Meme kanserinde uygulanabilir hedefler: tümör-spesifik hassasiyetleri ortaya çıkarmak için multi-omik bir yaklaşım

Yıl 2025, Cilt: 15 Sayı: 1, 260 - 273, 15.03.2025

Abdulkadir Elmas

Öz

Meme kanseri, dünya genelinde kanser kaynaklı ölümlerin başlıca nedenlerinden biri olup, önlenimi/tedavisi için yenilikçi terapötik stratejilerine gereksinim duyulmaktadır. Bu çalışma, meme kanserinde tümör-spesifik biyobelirteçleri ve zayıf noktaları sistematik olarak belirlemek için proteomik, transkriptomik ve fonksiyonel bağımlılık verilerini birleştirmektedir. 115 tümör örneği ve 18 eşlenmiş normal dokudan elde edilen kütle spektrometrisine dayalı proteomik veriler analiz edilmiş ve 10,468 adet protein incelenmiştir. Aşırı ekspresyon ve diferansiyel ekspresyon analizlerinin birleştirilmesiyle, ERBB2, EGFR ve CCND1 gibi iyi bilinen hedeflerin yanı sıra TRPS1, UBE2C ve FOXP4 gibi yeni adayların da bulunduğu 172 tümör-spesifik protein tespit edilmiştir. Bu aday hedeflerin fonksiyonel doğrulaması, BEACON yöntemi ve DepMap verileri kullanılarak CRISPR tabanlı ekspresyon odaklı bağımlılık analizi ile gerçekleştirilmiş olup, hem gen hem de protein düzeyinde bağımlılıkları ortaya koyarak proteine özgü yeni kanser hassasiyetlerini belirlemiştir. Özellikle UBE2C ve E2F3 gibi protein-spesifik bağımlılıklar, transkriptomik analizlerde gözden kaçan potansiyel terapötik hedefleri vurgulamaktadır. Özellikle, ekspresyon odaklı bağımlılık gösteren TRPS1 ve UBE2C gibi belirteçler, ilaç geliştirme ve hasta sınıflandırmasını yönlendiren hassas onkoloji yaklaşımları için potansiyel adaylar olarak hizmet edebilir. Bu çalışma, uygulanabilir hedefleri sistematik olarak önceliklendirerek, multi-omik entegrasyonun meme kanserinde hassas onkolojiyi ilerletmedeki kritik rolünü vurgulamaktadır.

Anahtar Kelimeler

BEACON yöntemi, Meme kanseri, Ekspresyon temelli bağımlılık, Çoklu-omik yaklaşım, Hassas onkoloji

Actionable targets in breast cancer: a multi-omics approach to uncover tumor-specific vulnerabilities

Öz

Breast cancer remains a leading cause of cancer-related deaths worldwide, necessitating innovative therapeutic strategies. This study integrates proteomic, transcriptomic, and functional dependency data to systematically identify tumor-specific markers and vulnerabilities in breast cancer. We analyzed mass-spectrometry-based proteomic data from 115 tumor samples and 18 matched normal tissues, quantifying 10,468 proteins. By combining overexpression and differential expression analyses, we identified 172 tumor-specific proteins, including well-characterized targets such as ERBB2, EGFR, and CCND1, as well as novel candidates like TRPS1, UBE2C, and FOXP4. Functional validation of these candidate targets was performed through CRISPR-based expression-driven dependency analysis using the BEACON method and DepMap data, which revealed both gene- and protein-level dependencies, uncovering novel protein-unique cancer vulnerabilities. Notably, protein-specific dependencies such as UBE2C and E2F3 highlight potential therapeutic targets overlooked in transcriptomic analyses. In particular, markers such as TRPS1 and UBE2C, which exhibit strong protein expression-driven dependencies, may serve as potential candidates for precision oncology approaches, guiding drug development and patient stratification. This study presents a systematically prioritized set of actionable targets, emphasizing the critical role of multi-omics integration in driving precision oncology advancements for breast cancer.

Anahtar Kelimeler

BEACON method, Breast cancer, Expression-driven dependency, Multi-omics approach, Precision oncology

Kaynakça

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