dealing with the heterogeneity of cancer

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

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Dealing with the heterogeneity of cancer. Department of Biological Sciences. Center for Computational Biology and Bioinformatics. Dana Pe ’ er. What is Cancer?. Weinberg, Cell 2001. Why these phenotypes?. Cells only proliferate when they are told to do so. - PowerPoint PPT Presentation

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Page 1: Dealing with the heterogeneity of cancer

Dealing with the heterogeneity of cancerDana Pe’er

Department ofBiological Sciences

Center for Computational Biology and Bioinformatics

Page 2: Dealing with the heterogeneity of cancer

What is Cancer?

Weinberg, Cell 2001

Page 3: Dealing with the heterogeneity of cancer

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

Page 4: Dealing with the heterogeneity of cancer

• 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

Page 5: Dealing with the heterogeneity of cancer

• 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).

Page 6: Dealing with the heterogeneity of cancer

• 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.

Page 7: Dealing with the heterogeneity of cancer

So what is cancer?

Weinberg, Cell 2011

Page 8: Dealing with the heterogeneity of cancer

The “Pathway” view of the cell

• We depict proteins and processes as “pathways”.

Page 9: Dealing with the heterogeneity of cancer

How a cell achieves these phenotypes

• Different types of mutations (alterations) can alter pathway activity– Activate “Oncogene”– Inhibit “Tumor

suppressor”

TCGA, Nature 2008

Page 10: Dealing with the heterogeneity of cancer

Point mutations

• Nucleotide change can lead to:– An early stop codon – making a protein non-

functional– Create a constitutively active protein

Page 11: Dealing with the heterogeneity of cancer

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

Page 12: Dealing with the heterogeneity of cancer

Subtypes of cancer – By expression

• Different cancers, and even subtypes of cancer, have dramatically different gene expression patterns

• These represent cellular states

Sandhu, 2010

Page 13: Dealing with the heterogeneity of cancer

Cancer development

Page 14: Dealing with the heterogeneity of cancer

Genetic alterations

alterations

functional

drivers

Identifying significantlyrecurrent alterations

across samples

Page 15: Dealing with the heterogeneity of cancer

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/

Page 16: Dealing with the heterogeneity of cancer

Prevalence of alterations by typeF

req

uen

cy

CN alterations

Fre

qu

ency

Sequence mutations

6 alt > 5% samples

87 alt > 5% samples

Page 17: Dealing with the heterogeneity of cancer

Distinguishing drivers from passengers

What Aberrations Make a Cell Go Bad?

Page 18: Dealing with the heterogeneity of cancer

Driver Aberrations:Significantly Recur Across Tumors

Breast Cancer Exome Sequencing Total mutations: 21713 Per patient: 48

Breast Copy Number Profile

Page 19: Dealing with the heterogeneity of cancer

Two forces driver copy number

Norwell, 1976

I. Selection of the Fittest

II. DNA secondary structure and

packing

Our ISAR algorithm tries to identify frequent alterations driven by fitness.

Page 20: Dealing with the heterogeneity of cancer

ISAR

~8Mbp

P-valueDistribution

Significance of number of alterations should be computed locally.

Page 21: Dealing with the heterogeneity of cancer

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?

# regions # genes per region

# genes per peak

ISAR 83 14

GISTIC2 33 14.39 1.18

Page 22: Dealing with the heterogeneity of cancer

Defining peaks: cut-off

9 of the 33 GISTIC2 peaks do not contain a single gene9 of the 33 GISTIC2 peaks do not contain a single gene

Page 23: Dealing with the heterogeneity of cancer

Helios approach

Sample 3Sample 2

Sample 4

Sample 1

GENE1 GENE2 GENE3 GENE4 GENE5Genome

Sequence Copy Number Expression shRNA

Features

Classic Approach

deterministic0/1 decision

Weightand

combine

IntegrativeScore

GENE1 GENE2 GENE3 GENE4 GENE5Genome

Page 24: Dealing with the heterogeneity of cancer

Primary tumor data (TCGA)

Page 25: Dealing with the heterogeneity of cancer

Functional assays (RNAi screens)

Page 26: Dealing with the heterogeneity of cancer

Helios: Data Integration

A ranked and scored list of driver genes

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

Primary tumor (many) Cell Line (few)

Page 27: Dealing with the heterogeneity of cancer

Features: Gene expression

Is the gene expressed ?

Diploid VS amplified :

Differentially expressed in subtypes:

AMPWTCCND1 CN

CCND1 EXP

SUBTYPE

FOXA1 EXP

BASAL LUMINAL

Page 28: Dealing with the heterogeneity of cancer

Driver genes may show a footprint of point mutations

We use p-value of frequency of alteration calculated by MutSig (Banerji, Nature 2012 )

Features: Sequence mutations

Page 29: Dealing with the heterogeneity of cancer

Training dataFeatures

Classifier

Labels

List of drivers and

passengers

Too small and biased !!!

Make frequency of alteration the center of the

system

Page 30: Dealing with the heterogeneity of cancer

PLX4720-Targeted Therapy

Page 31: Dealing with the heterogeneity of cancer

Proteins Form a Complex Network

BRAF

BRAF

BRAF exists in a networkFeedback

Crosstalk

Chandarpalaty et al. 2011

Page 32: Dealing with the heterogeneity of cancer

Networks Vary Across Genetic Backgrounds

Drastically different genetic backgrounds

Page 33: Dealing with the heterogeneity of cancer

Our Aims

Identify genetic determinants and master regulators of drug resistance

Predict additional target pathways for combinatorial drug treatment.

Page 34: Dealing with the heterogeneity of cancer

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

Page 35: Dealing with the heterogeneity of cancer

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

We capture the level of 45 protein epitopes simultaneously in single cells

For tens of thousands of cells

Page 36: Dealing with the heterogeneity of cancer

Mass cytometry

Page 37: Dealing with the heterogeneity of cancer

How do we view > 30 dimensions?

Parameters: 481432

Plots: 62891496

Page 38: Dealing with the heterogeneity of cancer

Acknowledgements

Felix Sanchez-Garcia

Dylan Kotliar

Uri David Akavia

El-ad David AmirJacob Levine

Smita Krishnaswamy

Jose Silva (CUMC)

Garry Nolan (Stanford)Sean Bendall

Erin SimondsDaniel Shenfeld

Michelle TadmorKara Davis

Junji Matsui

Bo-Juen Chen