Identifying Lymph Node Metastasis-Related Factors in Breast Cancer Using Differential Modular and Mutational Structural Analysis

Welch DR, Hurst DR (2019) Defining the hallmarks of metastasis. Can Res 79(12):3011–3027. https://doi.org/10.1158/0008-5472.CAN-19-0458

Fares J, Fares MY, Khachfe HH, Salhab HA, Fares Y (2020) Molecular principles of metastasis: a hallmark of cancer revisited. Signal Transduct Target Ther 5(1):28. https://doi.org/10.1038/s41392-020-0134-x

Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71(3):209–249. https://doi.org/10.3322/caac.21660

Dihge L, Vallon-Christersson J, Hegardt C, Saal LH, Hakkinen J, Larsson C, Ehinger A, Loman N, Malmberg M, Bendahl PO, Borg A, Staaf J, Ryden L (2019) Prediction of lymph node metastasis in breast cancer by gene expression and clinicopathological models: development and validation within a population-based cohort. Clin Cancer Res 25(21):6368–6381. https://doi.org/10.1158/1078-0432.CCR-19-0075

Angus L, Smid M, Wilting SM, van Riet J, Van Hoeck A, Nguyen L, Nik-Zainal S, Steenbruggen TG, Tjan-Heijnen VCG, Labots M, van Riel J, Bloemendal HJ, Steeghs N, Lolkema MP, Voest EE, van de Werken HJG, Jager A, Cuppen E, Sleijfer S, Martens JWM (2019) The genomic landscape of metastatic breast cancer highlights changes in mutation and signature frequencies. Nat Genet 51(10):1450–1458. https://doi.org/10.1038/s41588-019-0507-7

Khan A, Rehman Z, Hashmi HF, Khan AA, Junaid M, Sayaf AM, Ali SS, Hassan FU, Heng W, Wei DQ (2020) An integrated systems biology and network-based approaches to identify novel biomarkers in breast cancer cell lines using gene expression data. Interdiscip Sci 12(2):155–168. https://doi.org/10.1007/s12539-020-00360-0

Hozhabri H, GhasemiDehkohneh RS, Razavi SM, Razavi SM, Salarian F, Rasouli A, Azami J, GhasemiShiran M, Kardan Z, Farrokhzad N, MikaeiliNamini A, Salari A (2022) Comparative analysis of protein-protein interaction networks in metastatic breast cancer. PLoS ONE 17(1):e0260584. https://doi.org/10.1371/journal.pone.0260584

Liu C, Ma YF, Zhao J, Nussinov R, Zhang YC, Cheng FX, Zhang ZK (2020) Computational network biology: data, models, and applications. Phys Rep 846:1–66. https://doi.org/10.1016/j.physrep.2019.12.004

Yan WY, Liu XY, Wang YB, Han SQ, Wang F, Liu X, Xiao F, Hu G (2020) Identifying drug targets in pancreatic ductal adenocarcinoma through machine learning, analyzing biomolecular networks, and structural modeling. Front Pharmacol. https://doi.org/10.3389/Fphar.2020.00534

Basha O, Argov CM, Artzy R, Zoabi Y, Hekselman I, Alfandari L, Chalifa-Caspi V, Yeger-Lotem E (2020) Differential network analysis of multiple human tissue interactomes highlights tissue-selective processes and genetic disorder genes. Bioinformatics 36(9):2821–2828. https://doi.org/10.1093/bioinformatics/btaa034

Lichtblau Y, Zimmermann K, Haldemann B, Lenze D, Hummel M, Leser U (2017) Comparative assessment of differential network analysis methods. Brief Bioinform 18(5):837–850. https://doi.org/10.1093/bib/bbw061

Goenawan IH, Bryan K, Lynn DJ (2016) DyNet: visualization and analysis of dynamic molecular interaction networks. Bioinformatics 32(17):2713–2715. https://doi.org/10.1093/bioinformatics/btw187

Gulfidan G, Turanli B, Beklen H, Sinha R, Arga KY (2020) Pan-cancer mapping of differential protein-protein interactions. Sci Rep 10(1):3272. https://doi.org/10.1038/s41598-020-60127-x

Savino A, Provero P, Poli V (2020) Differential co-expression analyses allow the identification of critical signalling pathways altered during tumour transformation and progression. Int J Mol Sci. https://doi.org/10.3390/ijms21249461

Hu B, Chang X, Liu X (2019) Predicting functional modules of liver cancer based on differential network analysis. Interdiscip Sci 11(4):636–644. https://doi.org/10.1007/s12539-018-0314-3

Nussinov R, Jang H, Nir G, Tsai CJ, Cheng F (2022) Open structural data in precision medicine. Annu Rev Biomed Data Sci 5:95–117. https://doi.org/10.1146/annurev-biodatasci-122220-012951

Gorostiola Gonzalez M, Janssen APA, IJzerman AP, Heitman LH, van Westen GJP (2022) Oncological drug discovery: AI meets structure-based computational research. Drug Discover Today 27(6):1661–1670. https://doi.org/10.1016/j.drudis.2022.03.005

Robichaux JP, Le X, Vijayan RSK, Hicks JK, Heeke S, Elamin YY, Lin HY, Udagawa H, Skoulidis F, Tran H, Varghese S, He J, Zhang F, Nilsson MB, Hu L, Poteete A, Rinsurongkawong W, Zhang X, Ren C, Liu X, Hong L, Zhang J, Diao L, Madison R, Schrock AB, Saam J, Raymond V, Fang B, Wang J, Ha MJ, Cross JB, Gray JE, Heymach JV (2021) Structure-based classification predicts drug response in EGFR-mutant NSCLC. Nature 597(7878):732–737. https://doi.org/10.1038/s41586-021-03898-1

Wu H, Chen Z, Wu Y, Zhang H, Liu Q (2022) Integrating protein-protein interaction networks and somatic mutation data to detect driver modules in pan-cancer. Interdiscip Sci 14(1):151–167. https://doi.org/10.1007/s12539-021-00475-y

Kar G, Gursoy A, Keskin O (2009) Human cancer protein-protein interaction network: a structural perspective. PLoS Comput Biol 5(12):e1000601. https://doi.org/10.1371/journal.pcbi.1000601

Engin HB, Guney E, Keskin O, Oliva B, Gursoy A (2013) Integrating structure to protein-protein interaction networks that drive metastasis to brain and lung in breast cancer. PLoS ONE. https://doi.org/10.1371/journal.pone.0081035

AcunerOzbabacan SE, Gursoy A, Nussinov R, Keskin O (2014) The structural pathway of interleukin 1 (IL-1) initiated signaling reveals mechanisms of oncogenic mutations and SNPs in inflammation and cancer. PLoS Comput Biol 10(2):e1003470. https://doi.org/10.1371/journal.pcbi.1003470

Taylor IW, Linding R, Warde-Farley D, Liu Y, Pesquita C, Faria D, Bull S, Pawson T, Morris Q, Wrana JL (2009) Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat Biotechnol 27(2):199–204. https://doi.org/10.1038/nbt.1522

Chuang HY, Lee E, Liu YT, Lee D, Ideker T (2007) Network-based classification of breast cancer metastasis. Mol Syst Biol 3:140. https://doi.org/10.1038/msb4100180

Han JD, Bertin N, Hao T, Goldberg DS, Berriz GF, Zhang LV, Dupuy D, Walhout AJ, Cusick ME, Roth FP, Vidal M (2004) Evidence for dynamically organized modularity in the yeast protein-protein interaction network. Nature 430(6995):88–93. https://doi.org/10.1038/nature02555

Wang F, Han S, Yang J, Yan W, Hu G (2021) Knowledge-guided “Community Network” analysis reveals the functional modules and candidate targets in non-small-cell lung cancer. Cells. https://doi.org/10.3390/cells10020402

Mayakonda A, Lin DC, Assenov Y, Plass C, Koeffler HP (2018) Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res 28(11):1747–1756. https://doi.org/10.1101/gr.239244.118

von Mering C, Jensen LJ, Snel B, Hooper SD, Krupp M, Foglierini M, Jouffre N, Huynen MA, Bork P (2005) STRING: known and predicted protein-protein associations, integrated and transferred across organisms. Nucleic acids Res 33(Database issue):D433–D437. https://doi.org/10.1093/nar/gki005

Hotelling H (1953) New light on the correlation coefficient and its transforms. J R Stat Soc B 15(2):193–232. https://doi.org/10.1111/j.2517-6161.1953.tb00135.x

Subelj L, Bajec M (2011) Unfolding communities in large complex networks: combining defensive and offensive label propagation for core extraction. Phys Rev 83(3–2):036103. https://doi.org/10.1103/PhysRevE.83.036103

Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504. https://doi.org/10.1101/gr.1239303

Hu G, Yan W, Zhou J, Shen B (2014) Residue interaction network analysis of Dronpa and a DNA clamp. J Theor Biol 348:55–64. https://doi.org/10.1016/j.jtbi.2014.01.023

Yu G, Wang LG, Han Y, He QY (2012) clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16(5):284–287. https://doi.org/10.1089/omi.2011.0118

Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M (1999) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 27(1):29–34. https://doi.org/10.1093/nar/27.1.29

Liberzon A, Birger C, Thorvaldsdottir H, Ghandi M, Mesirov JP, Tamayo P (2015) The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst 1(6):417–425. https://doi.org/10.1016/j.cels.2015.12.004

Hanzelmann S, Castelo R, Guinney J (2013) GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinform 14:7. https://doi.org/10.1186/1471-2105-14-7

Jassal B, Matthews L, Viteri G, Gong C, Lorente P, Fabregat A, Sidiropoulos K, Cook J, Gillespie M, Haw R, Loney F, May B, Milacic M, Rothfels K, Sevilla C, Shamovsky V, Shorser S, Varusai T, Weiser J, Wu G, Stein L, Hermjakob H, D’Eustachio P (2020) The reactome pathway knowledgebase. Nucleic Acids Res 48(D1):D498–D503. https://doi.org/10.1093/nar/gkz1031

Forbes S, Clements J, Dawson E, Bamford S, Webb T, Dogan A, Flanagan A, Teague J, Wooster R, Futreal PA, Stratton MR (2006) Cosmic 2005. Br J Cancer 94(2):318–322. https://doi.org/10.1038/sj.bjc.6602928

Baspinar A, Cukuroglu E, Nussinov R, Keskin O, Gursoy A (2014) PRISM: a web server and repository for prediction of protein-protein interactions and modeling their 3D complexes. Nucleic Acids Res 42(Web Server issue):W285–W289. https://doi.org/10.1093/nar/gku397

Tuncbag N, Gursoy A, Nussinov R, Keskin O (2011) Predicting protein-protein interactions on a proteome scale by matching evolutionary and structural similarities at interfaces using PRISM. Nat Protoc 6(9):1341–1354. https://doi.org/10.1038/nprot.2011.367

Mashiach E, Nussinov R, Wolfson HJ (2010) FiberDock: flexible induced-fit backbone refinement in molecular docking. Proteins 78(6):1503–1519. https://doi.org/10.1002/prot.22668

Guarnera E, Tan ZW, Zheng Z, Berezovsky IN (2017) AlloSigMA: allosteric signaling and mutation analysis server. Bioinformatics 33(24):3996–3998. https://doi.org/10.1093/bioinformatics/btx430

Zhang Q, Lu S, Li T, Yu L, Zhang Y, Zeng H, Qian X, Bi J, Lin Y (2019) ACE2 inhibits breast cancer angiogenesis via suppressing the VEGFa/VEGFR2/ERK pathway. J Exp Clin Cancer Res 38(1):173. https://doi.org/10.1186/s13046-019-1156-5

Pecar G, Liu S, Hooda J, Atkinson JM, Oesterreich S, Lee AV (2023) RET signaling in breast cancer therapeutic resistance and metastasis. Breast cancer Res 25(1):26. https://doi.org/10.1186/s13058-023-01622-7

Peng M, Ball-Kell SM, Tyner AL (2015) Protein tyrosine kinase 6 promotes ERBB2-induced mammary gland tumorigenesis in the mouse. Cell Death Dis 6(8):e1848. https://doi.org/10.1038/cddis.2015.210

Jechlinger M, Sommer A, Moriggl R, Seither P, Kraut N, Capodiecci P, Donovan M, Cordon-Cardo C, Beug H, Grunert S (2006) Autocrine PDGFR signaling promotes mammary cancer metastasis. J Clin Invest 116(6):1561–1570. https://doi.org/10.1172/JCI24652

Xue C, Liang F, Mahmood R, Vuolo M, Wyckoff J, Qian H, Tsai KL, Kim M, Locker J, Zhang ZY, Segall JE (2006) ErbB3-dependent motility and intravasation in breast cancer metastasis. Can Res 66(3):1418–1426. https://doi.org/10.1158/0008-5472.CAN-05-0550

Jitariu AA, Raica M, Cimpean AM, Suciu SC (2018) The role of PDGF-B/PDGFR-BETA axis in the normal development and carcinogenesis of the breast. Crit Rev Oncol Hematol 131:46–52. https://doi.org/10.1016/j.critrevonc.2018.08.002

Zhou HB, Lei PJ, Padera TP (2021) Progression of metastasis through lymphatic system. Cells. https://doi.org/10.3390/Cells10030627

Hanahan D (2022) Hallmarks of cancer: new dimensions. Cancer Discov 12(1):31–46. https://doi.org/10.1158/2159-8290.CD-21-1059

Hunter DJ, Kraft P, Jacobs KB, Cox DG, Yeager M, Hankinson SE, Wacholder S, Wang Z, Welch R, Hutchinson A, Wang J, Yu K, Chatterjee N, Orr N, Willett WC, Colditz GA, Ziegler RG, Berg CD, Buys SS, McCarty CA, Feigelson HS, Calle EE, Thun MJ, Hayes RB, Tucker M, Gerhard DS, Fraumeni JF Jr, Hoover RN, Thomas G, Chanock SJ (2007) A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer. Nat Genet 39(7):870–874. https://doi.org/10.1038/ng2075

Konig SM, Rissler V, Terkelsen T, Lambrughi M, Papaleo E (2019) Alterations of the interactome of Bcl-2 proteins in breast cancer at the transcriptional, mutational and structural level. PLoS Comput Biol. https://doi.org/10.1371/journal.pcbi.1007485

Ghauri MA, Raza A, Hayat U, Atif N, Iqbal HMN, Bilal M (2022) Mechanistic insights expatiating the biological role and regulatory implications of estrogen and HER2 in breast cancer metastasis. Biochim Biophys Acta Gen Subj 1866(5):130113. https://doi.org/10.1016/j.bbagen.2022.130113

Hanker AB, Brown BP, Meiler J, Marin A, Jayanthan HS, Ye D, Lin CC, Akamatsu H, Lee KM, Chatterjee S, Sudhan DR, Servetto A, Brewer MR, Koch JP, Sheehan JH, He J, Lalani AS, Arteaga CL (2021) Co-occurring gain-of-function mutations in HER2 and HER3 modulate HER2/HER3 activation, oncogenesis, and HER2 inhibitor sensitivity. Cancer Cell 39(8):1099–1114. https://doi.org/10.1016/j.ccell.2021.06.001. (e8)

Makena MR, Rao R (2020) Subtype specific targeting of calcium signaling in breast cancer. Cell Calcium 85:102109. https://doi.org/10.1016/j.ceca.2019.102109

Lee D, Hong JH (2021) Ca2+ signaling as the untact mode during signaling in metastatic breast cancer. Cancers. https://doi.org/10.3390/Cancers13061473

Zhang Y (2021) The root cause of drug resistance in HER2-positive breast cancer and the therapeutic approaches to overcoming the resistance. Pharmacol Therapeutics 218:107677. https://doi.org/10.1016/j.pharmthera.2020.107677

Kennedy SP, Han JZR, Portman N, Nobis M, Hastings JF, Murphy KJ, Latham SL, Cadell AL, Miladinovic D, Marriott GR, O’Donnell YEI, Shearer RF, Williams JT, Munoz AG, Cox TR, Watkins DN, Saunders DN, Timpson P, Lim E, Kolch W, Croucher DR (2019) Targeting promiscuous heterodimerization overcomes innate resistance to ERBB2 dimerization inhibitors in breast cancer. Breast Cancer Res 21(1):43. https://doi.org/10.1186/s13058-019-1127-y

Nussinov R, Zhang M, Maloney R, Liu Y, Tsai CJ, Jang H (2022) Allostery: allosteric cancer drivers and innovative allosteric drugs. J Mol Biol. https://doi.org/10.1016/j.jmb.2022.167569

Paratala BS, Chung JH, Williams CB, Yilmazel B, Petrosky W, Williams K, Schrock AB, Gay LM, Lee E, Dolfi SC, Pham K, Lin S, Yao M, Kulkarni A, DiClemente F, Liu C, Rodriguez-Rodriguez L, Ganesan S, Ross JS, Ali SM, Leyland-Jones B, Hirshfield KM (2018) RET rearrangements are actionable alterations in breast cancer. Nat Commun 9(1):4821. https://doi.org/10.1038/s41467-018-07341-4