From Patient Engagement to Precision Oncology: Leveraging Informatics to Advance Cancer Care

Summary

Objectives: Conduct a survey of the literature for advancements in cancer informatics over the last three years in three specific areas where there has been unprecedented growth: 1) digital health; 2) machine learning; and 3) precision oncology. We also highlight the ethical implications and future opportunities within each area.

Methods: A search was conducted over a three-year period in two electronic databases (PubMed, Google Scholar) to identify peer-reviewed articles and conference proceedings. Search terms included variations of the following: neoplasms[MeSH], informatics[MeSH], cancer, oncology, clinical cancer informatics, medical cancer informatics. The search returned too many articles for practical review (23,994 from PubMed and 23,100 from Google Scholar). Thus, we conducted searches of key PubMed-indexed informatics journals and proceedings. We further limited our search to manuscripts that demonstrated a clear focus on clinical or translational cancer informatics. Manuscripts were then selected based on their methodological rigor, scientific impact, innovation, and contribution towards cancer informatics as a field or on their impact on cancer care and research.

Results: Key developments and opportunities in cancer informatics research in the areas of digital health, machine learning, and precision oncology were summarized.

Conclusion: While there are numerous innovations in the field of cancer informatics to advance prevention and clinical care, considerable challenges remain related to data sharing and privacy, digital accessibility, and algorithm biases and interpretation. The implementation and application of these findings in cancer care necessitates further consideration and research.

Keywords

Cancer informatics - precision medicine - oncology - machine learning - digital health

• References

• 1 SIMPRO Study Team. Electronic symptom management implementation of patient reported outcomes in oncology research; 2019. Available from: https://www.esymcancermoonshot.org [Accessed 14 Nov 2019]
• 2 U.S. National Cancer Institute. NCI and the Precision Medicine Initiative; 2017. Available from: https://www.cancer.gov/research/areas/treatment/pmi-oncology [Accessed 11 Nov 2019]
• 3 U.S. Food and Drug Administration. Digital Health. Available from: https://www.fda.gov/medical-devices/digital-health [Accessed 14 Nov 2019]
• 4 Chung AE, Jensen RE, Basch EM. Leveraging emerging technologies and the “Internet of Things” to improve the quality of cancer care. J Oncol Pract 2016; 12 (10) 863-6
• 5 Ariza-Garcia A, Lozano-Lozano M, Galiano-Castillo N, Postigo-Martin P, Arroyo-Morales M, Cantarero-Villanueva I. A web-based exercise system (e-CuidateChemo) to counter the side effects of chemotherapy in patients with breast cancer: randomized controlled trial. J Med Internet Res 2019; 21 (07) e14418
• 6 Jessup DL, Glover Iv M, Daye D, Banzi L, Jones P, Choy G. et al. Implementation of digital awareness strategies to engage patients and providers in a lung cancer screening program: retrospective study. J Med Internet Res 2018; 20 (02) e52
• 7 Sedrak MS, Salgia MM, Decat Bergerot C, Ashing-Giwa K, Cotta BN, Adashek JJ. et al Examining public communication about kidney cancer on Twitter. JCO Clin Cancer Inform 2019; 3: 1-6
• 8 Chen L, Wang X, Peng TQ. Nature and diffusion of gynecologic cancer-related misinformation on social media: analysis of tweets. J Med Internet Res 2018; 20 (10) e11515
• 9 Taylor J, Pagliari C. The social dynamics of lung cancer talk on Twitter, Facebook and Macmillan.org.uk. NPJ Digit Med 2019; 2: 51
• 10 Cho H, Silver N, Na K, Adams D, Luong KT, Song C. Visual cancer communication on social media: an examination of content and effects of #melanomasucks. J Med Internet Res 2018; 20 (09) e10501
• 11 Basch E, Deal AM, Dueck AC, Scher HI, Kris MG, Hudis C. et al. Overall survival results of a trial assessing patient-reported outcomes for symptom monitoring during routine cancer treatment. JAMA 2017; 318 (02) 197-8
• 12 Roth AJ, Kornblith AB, Batel-Copel L, Peabody E, Scher HI, Holland JC. Rapid screening for psychologic distress in men with prostate carcinoma: a pilot study. Cancer 1998; 82 (10) 1904-8
• 13 National Comprehensive Cancer Network. NCCN Clinical Practice Guidelines in Oncology: Distress Management. Available from: https://www.nccn.org/professionals/physician_gls/default.aspx [Accessed 17 Nov 2019]
• 14 Richardson L, Jones G. A review of the reliability and validity of the Edmonton Symptom Assessment System. Curr Oncol 2009; 16 (01) 55
• 15 Girgis A, Durcinoska I, Koh ES, Ng W, Arnold A, Delaney GP. et al Development of health pathways to standardize cancer care pathways informed by patient-reported outcomes and clinical practice guidelines. JCO Clin Cancer Inform 2018; 2: 1-13
• 16 Chung AE, Shoenbill K, Mitchell SA, Dueck AC, Schrag D, Bruner DW. et al. Patient free text reporting of symptomatic adverse events in cancer clinical research using the National Cancer Institute’s Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE). J Am Med Inform Assoc 2019; 26 (04) 276-85
• 17 Gresham G, Hendifar AE, Spiegel B, Neeman E, Tuli R, Rimel BJ. et al Wearable activity monitors to assess performance status and predict clinical outcomes in advanced cancer patients. NPJ Digit Med 2018; 1: 27
• 18 Gupta A, Stewart T, Bhulani N, Dong Y, Rahimi Z, Crane K. et al Feasibility of wearable physical activity monitors in patients with cancer. JCO Clin Cancer Inform 2018; 2: 1-10
• 19 Chung IY, Jung M, Lee SB, Lee JW, Park YR, Cho D. et al. An assessment of physical activity data collected via a smartphone app and a smart band in breast cancer survivors: observational study. J Med Internet Res 2019; 21 (09) 13463
• 20 Savova GK, Danciu I, Alamudun F, Miller T, Lin C, Bitterman DS. et al. Use of natural language processing to extract clinical cancer phenotypes from electronic medical records. Cancer Res 2019; 79 (21) 5463-70
• 21 Gonzalez-Hernandez G, Sarker A, O’Connor K, Savova G. Capturing the patient’s perspective: a review of advances in natural language processing of health-related text. Yearb Med Inform 2017; 214-17
• 22 Savova GK, Tseytlin E, Finan S, Castine M, Miller T, Medvedeva O. et al. DeepPhe: a natural language processing system for extracting cancer phenotypes from clinical records. Cancer Res 2017; 77 (21) e115-e118
• 23 Guan M, Cho S, Petro R, Zhang W, Pasche B, Topaloglu U. Natural language processing and recurrent network models for identifying genomic mutation-associated cancer treatment change from patient progress notes. JAMIA Open 2019; 2 (01) 139-49
• 24 Qiu JX, Yoon H, Fearn PA, Tourassi GD. Deep learning for automated extraction of primary sites from cancer pathology reports. IEEE J Biomed Health Inform 2018; 22 (01) 244-51
• 25 Observational Health Data Sciences and Informatics OMOP Common Data Model. Available from: https://www.ohdsi.org/data-standardization/the-common-data-model/ [Accessed 16 Dec 2019]
• 26 Seneviratne MG, Banda JM, Brooks JD, Shah NH, Hernandez-Boussard TM. Identifying cases of metastatic prostate cancer using machine learning on electronic health records. AMIA Annual Symp Proc 2018; 2018: 1498-504
• 27 Zhang S, Grave E, Sklar E, Elhadad N. Longitudinal analysis of discussion topics in online breast cancer community using convolutional neural networks. J Biomed Inform 2017; 69: 1-9
• 28 Lee YJ, Kamen C, Margolies L, Boehmer U. Online health community experiences of sexual minority women with cancer. J Am Med Inform Assoc 2019; 26 (8-9): 759-66
• 29 Temporal History of Your Medical Events (THYME) Corpus. Available from: https://clear.colorado.edu/TemporalWiki/index.php/Main_Page [Accessed 18 March 2020]
• 30 Styler WF, Bethard S, Finan S, Palmer M, Pradhan S, de Groen PC. et al. Temporal annotation in the clinical domain. Trans Assoc Comput Linguist 2014; 2: 143-54
• 31 Informatics for Integrating Biology & the Bedside (i2b2). NLP Research Datasets; 2020. Available from: https://www.i2b2.org/NLP/DataSets/Main.php [Accessed 19 March 2020]
• 32 Johnson AEW, Pollard TJ, Shen L, Lehman LH, Feng M, Ghassemi M. et al. MIMIC-III, a free accessible critical care database. Sci Data 2016(3).
• 33 Laajala TD, Murtojärvi M, Virkki A, Aittokallio T. ePCR: an R-package for survival and time-to-event prediction in advanced prostate cancer, applied to real-world patient cohorts. Bioinformatics 2018; 34 (22) 3957-9
• 34 DREAM Challenges. Available from: http://dreamchallenges.org/ [Accessed 18 Dec 2019]
• 35 Ardila D, Kiraly AP, Bharadwaj S, Choi B, Reicher JJ, Peng L. et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med 2019; 25 (06) 954-61
• 36 Yala A, Lehman C, Schuster T, Portnoi T, Barzilay R. A deep learning mammography-based model for improved breast cancer risk prediction. Radiology 2019; 292 (01) 60-6
• 37 Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542 7639 115-8
• 38 Haenssle HA, Fink C, Schneiderbauer R, Toberer F, Buhl T, Blum A. et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol 2018; 29 (08) 1836-42
• 39 Wei JW, Tafe LJ, Linnik YA, Vaickus LJ, Tomita N, Hassanpour S. Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks. Sci Rep 2019; 9 (01) 3358
• 40 Walsh S, de Jong EE, van Timmeren JE, Ibrahim A, Compter I, Peerlings J. et al. Decision support systems in oncology. JCO Clin Cancer Inform 2019 (03) 1-9
• 41 Senaras C, Niazi MKK, Sahiner B, Pennell MP, Tozbikian G, Lozanski G. et al. Optimized generation of high-resolution phantom images using cGAN: application to quantification of Ki67 breast cancer images. PLoS One 2018; 13 (05) e0196846
• 42 Guan S, Loew M. Breast cancer detection using synthetic mammograms from generative adversarial networks in convolutional neural networks. J Med Imaging (Bellingham) 2019; 6 (03) 031411
• 43 AlQuraishi M. AlphaFold at CASP13. Bioinformatics 2019; 35 (22) 4862-5
• 44 Zhan T, Rindtorff N, Betge J, Ebert MP, Boutros M. CRISPR/Cas9 for cancer research and therapy. Semin Cancer Biol 2019; 55: 106-19
• 45 Poudel P, Nyamundanda G, Patil Y, Cheang MCU, Sadanandam A. Heterocellular gene signatures reveal luminal-A breast cancer heterogeneity and differential therapeutic responses. NPJ Breast Cancer 2019; 5 (01) 21
• 46 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; 24 (10) 1559-67
• 47 American College of Radiology. FDA Cleared AI Algorithms. Available from: https://www.acrdsi.org/DSI-Services/FDA-Cleared-AI-Algorithms [Accessed 26 March 2020]
• 48 Wiedmeier JE, Noel P, Lin W, Von Hoff DD, Han H. Single-cell sequencing in precision medicine. Cancer Treat Res 2019; 178: 237-52
• 49 Zehir A, Benayed R, Shah RH, Syed A, Middha S, Kim HR. et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat Med 2017; 23 (06) 703-13
• 50 Coyne G, Takebe N, Chen A. Defining precision: the precision medicine initiative trials NCI-MPACT and NCI-MATCH. Curr Probl Cancer 2017; 41 (03) 182-93
• 51 Tate JG, Bamford S, Jubb HC, Sondka Z, Beare DM, Bindal N. et al. COSMIC: the catalogue of somatic mutations in cancer. Nucleic Acids Res 2019; 47 (D1): D941-d7
• 52 My Cancer Genome. Available from: https://www.mycancergenome.org/ . [Accessed 18 March 2020]
• 53 Chakravarty D, Gao J, Phillips SM, Kundra R, Zhang H, Wang J. et al. OncoKB: A precision oncology knowledge base. JCO Precis Oncol 2017.
• 54 Griffith M, Spies NC, Krysiak K, McMichael JF, Coffman AC, Danos AM. et al CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer. Nat Genet 2017; 49: 170-4
• 55 HemOnc.org. Available from: https://hemonc.org/wiki/Main_Page [Accessed 17 March 2020]
• 56 Levit LA, Kim ES, McAneny BL, Nadauld LD, Levit K, Schenkel C. et al. Implementing precision medicine in community-based oncology programs: three models. J Oncol Pract 2019; 15 (06) 325-9
• 57 Remon J, Dienstmann E. Precision oncology: separating the wheat from the chaff. ESMO Open 2018; 3: e000446
• 58 Harris L, Chen A, O’Dwyer P, Flaherty K, Hamilton S, McShane L. et al. Update on the NCI-Molecular Analysis for Therapy Choice (NCI-MATCH/EAY131) precision medicine trial. AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics. Philadelphia, PA; 2018.
• 59 Cutler D. Early returns from the era of precision medicine. JAMA Forum 2019.
• 60 Schachter J, Ribas A, Long GV, Arance A, Grob JJ, Mortier L. et al. Pembrolizumab versus ipilimumab for advanced melanoma: final overall survival results of a multicentre, randomized, open-label phase 3 study (KEYNOTE-006). Lancet 2017; 390 10105 1853-62
• 61 Kazandjian D, Gong Y, Keegan P, Pazdur R, Blumenthal GM. Prognostic value of the lung immune prognostic index for patients treated for metastatic non-small cell lung cancer. JAMA Oncol 2019; 5 (10) 1481-5
• 62 Townsend MH, Shrestha G, Robison RA, O’Neill KL. The expansion of targetable biomarkers for CAR T cell therapy. J Exp Clin Cancer Res 2018; 37 (01) 163
• 63 Yu W, Gwinn M, Dotson WD, Green RF, Clyne M, Wulf A. et al. A knowledge base for tracking the impact of genomics on population health. Genet Med 2016; 18 (12) 1312-4
• 64 U.S. National Cancer Institute. Toward Precision Cancer Surveillance. Available from: https://surveillance.cancer.gov/blog/home-page [Accessed 14 Dec 2019]
• 65 Nguyen MH, Smets EM, Bol N, Loos EF, van Laarhoven HW, Geijsen D. et al. Tailored web-based information for younger and older patients with cancer: randomized controlled trial of a preparatory educational intervention on patient outcomes. J Med Internet Res 2019; 21 (10) e14407
• 66 Cohen J. Google, Ascension data partnership sparks federal probe. Modern Healthcare. 2019. Available from: https://www.modernhealthcare.com/information-technology/google-ascension-data-partnership-sparks-federal-probe [Accessed 14 Dec 2019]
• 67 Kosman J. Google’s $2.1B acquisition of Fitbit faces DOJ probe. New York Post. 2019. Available from: https://nypost.com/2019/12/10/googles-2-1b-acquisition-of-fitbit-faces-doj-probe-sources/ [Accessed 15 Dec 2019]
• 68 Farr C. Apple and Aetna are teaming up on a new app to help track and reward healthy behavior. CNBC. 2019. Available from: https://www.cnbc.com/2019/01/28/apple-aetna-team-up-on-attain-health-tracking-app.html [Accessed 15 Dec 2019]
• 69 Farr C. Apple and Eli Lilly are studying whether data from iPhones and Apple Watches can detect signs of dementia. CNBC 2019. Available from: https://www.cnbc.com/2019/08/07/apple-eli-lilly-studying-if-iphones-apple-watches-can-spot-dementia.html [Accessed 15 Dec 2019]
• 70 Reuters. Fitbit in healthcare partnership to take on Apple Watch. 2019. Available from: https://www.reuters.com/article/us-fitbit-health/fitbit-in-healthcare-partnership-to-take-on-apple-watch-idUSKBN1WW263 [Accessed 15 Dec 2019]
• 71 Fitbit. Fitbit and Humana expand strategic partnership to drive healthy habits and prevent and manage chronic disease. 2018. Available from: https://investor.fitbit.com/press/press-releases/press-release-details/2018/Fitbit-and-Humana-Expand-Strategic-Partnership-to-Drive-Healthy-Habits-and-Prevent-and-Manage-Chronic-Disease/default.aspx [Accessed 15 Dec 2019]
• 72 Walker A. NHS gives Amazon free use of health data under Alexa advice deal. The Guardian; 2019. Available from: https://www.theguardian.com/society/2019/dec/08/nhs-gives-amazon-free-use-of-health-data-under-alexa-advice-deal [Accessed 15 Dec 2019]
• 73 Study reveals how well consumers feel health data is protected. HIPAA Journal. 2019. Available from: https://www.hipaajournal.com/study-reveals-how-well-consumers-feel-health-data-is-protected/ [Accessed 15 Dec 2019]
• 74 Etaati L. Azure Databricks. In: Etaati L, editor. Machine Learning with Microsoft Technologies: Selecting the Right Architecture and Tools for Your Project. Berkeley, CA: Apress; 2019. p. 159-71.
• 75 Bisong E. Google AutoML: Cloud Vision. In: Bisong E, editor. Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners. Berkeley, CA: Apress; 2019. p. 581-98.
• 76 Amazon Web Services. Machine Learning on AWS. 2019. Available from: https://aws.amazon.com/machine-learning/ [Accessed 20 Dec 2019]
• 77 Strubell E, Ganesh A, McCallum A. Energy and policy considerations for deep learning in NLP. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence, Italy; 2019. p. 3645-50.
• 78 HealthITAnalytics. Epic systems: machine learning is the EHR usability solution. 2017. Available from: https://healthitanalytics.com/features/epic-systems-machine-learning-is-the-ehr-usability-solution [Accessed 15 Dec 2019]
• 79 Charette RN. 450,000 women missed breast cancer screenings due to “algorithm failure.” IEEE Spectrum 2018. Available from: https://spectrum.ieee.org/riskfactor/computing/it/450000-woman-missed-breast-cancer-screening-exams-in-uk-due-to-algorithm-failure [Accessed 4 Dec 2019]
• 80 Ross C, Swetlitz I. IBM’s Watson supercomputer recommended ‘unsafe and incorrect’ cancer treatments, internal documents show. STAT 2018. Available from: https://www.statnews.com/2018/07/25/ibm-watson-recommended-unsafe-incorrect-treatments/ [Accessed 8 Nov 2019]
• 81 Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science 2019; 366 6464 447-53
• 82 Cabitza F, Rasoini R, Gensini GF. Unintended consequences of machine learning in medicine. JAMA 2017; 318 (06) 517-8
• 83 Hitaj B, Ateniese G, Perez-Cruz F. Deep models under the GAN: information leakage from collaborative deep learning. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. Dallas, Texas; 2017. p. 603-18.
• 84 Price WN. Big data and black-box medical algorithms. Sci Transl Med 2018; 10 471 eaao5333
• 85 Shah NH, Milstein A, Bagley P, Steven C. Making machine learning models clinically useful. JAMA 2019; 322 (14) 1351-2
• 86 U.S. Food and Drug Administration. Proposed regulatory framework for modifications to artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD). 2019. Available from: https://www.fda.gov/media/122535/download [Accessed 11 Nov 2019]
• 87 U.S. Food and Drug Administration. Clinical decision support software guidance for industry and FDA staff; 2019. Available from: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/clinical-decision-support-software [Accessed 11 Nov 2019]
• 88 U.S. Food and Drug Administration. Digital health software precertification (Pre-Cert) program; 2019. Available from: https://www.fda.gov/medical-devices/digital-health/digital-health-software-precertification-pre-cert-program [Accessed 11 Nov 2019]
• 89 Geis JR, Brady AP, Wu CC, Spencer J, Ranschaert E, Jaremko JL. et al. Ethics of artificial intelligence in radiology: summary of the Joint European and North American Multisociety Statement. J Am Coll Radiol 2019; 16 (11) 1516-21
• 90 Allen B, Agarwal S, Kalpathy-Cramer J, Dreyer K. Democratizing AI. J Am Coll Radiol 2019; 16 (07) 961-3
• 91 Saltz LB. Precision oncology giveth and precision oncology taketh away. Lancet Oncol 2019; 20 (04) 464-6
• 92 Gopishetty S, Gupta S, Kota V, Guddati AK. Analysis of racial distribution amongst patients in phase III cancer clinical trials. J Clin Oncol 2019; 37 (15_suppl): 6588
• 93 Vo KT, Parsons DW, Seibel NL. Precision medicine in pediatric oncology. Surg Oncol Clinics 2019; 29 (01) 63-72
• 94 Burnett-Hartman AN, Udaltsova N, Kushi LH, Neslund-Dudas C, Rahm AK, Pawloski PA. et al Clinical molecular marker testing data capture to promote precision medicine research within the cancer research network. JCO Clin Cancer Inform 2019; 3: 1-10
• 95 Schoenborn P, Osborne R, Toms N, Johnstone K, Milsom C, Muneer R. et al. OncoSim and OncoWiki: an authentic learning approach to teaching cancer genomics. BMC Med Educ 2019; 19 (01) 407
• 96 Bisong E. Google Collaboratory. In: Bisong E. Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners. Berkeley, CA: Apress; 2019: 59-64
• 97 National Cancer Institute. Informatics Technology for Cancer Research; 2020. Available from: https://itcr.cancer.gov/informatics-tools [Accessed 18 March 2020]