Unbiased discovery of cancer pathways and therapeutics using Pathway Ensemble Tool and Benchmark.

Humans Neoplasms / drug therapy Benchmarking Signal Transduction / drug effects Biomarkers, Tumor / metabolism Drug Repositioning Animals Prognosis Antineoplastic Agents / pharmacology Cell Line, Tumor Computational Biology / methods Mice

Journal

Nature communications

ISSN: 2041-1723

Titre abrégé: Nat Commun

Pays: England

ID NLM: 101528555

Informations de publication

Date de publication:
24 Aug 2024

Historique:

received: 06 03 2023

accepted: 19 08 2024

medline: 24 8 2024

pubmed: 24 8 2024

entrez: 23 8 2024

Statut: epublish

Résumé

Correctly identifying perturbed biological pathways is a critical step in uncovering basic disease mechanisms and developing much-needed therapeutic strategies. However, whether current tools are optimal for unbiased discovery of relevant pathways remains unclear. Here, we create "Benchmark" to critically evaluate existing tools and find that most function sub-optimally. We thus develop the "Pathway Ensemble Tool" (PET), which outperforms existing methods. Deploying PET, we identify prognostic pathways across 12 cancer types. PET-identified prognostic pathways offer additional insights, with genes within these pathways serving as reliable biomarkers for clinical outcomes. Additionally, normalizing these pathways using drug repurposing strategies represents therapeutic opportunities. For example, the top predicted repurposed drug for bladder cancer, a CDK2/9 inhibitor, represses cell growth in vitro and in vivo. We anticipate that using Benchmark and PET for unbiased pathway discovery will offer additional insights into disease mechanisms across a spectrum of diseases, enabling biomarker discovery and therapeutic strategies.

Identifiants

DOI: 10.1038/s41467-024-51859-9 PMID: 39179644

pubmed: 39179644

doi: 10.1038/s41467-024-51859-9

pii: 10.1038/s41467-024-51859-9

doi:

Substances chimiques

Biomarkers, Tumor 0

Antineoplastic Agents 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

Pagination

7288

Subventions

Organisme : U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)

ID : R35GM138283

Informations de copyright

Références

Howlader, N. et al. SEER Cancer Statistics Review, 1975–2018. National Cancer Institute. Bethesda, MD, https://seer.cancer.gov/csr/1975_2018/ , based on November 2020 SEER data submission posted to the SEER web site (2021).

Nguyen, T. M., Shafi, A., Nguyen, T. & Draghici, S. Identifying significantly impacted pathways: a comprehensive review and assessment. Genome Biol. 20, 203 (2019).

pubmed: 31597578 pmcid: 6784345 doi: 10.1186/s13059-019-1790-4

Khatri, P., Sirota, M. & Butte, A. J. Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput. Biol. 8, e1002375 (2012).

pubmed: 22383865 pmcid: 3285573 doi: 10.1371/journal.pcbi.1002375

Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

pubmed: 16199517 pmcid: 1239896 doi: 10.1073/pnas.0506580102

Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinforma. 14, 128 (2013).

doi: 10.1186/1471-2105-14-128

Alhamdoosh, M. et al. Combining multiple tools outperforms individual methods in gene set enrichment analyses. Bioinformatics 33, 414–424 (2017).

pubmed: 27694195 doi: 10.1093/bioinformatics/btw623

Geistlinger, L. et al. Toward a gold standard for benchmarking gene set enrichment analysis. Brief. Bioinform. 22, 545–556 (2021).

pubmed: 32026945 doi: 10.1093/bib/bbz158

Yu, C. et al. A strategy for evaluating pathway analysis methods. BMC Bioinforma. 18, 453 (2017).

doi: 10.1186/s12859-017-1866-7

Paczkowska, M. et al. Integrative pathway enrichment analysis of multivariate omics data. Nat. Commun. 11, 735 (2020).

pubmed: 32024846 pmcid: 7002665 doi: 10.1038/s41467-019-13983-9

Consortium, E. P. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

doi: 10.1038/nature11247

Badia, I. M. P. et al. decoupleR: ensemble of computational methods to infer biological activities from omics data. Bioinform. Adv. 2, vbac016 (2022).

doi: 10.1093/bioadv/vbac016

Varemo, L., Nielsen, J. & Nookaew, I. Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods. Nucleic Acids Res. 41, 4378–4391 (2013).

pubmed: 23444143 pmcid: 3632109 doi: 10.1093/nar/gkt111

Wu, D. & Smyth, G. K. Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Res. 40, e133 (2012).

pubmed: 22638577 pmcid: 3458527 doi: 10.1093/nar/gks461

Geistlinger, L., Csaba, G. & Zimmer, R. Bioconductor’s EnrichmentBrowser: seamless navigation through combined results of set- & network-based enrichment analysis. BMC Bioinforma. 17, 45 (2016).

doi: 10.1186/s12859-016-0884-1

Lee, E., Chuang, H. Y., Kim, J. W., Ideker, T. & Lee, D. Inferring pathway activity toward precise disease classification. PLoS Comput Biol. 4, e1000217 (2008).

pubmed: 18989396 pmcid: 2563693 doi: 10.1371/journal.pcbi.1000217

Hanzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinforma. 14, 7 (2013).

doi: 10.1186/1471-2105-14-7

Tarca, A. L., Draghici, S., Bhatti, G. & Romero, R. Down-weighting overlapping genes improves gene set analysis. BMC Bioinforma. 13, 136 (2012).

doi: 10.1186/1471-2105-13-136

Wu, D. et al. ROAST: rotation gene set tests for complex microarray experiments. Bioinformatics 26, 2176–2182 (2010).

pubmed: 20610611 pmcid: 2922896 doi: 10.1093/bioinformatics/btq401

Tomfohr, J., Lu, J. & Kepler, T. B. Pathway level analysis of gene expression using singular value decomposition. BMC Bioinforma. 6, 225 (2005).

doi: 10.1186/1471-2105-6-225

Luo, W., Friedman, M. S., Shedden, K., Hankenson, K. D. & Woolf, P. J. GAGE: generally applicable gene set enrichment for pathway analysis. BMC Bioinforma. 10, 161 (2009).

doi: 10.1186/1471-2105-10-161

Barry, W. T., Nobel, A. B. & Wright, F. A. Significance analysis of functional categories in gene expression studies: a structured permutation approach. Bioinformatics 21, 1943–1949 (2005).

pubmed: 15647293 doi: 10.1093/bioinformatics/bti260

Garraway, L. A. et al. Integrative genomic analyses identify MITF as a lineage survival oncogene amplified in malignant melanoma. Nature 436, 117–122 (2005).

pubmed: 16001072 doi: 10.1038/nature03664

Saha Roy, S. & Vadlamudi, R. K. Role of estrogen receptor signaling in breast cancer metastasis. Int J. Breast Cancer 2012, 654698 (2012).

pubmed: 22295247 doi: 10.1155/2012/654698

Clusan, L., Ferriere, F., Flouriot, G. & Pakdel, F. A Basic Review on Estrogen Receptor Signaling Pathways in Breast Cancer. Int J. Mol. Sci. 24, 6834 (2023).

pubmed: 37047814 pmcid: 10095386 doi: 10.3390/ijms24076834

Whitsett, J. A., Weaver, T. E., Lieberman, M. A., Clark, J. C. & Daugherty, C. Differential effects of epidermal growth factor and transforming growth factor-beta on synthesis of Mr = 35,000 surfactant-associated protein in fetal lung. J. Biol. Chem. 262, 7908–7913 (1987).

pubmed: 3495535 doi: 10.1016/S0021-9258(18)47654-5

Inoue, A. et al. Suppression of surfactant protein A by an epidermal growth factor receptor tyrosine kinase inhibitor exacerbates lung inflammation. Cancer Sci. 99, 1679–1684 (2008).

pubmed: 18754883 pmcid: 11158839 doi: 10.1111/j.1349-7006.2008.00857.x

Hasegawa, Y. et al. Surfactant protein D suppresses lung cancer progression by downregulation of epidermal growth factor signaling. Oncogene 34, 4285–4286 (2015).

pubmed: 26250851 doi: 10.1038/onc.2015.266

Ricono, J. M. et al. Specific cross-talk between epidermal growth factor receptor and integrin alphavbeta5 promotes carcinoma cell invasion and metastasis. Cancer Res. 69, 1383–1391 (2009).

pubmed: 19208836 pmcid: 2741736 doi: 10.1158/0008-5472.CAN-08-3612

Rubio, K. et al. Non-canonical integrin signaling activates EGFR and RAS-MAPK-ERK signaling in small cell lung cancer. Theranostics 13, 2384–2407 (2023).

pubmed: 37215577 pmcid: 10196829 doi: 10.7150/thno.79493

Mahe, M. et al. An FGFR3/MYC positive feedback loop provides new opportunities for targeted therapies in bladder cancers. EMBO Mol. Med. 10, e8163 (2018).

pubmed: 29463565 pmcid: 5887543 doi: 10.15252/emmm.201708163

Grandori, C. et al. c-Myc binds to human ribosomal DNA and stimulates transcription of rRNA genes by RNA polymerase I. Nat. Cell Biol. 7, 311–318 (2005).

pubmed: 15723054 doi: 10.1038/ncb1224

van Riggelen, J., Yetil, A. & Felsher, D. W. MYC as a regulator of ribosome biogenesis and protein synthesis. Nat. Rev. Cancer 10, 301–309 (2010).

pubmed: 20332779 doi: 10.1038/nrc2819

Arabi, A. et al. c-Myc associates with ribosomal DNA and activates RNA polymerase I transcription. Nat. Cell Biol. 7, 303–310 (2005).

pubmed: 15723053 doi: 10.1038/ncb1225

Grewal, S. S., Li, L., Orian, A., Eisenman, R. N. & Edgar, B. A. Myc-dependent regulation of ribosomal RNA synthesis during Drosophila development. Nat. Cell Biol. 7, 295–302 (2005).

pubmed: 15723055 doi: 10.1038/ncb1223

Kardos, J. et al. Development and validation of a NanoString BASE47 bladder cancer gene classifier. PLoS One 15, e0243935 (2020).

pubmed: 33332422 pmcid: 7745986 doi: 10.1371/journal.pone.0243935

Lee, J. S. et al. Expression signature of E2F1 and its associated genes predict superficial to invasive progression of bladder tumors. J. Clin. Oncol. 28, 2660–2667 (2010).

pubmed: 20421545 doi: 10.1200/JCO.2009.25.0977

Choi, W. et al. Identification of distinct basal and luminal subtypes of muscle-invasive bladder cancer with different sensitivities to frontline chemotherapy. Cancer Cell 25, 152–165 (2014).

pubmed: 24525232 pmcid: 4011497 doi: 10.1016/j.ccr.2014.01.009

Ferraro, G. B. et al. Fatty Acid Synthesis Is Required for Breast Cancer Brain Metastasis. Nat. Cancer 2, 414–428 (2021).

pubmed: 34179825 pmcid: 8223728 doi: 10.1038/s43018-021-00183-y

Przybytkowski, E. et al. Upregulation of cellular triacylglycerol - free fatty acid cycling by oleate is associated with long-term serum-free survival of human breast cancer cells. Biochem. Cell Biol. 85, 301–310 (2007).

pubmed: 17612624 doi: 10.1139/O07-001

Brunner, A. & Tzankov, A. The role of structural extracellular matrix proteins in urothelial bladder cancer (review). Biomark. Insights 2, 418–427 (2007).

pubmed: 19662222 pmcid: 2717820 doi: 10.4137/BMI.S294

Weniger, M., Honselmann, K. C. & Liss, A. S. The Extracellular Matrix and Pancreatic Cancer: A Complex Relationship. Cancers 10, 316 (2018).

pubmed: 30200666 pmcid: 6162452 doi: 10.3390/cancers10090316

Mahendra, A. et al. Vitamin D and gastrointestinal cancer. J. Lab Physicians 10, 1–5 (2018).

pubmed: 29403195 pmcid: 5784277 doi: 10.4103/JLP.JLP_49_17

Ren, C. et al. Prognostic effects of 25-hydroxyvitamin D levels in gastric cancer. J. Transl. Med. 10, 16 (2012).

pubmed: 22284859 pmcid: 3295723 doi: 10.1186/1479-5876-10-16

Kamel, H. F. M. & Al-Amodi, H. Exploitation of Gene Expression and Cancer Biomarkers in Paving the Path to Era of Personalized Medicine. Genomics Proteom. Bioinforma. 15, 220–235 (2017).

doi: 10.1016/j.gpb.2016.11.005

Willis, S. et al. Single Gene Prognostic Biomarkers in Ovarian Cancer: A Meta-Analysis. PLoS One 11, e0149183 (2016).

pubmed: 26886260 pmcid: 4757072 doi: 10.1371/journal.pone.0149183

Voronova, V. et al. Diagnostic Value of Combinatorial Markers in Colorectal Carcinoma. Front. Oncol. 10, 832 (2020).

pubmed: 32528895 pmcid: 7258084 doi: 10.3389/fonc.2020.00832

Rakha, E. A., Reis-Filho, J. S. & Ellis, I. O. Combinatorial biomarker expression in breast cancer. Breast Cancer Res. Treat. 120, 293–308 (2010).

pubmed: 20107892 doi: 10.1007/s10549-010-0746-x

Qian, Y. et al. Prognostic Cancer Gene Expression Signatures: Current Status and Challenges. Cells 10, 648 (2021).

pubmed: 33804045 pmcid: 8000474 doi: 10.3390/cells10030648

Farber, N. J. et al. Renal cell carcinoma: the search for a reliable biomarker. Transl. Cancer Res. 6, 620–632 (2017).

pubmed: 28775935 doi: 10.21037/tcr.2017.05.19

Xu, F. et al. The Oncogenic Role of COL23A1 in Clear Cell Renal Cell Carcinoma. Sci. Rep. 7, 9846 (2017).

pubmed: 28852123 pmcid: 5575106 doi: 10.1038/s41598-017-10134-2

Guo, L., An, T., Huang, Z., Wan, Z. & Chong, T. Comprehensive analysis of the collagen family members as prognostic markers in clear cell renal cell carcinoma. Transl. Cancer Res. 11, 1954–1969 (2022).

pubmed: 35966319 pmcid: 9372239 doi: 10.21037/tcr-22-398

Best, S. L. et al. Collagen organization of renal cell carcinoma differs between low and high grade tumors. BMC Cancer 19, 490 (2019).

pubmed: 31122202 pmcid: 6533752 doi: 10.1186/s12885-019-5708-z

Majo, S., Courtois, S., Souleyreau, W., Bikfalvi, A. & Auguste, P. Impact of Extracellular Matrix Components to Renal Cell Carcinoma Behavior. Front. Oncol. 10, 625 (2020).

pubmed: 32411604 pmcid: 7198871 doi: 10.3389/fonc.2020.00625

Hurt, E. M. et al. Identification of vitronectin as an extrinsic inducer of cancer stem cell differentiation and tumor formation. Stem Cells 28, 390–398 (2010).

pubmed: 19998373 doi: 10.1002/stem.271

Li, S. et al. Plasminogen activator inhibitor-1 in cancer research. Biomed. Pharmacother. 105, 83–94 (2018).

pubmed: 29852393 doi: 10.1016/j.biopha.2018.05.119

Gyorffy, B. Survival analysis across the entire transcriptome identifies biomarkers with the highest prognostic power in breast cancer. Comput Struct. Biotechnol. J. 19, 4101–4109 (2021).

pubmed: 34527184 pmcid: 8339292 doi: 10.1016/j.csbj.2021.07.014

Zhang, J. et al. The International Cancer Genome Consortium Data Portal. Nat. Biotechnol. 37, 367–369 (2019).

pubmed: 30877282 doi: 10.1038/s41587-019-0055-9

Yoon, S. J. et al. Deconvolution of diffuse gastric cancer and the suppression of CD34 on the BALB/c nude mice model. BMC Cancer 20, 314 (2020).

pubmed: 32293340 pmcid: 7160933 doi: 10.1186/s12885-020-06814-4

Cristescu, R. et al. Molecular analysis of gastric cancer identifies subtypes associated with distinct clinical outcomes. Nat. Med. 21, 449–456 (2015).

pubmed: 25894828 doi: 10.1038/nm.3850

Alkader, M. S. et al. Impact of KRAS Mutation on Survival Outcome of Patients With Metastatic Colorectal Cancer in Jordan. Cureus 15, e33736 (2023).

pubmed: 36788889 pmcid: 9922492

Tan, E. et al. BRAF Mutations Are Associated with Poor Survival Outcomes in Advanced-stage Mismatch Repair-deficient/Microsatellite High Colorectal Cancer. Oncologist 27, 191–197 (2022).

pubmed: 35274712 pmcid: 8914499 doi: 10.1093/oncolo/oyab055

Robles, A. I. & Harris, C. C. Clinical outcomes and correlates of TP53 mutations and cancer. Cold Spring Harb. Perspect. Biol. 2, a001016 (2010).

pubmed: 20300207 pmcid: 2829964 doi: 10.1101/cshperspect.a001016

Cancer Genome Atlas Research, N. et al. Comprehensive Molecular Characterization of Papillary Renal-Cell Carcinoma. N. Engl. J. Med. 374, 135–145 (2016).

doi: 10.1056/NEJMoa1505917

Seiler, R. et al. Impact of Molecular Subtypes in Muscle-invasive Bladder Cancer on Predicting Response and Survival after Neoadjuvant Chemotherapy. Eur. Urol. 72, 544–554 (2017).

pubmed: 28390739 doi: 10.1016/j.eururo.2017.03.030

Robertson, A. G. et al. Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer. Cell 171, 540–556 e525 (2017).

pubmed: 28988769 pmcid: 5687509 doi: 10.1016/j.cell.2017.09.007

Dhawan, D., Hahn, N. M., Ramos-Vara, J. A. & Knapp, D. W. Naturally-occurring canine invasive urothelial carcinoma harbors luminal and basal transcriptional subtypes found in human muscle invasive bladder cancer. PLoS Genet 14, e1007571 (2018).

pubmed: 30089113 pmcid: 6101404 doi: 10.1371/journal.pgen.1007571

Evangelista, J. E. et al. SigCom LINCS: data and metadata search engine for a million gene expression signatures. Nucleic Acids Res. 50, W697–W709 (2022).

pubmed: 35524556 pmcid: 9252724 doi: 10.1093/nar/gkac328

Yoo, M. et al. DSigDB: drug signatures database for gene set analysis. Bioinformatics 31, 3069–3071 (2015).

pubmed: 25990557 pmcid: 4668778 doi: 10.1093/bioinformatics/btv313

Yan, B. et al. SARS-CoV-2 drives JAK1/2-dependent local complement hyperactivation. Sci. Immunol. 6, eabg0833 (2021).

pubmed: 33827897 pmcid: 8139422 doi: 10.1126/sciimmunol.abg0833

Chauss, D. et al. Autocrine vitamin D signaling switches off pro-inflammatory programs of TH1 cells. Nat. Immunol. 23, 62–74 (2022).

pubmed: 34764490 doi: 10.1038/s41590-021-01080-3

Zhao, M. et al. Use of liposomal doxorubicin for adjuvant chemotherapy of breast cancer in clinical practice. J. Zhejiang Univ. Sci. B 18, 15–26 (2017).

pubmed: 28070993 pmcid: 5260474 doi: 10.1631/jzus.B1600303

Barrett-Lee, P. J. et al. Expert opinion on the use of anthracyclines in patients with advanced breast cancer at cardiac risk. Ann. Oncol. 20, 816–827 (2009).

pubmed: 19153118 doi: 10.1093/annonc/mdn728

Bufu, T. et al. Celastrol inhibits colorectal cancer cell proliferation and migration through suppression of MMP3 and MMP7 by the PI3K/AKT signaling pathway. Anticancer Drugs 29, 530–538 (2018).

pubmed: 29553945 doi: 10.1097/CAD.0000000000000621

Wilson, G. S. et al. Tumoricidal effects of the JAK inhibitor Ruxolitinib (INC424) on hepatocellular carcinoma in vitro. Cancer Lett. 341, 224–230 (2013).

pubmed: 23941832 doi: 10.1016/j.canlet.2013.08.009

Wainberg, Z. A. et al. First-line liposomal irinotecan with oxaliplatin, 5-fluorouracil and leucovorin (NALIRIFOX) in pancreatic ductal adenocarcinoma: A phase I/II study. Eur. J. Cancer 151, 14–24 (2021).

pubmed: 33957442 doi: 10.1016/j.ejca.2021.03.028

Hubner, R. A. et al. Quality of life in metastatic pancreatic cancer patients receiving liposomal irinotecan plus 5-fluorouracil and leucovorin. Eur. J. Cancer 106, 24–33 (2019).

pubmed: 30458340 doi: 10.1016/j.ejca.2018.09.029

Woo, W., Carey, E. T. & Choi, M. Spotlight on liposomal irinotecan for metastatic pancreatic cancer: patient selection and perspectives. Onco Targets Ther. 12, 1455–1463 (2019).

pubmed: 30863113 pmcid: 6391121 doi: 10.2147/OTT.S167590

Hessmann, E., Schneider, G., Ellenrieder, V. & Siveke, J. T. MYC in pancreatic cancer: novel mechanistic insights and their translation into therapeutic strategies. Oncogene 35, 1609–1618 (2016).

pubmed: 26119937 doi: 10.1038/onc.2015.216

Oliveira-Cunha, M., Newman, W. G. & Siriwardena, A. K. Epidermal growth factor receptor in pancreatic cancer. Cancers 3, 1513–1526 (2011).

pubmed: 24212772 pmcid: 3757375 doi: 10.3390/cancers3021513

Stewart, C. F. et al. Gefitinib enhances the antitumor activity and oral bioavailability of irinotecan in mice. Cancer Res. 64, 7491–7499 (2004).

pubmed: 15492275 doi: 10.1158/0008-5472.CAN-04-0096

Rubin, S. M., Sage, J. & Skotheim, J. M. Integrating Old and New Paradigms of G1/S Control. Mol. Cell 80, 183–192 (2020).

pubmed: 32946743 pmcid: 7582788 doi: 10.1016/j.molcel.2020.08.020

Klein, M. E., Kovatcheva, M., Davis, L. E., Tap, W. D. & Koff, A. CDK4/6 Inhibitors: The Mechanism of Action May Not Be as Simple as Once Thought. Cancer Cell 34, 9–20 (2018).

pubmed: 29731395 pmcid: 6039233 doi: 10.1016/j.ccell.2018.03.023

Du, Q. et al. The application and prospect of CDK4/6 inhibitors in malignant solid tumors. J. Hematol. Oncol. 13, 41 (2020).

pubmed: 32357912 pmcid: 7195725 doi: 10.1186/s13045-020-00880-8

Rose, T. L. et al. Phase II trial of palbociclib in patients with metastatic urothelial cancer after failure of first-line chemotherapy. Br. J. Cancer 119, 801–807 (2018).

pubmed: 30293995 pmcid: 6189143 doi: 10.1038/s41416-018-0229-0

Subramanian, A. et al. A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles. Cell 171, 1437–1452 e1417 (2017).

pubmed: 29195078 pmcid: 5990023 doi: 10.1016/j.cell.2017.10.049

Whittaker, S. R. et al. Molecular profiling and combinatorial activity of CCT068127: a potent CDK2 and CDK9 inhibitor. Mol. Oncol. 12, 287–304 (2018).

pubmed: 29063678 pmcid: 5830651 doi: 10.1002/1878-0261.12148

Yang, W. et al. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 41, D955–D961 (2013).

pubmed: 23180760 doi: 10.1093/nar/gks1111

Salviato, E., Djordjilovic, V., Chiogna, M. & Romualdi, C. simPATHy: a new method for simulating data from perturbed biological PATHways. Bioinformatics 33, 456–457 (2017).

pubmed: 28172414 doi: 10.1093/bioinformatics/btw642

Alhamdoosh, M. et al. Easy and efficient ensemble gene set testing with EGSEA. F1000Res 6, 2010 (2017).

pubmed: 29333246 pmcid: 5747338 doi: 10.12688/f1000research.12544.1

Schurch, N. J. et al. How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use? RNA 22, 839–851 (2016).

pubmed: 27022035 pmcid: 4878611 doi: 10.1261/rna.053959.115

Rubio, C. et al. CDK4/6 Inhibitor as a Novel Therapeutic Approach for Advanced Bladder Cancer Independently of RB1 Status. Clin. Cancer Res. 25, 390–402 (2019).

pubmed: 30242024 doi: 10.1158/1078-0432.CCR-18-0685

Long, Q. et al. Combination of cyclin-dependent kinase and immune checkpoint inhibitors for the treatment of bladder cancer. Cancer Immunol. Immunother. 69, 2305–2317 (2020).

pubmed: 32506263 pmcid: 7572640 doi: 10.1007/s00262-020-02609-5

Tong, Z. et al. Functional genomics identifies predictive markers and clinically actionable resistance mechanisms to CDK4/6 inhibition in bladder cancer. J. Exp. Clin. Cancer Res. 38, 322 (2019).

pubmed: 31331377 pmcid: 6647307 doi: 10.1186/s13046-019-1322-9

Pan, C. X. et al. Development and Characterization of Bladder Cancer Patient-Derived Xenografts for Molecularly Guided Targeted Therapy. PLoS One 10, e0134346 (2015).

pubmed: 26270481 pmcid: 4535951 doi: 10.1371/journal.pone.0134346

Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinforma. 12, 323 (2011).

doi: 10.1186/1471-2105-12-323

Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

pubmed: 20513432 pmcid: 2898526 doi: 10.1016/j.molcel.2010.05.004

Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

pubmed: 25516281 pmcid: 4302049 doi: 10.1186/s13059-014-0550-8

Muzellec, B., Telenczuk, M., Cabeli, V. & Andreux, M. PyDESeq2: a python package for bulk RNA-seq differential expression analysis. Bioinformatics 39, btad547 (2023).

pubmed: 37669147 pmcid: 10502239 doi: 10.1093/bioinformatics/btad547

Fang, Z., Liu, X. & Peltz, G. GSEApy: a comprehensive package for performing gene set enrichment analysis in Python. Bioinformatics 39, btac757 (2023).

pubmed: 36426870 doi: 10.1093/bioinformatics/btac757

Wang, L. et al. Unbiased discovery of cancer pathways and therapeutics using Pathway Ensemble Tool and Benchmark-Source data and code. Figshare https://doi.org/10.6084/m9.figshare.c.7252324 (2024).

doi: 10.6084/m9.figshare.c.7252324

Sher, S. et al. VIP152 is a selective CDK9 inhibitor with pre-clinical in vitro and in vivo efficacy in chronic lymphocytic leukemia. Leukemia 37, 326–338 (2023).

pubmed: 36376377 doi: 10.1038/s41375-022-01758-z