Personalized Medicine with IBM-Watson: Future of Cancer care

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Watson for Genomics uses IBM's Watson cognitive computing system to help personalize cancer care. It analyzes genomic sequencing data and clinical records to provide treatment suggestions and clinical trial matches for patients in minutes, compared to weeks for traditional approaches. Researchers are finalizing the algorithm and testing it in clinical trials. Watson draws from a large corpus of medical literature and patient data to understand questions, generate hypotheses, and provide evidence to support its answers. It could help reduce health professionals' workload and improve access to care, though challenges remain in developing the algorithm and acquiring sufficient data sets.

Health & Medicine

PERSONALIZED MEDICINE
WITH IBM-WATSON: FUTURE OF
CANCER CARE
SOFIA DARD

CANCER CARE NOW:
• Patient’s samples sequenced.
• Bioinformatics pipeline
• Molecular Tumor Board (MTB).
Reviews patient cases that are not
responding to standard care.
• This approach of personalized
medicine takes weeks.

EMERGING METHOD: WATSON FOR
GENOMICS
• After sequencing data is available,
oncologists will have Watson cull through
existing knowledge for clinical trial and
drug suggestions.
• IBM Watson…answers you in minutes.
• Finalizing algorithm and testing in clinical
trials.

HOW IT WORKS:
• Cognitive computing that draws from unstructured data and relies on
natural language = COMPLEX
• governed by grammar, context, and culture
• not like speech recognition.
• breaks sentence down into parts to understand it and infer users intent (Pai,
2014)
• Other researchers (ImageCLEF dataset, 2012): optimized query language but
vocab gap.• Corpus of Knowledge:
• Collection of knowledge for that domain.
• Uploaded by users
• Includes journal articles, patient health information, medical records.
• Format of materials: voice recordings, images, PDFs, etc.
• Curated by users

HOW IT WORKS:
• Ingestion of corpus
• Machine learning with experts:
• Question/ answer pairs uploaded to establish ground truth.
• More about learning grammar semantics.
• Watson learns but is periodically reviewed by experts.
• Receives a question
• Creates hypotheses and finds evidence to support each
one
• Weighted Evidence Scores: establishes Watson’s
confidence in itself.

EVALUATING WATSON
• Saves time, money, and
decreases health professionals
workload.
• Could potentially substitute/
replace MTB once proper
algorithm is established.
• Resource poor hospitals.
• Challenges: algorithm & acquiring
data sets.
Explorys Data Set = 50 million patient
records.

ENDLESS POSSIBILITIES WITH WATSON
• Save drug developers lots of
money by enhancing patient
recruitment.
• Population data from IBM Watson
Health Cloud can reveal new
patterns.
• Ex: gene mutation patterns by
patient location > underlying
environmental factor?

REFERENCES
1. http://www.jop.ascopubs.org/content/9/3/155.short#
2. https://scholar.google.com/scholar?start=10&q=ibm+watson+cancer&hl=en
&as_sdt=0,34
3. Pai VM, et al. J Am Med Inform Assoc 2014;21:e2–e5. doi:10.1136/amiajnl-
2013-001896
4. http://jamia.oxfordjournals.org/content/early/2016/03/28/jamia.ocw022
5. http://jamia.oxfordjournals.org/content/19/5/851
6. http://www.businessinsider.com/sc/ibm-watson-and-personalized-
healthcare-for-cancer-2016-1
7. http://www.ibm.com/smarterplanet/us/en/wild-ducks-podcast/episode-7-
personalized-medicine-podcast.html
8. http://www.iconplc.com/news-events/news/icon-and-ibm-to-
revolutio/index.xml
9. http://ij-healthgeographics.biomedcentral.com/articles/10.1186/1476-
072X-4-29

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i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource. This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and Associate Division Director for Ambulatory Operations The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma. STATEMENT OF NEED Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL. Target Audience Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL). Learning Objectives 1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL 2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults 3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection

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New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...

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Personalized Medicine with IBM-Watson: Future of Cancer care

1. PERSONALIZED MEDICINE WITH IBM-WATSON: FUTURE OF CANCER CARE SOFIA DARD

2. CANCER CARE NOW: • Patient’s samples sequenced. • Bioinformatics pipeline • Molecular Tumor Board (MTB). Reviews patient cases that are not responding to standard care. • This approach of personalized medicine takes weeks.

3. EMERGING METHOD: WATSON FOR GENOMICS • After sequencing data is available, oncologists will have Watson cull through existing knowledge for clinical trial and drug suggestions. • IBM Watson…answers you in minutes. • Finalizing algorithm and testing in clinical trials.

4. HOW IT WORKS: • Cognitive computing that draws from unstructured data and relies on natural language = COMPLEX • governed by grammar, context, and culture • not like speech recognition. • breaks sentence down into parts to understand it and infer users intent (Pai, 2014) • Other researchers (ImageCLEF dataset, 2012): optimized query language but vocab gap.• Corpus of Knowledge: • Collection of knowledge for that domain. • Uploaded by users • Includes journal articles, patient health information, medical records. • Format of materials: voice recordings, images, PDFs, etc. • Curated by users

5. HOW IT WORKS: • Ingestion of corpus • Machine learning with experts: • Question/ answer pairs uploaded to establish ground truth. • More about learning grammar semantics. • Watson learns but is periodically reviewed by experts. • Receives a question • Creates hypotheses and finds evidence to support each one • Weighted Evidence Scores: establishes Watson’s confidence in itself.

6. EVALUATING WATSON • Saves time, money, and decreases health professionals workload. • Could potentially substitute/ replace MTB once proper algorithm is established. • Resource poor hospitals. • Challenges: algorithm & acquiring data sets. Explorys Data Set = 50 million patient records.

7. ENDLESS POSSIBILITIES WITH WATSON • Save drug developers lots of money by enhancing patient recruitment. • Population data from IBM Watson Health Cloud can reveal new patterns. • Ex: gene mutation patterns by patient location > underlying environmental factor?

8. REFERENCES 1. http://www.jop.ascopubs.org/content/9/3/155.short# 2. https://scholar.google.com/scholar?start=10&q=ibm+watson+cancer&hl=en &as_sdt=0,34 3. Pai VM, et al. J Am Med Inform Assoc 2014;21:e2–e5. doi:10.1136/amiajnl- 2013-001896 4. http://jamia.oxfordjournals.org/content/early/2016/03/28/jamia.ocw022 5. http://jamia.oxfordjournals.org/content/19/5/851 6. http://www.businessinsider.com/sc/ibm-watson-and-personalized- healthcare-for-cancer-2016-1 7. http://www.ibm.com/smarterplanet/us/en/wild-ducks-podcast/episode-7- personalized-medicine-podcast.html 8. http://www.iconplc.com/news-events/news/icon-and-ibm-to- revolutio/index.xml 9. http://ij-healthgeographics.biomedcentral.com/articles/10.1186/1476- 072X-4-29

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