Skip to main content
SpringerLink
Log in
Book cover

Artificial Intelligence and Precision Oncology pp 29–48Cite as

Application of AI in Novel Biomarkers Detection that Induces Drug Resistance, Enhance Treatment Regimens, and Advancing Precision Oncology

  • Chapter
  • First Online:

Abstract

Artificial intelligence (AI) is changing the medical research and patient care field by showing data patterns that allow predicting disease, disease progress, and treatment outcomes for individual patients. Big-data sets from these fields require advanced technology for analysis. High cancer mortality negates advances in oncology research. Traditional approaches are becoming inadequate to efficiently combat cancer due to cancer’s heterogenous nature. Accurate risk assessment, prevention, detection, segmentation, and cancer treatment present major challenges for successful patient outcomes. AI-based tool advancement presents a potent weapon for improved cancer care by advancing personalized patient care. These tools have promise for improved therapeutic potential and identifying novel biomarkers and drug targets. Effective implementation of precision oncology needs a positive impact on patient outcome, provides decision support in real time, and discovery of unique patient patterns of disease progression. Emerging technologies present with new challenges; the benefits of AI technology in precision oncology outweigh the challenges. AI-based precision oncology provides augmented intelligence to aid clinician decision-making. Advancement of wet-lab-based assays, high throughput NGS data, bioinformatics tools, and strategies to detect novel biomarkers that accurately predict prognosis and enhance treatment regimens are urgently warranted. This review will focus on AI-based tools in the detection and identification of cancer biomarkers for accurate prognosis with the overall aim of enhancing treatment regimens, advancing precision oncology, and improving patient outcomes.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • AACR. (n.d.) Project GENIE [Online]. Available: http://www.aacr.org/Research/Research/Pages/aacr-project-genie.aspx [Accessed].

  • Ahmed, Z., Mohamed, K., Zeeshan, S., & Dong, X. (2020). Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database (Oxford).

    Book  Google Scholar 

  • Aktas, B., Kasimir-Bauer, S., Heubner, M., Kimmig, R., & Wimberger, P. (2011). Molecular profiling and prognostic relevance of circulating tumor cells in the blood of ovarian cancer patients at primary diagnosis and after platinum-based chemotherapy. International Journal of Gynecological Cancer, 21, 822–830.

    Article  Google Scholar 

  • Alizadeh, A. A., Eisen, M. B., Davis, R. E., Ma, C., Lossos, I. S., Rosenwald, A., Boldrick, J. C., Sabet, H., Tran, T., & Yu, X. (2000). Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature, 403, 503–511.

    Article  CAS  Google Scholar 

  • Ardila, D., Kiraly, A. P., Bharadwaj, S., Choi, B., Reicher, J. J., Peng, L., Tse, D., Etemadi, M., Ye, W., & Corrado, G. (2019). End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nature Medicine, 25, 954–961.

    Article  CAS  Google Scholar 

  • Banerjee, J., Pradhan, R., Gupta, A., Kumar, R., Sahu, V., Upadhyay, A. D., Chaterjee, P., Dwivedi, S., Dey, S., & Dey, A. B. (2017). CDK4 in lung, and head and neck cancers in old age: Evaluation as a biomarker. Clinical and Translational Oncology, 19, 571–578.

    Article  CAS  Google Scholar 

  • Barreto, L., Azambuja, D., & Morais, J. C. D. (2012). Expression of immunohistochemical markers in patients with AIDS-related lymphoma. Brazilian Journal of Infectious Diseases, 16, 74–77.

    Article  Google Scholar 

  • Cai, Q., Verma, S. C., Choi, J.-Y., Ma, M., & Robertson, E. S. (2010). Kaposi's sarcoma-associated herpesvirus inhibits interleukin-4-mediated STAT6 phosphorylation to regulate apoptosis and maintain latency. Journal of Virology, 84, 11134–11144.

    Article  CAS  Google Scholar 

  • Chen, J., Sun, M., & Shen, B. (2015). Deciphering oncogenic drivers: From single genes to integrated pathways. Briefings in Bioinformatics, 16, 413–428.

    Article  CAS  Google Scholar 

  • Chen, H. Y., Yu, S. L., Chen, C. H., Chang, G. C., Chen, C. Y., Yuan, A., Cheng, C. L., Wang, C. H., Terng, H. J., Kao, S. F., Chan, W. K., Li, H. N., Liu, C. C., Singh, S., Chen, W. J., Chen, J. J., & Yang, P. C. (2007). A five-gene signature and clinical outcome in non-small-cell lung cancer. The New England Journal of Medicine, 356, 11–20.

    Article  CAS  Google Scholar 

  • Cheng, L., Davison, D. D., Adams, J., Lopez-Beltran, A., Wang, L., Montironi, R., & Zhang, S. (2014). Biomarkers in bladder cancer: Translational and clinical implications. Critical Reviews in Oncology/Hematology, 89, 73–111.

    Article  Google Scholar 

  • Chyla, B., Daver, N., Doyle, K., Mckeegan, E., Huang, X., Ruvolo, V., Wang, Z., Chen, K., Souers, A., & Leverson, J. (2018). Genetic biomarkers of sensitivity and resistance to venetoclax monotherapy in patients with relapsed acute myeloid leukemia. American Journal of Hematology, 93, E202.

    Article  Google Scholar 

  • Colling, R., Pitman, H., Oien, K., Rajpoot, N., Macklin, P., Snead, D., Sackville, T., & Verrill, C. (2019). Artificial intelligence in digital pathology: A roadmap to routine use in clinical practice. The Journal of Pathology, 249, 143–150.

    Article  Google Scholar 

  • Costa, R. L. B., & Czerniecki, B. J. (2020). Clinical development of immunotherapies for HER2+ breast cancer: A review of HER2-directed monoclonal antibodies and beyond. NPJ Breast Cancer, 6, 1–11.

    Article  Google Scholar 

  • Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A. L., Razavian, N., & Tsirigos, A. (2018). Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning. Nature Medicine, 24, 1559–1567.

    Article  CAS  Google Scholar 

  • Dlamini, Z., Francies, F. Z., Hull, R., & Marima, R. (2020). Artificial intelligence (AI) and big data in cancer and precision oncology. Computational and Structural Biotechnology Journal, 18, 2300–2311.

    Article  CAS  Google Scholar 

  • Dlamini, Z., Mbele, M., Makhafola, T. J., Hull, R., & Marima, R. (2021). HIV-associated cancer biomarkers: A requirement for early diagnosis. International Journal of Molecular Sciences, 22, 8127.

    Article  CAS  Google Scholar 

  • Dlamini, Z., Skepu, A., Kim, N., Mkhabele, M., Khanyile, R., Molefi, T., Mbatha, S., Setlai, B., Mulaudzi, T., Mabongo, M., Bida, M., Kgoebane-Maseko, M., Mathabe, K., Lockhat, Z., Kgokolo, M., Chauke-Malinga, N., Ramagaga, S., & Hull, R. (2022). AI and precision oncology in clinical cancer genomics: From prevention to targeted cancer therapies-an outcomes based patient care. Informatics in Medicine Unlocked, 31, 100965.

    Article  Google Scholar 

  • Echle, A., Rindtorff, N. T., Brinker, T. J., Luedde, T., Pearson, A. T., & Kather, J. N. (2021). Deep learning in cancer pathology: A new generation of clinical biomarkers. British Journal of Cancer, 124, 686–696.

    Article  Google Scholar 

  • Elton, D. C., Chen, A., Pickhardt, P. J., & Summers, R. M. (2021). Cardiovascular disease and all-cause mortality risk prediction from abdominal CT using deep learning (pp. 694–701). SPIE.

    Google Scholar 

  • Fang, B. (2020). Introduction to this special issue: “Biomarker discovery and precision medicine”. Journal of Cancer Metastasis and Treatment, 6, 1.

    CAS  Google Scholar 

  • Farina, E., Nabhen, J. J., Dacoregio, M. I., Batalini, F., & Moraes, F. Y. (2022). An overview of artificial intelligence in oncology. Future Science Open Access, 8, Fso787.

    CAS  Google Scholar 

  • Feng, H., Jin, P., & Wu, H. (2019). Disease prediction by cell-free DNA methylation. Briefings in Bioinformatics, 20, 585–597.

    Article  CAS  Google Scholar 

  • Fiala, C., & Diamandis, E. P. (2018). Utility of circulating tumor DNA in cancer diagnostics with emphasis on early detection. BMC Medicine, 16, 166.

    Article  CAS  Google Scholar 

  • Fitzgerald, J., Higgins, D., Mazo Vargas, C., Watson, W., Mooney, C., Rahman, A., Aspell, N., Connolly, A., Aura Gonzalez, C., & Gallagher, W. (2021). Future of biomarker evaluation in the realm of artificial intelligence algorithms: Application in improved therapeutic stratification of patients with breast and prostate cancer. Journal of Clinical Pathology, 74, 429.

    Article  CAS  Google Scholar 

  • Fountzilas, E., & Tsimberidou, A. M. (2018). Overview of precision oncology trials: Challenges and opportunities. Expert Review of Clinical Pharmacology, 11, 797–804.

    Article  CAS  Google Scholar 

  • Gerlinger, M., Horswell, S., Larkin, J., Rowan, A. J., Salm, M. P., Varela, I., Fisher, R., Mcgranahan, N., Matthews, N., Santos, C. R., Martinez, P., Phillimore, B., Begum, S., Rabinowitz, A., Spencer-Dene, B., Gulati, S., Bates, P. A., Stamp, G., Pickering, L., Gore, M., Nicol, D. L., Hazell, S., Futreal, P. A., Stewart, A., & Swanton, C. (2014). Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing. Nature Genetics, 46, 225–233.

    Article  CAS  Google Scholar 

  • Gordevičius, J., Kriščiūnas, A., Groot, D. E., Yip, S. M., Susic, M., Kwan, A., Kustra, R., Joshua, A. M., Chi, K. N., Petronis, A., & Oh, G. (2018). Cell-free DNA modification dynamics in abiraterone acetate-treated prostate cancer patients. Clinical Cancer Research, 24, 3317–3324.

    Article  Google Scholar 

  • Guo, S., Diep, D., Plongthongkum, N., Fung, H. L., Zhang, K., & Zhang, K. (2017). Identification of methylation haplotype blocks aids in deconvolution of heterogeneous tissue samples and tumor tissue-of-origin mapping from plasma DNA. Nature Genetics, 49, 635–642.

    Article  CAS  Google Scholar 

  • Haenssle, H. A., Fink, C., Schneiderbauer, R., Toberer, F., Buhl, T., Blum, A., Kalloo, A., Hassen, A. B. H., Thomas, L., & Enk, A. (2018). Man against machine: Diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Annals of Oncology, 29, 1836–1842.

    Article  CAS  Google Scholar 

  • Heckman-Stoddard, B. M. (2012). Oncology biomarkers: Discovery, validation, and clinical use (pp. 93–98). Elsevier.

    Google Scholar 

  • Hildebrand, L. A., Pierce, C. J., Dennis, M., Paracha, M., & Maoz, A. (2021). Artificial intelligence for histology-based detection of microsatellite instability and prediction of response to immunotherapy in colorectal cancer. Cancers, 13, 391.

    Article  CAS  Google Scholar 

  • Hoffman, R. M., Gilliland, F. D., Adams-Cameron, M., Hunt, W. C., & Key, C. R. (2002). Prostate-specific antigen testing accuracy in community practice. BMC Family Practice, 3, 19.

    Article  Google Scholar 

  • Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018). Artificial intelligence in radiology. Nature Reviews Cancer, 18, 500–510.

    Article  CAS  Google Scholar 

  • Ilié, M., & Hofman, P. (2016). Pros: Can tissue biopsy be replaced by liquid biopsy? Translational Lung Cancer Research, 5, 420–423.

    Article  Google Scholar 

  • Ioannidis, J. P. A., & Khoury, M. J. (2018). Evidence-based medicine and big genomic data. Human Molecular Genetics, 27, R2–r7.

    Article  CAS  Google Scholar 

  • Ivanov, M., Baranova, A., Butler, T., Spellman, P., & Mileyko, V. (2015). Non-random fragmentation patterns in circulating cell-free DNA reflect epigenetic regulation. BMC Genomics, 16(Suppl 13), S1.

    Article  Google Scholar 

  • Jacobs, I. J., Menon, U., Ryan, A., Gentry-Maharaj, A., Burnell, M., Kalsi, J. K., Amso, N. N., Apostolidou, S., Benjamin, E., Cruickshank, D., Crump, D. N., Davies, S. K., Dawnay, A., Dobbs, S., Fletcher, G., Ford, J., Godfrey, K., Gunu, R., Habib, M., Hallett, R., Herod, J., Jenkins, H., Karpinskyj, C., Leeson, S., Lewis, S. J., Liston, W. R., Lopes, A., Mould, T., Murdoch, J., Oram, D., Rabideau, D. J., Reynolds, K., Scott, I., Seif, M. W., Sharma, A., Singh, N., Taylor, J., Warburton, F., Widschwendter, M., Williamson, K., Woolas, R., Fallowfield, L., Mcguire, A. J., Campbell, S., Parmar, M., & Skates, S. J. (2016). Ovarian cancer screening and mortality in the UK collaborative trial of ovarian cancer screening (UKCTOCS): A randomised controlled trial. Lancet, 387, 945–956.

    Article  Google Scholar 

  • Jahr, S., Hentze, H., Englisch, S., Hardt, D., Fackelmayer, F. O., Hesch, R. D., & Knippers, R. (2001). DNA fragments in the blood plasma of cancer patients: Quantitations and evidence for their origin from apoptotic and necrotic cells. Cancer Research, 61, 1659–1665.

    CAS  Google Scholar 

  • Jain, A. (2016). The 5 Vs of big data. Available from https://www.ibm.com/blogs/watson-health/the-5-vs-of-big-data

  • Kang, J., D’Andrea, A. D., & Kozono, D. (2012). A DNA repair pathway-focused score for prediction of outcomes in ovarian cancer treated with platinum-based chemotherapy. Journal of the National Cancer Institute, 104, 670–681.

    Article  CAS  Google Scholar 

  • Kang, S., Li, Q., Chen, Q., Zhou, Y., Park, S., Lee, G., Grimes, B., Krysan, K., Yu, M., Wang, W., Alber, F., Sun, F., Dubinett, S. M., Li, W., & Zhou, X. J. (2017). CancerLocator: Non-invasive cancer diagnosis and tissue-of-origin prediction using methylation profiles of cell-free DNA. Genome Biology, 18, 53.

    Article  Google Scholar 

  • Kather, J. N., Heij, L. R., Grabsch, H. I., Loeffler, C., Echle, A., Muti, H. S., Krause, J., Niehues, J. M., Sommer, K. A. J., & Bankhead, P. (2020). Pan-cancer image-based detection of clinically actionable genetic alterations. Nature Cancer, 1, 789–799.

    Article  CAS  Google Scholar 

  • Kather, J. N., Krisam, J., Charoentong, P., Luedde, T., Herpel, E., Weis, C.-A., Gaiser, T., Marx, A., Valous, N. A., Ferber, D., Jansen, L., Reyes-Aldasoro, C. C., Zörnig, I., Jäger, D., Brenner, H., Chang-Claude, J., Hoffmeister, M., & Halama, N. (2019). Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study. PLoS Medicine, 16, e1002730.

    Article  Google Scholar 

  • Kather, J. N., Pearson, A. T., Halama, N., Jäger, D., Krause, J., Loosen, S. H., Marx, A., Boor, P., Tacke, F., & Neumann, U. P. (2019). Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nature Medicine, 25, 1054–1056.

    Article  CAS  Google Scholar 

  • Khan, K. H., Cunningham, D., Werner, B., Vlachogiannis, G., Spiteri, I., Heide, T., Mateos, J. F., Vatsiou, A., Lampis, A., Damavandi, M. D., Lote, H., Huntingford, I. S., Hedayat, S., Chau, I., Tunariu, N., Mentrasti, G., Trevisani, F., Rao, S., Anandappa, G., Watkins, D., Starling, N., Thomas, J., Peckitt, C., Khan, N., Rugge, M., Begum, R., Hezelova, B., Bryant, A., Jones, T., Proszek, P., Fassan, M., Hahne, J. C., Hubank, M., Braconi, C., Sottoriva, A., & Valeri, N. (2018). Longitudinal liquid biopsy and mathematical Modeling of clonal evolution forecast time to treatment failure in the PROSPECT-C phase II colorectal cancer clinical trial. Cancer Discovery, 8, 1270–1285.

    Article  CAS  Google Scholar 

  • Koch, A., Joosten, S. C., Feng, Z., De Ruijter, T. C., Draht, M. X., Melotte, V., Smits, K. M., Veeck, J., Herman, J. G., Van Neste, L., Van Criekinge, W., De Meyer, T., & Van Engeland, M. (2018). Analysis of DNA methylation in cancer: Location revisited. Nature Reviews. Clinical Oncology, 15, 459–466.

    Article  CAS  Google Scholar 

  • Kohn, M. S., Sun, J., Knoop, S., Shabo, A., Carmeli, B., Sow, D., Syed-Mahmood, T., & Rapp, W. (2014). IBM's health analytics and clinical decision support. Yearbook of Medical Informatics, 9, 154–162.

    CAS  Google Scholar 

  • Lancellotti, C., Cancian, P., Savevski, V., Kotha, S. R. R., Fraggetta, F., Graziano, P., & Di Tommaso, L. (2021). Artificial Intelligence & tissue biomarkers: advantages, risks and perspectives for pathology. Cells, 10, 787.

    Article  Google Scholar 

  • Le, D. T., Durham, J. N., Smith, K. N., Wang, H., Bartlett, B. R., Aulakh, L. K., Lu, S., Kemberling, H., Wilt, C., & Luber, B. S. (2017). Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science, 357, 409–413.

    Article  CAS  Google Scholar 

  • Lehmann-Werman, R., Neiman, D., Zemmour, H., Moss, J., Magenheim, J., Vaknin-Dembinsky, A., Rubertsson, S., Nellgård, B., Blennow, K., Zetterberg, H., Spalding, K., Haller, M. J., Wasserfall, C. H., Schatz, D. A., Greenbaum, C. J., Dorrell, C., Grompe, M., Zick, A., Hubert, A., Maoz, M., Fendrich, V., Bartsch, D. K., Golan, T., Ben Sasson, S. A., Zamir, G., Razin, A., Cedar, H., Shapiro, A. M., Glaser, B., Shemer, R., & Dor, Y. (2016). Identification of tissue-specific cell death using methylation patterns of circulating DNA. Proceedings of the National Academy of Sciences of the United States of America, 113, E1826–E1834.

    CAS  Google Scholar 

  • Lesko, L. J., & Atkinson, A. J., Jr. (2001). Use of biomarkers and surrogate endpoints in drug development and regulatory decision making: Criteria, validation, strategies. Annual Review of Pharmacology and Toxicology, 41, 347.

    Article  CAS  Google Scholar 

  • Leygo, C., Williams, M., Jin, H. C., Chan, M. W. Y., Chu, W. K., Grusch, M., & Cheng, Y. Y. (2017). DNA methylation as a noninvasive epigenetic biomarker for the detection of cancer. Disease Markers, 2017, 3726595.

    Article  Google Scholar 

  • Li, W., Zhang, X., Lu, X., You, L., Song, Y., Luo, Z., Zhang, J., Nie, J., Zheng, W., Xu, D., Wang, Y., Dong, Y., Yu, S., Hong, J., Shi, J., Hao, H., Luo, F., Hua, L., Wang, P., Qian, X., Yuan, F., Wei, L., Cui, M., Zhang, T., Liao, Q., Dai, M., Liu, Z., Chen, G., Meckel, K., Adhikari, S., Jia, G., Bissonnette, M. B., Zhang, X., Zhao, Y., Zhang, W., He, C., & Liu, J. (2017). 5-Hydroxymethylcytosine signatures in circulating cell-free DNA as diagnostic biomarkers for human cancers. Cell Research, 27, 1243–1257.

    Article  CAS  Google Scholar 

  • Lim, S. M., Hong, M. H., & Kim, H. R. (2020). Immunotherapy for non-small cell lung cancer: Current landscape and future perspectives. Immune Network, 20, e10.

    Article  Google Scholar 

  • Lin, Y., Qian, F., Shen, L., Chen, F., Chen, J., & Shen, B. (2019). Computer-aided biomarker discovery for precision medicine: Data resources, models and applications. Briefings in Bioinformatics, 20, 952–975.

    Article  CAS  Google Scholar 

  • Liu, L., Toung, J. M., Jassowicz, A. F., Vijayaraghavan, R., Kang, H., Zhang, R., Kruglyak, K. M., Huang, H. J., Hinoue, T., Shen, H., Salathia, N. S., Hong, D. S., Naing, A., Subbiah, V., Piha-Paul, S. A., Bibikova, M., Granger, G., Barnes, B., Shen, R., Gutekunst, K., Fu, S., Tsimberidou, A. M., Lu, C., Eng, C., Moulder, S. L., Kopetz, E. S., Amaria, R. N., Meric-Bernstam, F., Laird, P. W., Fan, J. B., & Janku, F. (2018). Targeted methylation sequencing of plasma cell-free DNA for cancer detection and classification. Annals of Oncology, 29, 1445–1453.

    Article  CAS  Google Scholar 

  • Lundervold, A. S., & Lundervold, A. (2019). An overview of deep learning in medical imaging focusing on MRI. Zeitschrift für Medizinische Physik, 29, 102–127.

    Article  Google Scholar 

  • Luo, H., Xu, G., Li, C., He, L., Luo, L., Wang, Z., Jing, B., Deng, Y., Jin, Y., & Li, Y. (2019). Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: A multicentre, case-control, diagnostic study. The Lancet Oncology, 20, 1645–1654.

    Article  CAS  Google Scholar 

  • Ma, J., Yang, J., Jin, Y., Cheng, S., Huang, S., Zhang, N., & Wang, Y. (2021). Artificial intelligence based on blood biomarkers including CTCs predicts outcomes in epithelial ovarian cancer: A prospective study. Oncotargets and Therapy, 14, 3267–3280.

    Article  Google Scholar 

  • Maisel, A. S., Katz, N., Hillege, H. L., Shaw, A., Zanco, P., Bellomo, R., Anand, I., Anker, S. D., Aspromonte, N., & Bagshaw, S. M. (2011). Biomarkers in kidney and heart disease. Nephrology Dialysis Transplantation, 26, 62–74.

    Article  Google Scholar 

  • Makary, M. A., & Daniel, M. (2016). Medical error-the third leading cause of death in the US. BMJ, 353, i2139.

    Article  Google Scholar 

  • Mayekar, M. K., & Bivona, T. G. (2017). Current landscape of targeted therapy in lung cancer. Clinical Pharmacology and Therapeutics, 102, 757–764.

    Article  Google Scholar 

  • Merker, J. D., Oxnard, G. R., Compton, C., Diehn, M., Hurley, P., Lazar, A. J., Lindeman, N., Lockwood, C. M., Rai, A. J., Schilsky, R. L., Tsimberidou, A. M., Vasalos, P., Billman, B. L., Oliver, T. K., Bruinooge, S. S., Hayes, D. F., & Turner, N. C. (2018). Circulating tumor DNA analysis in patients with cancer: American Society of Clinical Oncology and College of American Pathologists Joint Review. Journal of Clinical Oncology, 36, 1631–1641.

    Article  CAS  Google Scholar 

  • Mesko, B. (2017). The role of artificial intelligence in precision medicine. Expert Review of Precision Medicine and Drug Development, 2, 239–241.

    Article  Google Scholar 

  • Miller, R. S., & Wong, J. L. (2018). Using oncology real-world evidence for quality improvement and discovery: The case for ASCO’s CancerLinQ. Future Oncology, 14, 5–8.

    Article  CAS  Google Scholar 

  • Mondelo-Macía, P., García-González, J., León-Mateos, L., Castillo-García, A., López-López, R., Muinelo-Romay, L., & Díaz-Peña, R. (2021). Current status and future perspectives of liquid biopsy in small cell lung cancer. Biomedicines, 9(1), 48. https://doi.org/10.3390/biomedicines9010048

    Article  CAS  Google Scholar 

  • Naito, Y., & Urasaki, T. (2018). Precision medicine in breast cancer. Chinese Clinical Oncology, 7, 29.

    Article  Google Scholar 

  • Narod, S. (2016). Can advanced-stage ovarian cancer be cured? Nature Reviews. Clinical Oncology, 13, 255–261.

    Article  CAS  Google Scholar 

  • Niazi, M. K. K., Parwani, A. V., & Gurcan, M. N. (2019). Digital pathology and artificial intelligence. The Lancet Oncology, 20, e253–e261.

    Article  Google Scholar 

  • Ossandon, M. R., Agrawal, L., Bernhard, E. J., Conley, B. A., Dey, S. M., Divi, R. L., Guan, P., Lively, T. G., Mckee, T. C., Sorg, B. S., & Tricoli, J. V. (2018). Circulating tumor DNA assays in clinical cancer research. Journal of the National Cancer Institute, 110, 929–934.

    Article  Google Scholar 

  • Pantel, K. (2016). Blood-based analysis of circulating cell-free DNA and tumor cells for early cancer detection. PLoS Medicine, 13, e1002205.

    Article  Google Scholar 

  • Parwani, A. V., & Amin, M. B. (2020). Convergence of digital pathology and artificial intelligence tools in anatomic pathology practice: Current landscape and future directions. Advances in Anatomic Pathology, 27, 221–226.

    Article  Google Scholar 

  • Peltomäki, P. (2012). Mutations and epimutations in the origin of cancer. Experimental Cell Research, 318, 299–310.

    Article  Google Scholar 

  • Pickhardt, P. J., Graffy, P. M., Zea, R., Lee, S. J., Liu, J., Sandfort, V., & Summers, R. M. (2020). Automated CT biomarkers for opportunistic prediction of future cardiovascular events and mortality in an asymptomatic screening population: A retrospective cohort study. The Lancet Digital Health, 2, e192–e200.

    Article  Google Scholar 

  • Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380, 1347–1358.

    Article  Google Scholar 

  • Rezvantalab, A., Safigholi, H. & Karimijeshni, S. (2018). Dermatologist level dermoscopy skin cancer classification using different deep learning convolutional neural networks algorithms. arXiv preprint arXiv:1810.10348.

    Google Scholar 

  • Sabaawy, H. E. (2013). Genetic heterogeneity and clonal evolution of tumor cells and their impact on precision cancer medicine. Journal of Leukemia (Los Angel), 1, 1000124.

    Google Scholar 

  • Sahutoglu, T., Sakaci, T., Hasbal, N. B., Ahbap, E., Kara, E., Sumerkan, M. C., Sevinc, M., Akgol, C., Koc, Y., & Basturk, T. (2017). Serum VEGF-C levels as a candidate biomarker of hypervolemia in chronic kidney disease. Medicine, 96, e6543.

    Article  CAS  Google Scholar 

  • Saltz, J., Gupta, R., Hou, L., Kurc, T., Singh, P., Nguyen, V., Samaras, D., Shroyer, K. R., Zhao, T., Batiste, R., Van Arnam, J., Shmulevich, I., Rao, A. U. K., Lazar, A. J., Sharma, A., & Thorsson, V. (2018). Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. Cell Reports, 23, 181–193.e7.

    Article  CAS  Google Scholar 

  • Skrede, O. J., De Raedt, S., Kleppe, A., Hveem, T. S., Liestøl, K., Maddison, J., Askautrud, H. A., Pradhan, M., Nesheim, J. A., Albregtsen, F., Farstad, I. N., Domingo, E., Church, D. N., Nesbakken, A., Shepherd, N. A., Tomlinson, I., Kerr, R., Novelli, M., Kerr, D. J., & Danielsen, H. E. (2020). Deep learning for prediction of colorectal cancer outcome: A discovery and validation study. Lancet, 395, 350–360.

    Article  CAS  Google Scholar 

  • Snyder, M. W., Kircher, M., Hill, A. J., Daza, R. M., & Shendure, J. (2016). Cell-free DNA comprises an in vivo nucleosome footprint that informs its tissues-of-origin. Cell, 164, 57–68.

    Article  CAS  Google Scholar 

  • Song, C. X., Yin, S., Ma, L., Wheeler, A., Chen, Y., Zhang, Y., Liu, B., Xiong, J., Zhang, W., Hu, J., Zhou, Z., Dong, B., Tian, Z., Jeffrey, S. S., Chua, M. S., So, S., Li, W., Wei, Y., Diao, J., Xie, D., & Quake, S. R. (2017). 5-Hydroxymethylcytosine signatures in cell-free DNA provide information about tumor types and stages. Cell Research, 27, 1231–1242.

    Article  CAS  Google Scholar 

  • Strimbu, K., & Tavel, J. A. (2010). What are biomarkers? Current Opinion in HIV and AIDS, 5, 463–466.

    Article  Google Scholar 

  • Tian, X., Sun, B., Chen, C., Gao, C., Zhang, J., Lu, X., Wang, L., Li, X., Xing, Y., Liu, R., Han, X., Qi, Z., Zhang, X., He, C., Han, D., Yang, Y. G., & Kan, Q. (2018). Circulating tumor DNA 5-hydroxymethylcytosine as a novel diagnostic biomarker for esophageal cancer. Cell Research, 28, 597–600.

    Article  CAS  Google Scholar 

  • Verma, M. (2012). Personalized medicine and cancer. Journal of Personalized Medicine, 2, 1–14.

    Article  CAS  Google Scholar 

  • Wan, J. C. M., Massie, C., Garcia-Corbacho, J., Mouliere, F., Brenton, J. D., Caldas, C., Pacey, S., Baird, R., & Rosenfeld, N. (2017). Liquid biopsies come of age: Towards implementation of circulating tumour DNA. Nature Reviews. Cancer, 17, 223–238.

    Article  CAS  Google Scholar 

  • Wang, F., Casalino, L. P., & Khullar, D. (2019). Deep learning in medicine—Promise, progress, and challenges. JAMA Internal Medicine, 179, 293–294.

    Article  Google Scholar 

  • Wang, D., Khosla, A., Gargeya, R., Irshad, H. & Beck, A. H. (2016). Deep learning for identifying metastatic breast cancer. ArXiv, abs/1606.05718.

    Google Scholar 

  • Waseem, M., Ahmad, M. K., Srivatava, V. K., Rastogi, N., Serajuddin, M., Kumar, S., Mishra, D. P., Sankhwar, S. N., & Mahdi, A. A. (2017). Evaluation of miR-711 as novel biomarker in prostate cancer progression. Asian Pacific Journal of Cancer Prevention: APJCP, 18, 2185.

    Google Scholar 

  • Wedge, E., Hansen, J. W., Garde, C., Asmar, F., Tholstrup, D., Kristensen, S. S., Munch-Petersen, H. D., Ralfkiaer, E., Brown, P., Grønbaek, K., & Kristensen, L. S. (2017). Global hypomethylation is an independent prognostic factor in diffuse large B cell lymphoma. American Journal of Hematology, 92, 689–694.

    Article  CAS  Google Scholar 

  • Williams, B. J., Lee, J., Oien, K. A., & Treanor, D. (2018). Digital pathology access and usage in the UK: Results from a national survey on behalf of the National Cancer Research Institute's CM-path initiative. Journal of Clinical Pathology, 71, 463–466.

    Article  Google Scholar 

  • Wrzeszczynski, K. O., Frank, M. O., Koyama, T., Rhrissorrakrai, K., Robine, N., Utro, F., Emde, A.-K., Chen, B.-J., Arora, K., & Shah, M. (2017). Comparing sequencing assays and human-machine analyses in actionable genomics for glioblastoma. Neurology Genetics, 3, e164.

    Article  CAS  Google Scholar 

  • Wulczyn, E., Steiner, D. F., Xu, Z., Sadhwani, A., Wang, H., Flament-Auvigne, I., Mermel, C. H., Chen, P.-H. C., Liu, Y., & Stumpe, M. C. (2020). Deep learning-based survival prediction for multiple cancer types using histopathology images. PLoS One, 15, e0233678.

    Article  CAS  Google Scholar 

  • Wyatt, A. W., Annala, M., Aggarwal, R., Beja, K., Feng, F., Youngren, J., Foye, A., Lloyd, P., Nykter, M., Beer, T. M., Alumkal, J. J., Thomas, G. V., Reiter, R. E., Rettig, M. B., Evans, C. P., Gao, A. C., Chi, K. N., Small, E. J., & Gleave, M. E. (2017). Concordance of circulating tumor DNA and matched metastatic tissue biopsy in prostate cancer. Journal of the National Cancer Institute, 109, 78.

    Article  Google Scholar 

  • Xu, R. H., Wei, W., Krawczyk, M., Wang, W., Luo, H., Flagg, K., Yi, S., Shi, W., Quan, Q., Li, K., Zheng, L., Zhang, H., Caughey, B. A., Zhao, Q., Hou, J., Zhang, R., Xu, Y., Cai, H., Li, G., Hou, R., Zhong, Z., Lin, D., Fu, X., Zhu, J., Duan, Y., Yu, M., Ying, B., Zhang, W., Wang, J., Zhang, E., Zhang, C., Li, O., Guo, R., Carter, H., Zhu, J. K., Hao, X., & Zhang, K. (2017). Circulating tumour DNA methylation markers for diagnosis and prognosis of hepatocellular carcinoma. Nature Materials, 16, 1155–1161.

    Article  CAS  Google Scholar 

  • Yamada, M., Saito, Y., Imaoka, H., Saiko, M., Yamada, S., Kondo, H., Takamaru, H., Sakamoto, T., Sese, J., & Kuchiba, A. (2019). Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy. Scientific Reports, 9, 1–9.

    Article  Google Scholar 

  • Yap, J., Yolland, W., & Tschandl, P. (2018). Multimodal skin lesion classification using deep learning. Experimental Dermatology, 27, 1261–1267.

    Article  Google Scholar 

  • Zeng, H., He, B., Yi, C., & Peng, J. (2018). Liquid biopsies: DNA methylation analyses in circulating cell-free DNA. Journal of Genetics and Genomics, 45, 185–192.

    Article  Google Scholar 

  • Zeng, C., Stroup, E. K., Zhang, Z., Chiu, B. C., & Zhang, W. (2019). Towards precision medicine: Advances in 5-hydroxymethylcytosine cancer biomarker discovery in liquid biopsy. Cancer Communications (London), 39, 12.

    Article  Google Scholar 

  • Zhang, J., Fujimoto, J., Zhang, J., Wedge, D. C., Song, X., Zhang, J., Seth, S., Chow, C. W., Cao, Y., Gumbs, C., Gold, K. A., Kalhor, N., Little, L., Mahadeshwar, H., Moran, C., Protopopov, A., Sun, H., Tang, J., Wu, X., Ye, Y., William, W. N., Lee, J. J., Heymach, J. V., Hong, W. K., Swisher, S., Wistuba, I. I., & Futreal, P. A. (2014). Intratumor heterogeneity in localized lung adenocarcinomas delineated by multiregion sequencing. Science, 346, 256–259.

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. SAMRC Precision Oncology Research Unit (PORU), DSI/NRF SARChI Chair in Precision Oncology and Cancer Prevention (POCP), Pan African Cancer Research Institute (PACRI), University of Pretoria, Pretoria, South Africa

    Rahaba Marima, Kgomotso Mathabe, Afra Basera, Mahlori Mkhabele, Meshack Bida & Zodwa Dlamini

  2. Department of Anatomical Pathology, Faculty of Health Sciences, University of the Witwatersrand, Parktown, South Africa

    Nompumelelo Mtshali

  3. Department of Urology, Faculty of Health Sciences, Steve Biko Academic Hospital, University of Pretoria, Pretoria, South Africa

    Kgomotso Mathabe

  4. Department of Medical Oncology, Faculty of Health Sciences, Steve Biko Academic Hospital, University of Pretoria, Pretoria, South Africa

    Afra Basera

  5. Department of Anatomical Pathology, National Health Laboratory Service (NHLS), University of Pretoria, Pretoria, South Africa

    Meshack Bida

Authors
  1. Rahaba Marima
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nompumelelo Mtshali
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kgomotso Mathabe
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Afra Basera
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mahlori Mkhabele
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Meshack Bida
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Zodwa Dlamini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rahaba Marima .

Editor information

Editors and Affiliations

  1. SAMRC Precision Oncology Research Unit (PORU), DSI/NRF SARChI Chair in Precision Oncology and Cancer Prevention (POCP), Pan African Cancer Research Institute (PACRI), University of Pretoria, Pretoria, South Africa

    Zodwa Dlamini

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Marima, R. et al. (2023). Application of AI in Novel Biomarkers Detection that Induces Drug Resistance, Enhance Treatment Regimens, and Advancing Precision Oncology. In: Dlamini, Z. (eds) Artificial Intelligence and Precision Oncology. Springer, Cham. https://doi.org/10.1007/978-3-031-21506-3_2

Download citation

Publish with us

Policies and ethics

Search

Navigation