AURIA BIOBANK

Samu Kurki, PhD, Senior Data Scientist,Auria Biobank, Turku University Hospital,University of Turku, Finland

Nationell Biobankskonferens 2019,Göteborg, Sweden

Turning Hospital Biobank Data intoReal-World Evidence usingArtificial Intelligence Tools

Terminology Hospital biobank: Biobank that processes clinical specimens and

data in a hospital setting

Real-world data: Patient data that is generated outside clinicaltrials (such as hospitals)

Artificial intelligence: Use of computer algorithms / tools / software to process data

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Auria Biobank

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• Finland’s first clinical biobank (founded in 2012), integrated with Turku University Hospital

• Jointly owned by University of Turku and HospitalDistricts of Southwest Finland, Satakunta and Vaasa

• Finnish Biobank Act in 2013

• Catchment population ~900 000 people

• Ongoing collection of blood samples from every newconsented patient

• >1,5 million FFPE samples and prospectivecollections of fresh tissue samples

• >160 biobank studies with pharma industry and academic researchers

Finnish Biobanks

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FoundersHelsingin ja Uudenmaan Sairaanhoitopiirin kuntayhtymä

Helsingin yliopisto

Keski-Suomen Sairaanhoitopiirin kuntayhtymä

Jyväskylän yliopisto

Pirkanmaan Sairaanhoitopiirin kuntayhtymä

Tampereen yliopisto

Pohjois-Pohjanmaan Sairaanhoitopiirin kuntayhtymä

Oulun yliopisto

Pohjois-Savon Sairaanhoitopiirin kuntayhtymä

Itä-Suomen yliopisto

Varsinais-Suomen Sairaanhoitopiirin kuntayhtymä

Turun yliopisto

Finnish BioBanks Cooperative FINBBFounded in 2017, domicile Turku

Bio(data)banks for drug development

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Real-world data linked to specimens

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Provides the possibility to combine hospitaldatabases (typically in electronic form fromyear 2004 on) with biobank specimens

Longitudinal information on the course of disease, operations, response to treatments, outcome etc.

Digital timeline of a patient

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Clinicaldiagnoses

Pathologyspecimens

Oncologytreatments

Laboratorymeasurements

...

Digital pathology for drug discovery

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+ black for DNA isolation

Digital pathology for drug discovery

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• Coudray et al. (Nature Med 2018)

• Lung squamous cell and adenocarcinoma

• Google inception v3 neural net

• Histopathological diagnosis (AUC 0.97)

• Could predict mutations in (STK11, EGFR, FAT1, SETBP1, KRAS and TP53) from HE images (AUC 0.733-0.856)

• We are implementing and training on our data

Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning. Coudray N. et al. Nature Medicine 24, 1559–1567 (2018)

Biobank supporting diagnostics and treatment

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Major improvement in survival of CRC patients was observed in 2004, at the time of

The observed changes have resulted in improved survival in CRC and a marked

decrease of non-operable rectal cancer.

Trends in presentation, treatment and survival of 1777 patientswith colorectal cancer over a decade: a Biobank study. Heervä E et al.Acta Oncol. 2017 Dec 23:1-8.

• centralization of rectal cancer surgery

• introduction of multidisciplinary teams

• higher number of lymph nodes examined

• implementation of preoperative radiotherapy in rectal cancer

• the use of adjuvant chemotherapy in stage III CRC became also slightly more frequent

The clinical presentation of CRC has remained essentially the same between 2001 and 2012.

Biobank supporting diagnostics and treatment

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Text mining hospital infections

alg. no-SAI alg. yes-SAI

rek. no-SAI 1123 16

rek. yes-SAI 6 15

XgBoost

alg. no-SAI alg. yes-SAI

rek. no-SAI 1075 64

rek. yes-SAI 3 18

Gaussian Naive Bayes

Karlsson A.

Challenge of free text Majority of clinical data is not yet in structured form Mining of relevant information from among big data Modification of data in a form that is easy to analyze

Important example: smoking status Not in structured form - needs to be mined from

unstructured reports Only a very small part of the document is relevant for

the question (smoking)

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Artificial intelligence methods

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The accuracy of the tool tested against an expert clinical investigator is 90% - 95%.

The results are reproducible, and time spent can be hours vs. months.

Graphical UI for data curation

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Visualization for hypothesis generation

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Comorbiditynetwork

Final words

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Who benefits?

• Researchers: Unique samples with large amounts of clinical data

• Industry: Research innovations, new forms of collaboration

• Healthcare: Data-driven healthcare, clinical decision support

• Patients: Better healthcare, personalized treatments

What is needed?

• Bio(data)banks and data lakes with big data

• Data scientists to turn data into knowledge

• Courage to work with the new methods

• (Legal framework, sufficient funding, ...)

www.auria.fi

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+358 50 309 3546info@auria.fi

samu.kurki@auria.fiTwitter @SamuKurki