Proteogenomic profiling of lung adenocarcinoma reveals therapeutic targets for precision medicine

Year 2024, , 1545 - 1552, 15.10.2024

Abdülkadir Elmas

https://doi.org/10.28948/ngumuh.1542153

Abstract

Lung cancer is the top cause of cancer-related fatalities worldwide, impacting both men and women. A major challenge is its frequent diagnosis at advanced stages, which limits treatment options. While genomic and transcriptomic analyses have traditionally been used to identify potential drug targets, there remains an unexplored potential in targeting protein-level anomalies. This study systematically investigates the proteomic landscape of 109 primary lung adenocarcinoma (LUAD) tumors using comprehensive mass-spectrometry (MS) proteomics data. By focusing on kinases, the key actors in oncogenic signaling pathways, we aim to find new therapeutic targets for LUAD. Through intricate analyses encompassing tumor-normal differentials and inter-tumor variations, our study identifies notable overexpressed targets, including PLAU, MET, ERBB2, EGFR, PDK1 kinases, and THBS2, CRABP2, INPP4B proteins, many of which present no evidence of transcriptomic alteration. Several targets we identified through proposed approaches have corresponding inhibitor drugs, including ERBB2 kinase (Afatinib) and VEGF-A protein (Bevacizumab). Our findings validate known therapeutic markers in lung cancer and reveal candidate protein targets specific to LUAD, underscoring the efficacy of proteomic methodologies in advancing precision medicine for cancer.

Keywords

Weighted k-nearest neighbor (KNN) algorithm, lung adenocarcinoma, proteomics, targeted therapy, precision oncology

Akciğer adenokarsinomunun proteogenomik analizi: hassas tıp için terapötik hedeflerin belirlenmesi

Abstract

Akciğer kanseri, dünya genelinde kanserle ilişkili ölümlerin başlıca nedeni olup, hem erkekleri hem de kadınları etkilemektedir. En büyük zorluklardan biri, genellikle hastalığın ileri evrelerinde teşhis edilmesidir; bu da tedavi seçeneklerini kısıtlamaktadır. Genomik ve transkriptomik analizler geleneksel olarak potansiyel ilaç hedeflerini belirlemede kullanılmıştır; ancak protein düzeyindeki anormallikleri hedeflemekte henüz keşfedilmemiş bir potansiyel bulunmaktadır. Bu çalışma, 109 birincil akciğer adenokarsinomu (LUAD) tümörünün proteomik profilini kapsamlı kütle spektrometrisi (MS) verileri kullanarak sistematik bir şekilde incelemektedir. Onkogenik sinyal yolarında kritik rol oynayan kinazlara odaklanarak, LUAD için yeni terapötik hedefler bulmayı amaçlıyoruz. Tümör-normal farklılıkları ve tümörler arası varyasyonları içeren ayrıntılı analizler sonucunda, PLAU, MET, ERBB2, EGFR, PDK1 kinazları ve THBS2, CRABP2, INPP4B proteinleri gibi önemli aşırı ekspres edilen hedefler belirlenmiştir. Bu hedeflerin çoğunda transkriptomik değişim kanıtı bulunmamaktadır. Önerilen yaklaşımlar aracılığıyla belirlediğimiz bazı hedefler için mevcut inhibitör ilaçlar geliştirilmiştir, ERBB2 kinazı (Afatinib) ve VEGF-A proteini (Bevacizumab) gibi. Bulgularımız, akciğer kanserindeki bilinen terapötik belirteçleri doğrulamakta ve LUAD’e özgü aday protein hedeflerini ortaya koyarak, proteomik yöntemlerin kanser tedavisinde kişiselleştirilmiş tıbbın ilerletilmesindeki etkinliğini vurgulamaktadır.

Keywords

Ağırlıklı k-en yakın komşu (KNN) algoritması, akciğer kanseri, proteomik metodolojiler, hedefe yönelik tedavi, hassas onkoloji

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