Multi-Omics Investigation of Innate Navitoclax Resistance in Triple-Negative Breast Cancer Cells
Abstract
:Simple Summary
Abstract
1. Introduction
2. Results
2.1. The Effect of Navitoclax Treatment on MDA-MB-231 Cell Growth and Concordance of Results in Replicate Measurements
2.2. Multi-Omics Effects of 72 h of Navitoclax Exposure
2.3. Multi-Omics Changes after 10 Days of Drug-Free Recovery
2.4. Single-Cell Assessment of Changes in Cell-Cycle States and Expression Changes of Known Navitoclax Response-Related Genes
2.5. Development of a Novel Navitoclax Resistance Gene Expression Signature from Single-Cell Analysis
2.6. Validation of the Navitoclax Resistance Signature In Vitro
2.7. Validation of the 18-Gene Resistance Signature in the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) Databases
2.8. Navitoclax Resistance Signature in the The Cancer Genome Atlas (TCGA) Human Samples
3. Discussion
4. Materials and Methods
4.1. Cell Culture and Treatment
4.2. qPCR Experiments
4.3. Cancer Cell Line Drug Response Database
4.4. Gene Expression Data of Human Breast Cancer Samples
4.5. Genome and Trascriptome Annotation
4.6. Single-Cell RNA Sequencing
4.7. Bulk RNA Sequencing
4.8. ATAC Sequencing
4.9. DNA Methylation
4.10. Copy Number Variants
4.11. Data Availability
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
- Chircop, M.; Speidel, D. Cellular stress responses in cancer and cancer therapy. Front. Oncol. 2014, 4, 304. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Niepel, M.; Hafner, M.; Duan, Q.; Wang, Z.; Paull, E.O.; Chung, M.; Lu, X.; Stuart, J.M.; Golub, T.R.; Subramanian, A.; et al. Common and cell-type specific responses to anti-cancer drugs revealed by high throughput transcript profiling. Nat. Commun. 2017, 8, 1186. [Google Scholar] [CrossRef]
- Debouck, C.; Goodfellow, P.N. DNA microarrays in drug discovery and development. Nat. Genet. 1999, 21, 48–50. [Google Scholar] [CrossRef] [PubMed]
- Garnett, M.J.; Edelman, E.J.; Heidorn, S.J.; Greenman, C.D.; Dastur, A.; Lau, K.W.; Greninger, P.; Thompson, I.R.; Luo, X.; Soares, J.; et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 2012, 483, 570–575. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ivanov, M.; Barragan, I.; Ingelman-Sundberg, M. Epigenetic mechanisms of importance for drug treatment. Trends Pharmacol. Sci. 2014, 35, 384–396. [Google Scholar] [CrossRef] [PubMed]
- Brown, R.; Curry, E.; Magnani, L.; Wilhelm-Benartzi, C.S.; Borley, J. Poised epigenetic states and acquired drug resistance in cancer. Nat. Rev. Cancer 2014, 14, 747–753. [Google Scholar] [CrossRef] [PubMed]
- Greaves, M.; Maley, C.C. Clonal evolution in cancer. Nature 2012, 481, 306–313. [Google Scholar] [CrossRef]
- Hanahan, D.; Weinberg, R.A. The hallmarks of cancer. Cell 2000, 100, 57–70. [Google Scholar] [CrossRef] [Green Version]
- Wilson, W.H.; O’Connor, O.A.; Czuczman, M.S.; LaCasce, A.S.; Gerecitano, J.F.; Leonard, J.P.; Tulpule, A.; Dunleavy, K.; Xiong, H.; Chiu, Y.-L.; et al. Navitoclax, a targeted high-affinity inhibitor of bcl-2, in lymphoid malignancies: A phase 1 dose-escalation study of safety, pharmacokinetics, pharmacodynamics, and antitumour activity. Lancet Oncol. 2010, 11, 1149–1159. [Google Scholar] [CrossRef] [Green Version]
- Tse, C.; Shoemaker, A.R.; Adickes, J.; Anderson, M.G.; Chen, J.; Jin, S.; Johnson, E.F.; Marsh, K.C.; Mitten, M.J.; Nimmer, P.; et al. Abt-263: A potent and orally bioavailable bcl-2 family inhibitor. Cancer Res. 2008, 68, 3421–3428. [Google Scholar] [CrossRef] [Green Version]
- Gandhi, L.; Camidge, D.R.; Ribeiro de Oliveira, M.; Bonomi, P.; Gandara, D.; Khaira, D.; Hann, C.L.; McKeegan, E.M.; Litvinovich, E.; Hemken, P.M.; et al. Phase i study of navitoclax (abt-263), a novel bcl-2 family inhibitor, in patients with small-cell lung cancer and other solid tumors. J. Clin. Oncol. 2011, 29, 909–916. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Rudin, C.M.; Hann, C.L.; Garon, E.B.; Ribeiro de Oliveira, M.; Bonomi, P.D.; Camidge, D.R.; Chu, Q.; Giaccone, G.; Khaira, D.; Ramalingam, S.S.; et al. Phase ii study of single-agent navitoclax (abt-263) and biomarker correlates in patients with relapsed small cell lung cancer. Clin. Cancer Res. Off. J. Am. Assoc. Cancer Res. 2012, 18, 3163–3169. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Cleary, J.M.; Lima, C.M.; Hurwitz, H.I.; Montero, A.J.; Franklin, C.; Yang, J.; Graham, A.; Busman, T.; Mabry, M.; Holen, K.; et al. A phase i clinical trial of navitoclax, a targeted high-affinity bcl-2 family inhibitor, in combination with gemcitabine in patients with solid tumors. Investig. New Drugs 2014, 32, 937–945. [Google Scholar] [CrossRef] [PubMed]
- Ju, W.; Zhang, M.; Wilson, K.M.; Petrus, M.N.; Bamford, R.N.; Zhang, X.; Guha, R.; Ferrer, M.; Thomas, C.J.; Waldmann, T.A. Augmented efficacy of brentuximab vedotin combined with ruxolitinib and/or navitoclax in a murine model of human hodgkin’s lymphoma. Proc. Natl. Acad. Sci. USA 2016, 113, 1624–1629. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Xu, H.; Sun, Y.; Huang, C.-P.; You, B.; Ye, D.; Chang, C. Preclinical study using abt263 to increase enzalutamide sensitivity to suppress prostate cancer progression via targeting bcl2/ros/usp26 axis through altering arv7 protein degradation. Cancers 2020, 12, 831. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zoeller, J.J.; Vagodny, A.; Taneja, K.; Tan, B.Y.; Brien, N.; Slamon, D.J.; Sampath, D.; Leverson, J.D.; Bronson, R.T.; Dillon, D.A.; et al. Neutralization of BCL-2/XL Enhances the Cytotoxicity of T-DM1 In Vivo. Mol. Cancer Ther. 2019, 18, 1115. [Google Scholar] [CrossRef] [Green Version]
- Jiang, T.; Shi, W.; Natowicz, R.; Ononye, S.N.; Wali, V.B.; Kluger, Y.; Pusztai, L.; Hatzis, C. Statistical measures of transcriptional diversity capture genomic heterogeneity of cancer. BMC Genomics 2014, 15, 876. [Google Scholar] [CrossRef] [Green Version]
- Diana, A.; Carlino, F.; Franzese, E.; Oikonomidou, O.; Criscitiello, C.; De Vita, F.; Ciardiello, F.; Orditura, M. Early triple negative breast cancer: Conventional treatment and emerging therapeutic landscapes. Cancers 2020, 12, 819. [Google Scholar] [CrossRef] [Green Version]
- Vagia, E.; Mahalingam, D.; Cristofanilli, M. The landscape of targeted therapies in tnbc. Cancers 2020, 12, 916. [Google Scholar] [CrossRef] [Green Version]
- Metzger-Filho, O.; Tutt, A.; de Azambuja, E.; Saini, K.S.; Viale, G.; Loi, S.; Bradbury, I.; Bliss, J.M.; Azim, H.A., Jr.; Ellis, P.; et al. Dissecting the heterogeneity of triple-negative breast cancer. J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol. 2012, 30, 1879–1887. [Google Scholar] [CrossRef] [Green Version]
- Wali, V.B.; Langdon, C.G.; Held, M.A.; Platt, J.T.; Patwardhan, G.A.; Safonov, A.; Aktas, B.; Pusztai, L.; Stern, D.F.; Hatzis, C. Systematic drug screening identifies tractable targeted combination therapies in triple-negative breast cancer. Cancer Res. 2017, 77, 566–578. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Patwardhan, G.A.; Wali, V.B.; Pusztai, L.; Hatzis, C. Abstract 2073: A systematic investigation of the effect of scheduling of targeted combination therapies on response and dynamics of relapse in triple negative breast cancer cells. Cancer Res. 2017, 77, 2073. [Google Scholar]
- Tominaga, H.; Kodama, S.; Matsuda, N.; Suzuki, K.; Watanabe, M. Involvement of reactive oxygen species (ros) in the induction of genetic instability by radiation. J. Radiat. Res. 2004, 45, 181–188. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Fuhrmann, D.C.; Brüne, B. Mitochondrial composition and function under the control of hypoxia. Redox Biol. 2017, 12, 208–215. [Google Scholar] [CrossRef] [PubMed]
- Kang, M.A.; So, E.Y.; Simons, A.L.; Spitz, D.R.; Ouchi, T. DNA damage induces reactive oxygen species generation through the h2ax-nox1/rac1 pathway. Cell Death Dis. 2012, 3, e249. [Google Scholar] [CrossRef] [PubMed]
- Sullivan, L.B.; Chandel, N.S. Mitochondrial reactive oxygen species and cancer. Cancer Metab. 2014, 2, 17. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Green, M.M.; Shekhar, T.M.; Hawkins, C.J. Data on the DNA damaging and mutagenic potential of the bh3-mimetics abt-263/navitoclax and tw-37. Data Brief. 2016, 6, 710–714. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Mehta, K.; Laimins, L. Human papillomaviruses preferentially recruit DNA repair factors to viral genomes for rapid repair and amplification. mBio 2018, 9, e00064-18. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zaal, E.A.; Berkers, C.R. The influence of metabolism on drug response in cancer. Front. Oncol. 2018, 8, 500. [Google Scholar] [CrossRef]
- Stincone, A.; Prigione, A.; Cramer, T.; Wamelink, M.M.; Campbell, K.; Cheung, E.; Olin-Sandoval, V.; Grüning, N.M.; Krüger, A.; Tauqeer Alam, M.; et al. The return of metabolism: Biochemistry and physiology of the pentose phosphate pathway. Biol. Rev. Camb. Philos. Soc. 2015, 90, 927–963. [Google Scholar] [CrossRef] [Green Version]
- Patra, K.C.; Hay, N. The pentose phosphate pathway and cancer. Trends Biochem. Sci. 2014, 39, 347–354. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Benito, A.; Polat, I.H.; Noé, V.; Ciudad, C.J.; Marin, S.; Cascante, M. Glucose-6-phosphate dehydrogenase and transketolase modulate breast cancer cell metabolic reprogramming and correlate with poor patient outcome. Oncotarget 2017, 8, 106693–106706. [Google Scholar] [CrossRef]
- Maddocks, O.D.; Labuschagne, C.F.; Adams, P.D.; Vousden, K.H. Serine metabolism supports the methionine cycle and DNA/rna methylation through de novo atp synthesis in cancer cells. Mol. Cell 2016, 61, 210–221. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Yang, M.; Vousden, K.H. Serine and one-carbon metabolism in cancer. Nat. Rev. Cancer 2016, 16, 650–662. [Google Scholar] [CrossRef] [PubMed]
- Maddocks, O.D.; Berkers, C.R.; Mason, S.M.; Zheng, L.; Blyth, K.; Gottlieb, E.; Vousden, K.H. Serine starvation induces stress and p53-dependent metabolic remodelling in cancer cells. Nature 2013, 493, 542–546. [Google Scholar] [CrossRef]
- Jia, D.; Lu, M.; Jung, K.H.; Park, J.H.; Yu, L.; Onuchic, J.N.; Kaipparettu, B.A.; Levine, H. Elucidating cancer metabolic plasticity by coupling gene regulation with metabolic pathways. Proc. Natl. Acad. Sci. USA 2019, 116, 3909–3918. [Google Scholar] [CrossRef] [Green Version]
- Stine, Z.E.; Walton, Z.E.; Altman, B.J.; Hsieh, A.L.; Dang, C.V. Myc, metabolism, and cancer. Cancer Discov. 2015, 5, 1024–1039. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sciacovelli, M.; Frezza, C. Metabolic reprogramming and epithelial-to-mesenchymal transition in cancer. FEBS J. 2017, 284, 3132–3144. [Google Scholar] [CrossRef]
- Klanova, M.; Klener, P. Bcl-2 proteins in pathogenesis and therapy of b-cell non-hodgkin lymphomas. Cancers 2020, 12, 938. [Google Scholar] [CrossRef] [Green Version]
- Doherty, M.R.; Smigiel, J.M.; Junk, D.J.; Jackson, M.W. Cancer stem cell plasticity drives therapeutic resistance. Cancers 2016, 8, 8. [Google Scholar] [CrossRef] [Green Version]
- Tsvetkov, P.; Sokol, E.; Jin, D.; Brune, Z.; Thiru, P.; Ghandi, M.; Garraway, L.A.; Gupta, P.B.; Santagata, S.; Whitesell, L.; et al. Suppression of 19s proteasome subunits marks emergence of an altered cell state in diverse cancers. Proc. Natl. Acad. Sci. USA 2017, 114, 382. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Tanaka, K. The proteasome: From basic mechanisms to emerging roles. Keio J. Med. 2013, 62, 1–12. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lander, G.C.; Estrin, E.; Matyskiela, M.E.; Bashore, C.; Nogales, E.; Martin, A. Complete subunit architecture of the proteasome regulatory particle. Nature 2012, 482, 186–191. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Fell, V.L.; Schild-Poulter, C. The ku heterodimer: Function in DNA repair and beyond. Mutat. Res. Rev. Mutat. Res. 2015, 763, 15–29. [Google Scholar] [CrossRef] [PubMed]
- Iorio, F.; Knijnenburg, T.A.; Vis, D.J.; Bignell, G.R.; Menden, M.P.; Schubert, M.; Aben, N.; Gonçalves, E.; Barthorpe, S.; Lightfoot, H.; et al. A landscape of pharmacogenomic interactions in cancer. Cell 2016, 166, 740–754. [Google Scholar] [CrossRef] [Green Version]
- The Cancer Genome Atlas Network; Koboldt, D.C.; Fulton, R.S.; McLellan, M.D.; Schmidt, H.; Kalicki-Veizer, J.; McMichael, J.F.; Fulton, L.L.; Dooling, D.J.; Ding, L.; et al. Comprehensive molecular portraits of human breast tumours. Nature 2012, 490, 61. [Google Scholar]
- Marczyk, M.; Jaksik, R.; Polanski, A.; Polanska, J. Gamred—Adaptive filtering of high-throughput biological data. IEEE/ACM Trans. Comput. Biol. Bioinform. 2020, 17, 149–157. [Google Scholar] [CrossRef]
- Zheng, G.X.Y.; Terry, J.M.; Belgrader, P.; Ryvkin, P.; Bent, Z.W.; Wilson, R.; Ziraldo, S.B.; Wheeler, T.D.; McDermott, G.P.; Zhu, J.; et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 2017, 8, 14049. [Google Scholar] [CrossRef] [Green Version]
- Maaten, L.v.d.; Hinton, G. Visualizing data using t-sne. J. Mach. Learn. Res. 2008, 9, 2579–2605. [Google Scholar]
- Drost, H. Philentropy: Information theory and distance quantification with r. J. Open Source Softw. 2018, 3. [Google Scholar] [CrossRef]
- Wolf, F.A.; Hamey, F.K.; Plass, M.; Solana, J.; Dahlin, J.S.; Göttgens, B.; Rajewsky, N.; Simon, L.; Theis, F.J. Paga: Graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biol. 2019, 20, 59. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Whitfield, M.L.; Sherlock, G.; Saldanha, A.J.; Murray, J.I.; Ball, C.A.; Alexander, K.E.; Matese, J.C.; Perou, C.M.; Hurt, M.M.; Brown, P.O.; et al. Identification of genes periodically expressed in the human cell cycle and their expression in tumors. Mol. Biol. Cell 2002, 13, 1977–2000. [Google Scholar] [CrossRef] [PubMed]
- Finak, G.; McDavid, A.; Yajima, M.; Deng, J.; Gersuk, V.; Shalek, A.K.; Slichter, C.K.; Miller, H.W.; McElrath, M.J.; Prlic, M.; et al. Mast: A flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell rna sequencing data. Genome Biol. 2015, 16, 278. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Andrews, S. Fastqc: A Quality Control Tool for High Throughput Sequence Data. Available online: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (accessed on 11 February 2018).
- Bolger, A.M.; Lohse, M.; Usadel, B. Trimmomatic: A flexible trimmer for illumina sequence data. Bioinformatics 2014, 30, 2114–2120. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Dobin, A.; Davis, C.A.; Schlesinger, F.; Drenkow, J.; Zaleski, C.; Jha, S.; Batut, P.; Chaisson, M.; Gingeras, T.R. Star: Ultrafast universal rna-seq aligner. Bioinformatics 2013, 29, 15–21. [Google Scholar] [CrossRef]
- Li, B.; Dewey, C.N. Rsem: Accurate transcript quantification from rna-seq data with or without a reference genome. BMC Bioinform. 2011, 12, 323. [Google Scholar] [CrossRef] [Green Version]
- Love, M.I.; Huber, W.; Anders, S. Moderated estimation of fold change and dispersion for rna-seq data with deseq2. Genome Biol. 2014, 15, 550. [Google Scholar] [CrossRef] [Green Version]
- Liberzon, A.; Birger, C.; Thorvaldsdottir, H.; Ghandi, M.; Mesirov, J.P.; Tamayo, P. The molecular signatures database (msigdb) hallmark gene set collection. Cell Syst. 2015, 1, 417–425. [Google Scholar] [CrossRef] [Green Version]
- Korotkevich, G.; Sukhov, V.; Sergushichev, A. Fast gene set enrichment analysis. BioRxiv 2019, 060012. [Google Scholar] [CrossRef] [Green Version]
- Zyla, J.; Marczyk, M.; Weiner, J.; Polanska, J. Ranking metrics in gene set enrichment analysis: Do they matter? BMC Bioinform. 2017, 18, 256. [Google Scholar] [CrossRef]
- Buenrostro, J.D.; Wu, B.; Chang, H.Y.; Greenleaf, W.J. Atac-seq: A method for assaying chromatin accessibility genome-wide. Curr. Protoc. Mol. Biol. 2015, 109, 21.29.21–21.29.29. [Google Scholar] [CrossRef] [PubMed]
- Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011, 17, 3. [Google Scholar] [CrossRef]
- Langmead, B.; Salzberg, S.L. Fast gapped-read alignment with bowtie 2. Nat. Methods 2012, 9, 357–359. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- BroadInstitute. Picard Tools. Available online: http://broadinstitute.github.io/picard/ (accessed on 11 February 2018).
- Li, H.; Handsaker, B.; Wysoker, A.; Fennell, T.; Ruan, J.; Homer, N.; Marth, G.; Abecasis, G.; Durbin, R. The sequence alignment/map format and samtools. Bioinformatics 2009, 25, 2078–2079. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zhang, Y.; Liu, T.; Meyer, C.A.; Eeckhoute, J.; Johnson, D.S.; Bernstein, B.E.; Nusbaum, C.; Myers, R.M.; Brown, M.; Li, W.; et al. Model-based analysis of chip-seq (macs). Genome Biol. 2008, 9, R137. [Google Scholar] [CrossRef] [Green Version]
- Quinlan, A.R.; Hall, I.M. Bedtools: A flexible suite of utilities for comparing genomic features. Bioinformatics 2010, 26, 841–842. [Google Scholar] [CrossRef] [Green Version]
- Cheng, H.; Xu, Y. Bitmapperbs: A fast and accurate read aligner for whole-genome bisulfite sequencing. BioRxiv 2018, 442798. [Google Scholar] [CrossRef] [Green Version]
- Akalin, A.; Franke, V.; Vlahoviček, K.; Mason, C.E.; Schübeler, D. Genomation: A toolkit to summarize, annotate and visualize genomic intervals. Bioinformatics 2014, 31, 1127–1129. [Google Scholar] [CrossRef]
- Wu, H.; Wang, C.; Wu, Z. A new shrinkage estimator for dispersion improves differential expression detection in rna-seq data. Biostatistics 2012, 14, 232–243. [Google Scholar] [CrossRef] [Green Version]
- Li, H.; Durbin, R. Fast and accurate short read alignment with burrows-wheeler transform. Bioinformatics 2009, 25, 1754–1760. [Google Scholar] [CrossRef] [Green Version]
- Abyzov, A.; Urban, A.E.; Snyder, M.; Gerstein, M. Cnvnator: An approach to discover, genotype, and characterize typical and atypical cnvs from family and population genome sequencing. Genome Res. 2011, 21, 974–984. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Cui, Y.; Chen, X.; Luo, H.; Fan, Z.; Luo, J.; He, S.; Yue, H.; Zhang, P.; Chen, R. Biocircos.Js: An interactive circos javascript library for biological data visualization on web applications. Bioinformatics 2016, 32, 1740–1742. [Google Scholar] [CrossRef] [PubMed] [Green Version]
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Marczyk, M.; Patwardhan, G.A.; Zhao, J.; Qu, R.; Li, X.; Wali, V.B.; Gupta, A.K.; Pillai, M.M.; Kluger, Y.; Yan, Q.; et al. Multi-Omics Investigation of Innate Navitoclax Resistance in Triple-Negative Breast Cancer Cells. Cancers 2020, 12, 2551. https://doi.org/10.3390/cancers12092551
Marczyk M, Patwardhan GA, Zhao J, Qu R, Li X, Wali VB, Gupta AK, Pillai MM, Kluger Y, Yan Q, et al. Multi-Omics Investigation of Innate Navitoclax Resistance in Triple-Negative Breast Cancer Cells. Cancers. 2020; 12(9):2551. https://doi.org/10.3390/cancers12092551
Chicago/Turabian StyleMarczyk, Michal, Gauri A. Patwardhan, Jun Zhao, Rihao Qu, Xiaotong Li, Vikram B. Wali, Abhishek K. Gupta, Manoj M. Pillai, Yuval Kluger, Qin Yan, and et al. 2020. "Multi-Omics Investigation of Innate Navitoclax Resistance in Triple-Negative Breast Cancer Cells" Cancers 12, no. 9: 2551. https://doi.org/10.3390/cancers12092551