The Evolutionary Biology of Cancer - Carlo C. Maley
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Transcript of The Evolutionary Biology of Cancer - Carlo C. Maley
The Evolutionary Biology of Cancer
Carlo C. Maley
Associate Professor School of Life Sciences Arizona State University
Director, Center for Evolution and Cancer University of California San Francisco
Evolution explains
• Why we don’t get more cancer
• Why it has been hard to cure
• How we get cancer
Myriad forms most wonderful had appeared
www.palaeos.com D.W. Miller from American Scientist, March-April, 1997
By 500 million years ago"
The Multicellular Covenant
Cells of our bodies (somatic cells) curtail their reproduction Sperm and egg cells propagate the genes Cancer is the breaking of that covenant
The Promise of Comparative Oncology
Transition from unicellularity to multicellularity
Comparative oncology
Peto’s Paradox
Experimental evolution in microbes
Tumors Evolve by Natural Selection
• Variation in the population of cells:
– Somatic genetic alterations
• Variation amongst cells is heritable:
– Alterations in DNA and methylation patterns
• Variation affects reproduction and survival:
– e.g., suppression of apoptosis etc.
Nowell (1976) Science 1976; 194:23-28 Greaves & Maley (2012) Nature 481:306
Regularities across cancers are due to natural selection
• Each hallmark increases cell fitness
• Selection for phenotypes that increase proliferation or survival
– There are tradeoffs that limit this selection
• Little consistency in underlying genetics
• Stochastic mutational process
The dynamics of this are unknown Evolution Within a Neoplasm
Time
Frequency within the neoplasm
p53-
p16+/- p16-/- p53-
cancer
neutral neutral neutral
neutral
dysplasia
Typical Cancer Data
Time
Frequency within the neoplasm
p53-
p16+/- p16-/- p53-
cancer
neutral neutral neutral
neutral
dysplasia
One time point Majority genotype
Use tools from evolutionary biology The history of a tumor is not a single sequence of events
Every cell has its own, intertwined history – a phylogeny
Phylogenies can reconstruct the history of a tumor
Gerlinger et al. NEJM 366:883-892 (2012)
Tumors are spatially heterogeneous
• Different regions of the tumor:
– make different predictions
– suggest different therapies
• Need to take multiple biopsies
Gerlinger et al. NEJM 366:883-892 (2012)
Problems with measuring the products of evolution
• Most biomarkers are panels of the presence/absence of a mutation or expression of a gene – Her2/Neu, EGFR, p53, Mammoprint, Oncotype, etc.
• Spatial heterogeneity limits utility of sampling/biopsies
• Historical accidents
• Many ways of generating the same phenotype
• Critical event may not have happened yet
Universal biomarkers
We should measure the process of somatic evolution
– Mutation rate
– Population sizemore cells = more chances for mutations
– Generation timemore cell divisions = more mutations
– Rate of clonal expansion (strength of selection)faster expansion = larger target population for next mutation
Can we target these parameters?
Kostadinov et al. PLoS Genetics 2013 Published Today
Trevor Graham: Fri 1pm
When a tumor recurs it is reincarnated as a different tumor
Therapy selected for resistance Additional mutations have accumulated
Need to biopsy recurrent tumors to see how they have changed
Ding et al. Nature 2011
Natural selection favors cells resistant to therapy True for every cancer drug invented Should assay for presence of resistant cells prior to drug selection
Before Therapy
After Therapy
Wang et al. 2004 PNAS 101:3089
Delay the evolution of resistance
• Practically no one in the developed world dies of chemosensitive cancer
– Resistance is the problem in cancer therapy
• Drug development screens should test for time to resistance
• Drugs should be combined based on the probability of and time to resistance
• Speed of evolution is proportional to the fitness differentials
– Cytotoxic drugs select for resistance the fastest!
Should we treat for survival or tumor eradication?
Tumor eradication
• Most effective for homogeneous cancers
• Should design treatment to select against resistant clones (“sucker’s gambit”)
Cure via control
• Select against lethal clones
• Select for benign cells
• Maintain the tumor in a manageable state
• Slow down somatic evolution
• Prevent the proximal cause of death
Can we transform cancer into a chronic disease?
• Cancer could be like diabetes, something we live with, rather than die of
– With standard therapy, you never know if there are lingering cancer cells
– We all live with some neoplastic cells
• Opens alternatives to killing all the cancer cells
– Try to keep tumor at a stable size… Brash et al. Sem. Can. Bio. 2005
Adaptive Therapy: My enemy’s enemy is my friend Maintain a population of sensitive cells to compete with resistant cells Algorithm: Adjust dose to maintain a stable tumor
Gatenby et al. Cancer Research 69, 4894-4903 (2009)
Adaptive Therapy Results"
Kept the mice alive indefinitely
Dose required decreased to the minimum!
We don’t know why
Days after Cell Injection
Mea
n Tu
mor
Bur
den
(mm
3)
Vehicle Only Standard Regimen
Adaptive
Gatenby et al. Cancer Research 69, 4894-4903 (2009)
Implant OVCAR3 cells
Implications of Adaptive Therapy
• Could be used on any cancer
• Does not require the development and approval of a new drug
• Caveats: – Shown and repeated in one
type of cancer in mice – Intermittent therapy hasn’t
worked well in human trials in prostate cancer
• Now testing in breast cancer
Control Standard Therapy Adaptive Therapy
Clinical Messages
• Profile more than one area of a tumor
• Test for the presence of a rare resistant clone
• Biopsy at recurrence
• Combine drugs that prevent or delay resistance
• Approach management with the goal of delaying the evolution of resistance
• Treat for survival rather than tumor eradication
The evolutionary biology of cancer is expanding rapidly
• Evolution and ecology provide a theoretical framework for integrating big data from new technologies
• Microenvironment introduces ecology
• Multiple sessions at the major national conferences
• Phylogenetic analyses in publications
• Characterizing intratumor heterogeneity in genome sequencing data
• Increases in studies of the evolution of resistance
Evolution explains
• Why we get cancer?
• Why we don’t get more cancer?
• Why it has been hard to cure?
• What to do about it!
Acknowledgements Grant Support
Maley NIH R01 CA140657
Maley ACS RSG-09-163-01-CNE
Reid NIH P01 CA91955
Carroll NIH R01 CA149566
Gatenby NIH R01 CA170595
Maley UCSF School of Medicine Discretionary Funds
Bonnie J. Addario Lung Cancer Foundation
Collaborators
Athena Aktipis (UCSF & ASU)
Amy Boddy (UCSF)
Lauren Merlo (Lankenau)
Brian Reid (FHCRC)
Tom Vaughan (FHCRC)
Rob Odze (Harvard)
Shane Jensen (U. Penn)
Paul Sniegowski (U. Penn)
Kathleen Sprouffske (U. Zurich)
Rumen Kostadinov (Johns Hopkins)
Toni Bedalov (FHCRC)
David Jablons (UCSF)
Nader Pourmand (UCSC)
John Pepper (NCI)
Mary Kuhner (U. Washington)
Qihong Huang (Wistar)
Simon Tavaré (Cambridge)
Martin Carroll (U. Penn)
Jerry Radich (FHCRC)
Per Palsboll (U. Sweden)
Aurora Nedelcu (U. New Brunswick)
Bob Gatenby (Moffitt Cancer Center)
Virginia Kwan (ASU)
Steven Neuberg (ASU)
Shelly Hwang (Duke)
Joshua Schiffman (U. Utah)