Integrative genomic and transcriptomic profiling of pulmonary sarcomatoid carcinoma identifies molecular subtypes associated with distinct immune features and clinical outcomes.

Background: Pulmonary sarcomatoid carcinoma (PSC) is a rare and aggressive subtype of non-small cell lung cancer (NSCLC), characterized by the presence of epithelial and sarcoma-like components. The molecular and immune landscape of PSC has not been well defined. Methods: Multiomics profiling of 21 pairs of PSCs with matched normal lung tissues was performed through targeted high-depth DNA panel, whole-exome, and RNA sequencing. We describe molecular and immune features that define subgroups of PSC with disparate genomic and immunogenic features as well as distinct clinical outcomes. Results: In total, 27 canonical cancer gene mutations were identified, with TP53 the most frequently mutated gene, followed by KRAS. Interestingly, most TP53 and KRAS mutations were earlier genomic events mapped to the trunks of the tumors, suggesting branching evolution in most PSC tumors. We identified two distinct molecular subtypes of PSC, driven primarily by immune infiltration and signaling. The Immune High (IM-H) subtype was associated with superior survival, highlighting the impact of immune infiltration on the biological and clinical features of localized PSCs. Conclusions: We provided detailed insight into the mutational landscape of PSC and identified two molecular subtypes associated with prognosis. IM-H tumors were associated with favorable recurrence-free survival and overall survival, highlighting the importance of tumor immune infiltration in the biological and clinical features of PSCs.

Mechanisms of response and tolerance to active RAS inhibition in KRAS-mutant NSCLC.

Resistance to inactive state-selective RASG12C inhibitors frequently entails accumulation of RASGTP, rendering effective inhibition of active RAS potentially desirable. Here, we evaluated the anti-tumor activity of the RAS(ON) multi-selective tri-complex inhibitor RMC-7977 and dissected mechanisms of response and tolerance in KRASG12C-mutant NSCLC. Broad-spectrum, reversible RASGTP inhibition with or without concurrent covalent targeting of active RASG12C yielded superior and differentiated antitumor activity across diverse co-mutational KRASG12C-mutant NSCLC mouse models of primary or acquired RASG12C(ON) or (OFF) inhibitor resistance. Interrogation of time-resolved single cell transcriptional responses established an in vivo atlas of multi-modal acute and chronic RAS pathway inhibition in the NSCLC ecosystem and uncovered a regenerative mucinous transcriptional program that supports long-term tumor cell persistence. In patients with advanced KRASG12C-mutant NSCLC, the presence of mucinous histological features portended poor response to sotorasib or adagrasib. Our results have potential implications for personalized medicine and the development of rational RAS inhibitor-anchored therapeutic strategies.

CoCo-ST: Comparing and Contrasting Spatial Transcriptomics data sets using graph contrastive learning.

Traditional feature dimension reduction methods have been widely used to uncover biological patterns or structures within individual spatial transcriptomics data. However, these methods are designed to yield feature representations that emphasize patterns or structures with dominant high variance, such as the normal tissue spatial pattern in a precancer setting. Consequently, they may inadvertently overlook patterns of interest that are potentially masked by these high-variance structures. Herein we present our graph contrastive feature representation method called CoCo-ST (Comparing and Contrasting Spatial Transcriptomics) to overcome this limitation. By incorporating a background data set representing normal tissue, this approach enhances the identification of interesting patterns in a target data set representing precancerous tissue. Simultaneously, it mitigates the influence of dominant common patterns shared by the background and target data sets. This enables discerning biologically relevant features crucial for capturing tissue-specific patterns, a capability we showcased through the analysis of serial mouse precancerous lung tissue samples.

Immune checkpoint blockade resistance in lung cancer: emerging mechanisms and therapeutic opportunities.

Immune checkpoint blockade (ICB) therapy works by inhibiting suppressive checkpoints that become upregulated after T cell activation, like PD-1/PD-L1 and CTLA-4. While the initial FDA approvals of ICB have revolutionized cancer therapies and fueled a burgeoning immuno-oncology field, more recent clinical development of new agents has been slow. Here, focusing on lung cancer, we review the latest research uncovering tumor cell intrinsic and extrinsic ICB resistance mechanisms as major hurdles to treatment efficacy and clinical progress. These include genomic and non-genomic tumor cell alterations, along with host and microenvironmental factors like the microbiome, metabolite accumulation, and hypoxia. Together, these factors can cooperate to promote immunosuppression and ICB resistance. Opportunities to prevent resistance are constantly evolving in this rapidly expanding field, with the goal of moving toward personalized immunotherapeutic regimens.

Joint multi-omics discriminant analysis with consistent representation learning using PANDA.

Integrative multi-omics analysis provides deeper insight and enables better and more realistic modeling of the underlying biology and causes of diseases than does single omics analysis. Although several integrative multi-omics analysis methods have been proposed and demonstrated promising results in integrating distinct omics datasets, inconsistent distribution of the different omics data, which is caused by technology variations, poses a challenge for paired integrative multi-omics methods. In addition, the existing discriminant analysis-based integrative methods do not effectively exploit correlation and consistent discriminant structures, necessitating a compromise between correlation and discrimination in using these methods. Herein we present PAN-omics Discriminant Analysis (PANDA), a joint discriminant analysis method that seeks omics-specific discriminant common spaces by jointly learning consistent discriminant latent representations for each omics. PANDA jointly maximizes between-class and minimizes within-class omics variations in a common space and simultaneously models the relationships among omics at the consistency representation and cross-omics correlation levels, overcoming the need for compromise between discrimination and correlation as with the existing integrative multi-omics methods. Because of the consistency representation learning incorporated into the objective function of PANDA, this method seeks a common discriminant space to minimize the differences in distributions among omics, can lead to a more robust latent representations than other methods, and is against the inconsistency of the different omics. We compared PANDA to 10 other state-of-the-art multi-omics data integration methods using both simulated and real-world multi-omics datasets and found that PANDA consistently outperformed them while providing meaningful discriminant latent representations. PANDA is implemented using both R and MATLAB, with codes available at https://github.com/WuLabMDA/PANDA.

The evolution of lung adenocarcinoma precursors is associated with chromosomal instability and transition from innate to adaptive immune response/evasion.

Studying lung adenocarcinoma (LUAD) early carcinogenesis is challenging, primarily due to the lack of LUAD precursors specimens. We amassed multi-omics data from 213 LUAD and LUAD precursors to identify molecular features underlying LUAD precancer evolution. We observed progressively increasing mutations, chromosomal aberrations, whole genome doubling and genomic instability from precancer to invasive LUAD, indicating aggravating chromosomal instability (CIN). Telomere shortening, a crucial genomic alteration linked to CIN, emerged at precancer stage. Moreover, later-stage lesions demonstrated increasing cancer stemness and decreasing alveolar identity, suggesting epithelial de-differentiation during early LUAD carcinogenesis. The innate immune cells progressively diminished from precancer to invasive LUAD, concomitant with a gradual recruitment of adaptive immune cells (except CD8+ and gamma-delta T cells that decreased in later stages) and upregulation of numerous immune checkpoints, suggesting LUAD precancer evolution is associated with a shift from innate to adaptive immune response and immune evasion mediated by various mechanisms.

Enhancing NSCLC recurrence prediction with PET/CT habitat imaging, ctDNA, and integrative radiogenomics-blood insights.

While we recognize the prognostic importance of clinicopathological measures and circulating tumor DNA (ctDNA), the independent contribution of quantitative image markers to prognosis in non-small cell lung cancer (NSCLC) remains underexplored. In our multi-institutional study of 394 NSCLC patients, we utilize pre-treatment computed tomography (CT) and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) to establish a habitat imaging framework for assessing regional heterogeneity within individual tumors. This framework identifies three PET/CT subtypes, which maintain prognostic value after adjusting for clinicopathologic risk factors including tumor volume. Additionally, these subtypes complement ctDNA in predicting disease recurrence. Radiogenomics analysis unveil the molecular underpinnings of these imaging subtypes, highlighting downregulation in interferon alpha and gamma pathways in the high-risk subtype. In summary, our study demonstrates that these habitat imaging subtypes effectively stratify NSCLC patients based on their risk levels for disease recurrence after initial curative surgery or radiotherapy, providing valuable insights for personalized treatment approaches.

Anti-tumor activity of a novel LAIR1 antagonist in combination with anti-PD-1 to treat collagen-rich solid tumors.

We recently reported that resistance to PD-1-blockade in a refractory lung cancer-derived model involved increased collagen deposition and the collagen-binding inhibitory receptor leukocyte-associated immunoglobulin-like receptor 1 (LAIR1), and thus we hypothesized that LAIR1 and collagen cooperated to suppress therapeutic response. Here, we report LAIR1 is associated with tumor stroma and is highly expressed by intratumoral myeloid cells in both human tumors and mouse models of cancer. Stroma-associated myeloid cells exhibit a suppressive phenotype and correlate with LAIR1 expression in human cancer. NGM438, a novel humanized LAIR1 antagonist monoclonal antibody, elicits myeloid inflammation and allogeneic T cell responses by binding to LAIR1 and blocking collagen engagement. Further, a mouse-reactive NGM438 surrogate antibody sensitized refractory KP mouse lung tumors to anti-PD-1 therapy and resulted in increased intratumoral CD8+ T cell content and inflammatory gene expression. These data place LAIR1 at the intersection of stroma and suppressive myeloid cells and support the notion that blockade of the LAIR1/collagen axis can potentially address resistance to checkpoint inhibitor therapy in the clinic.

Case report: Molecular profiling facilitates the diagnosis of a challenging case of lung cancer with choriocarcinoma features.

Accurate diagnoses are crucial in determining the most effective treatment across different cancers. In challenging cases, morphology-based traditional pathology methods have important limitations, while molecular profiling can provide valuable information to guide clinical decisions. We present a 35-year female with lung cancer with choriocarcinoma features. Her disease involved the right lower lung, brain, and thoracic lymph nodes. The pathology from brain metastasis was reported as "metastatic choriocarcinoma" (a germ cell tumor) by local pathologists. She initiated carboplatin and etoposide, a regimen for choriocarcinoma. Subsequently, her case was assessed by pathologists from an academic cancer center, who gave the diagnosis of "adenocarcinoma with aberrant expression of ß-hCG" and finally pathologists at our hospital, who gave the diagnosis of "poorly differentiated carcinoma with choriocarcinoma features". Genomic profiling detected a KRAS G13R mutation and transcriptomics profiling was suggestive of lung origin. The patient was treated with carboplatin/paclitaxel/ipilimumab/nivolumab followed by consolidation radiation therapy. She had no evidence of progression to date, 16 months after the initial presentation. The molecular profiling could facilitate diagnosing of challenging cancer cases. In addition, chemoimmunotherapy and local consolidation radiation therapy may provide promising therapeutic options for patients with lung cancer exhibiting choriocarcinoma features.

Synthetic PET from CT improves diagnosis and prognosis for lung cancer: Proof of concept.

[18F]Fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) are indispensable components in modern medicine. Although PET can provide additional diagnostic value, it is costly and not universally accessible, particularly in low-income countries. To bridge this gap, we have developed a conditional generative adversarial network pipeline that can produce FDG-PET from diagnostic CT scans based on multi-center multi-modal lung cancer datasets (n = 1,478). Synthetic PET images are validated across imaging, biological, and clinical aspects. Radiologists confirm comparable imaging quality and tumor contrast between synthetic and actual PET scans. Radiogenomics analysis further proves that the dysregulated cancer hallmark pathways of synthetic PET are consistent with actual PET. We also demonstrate the clinical values of synthetic PET in improving lung cancer diagnosis, staging, risk prediction, and prognosis. Taken together, this proof-of-concept study testifies to the feasibility of applying deep learning to obtain high-fidelity PET translated from CT.

Clinical Benefit from Docetaxel +/- Ramucirumab Is Not Associated with Mutation Status in Metastatic Non-Small-Cell Lung Cancer Patients Who Progressed on Platinum Doublets and Immunotherapy.

Docetaxel +/- ramucirumab remains the standard-of-care therapy for patients with metastatic non-small-cell lung cancer (NSCLC) after progression on platinum doublets and immune checkpoint inhibitors (ICIs). The aim of our study was to investigate whether the cancer gene mutation status was associated with clinical benefits from docetaxel +/- ramucirumab. We also investigated whether platinum/taxane-based regimens offered a better clinical benefit in this patient population. A total of 454 patients were analyzed (docetaxel +/- ramucirumab n=381; platinum/taxane-based regimens n=73). Progression-free survival (PFS) and overall survival (OS) were compared among different subpopulations with different cancer gene mutations and between patients who received docetaxel +/- ramucirumab versus platinum/taxane-based regimens. Among patients who received docetaxel +/- ramucirumab, the top mutated cancer genes included TP53 (n=167), KRAS (n=127), EGFR (n=65), STK11 (n=32), ERBB2 (HER2) (n=26), etc. None of these cancer gene mutations or PD-L1 expression was associated with PFS or OS. Platinum/taxane-based regimens were associated with a significantly longer mQS (13.00 m, 95% Cl: 11.20-14.80 m versus 8.40 m, 95% Cl: 7.12-9.68 m, LogRank P=0.019) than docetaxel +/- ramcirumab. Key prognostic factors including age, histology, and performance status were not different between these two groups. In conclusion, in patients with metastatic NSCLC who have progressed on platinum doublets and ICIs, the clinical benefit from docetaxel +/- ramucirumab is not associated with the cancer gene mutation status. Platinum/taxane-based regimens may offer a superior clinical benefit over docetaxel +/- ramucirumab in this patient population.

Brief Report: Comprehensive Clinicogenomic Profiling of Small Cell Transformation From EGFR-Mutant NSCLC Informs Potential Therapeutic Targets.

Introduction: NSCLC transformation to SCLC has been best characterized with EGFR-mutant NSCLC, with emerging case reports seen in ALK, RET, and KRAS-altered NSCLC. Previous reports revealed transformed SCLC from EGFR-mutant NSCLC portends very poor prognosis and lack effective treatment. Genomic analyses revealed TP53 and RB1 loss of function increase the risk of SCLC transformation. Little has been reported on the detailed clinicogenomic characteristics and potential therapeutic targets for this patient population. Methods: In this study, we conducted a single-center retrospective analysis of clinical and genomic characteristics of patients with EGFR-mutant NSCLC transformed to SCLC. Demographic data, treatment course, and clinical molecular testing reports were extracted from electronic medical records. Kaplan-Meier analyses were used to estimate survival outcomes. Next generation sequencing-based assays was used to identify EGFR and co-occurring genetic alterations in tissue or plasma before and after SCLC transformation. Single-cell RNA sequencing (scRNA-seq) was performed on a patient-derived-xenograft model generated from a patient with EGFR-NSCLC transformed SCLC tumor. Results: A total of 34 patients were identified in our study. Median age at initial diagnosis was 58, and median time to SCLC transformation was 24.2 months. 68% were female and 82% were never smokers. 79% of patients were diagnosed as stage IV disease, and over half had brain metastases at baseline. Median overall survival of the entire cohort was 38.3 months from initial diagnoses and 12.4 months from time of SCLC transformation. Most patients harbored EGFR exon19 deletions as opposed to exon21 L858R alteration. Continuing EGFR tyrosine kinase inhibitor post-transformation did not improve overall survival compared with those patients where tyrosine kinase inhibitor was stopped in our cohort. In the 20 paired pretransformed and post-transformed patient samples, statistically significant enrichment was seen with PIK3CA alterations (p = 0.04) post-transformation. Profiling of longitudinal liquid biopsy samples suggest emergence of SCLC genetic alterations before biopsy-proven SCLC, as shown by increasing variant allele frequency of TP53, RB1, PIK3CA alterations. ScRNA-seq revealed potential therapeutic targets including DLL3, CD276 (B7-H3) and PTK7 were widely expressed in transformed SCLC. Conclusions: SCLC transformation is a potential treatment resistance mechanism in driver-mutant NSCLC. In our cohort of 34 EGFR-mutant NSCLC, poor prognosis was observed after SCLC transformation. Clinicogenomic analyses of paired and longitudinal samples identified genomic alterations emerging post-transformation and scRNA-seq reveal potential therapeutic targets in this population. Further studies are needed to rigorously validate biomarkers and therapeutic targets for this patient population.

Surgical outcomes after chemotherapy plus nivolumab and chemotherapy plus nivolumab and ipilimumab in patients with non-small cell lung cancer.

OBJECTIVE: Chemotherapy plus nivolumab is the standard of care neoadjuvant treatment for patients with resectable stage IB to IIIA non-small cell lung cancer. The influence of dual checkpoint blockade with chemotherapy on surgical outcomes remains unknown. We aimed to determine operative complexity and perioperative outcomes associated with neoadjuvant chemotherapy and nivolumab with or without ipilimumab. METHODS: A total of 44 patients with stage IB (≥4 cm) to IIIA non-small cell lung cancer were treated on sequential platform arms of the NEOSTAR trial. A total of 22 patients were treated with nivolumab + chemotherapy, and 22 patients were treated with ipilimumab + nivolumab + chemotherapy. The safety of surgical resection after neoadjuvant therapy was estimated using 30-day complication rates. Operative reports and surgeons' narratives were evaluated to determine procedural complexity and operative conduct. RESULTS: All 22 of 22 patients (100%) treated with nivolumab + chemotherapy underwent surgical resection: 20 R0 (90.9%), 17 (77.3%) lobectomies, 1 wedge resection, 2 segmentectomies, and 2 pneumonectomies. The majority, 21 of 22 (95%), were performed by thoracotomy. A total of 13 of 22 (59.1%) were rated as challenging resections. A total of 4 of 22 patients (18.2%) experienced grade 3 or greater Clavien-Dindo complication. A total of 20 of 22 patients (90.9%) treated with ipilimumab + nivolumab + chemotherapy underwent surgical resection: 19 R0 (95%), 18 (90%) lobectomies, 1 pneumonectomy, and 1 segmentectomy. A total of 16 of 20 (80%) resections were performed via thoracotomy, 3 of 20 (15%) via robotics, and 1 of 20 (5%) via thoracoscopy. A total of 9 of 20 (45%) resections were considered challenging. A total of 4 of 20 patients (20%) experienced grade 3 or greater Clavien-Dindo complication. CONCLUSIONS: Surgical resections are feasible and safe, with high rates of R0 after neoadjuvant chemotherapy and nivolumab with or without ipilimumab. Overall, approximately half of cases (22/42, 52.3%) were considered to be more challenging than a standard lobectomy.

Real-World Efficacy and Safety of Amivantamab for EGFR-Mutant NSCLC.

INTRODUCTION: Amivantamab-vmjw (amivantamab) is a bispecific EGFR/MET antibody approved for patients with advanced NSCLC with EGFR exon 20 insertion mutations, after prior therapy. Nevertheless, the benefits and safety of amivantamab in other EGFR-mutant lung cancer, with or without osimertinib, and with concurrent radiation therapy, are less known. METHODS: We queried the MD Anderson Lung Cancer GEMINI, Fred Hutchinson Cancer Research Center, University of California Davis Comprehensive Cancer Center, and Stanford Cancer Center's database for patients with EGFR-mutant NSCLC treated with amivantamab, not on a clinical trial. The data analyzed included initial response, duration of treatment, and concomitant radiation safety in overall population and prespecified subgroups. RESULTS: A total of 61 patients received amivantamab. Median age was 65 (31-81) years old; 72.1% were female; and 77% were patients with never smoking history. Median number of prior lines of therapies was four. On the basis of tumor's EGFR mutation, 39 patients were in the classical mutation cohort, 15 patients in the exon 20 cohort, and seven patients in the atypical cohort. There were 37 patients (58.7%) who received amivantamab concomitantly with osimertinib and 25 patients (39.1%) who received concomitant radiation. Furthermore, 54 patients were assessable for response in the overall population; 19 patients (45.2%) had clinical response and disease control rate (DCR) was 64.3%. In the classical mutation cohort of the 33 assessable patients, 12 (36.4%) had clinical response and DCR was 48.5%. In the atypical mutation cohort, six of the seven patients (85.7%) had clinical response and DCR was 100%. Of the 13 assessable patients in the exon 20 cohort, five patients (35.7%) had clinical response and DCR was 64.3%. Adverse events reported with amivantamab use were similar as previously described in product labeling. No additional toxicities were noted when amivantamab was given with radiation with or without osimertinib. CONCLUSIONS: Our real-world multicenter analysis revealed that amivantamab is a potentially effective treatment option for patients with EGFR mutations outside of exon 20 insertion mutations. The combination of osimertinib with amivantamab is safe and feasible. Radiation therapy also seems safe when administered sequentially or concurrently with amivantamab.

pan-MHC and cross-Species Prediction of T Cell Receptor-Antigen Binding.

Profiling the binding of T cell receptors (TCRs) of T cells to antigenic peptides presented by MHC proteins is one of the most important unsolved problems in modern immunology. Experimental methods to probe TCR-antigen interactions are slow, labor-intensive, costly, and yield moderate throughput. To address this problem, we developed pMTnet-omni, an Artificial Intelligence (AI) system based on hybrid protein sequence and structure information, to predict the pairing of TCRs of αß T cells with peptide-MHC complexes (pMHCs). pMTnet-omni is capable of handling peptides presented by both class I and II pMHCs, and capable of handling both human and mouse TCR-pMHC pairs, through information sharing enabled this hybrid design. pMTnet-omni achieves a high overall Area Under the Curve of Receiver Operator Characteristics (AUROC) of 0.888, which surpasses competing tools by a large margin. We showed that pMTnet-omni can distinguish binding affinity of TCRs with similar sequences. Across a range of datasets from various biological contexts, pMTnet-omni characterized the longitudinal evolution and spatial heterogeneity of TCR-pMHC interactions and their functional impact. We successfully developed a biomarker based on pMTnet-omni for predicting immune-related adverse events of immune checkpoint inhibitor (ICI) treatment in a cohort of 57 ICI-treated patients. pMTnet-omni represents a major advance towards developing a clinically usable AI system for TCR-pMHC pairing prediction that can aid the design and implementation of TCR-based immunotherapeutics.

Automated Cellular-Level Dual Global Fusion of Whole-Slide Imaging for Lung Adenocarcinoma Prognosis.

Histopathologic whole-slide images (WSI) are generally considered the gold standard for cancer diagnosis and prognosis. Survival prediction based on WSI has recently attracted substantial attention. Nevertheless, it remains a central challenge owing to the inherent difficulties of predicting patient prognosis and effectively extracting informative survival-specific representations from WSI with highly compounded gigapixels. In this study, we present a fully automated cellular-level dual global fusion pipeline for survival prediction. Specifically, the proposed method first describes the composition of different cell populations on WSI. Then, it generates dimension-reduced WSI-embedded maps, allowing for efficient investigation of the tumor microenvironment. In addition, we introduce a novel dual global fusion network to incorporate global and inter-patch features of cell distribution, which enables the sufficient fusion of different types and locations of cells. We further validate the proposed pipeline using The Cancer Genome Atlas lung adenocarcinoma dataset. Our model achieves a C-index of 0.675 (±0.05) in the five-fold cross-validation setting and surpasses comparable methods. Further, we extensively analyze embedded map features and survival probabilities. These experimental results manifest the potential of our proposed pipeline for applications using WSI in lung adenocarcinoma and other malignancies.

Autotaxin suppresses cytotoxic T cells via LPAR5 to promote anti-PD-1 resistance in non-small cell lung cancer.

Non-small cell lung cancers that harbor concurrent KRAS and TP53 (KP) mutations are immunologically warm tumors with partial responsiveness to anti-PD-(L)1 blockade; however, most patients observe little or no durable clinical benefit. To identify novel tumor-driven resistance mechanisms, we developed a panel of KP murine lung cancer models with intrinsic resistance to anti-PD-1 and queried differential gene expression between these tumors and anti-PD-1-sensitive tumors. We found that the enzyme autotaxin (ATX), and the metabolite it produces, lysophosphatidic acid (LPA), were significantly upregulated in resistant tumors and that ATX directly modulated antitumor immunity, with its expression negatively correlating with total and effector tumor-infiltrating CD8+ T cells. Pharmacological inhibition of ATX, or the downstream receptor LPAR5, in combination with anti-PD-1 was sufficient to restore the antitumor immune response and efficaciously control lung tumor growth in multiple KP tumor models. Additionally, ATX was significantly correlated with inflammatory gene signatures, including a CD8+ cytolytic score in multiple lung adenocarcinoma patient data sets, suggesting that an activated tumor-immune microenvironment upregulates ATX and thus provides an opportunity for cotargeting to prevent acquired resistance to anti-PD-1 treatment. These data reveal the ATX/LPA axis as an immunosuppressive pathway that diminishes the immune checkpoint blockade response in lung cancer.

AXL-initiated paracrine activation of pSTAT3 enhances mesenchymal and vasculogenic supportive features of tumor-associated macrophages.

Tumor-associated macrophages (TAMs) are integral to the development of complex tumor microenvironments (TMEs) and can execute disparate cellular programs in response to extracellular cues. However, upstream signaling processes underpinning this phenotypic plasticity remain to be elucidated. Here, we report that concordant AXL-STAT3 signaling in TAMs is triggered by lung cancer cells or cancer-associated fibroblasts in the cytokine milieu. This paracrine action drives TAM differentiation toward a tumor-promoting "M2-like" phenotype with upregulation of CD163 and putative mesenchymal markers, contributing to TAM heterogeneity and diverse cellular functions. One of the upregulated markers, CD44, mediated by AXL-IL-11-pSTAT3 signaling cascade, enhances macrophage ability to interact with endothelial cells and facilitate formation of primitive vascular networks. We also found that AXL-STAT3 inhibition can impede the recruitment of TAMs in a xenograft mouse model, thereby suppressing tumor growth. These findings suggest the potential application of AXL-STAT3-related markers to quantitatively assess metastatic potential and inform therapeutic strategies in lung cancer.