arXiv:2410.01755v3 Announce Type: replace-cross 
Abstract: Breast cancer's complexity and variability pose significant challenges in understanding its progression and guiding effective treatment. This study aims to integrate protein sequence data with expression levels to improve the molecular characterization of breast cancer subtypes and predict clinical outcomes. Using ProtGPT2, a language model specifically designed for protein sequences, we generated embeddings that capture the functional and structural properties of proteins. These embeddings were integrated with protein expression levels to form enriched biological representations, which were analyzed using machine learning methods, such as ensemble K-means for clustering and XGBoost for classification. Our approach enabled the successful clustering of patients into biologically distinct groups and accurately predicted clinical outcomes such as survival and biomarker status, achieving high performance metrics, notably an F1 score of 0.88 for survival and 0.87 for biomarker status prediction. Feature importance analysis identified KMT2C, CLASP2, and MYO1B as key proteins involved in hormone signaling, cytoskeletal remodeling, and therapy resistance in hormone receptor-positive and triple-negative breast cancer, with potential influence on breast cancer subtype behavior and progression. Furthermore, protein-protein interaction networks and correlation analyses revealed functional interdependencies among proteins that may influence the behavior and progression of breast cancer subtypes. These findings suggest that integrating protein sequence and expression data provides valuable insights into tumor biology and has significant potential to enhance personalized treatment strategies in breast cancer care.

أحرزت دراسة حديثة تقدمًا كبيرًا في فهم سرطان الثدي من خلال دمج بيانات تسلسل البروتين مع مستويات التعبير. تهدف هذه الطريقة المبتكرة إلى تحسين التوصيف الجزيئي لأنواع سرطان الثدي المختلفة، وهو أمر حاسم للتنبؤ بالنتائج السريرية وتكييف العلاجات الفعالة. باستخدام ProtGPT2، وهو نموذج لغوي مصمم خصيصًا لتسلسلات البروتين، يمكن للباحثين توليد تمثيلات تفصيلية تلتقط الجوانب الوظيفية لهذه البروتينات. لا تسلط هذه الخطوة الضوء فقط على تعقيدات سرطان الثدي، بل تعد أيضًا بتحسين رعاية المرضى.

Un estudio reciente ha logrado avances significativos en la comprensión del cáncer de mama al integrar datos de secuencias de proteínas con niveles de expresión. Este enfoque innovador tiene como objetivo mejorar la caracterización molecular de los diferentes subtipos de cáncer de mama, lo cual es crucial para predecir los resultados clínicos y adaptar tratamientos efectivos. Al utilizar ProtGPT2, un modelo de lenguaje especializado para secuencias de proteínas, los investigadores pueden generar embeddings detallados que capturan los aspectos funcionales de estas proteínas. Este avance no solo arroja luz sobre las complejidades del cáncer de mama, sino que también promete mejorar la atención al paciente.

Une étude récente a fait des progrès significatifs dans la compréhension du cancer du sein en intégrant des données de séquence de protéines avec des niveaux d'expression. Cette approche innovante vise à améliorer la caractérisation moléculaire des différents sous-types de cancer du sein, ce qui est crucial pour prédire les résultats cliniques et adapter les traitements efficaces. En utilisant ProtGPT2, un modèle de langage spécialisé pour les séquences de protéines, les chercheurs peuvent générer des embeddings détaillés qui capturent les aspects fonctionnels de ces protéines. Cette avancée éclaire non seulement les complexités du cancer du sein, mais promet également d'améliorer les soins aux patients.

A recent study has made significant strides in understanding breast cancer by integrating protein sequence data with expression levels. This innovative approach aims to enhance the molecular characterization of different breast cancer subtypes, which is crucial for predicting clinical outcomes and tailoring effective treatments. By utilizing ProtGPT2, a specialized language model for protein sequences, researchers are able to generate detailed embeddings that capture the functional aspects of these proteins. This advancement not only sheds light on the complexities of breast cancer but also holds promise for improving patient care.

Integrating Protein Sequence and Expression Level to Analysis Molecular Characterization of Breast Cancer Subtypes

arXiv:2601.08336v1 Announce Type: new 
Abstract: Automatic integration of whole slide images (WSIs) and gene expression profiles has demonstrated substantial potential in precision clinical diagnosis and cancer progression studies. However, most existing studies focus on individual gene sequences and slide level classification tasks, with limited attention to spatial transcriptomics and patch level applications. To address this limitation, we propose a multimodal network, BioMorphNet, which automatically integrates tissue morphological features and spatial gene expression to support tissue classification and differential gene analysis. For considering morphological features, BioMorphNet constructs a graph to model the relationships between target patches and their neighbors, and adjusts the response strength based on morphological and molecular level similarity, to better characterize the tumor microenvironment. In terms of multimodal interactions, BioMorphNet derives clinical pathway features from spatial transcriptomic data based on a predefined pathway database, serving as a bridge between tissue morphology and gene expression. In addition, a novel learnable pathway module is designed to automatically simulate the biological pathway formation process, providing a complementary representation to existing clinical pathways. Compared with the latest morphology gene multimodal methods, BioMorphNet's average classification metrics improve by 2.67%, 5.48%, and 6.29% for prostate cancer, colorectal cancer, and breast cancer datasets, respectively. BioMorphNet not only classifies tissue categories within WSIs accurately to support tumor localization, but also analyzes differential gene expression between tissue categories based on prediction confidence, contributing to the discovery of potential tumor biomarkers.

تم اقتراح شبكة متعددة الوسائط جديدة تُدعى BioMorphNet لتحسين تصنيف الأنسجة وتحليل صور الشرائح الكاملة من خلال دمج الميزات الشكلية والتعبير الجيني المكاني، مما يعالج القيود في منهجيات البحث الحالية حول السرطان.

Se ha propuesto una nueva red multimodal llamada BioMorphNet para mejorar la clasificación de tejidos y el análisis de imágenes de diapositivas completas mediante la integración de características morfológicas y expresión génica espacial, abordando las limitaciones de las metodologías actuales en la investigación del cáncer.

Un nouveau réseau multimodal nommé BioMorphNet a été proposé pour améliorer la classification des tissus et l'analyse des images à grande échelle en intégrant des caractéristiques morphologiques et l'expression génique spatiale, répondant aux limitations des méthodologies de recherche sur le cancer actuelles.

A new multimodal network named BioMorphNet has been proposed to enhance tissue classification and whole-slide image analysis by integrating morphological features and spatial gene expression, addressing limitations in current cancer research methodologies.

Integrating Protein Sequence and Expression Level to Analysis Molecular Characterization of Breast Cancer Subtypes

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