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

Abstract

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.

Keywords

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

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

Abstract

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.

Keywords

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

References

1. Anders, S., Huber, W., & Love, M. I. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550. https://doi.org/10.1186/s13059-014-0550-8
2. Argelaguet, R., Velten, B., Arnol, D., Dietrich, S., Zenz, T., Marioni, J. C., Buettner, F., Huber, W., & Stegle, O. (2018). Multi-omics factor analysis—A framework for unsupervised integration of multi-omics data sets. Molecular Systems Biology, 14(6), e8124.
3. Baião, A. R., Cai, Z., Poulos, R. C., Robinson, P. J., Reddel, R. R., Zhong, Q., Vinga, S., & Gonçalves, E. (2025). A technical review of multi-omics data integration methods: From classical statistical to deep generative approaches. Briefings in Bioinformatics, 26(4), bbaf355. https://doi.org/10.1093/bib/bbaf355
4. Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A. A., Kim, S., Wilson, C. J., Lehár, J., Kryukov, G. V., Sonkin, D., Reddy, A., Liu, M., Murray, L., Berger, M. F., Monahan, J. E., Morais, P., Meltzer, J., Korejwa, A., Jané-Valbuena, J., & Garraway, L. A. (2012). The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature, 483(7391), 603–607.
5. Bertucci, F., Finetti, P., Rougemont, J., Cervera, N., Charafe-Jauffret, E., Tarpin, C., Viens, P., Jacquemier, J., Birnbaum, D., & Bertone, P. (2005). Gene expression profiling identifies molecular subtypes of inflammatory breast cancer. Cancer Research, 65(6), 2170–2178. https://doi.org/10.1158/0008-5472.CAN-04-4115
6. Conway, M. E., McDaniel, J. M., Graham, J. D., Weigel, N. L., & Richer, J. K. (2020). STAT3 and GR cooperate to drive gene expression and growth of basal-like triple-negative breast cancer. Cancer Research, 80(21), 4663–4676. https://doi.org/10.1158/0008-5472.CAN-20-1379
7. Elkabets, M., Vora, S., Juric, D., Morse, N., De Pinho, R. A., Polyak, K., & Wagle, N. (2013). mTORC1 inhibition is required for sensitivity to PI3K p110α inhibitors in PIK3CA-mutant breast cancer. Science Translational Medicine, 5(196), 196ra99. https://doi.org/10.1126/scitranslmed.3005747
8. Hasin, Y., Seldin, M., & Lusis, A. (2017). Multi-omics approaches to disease. Genome Biology, 18, 83. https://doi.org/10.1186/s13059-017-1215-1

9. Haworth, A. S., & Brackenbury, W. J. (2019). Emerging roles for multifunctional ion channel auxiliary subunits in cancer. Cell Calcium, 80, 125–134. https://doi.org/10.1016/j.ceca.2019.04.005
10. Hu, J., Li, G., Qu, L., et al. (2016). TMEM166/EVA1A interacts with ATG16L1 and induces autophagosome formation and cell death. Cell Death & Disease, 7, e2323. https://doi.org/10.1038/cddis.2016.230
11. Huynh, M., Jayanthan, A., Pambid, M. R., & Lai, R. (2020). RSK2: A promising therapeutic target for the treatment of triple-negative breast cancer. Expert Opinion on Therapeutic Targets, 24(2), 157–167. https://doi.org/10.1080/14728222.2020.1709824
12. Iorio, M. V., Ferracin, M., Liu, C. G., Veronese, A., Spizzo, R., Sabbioni, S., Magri, E., Pedriali, M., Fabbri, M., Campiglio, M., Ménard, S., Palazzo, J. P., Rosenberg, A., & Croce, C. M. (2005). MicroRNA gene expression deregulation in human breast cancer. Cancer Research, 65(16), 7065–7070. https://doi.org/10.1158/0008-5472.CAN-05-1783
13. Jastrzebski, K., Thijssen, B., Kluin, R. J. C., de Lint, K., Wessels, L. F. A., & Linn, S. C. (2018). Integrative modeling identifies key determinants of inhibitor sensitivity in breast cancer cell lines. Cancer Research, 78(15), 4396–4410. https://doi.org/10.1158/0008-5472.CAN-17-2698
14. Jiang, Y. Z., Ma, D., Suo, C., et al. (2019). Genomic and transcriptomic landscape of triple-negative breast cancers: Subtypes and treatment strategies. Cancer Cell, 35(3), 428–440. https://doi.org/10.1016/j.ccell.2019.02.001
15. Koçak, M., Kırtay, S., & Akçalı, Z. (2025). Exploring the trends in multiomics research: A comprehensive bibliometric analysis with interactive visualization tools (BiblioMaps). Journal of Advanced Research in Health Sciences, 8(3), 236–247. https://doi.org/10.26650/JARHS2025-1759179
16. Lehmann, B. D., Bauer, J. A., Chen, X., Sanders, M. E., Chakravarthy, A. B., Shyr, Y., & Pietenpol, J. A. (2011). Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. Journal of Clinical Investigation, 121(7), 2750–2767. https://doi.org/10.1172/JCI45014
17. Mertins, P., Mani, D., Ruggles, K., et al. (2016). Proteogenomics connects somatic mutations to signalling in breast cancer. Nature, 534, 55–62. https://doi.org/10.1038/nature18003
18. Nieto, M. A., Huang, R. Y., Jackson, R. A., & Thiery, J. P. (2016). EMT: 2016. Cell, 166(1), 21–45. https://doi.org/10.1016/j.cell.2016.06.028
19. Perou, C. M., Sørlie, T., Eisen, M. B., et al. (2000). Molecular portraits of human breast tumours. Nature, 406(6797), 747–752. https://doi.org/10.1038/35021093
20. Picard, M., Scott-Boyer, M. P., Bodein, A., Périn, O., & Droit, A. (2021). Integration strategies of multi-omics data for machine learning analysis. Computational and Structural Biotechnology Journal, 19, 3735–3746.
21. Polyak, K., Haviv, I., & Campbell, I. G. (2009). Co-evolution of tumor cells and their microenvironment. Trends in Genetics, 25(1), 30–38. https://doi.org/10.1016/j.tig.2008.10.012
22. Schmid, P., Adams, S., Rugo, H. S., et al. (2018). Atezolizumab and nab-paclitaxel in advanced triple-negative breast cancer. New England Journal of Medicine, 379(22), 2108–2121. https://doi.org/10.1056/NEJMoa1809615
23. Sørlie, T., Perou, C. M., Tibshirani, R., et al. (2001). Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proceedings of the National Academy of Sciences, 98(19), 10869–10874. https://doi.org/10.1073/pnas.191367098
24. The Cancer Genome Atlas Network. (2012). Comprehensive molecular portraits of human breast tumours. Nature, 490, 61–70. https://doi.org/10.1038/nature11412
25. Wang, B., Mezlini, A. M., Demir, F., Fiume, M., Tu, Z., Brudno, M., Haibe-Kains, B., & Goldenberg, A. (2014). Similarity network fusion for aggregating data types on a genomic scale. Nature Methods, 11(3), 333–337.
26. Wei, Z., Han, D., Zhang, C., et al. (2022). Deep learning-based multi-omics integration robustly predicts relapse in prostate cancer. Frontiers in Oncology, 12, 893424. https://doi.org/10.3389/fonc.2022.893424
27. Wörheide, M. A., Krumsiek, J., Kastenmüller, G., & Arnold, M. (2020). Multi-omics integration in biomedical research—A metabolomics-centric review. Analytical Chemistry, 92(1), 386–402.
28. Yap, F. Y., Chong, P. F., Ahmad, K. A., Nordin, A., & Tan, Y. C. (2019). Predicting factors for survival of breast cancer patients using machine learning techniques. BMC Medical Informatics and Decision Making, 19(1), 48. https://doi.org/10.1186/s12911-019-0801-4
29. Yu, G., Wang, L. G., Han, Y., & He, Q. Y. (2012). clusterProfiler: An R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology, 16(5), 284–287. https://doi.org/10.1089/omi.2011.0118