Biología de sistemas y computacional en cáncer: de lo básico a la próxima frontera
DOI:
https://doi.org/10.56050/01205498.1651Palabras clave:
Inteligencia artificial (IA), cáncer, oncología, tratamiento personalizado, terapia, diagnóstico, medicina de precisiónResumen
La inteligencia artificial (IA) está remodelando rápidamente la investigación en cáncer, al igual que la atención clínica personalizada. La disponibilidad de conjuntos de datos de alta dimensionalidad junto con los avances en la computación de alto rendimiento y de las arquitecturas innovadoras de aprendizaje profundo ha llevado a una explosión en el uso de la IA en varios aspectos de la práctica e investigación oncológica. Estas aplicaciones van desde la detección y clasificación de diversas neoplasias, su caracterización molecular incluyendo la evaluación del microambiente tumoral, el descubrimiento y la reutilización de medicamentos, y la predicción de los resultados derivados del tratamiento. A medida que estos avances penetren en la práctica clínica, se prevé un cambio de paradigma en la atención que se verá fuertemente impulsado por la IA.
Biografía del autor/a
Andrés F. Cardona, entro de Tratamiento e Investigación sobre cáncer Luis Carlos Sarmiento Angulo (CTIC)
MD. MSc. PhD. MBA. Dirección de Investigación y Educación, Centro de Tratamiento e Investigación sobre cáncer Luis Carlos Sarmiento Angulo (CTIC), Bogotá, Colombia.
Fundación para la Investigación Clínica y Molecular Aplicada del Cáncer – FICMAC, Bogotá, Colombia.
Grupo de Investigación en Oncología Molecular y Sistemas Biológicos (Fox-G), Universidad El Bosque, Bogotá, Colombia.
Luis Eduardo Pino, Fundación Santa Fe de Bogotá, Bogotá
Grupo Oncología Clínica, Instituto de Cáncer Carlos Ardila Lülle, Fundación Santa Fe de Bogotá, Bogotá, Colombia.
Erick Cantor, Fundación Santa Fe de Bogotá, Bogotá
Grupo Oncología Clínica, Instituto de Cáncer Carlos Ardila Lülle, Fundación Santa Fe de Bogotá, Bogotá, Colombia.
Referencias bibliográficas
1. Torre LA, Siegel RL, Ward EM, Jemal A. Global Cancer Incidence and Mortality Rates and Trends--An Update. Cancer Epidemiol Biomarkers Prev. 2016 Jan;25(1):16-27. doi: 10.1158/1055-9965.EPI-15-0578. Epub 2015 Dec 14.
2. Tufail AB, Ma YK, Kaabar MKA, Martínez F, Junejo AR, Ullah I, Khan R. Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions. Comput Math Methods Med. 2021 Oct 31;2021:9025470. doi: 10.1155/2021/9025470.
3. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015 May 28;521(7553):436-44. doi: 10.1038/nature14539.
4. Pellegrino E, Jacques C, Beaufils N, Nanni I, Carlioz A, Metellus P, Ouafik L. Machine learning random forest for predicting oncosomatic variant NGS analysis. Sci Rep. 2021 Nov 8;11(1):21820. doi: 10.1038/s41598-021-01253-y.
5. Ding L, Bailey MH, Porta-Pardo E, Thorsson V, Colaprico A, et al.; Cancer Genome Atlas Research Network. Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics. Cell. 2018 Apr 5;173(2):305-320.e10. doi: 10.1016/j.cell.2018.03.033.
6. Li F, Wu T, Xu Y, Dong Q, Xiao J, Xu Y, et al. A comprehensive overview of oncogenic pathways in human cancer. Brief Bioinform. 2020 May 21;21(3):957-969. doi: 10.1093/bib/bbz046.
7. Madhukar NS, Khade PK, Huang L, Gayvert K, Galletti G, Stogniew M, et al. A Bayesian machine learning approach for drug target identification using diverse data types. Nat Commun. 2019 Nov 19;10(1):5221. doi: 10.1038/s41467-019-12928-6.
8. Madhukar NS, Khade PK, Huang L, Gayvert K, Galletti G, Stogniew M, et al. A Bayesian machine learning approach for drug target identification using diverse data types. Nat Commun. 2019 Nov 19;10(1):5221. doi: 10.1038/s41467-019-12928-6.
9. Cheerla A, Gevaert O. Deep learning with multimodal representation for pancancer prognosis prediction. Bioinformatics. 2019 Jul 15;35(14):i446-i454. doi: 10.1093/ bioinformatics/btz342.
10. Bhinder B, Gilvary C, Madhukar NS, Elemento O. Artificial Intelligence in Cancer Research and Precision Medicine. Cancer Discov. 2021 Apr;11(4):900-915. doi: 10.1158/2159-8290.CD-21-0090.
11. Khosravi P, Kazemi E, Imielinski M, Elemento O, Hajirasouliha I. Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images. EBioMedicine. 2018 Jan;27:317-328. doi:
10.1016/j.ebiom.2017.12.026. Epub 2017 Dec 28.
12. Munir K, Elahi H, Ayub A, Frezza F, Rizzi A. Cancer Diagnosis Using Deep Learning: A Bibliographic Review. Cancers (Basel). 2019 Aug 23;11(9):1235. doi: 10.3390/ cancers11091235.
13. Patil PD, Hobbs B, Pennell NA. The promise and challenges of deep learning models for automated histopathologic classification and mutation prediction in lung cancer. J Thorac Dis. 2019 Feb;11(2):369-372. doi: 10.21037/jtd.2018.12.55.
14. Coudray N, Ocampo PS, Sakellaropoulos T, Narula N, Snuderl M, Fenyö D, et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med. 2018 Oct;24(10):1559-1567. doi: 10.1038/s41591-018-0177- 5. Epub 2018 Sep 17.
15. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017 Feb 2;542(7639):115-118. doi: 10.1038/nature21056. Epub 2017 Jan 25. Erratum in: Nature. 2017 Jun 28;546(7660):686.
16. Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S. Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network. IEEE Trans Med Imaging. 2016
May;35(5):1207-1216. doi: 10.1109/TMI.2016.2535865. Epub 2016 Feb 29.
17. Jiang Y, Liang X, Wang W, Chen C, Yuan Q, Zhang X, et al. Noninvasive Prediction of Occult Peritoneal Metastasis in Gastric Cancer Using Deep Learning. JAMA Netw Open. 2021 Jan 4;4(1):e2032269. doi: 10.1001/jamanetworkopen.2020.32269.
18. Wang X, Yang W, Weinreb J, Han J, Li Q, Kong X, et al. Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning. Sci Rep. 2017 Nov 13;7(1):15415. doi: 10.1038/s41598-017-15720-y.
19. McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020 Jan;577(7788):89-94. doi: 10.1038/s41586-019- 1799-6. Epub 2020 Jan 1. Erratum in: Nature. 2020 Oct;586(7829):E19.
20. Freeman K, Dinnes J, Chuchu N, Takwoingi Y, Bayliss SE, Matin RN, et al. Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies. BMJ. 2020 Feb 10;368:m127. doi: 10.1136/bmj.m127. Erratum in: BMJ. 2020 Feb 25;368:m645.
21. Dasgupta S, Vaughan AS, Kramer MR, Sanchez TH, Sullivan PS. Use of a Google Map Tool Embedded in an Internet Survey Instrument: Is it a Valid and Reliable Alternative to Geocoded Address Data? JMIR Res Protoc. 2014 Apr 10;3(2):e24. doi: 10.2196/resprot.2946.
22. Nagpal K, Foote D, Liu Y, Chen PC, Wulczyn E, Tan F, et al. Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer. NPJ Digit Med. 2019 Jun 7;2:48. doi: 10.1038/ s41746-019-0112-2. Erratum in: NPJ Digit Med. 2019 Nov 19;2:113.
23. Zhou Q, Zhou Z, Chen C, Fan G, Chen G, Heng H, et al. Grading of hepatocellular carcinoma using 3D SEDenseNet in dynamic enhanced MR images. Comput Biol Med. 2019 Apr;107:47-57. doi: 10.1016/j.compbiomed.2019.01.026. Epub 2019 Feb 4.
24. Gong XQ, Tao YY, Wu YK, Liu N, Yu X, Wang R, et al. Progress of MRI Radiomics in Hepatocellular Carcinoma. Front Oncol. 2021 Sep 20;11:698373. doi: 10.3389/ fonc.2021.698373.
25. Grewal JK, Tessier-Cloutier B, Jones M, Gakkhar S, Ma Y, Moore R, et al. Application of a Neural Network Whole Transcriptome-Based Pan-Cancer Method for Diagnosis of Primary and Metastatic Cancers. JAMA Netw Open. 2019 Apr 5;2(4):e192597. doi: 10.1001/jamanetworkopen.2019.2597.
26. Penson A, Camacho N, Zheng Y, Varghese AM, AlAhmadie H, Razavi P, et al. Development of GenomeDerived Tumor Type Prediction to Inform Clinical Cancer Care. JAMA Oncol. 2020 Jan 1;6(1):84-91. doi: 10.1001/ jamaoncol.2019.3985.
27. Capper D, Jones DTW, Sill M, Hovestadt V, Schrimpf D, Sturm D, et al. DNA methylation-based classification of central nervous system tumours. Nature. 2018 Mar 22;555(7697):469-474. doi: 10.1038/nature26000. Epub 2018 Mar 14.
28. Sun Y, Zhu S, Ma K, Liu W, Yue Y, Hu G, et al. Identification of 12 cancer types through genome deep learning. Sci Rep. 2019 Nov 21;9(1):17256. doi: 10.1038/s41598- 019-53989-3.
29. Chabon JJ, Hamilton EG, Kurtz DM, Esfahani MS, Moding EJ, Stehr H, et al. Integrating genomic features for non-invasive early lung cancer detection. Nature. 2020 Apr;580(7802):245-251. doi: 10.1038/s41586-020- 2140-0. Epub 2020 Mar 25.
30. Mouliere F, Chandrananda D, Piskorz AM, Moore EK, Morris J, Ahlborn LB, et al. Enhanced detection of circulating tumor DNA by fragment size analysis. Sci Transl Med. 2018 Nov 7;10(466):eaat4921. doi: 10.1126/scitranslmed.aat4921.
31. Cohen JD, Li L, Wang Y, Thoburn C, Afsari B, Danilova L, et al. Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science. 2018 Feb 23;359(6378):926-930. doi: 10.1126/science. aar3247. Epub 2018 Jan 18.
32. Poplin R, Chang PC, Alexander D, Schwartz S, Colthurst T, Ku A, et al. A universal SNP and small-indel variant caller using deep neural networks. Nat Biotechnol. 2018 Nov;36(10):983-987. doi: 10.1038/nbt.4235. Epub 2018 Sep 24.
33. Park H, Chun SM, Shim J, Oh JH, Cho EJ, Hwang HS, et al. Detection of chromosome structural variation by targeted next-generation sequencing and a deep learning application. Sci Rep. 2019 Mar 6;9(1):3644. doi: 10.1038/s41598-019-40364-5.
34. Yin G, Wang Z, Song Y, Li X, Chen Y, Zhu L, et al. Prediction of EGFR Mutation Status Based on 18F-FDG PET/ CT Imaging Using Deep Learning-Based Model in Lung Adenocarcinoma. Front Oncol. 2021 Jul 22;11:709137. doi: 10.3389/fonc.2021.709137. Erratum in: Front Oncol. 2021 Sep 07;11:747316.
35. Mu W, Jiang L, Zhang J, Shi Y, Gray JE, Tunali I, et al. Non-invasive decision support for NSCLC treatment using PET/CT radiomics. Nat Commun. 2020 Oct 16;11(1):5228. doi: 10.1038/s41467-020-19116-x.
36. Shboul ZA, Chen J, M Iftekharuddin K. Prediction of Molecular Mutations in Diffuse Low-Grade Gliomas using MR Imaging Features. Sci Rep. 2020 Feb 28;10(1):3711. doi: 10.1038/s41598-020-60550-0.
37. Kha QH, Le VH, Hung TNK, Le NQK. Development and Validation of an Efficient MRI Radiomics Signature for Improving the Predictive Performance of 1p/19q Co-Deletion in Lower-Grade Gliomas. Cancers (Basel). 2021 Oct 27;13(21):5398. doi: 10.3390/cancers13215398.
38. Han Y, Xie Z, Zang Y, Zhang S, Gu D, Zhou M, et al. Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas. J Neurooncol. 2018 Nov;140(2):297-306. doi: 10.1007/s11060-018-2953-y. Epub 2018 Aug 10.
39. Zhou H, Chang K, Bai HX, Xiao B, Su C, Bi WL, et al. Machine learning reveals multimodal MRI patterns predictive of isocitrate dehydrogenase and 1p/19q status in diffuse low- and high-grade gliomas. J Neurooncol. 2019 Apr;142(2):299-307. doi: 10.1007/s11060-019-03096-0. Epub 2019 Jan 19.
40. Zhang B, Chang K, Ramkissoon S, Tanguturi S, Bi WL, Reardon DA, et al. Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas. Neuro Oncol. 2017 Jan;19(1):109-117. doi: 10.1093/neuonc/now121. Epub 2016 Jun 26.
41. Kim M, Jung SY, Park JE, Jo Y, Park SY, Nam SJ, et al. Diffusion- and perfusion-weighted MRI radiomics model may predict isocitrate dehydrogenase (IDH) mutation and tumor aggressiveness in diffuse lower grade glioma. Eur Radiol. 2020 Apr;30(4):2142-2151. doi: 10.1007/s00330-019-06548-3. Epub 2019 Dec 11.
42. Tan Y, Zhang ST, Wei JW, Dong D, Wang XC, Yang GQ, et al. A radiomics nomogram may improve the prediction of IDH genotype for astrocytoma before surgery. Eur Radiol. 2019 Jul;29(7):3325-3337. doi: 10.1007/s00330- 019-06056-4. Epub 2019 Apr 10.
43. Chen M, Zhang B, Topatana W, Cao J, Zhu H, Juengpanich S, et al. Classification and mutation prediction based on histopathology H&E images in liver cancer using deep learning. NPJ Precis Oncol. 2020 Jun 8;4:14. doi: 10.1038/s41698-020-0120-3.
44. Lu L, Daigle BJ Jr. Prognostic analysis of histopathological images using pre-trained convolutional neural networks: application to hepatocellular carcinoma. PeerJ. 2020 Mar 12;8:e8668. doi: 10.7717/peerj.8668.
45. Sidaway P. MSI-H: a truly agnostic biomarker? Nat Rev Clin Oncol. 2020 Feb;17(2):68. doi: 10.1038/s41571-019-0310-5.
46. Marabelle A, Le DT, Ascierto PA, Di Giacomo AM, De Jesus-Acosta A, Delord JP, et al. Efficacy of Pembrolizumab in Patients With Noncolorectal High Microsatellite Instability/Mismatch Repair-Deficient Cancer: Results From the Phase II KEYNOTE-158 Study. J Clin Oncol. 2020 Jan 1;38(1):1-10. doi: 10.1200/JCO.19.02105. Epub 2019 Nov 4.
47. Kather JN, Pearson AT, Halama N, Jäger D, Krause J, Loosen SH, et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat Med. 2019 Jul;25(7):1054-1056. doi: 10.1038/s41591-019-0462-y. Epub 2019 Jun 3.
48. Yamashita R, Long J, Longacre T, Peng L, Berry G, Martin B, et al. Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study. Lancet Oncol. 2021 Jan;22(1):132-141. doi: 10.1016/S1470-2045(20)30535-0.
49. Chan TA, Yarchoan M, Jaffee E, Swanton C, Quezada SA, Stenzinger A, et al. Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic. Ann Oncol. 2019 Jan 1;30(1):44-56. doi: 10.1093/annonc/mdy495.
50. Jain MS, Massoud TF. Predicting tumour mutational burden from histopathological images using multiscale deep learning. Nat Mach Intell. 2020;2:356-62.
51. Wang L, Jiao Y, Qiao Y, Zeng N, Yu R. A novel approach combined transfer learning and deep learning to predict TMB from histology image. Pattern Recognit. Lett 2020;135:244–48.
52. He B, Dong D, She Y, Zhou C, Fang M, Zhu Y, et al. Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker. J Immunother Cancer. 2020 Jul;8(2):e000550. doi: 10.1136/jitc-2020-000550.
53. Xu Z, Verma A, Naveed U, Bakhoum S, Khosravi P, Elemento O. Using Histopathology Images to Predict Chromosomal Instability in Breast Cancer: A Deep Learning Approach. medRxiv 2020.09.23.20200139. 10.1101/2020.09.23.20200139.
54. Hainsworth JD, Rubin MS, Spigel DR, Boccia RV, Raby S, Quinn R, et al. Molecular gene expression profiling to predict the tissue of origin and direct site-specific therapy in patients with carcinoma of unknown primary site: a prospective trial of the Sarah Cannon research institute. J Clin Oncol. 2013 Jan 10;31(2):217-23. doi: 10.1200/JCO.2012.43.3755. Epub 2012 Oct 1.
55. Greco FA. Molecular diagnosis of the tissue of origin in cancer of unknown primary site: useful in patient management. Curr Treat Options Oncol. 2013 Dec;14(4):634- 42. doi: 10.1007/s11864-013-0257-1.
56. Jiao W, Atwal G, Polak P, Karlic R, Cuppen E; PCAWG Tumor Subtypes and Clinical Translation Working Group; PCAWG Consortium. A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns. Nat Commun. 2020 Feb 5;11(1):728. doi: 10.1038/s41467-019-13825-8.
57. Zhou J, Theesfeld CL, Yao K, Chen KM, Wong AK, Troyanskaya OG. Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk. Nat Genet. 2018 Aug;50(8):1171-1179. doi: 10.1038/s41588-018-0160-6. Epub 2018 Jul 16.
58. Hoffman GE, Bendl J, Girdhar K, Schadt EE, Roussos P. Functional interpretation of genetic variants using deep learning predicts impact on chromatin accessibility and histone modification. Nucleic Acids Res. 2019 Nov 18;47(20):10597-10611. doi: 10.1093/nar/gkz808.
59. Haider S, Tyekucheva S, Prandi D, Fox NS, Ahn J, Xu AW, et al.; Cancer Genome Atlas Research Network. Systematic Assessment of Tumor Purity and Its Clinical Implications. JCO Precis Oncol. 2020 Sep
4;4:PO.20.00016. doi: 10.1200/PO.20.00016.
60. Akbar S, Peikari M, Salama S, Panah AY, Nofech-Mozes S, Martel AL. Automated and Manual Quantification of Tumour Cellularity in Digital Slides for Tumour Burden Assessment. Sci Rep. 2019 Oct 1;9(1):14099. doi: 10.1038/s41598-019-50568-4.
61. Bhinder B, Elemento O. Computational methods in tumor immunology. Methods Enzymol. 2020;636:209-259. doi: 10.1016/bs.mie.2020.01.001. Epub 2020 Jan 25.
62. Saltz J, Gupta R, Hou L, Kurc T, Singh P, Nguyen V, et al. Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. Cell Rep. 2018 Apr 3;23(1):181-193. e7. doi: 10.1016/j.celrep.2018.03.086.
63. Fassler DJ, Abousamra S, Gupta R, Chen C, Zhao M, Paredes D, et al. Deep learning-based image analysis methods for brightfield-acquired multiplex immunohistochemistry images. Diagn Pathol. 2020 Jul 28;15(1):100. doi: 10.1186/s13000-020-01003-0. Erratum in: Diagn Pathol. 2020 Sep 24;15(1):116.
64. Tong Z, Zhou Y, Wang J. Identifying potential drug targets in hepatocellular carcinoma based on network analysis and one-class support vector machine. Sci Rep. 2019 Jul 18;9(1):10442. doi: 10.1038/s41598-019-46540-x.
65. López-Cortés A, Paz-Y-Miño C, Cabrera-Andrade A, Barigye SJ, Munteanu CR, González-Díaz H, et al. Gene prioritization, communality analysis, networking and metabolic integrated pathway to better understand breast cancer pathogenesis. Sci Rep. 2018 Nov 12;8(1):16679. doi: 10.1038/s41598-018-35149-1.
66. Tamborero D, Rubio-Perez C, Deu-Pons J, Schroeder MP, Vivancos A, Rovira A, et al. Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations. Genome Med. 2018 Mar 28;10(1):25. doi: 10.1186/s13073-018-0531-8.
67. Tsherniak A, Vazquez F, Montgomery PG, Weir BA, Kryukov G, Cowley GS, et al. Defining a Cancer Dependency Map. Cell. 2017 Jul 27;170(3):564-576.e16. doi: 10.1016/j.cell.2017.06.010.
68. Gilvary C, Madhukar NS, Gayvert K, Foronda M, Perez A, Leslie CS, et al. A machine learning approach predicts essential genes and pharmacological targets in cancer. bioRxiv 692277; doi: 10.1101/692277
69. Chen MM, Li J, Mills GB, Liang H. Predicting Cancer Cell Line Dependencies From the Protein Expression Data of Reverse-Phase Protein Arrays. JCO Clin Cancer Inform. 2020 Apr;4:357-366. doi: 10.1200/CCI.19.00144.
70. Olivecrona M, Blaschke T, Engkvist O, Chen H. Molecular de-novo design through deep reinforcement learning. J Cheminform. 2017 Sep 4;9(1):48. doi: 10.1186/s13321-017-0235-x.
71. You J, Liu B, Ying R, Pande V, Leskovec J. Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation. arXiv:1806.02473v3.
72. Maziarka Ł, Pocha A, Kaczmarczyk J, Rataj K, Danel T, Warchoł M. Mol-CycleGAN: a generative model for molecular optimization. J Cheminform. 2020 Jan 8;12(1):2. doi: 10.1186/s13321-019-0404-1.
73. Gayvert KM, Madhukar NS, Elemento O. A Data-Driven Approach to Predicting Successes and Failures of Clinical Trials. Cell Chem Biol. 2016 Oct 20;23(10):1294-1301. doi: 10.1016/j.chembiol.2016.07.023. Epub 2016 Sep 15.
74. Shen J, Cheng F, Xu Y, Li W, Tang Y. Estimation of ADME properties with substructure pattern recognition. J Chem Inf Model. 2010 Jun 28;50(6):1034-41. doi: 10.1021/ci100104j.
75. Liu D, Schilling B, Liu D, Sucker A, Livingstone E, JerbyArnon L, et al. Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma. Nat Med. 2019 Dec;25(12):1916- 1927. doi: 10.1038/s41591-019-0654-5. Epub 2019 Dec 2. Erratum in: Nat Med. 2020 Jul;26(7):1147.
76. Litchfield K, Reading JL, Puttick C, Thakkar K, Abbosh C, Bentham R, et al. Meta-analysis of tumor- and T cell-intrinsic mechanisms of sensitization to checkpoint inhibition. Cell. 2021 Feb 4;184(3):596-614.e14. doi: 10.1016/j.cell.2021.01.002. Epub 2021 Jan 27.
77. Johannet P, Coudray N, Donnelly DM, Jour G, IllaBochaca I, Xia Y, et al. Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma. Clin Cancer Res. 2021 Jan 1;27(1):131-140. doi: 10.1158/1078-0432.CCR-20- 2415. Epub 2020 Nov 18.
78. Sun D, Wang M, Li A. A multimodal deep neural network for human breast cancer prognosis prediction by integrating multi-dimensional data. IEEE/ACM Trans Comput Biol Bioinform. 2018 Feb 15. doi: 10.1109/ TCBB.2018.2806438. Epub ahead of print.
79. Chaudhary K, Poirion OB, Lu L, Garmire LX. Deep Learning-Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer. Clin Cancer Res. 2018 Mar 15;24(6):1248-1259. doi: 10.1158/1078-0432.CCR-17- 0853. Epub 2017 Oct 5.
80. Korfiatis P, Kline TL, Lachance DH, Parney IF, Buckner JC, Erickson BJ. Residual Deep Convolutional Neural Network Predicts MGMT Methylation Status. J Digit Imaging. 2017 Oct;30(5):622-628. doi: 10.1007/s10278-017-0009-z.
81. Mobadersany P, Yousefi S, Amgad M, Gutman DA, Barnholtz-Sloan JS, Velázquez Vega JE, et al. Predicting cancer outcomes from histology and genomics using convolutional networks. Proc Natl Acad Sci U S A. 2018 Mar 27;115(13):E2970-E2979. doi: 10.1073/pnas.1717139115. Epub 2018 Mar 12.
82. Bychkov D, Linder N, Turkki R, Nordling S, Kovanen PE, Verrill C, et al. Deep learning based tissue analysis predicts outcome in colorectal cancer. Sci Rep. 2018 Feb 21;8(1):3395. doi: 10.1038/s41598-018-21758-3.
83. Skrede OJ, De Raedt S, Kleppe A, Hveem TS, Liestøl K, Maddison J, et al. Deep learning for prediction of colorectal cancer outcome: a discovery and validation study. Lancet. 2020 Feb 1;395(10221):350-360. doi: 10.1016/S0140-6736(19)32998-8.
84. Bates DW, Auerbach A, Schulam P, Wright A, Saria S. Reporting and Implementing Interventions Involving Machine Learning and Artificial Intelligence. Ann Intern Med. 2020 Jun 2;172(11 Suppl):S137-S144. doi: 10.7326/M19-0872.
85. Cavallo J. How Watson for Oncology is advancing personalized patient care. http://www.ascopost.com/issues/june-25-2017/how-watson-for-oncology-is-advancing-personalized-patient-care/
86. Zhou N, Zhang CT, Lv HY, Hao CX, Li TJ, Zhu JJ, et al. Concordance Study Between IBM Watson for Oncology and Clinical Practice for Patients with Cancer in China. Oncologist. 2019 Jun;24(6):812-819. doi: 10.1634/theoncologist.2018-0255. Epub 2018 Sep 4.
87. Seidman AD, Pilewskie ML, Robson ME, et al. Integration of multi-modality treatment planning for early stage breast cancer into Watson for Oncology, a decision support system: Seeing the forest and the trees. 2015 ASCO Annual Meeting. Abstract e12042. Presented May 29, 2015.
88. Kris MG, Gucalp A, Epstein AS, et al. Assessing the performance of Watson for oncology, a decision support system, using actual contemporary clinical cases. 2015 ASCO Annual Meeting. Abstract 8023. Presented May 29, 2015.
89. Cavallo J. Confronting the Criticisms Facing Watson for Oncology https://ascopost.com/issues/september-10-2019/
90. Somashekhar SP, Sepúlveda MJ, Shortliffe, et al. A prospective blinded study of 1,000 cases analyzing the role of artificial intelligence: Watson for Oncology and change in decision-making of a multidisciplinary tumor board from a tertiary care cancer center. 2019 ASCO Annual Meeting. Abstract 6533. Presented June 1, 2019.
91. Jie Z, Zhiying Z, Li L. A meta-analysis of Watson for Oncology in clinical application. Sci Rep. 2021 Mar 11;11(1):5792. doi: 10.1038/s41598-021-84973-5. PMID: 33707577.
2. Tufail AB, Ma YK, Kaabar MKA, Martínez F, Junejo AR, Ullah I, Khan R. Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions. Comput Math Methods Med. 2021 Oct 31;2021:9025470. doi: 10.1155/2021/9025470.
3. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015 May 28;521(7553):436-44. doi: 10.1038/nature14539.
4. Pellegrino E, Jacques C, Beaufils N, Nanni I, Carlioz A, Metellus P, Ouafik L. Machine learning random forest for predicting oncosomatic variant NGS analysis. Sci Rep. 2021 Nov 8;11(1):21820. doi: 10.1038/s41598-021-01253-y.
5. Ding L, Bailey MH, Porta-Pardo E, Thorsson V, Colaprico A, et al.; Cancer Genome Atlas Research Network. Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics. Cell. 2018 Apr 5;173(2):305-320.e10. doi: 10.1016/j.cell.2018.03.033.
6. Li F, Wu T, Xu Y, Dong Q, Xiao J, Xu Y, et al. A comprehensive overview of oncogenic pathways in human cancer. Brief Bioinform. 2020 May 21;21(3):957-969. doi: 10.1093/bib/bbz046.
7. Madhukar NS, Khade PK, Huang L, Gayvert K, Galletti G, Stogniew M, et al. A Bayesian machine learning approach for drug target identification using diverse data types. Nat Commun. 2019 Nov 19;10(1):5221. doi: 10.1038/s41467-019-12928-6.
8. Madhukar NS, Khade PK, Huang L, Gayvert K, Galletti G, Stogniew M, et al. A Bayesian machine learning approach for drug target identification using diverse data types. Nat Commun. 2019 Nov 19;10(1):5221. doi: 10.1038/s41467-019-12928-6.
9. Cheerla A, Gevaert O. Deep learning with multimodal representation for pancancer prognosis prediction. Bioinformatics. 2019 Jul 15;35(14):i446-i454. doi: 10.1093/ bioinformatics/btz342.
10. Bhinder B, Gilvary C, Madhukar NS, Elemento O. Artificial Intelligence in Cancer Research and Precision Medicine. Cancer Discov. 2021 Apr;11(4):900-915. doi: 10.1158/2159-8290.CD-21-0090.
11. Khosravi P, Kazemi E, Imielinski M, Elemento O, Hajirasouliha I. Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images. EBioMedicine. 2018 Jan;27:317-328. doi:
10.1016/j.ebiom.2017.12.026. Epub 2017 Dec 28.
12. Munir K, Elahi H, Ayub A, Frezza F, Rizzi A. Cancer Diagnosis Using Deep Learning: A Bibliographic Review. Cancers (Basel). 2019 Aug 23;11(9):1235. doi: 10.3390/ cancers11091235.
13. Patil PD, Hobbs B, Pennell NA. The promise and challenges of deep learning models for automated histopathologic classification and mutation prediction in lung cancer. J Thorac Dis. 2019 Feb;11(2):369-372. doi: 10.21037/jtd.2018.12.55.
14. Coudray N, Ocampo PS, Sakellaropoulos T, Narula N, Snuderl M, Fenyö D, et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med. 2018 Oct;24(10):1559-1567. doi: 10.1038/s41591-018-0177- 5. Epub 2018 Sep 17.
15. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017 Feb 2;542(7639):115-118. doi: 10.1038/nature21056. Epub 2017 Jan 25. Erratum in: Nature. 2017 Jun 28;546(7660):686.
16. Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S. Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network. IEEE Trans Med Imaging. 2016
May;35(5):1207-1216. doi: 10.1109/TMI.2016.2535865. Epub 2016 Feb 29.
17. Jiang Y, Liang X, Wang W, Chen C, Yuan Q, Zhang X, et al. Noninvasive Prediction of Occult Peritoneal Metastasis in Gastric Cancer Using Deep Learning. JAMA Netw Open. 2021 Jan 4;4(1):e2032269. doi: 10.1001/jamanetworkopen.2020.32269.
18. Wang X, Yang W, Weinreb J, Han J, Li Q, Kong X, et al. Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning. Sci Rep. 2017 Nov 13;7(1):15415. doi: 10.1038/s41598-017-15720-y.
19. McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020 Jan;577(7788):89-94. doi: 10.1038/s41586-019- 1799-6. Epub 2020 Jan 1. Erratum in: Nature. 2020 Oct;586(7829):E19.
20. Freeman K, Dinnes J, Chuchu N, Takwoingi Y, Bayliss SE, Matin RN, et al. Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies. BMJ. 2020 Feb 10;368:m127. doi: 10.1136/bmj.m127. Erratum in: BMJ. 2020 Feb 25;368:m645.
21. Dasgupta S, Vaughan AS, Kramer MR, Sanchez TH, Sullivan PS. Use of a Google Map Tool Embedded in an Internet Survey Instrument: Is it a Valid and Reliable Alternative to Geocoded Address Data? JMIR Res Protoc. 2014 Apr 10;3(2):e24. doi: 10.2196/resprot.2946.
22. Nagpal K, Foote D, Liu Y, Chen PC, Wulczyn E, Tan F, et al. Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer. NPJ Digit Med. 2019 Jun 7;2:48. doi: 10.1038/ s41746-019-0112-2. Erratum in: NPJ Digit Med. 2019 Nov 19;2:113.
23. Zhou Q, Zhou Z, Chen C, Fan G, Chen G, Heng H, et al. Grading of hepatocellular carcinoma using 3D SEDenseNet in dynamic enhanced MR images. Comput Biol Med. 2019 Apr;107:47-57. doi: 10.1016/j.compbiomed.2019.01.026. Epub 2019 Feb 4.
24. Gong XQ, Tao YY, Wu YK, Liu N, Yu X, Wang R, et al. Progress of MRI Radiomics in Hepatocellular Carcinoma. Front Oncol. 2021 Sep 20;11:698373. doi: 10.3389/ fonc.2021.698373.
25. Grewal JK, Tessier-Cloutier B, Jones M, Gakkhar S, Ma Y, Moore R, et al. Application of a Neural Network Whole Transcriptome-Based Pan-Cancer Method for Diagnosis of Primary and Metastatic Cancers. JAMA Netw Open. 2019 Apr 5;2(4):e192597. doi: 10.1001/jamanetworkopen.2019.2597.
26. Penson A, Camacho N, Zheng Y, Varghese AM, AlAhmadie H, Razavi P, et al. Development of GenomeDerived Tumor Type Prediction to Inform Clinical Cancer Care. JAMA Oncol. 2020 Jan 1;6(1):84-91. doi: 10.1001/ jamaoncol.2019.3985.
27. Capper D, Jones DTW, Sill M, Hovestadt V, Schrimpf D, Sturm D, et al. DNA methylation-based classification of central nervous system tumours. Nature. 2018 Mar 22;555(7697):469-474. doi: 10.1038/nature26000. Epub 2018 Mar 14.
28. Sun Y, Zhu S, Ma K, Liu W, Yue Y, Hu G, et al. Identification of 12 cancer types through genome deep learning. Sci Rep. 2019 Nov 21;9(1):17256. doi: 10.1038/s41598- 019-53989-3.
29. Chabon JJ, Hamilton EG, Kurtz DM, Esfahani MS, Moding EJ, Stehr H, et al. Integrating genomic features for non-invasive early lung cancer detection. Nature. 2020 Apr;580(7802):245-251. doi: 10.1038/s41586-020- 2140-0. Epub 2020 Mar 25.
30. Mouliere F, Chandrananda D, Piskorz AM, Moore EK, Morris J, Ahlborn LB, et al. Enhanced detection of circulating tumor DNA by fragment size analysis. Sci Transl Med. 2018 Nov 7;10(466):eaat4921. doi: 10.1126/scitranslmed.aat4921.
31. Cohen JD, Li L, Wang Y, Thoburn C, Afsari B, Danilova L, et al. Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science. 2018 Feb 23;359(6378):926-930. doi: 10.1126/science. aar3247. Epub 2018 Jan 18.
32. Poplin R, Chang PC, Alexander D, Schwartz S, Colthurst T, Ku A, et al. A universal SNP and small-indel variant caller using deep neural networks. Nat Biotechnol. 2018 Nov;36(10):983-987. doi: 10.1038/nbt.4235. Epub 2018 Sep 24.
33. Park H, Chun SM, Shim J, Oh JH, Cho EJ, Hwang HS, et al. Detection of chromosome structural variation by targeted next-generation sequencing and a deep learning application. Sci Rep. 2019 Mar 6;9(1):3644. doi: 10.1038/s41598-019-40364-5.
34. Yin G, Wang Z, Song Y, Li X, Chen Y, Zhu L, et al. Prediction of EGFR Mutation Status Based on 18F-FDG PET/ CT Imaging Using Deep Learning-Based Model in Lung Adenocarcinoma. Front Oncol. 2021 Jul 22;11:709137. doi: 10.3389/fonc.2021.709137. Erratum in: Front Oncol. 2021 Sep 07;11:747316.
35. Mu W, Jiang L, Zhang J, Shi Y, Gray JE, Tunali I, et al. Non-invasive decision support for NSCLC treatment using PET/CT radiomics. Nat Commun. 2020 Oct 16;11(1):5228. doi: 10.1038/s41467-020-19116-x.
36. Shboul ZA, Chen J, M Iftekharuddin K. Prediction of Molecular Mutations in Diffuse Low-Grade Gliomas using MR Imaging Features. Sci Rep. 2020 Feb 28;10(1):3711. doi: 10.1038/s41598-020-60550-0.
37. Kha QH, Le VH, Hung TNK, Le NQK. Development and Validation of an Efficient MRI Radiomics Signature for Improving the Predictive Performance of 1p/19q Co-Deletion in Lower-Grade Gliomas. Cancers (Basel). 2021 Oct 27;13(21):5398. doi: 10.3390/cancers13215398.
38. Han Y, Xie Z, Zang Y, Zhang S, Gu D, Zhou M, et al. Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas. J Neurooncol. 2018 Nov;140(2):297-306. doi: 10.1007/s11060-018-2953-y. Epub 2018 Aug 10.
39. Zhou H, Chang K, Bai HX, Xiao B, Su C, Bi WL, et al. Machine learning reveals multimodal MRI patterns predictive of isocitrate dehydrogenase and 1p/19q status in diffuse low- and high-grade gliomas. J Neurooncol. 2019 Apr;142(2):299-307. doi: 10.1007/s11060-019-03096-0. Epub 2019 Jan 19.
40. Zhang B, Chang K, Ramkissoon S, Tanguturi S, Bi WL, Reardon DA, et al. Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas. Neuro Oncol. 2017 Jan;19(1):109-117. doi: 10.1093/neuonc/now121. Epub 2016 Jun 26.
41. Kim M, Jung SY, Park JE, Jo Y, Park SY, Nam SJ, et al. Diffusion- and perfusion-weighted MRI radiomics model may predict isocitrate dehydrogenase (IDH) mutation and tumor aggressiveness in diffuse lower grade glioma. Eur Radiol. 2020 Apr;30(4):2142-2151. doi: 10.1007/s00330-019-06548-3. Epub 2019 Dec 11.
42. Tan Y, Zhang ST, Wei JW, Dong D, Wang XC, Yang GQ, et al. A radiomics nomogram may improve the prediction of IDH genotype for astrocytoma before surgery. Eur Radiol. 2019 Jul;29(7):3325-3337. doi: 10.1007/s00330- 019-06056-4. Epub 2019 Apr 10.
43. Chen M, Zhang B, Topatana W, Cao J, Zhu H, Juengpanich S, et al. Classification and mutation prediction based on histopathology H&E images in liver cancer using deep learning. NPJ Precis Oncol. 2020 Jun 8;4:14. doi: 10.1038/s41698-020-0120-3.
44. Lu L, Daigle BJ Jr. Prognostic analysis of histopathological images using pre-trained convolutional neural networks: application to hepatocellular carcinoma. PeerJ. 2020 Mar 12;8:e8668. doi: 10.7717/peerj.8668.
45. Sidaway P. MSI-H: a truly agnostic biomarker? Nat Rev Clin Oncol. 2020 Feb;17(2):68. doi: 10.1038/s41571-019-0310-5.
46. Marabelle A, Le DT, Ascierto PA, Di Giacomo AM, De Jesus-Acosta A, Delord JP, et al. Efficacy of Pembrolizumab in Patients With Noncolorectal High Microsatellite Instability/Mismatch Repair-Deficient Cancer: Results From the Phase II KEYNOTE-158 Study. J Clin Oncol. 2020 Jan 1;38(1):1-10. doi: 10.1200/JCO.19.02105. Epub 2019 Nov 4.
47. Kather JN, Pearson AT, Halama N, Jäger D, Krause J, Loosen SH, et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat Med. 2019 Jul;25(7):1054-1056. doi: 10.1038/s41591-019-0462-y. Epub 2019 Jun 3.
48. Yamashita R, Long J, Longacre T, Peng L, Berry G, Martin B, et al. Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study. Lancet Oncol. 2021 Jan;22(1):132-141. doi: 10.1016/S1470-2045(20)30535-0.
49. Chan TA, Yarchoan M, Jaffee E, Swanton C, Quezada SA, Stenzinger A, et al. Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic. Ann Oncol. 2019 Jan 1;30(1):44-56. doi: 10.1093/annonc/mdy495.
50. Jain MS, Massoud TF. Predicting tumour mutational burden from histopathological images using multiscale deep learning. Nat Mach Intell. 2020;2:356-62.
51. Wang L, Jiao Y, Qiao Y, Zeng N, Yu R. A novel approach combined transfer learning and deep learning to predict TMB from histology image. Pattern Recognit. Lett 2020;135:244–48.
52. He B, Dong D, She Y, Zhou C, Fang M, Zhu Y, et al. Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker. J Immunother Cancer. 2020 Jul;8(2):e000550. doi: 10.1136/jitc-2020-000550.
53. Xu Z, Verma A, Naveed U, Bakhoum S, Khosravi P, Elemento O. Using Histopathology Images to Predict Chromosomal Instability in Breast Cancer: A Deep Learning Approach. medRxiv 2020.09.23.20200139. 10.1101/2020.09.23.20200139.
54. Hainsworth JD, Rubin MS, Spigel DR, Boccia RV, Raby S, Quinn R, et al. Molecular gene expression profiling to predict the tissue of origin and direct site-specific therapy in patients with carcinoma of unknown primary site: a prospective trial of the Sarah Cannon research institute. J Clin Oncol. 2013 Jan 10;31(2):217-23. doi: 10.1200/JCO.2012.43.3755. Epub 2012 Oct 1.
55. Greco FA. Molecular diagnosis of the tissue of origin in cancer of unknown primary site: useful in patient management. Curr Treat Options Oncol. 2013 Dec;14(4):634- 42. doi: 10.1007/s11864-013-0257-1.
56. Jiao W, Atwal G, Polak P, Karlic R, Cuppen E; PCAWG Tumor Subtypes and Clinical Translation Working Group; PCAWG Consortium. A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns. Nat Commun. 2020 Feb 5;11(1):728. doi: 10.1038/s41467-019-13825-8.
57. Zhou J, Theesfeld CL, Yao K, Chen KM, Wong AK, Troyanskaya OG. Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk. Nat Genet. 2018 Aug;50(8):1171-1179. doi: 10.1038/s41588-018-0160-6. Epub 2018 Jul 16.
58. Hoffman GE, Bendl J, Girdhar K, Schadt EE, Roussos P. Functional interpretation of genetic variants using deep learning predicts impact on chromatin accessibility and histone modification. Nucleic Acids Res. 2019 Nov 18;47(20):10597-10611. doi: 10.1093/nar/gkz808.
59. Haider S, Tyekucheva S, Prandi D, Fox NS, Ahn J, Xu AW, et al.; Cancer Genome Atlas Research Network. Systematic Assessment of Tumor Purity and Its Clinical Implications. JCO Precis Oncol. 2020 Sep
4;4:PO.20.00016. doi: 10.1200/PO.20.00016.
60. Akbar S, Peikari M, Salama S, Panah AY, Nofech-Mozes S, Martel AL. Automated and Manual Quantification of Tumour Cellularity in Digital Slides for Tumour Burden Assessment. Sci Rep. 2019 Oct 1;9(1):14099. doi: 10.1038/s41598-019-50568-4.
61. Bhinder B, Elemento O. Computational methods in tumor immunology. Methods Enzymol. 2020;636:209-259. doi: 10.1016/bs.mie.2020.01.001. Epub 2020 Jan 25.
62. Saltz J, Gupta R, Hou L, Kurc T, Singh P, Nguyen V, et al. Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. Cell Rep. 2018 Apr 3;23(1):181-193. e7. doi: 10.1016/j.celrep.2018.03.086.
63. Fassler DJ, Abousamra S, Gupta R, Chen C, Zhao M, Paredes D, et al. Deep learning-based image analysis methods for brightfield-acquired multiplex immunohistochemistry images. Diagn Pathol. 2020 Jul 28;15(1):100. doi: 10.1186/s13000-020-01003-0. Erratum in: Diagn Pathol. 2020 Sep 24;15(1):116.
64. Tong Z, Zhou Y, Wang J. Identifying potential drug targets in hepatocellular carcinoma based on network analysis and one-class support vector machine. Sci Rep. 2019 Jul 18;9(1):10442. doi: 10.1038/s41598-019-46540-x.
65. López-Cortés A, Paz-Y-Miño C, Cabrera-Andrade A, Barigye SJ, Munteanu CR, González-Díaz H, et al. Gene prioritization, communality analysis, networking and metabolic integrated pathway to better understand breast cancer pathogenesis. Sci Rep. 2018 Nov 12;8(1):16679. doi: 10.1038/s41598-018-35149-1.
66. Tamborero D, Rubio-Perez C, Deu-Pons J, Schroeder MP, Vivancos A, Rovira A, et al. Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations. Genome Med. 2018 Mar 28;10(1):25. doi: 10.1186/s13073-018-0531-8.
67. Tsherniak A, Vazquez F, Montgomery PG, Weir BA, Kryukov G, Cowley GS, et al. Defining a Cancer Dependency Map. Cell. 2017 Jul 27;170(3):564-576.e16. doi: 10.1016/j.cell.2017.06.010.
68. Gilvary C, Madhukar NS, Gayvert K, Foronda M, Perez A, Leslie CS, et al. A machine learning approach predicts essential genes and pharmacological targets in cancer. bioRxiv 692277; doi: 10.1101/692277
69. Chen MM, Li J, Mills GB, Liang H. Predicting Cancer Cell Line Dependencies From the Protein Expression Data of Reverse-Phase Protein Arrays. JCO Clin Cancer Inform. 2020 Apr;4:357-366. doi: 10.1200/CCI.19.00144.
70. Olivecrona M, Blaschke T, Engkvist O, Chen H. Molecular de-novo design through deep reinforcement learning. J Cheminform. 2017 Sep 4;9(1):48. doi: 10.1186/s13321-017-0235-x.
71. You J, Liu B, Ying R, Pande V, Leskovec J. Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation. arXiv:1806.02473v3.
72. Maziarka Ł, Pocha A, Kaczmarczyk J, Rataj K, Danel T, Warchoł M. Mol-CycleGAN: a generative model for molecular optimization. J Cheminform. 2020 Jan 8;12(1):2. doi: 10.1186/s13321-019-0404-1.
73. Gayvert KM, Madhukar NS, Elemento O. A Data-Driven Approach to Predicting Successes and Failures of Clinical Trials. Cell Chem Biol. 2016 Oct 20;23(10):1294-1301. doi: 10.1016/j.chembiol.2016.07.023. Epub 2016 Sep 15.
74. Shen J, Cheng F, Xu Y, Li W, Tang Y. Estimation of ADME properties with substructure pattern recognition. J Chem Inf Model. 2010 Jun 28;50(6):1034-41. doi: 10.1021/ci100104j.
75. Liu D, Schilling B, Liu D, Sucker A, Livingstone E, JerbyArnon L, et al. Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma. Nat Med. 2019 Dec;25(12):1916- 1927. doi: 10.1038/s41591-019-0654-5. Epub 2019 Dec 2. Erratum in: Nat Med. 2020 Jul;26(7):1147.
76. Litchfield K, Reading JL, Puttick C, Thakkar K, Abbosh C, Bentham R, et al. Meta-analysis of tumor- and T cell-intrinsic mechanisms of sensitization to checkpoint inhibition. Cell. 2021 Feb 4;184(3):596-614.e14. doi: 10.1016/j.cell.2021.01.002. Epub 2021 Jan 27.
77. Johannet P, Coudray N, Donnelly DM, Jour G, IllaBochaca I, Xia Y, et al. Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma. Clin Cancer Res. 2021 Jan 1;27(1):131-140. doi: 10.1158/1078-0432.CCR-20- 2415. Epub 2020 Nov 18.
78. Sun D, Wang M, Li A. A multimodal deep neural network for human breast cancer prognosis prediction by integrating multi-dimensional data. IEEE/ACM Trans Comput Biol Bioinform. 2018 Feb 15. doi: 10.1109/ TCBB.2018.2806438. Epub ahead of print.
79. Chaudhary K, Poirion OB, Lu L, Garmire LX. Deep Learning-Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer. Clin Cancer Res. 2018 Mar 15;24(6):1248-1259. doi: 10.1158/1078-0432.CCR-17- 0853. Epub 2017 Oct 5.
80. Korfiatis P, Kline TL, Lachance DH, Parney IF, Buckner JC, Erickson BJ. Residual Deep Convolutional Neural Network Predicts MGMT Methylation Status. J Digit Imaging. 2017 Oct;30(5):622-628. doi: 10.1007/s10278-017-0009-z.
81. Mobadersany P, Yousefi S, Amgad M, Gutman DA, Barnholtz-Sloan JS, Velázquez Vega JE, et al. Predicting cancer outcomes from histology and genomics using convolutional networks. Proc Natl Acad Sci U S A. 2018 Mar 27;115(13):E2970-E2979. doi: 10.1073/pnas.1717139115. Epub 2018 Mar 12.
82. Bychkov D, Linder N, Turkki R, Nordling S, Kovanen PE, Verrill C, et al. Deep learning based tissue analysis predicts outcome in colorectal cancer. Sci Rep. 2018 Feb 21;8(1):3395. doi: 10.1038/s41598-018-21758-3.
83. Skrede OJ, De Raedt S, Kleppe A, Hveem TS, Liestøl K, Maddison J, et al. Deep learning for prediction of colorectal cancer outcome: a discovery and validation study. Lancet. 2020 Feb 1;395(10221):350-360. doi: 10.1016/S0140-6736(19)32998-8.
84. Bates DW, Auerbach A, Schulam P, Wright A, Saria S. Reporting and Implementing Interventions Involving Machine Learning and Artificial Intelligence. Ann Intern Med. 2020 Jun 2;172(11 Suppl):S137-S144. doi: 10.7326/M19-0872.
85. Cavallo J. How Watson for Oncology is advancing personalized patient care. http://www.ascopost.com/issues/june-25-2017/how-watson-for-oncology-is-advancing-personalized-patient-care/
86. Zhou N, Zhang CT, Lv HY, Hao CX, Li TJ, Zhu JJ, et al. Concordance Study Between IBM Watson for Oncology and Clinical Practice for Patients with Cancer in China. Oncologist. 2019 Jun;24(6):812-819. doi: 10.1634/theoncologist.2018-0255. Epub 2018 Sep 4.
87. Seidman AD, Pilewskie ML, Robson ME, et al. Integration of multi-modality treatment planning for early stage breast cancer into Watson for Oncology, a decision support system: Seeing the forest and the trees. 2015 ASCO Annual Meeting. Abstract e12042. Presented May 29, 2015.
88. Kris MG, Gucalp A, Epstein AS, et al. Assessing the performance of Watson for oncology, a decision support system, using actual contemporary clinical cases. 2015 ASCO Annual Meeting. Abstract 8023. Presented May 29, 2015.
89. Cavallo J. Confronting the Criticisms Facing Watson for Oncology https://ascopost.com/issues/september-10-2019/
90. Somashekhar SP, Sepúlveda MJ, Shortliffe, et al. A prospective blinded study of 1,000 cases analyzing the role of artificial intelligence: Watson for Oncology and change in decision-making of a multidisciplinary tumor board from a tertiary care cancer center. 2019 ASCO Annual Meeting. Abstract 6533. Presented June 1, 2019.
91. Jie Z, Zhiying Z, Li L. A meta-analysis of Watson for Oncology in clinical application. Sci Rep. 2021 Mar 11;11(1):5792. doi: 10.1038/s41598-021-84973-5. PMID: 33707577.
Cómo citar
[1]
Cardona, A.F. et al. 2022. Biología de sistemas y computacional en cáncer: de lo básico a la próxima frontera. Medicina. 43, 4 (ene. 2022), 631–651. DOI:https://doi.org/10.56050/01205498.1651.
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