The response rate of most anti-cancer drugs is limited because of the high heterogeneity of cancer and the complex mechanism of drug action. Personalized treatment that stratifies patients into subgroups using molecular biomarkers is promising to improve clinical benefit. With the accumulation of preclinical models and advances in computational approaches of drug response prediction, pharmacogenomics has made great success over the last 20 years and is increasingly used in the clinical practice of personalized cancer medicine. In this article, we first summarize FDA-approved pharmacogenomic biomarkers and large-scale pharmacogenomic studies of preclinical cancer models such as patient-derived cell lines, organoids, and xenografts. Furthermore, we comprehensively review the recent developments of computational methods in drug response prediction, covering network, machine learning, and deep learning technologies and strategies to evaluate immunotherapy response. In the end, we discuss challenges and propose possible solutions for further improvement.
|
Anchang, B., Davis, K.L., Fienberg, H.G., Williamson, B.D., Bendall, S.C., Karacosta, L.G., Tibshirani, R., Nolan, G.P., Plevritis, S.K., 2018. DRUG-NEM:optimizing drug combinations using single-cell perturbation response to account for intratumoral heterogeneity. Proc. Natl. Acad. Sci. U. S. A. 115, E4294-E4303.
|
|
Auslander, N., Zhang, G., Lee, J.S., Frederick, D.T., Miao, B., Moll, T., Tian, T., Wei, Z., Madan, S., Sullivan, R.J., et al., 2018. Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma. Nat. Med. 24, 1545-1549.
|
|
Ayers, M., Lunceford, J., Nebozhyn, M., Murphy, E., Loboda, A., Kaufman, D.R., Albright, A., Cheng, J.D., Kang, S.P., Shankaran, V., et al., 2017. IFN-gammarelated mRNA profile predicts clinical response to PD-1 blockade. J. Clin. Invest. 127, 2930-2940.
|
|
Baptista, D., Ferreira, P.G., Rocha, M., 2021. Deep learning for drug response prediction in cancer. Briefings Bioinf. 22, 360-379.
|
|
Basu, A., Bodycombe, N.E., Cheah, J.H., Price, E.V., Liu, K., Schaefer, G.I., Ebright, R.Y., Stewart, M.L., Ito, D., Wang, S., et al., 2013. An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules. Cell 154, 1151-1161.
|
|
Bock, C., Boutros, M., Camp, J.G., Clarke, L., Clevers, H., Knoblich, J.A., Liberali, P., Regev, A., Rios, A.C., Stegle, O., et al., 2021. The organoid cell atlas. Nat. Biotechnol. 39, 13-17.
|
|
Bouhaddou, M., DiStefano, M.S., Riesel, E.A., Carrasco, E., Holzapfel, H.Y., Jones, D.C., Smith, G.R., Stern, A.D., Somani, S.S., Thompson, T.V., et al., 2016. Drug response consistency in CCLE and CGP. Nature 540, E9-E10.
|
|
Boyiadzis, M.M., Kirkwood, J.M., Marshall, J.L., Pritchard, C.C., Azad, N.S., Gulley, J.L., 2018. Significance and implications of S approval of pembrolizumab for biomarker-defined disease. J. Immunother. Cancer 6, 35.
|
|
Brady, S.W., McQuerry, J.A., Qiao, Y., Piccolo, S.R., Shrestha, G., Jenkins, D.F., Layer, R.M., Pedersen, B.S., Miller, R.H., Esch, A., et al., 2017. Combating subclonal evolution of resistant cancer phenotypes. Nat. Commun. 8, 1231.
|
|
Bruni, D., Angell, H.K., Galon, J., 2020. The immune contexture and immunoscore in cancer prognosis and therapeutic efficacy. Nat. Rev. Cancer 20, 662-680.
|
|
Cadow, J., Born, J., Manica, M., Oskooei, A., Rodriguez Martinez, M., 2020. PaccMann:a web service for interpretable anticancer compound sensitivity prediction. Nucleic Acids Res. 48, W502-W508.
|
|
Caroli, J., Dori, M., Bicciato, S., 2020. Computational methods for the integrative analysis of genomics and pharmacological data. Front. Oncol. 10, 185.
|
|
Cerami, E., Gao, J., Dogrusoz, U., Gross, B.E., Sumer, S.O., Aksoy, B.A., Jacobsen, A., Byrne, C.J., Heuer, M.L., Larsson, E., et al., 2012. The cBio cancer genomics portal:an open platform for exploring multidimensional cancer genomics data. Canc. Discov. 2, 401-404.
|
|
Chang, Y., Park, H., Yang, H.J., Lee, S., Lee, K.Y., Kim, T.S., Jung, J., Shin, J.M., 2018. Cancer drug response profile scan (CDRscan):a deep learning model that predicts drug effectiveness from cancer genomic signature. Sci. Rep. 8, 8857.
|
|
Charoentong, P., Finotello, F., Angelova, M., Mayer, C., Efremova, M., Rieder, D., Hackl, H., Trajanoski, Z., 2017. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep. 18, 248-262.
|
|
Chen, J., Zhang, L., 2021. A survey and systematic assessment of computational methods for drug response prediction. Briefings Bioinf. 22, 232-246.
|
|
Chen, P.L., Roh, W., Reuben, A., Cooper, Z.A., Spencer, C.N., Prieto, P.A., Miller, J.P., Bassett,R.L.,Gopalakrishnan,V.,Wani,K.,etal.,2016.Analysisofimmunesignatures in longitudinal tumor samples yields insight into biomarkers of response and mechanisms of resistance to immune checkpoint blockade. Canc. Discov. 6, 827-837.
|
|
Chen, E.Y., Raghunathan, V., Prasad, V., 2019. An overview of cancer drugs approved by the US Food and Drug Administration based on the surrogate end point of response rate. JAMA Intern. Med. 179, 915-921.
|
|
Chiu, Y.C., Chen, H.H., Gorthi, A., Mostavi, M., Zheng, S., Huang, Y., Chen, Y., 2020. Deep learning of pharmacogenomics resources:moving towards precision oncology. Briefings Bioinf. 21, 2066-2083.
|
|
Cichonska, A., Pahikkala, T., Szedmak, S., Julkunen, H., Airola, A., Heinonen, M., Aittokallio, T., Rousu, J., 2018. Learning with multiple pairwise kernels for drug bioactivity prediction. Bioinformatics 34, i509-i518.
|
|
Conte, N., Mason, J.C., Halmagyi, C., Neuhauser, S., Mosaku, A., Yordanova, G., Chatzipli, A., Begley, D.A., Krupke, D.M., Parkinson, H., et al., 2019. PDX Finder:a portal for patient-derived tumor xenograft model discovery. Nucleic Acids Res. 47, D1073-D1079.
|
|
Costello, J.C., Heiser, L.M., Georgii, E., Gonen, M., Menden, M.P., Wang, N.J., Bansal, M., Ammad-ud-din, M., Hintsanen, P., Khan, S.A., et al., 2014. A community effort to assess and improve drug sensitivity prediction algorithms. Nat. Biotechnol. 32, 1202-1212.
|
|
Crown Bioscience Inc, 2021. Oncology Databases. https://www.crownbio.com/oncology/oncology-databases.
|
|
Daemen, A., Griffith, O.L., Heiser, L.M., Wang, N.J., Enache, O.M., Sanborn, Z., Pepin, F., Durinck, S., Korkola, J.E., Griffith, M., et al., 2013. Modeling precision treatment of breast cancer. Genome Biol. 14, R110.
|
|
Damotte, D., Warren, S., Arrondeau, J., Boudou-Rouquette, P., Mansuet-Lupo, A., Biton, J., Ouakrim, H., Alifano, M., Gervais, C., Bellesoeur, A., et al., 2019. The tumor inflammation signature (TIS) is associated with anti-PD-1 treatment benefit in the CERTIM pan-cancer cohort. J. Transl. Med. 17, 357.
|
|
Ding, Z., Zu, S., Gu, J., 2016. Evaluating the molecule-based prediction of clinical drug responses in cancer. Bioinformatics 32, 2891-2895.
|
|
Emad, A., Cairns, J., Kalari, K.R., Wang, L., Sinha, S., 2017. Knowledge-guided gene prioritization reveals new insights into the mechanisms of chemoresistance. Genome Biol. 18, 153.
|
|
Emdadi, A., Eslahchi, C., 2020. DSPLMF:a method for cancer drug sensitivity prediction using a novel regularization approach in logistic matrix factorization. Front. Genet. 11, 75.
|
|
Espin-Perez, A., Portier, C., Chadeau-Hyam, M., van Veldhoven, K., Kleinjans, J.C.S., de Kok, T., 2018. Comparison of statistical methods and the use of quality control samples for batch effect correction in human transcriptome data. PLoS One 13,e0202947.
|
|
Evrard, Y.A., Ahalt, M.M.G., Alcoser, S.Y., Arthur, K., Baldwin, M., Blumenauer, L.L., Bonomi, C., Borgel, S., Bradtke, E., Camalier, C., et al., 2018. Abstract 986:the national cancer institute's patient-derived models repository (PDMR). Cancer Res. 78, 986-986.
|
|
Fang, Y., Xu, P., Yang, J., Qin, Y., 2018. A quantile regression forest based method to predict drug response and assess prediction reliability. PLoS One 13, e0205155.
|
|
Farkkila, A., Gulhan, D.C., Casado, J., Jacobson, C.A., Nguyen, H., Kochupurakkal, B., Maliga, Z., Yapp, C., Chen, Y.A., Schapiro, D., et al., 2020.
|
|
Immunogenomic profiling determines responses to combined PARP and PD-1 inhibition in ovarian cancer. Nat. Commun. 11, 1459.
|
|
Feins, S., Kong, W., Williams, E.F., Milone, M.C., Fraietta, J.A., 2019. An introduction to chimeric antigen receptor (CAR) T-cell immunotherapy for human cancer. Am. J. Hematol. 94, S3-S9.
|
|
Friedman, J., Hastie, T., Tibshirani, R., 2010. Regularization paths for generalized linear models via coordinate descent. J. Stat. Software 33, 1-22.
|
|
Gao, H., Korn, J.M., Ferretti, S., Monahan, J.E., Wang, Y., Singh, M., Zhang, C., Schnell, C., Yang, G., Zhang, Y., et al., 2015. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nat. Med. 21, 1318-1325.
|
|
Geeleher, P., Cox, N.J., Huang, R.S., 2014. Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines. Genome Biol. 15, R47.
|
|
Ghandi,M.,Huang, F.W.,Jane-Valbuena,J., Kryukov,G.V.,Lo, C.C.,McDonald 3rd,E.R., Barretina, J., Gelfand, E.T., Bielski, C.M., Li, H., et al., 2019. Next-generation characterization of the cancer cell line encyclopedia. Nature 569, 503-508.
|
|
Goto, T., 2020. Patient-derived tumor xenograft models:toward the establishment of precision cancer medicine. J. Personalized Med. 10, 64.
|
|
Greshock, J., Bachman, K.E., Degenhardt, Y.Y., Jing, J., Wen, Y.H., Eastman, S., McNeil, E., Moy, C., Wegrzyn, R., Auger, K., et al., 2010. Molecular target class is predictive of in vitro response profile. Cancer Res. 70, 3677-3686.
|
|
Han, S.W., Kim, T.Y., Hwang, P.G., Jeong, S., Kim, J., Choi, I.S., Oh, D.Y., Kim, J.H., Kim, D.W., Chung, D.H., et al., 2005. Predictive and prognostic impact of epidermal growth factor receptor mutation in non-small-cell lung cancer patients treated with gefitinib. J. Clin. Oncol. 23, 2493-2501.
|
|
Haverty, P.M., Lin, E., Tan, J., Yu, Y., Lam, B., Lianoglou, S., Neve, R.M., Martin, S., Settleman, J., Yauch, R.L., et al., 2016. Reproducible pharmacogenomic profiling of cancer cell line panels. Nature 533, 333-337.
|
|
He, S., Hu, B., Li, C., Lin, P., Tang, W.G., Sun, Y.F., Feng, F.Y., Guo, W., Li, J., Xu, Y., et al., 2018. Pdxliver:a database of liver cancer patient derived xenograft mouse models. BMC Canc. 18, 550.
|
|
Heiser, L.M., Sadanandam, A., Kuo, W.L., Benz, S.C., Goldstein, T.C., Ng, S., Gibb, W.J., Wang, N.J., Ziyad, S., Tong, F., et al., 2012. Subtype and pathway specific responses to anticancer compounds in breast cancer. Proc. Natl. Acad. Sci. U. S. A. 109, 2724-2729.
|
|
Hu, B., Li, H., Guo, W., Sun, Y.F., Zhang, X., Tang, W.G., Yang, L.X., Xu, Y., Tang, X.Y., Ding, G.H., et al., 2020. Establishment of a hepatocellular carcinoma patient-derived xenograft platform and its application in biomarker identification. Int. J. Canc. 146, 1606-1617.
|
|
Huang, C., Mezencev, R., McDonald, J.F., Vannberg, F., 2017. Open source machinelearning algorithms for the prediction of optimal cancer drug therapies. PLoS One 12, e0186906.
|
|
Huang, E.W., Bhope, A., Lim, J., Sinha, S., Emad, A., 2020. Tissue-guided LASSO for prediction of clinical drug response using preclinical samples. PLoS Comput. Biol. 16, e1007607.
|
|
Ibarrola-Villava, M., Cervantes, A., Bardelli, A., 2018. Preclinical models for precision oncology. Biochim. Biophys. Acta Rev. Canc 1870, 239-246.
|
|
Izumchenko, E., Paz, K., Ciznadija, D., Sloma, I., Katz, A., Vasquez-Dunddel, D., BenZvi, I., Stebbing, J., McGuire, W., Harris, W., et al., 2017. Patient-derived xenografts effectively capture responses to oncology therapy in a heterogeneous cohort of patients with solid tumors. Ann. Oncol. 28, 2595-2605.
|
|
Jafarzadeh, L., Khakpoor-Koosheh, M., Mirzaei, H., Mirzaei, H.R., 2021. Biomarkers for predicting the outcome of various cancer immunotherapies. Crit. Rev. Oncol. Hematol. 157, 103161.
|
|
Jerby-Arnon, L., Shah, P., Cuoco, M.S., Rodman, C., Su, M.J., Melms, J.C., Leeson, R., Kanodia, A., Mei, S., Lin, J.R., et al., 2018. A cancer cell program promotes T cell exclusion and resistance to checkpoint blockade. Cell 175, 984-997. e24.
|
|
Jiang, P., Gu, S., Pan, D., Fu, J., Sahu, A., Hu, X., Li, Z., Traugh, N., Bu, X., Li, B., et al., 2018. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat. Med. 24, 1550-1558.
|
|
Karamboulas, C., Bruce, J.P., Hope, A.J., Meens, J., Huang, S.H., Erdmann, N., Hyatt, E., Pereira, K., Goldstein, D.P., Weinreb, I., et al., 2018. Patient-derived xenografts for prognostication and personalized treatment for head and neck squamous cell carcinoma. Cell Rep. 25, 1318-1331 e1314.
|
|
Kim, Y.A., Sarto Basso, R., Wojtowicz, D., Liu, A.S., Hochbaum, D.S., Vandin, F., Przytycka, T.M., 2020. Identifying drug sensitivity subnetworks with NETPHIX. iScience 23, 101619.
|
|
Kim, Y., Zheng, S., Tang, J., Jim Zheng, W., Li, Z., Jiang, X., 2021. Anticancer drug synergy prediction in understudied tissues using transfer learning. J. Am. Med. Inf. Assoc. 28, 42-51.
|
|
Koleti, A., Terryn, R., Stathias, V., Chung, C., Cooper, D.J., Turner, J.P., Vidovic, D., Forlin, M., Kelley, T.T., D'Urso, A., et al., 2018. Data portal for the library of integrated network-based cellular signatures (LINCS) program:integrated access to diverse large-scale cellular perturbation response data. Nucleic Acids Res. 46, D558-D566.
|
|
Kondo, J., Ekawa, T., Endo, H., Yamazaki, K., Tanaka, N., Kukita, Y., Okuyama, H., Okami, J., Imamura, F., Ohue, M., et al., 2019. High-throughput screening in colorectal cancer tissue-originated spheroids. Canc. Sci. 110, 345-355.
|
|
Kong, J., Lee, H., Kim, D., Han, S.K., Ha, D., Shin, K., Kim, S., 2020. Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients. Nat. Commun. 11, 5485.
|
|
Kuenzi, B.M., Park, J., Fong, S.H., Sanchez, K.S., Lee, J., Kreisberg, J.F., Ma, J., Ideker, T., 2020. Predicting drug response and synergy using a deep learning model of human cancer cells. Canc. Cell 38, 672-684. e6.
|
|
Kurilov, R., Haibe-Kains, B., Brors, B., 2020. Assessment of modelling strategies for drug response prediction in cell lines and xenografts. Sci. Rep. 10, 2849.
|
|
Lamb, J., Crawford, E.D., Peck, D., Modell, J.W., Blat, I.C., Wrobel, M.J., Lerner, J., Brunet, J.P., Subramanian, A., Ross, K.N., et al., 2006. The Connectivity Map:using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929-1935.
|
|
Le, D.H., Pham, V.H., 2018. Drug response prediction by globally capturing drug and cell line information in a heterogeneous network. J. Mol. Biol. 430, 2993-3004.
|
|
Liu, P., Li, H., Li, S., Leung, K.S., 2019a. Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network. BMC Bioinf. 20, 408.
|
|
Liu, Q., Muglia, L.J., Huang, L.F., 2019b. Network as a biomarker:a novel networkbased sparse bayesian machine for pathway-driven drug response prediction. Genes (Basel) 10, 602.
|
|
Liu, C., Qin, T., Huang, Y., Li, Y., Chen, G., Sun, C., 2020a. Drug screening model meets cancer organoid technology. Transl. Oncol. 13, 100840.
|
|
Liu, C., Wei, D., Xiang, J., Ren, F., Huang, L., Lang, J., Tian, G., Li, Y., Yang, J., 2020b. An improved anticancer drug-response prediction based on an ensemble method integrating matrix completion and ridge regression. Mol. Ther. Nucleic Acids 21, 676-686.
|
|
Maynard, A., McCoach, C.E., Rotow, J.K., Harris, L., Haderk, F., Kerr, D.L., Yu, E.A., Schenk, E.L., Tan, W., Zee, A., et al., 2020. Therapy-induced evolution of human lung cancer revealed by Single-Cell RNA sequencing. Cell 182, 1232-1251. e22.
|
|
Mpindi, J.P., Yadav, B., Ostling, P., Gautam, P., Malani, D., Murumagi, A., Hirasawa, A., Kangaspeska, S., Wennerberg, K., Kallioniemi, O., et al., 2016. Consistency in drug response profiling. Nature 540, E5-E6.
|
|
Olson, B., Li, Y., Lin, Y., Liu, E.T., Patnaik, A., 2018. Mouse models for cancer immunotherapy research. Canc. Discov. 8, 1358-1365.
|
|
Ooft, S.N., Weeber, F., Dijkstra, K.K., McLean, C.M., Kaing, S., van Werkhoven, E., Schipper, L., Hoes, L., Vis, D.J., van de Haar, J., et al., 2019. Patient-derived organoids can predict response to chemotherapy in metastatic colorectal cancer patients. Sci. Transl. Med. 11, eaay2574.
|
|
Park, J.J.H., Hsu, G., Siden, E.G., Thorlund, K., Mills, E.J., 2020. An overview of precision oncology basket and umbrella trials for clinicians. CA. Cancer J. Clin. 70, 125-137.
|
|
Pauli, C., Hopkins, B.D., Prandi, D., Shaw, R., Fedrizzi, T., Sboner, A., Sailer, V., Augello, M., Puca, L., Rosati, R., et al., 2017. Personalized in vitro and in vivo cancer models to guide precision medicine. Canc. Discov. 7, 462-477.
|
|
Petitprez, F., Meylan, M., de Reynies, A., Sautes-Fridman, C., Fridman, W.H., 2020. The tumor microenvironment in the response to immune checkpoint blockade therapies. Front. Immunol. 11, 784.
|
|
Polley, E., Kunkel, M., Evans, D., Silvers, T., Delosh, R., Laudeman, J., Ogle, C., Reinhart, R., Selby, M., Connelly, J., et al., 2016. Small cell lung cancer screen of oncology drugs, investigational agents, and gene and microrna expression. J. Natl. Cancer Inst. 108, djw122.
|
|
Qiu, Z., Li, H., Zhang, Z., Zhu, Z., He, S., Wang, X., Wang, P., Qin, J., Zhuang, L., Wang, W., et al., 2019. A pharmacogenomic landscape in human liver cancers. Canc. Cell 36, 179-193 e111.
|
|
Rahman, R., Dhruba, S.R., Matlock, K., De-Niz, C., Ghosh, S., Pal, R., 2019. Evaluating the consistency of large-scale pharmacogenomic studies. Briefings Bioinf. 20, 1734-1753.
|
|
Roider, T., Seufert, J., Uvarovskii, A., Frauhammer, F., Bordas, M., Abedpour, N., Stolarczyk, M., Mallm, J.P., Herbst, S.A., Bruch, P.M., et al., 2020. Dissecting intratumour heterogeneity of nodal B-cell lymphomas at the transcriptional, genetic and drug-response levels. Nat. Cell Biol. 22, 896-906.
|
|
Sachs, N., de Ligt, J., Kopper, O., Gogola, E., Bounova, G., Weeber, F., Balgobind, A.V., Wind, K., Gracanin, A., Begthel, H., et al., 2018. A living biobank of breast cancer organoids captures disease heterogeneity. Cell 172, 373-386. e10.
|
|
Sakellaropoulos, T., Vougas, K., Narang, S., Koinis, F., Kotsinas, A., Polyzos, A., Moss, T.J., Piha-Paul, S., Zhou, H., Kardala, E., et al., 2019. A deep learning framework for predicting response to therapy in cancer. Cell Rep. 29, 3367-3373. e64.
|
|
Shanks, N., Greek, R., Greek, J., 2009. Are animal models predictive for humans? Philos. Ethics Humanit. Med. 4, 2.
|
|
Sharifi-Noghabi, H., Zolotareva, O., Collins, C.C., Ester, M., 2019. MOLI:multi-omics late integration with deep neural networks for drug response prediction. Bioinformatics 35, i501-i509.
|
|
Sharma, P., Wagner, K., Wolchok, J.D., Allison, J.P., 2011. Novel cancer immunotherapy agents with survival benefit:recent successes and next steps. Nat. Rev. Cancer 11, 805-812.
|
|
Sharpless, N.E., Depinho, R.A., 2006. The mighty mouse:genetically engineered mouse models in cancer drug development. Nat. Rev. Drug Discov. 5, 741-754.
|
|
Shoemaker, R.H., 2006. The NCI60 human tumour cell line anticancer drug screen. Nat. Rev. Cancer 6, 813-823.
|
|
Stanfield, Z., Coskun, M., Koyuturk, M., 2017. Drug response prediction as a link prediction problem. Sci. Rep. 7, 40321.
|
|
Su, R., Liu, X., Wei, L., Zou, Q., 2019. Deep-Resp-Forest:a deep forest model to predict anti-cancer drug response. Methods 166, 91-102.
|
|
Su, Y., Wei, W., Robert, L., Xue, M., Tsoi, J., Garcia-Diaz, A., Homet Moreno, B., Kim, J., Ng, R.H., Lee, J.W., et al., 2017. Single-cell analysis resolves the cell state transition and signaling dynamics associated with melanoma drug-induced resistance. Proc. Natl. Acad. Sci. U. S. A. 114, 13679-13684.
|
|
Teicher, B.A., Polley, E., Kunkel, M., Evans, D., Silvers, T., Delosh, R., Laudeman, J., Ogle, C., Reinhart, R., Selby, M., et al., 2015. Sarcoma cell line screen of oncology drugs and investigational agents identifies patterns associated with gene and microRNA expression. Mol. Canc. Therapeut. 14, 2452-2462.
|
|
U.S. Food and Drug Administration, 2020. Table of pharmacogenomic biomarkers in drug labeling with labeling text. https://www.fda.gov/drugs/science-andresearch-drugs/table-pharmacogenomic-biomarkers-drug-labeling.
|
|
van de Wetering, M., Francies, H.E., Francis, J.M., Bounova, G., Iorio, F., Pronk, A., van Houdt, W., van Gorp, J., Taylor-Weiner, A., Kester, L., et al., 2015. Prospective derivation of a living organoid biobank of colorectal cancer patients. Cell 161, 933-945.
|
|
Vidyasagar, M., 2015. Identifying predictive features in drug response using machine learning:opportunities and challenges. Annu. Rev. Pharmacol. Toxicol. 55, 15-34.
|
|
Wang, H., Li, S., Wang, Q., Jin, Z., Shao, W., Gao, Y., Li, L., Lin, K., Zhu, L., Wang, H., et al., 2021. Tumor immunological phenotype signature-based high-throughput screening for the discovery of combination immunotherapy compounds. Sci. Adv. 7, eabd7851.
|
|
Wang, L., Li, X., Zhang, L., Gao, Q., 2017. Improved anticancer drug response prediction in cell lines using matrix factorization with similarity regularization. BMC Canc. 17, 513.
|
|
Weeber, F., Ooft, S.N., Dijkstra, K.K., Voest, E.E., 2017. Tumor organoids as a preclinical cancer model for drug discovery. Cell Chem. Biol. 24, 1092-1100.
|
|
Wishart, D.S., Feunang, Y.D., Guo, A.C., Lo, E.J., Marcu, A., Grant, J.R., Sajed, T., Johnson, D., Li, C., Sayeeda, Z., et al., 2018. Drugbank 5.0:a major update to the drugbank database for 2018. Nucleic Acids Res. 46, D1074-D1082.
|
|
Woo, X.Y., Srivastava, A., Graber, J.H., Yadav, V., Sarsani, V.K., Simons, A., Beane, G., Grubb, S., Ananda, G., Liu, R., et al., 2019. Genomic data analysis workflows for tumors from patient-derived xenografts (PDXs):challenges and guidelines. BMC Med. Genom. 12, 92.
|
|
Yang, W., Soares, J., Greninger, P., Edelman, E.J., Lightfoot, H., Forbes, S., Bindal, N., Beare, D., Smith, J.A., Thompson, I.R., et al., 2013. Genomics of drug sensitivity in cancer (GDSC):a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 41, D955-D961.
|
|
Yuki, K., Cheng, N., Nakano, M., Kuo, C.J., 2020. Organoid models of tumor immunology. Trends Immunol. 41, 652-664.
|
|
Zhang, N., Wang, H., Fang, Y., Wang, J., Zheng, X., Liu, X.S., 2015. Predicting anticancer drug responses using a dual-layer integrated cell line-drug network model. PLoS Comput. Biol. 11, e1004498.
|
|
Zhang, F., Wang, M., Xi, J., Yang, J., Li, A., 2018. A novel heterogeneous networkbased method for drug response prediction in cancer cell lines. Sci. Rep. 8, 3355.
|
|
Zhao, W., Li, J., Chen, M.M., Luo, Y., Ju, Z., Nesser, N.K., Johnson-Camacho, K., Boniface, C.T., Lawrence, Y., Pande, N.T., et al., 2020. Large-scale characterization of drug responses of clinically relevant proteins in cancer cell lines. Canc. Cell 38, 829-843. e4.
|
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