Enhancing Next-Generation Sequencing-Guided Cancer Care Through Cognitive Computing

Oncologist

Volumn 23, Issue 2, 2018, Pages 179-185

  Patel, Nirali M ^a,b   Michelini, Vanessa V ^c   Snell, Jeff M ^a,b   Balu, Saianand ^a   Hoyle, Alan P ^a   Parker, Joel S ^a,b   Hayward, Michele C ^a   Eberhard, David A ^a,b   Salazar, Ashley H ^a   McNeillie, Patrick ^c   Xu, Jia ^c   Huettner, Claudia S ^c   Koyama, Takahiko ^d   Utro, Filippo ^d   Rhrissorrakrai, Kahn ^d   Norel, Raquel ^d   Bilal, Erhan ^d   Royyuru, Ajay ^d   Parida, Laxmi ^d   Earp, H Shelton ^a,b   Grilley Olson, Juneko E ^a,b   Hayes, D Neil ^a,b   Harvey, Stephen J ^c   Sharpless, Norman E ^a,b   Kim, William Y ^a,b   more..

^a UNIVERSITY OF NORTH CAROLINA   (United States)

^b UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

^c IBM WATSON HEALTH   (United States)

^d IBM RESEARCH   (United States)

Author keywords

Artificial intelligence; Genomics; High throughput nucleotide sequencing; Precision medicine

Indexed keywords

ATR PROTEIN; CYCLIN DEPENDENT KINASE INHIBITOR 2B; F BOX/WD REPEAT CONTAINING PROTEIN 7; MICROPHTHALMIA ASSOCIATED TRANSCRIPTION FACTOR; RAD50 PROTEIN; TUMOR MARKER; ANTINEOPLASTIC AGENT;

ARID1A GENE; ARTICLE; BRIP1 GENE; COGNITION; COMPUTER ANALYSIS; COMPUTER MODEL; GENE; GENE MUTATION; GENOMICS; HUMAN; MAJOR CLINICAL STUDY; NATURAL LANGUAGE PROCESSING; NEXT GENERATION SEQUENCING; PERSONALIZED MEDICINE; PRIORITY JOURNAL; RETROSPECTIVE STUDY; TUMOR SUPPRESSOR GENE; CASE CONTROL STUDY; FOLLOW UP; GENE EXPRESSION REGULATION; HIGH THROUGHPUT SEQUENCING; LYMPH NODE METASTASIS; MULTIMODALITY CANCER THERAPY; NEOPLASM; PATHOLOGY; PROGNOSIS; SURVIVAL RATE; TUMOR INVASION; TUMOR RECURRENCE;

ANTINEOPLASTIC COMBINED CHEMOTHERAPY PROTOCOLS; BIOMARKERS, TUMOR; CASE-CONTROL STUDIES; COMBINED MODALITY THERAPY; FOLLOW-UP STUDIES; GENE EXPRESSION REGULATION, NEOPLASTIC; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; LYMPHATIC METASTASIS; NEOPLASM INVASIVENESS; NEOPLASM RECURRENCE, LOCAL; NEOPLASMS; PROGNOSIS; RETROSPECTIVE STUDIES; SURVIVAL RATE;

EID: 85042131845     PISSN: 10837159     EISSN: 1549490X     Source Type: Journal    
DOI: 10.1634/theoncologist.2017-0170     Document Type: Article

References (17)

1. Hayes DN, Kim WY. The next steps in next-gen sequencing of cancer genomes. J Clin Invest 2015;125:462–468.
2. Garraway LA. Genomics-driven oncology: Framework for an emerging paradigm. J Clin Oncol 2013;31:1806–1814.
3. National Science Board, National Science Foundation. Science and Engineering Indicators 2014. Available at http://www.nsf.gov/statistics/seind14/. Accessed September 10, 2016.
4. Jeck WR, Parker J, Carson CC et al. Targeted next generation sequencing identifies clinically actionable mutations in patients with melanoma. Pigment Cell Melanoma Res 2014;27:653–663.
5. Zhao X, Wang A, Walter V et al. Combined targeted DNA sequencing in non-small cell lung cancer (NSCLC) using UNCseq and NGScopy, and RNA sequencing using UNCqeR for the detection of genetic aberrations in NSCLC. PLoS ONE 2015;10:e0129280.
6. Mose LE, Wilkerson MD, Hayes DN et al. ABRA: Improved coding indel detection via assembly-based realignment. Bioinformatics 2014;30:2813–2815.
7. Wilkerson MD, Cabanski CR, Sun W et al. Integrated RNA and DNA sequencing improves mutation detection in low purity tumors. Nucleic Acids Res 2014;42:e107.
8. Saunders CT, Wong WS, Swamy S et al. Strelka: Accurate somatic small-variant calling from sequenced tumor-normal sample pairs. Bioinformatics 2012;28:1811–1817.
9. American Association for Cancer Research. News in Brief: Oncologists partner with Watson on genomics. Cancer Discov 2015;5:788.
10. Wrzeszczynski KO, Frank MO, Koyama T et al. Comparing sequencing assays and human-machine analyses in actionable genomics for glioblastoma. Neurol Genet 2017;3:e164.
11. Kandoth C, McLellan MD, Vandin F et al. Mutational landscape and significance across 12 major cancer types. Nature 2013;502:333–339.
12. Ogiela L, Tadeusiewicz R, Ogiela MR. Cognitive techniques in medical information systems. Comput Biol Med 2008;38:501–507.
13. Philipp P, Maleshkova M, Katic D et al. Toward cognitive pipelines of medical assistance algorithms. Int J Comput Assist Radiol Surg 2016;11:1743–1753.
14. Chen Y, Elenee Argentinis JD, Weber G. IBM Watson: How cognitive computing can be applied to big data challenges in life sciences research. Clin Ther 2016;38:688–701.
15. West HJ. No solid evidence, only hollow argument for universal tumor sequencing: Show me the data. JAMA Oncol 2016;2:717–718.
16. Le Tourneau C, Delord JP, Gonçalves A et al. Molecularly targeted therapy based on tumour molecular profiling versus conventional therapy for advanced cancer (SHIVA): A multicentre, open-label, proof-of-concept, randomised, controlled phase 2 trial. Lancet Oncol 2015;16:1324–1334.
17. Subbiah V, Kurzrock R. Universal genomic testing needed to win the war against cancer: Genomics IS the diagnosis. JAMA Oncol 2016;2:719–720.