Meme Kanseri Alt Tiplerinde Gen Ekspresyonu ve İlaç Duyarlılığı İlişkisinin Multiomik Analizi

Öz

Meme kanseri, moleküler heterojenitesi nedeniyle farklı klinik davranışlar sergileyen karmaşık bir hastalıktır. Bu çalışmada, 30 meme kanseri hücre hattı, 'Hormon/HER2 Pozitif' (n=12) ve 'TNBC' (üçlü negatif, meme kanseri) olmak üzere iki ana analiz grubuna ayrılarak incelenmiştir. Bu gruplar arasındaki gen ekspresyon profilleri (RNA-seq), proteomik veriler (RPPA) ve yedi farklı kinaz inhibitörüne karşı ilaç duyarlılıkları, E-MTAB-4801 veri seti kullanılarak analiz edilmiştir. t-SNE ve hiyerarşik kümeleme analizleri, bu iki grubun genel ekspresyon profillerine göre ayrıştığını göstermiştir. Diferansiyel gen ekspresyon analizi edgeR ile gerçekleştirilmiş; anlamlı genler FDR<0.05 ve |logFC|>1 ölçütleriyle belirlenmiştir. Fonksiyonel zenginleştirme analizleri (GO ve KEGG: clusterProfiler; Hallmark: MSigDB/msigdbr ve fgsea), Hormon/HER2 Pozitif grubunda östrojen yanıtı ve metabolik yolakların, TNBC grubunda ise immün yanıt ve epiteliyal-mezenkimal geçiş ile ilişkili yolakların aktive olduğunu belirlemiştir. Makine öğrenimi yöntemleri ile iki grubu ayırt etmede önemli olan potansiyel biyobelirteçler (FAM176A, CACNG1, GPR77) tanımlanmıştır. Modellerin performansı nested çapraz doğrulama (5 dış kat/outer folds) ile değerlendirilmiştir; LASSO lojistik regresyon ve Random Forest modelleri ortalama olarak sırasıyla %83.3 ve %80.0 doğruluk, %86.5 ve %83.3 F1-skoru, %88.9 ve %83.3 duyarlılık ve %75.0 ve %75.0 özgüllük sağlamıştır; karar ağacı modeli ise daha değişken olup ortalama %70.0 doğruluk göstermiştir. Ayrıca, RPPA verileri kullanılarak, 4E-BP1 protein ekspresyon seviyesinin mTOR inhibitörlerine duyarlılıkta önemli bir belirleyici olduğu doğrulanmıştır. Bu çalışma, multi-omik verilerin entegratif analizinin meme kanseri alt tiplerini ayırt etmede ve potansiyel biyobelirteç adaylarını ortaya koymada değerli bilgiler sağlayabileceğini göstermektedir. Ancak bulgular 30 hücre hattına dayandığından istatistiksel güç sınırlıdır ve overfitting riski artabilir; ayrıca bağımsız klinik kohortlarda dış doğrulama bulunmadığı için tanımlanan biyobelirteçler aday biyobelirteçler olarak değerlendirilmelidir. Sonuçlar, klinik karar veya ilaç seçimi yerine, araştırma düzeyinde alt tip stratifikasyonu ve hasta tümör kohortlarında doğrulanacak adayların önceliklendirilmesine katkı sağlamayı amaçlamaktadır.

Anahtar Kelimeler

• Meme kanseri
• Multiomiks
• Gen ekspresyon profili
• İlaç duyarlılığı
• Biyobelirteç-ler
• TNBC

Multiomics Analysis of Gene Expression and Drug Sensitivity Relationship in Breast Cancer Subtypes

Öz

Breast cancer is a complex disease exhibiting distinct clinical behaviors due to its molecular heterogeneity. In this study, 30 breast cancer cell lines were categorized into two main analysis groups: 'Hormone/HER2 Positive' (n=12) and 'TNBC' (triple-negative breast cancer). Gene expression profiles (RNA-seq), proteomic data (RPPA), and drug sensitivity to seven different kinase inhibitors were analyzed using the E-MTAB-4801 dataset. t-SNE and hierarchical clustering demonstrated a separation based on the overall expression profiles of these two groups DEG (edgeR; FDR<0.05 and |logFC|>1) and enrichment analyses (GO/KEGG via clusterProfiler; Hallmark via msigdbr/fgsea) identified significant activation of estrogen response and metabolic pathways in the Hormone/HER2 Positive group, whereas immune response and epithelial-mesenchymal transition pathways were active in the TNBC group. Machine learning methods identified potential biomarker candidates (FAM176A, CACNG1, GPR77) crucial for discriminating between the two groups. Model performance was evaluated using nested cross-validation (five outer folds). Across outer folds, LASSO achieved mean Accuracy=0.833, F1=0.865, Sensitivity=0.889, Specificity=0.750; Random Forest achieved Accuracy=0.800, F1=0.833, Sensitivity=0.833, Specificity=0.750; and the Decision Tree showed more variable performance with mean Accuracy=0.700. Furthermore, analyses using RPPA data confirmed that the 4E-BP1 protein expression level is an important determinant of sensitivity to mTOR inhibitors. However, findings are based on 30 breast cancer cell lines, which limits statistical power and increases the risk of overfitting, and no independent clinical cohort was available for external validation; therefore, the identified biomarkers should be considered candidate biomarkers requiring validation in patient cohorts. Accordingly, the results are intended to support research-stage subtype stratification and prioritization of biomarker candidates rather than immediate clinical decision-making or drug selection providing a prioritized shortlist of candidates for follow-up validation in patient tumor cohorts.

Anahtar Kelimeler

• Breast cancer
• Multi-omics
• Gene expression profile
• Drug sensitivity
• Bi-omarkers
• TNBC

Kaynakça

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