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.

A structured curriculum supporting biomedical trainees' transition into independent academic positions and early career success.

The United States government makes a substantial investment in biomedical training programs each year. However, for most trainees, these opportunities do not translate into career progression in academic research pathways. Only about one-fifth of postdoctoral fellows eventually secure a tenure-track faculty position, and even among these candidates, attrition is high. Although a number of factors govern career choices and career longevity, the transition from trainee to faculty is a challenging process and requires knowledge and skills that are not necessarily developed during a traditional university experience. Many postdoctoral fellows receive adequate training in research skills and scientific communication, but new faculty report not being sufficiently prepared for the job search process and for starting their labs. To address this critical training gap, the ITERT core (Interdisciplinary Translational Education and Research Training) and the Office of Postdoctoral Fellows at the University of Texas MD Anderson Cancer Center implemented a structured course for both postdoctoral fellows and senior PhD students to provide formalized training for successfully navigating academic positions in biomedical research. Here we report on the pilot Navigating Academic Careers course conducted in 2021-2022 for 30 PhD students and postdocs. The nine-module course was conducted over 13 weeks in 25.5 h instructional sessions. The key educational objectives included 1) navigating the job application and the interview/negotiation process, 2) hiring, leading, and mentoring lab personnel and program support staff, 3) project administration and financial stewardship, 4) managing time and work-life balance and 5) developing collaborations, branding, personalized niche, and networking. Survey-based analysis at the time of the course was used to capture the participants' assessment of the course content, organization, and delivery, with a follow-up survey conducted approximately 2 years post-course (2024) to evaluate longer-term impacts of the training. Initial in-course assessment revealed that 89.9% of respondents found the scope and instructional content appropriate, and 91.1% found the course relevant and applicable to their career needs. Longer-term post-course evaluation indicated that 80% of respondents applied the learnings of the course, that 80% reported feeling more confident in navigating an academic job search, and that 66.6% continued to report agreement with the course preparing them for their current role/ongoing job search, with 46.7% already securing jobs in academic research, including as independent faculty. The outcomes of this pilot course suggest that integrating this into the broader postdoctoral training curriculum can enhance both the transition and early-career success of talented scientists-in-training into working professionals in biomedical careers, as faculty and science-trained staff.

Genetic Deletion of Galectin-3 Inhibits Pancreatic Cancer Progression and Enhances the Efficacy of Immunotherapy.

BACKGROUND & AIMS: Pancreatic ductal adenocarcinoma (PDAC) has a desmoplastic tumor stroma and immunosuppressive microenvironment. Galectin-3 (GAL3) is enriched in PDAC, highly expressed by cancer cells and myeloid cells. However, the functional roles of GAL3 in the PDAC microenvironment remain elusive. METHODS: We generated a novel transgenic mouse model (LSL-KrasG12D/+;Trp53loxP/loxP;Pdx1-Cre;Lgals3-/- [KPPC;Lgals3-/-]) that allows the genetic depletion of GAL3 from both cancer cells and myeloid cells in spontaneous PDAC formation. Single-cell RNA-sequencing analysis was used to identify the alterations in the tumor microenvironment upon GAL3 depletion. We investigated both the cancer cell-intrinsic function and immunosuppressive function of GAL3. We also evaluated the therapeutic efficacy of GAL3 inhibition in combination with immunotherapy. RESULTS: Genetic deletion of GAL3 significantly inhibited the spontaneous pancreatic tumor progression and prolonged the survival of KPPC;Lgals3-/- mice. Single-cell analysis revealed that genetic deletion of GAL3 altered the phenotypes of immune cells, cancer cells, and other cell populations. GAL3 deletion significantly enriched the antitumor myeloid cell subpopulation with high major histocompatibility complex class II expression. We also identified that GAL3 depletion resulted in CXCL12 upregulation, which could act as a potential compensating mechanism on GAL3 deficiency. Combined inhibition of the CXCL12-CXCR4 axis and GAL3 enhanced the efficacy of anti-PD-1 immunotherapy, leading to significantly inhibited PDAC progression. In addition, deletion of GAL3 also inhibited the basal/mesenchymal-like phenotype of pancreatic cancer cells. CONCLUSIONS: GAL3 promotes PDAC progression and immunosuppression via both cancer cell-intrinsic and immune-related mechanisms. Combined treatment targeting GAL3, CXCL12-CXCR4 axis, and PD-1 represents a novel therapeutic strategy for PDAC.

Cancer biomarkers: Emerging trends and clinical implications for personalized treatment.

The integration of cancer biomarkers into oncology has revolutionized cancer treatment, yielding remarkable advancements in cancer therapeutics and the prognosis of cancer patients. The development of personalized medicine represents a turning point and a new paradigm in cancer management, as biomarkers enable oncologists to tailor treatments based on the unique molecular profile of each patient's tumor. In this review, we discuss the scientific milestones of cancer biomarkers and explore future possibilities to improve the management of patients with solid tumors. This progress is primarily attributed to the biological characterization of cancers, advancements in testing methodologies, elucidation of the immune microenvironment, and the ability to profile circulating tumor fractions. Integrating these insights promises to continually advance the precision oncology field, fostering better patient outcomes.

eEF1A2 promotes PTEN-GSK3ß-SCF complex-dependent degradation of Aurora kinase A and is inactivated in breast cancer.

The translation elongation factor eEF1A promotes protein synthesis. Its methylation by METTL13 increases its activity, supporting tumor growth. However, in some cancers, a high abundance of eEF1A isoforms is associated with a good prognosis. Here, we found that eEF1A2 exhibited oncogenic or tumor-suppressor functions depending on its interaction with METTL13 or the phosphatase PTEN, respectively. METTL13 and PTEN competed for interaction with eEF1A2 in the same structural domain. PTEN-bound eEF1A2 promoted the ubiquitination and degradation of the mitosis-promoting Aurora kinase A in the S and G2 phases of the cell cycle. eEF1A2 bridged the interactions between the SKP1-CUL1-FBXW7 (SCF) ubiquitin ligase complex, the kinase GSK3ß, and Aurora-A, thereby facilitating the phosphorylation of Aurora-A in a degron site that was recognized by FBXW7. Genetic ablation of Eef1a2 or Pten in mice resulted in a greater abundance of Aurora-A and increased cell cycling in mammary tumors, which was corroborated in breast cancer tissues from patients. Reactivating this pathway using fimepinostat, which relieves inhibitory signaling directed at PTEN and increases FBXW7 expression, combined with inhibiting Aurora-A with alisertib, suppressed breast cancer cell proliferation in culture and tumor growth in vivo. The findings demonstrate a therapeutically exploitable, tumor-suppressive role for eEF1A2 in breast cancer.

Differential Upregulation of Th1/Th17-Associated Proteins and PD-L1 in Granulomatous Mycosis Fungoides.

Granulomatous Mycosis Fungoides (GMF) is a rare form of mycosis fungoides (MF) characterized by a granulomatous infiltrate associated with the neoplastic lymphoid population and is considered to have a worse prognosis compared with regular MF. The upregulation of the T helper (Th) axis, especially Th17, plays an important role in the pathogenesis of several inflammatory/infectious granulomatous cutaneous diseases, but its role in GMF is still not elucidated to date. In this study, we evaluated the immunohistochemical expression of Th1 (Tbet), Th2 (GATA-3), Th17 (RORγT), T regulatory (Foxp3), and immune checkpoint (IC) (PD-1 and PD-L1) markers in a cohort of patients with GMF and MF with large cell transformation (MFLCT). Skin biopsies from 49 patients (28 GMF and 21 MFLCT) were studied. Patients with GMF were associated with early clinical stage (p = 0.036) and lower levels of lactate dehydrogenase (p = 0.042). An increased percentage of cells positive for Tbet (p = 0.017), RORγT (p = 0.001), and PD-L1 (p = 0.011) was also observed among the GMF specimens, while a stronger PD-1 intensity was detected in cases of MFLCT. In this cohort, LCT, RORγT < 10%, Foxp3 < 10%, age, and advanced stage were associated with worse overall survival (OS) in univariate analysis. GMF demonstrated Th1 (cellular response) and Th17 (autoimmunity) phenotype, seen in early MF and granulomatous processes, respectively, which may be related to the histopathological appearance and biological behavior of GMF. Further studies involving larger series of cases and more sensitive techniques are warranted.

An atlas of epithelial cell states and plasticity in lung adenocarcinoma.

Understanding the cellular processes that underlie early lung adenocarcinoma (LUAD) development is needed to devise intervention strategies1. Here we studied 246,102 single epithelial cells from 16 early-stage LUADs and 47 matched normal lung samples. Epithelial cells comprised diverse normal and cancer cell states, and diversity among cancer cells was strongly linked to LUAD-specific oncogenic drivers. KRAS mutant cancer cells showed distinct transcriptional features, reduced differentiation and low levels of aneuploidy. Non-malignant areas surrounding human LUAD samples were enriched with alveolar intermediate cells that displayed elevated KRT8 expression (termed KRT8+ alveolar intermediate cells (KACs) here), reduced differentiation, increased plasticity and driver KRAS mutations. Expression profiles of KACs were enriched in lung precancer cells and in LUAD cells and signified poor survival. In mice exposed to tobacco carcinogen, KACs emerged before lung tumours and persisted for months after cessation of carcinogen exposure. Moreover, they acquired Kras mutations and conveyed sensitivity to targeted KRAS inhibition in KAC-enriched organoids derived from alveolar type 2 (AT2) cells. Last, lineage-labelling of AT2 cells or KRT8+ cells following carcinogen exposure showed that KACs are possible intermediates in AT2-to-tumour cell transformation. This study provides new insights into epithelial cell states at the root of LUAD development, and such states could harbour potential targets for prevention or intervention.

Circulating microRNA Panel for Prediction of Recurrence and Survival in Early-Stage Lung Adenocarcinoma.

Early-stage lung adenocarcinoma (LUAD) patients remain at substantial risk for recurrence and disease-related death, highlighting the unmet need of biomarkers for the assessment and identification of those in an early stage who would likely benefit from adjuvant chemotherapy. To identify circulating miRNAs useful for predicting recurrence in early-stage LUAD, we performed miRNA microarray analysis with pools of pretreatment plasma samples from patients with stage I LUAD who developed recurrence or remained recurrence-free during the follow-up period. Subsequent validation in 85 patients with stage I LUAD resulted in the development of a circulating miRNA panel comprising miR-23a-3p, miR-320c, and miR-125b-5p and yielding an area under the curve (AUC) of 0.776 in predicting recurrence. Furthermore, the three-miRNA panel yielded an AUC of 0.804, with a sensitivity of 45.8% at 95% specificity in the independent test set of 57 stage I and II LUAD patients. The miRNA panel score was a significant and independent factor for predicting disease-free survival (p < 0.001, hazard ratio [HR] = 1.64, 95% confidence interval [CI] = 1.51-4.22) and overall survival (p = 0.001, HR = 1.51, 95% CI = 1.17-1.94). This circulating miRNA panel is a useful noninvasive tool to stratify early-stage LUAD patients and determine an appropriate treatment plan with maximal efficacy.

Multi-omics Analysis Reveals Immune Features Associated with Immunotherapy Benefit in Patients with Squamous Cell Lung Cancer from Phase III Lung-MAP S1400I Trial.

PURPOSE: Identifying molecular and immune features to guide immune checkpoint inhibitor (ICI)-based regimens remains an unmet clinical need. EXPERIMENTAL DESIGN: Tissue and longitudinal blood specimens from phase III trial S1400I in patients with metastatic squamous non-small cell carcinoma (SqNSCLC) treated with nivolumab monotherapy (nivo) or nivolumab plus ipilimumab (nivo+ipi) were subjected to multi-omics analyses including multiplex immunofluorescence (mIF), nCounter PanCancer Immune Profiling Panel, whole-exome sequencing, and Olink. RESULTS: Higher immune scores from immune gene expression profiling or immune cell infiltration by mIF were associated with response to ICIs and improved survival, except regulatory T cells, which were associated with worse overall survival (OS) for patients receiving nivo+ipi. Immune cell density and closer proximity of CD8+GZB+ T cells to malignant cells were associated with superior progression-free survival and OS. The cold immune landscape of NSCLC was associated with a higher level of chromosomal copy-number variation (CNV) burden. Patients with LRP1B-mutant tumors had a shorter survival than patients with LRP1B-wild-type tumors. Olink assays revealed soluble proteins such as LAMP3 increased in responders while IL6 and CXCL13 increased in nonresponders. Upregulation of serum CXCL13, MMP12, CSF-1, and IL8 were associated with worse survival before radiologic progression. CONCLUSIONS: The frequency, distribution, and clustering of immune cells relative to malignant ones can impact ICI efficacy in patients with SqNSCLC. High CNV burden may contribute to the cold immune microenvironment. Soluble inflammation/immune-related proteins in the blood have the potential to monitor therapeutic benefit from ICI treatment in patients with SqNSCLC.

Tumor- and circulating-free DNA methylation identifies clinically relevant small cell lung cancer subtypes.

Small cell lung cancer (SCLC) is an aggressive malignancy composed of distinct transcriptional subtypes, but implementing subtyping in the clinic has remained challenging, particularly due to limited tissue availability. Given the known epigenetic regulation of critical SCLC transcriptional programs, we hypothesized that subtype-specific patterns of DNA methylation could be detected in tumor or blood from SCLC patients. Using genomic-wide reduced-representation bisulfite sequencing (RRBS) in two cohorts totaling 179 SCLC patients and using machine learning approaches, we report a highly accurate DNA methylation-based classifier (SCLC-DMC) that can distinguish SCLC subtypes. We further adjust the classifier for circulating-free DNA (cfDNA) to subtype SCLC from plasma. Using the cfDNA classifier (cfDMC), we demonstrate that SCLC phenotypes can evolve during disease progression, highlighting the need for longitudinal tracking of SCLC during clinical treatment. These data establish that tumor and cfDNA methylation can be used to identify SCLC subtypes and might guide precision SCLC therapy.

LncRNA Malat1 suppresses pyroptosis and T cell-mediated killing of incipient metastatic cells.

The contribution of antitumor immunity to metastatic dormancy is poorly understood. Here we show that the long noncoding RNA Malat1 is required for tumor initiation and metastatic reactivation in mouse models of breast cancer and other tumor types. Malat1 localizes to nuclear speckles to couple transcription, splicing and mRNA maturation. In metastatic cells, Malat1 induces WNT ligands, autocrine loops to promote self-renewal and the expression of Serpin protease inhibitors. Through inhibition of caspase-1 and cathepsin G, SERPINB6B prevents gasdermin D-mediated induction of pyroptosis. In this way, SERPINB6B suppresses immunogenic cell death and confers evasion of T cell-mediated tumor lysis of incipient metastatic cells. On-target inhibition of Malat1 using therapeutic antisense nucleotides suppresses metastasis in a SERPINB6B-dependent manner. These results suggest that Malat1-induced expression of SERPINB6B can titrate pyroptosis and immune recognition at metastatic sites. Thus, Malat1 is at the nexus of tumor initiation, reactivation and immune evasion and represents a tractable and clinically relevant drug target.

Nectin-4 expression in a subset of cutaneous adnexal carcinomas: A potential target for therapy with enfortumab vedotin.

BACKGROUND: Enfortumab vedotin (EV) is an antibody-drug conjugate directed against Nectin-4 that is used to treat urothelial carcinoma. Nectin-4 is inherently expressed in the skin and adnexal structures. Since therapeutic options for cutaneous adnexal carcinomas are limited, we sought to evaluate Nectin-4 expression in adnexal carcinomas and benign adnexal neoplasms to identify tumors that are potentially targetable with EV. METHODS: Eight sebaceous carcinomas (seven periocular and one lymph node metastasis), eight digital papillary adenocarcinomas, seven squamoid eccrine ductal carcinomas, eight poromas, eight trichilemmomas, and seven sebaceous adenomas were subjected to immunohistochemical staining for anti-Nectin-4 antibody. H-scores for Nectin-4 expression were calculated. RESULTS: Benign adnexal neoplasms had a significantly lower mean (±SD) Nectin-4 H-score (142.6 ± 39.1) than did the adnexal carcinomas (198 ± 90.8; p = 0.006). Nectin-4 was expressed in 91% (21/23) of adnexal carcinomas. Sebaceous carcinomas frequently exhibited high expression of Nectin-4 (88% [7/8]), with a mean (±SD) H-score (258.1 ± 58.4) significantly higher than those for digital papillary adenocarcinomas (197.5 ± 52.5; p = 0.035) and squamoid eccrine ductal carcinomas (131.4 ± 114.1; p = 0.031). Sebaceous carcinomas also had significantly higher H-scores than did sebaceous adenomas (186.4 ± 25.0; p = 0.013). CONCLUSIONS: Increased Nectin-4 expression in a subset of cutaneous adnexal carcinomas, particularly sebaceous carcinomas, reveals that EV is a potential therapeutic option for these tumors.

Deep Learning-Based H-Score Quantification of Immunohistochemistry-Stained Images.

Immunohistochemistry (IHC) is a well-established and commonly used staining method for clinical diagnosis and biomedical research. In most IHC images, the target protein is conjugated with a specific antibody and stained using diaminobenzidine (DAB), resulting in a brown coloration, whereas hematoxylin serves as a blue counterstain for cell nuclei. The protein expression level is quantified through the H-score, calculated from DAB staining intensity within the target cell region. Traditionally, this process requires evaluation by 2 expert pathologists, which is both time consuming and subjective. To enhance the efficiency and accuracy of this process, we have developed an automatic algorithm for quantifying the H-score of IHC images. To characterize protein expression in specific cell regions, a deep learning model for region recognition was trained based on hematoxylin staining only, achieving pixel accuracy for each class ranging from 0.92 to 0.99. Within the desired area, the algorithm categorizes DAB intensity of each pixel as negative, weak, moderate, or strong staining and calculates the final H-score based on the percentage of each intensity category. Overall, this algorithm takes an IHC image as input and directly outputs the H-score within a few seconds, significantly enhancing the speed of IHC image analysis. This automated tool provides H-score quantification with precision and consistency comparable to experienced pathologists but at a significantly reduced cost during IHC diagnostic workups. It holds significant potential to advance biomedical research reliant on IHC staining for protein expression quantification.

Genomic Staging of Multifocal Lung Squamous Cell Carcinomas Is Independent of the Comprehensive Morphologic Assessment.

INTRODUCTION: Morphologic and molecular data for staging of multifocal lung squamous cell carcinomas (LSCCs) are limited. In this study, whole exome sequencing (WES) was used as the gold standard to determine whether multifocal LSCC represented separate primary lung cancers (SPLCs) or intrapulmonary metastases (IPMs). Genomic profiles were compared with the comprehensive morphologic assessment. METHODS: WES was performed on 20 tumor pairs of multifocal LSCC and matched normal lymph nodes using the Illumina NovaSeq6000 S4-Xp (Illumina, San Diego, CA). WES clonal and subclonal analysis data were compared with histologic assessment by 16 thoracic pathologists. In addition, the immune gene profiling of the study cases was characterized by the HTG EdgeSeq Precision Immuno-Oncology Panel. RESULTS: By WES data, 11 cases were classified as SPLC and seven cases as IPM. Two cases were technically suboptimal. Analysis revealed marked genomic and immunogenic heterogeneity, but immune gene expression profiles highly correlated with mutation profiles. Tumors classified as IPM have a large number of shared mutations (ranging from 33.5% to 80.7%). The agreement between individual morphologic assessments for each case and WES was 58.3%. One case was unanimously interpreted morphologically as IPM and was in agreement with WES. In a further 17 cases, the number of pathologists whose morphologic interpretation was in agreement with WES ranged from two (one case) to 15 pathologists (one case) per case. Pathologists showed a fair interobserver agreement in the morphologic staging of multiple LSCCs, with an overall kappa of 0.232. CONCLUSIONS: Staging of multifocal LSCC based on morphologic assessment is unreliable. Comprehensive genomic analyses should be adopted for the staging of multifocal LSCC.

Artificial Intelligence-Powered Assessment of Pathologic Response to Neoadjuvant Atezolizumab in Patients With NSCLC: Results From the LCMC3 Study.

INTRODUCTION: Pathologic response (PathR) by histopathologic assessment of resected specimens may be an early clinical end point associated with long-term outcomes with neoadjuvant therapy. Digital pathology may improve the efficiency and precision of PathR assessment. LCMC3 (NCT02927301) evaluated neoadjuvant atezolizumab in patients with resectable NSCLC and reported a 20% major PathR rate. METHODS: We determined PathR in primary tumor resection specimens using guidelines-based visual techniques and developed a convolutional neural network model using the same criteria to digitally measure the percent viable tumor on whole-slide images. Concordance was evaluated between visual determination of percent viable tumor (n = 151) performed by one of the 47 local pathologists and three central pathologists. RESULTS: For concordance among visual determination of percent viable tumor, the interclass correlation coefficient was 0.87 (95% confidence interval [CI]: 0.84-0.90). Agreement for visually assessed 10% or less viable tumor (major PathR [MPR]) in the primary tumor was 92.1% (Fleiss kappa = 0.83). Digitally assessed percent viable tumor (n = 136) correlated with visual assessment (Pearson r = 0.73; digital/visual slope = 0.28). Digitally assessed MPR predicted visually assessed MPR with outstanding discrimination (area under receiver operating characteristic curve, 0.98) and was associated with longer disease-free survival (hazard ratio [HR] = 0.30; 95% CI: 0.09-0.97, p = 0.033) and overall survival (HR = 0.14, 95% CI: 0.02-1.06, p = 0.027) versus no MPR. Digitally assessed PathR strongly correlated with visual measurements. CONCLUSIONS: Artificial intelligence-powered digital pathology exhibits promise in assisting pathologic assessments in neoadjuvant NSCLC clinical trials. The development of artificial intelligence-powered approaches in clinical settings may aid pathologists in clinical operations, including routine PathR assessments, and subsequently support improved patient care and long-term outcomes.

New Insights into Macrophage Polarization and its Prognostic Role in Patients with Colorectal Cancer Liver Metastasis.

Background: As liver metastasis is the most common cause of mortality in patients with colorectal cancer, studying colorectal cancer liver metastasis (CLM) microenvironment is essential for improved understanding of tumor biology and to identify novel therapeutic targets. Methods: We used multiplex immunofluorescence platform to study tumor associated macrophage (TAM) polarization and adaptive T cell subtypes in tumor samples from 105 CLM patients (49 without and 56 with preoperative chemotherapy). Results: CLM exhibited M2 macrophage polarization, and helper T cells were the prevalent adaptive T cell subtype. The density of total, M2 and TGFß-expressing macrophages, and regulatory T cells was lower in CLM treated with preoperative chemotherapy. CLM with right-sided primary demonstrated enrichment of TGFß-expressing macrophages, and with left-sided primary had higher densities of helper and cytotoxic T cells. In multivariate analysis, high density of M2 macrophages correlated with longer recurrence-free survival (RFS) in the entire cohort [hazard ratio (HR) 0.425, 95% CI 0.219-0.825, p=0.011) and in patients without preoperative chemotherapy (HR 0.45, 95% CI 0.221-0.932, p=0.032). High pSMAD3-expressing macrophages were associated with shorter RFS in CLM after preoperative chemotherapy. Conclusions: Our results highlight the significance of a multi-marker approach to define the macrophage subtypes and identify M2 macrophages as a predictor of favorable prognosis in CLM.

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.