Physical Sciences in Oncology Symposium: Take Home Thoughts

Final comments from the speakers and people from the audience.

• (???) It's really difficult to know actually what's going on in the patient.
• (David Parkinson) This is actually an issue of clinical priorities.
• Need to link all of the work in cancer research to clinical activities
• (Larry Nagahara) There are going to be a wealth of research and applications coming out of the PSOC centers.
• (Peter Kuhn) 
• (David Agus ?) Would be nice to all get drug response data in a reproducible fashion
• Should start getting people together more
• (woman from the audience) Would be great to aim all of this technology at clinically relevant questions.
• First step: Identifying the questions!  What do we want to answer?
• (Bob Austin) Technology is still somewhat misplaced.
• (Dr. Lee ?) Can the medical doctors give feedback to the physical scientist to make their work relevant to the clinician sooner
• (Bittorio) Good that the community is developing a common language.
• Journals should be coming out around this common language
• Trans-network collaboration is good, as long as it stays focused on key questions
• (David Parkinson) Comment from a physicist:
• "You're where the physicists were 100 years ago.  We were taking the atom apart and discovering it's components.  But the real value came when we started putting it back together."
• Again, focus on the goal
• e.g., in drug development, start with the end goal of what the drug should do, and then work backwards

Shan Wang, USC, "Magneto-Nano Chip Platform for Cancer Research and Diagnostics"

Working on a diagnostics mageto-nano sensor for diagnostics and tracking changes during cancer therapy.

• Higher sensitivity, earlier detection possible
• Add biomarker, related antibody with nanoparticle attached
• By applying a magnetic field, the nanoparticle resonates, and can be read
• Sensor: Giant magnetoresistance (GMR) sensor
• Same sensor used in hard disk drives, ... so should be cheap!
• Signal available in about 3-7 minutes
• 8-plex experiment in 1 hour.
• high SNR (> 4:1), low intrasample variation

Scott Manalis, MIT, "Measuring the Physical Properties of Single Cells"

Physical properties of single cells: Clinical applications

• Patel et. al, GSA60: Red Cell Distribution
• high variability in an individual cell sizes are highly correlated with disease

Suspended microchannel resonator (SMR)

• cantalever based, very small measurements
• can measure mass, volume, density, physical properties of cells

Complete picture of cells:

• mass
• volume
• density

Density is hardest

• measured with density gradient centrifugation
• issues:
• not single cell
• no mass/volume info
• etc.

Solution: Archmedes solution to determine whether or not a crown was really gold

• weigh in water, and air, and compare
• constant ratio
• repeat this experiment in SMR with single cell
• 1 cell/second
• mass & density can be used to distinguish iron-deficiency anemia

Michael Snyder, Stanford, "Tech. for Analyzing Transcriptomes and Genomes"

Cost of DNA Sequencing: dropped at 10-fold per year!

Prediction: $1000 genome by end of 2011.

From DNA sequence: how do we understand the genome?

1. RNA Seq for gene expression
2. Mapping structural variation using paired sequencing and sequencing depth.
3. Transcription factor binding sites (ChIP Seq)

RNA Seq

• Start with mRNA
• fragment into bits, or copy to cDNA (and then fragment)
• EST library with adaptation
• generate short sequence reads
• map back to genome
• Get a signal read
• exon regions
• poly-A tail
• reads which span introns (ends map to two exons)
• Advantages
• define exons and introns
• follow espression of genes, exons, and splicing isoforms
• discover new exons and genes
• Types
• Single end (short or long reads)
• long reads are expensive (454, Sanger)
• Paired end reads
• Allow detection of novel transcripts
• RNA Seq is quantitative information
• High correlation with QPCR
• 8000 fold dynamic range (vs 100 fold for microarray)
• Microarray: low expression levels are not able to be measured
• Lot of splice junctions being discovered through RNA-Seq
• some (much?) background, not used
• Single Cell Analysis with RNA-Seq
• Not very detailed yet

Mapping variation among people

• SNPs (Single nucleotide polymorphisms)
• Structural variation
• deletion, insertions (copy number variation (CNV)), inversions
• 3-4% difference per person
• likely involved in phenotype variation and disease
• most detection methods are low resolution (> 50kb)
• can be detected to a certain extent using sequencing
• 17% of variations affect genes

 All of these technologies can and are being used to characterize cancer.

David Parkinson, Nodality, "Single Cell-based signaling network functional characterization as a basis for biologically driven clinic decision-making and drug development in oncology"

Rational Efficient Treatment

• Biological understanding of malignancy
• Dev. of targeted therapeutics
• Biological characterization of individual patients

Anticancer drugs development timeline

• 1980s: 
• conventional chemotherapy
• cytotoxics: platinums, taxanes, topoisomerase in hibitors, alkylating agents, etc.
• 1990s:
• 1st generation targeted (ERBB, VEGF)
• herceptin, gleevec...
• 2000s
• 2nd generation targeted (kinases)
• 2010
• Novel Targets (cytotoxic, cytostatic)

Progression over past 20 years:

• Anatomical => Biological characterization of cancer
• Tumor (histological) => Target (Tumor subsets) => Biology (pathways/networks/processes)

Biological characterization

• Must reflect pathophysiology (pathway and network characterization)
• Need to be able to characterize single/rare cell populations
• Must follow patient over time as tumor evolves
• Response and resistance biological profiles

Example: Morphological to Biological Classification in AML

• Today:
• morphology (does not characterize cell type)
• immuno-phenotype
• cytogenetic characterization (closer)
• Tomorrow: pathophysiology-based characterization
• core signaling pathways
• Hypothesis:
• internal signaling networks are superior for patient stratification, rx selection, prediction of outcomes

Disease downstream activity:

• DNA -> RNA -> protein -> ... -> cell signaling

Characterizing pathways

• Transport
• Signaling
• Cell division
• DNA damage
• Apoptosis
• Differentiation
• Patient response

(Nice laundry list...)

SCNP: Flow cytometry

• Gary Nolan (Stanford)
1. obtain sample
2. evoke and fix cels
3. stain with antibodies
4. single cell analysis
• -> can compare basal to evoked cell signaling response

Recent accomplishments in AML

• ID of pathway signatures highly predictive of induction response
• Can monitor individual drug response.

Personalized Medicine: Hurdles:

• Technological
• Regulatoryt
• Reimbursment
• vested interests
• cultural
• Resistance to change

Summary

• Systems approach to characterizing complex biology
• Function rather than anatomical chacterization
• Single cell resolution
• Defining biology/therapeutic outcome at key clinical decision points
• Toward biologically driven clinical decisions and drug development

John Pepper, U. Arizona, "How to avoid acquired drug resistance to cancer"

Paradox of standard cancer therapy:

• Killing cancer cells does not necessarily promote health

We need alternatives to cytotoxins.  They impose strong selection, which causes harm in the long term

Cancer cells thrive by modifying their microenvironment, with shared secreted molecules that increase the fitness of both producer and consumer.

• Entail a cost to producer, but
• ...not evolutionarily robust
• idea: attack these targets

Shared secreted molecules

• angiogneisis factors
• secreted growht factors
• secreted invasion factors (MMPs)
• secreted immune factors...
• others...

 Why will this work: avoids drug resistance by disruption cooeperations, instead of trying to kill individual cells...

• These factors are not strongly selected for (?), since they confer a cost to producers, though are beneficial to the tumor as a whole

Take home points

• Killing cancer cells is not the only/best way to reduce morbidity/mortality
• Alternatives
• Blocking angiogeneisis
• Blocking acidosis
• Blocking motility and invasion

Problem:

• Blocking angiogenesis: can cause cancers to come back strong (according to some 

Critical assumption

• Cancer cells are related to each other through mitosis (as opposed to reprogrammed epithelial cells)
• Evidence from Simon Tavare, Shibata, et al.

Others:

• Tumor cells may recruit cancer cells in the blood stream (e.g., from metastatic tumors, or metastatic tumors may recruit blood-born cells from the primary tumor)

Darryl Shibata, USC, "Reconstruction Human Cancer Evolution with Epigenetic Somatic Cell Molecular Clocks"

How did a tumor form?

1. Sequential evolution?
2. Single clonal expansion ("Big-bang"? (from a single cell)

To study, using tools from pop. gen.

Molecular Clock Hypothesis:

• Genomes are almost perfect copies of copies
• Measurements

1. Pairwise distances (PWD) (comparative genomics)
2. Selection: Hard to measure
• use PWD to infer selection
• between tumor/germline; <1 per 100,000 bp (~70 years difference estimated)
• doesn't work for comparing tumor to germline, or tumor to tumor
• but we can use methylation for this comparison!
• changes faster

Testing sequential vs. big bang theory:

• sequential evolution: wide heterogeneity of ages = wide variety in methylation patters
• single clonal expansion: narrow range of ages  = similar methylation patterns
• differs for individual cancers

Also: can test physical vs. pairwise distances

• sequential evolution: neighbors are more related than distant cells
• single clonal expansion: methylation similarity not related to physical distance

Side notes:

• Using genetic clock analysis, metastatic cells are generally found to be quite old
• Collection process may introduce homogeneity (e.g., can use microenvironments to influence methylation patterns)

Jasmine Foo, Sloan Kettering, "Evolution of resistance to cancer therapy"

Evolutionary model of resistance

• Stochastic birth/death process: 
• within any time interval, the following can happen:
• cell division
• cell death
• nothing happens
• Mutation might happen during cell division (mutation rate u)
• Multiple mutations might confer resistance to the same therapy
• evolve according to the same process as above
• Questions model can answer:
• prob of de novo resistance
• expected size of resistaant pop.
• diversity of resistant pop (number of celltypes, relative pop sizes)
• prob of resistance development for treatment schedules
• Treatment schedule models birth and death rates of sensitive and resistant cell populations

Resistance in chronic myeloid leukemia (CML):

• Gleevec (imatinib) inhibits activity of oncoprotein BCR-ABL
• point mutation in kinase domain confer resistance to this drug
• Prior to treatment: wide variety of cell types in population

Parig Mallick, USC, "Developing Models of Therapeutic Response"

Therapeutic response: for many drugs, they only work in a small percentage of the population.

Components:

1. De novo resistance
• want to ID which patience likely to respond
• studying Oncogene addiction pathway
2. Acquired resitance
• How do we detect this?
• How do we overcome this?
• How does it develop and spread?
• Mechanical explanation:
• oncogene escape

Where does resistance come from?

• Environment or host
• drug never hits target, hypoxia or other mechanisms change interaction
• Target
• something about the target itself is "broken"
• Downstream of target
• cell's response circuitry is broken or something is compensating

Modeling:

• General cell/drug response model
• How does heterogeneity affect tumor drug response?

Acquired resistance:

• Chacterize the impact of resistance on cellular physiology
• environmental : not much 
• level of the target (cell addicted to a particular growth axis)

Studying EGFR resistance

• Developed multiple resistance strains of HCC827 to drug response
• Studied proteomics of these to try to understand the pathway
• Showed novel compensating pathways (example of oncogene escape)
• Magnitude of proteomic change between resistant strains of HCC827 and parent was much larger than the general proteomic change between any two random (cancer?) cell lines

Question: can we then look for proteomic changes in the blood stream

• Which cellular compartments do tumor-derived proteins in the circulation come from?
• Ans: cell surface and secreted proteins (15x more likely than intracellular proteins)

 

Robet Gatenbt, Moffit Cancer Center, "Tumor physical microenvironment from Mathematical Models to Clinical Trials"

"Cancer is a disease of the genes": not a complete picture.

Cancer is not at all homogeneous.  Better viewed as a multi-scale tissue ecosystem

Cancer is a disease of evolution:

1. Somatic mutations occur
2. Failure of host defenses to deal with those changes

 PET imaging: uses FDG (f-deoxyglucose).  Cancer cells take up glucose at a much higher rate, easily imaged.  Result:

Glycolosys is a hallmark of cancer

Tumors are hypoxic.  Cause: "oxygen inhibits the fermentation of glucose." (Louis Pasteur, 1855)... but "tumors have a remakable capacity to ferment glucose even in the presence of oxygen (Otto Warberg, 1934).  So, increased glucose uptake does not necessarily correlate with hypoxia. (Pasteur and Warberg effects)




Cancer grows on an epithelial membrane: blood on one side, cancer cells on other.  As they develop away from the membrane:

• less oxygen available
• more acidic
• => already adapted to this environment (Warberg effect)

Question: why does hypoxic glycolosis increase fitness advantage?

• produces more acid: toxic to normal cells
• doesn't require oxygen (required by normal cells
• => well adapted to their environment

Hypotheses:

• adding sodium bicarbonate (base) to the tumor environment reduced tumor growth 
• tumor-associated pain related to acidic environment

 Testing these hypotheses:

• Shown to be true (missed ref...)
• About to start clinical trials of oral ingestion of sodium bicarbonate in conjunction with prostate cancer treatment
• Does not change pH of blood!  But does change pH of tumor region (from animal studies)
• Looking at other buffers for clinical use (because of high sodium content of sodium bicarbonate)

Jan Liphardt, Cal Berkeley, Mechanobiology of Tumor Progressoin

Question:

• Why are cell and tissue mechanics changed in virtually all cancers?
• Do the mechanical properties of cancer tumors drive tumor growth, or are they passive bystanders?
• Implications for diagnosis and therapy?

There exists geometrical modulation of oncogenic signaling:

• two cells, with identical number of progeins (and mRNAs) have entirely different phenotypes.
• related to intercellular networks

Tension homeostasis

• cells measure forces/compliance
• cell generate forces
• cells change compliance in response to changes in these
• Result: matrix stiffness promotes malignant progression (Levental et al., Cell (2009)
• Hardening can be reversed by LOX inhibitor, which reduces incidence and growth of tumors

Question: mutations related to cell adhesion/structure/firmness relevant to cancer?

• What combination of signals control cell shape?

Cancer book: Weinberg book (cancer diagram)

Question

• What are the difference in the info transfered across the nuclear membrane (proteins, DNA, RNA) between normal and cancer cells?
• Shape and size of nucleus is regulated during development and differentiation (unpublished data)
• Able to measure this info transfer using quantum dot networks on nuclear membrane

Robert "Bob" Austin, Princeton, "Junior Mints"

Junior Mints: references a Seinfeld episode where Jerry and Kramer are watching a surgery, and Kramer flips a Junior Mint into the open wound...

Elephant in the room: evolution of a cancer to become resistant to therapy.

Statement: Evolution here is not random (!?).

Bacterial experiment to probe for accelerated and directed evolution of drug resistance: The Death Galaxy

• attempt to realize the fitness landscape dynamics in a dD ecologywith a large range of stress gradients
• Goal: evolve resistance to CIPRO (in bacteria)
• CIPRO binds to a gyrase
• bacteria evolves different forms of gyrases, and eventually one is resistant to CIPRO
• Creating a stress gradient across a population on a 1-inch slide
• Food on one side, cipro and food on other (10 ug/ml CIPRO -- high dosage)
• Resistance to 10ug/ml CIPRO in 20 hours!
• Resistant bacteria spread very quickly
• Bacterial morphology different in different parts of gradient

Point:  by the time you've detected the cancer/infection, it's already spread throughout the organism...

Further research:

1. vary initial population
2. sequence!
3. model dynamics as function of antibiotic stress

Questions from audience:

• What happens when you change the topology/connectivity of the gradient?
• Are the dynamics universal?  Do the results here apply broadly?
• Do the resistant-allowing mutations already exist in the bacteria, or do they happen during the course of the experiment?

Peter Kuhn, Scripps, "Fluid Phase Biopsy in Solid Tumors"

Pointed out the AACR talk, "It's our time."

Step back now and then and then try to see if there's a simple solution (e.g., look for a single mutation in a single gene!).

Fluid-phase biopsy: first studied in 1869.

Trying to study the fluid phase of solid tumors, for

• enabling earlier diagnosis
• better staging
• improved therapeutic selection

by chacterization of circulating tumor cells (CTCs).

How to study:

1. collect cancer tissue from primary site and circulatory system
2. investigate physics, topology, genomics
3. correlate through math and stats

 Areas:

• Cytophysics
• Topology
• Dynomics (?! that's not a misspelling)

 Have developed next-gen solution to detecting CTCs in blood.

• With this technology, can distinguish the difference between HER2/non-HER2 populations!
• Can be used to track course of cancer (e.g., by looking a CTC load vs. time)

Important questions that can be answered by this research:

• tumor growth over time
• kinds of tumor cells that can be CTCs

Needs to be coupled with study of heterogeneity of CTCs:

• what makes them different from non-circulating tumor cells?
• how heterogeneous are the CTCs themselves?

Important question: how important is the data coming out of this technology for diagnosis, treatment, and outcome?

Interesting discussion afterwards contrasting counting vs. characterization of cell types.

Denis Wirtz, Johns Hopkins, Focal Adhesion Proteins in 3D Cancer Cell Motility

Main points:

• What we know about cancer motility in 2D is not predictive of what happens in 3D
• Looking for factors which predict motility in 3D
• Protusion activity is the best predictor
• Not true in 2D
• 3D Motility requires
• integrins
• MMPs
• actomyosin inhibition
• FA proteins play a role in 3D motility (but not in 2D)
• Normal motility: random walk
• Zyxin: causes occilitory motion

For exploration: can we predict protusion activity from transcription or mutation information?

See:
Fraley et al., Nature Cell Biology 2010
Bloom et al., Biophys. J. 2010

Deceptive Practices, Michael Weber, "Arcane Knowledge on a Need-to-Know Basis"

Michael Weber is a magician, and talked about historical "magician inventors".  This was meant to inspire thoughts of innovation.  I'm not sure if it did that, but it was quite interesting!

Hero of Alexander: described a number of remarkable inventions. 

Robert Houdan helped to put down an uprising in Algeria.  He used electromagnetism to hold a box down, so that a strong Algerian could not pick up the box.

The Mechanical Turk: it wasn't really an automaton, but it inspired Joseph Marie Jacquard's invention of the mechanical loom, and Babbage's work on the differential engine.

Rules of the Magician Inventor:

1) the impossible is preferred
2) there are many paths to a solution
3) think as wildly and freely as is possible
4) look outside of your discipline and areas of expertise
5) we must often unlearn to make way for progress
6) enduring hopefulness in the pursuit of magic

Physical Sciences in Oncology Symposium--USC

I'm currently at the Physical Sciences in Oncology Symposium at USC. I'm going to try to blog on some of the talks here. We'll see what happens.

Overview: NCI has recently created 10 Physical Sciences in Oncology centers around the nation. The thought is to integrate the physical sciences into cancer research, to make it a more holistic discipline.

Most of the talks here are focused not on the biology of cancer, but on physical characterization and modeling of cancer cells, cancer cell signaling, cancer evolution, diagnostics, and therapeutic response.

There are at least some information on most of the talks.