WP8 Personalized medicine usage scenario

Henrik Edgren First BioMedBridges Annual General Meeting

11-12 March 2013, Düsseldorf

PM usage scenario

¡ The PM project is a collaboration between University of Helsinki / Institute for Molecular Medicine Finland and the HUCS Hematology clinic.

¡ Concentrates on acute myeloid leukemia (AML), will be expanded in the future.

¡ Aims to improve AML treatment, by using drug testing and molecular profiling data to guide patient treatment.

Leukemia sample work flow

3

Hematology Clinic FIMM

1 h 1 day

Transport 1-24 hours

DSRT 4 days

NGS 3-4 weeks

Proteomics 2 days

FIMM

Sample collection •  Bone marrow aspirate •  Peripheral blood •  Skin biopsy

Sample processing •  Mononuclear cell separation •  Protein lysates •  DNA extraction •  RNA extraction

Sample analysis •  Drug screening •  Phospho-protein analysis •  Whole genome/exome sequencing •  RNA sequencing

Data types – one or more types per patient

¡ Drug dose response ¡ Single nucleotide variants ¡ Small indels ¡ Structural rearrangements (duplications, translocations,

inversions) ¡ Copy number changes (gains, losses), heterozygosity data ¡ Fusion genes ¡ Gene expression data ¡ Protein phosphorylation data ¡ Frequencies, consecutive samples

Presentation name or name of the guest dd.mm.yyyy 4

The most important question

¡ How can we improve the treatment of the patient based on the data?

Questions we would like to answer (preferably easily)

¡  Is a gene that we find mutated a known cancer gene? ¡  If the mutated gene is not a "recognized" known cancer gene (Cancer gene census), is

it known to be mutated all the same? ¡  Is the gene likely to be an oncogene or tumor suppressor? ¡  Is the mutation we see in our patient located in a mutational hotspot? ¡  Are mutations in the gene expressed in other cases? ¡  We observe mutation of two or more genes in the same patient. Are these same genes

mutated together in other patients? ¡  Is the gene directly druggable? ¡  Is the gene indirectly druggable? ¡  Do mutations in the gene have clinical consequences? ¡  Are mutations, the type of genomic rearrangements observed, expression profile etc.

biomarkers for anything clinically useful? ¡  What was the disease course of other patients with mutations in gene X, or more

generally, patients that resemble our case? ¡  Based on gene expression data, what kind of cancer or what kinds of patients does

our case resemble? ¡  What are the molecular targets of the drugs that are effective in our drug screen? ¡  ........

Presentation name or name of the guest dd.mm.yyyy 6

A bit more specifically

¡ How can we improve the treatment of the patient based on the data?

¡ How do you interpret the data? ¡ WP8 becomes relevant to WP5 when linking data to external

sources or making PM data accessible. ¡ The process of accessing data can broadly be divided into two

different categories: ¡  Completely open data provided by databases such as Cosmic or

ChEMBL (EBI/Elixir). ¡  Restricted access databases such as EGA (EBI/Elixir) or biobanks

(BBMRI). ¡  Projects, such as TCGA and ICGC with open and limited access

data.

Data bridges planned under WP8

8

Clinician interacts with patient, takes

sample

Profile interpretation

Hos

pita

l

Patient’s own data

Clinical presentatio

n

Researcher acquires sample

Molecular profiling

Drug screening

Treatment suggestions,

prognosis, followup plan

Knowledge bases ChEMBL, Ensembl,

Reactom etc.

Reference data Cosmic, TCGA, ICGC

etc.

Personalized medicine flow diagram FI

MM

E

xter

nal d

ata

prov

ider

Added benefit of data bridges

¡ Easing of access to reference patients. ¡ Annotation of FIMM data -> helping data interpretation.