Dealing with the heterogeneity of cancer What is Cancer Why these phenotypes So what is cancer The “Pathway” view of the cell

Dealing with the heterogeneity of cancer What is Cancer Why these phenotypes So what is cancer The “Pathway” view of the cell www.phwiki.com

Dealing with the heterogeneity of cancer What is Cancer Why these phenotypes So what is cancer The “Pathway” view of the cell

McPherson, Doug, Contributor has reference to this Academic Journal, PHwiki organized this Journal Dealing with the heterogeneity of cancer Dana Pe’er Department of Biological Sciences Center as long as Computational Biology in addition to Bioin as long as matics 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

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Proliferation rate is controlled by external in addition to 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 in addition to affects other organs Our chromosomes end with “telomeres”, a chunk of DNA that isn’t replicated in addition to gets smaller when a new DNA is synthesized. When they are too short, the “important” DNA is unable to be copied in addition to the cell dies Tumors activate the process that elongates telomeres ( in addition to 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 in addition to 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 in addition to 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, in addition to even subtypes of cancer, have dramatically different gene expression patterns These represent cellular states S in addition to hu, 2010

Cancer development Genetic alterations Identifying significantly recurrent 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 type Frequency CN alterations Frequency 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 as long as ces driver copy number Norwell, 1976 I. Selection of the Fittest Our ISAR algorithm tries to identify frequent alterations driven by fitness. ISAR 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

Defining peaks: cut-off 9 of the 33 GISTIC2 peaks do not contain a single gene Helios approach Sample 3 Sample 2 Sample 4 Sample 1 Classic Approach deterministic 0/1 decision Weight in addition to combine Primary tumor data (TCGA)

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Functional assays (RNAi screens) Helios: Data Integration A ranked in addition to 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: AMP WT CCND1 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 data Features Classifier Labels List of drivers in addition to passengers Too small in addition to biased !!! PLX4720-Targeted Therapy

How do we view > 30 dimensions Parameters: 4 8 14 32 Plots: 6 28 91 496 Acknowledgements Felix Sanchez-Garcia Dylan Kotliar Uri David Akavia El-ad David Amir Jacob Levine Smita Krishnaswamy Garry Nolan (Stan as long as d) Sean Bendall Erin Simonds Daniel Shenfeld Michelle Tadmor Kara Davis Junji Matsui Bo-Juen Chen

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