Biology subjects

Moreira, A. L.

Publications and source records attributed to Moreira, A. L..

6 recordsLinked to original sources

bioRxiv2024

Characterization of tumor heterogeneity through segmentation-free representation learning

The interaction between tumors and their microenvironment is complex and heterogeneous. Recent developments in high-dimensional multiplexed imaging have revealed the spatial organization of tumor tissues at the molecular level. However, the discovery and thorough characterization of the tumor microenvironment (TME) remains challenging due to the scale and complexity of the images. Here, we propose a self-supervised representation learning framework, CANVAS, that enables discovery of novel types of TMEs. CANVAS is a vision transformer that directly takes high-dimensional multiplexed images and is trained using self-supervised masked image modeling. In contrast to traditional spatial analysis approaches which rely on cell segmentations, CANVAS is segmentation-free, utilizes pixel-level information, and retains local morphology and biomarker distribution information. This approach allows the model to distinguish subtle morphological differences, leading to precise separation and characterization of distinct TME signatures. We applied CANVAS to a lung tumor dataset and identified and validated a monocytic signature that is associated with poor prognosis.

cancer biology↗

bioRxiv2024

Regulatory T cells and IFN-γ-producing Th1 cells play a critical role in the pathogenesis of Sjögren's Syndrome

ObjectivesSjogrens Disease (SjD) is an autoimmune disorder characterized by progressive dysfunction, inflammation and destruction of salivary and lacrimal glands, and by extraglandular manifestations. Its etiology and pathophysiology remain incompletely understood, though a role for autoreactive B cells has been considered key. Here, we investigated the role of effector and regulatory T cells in the pathogenesis of SjD. MethodsHistological analysis, RNA-sequencing and flow cytometry were conducted on glands, lungs, eyes and lymphoid tissues of mice with regulatory T cell-specific deletion of stromal interaction proteins (STIM) 1 and 2 (Stim1/2Foxp3), which play key roles in calcium signaling and T cell function. The pathogenicity of T cells from Stim1/2Foxp3mice was investigated through adoptively transfer into lymphopenic host mice. Additionally, single-cell transcriptomic analysis was performed on peripheral blood mononuclear cells (PBMCs) of patients with SjD and control subjects. ResultsStim1/2Foxp3 mice develop a severe SjD-like disorder including salivary gland (SG) and lacrimal gland (LG) inflammation and dysfunction, autoantibodies and extraglandular symptoms. SG inflammation in Stim1/2Foxp3 mice is characterized by T and B cell infiltration, and transcriptionally by a Th1 immune response that correlates strongly with the dysregulation observed in patients with SjD. Adoptive transfer of effector T cells from Stim1/2Foxp3 mice demonstrates that the SjD-like disease is driven by interferon (IFN)-{gamma} producing autoreactive CD4+ T cells independently of B cells and autoantiboodies. scRNA-seq analysis identifies increased Th1 responses and attenuated memory Treg function in PBMCs of patients with SjD. ConclusionsWe report a more accurate mouse model of SjD while providing evidence for a critical role of Treg cells and IFN-{gamma} producing Th1 cells in the pathogenesis of SjD, which may be effective targets for therapy.

immunology↗

bioRxiv2023

Tumor-intrinsic LKB1-LIF signaling axis establishes a myeloid niche to promote immune evasion and tumor growth

Tumor mutations can influence the surrounding microenvironment leading to suppression of anti-tumor immune responses and thereby contributing to tumor progression and failure of cancer therapies. Here we use genetically engineered lung cancer mouse models and patient samples to dissect how LKB1 mutations accelerate tumor growth by reshaping the immune microenvironment. Comprehensive immune profiling of LKB1-mutant vs wildtype tumors revealed dramatic changes in myeloid cells, specifically enrichment of Arg1+ interstitial macrophages and SiglecFHi neutrophils. We discovered a novel mechanism whereby autocrine LIF signaling in Lkb1-mutant tumors drives tumorigenesis by reprogramming myeloid cells in the immune microenvironment. Inhibiting LIF signaling in Lkb1-mutant tumors, via gene targeting or with a neutralizing antibody, resulted in a striking reduction in Arg1+ interstitial macrophages and SiglecFHi neutrophils, expansion of antigen specific T cells, and inhibition of tumor progression. Thus, targeting LIF signaling provides a new therapeutic approach to reverse the immunosuppressive microenvironment of LKB1-mutant tumors.

cancer biology↗

bioRxiv2022

Inflammation in the tumor-adjacent lung as a predictor of clinical outcome in lung adenocarcinoma

Early-stage lung adenocarcinoma is typically treated by surgical resection of the tumor. While in the majority of cases surgery can lead to cure, approximately 30% of patients progress. Despite intense efforts to map the genetic landscape of early-stage lung tumors, there has been limited success in discovering accurate biomarkers that can predict clinical outcomes. Meanwhile, the role of the tumor-adjacent tissue in cancer progression has been largely ignored. To test whether tumor-adjacent tissue can be informative of progression-free survival and to probe the underlying molecular pathways involved, we designed a multi-omic study in both tumor and matched tumor-adjacent histologically normal lung tissue from the same patient. Our study includes 143 treatment naive stage I cases with long-term patient follow-up and is, to our knowledge, the largest such study with the longest follow-up. We performed a comprehensive histologic characterization of all tumors, mapped the mutational landscape and probed the transcriptome of both tumor and adjacent normal tissue. We evaluated the predictive power of each data modality and showed that the transcriptome of tumor-adjacent histologically normal lung tissue is the only reliable predictor of clinical outcome. Unbiased discovery of co-expressed gene modules revealed that inflammatory pathways are upregulated in the tumor-adjacent tissue of patients at high risk for disease progression. Furthermore, single-cell transcriptome analysis in the tumor-adjacent lung demonstrated that progression-associated inflammatory signatures were broadly expressed by both immune and non-immune cells including mesothelial cells, alveolar type 2 cells and fibroblasts, CD1 dendritic cells and MAST cells. Collectively, our studies suggest that molecular profiling of tumor-adjacent tissue can identify patients that are at high risk for disease progression.

bioinformatics↗

bioRxiv2021

KEAP1 mutation in lung adenocarcinoma promotes immune evasion and immunotherapy resistance

Lung cancer treatment has benefited greatly from the development of effective immune-based therapies. However, these strategies still fail in a large subset of patients. Tumor-intrinsic mutations can drive immune evasion via recruiting immunosuppressive populations or suppressing anti-tumor immune responses. KEAP1 is one of the most frequently mutated genes in lung adenocarcinoma patients and is associated with poor prognosis and inferior response to all therapies, including checkpoint blockade. Here, we established a novel antigenic lung cancer model and showed that Keap1-mutant tumors promote dramatic remodeling of the tumor immune microenvironment. Combining single-cell technology and depletion studies, we demonstrate that Keap1-mutant tumors diminish dendritic cell and T cell responses driving immunotherapy resistance. Importantly, analysis of KEAP1 mutant patient tumors revealed analogous decrease in dendritic cell and T cell infiltration. Our study provides new insight into the role of KEAP1 mutations in promoting immune evasion and suggests a path to novel immune-based therapeutic strategies for KEAP1 mutant lung cancer. Statement of significanceThis study establishes that tumor-intrinsic KEAP1 mutations contribute to immune evasion through suppression of dendritic cell and T cell responses, explaining the observed resistance to immunotherapy of KEAP1 mutant tumors. These results highlight the importance of stratifying patients based on KEAP1 status and paves the way for novel therapeutic strategies.

cancer biology↗

bioRxiv2017

Classification and Mutation Prediction from Non-Small Cell Lung Cancer Histopathology Images using Deep Learning

Visual analysis of histopathology slides of lung cell tissues is one of the main methods used by pathologists to assess the stage, types and sub-types of lung cancers. Adenocarcinoma and squamous cell carcinoma are two most prevalent sub-types of lung cancer, but their distinction can be challenging and time-consuming even for the expert eye. In this study, we trained a deep learning convolutional neural network (CNN) model (inception v3) on histopathology images obtained from The Cancer Genome Atlas (TCGA) to accurately classify whole-slide pathology images into adenocarcinoma, squamous cell carcinoma or normal lung tissue. Our method slightly outperforms a human pathologist, achieving better sensitivity and specificity, with [~]0.97 average Area Under the Curve (AUC) on a held-out population of whole-slide scans. Furthermore, we trained the neural network to predict the ten most commonly mutated genes in lung adenocarcinoma. We found that six of these genes - STK11, EGFR, FAT1, SETBP1, KRAS and TP53 - can be predicted from pathology images with an accuracy ranging from 0.733 to 0.856, as measured by the AUC on the held-out population. These findings suggest that deep learning models can offer both specialists and patients a fast, accurate and inexpensive detection of cancer types or gene mutations, and thus have a significant impact on cancer treatment.

cancer biology↗