Dealing with the heterogeneity of cancer

1. Dealing with the heterogeneity of cancer Department of Biological Sciences Center for Computational Biology and Bioinformatics Dana Pe’er

2. What is Cancer? Weinberg, Cell 2001

3. Why these phenotypes? • Cells only proliferate when they are told to do so. • Usually achieved by growth factors or cell-to-cell interaction. • Malignant cells proliferate independent of external signals

4. Proliferation rate is controlled by external and internal signals. • Cells that interfere with their environment receive signals to die • Tumors evade these signals • A local tumor is almost always surgically removable. • Cancer is such a terrible disease because it metastasizes and affects other organs

5. Our chromosomes end with “telomeres”, a chunk of DNA that isn’t replicated and gets smaller when a new DNA is synthesized. • When they are too short, the “important” DNA is unable to be copied and the cell dies • Tumors activate the process that elongates telomeres (and don’t die).

6. Cells need blood. More cells need more blood • Tumors, which spread into new areas, need new blood vessels • Our cells aren’t designed to proliferate indefinitely, metastasize, divide whenever they want and ignore extracellular signals • There are checkpoints in place that prevent all of the above by a suicide. • These are lost in cancer.

7. So what is cancer? Weinberg, Cell 2011

8. The “Pathway” view of the cell • We depict proteins and processes as “pathways”.

9. How a cell achieves these phenotypes • Different types of mutations (alterations) can alter pathway activity • Activate “Oncogene” • Inhibit “Tumor suppressor” TCGA, Nature 2008

10. Point mutations • Nucleotide change can lead to: • An early stop codon – making a protein non-functional • Create a constitutively active protein

11. DNA Copy Number Alterations • Chunks of the genome can be amplified • Leading to many copies of an oncogene • Which leads to overexpression of the gene • Chunks can also be lost (deleted) • And that is one mechanism to lose a tumor suppressor

12. Subtypes of cancer – By expression • Different cancers, and even subtypes of cancer, have dramatically different gene expression patterns • These represent cellular states Sandhu, 2010

13. Cancer development

14. Genetic alterations alterations functional drivers Identifying significantly recurrent alterations across samples

15. The Cancer Genome Atlas (TCGA) • Characterization of 20 cancers x 1000 tumors each • Assays include: • How is the DNA changing: DNA sequencing (mostly exon), copy number variation • How is expression different: RNA-seq, miRNAs • Extras: methylation, clinical annotation • https://tcga-data.nci.nih.gov/tcga/

16. Prevalence of alterations by type Sequence mutations Frequency 6 alt > 5% samples CN alterations Frequency 87 alt > 5% samples

17. Distinguishing drivers from passengers What Aberrations Make a Cell Go Bad?

18. Driver Aberrations:Significantly Recur Across Tumors Breast Copy Number Profile • Breast Cancer Exome Sequencing • Total mutations: 21713 • Per patient: 48

19. Two forces driver copy number I. Selection of the Fittest II. DNA secondary structure and packing Norwell, 1976 • Our ISAR algorithm tries to identify frequent alterations driven by fitness.

20. ISAR • Significance of number of alterations should be computed locally. ~8Mbp P-value Distribution

21. ISAR regions • A better null model helps sensitivity • ~1200 genes in ISAR regions: we need to identify drivers within these regions. • GISTIC2 narrows down regions to deterministic peaks containing 1.18 genes. Problem solved?

22. Defining peaks: cut-off 9 of the 33 GISTIC2 peaks do not contain a single gene

23. Helios approach Sample 1 Sample 2 Sample 3 Sample 4 Genome Genome GENE1 GENE1 GENE2 GENE2 GENE3 GENE3 GENE4 GENE4 GENE5 GENE5 deterministic 0/1 decision Classic Approach Features Sequence Copy Number Expression shRNA Weight and combine Integrative Score

24. Primary tumor data (TCGA)

25. Functional assays (RNAi screens)

26. Helios: Data Integration Cell Line (few) Primary tumor (many) • Making use of the large-scale of functional screens that are quickly accumulating • Best of both worlds: Integrating primary tumor data with functional screens on cell lines … A ranked and scored list of driver genes

27. Features: Gene expression • Is the gene expressed ? • Diploid VS amplified : • Differentially expressed in subtypes: CCND1 CN AMP WT CCND1 EXP SUBTYPE BASAL LUMINAL FOXA1 EXP

28. Features: Sequence mutations • Driver genes may show a footprint of point mutations • We use p-value of frequency of alteration calculated by MutSig(Banerji, Nature 2012 )

29. Training data Features Classifier Labels List of drivers and passengers Too small and biased !!! Make frequency of alteration the center of the system

30. PLX4720-Targeted Therapy

31. Proteins Form a Complex Network Chandarpalaty et al. 2011 Feedback Crosstalk BRAF exists in a network BRAF

32. Networks Vary Across Genetic Backgrounds Drastically different genetic backgrounds

33. Our Aims • Identify genetic determinants and master regulators of drug resistance • Predict additional target pathways for combinatorial drug treatment.

34. Heterogeneity within a tumor • If even < 1% of cells evade therapy, tumor will recur. • The influence of this population on any bulk assay is negligent

35. Mass cytometry: A powerful new technology Single cell droplets Time of flight Mass spectrometer • We capture the level of 45 protein epitopes simultaneously in single cells • For tens of thousands of cells

36. Mass cytometry

37. How do we view > 30 dimensions? Parameters: 4 8 14 32 Plots: 6 28 91 496

38. Acknowledgements Felix Sanchez-Garcia Dylan Kotliar Junji Matsui Uri David Akavia Bo-Juen Chen Jose Silva (CUMC) Garry Nolan (Stanford) El-ad David Amir Jacob Levine Sean Bendall Smita Krishnaswamy Erin Simonds Daniel Shenfeld Kara Davis Michelle Tadmor