dealing with the heterogeneity of cancer
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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 PresentationTRANSCRIPT
Dealing with the heterogeneity of cancerDana Pe’er
Department ofBiological Sciences
Center for Computational Biology and Bioinformatics
What is Cancer?
Weinberg, Cell 2001
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
• 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
• 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).
• 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.
So what is cancer?
Weinberg, Cell 2011
The “Pathway” view of the cell
• We depict proteins and processes as “pathways”.
How a cell achieves these phenotypes
• Different types of mutations (alterations) can alter pathway activity– Activate “Oncogene”– Inhibit “Tumor
suppressor”
TCGA, Nature 2008
Point mutations
• Nucleotide change can lead to:– An early stop codon – making a protein non-
functional– Create a constitutively active protein
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
Subtypes of cancer – By expression
• Different cancers, and even subtypes of cancer, have dramatically different gene expression patterns
• These represent cellular states
Sandhu, 2010
Cancer development
Genetic alterations
alterations
functional
drivers
Identifying significantlyrecurrent alterations
across samples
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/
Prevalence of alterations by typeF
req
uen
cy
CN alterations
Fre
qu
ency
Sequence mutations
6 alt > 5% samples
87 alt > 5% samples
Distinguishing drivers from passengers
What Aberrations Make a Cell Go Bad?
Driver Aberrations:Significantly Recur Across Tumors
Breast Cancer Exome Sequencing Total mutations: 21713 Per patient: 48
Breast Copy Number Profile
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.
ISAR
~8Mbp
P-valueDistribution
Significance of number of alterations should be computed locally.
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
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
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
Primary tumor data (TCGA)
•
Functional assays (RNAi screens)
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)
Features: Gene expression
Is the gene expressed ?
Diploid VS amplified :
Differentially expressed in subtypes:
AMPWTCCND1 CN
CCND1 EXP
SUBTYPE
FOXA1 EXP
BASAL LUMINAL
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
Training dataFeatures
Classifier
Labels
List of drivers and
passengers
Too small and biased !!!
Make frequency of alteration the center of the
system
PLX4720-Targeted Therapy
Proteins Form a Complex Network
BRAF
BRAF
BRAF exists in a networkFeedback
Crosstalk
Chandarpalaty et al. 2011
Networks Vary Across Genetic Backgrounds
Drastically different genetic backgrounds
Our Aims
Identify genetic determinants and master regulators of drug resistance
Predict additional target pathways for combinatorial drug treatment.
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
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
Mass cytometry
How do we view > 30 dimensions?
Parameters: 481432
Plots: 62891496
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