrichment scores, the R package GSVA (v1.42.0) \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e with the default GSVA method was employed. Following the analytical approach of the previous study\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e, the hallmark gene set (h.all.v7.5.symbols.gmt) was downloaded from the GSEA website\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. Enrichment scoring for each sample per hallmark gene set was conducted using GSVA, with the method parameters set to ssgsea. Identification of hallmarks significantly enriched with genes from the input set was based on an adjusted P-value (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The differences among the pure GGO, mixed GGO, and Solid groups were assessed using the ANOVA statistical test. The p-values were corrected for false discovery rate (FDR), and Hallmarks with an FDR-adjusted p-value of less than 0.05 were selected for further analysis.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of molecular events related to recurrence and site of recurrence\u003c/h2\u003e \u003cp\u003ePatients who did not experience recurrence for over 5 years were defined as non-recurrent, while those who experienced recurrence during follow-up were classified as the recurrent. For patients with recurrence, the site of recurrence was documented in as much detail as possible based on CT. We conducted a comprehensive analysis of recurrence, along with gene mutations and copy number variants associated with the site of recurrence, using Fisher's exact test (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Gene mutations and copy number variants were analyzed for their association with time to relapse or site-specific relapse using COX regression (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). q-value represents the p-value after FDR correction. Since multiple genes with copy number variations on a chromosome cytoband may be relevant to relapse, relapse site, or relapse timing, we present only the chromosome cytoband where these genes are located in the final presentation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted using R (v4.1.2). Various statistical tests, including t-tests, Fisher's exact tests, and Pearson correlation, were applied. Kaplan-Meier survival analysis and Cox regression hazard modelling were performed using the R packages survival (v3.2-13) and survminer (0.4.9). Kaplan-Meier survival analysis, coupled with the log-rank test, was employed for both overall survival (OS) and relapse-free survival (RFS) assessments. Heatmaps were generated using the R package ComplexHeatmap (v2.15.4)\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. Principal component analysis (PCA) was executed with the R package stats (v4.1.2). Oncoplot and lollipop plots were created using maftools (v2.10.05) \u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. Boxplots and scatter plots were crafted using R packages ggpubr (v0.4.0) and ggplot2 (v3.4.0). This comprehensive suite of analyses and visualizations ensures a thorough exploration and presentation of the dataset.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMice studies\u003c/h3\u003e\n\u003cp\u003e All mouse work was approved by the Animal Ethics committee of School of Basic Medical Sciences at Fudan University and performed in compliance with the NIH Guide for the Care and Use of Laboratory Animals. \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eL/L\u003c/sup\u003e;Cas9\u003csup\u003etdTomato\u003c/sup\u003e mice (\u003cem\u003eTrp53\u003c/em\u003e homogenous and LSL-Cas9\u003csup\u003etdTomato\u003c/sup\u003e heterozygous) were obtained to generate \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eL/L\u003c/sup\u003e;Cas9\u003csup\u003etdTomato\u003c/sup\u003e ATII lung organoids by crossing \u003cem\u003eTrp53\u003c/em\u003e conditional knock-out mice with Cas9\u003csup\u003etdTomato\u003c/sup\u003e conditional knock-in mice. Briefly, LoxP sites were inserted flanking the exon 5\u0026ndash;7 region of Trp53 via homologous recombination. \u003cem\u003eTrp53\u003c/em\u003e was knockout by Cre recombinase-mediated removal of \u003cem\u003eTrp53\u003c/em\u003e exons 5\u0026ndash;7 using Ad-Cre system. NOD/ShiLtGpt-Prkdc\u003csup\u003eem26cd52\u003c/sup\u003eIl2rg\u003csup\u003eem26cd22\u003c/sup\u003e/cpt mice (NSG, procured from Gem Pharmatech Co., Ltd., Nanjing, China) were used for the human ATII organoid allografts.\u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eOrganoid culture and manipulation\u003c/h2\u003e \u003cp\u003e \u003cem\u003eTrp53\u003c/em\u003e \u003csup\u003eL/L\u003c/sup\u003e;Cas9\u003csup\u003etdTomato\u003c/sup\u003e ATII lung organoids were generated from 6\u0026ndash;8 weeks \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eL/L\u003c/sup\u003e;Cas9\u003csup\u003etdTomato\u003c/sup\u003e mice of the C57BL/6J background. In brief, the lungs were dissected and rinsed twice with phosphate-buffered saline (PBS). The tissues were finely chopped using scissors and then digested in a solution of collagenase D and DNase I in Hank\u0026rsquo;s Balanced Salt Solution (HBSS) at 37\u0026deg;C for 30 minutes. Following incubation, the digested tissue was filtered through a 70 \u0026micro;m cell strainer to obtain single-cell suspensions. The cells were pelleted by centrifugation at 200g for 5 minutes, resuspended in Advanced-DMEM media, and plated in a 6 cm dish. After 36 hours of culture, the cells were washed 5\u0026ndash;6 times with PBS and treated with 1 ml of Trypsin-EDTA (Meilunbio, Cat#: MA0233) to digest epithelial cells. The ATII lung organoids were maintained for successive passages using a 1:2 mixture of PneumaCult\u0026trade; Alveolar Organoid Expansion Medium (Stemcell, Cat#:) and growth factor-reduced Matrigel (Corning, Cat#: 354230). \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e;Cas9\u003csup\u003etdTomato\u003c/sup\u003e organoids were generated from \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003e\u003cem\u003eL/L\u003c/em\u003e\u003c/sup\u003e;Cas9\u003csup\u003etdTomato\u003c/sup\u003e ATII lung organoids by Ad-Cre virus infection, followed by flow cytometry sorting of tdTomato\u0026thinsp;+\u0026thinsp;cells.\u003c/p\u003e \u003cp\u003eLentiviral plasmids expressing \u003cem\u003eMAP2K1\u003c/em\u003e (NM_002755) wild-type (WT) and \u003cem\u003eMAP2K1\u003c/em\u003e (NM_002755) mutant (MT, p.E102_I103del) were constructed by ligating the corresponding PCR products into MCS-3FLAG-SV40-Cherry-IRES-Blasticidin vector. Lentiviral particles were packaged using ViraPower Lentiviral Expression Systems (Thermo Fisher Scientific) following the manufacturer's manual.\u003c/p\u003e \u003cp\u003eTo generate \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e;\u003cem\u003eMAP2K1\u003c/em\u003e WT or \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e;\u003cem\u003eMAP2K1\u003c/em\u003e MT AT Organoids were isolated by digesting the Matrigel with 0.25% Trypsin-EDTA (Meilunbio, Cat#: MA0233) in culture plates for 10 minutes at 37\u0026deg;C and washed with PBS. Once organoids were dissociated, cells were pelleted and resuspended in 50 \u0026micro;L lentiviral solution. Spinoculation was performed by transferring the suspension into a 24-well plate and centrifuging the plate at 600g for 1 hour at 32\u0026deg;C. Plates were then incubated at 37\u0026deg;C for 6 hours before washing the suspension with fresh media and pelleting the cells to be embedded in fresh Matrigel media mixture. Antibiotics (blasticidin, 5 \u0026micro;g/mL) were added to the media to select the infected organoids. Organoid viability was assessed using the bioGenous LivingCell-Fluo\u0026trade; Organoid Vitality Assay Kit (bioGenous, Catalog: E238004). Morphological images were captured 5 days after passaging under various experimental conditions and analyzed with ImageJ to evaluate the organoid area.\u003c/p\u003e \u003cp\u003eTo investigate whether \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e;\u003cem\u003eMAP2K1\u003c/em\u003e MT organoids could form tumors in vivo. 1\u0026times;10\u003csup\u003e6\u003c/sup\u003e \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e;NC or overexpressing \u003cem\u003eMAP2K1\u003c/em\u003e WT/MT ATII cells were resuspended in ice-cold 100 \u0026micro;L PBS buffer and implanted subcutaneously into the lower flanks of NSG mice. After tumor formation, tumor volume was estimated every 2\u0026ndash;3 days with the following formula: (L * W ^ 2) / 2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemistry staining and analysis\u003c/h2\u003e \u003cp\u003eFor immunohistochemistry (IHC) analysis, paraffin-embedded tissue or organoid sections were first deparaffinized using xylene and then rehydrated through a series of graded alcohols into water. Antigen retrieval was performed by heating the sections in citrate buffer (10 mM sodium citrate buffer, pH 6.0) at sub-boiling temperature for 15 minutes. The sections were then permeabilized with 0.5% Triton-100 in PBS for 20 minutes. To block endogenous peroxidase activity, a 3% H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e solution was applied for 10 minutes, followed by three PBS washes. The sections were then incubated with blocking buffer (3% BSA in PBS) for 30 minutes. Primary antibodies were applied, and the sections were incubated overnight at 4℃. After three washes, the sections were incubated with the appropriate secondary antibodies for 30 minutes at room temperature. Signals were visualized using freshly prepared DAB substrate solution (ZSGB-BIO Company, Beijing, China). Finally, the sections were counterstained with hematoxylin, dehydrated, and mounted with coverslips.\u003c/p\u003e \u003cp\u003eThe following primary antibodies were used: EpCam (Abcam, Cat#: ab71916), Ttf1 (Abcam, Cat#: ab76013), Krt7 (Proteintech, Cat#: 17513-1-AP).\u003c/p\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003eWestern blotting and analysis\u003c/h2\u003e \u003cp\u003eWestern blotting was conducted following previously established protocols\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. In brief, cells were lysed in RIPA buffer (Meilunbio) containing 50 mM Tris (pH 7.4), 150 mM NaCl, 1% Triton X-100, 1% sodium deoxycholate, 0.1% SDS, and EDTA, supplemented with protease and phosphatase inhibitors (Cat. No. HY-K0010, MCE Chemicals). A total of 30 \u0026micro;g of protein was loaded onto SDS\u0026ndash;polyacrylamide gels, transferred to PVDF membranes (Millipore), probed with specific antibodies, and visualized.\u003c/p\u003e \u003cp\u003eThe following primary antibodies were used: ERK1/2 (1:2000 dilution; Cell Signaling Technology Cat, Cat#: 4695), pERK1/2 (1:2000 dilution; Cell Signaling Technology, Cat#: 9101), MEK1(1:2000 dilution; Abcam. Cat#: ab32091), pMEK1 (1:2000 dilution; Abcam, Cat#: ab96379) and GAPDH (1:5000 dilution; Proteintech, Cat#: CL594-60004)\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (81930073, 82203037), Shanghai Science and Technology Innovation Action Project (20JC1417200), Shanghai Municipal Science and Technology Major Project (2017SHZDZX01, VBH1323001/026), Shanghai Municipal Key Clinical Specialty Project (SHSLCZDZK02104), Pilot Project of Fudan University (IDF159045), Shanghai Sailing Program (22YF1408900) and State Sponsored Postdoctoral Fellowship Programme (GZC20230501).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting Financial Interest Declarations\u003c/h2\u003e \u003cp\u003eThe authors declare no competing financial interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Natural Science Foundation of China (81930073, 82203037), Shanghai Science and Technology Innovation Action Project (20JC1417200), Shanghai Municipal Science and Technology Major Project (2017SHZDZX01, VBH1323001/026), Shanghai Municipal Key Clinical Specialty Project (SHSLCZDZK02104), Pilot Project of Fudan University (IDF159045), Shanghai Sailing Program (22YF1408900) and State Sponsored Postdoctoral Fellowship Programme (GZC20230501).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003eThe raw data from whole-genome sequencing (WGS) and RNA-seq have been deposited in the National Omics Data Encyclopedia (NODE) (Accession number: OEP002580) and Genome Sequence Archive (GSA) (DNA accession number: HRA002624, RNA accession number: HRA002983). Data on copy number variation, gene expression, and survival prognosis for the TCGA-LUAD cohort were retrieved from the GDC database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel, R.L., Miller, K.D., Wagle, N.S. \u0026amp; Jemal, A. Cancer statistics, 2023. CA Cancer J Clin 73, 17\u0026ndash;48 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, Y. \u003cem\u003eet al.\u003c/em\u003e Global variations in lung cancer incidence by histological subtype in 2020: a population-based study. 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Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res 28, 1747\u0026ndash;1756 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu, N. \u003cem\u003eet al.\u003c/em\u003e PUF60 promotes cell cycle and lung cancer progression by regulating alternative splicing of CDC25C. Cell Rep 42, 113041 (2023).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4977481/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4977481/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLung adenocarcinoma (LUAD) progresses from pre-invasive to invasive stages, as well as from ground-glass opacities (GGOs) to solid nodules. However, the dynamic genomic and transcriptomic changes underlying LUAD progression remain incompletely understood. Here we analysed genomic and transcriptomic profiles of 1,008 LUAD samples, encompassing all developmental stages in 954 patients, with comprehensive follow-up data. Frequently mutated driver genes were identified, with more mutations in tumor suppressor genes and higher levels of genomic instability observed as the disease progressed. Notably, \u003cem\u003eMAP2K1\u003c/em\u003e E102-I103 deletion was frequently identified in pre-invasive samples, which endowed Alveolar Type II (ATII) cells with increased growth potential and initiated tumor formation, suggesting it a potential driver mutation of LUAD. In summary, our study highlights key molecular changes in the stepwise progression of lung adenocarcinoma, provides insights into identifying novel therapeutic targets, and helps define the curative time window for this disease.\u003c/p\u003e","manuscriptTitle":"Genomic and Transcriptomic Dynamics in the Stepwise Progression of Lung Adenocarcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-06 11:11:35","doi":"10.21203/rs.3.rs-4977481/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0c1842cf-cc6b-4027-8c3c-74be322f12a9","owner":[],"postedDate":"November 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":39802567,"name":"Health sciences/Diseases/Cancer/Lung cancer/Non-small-cell lung cancer"},{"id":39802568,"name":"Health sciences/Diseases/Cancer/Oncogenes"},{"id":39802569,"name":"Health sciences/Medical research/Genetics research"}],"tags":[],"updatedAt":"2024-12-11T09:00:19+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-06 11:11:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4977481","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4977481","identity":"rs-4977481","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}