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

In the beginning, life on earth was all single cells No cancer

>1 Billion Years Ago"

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

The destiny of cancer medicine

Implications and new opportunities

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

What phenotypes do you want to select for?

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)