1. Results grade1 grade2 grade3 (1) Can we identify metabolites or metabolic pathways that are associated with breast cancer clinical parameters? Alex-CIS-GCTOF MS w/ BinBase: 470 detected compounds 161 known metabolites, 309 without identified structure. Partial Least Square (multivariate stats) grade3 grade2 grade1 breast adipose

2. Results Grade n  1  2  3 tumors show differential up-regulation in Pentose Phosphate Pathway, nucleotides, amino acids, arachidonic acid (but not free fatty acids, gln) production of NADPH, ribose unitsproduction of DNA, RNA

3. Results Grade n  1  2  3 tumors show differential up-regulation in Pentose Phosphate Pathway, nucleotides, amino acids, arachidonic acid (but not free fatty acids, gln) Amino acids lactate glu  -ala malate citrate n 1 2 3 grade C20:4 2HOglutarate n 1 2 3 grade IDH1?

4. Results Can we validate biomarkers in a fully independent study? Cohort 2 (113 patients)

5. Results Can we validate lipidomic biomarkers in a fully independent study? Cohort 2 (113 patients) Matej Orešič et al. (subm.)

6. Results Can we validate lipidomic biomarkers in a fully independent study? Immunohistochemistry on tissue slides. Orešič et al. (subm)

7. Can we get biochemical maps of differential regulation for hormone receptor status? Methods (1)Up to 20% of the identified metabolites lack enzyme annotations. (2)Metabolic endpoint metabolites accumulate more than intermediates. ‘Comprehensive maps’ do not work.

8. Can we get biochemical maps of differential regulation for hormone receptor status? Methods (1)Up to 20% of the identified metabolites lack enzyme annotations. (2)Metabolic endpoint metabolites accumulate more than intermediates. ‘Comprehensive maps’ do not work.

9. Chemical and biochemical mapping of metabolomic results Methods

10. Results Grade 1 tumors vs normal Red nodes: up-regulated in tumors at p<0.05, size ~ x-fold Blue nodes: down-regulated in tumors at p<0.05, size~x-fold Red edges: enzyme substrate/product in KEGGRpair DB Green edges: chemical similarity > 0.7

11. Methods % of patients Estrogen negativeEstrogen positive triple neg. ER+ PR+ HER2- ER- PR- HER2- single neg. Hormone receptor @ grade 3

12. Results Metabolic phenotype of triple negative tumors Patients without Estrogen, Herceptin neu2, Progesteron receptor

13. Methods % of patients Estrogen negativeEstrogen positive triple neg. ER- PR- HER2+ ER- PR- HER2- double neg. Hormone receptor @ grade 3

14. Results Effect of HER2 status on metabolic phenotype of triple negative tumors

15. Methods % of patients Estrogen negativeEstrogen positive ER+ PR+ HER2- double pos. ER+ PR+ HER2+ triple pos. Hormone receptor @ grade 2