THE PLANNING: Asking questions and planning studies Chi-Ping Day, Ph.D.

Laboratory of Cancer Biology and Genetics

National Cancer Institute

NCI Experimental Design CourseApril 19th, 2017

1. The Science of Doing Scientific Research

What is scientific research:

Popper, Kuhn, and Occam' razor.

Karl Popper’s question: What is a scientific theory?

Thomas Kuhn: We see the world through tinted glasses

Occam' razor: the practical reason of science

Structured hypothesis → “testable” hypothesis → experiment → validation → modifying hypothesis

If a theory can explain everything, it is useless!

Is Intelligent Design/Evolution a Scientific Theory?

Goal Essential Factors

Hypothesis Test Outcome Improvement

Evolution To identify factors that determine the function and structure of life forms.

Genetic variations, environment stress, population sizes, etc.

Neo-Darwinistshypothesize natural selection dominates the evolutionary outcomes

Without natural selection, isolated descendant population should have similar genotypes.

Isolated descendant population developed different genotypes.

Genetic drift also drives evolution in isolated population.

Intelligent Design

To Prove the existence of the creator.

“irreducible complexity”

“irreducible complexity” can be found in life form

To find “irreducible complexity”.

Identified astructure as “irreducible complexity” by self-defining.

No need.

Learning the world by mapping

Design the map• Determine the function• Determine the scope• Consider what to include

(“essential elements)”)• Determine how detail

Generate the map• Outline the boundary• Add “essential elements”• Label the elements

MeasurementOrientatingSurveying

Use the map• Determine the path from the map• Run the path• Measure the errors

Feedback from user • Report the errors• Include new details• Include new function

Research as a modeling cycle

Cancer Res; 2016. 76(20):5921

Defining the question

Pattern formationBoundary conditionResolution & scope

Unexpected observation

and/orPreliminary

studies

Input Output

How?Why?

What if?

Mendel’s question• During cross-breeding of pea plants, certain traits show up in offspring without any

blending of parent characteristics. • Boundary: the “non-blending” traits from two genders• How are the genetic traits transferred to the next generation?• Input: the traits of parents• Output: the traits of the offspring

Hypothesizing the driving factors

Input OutputA

B

C

Factor that can be

triggered by the input

Factors that can

determine the output

Preliminary results

Thought experiment

Driving factors in Mendel’s question• Genetic traits as factors independent of environmental influence. • Each factor determines one phenotype.• Input: Parents with different traits.• Output: phenotypes of offspring.

Constructing the model

Input parameters Output readoutA

B

C

Base of the model

(“grey box”)

Boundary condition:

resolution/scope

Mendel’s model of genetic traits• Base: pea plants. • Boundary condition: Reproduction with two genders• Driving factors: the “non-blending” traits• Input parameters: Selected traits of the parents.• Output: phenotypes of offspring.

Testing the model

Input parameters to control and treated groups

Measure the output readout

123…

Mendel’s model testing• Model setting: Cross-pollinating the pea plants• Input parameters: Purebred plants with particular traits.• Output readout: observe the defined traits in offspring over many generations.

A

B

C

Grey box

Evaluating the output

Evaluating Mendel’s results: 1. Principle of segregation and independent assortment.2. Measure the gap: (1) other species; (2) “blending” factors3. Exception to the rules

output readout

The Grey Box The Black Box

ABCD…

αβγδ…

The studied system

The model

Input parameters

“Gap”

Factors of the real world

Improving the model for next round

(1) Evaluate the driving factors to be retained or removed.(2) Hypothesize new driving factors(3) Build new a model base on the new combination of driving factors.

What can be added to Mendel’s model of genetic traits?

Input parameters to control and treated groups

Measure the output readout

123…

Grey box

AB

D

Technologies for model building and testing

• Model systems: species, genetic engineered animals, cultured cells, in silico model, etc.

• Hypothesis generation: big data

• Input: Library screening

• Output: Biosensor, parallel processing (e.g. next generation sequencing, mass spectrum, image analysis)

• Outcome evaluation: computational power.

Logistics, cost, and time

Logistics

Model complexity

Cost & TimeScope & resolution of the question

Contribution to scientific knowledge

• Every kind of knowledge is valuable, but not each one of them is scientific.

• If the result can be used to improve the model, it deserve your time.

• Novelty is not a criterion for a good research.

• Is the result generated by “shooting first and then drawing a target”?

• Is the evidence identified because it supports my hypothesis?

2. Historical Examples

Min Chiu Li's chemotherapy for cancer treatment

• Question: What if the dosing continues until tumor marker stops persisting?

• Hypothesis: Residual disease caused the failure of cancer treatment. It can be eliminated by cyclic chemotherapy.

• Model: Metastatic choriocarcinoma patients that received methotrexate.

• Testing: Multiple dosing on patients with methotrexate until no detection of urine hCGresulted in complete regression.

Outcome evaluation: Cyclic treatment enhance the efficacy of cancer chemotherapy. However, many other types of cancer are still resistant

Min Chiu Li (李敏求)

• Improvement: “log-kill” theory and combinational chemotherapy.

• Preliminary results: One dose of chemo drug reduced tumor marker level but did not improve patient survival.

Residual disease

Accumulated toxicity

Biomarker (hCG)

Tumor growth kinetics

Nature Rev Cancer 5: 516 (2005)

Heterogeneity of cell populations: From Delbrück to Fidler

• Question: How cancer becomes metastatic? Does the tumor have pre-existed metastatic cells or acquire metastatic capacity?

• Hypothesis: Metastatic cells pre-exist in primary tumor (or not).

• Model: Lung colonization B16 melanoma cells in mice via i.v. injection.

• Testing: Injection i.v. clonally cultured B16 sublines into mice, and calculate distribution of metastatic nodule number.

Outcome evaluation: Each subline gave significantly different metastatic nodule number. In Delbruck model, it indicated the pre-existence of metastatic cells.

• Improvement: Metastatic signature in patient tumors

• Preliminary results: “Seed-and-soil” theory of melanoma

The Luria–Delbrück experiment(the Fluctuation Test), 1943

The Fidler–Kripke experimentScience 197:893, 1977

Drug resistance in targeted therapy

• Question: How do tumors acquire resistance?

• Hypothesis: Targeted therapy selects pre-existing cells whose survival does not depend on the “driver” oncogene.

• Model: Cultured cancer cells driven by the oncogene of target.

• Testing: The cells received continuous treatment of the targeted drug.

Outcome evaluation: The survival cells become resistant to the drug. Several different mechanisms have been identified.

• Improvement: Which mechanism is clinically relevant? Why?

• Preliminary results: Most targeted therapy in cancer treatment resulted in resistance within a short period.

Cellular signaling

Tumor microenvironment

Immune response

Metabolism

Yamanaka factors of iPS cells

• Question: Which TFs can induce pluripotency in somatic cells. ?

• Hypothesis: A specific, limited number of TFs can re-program somatic cells.

• Model: Mouse fibroblasts with an ES cell reporter were transduced with different combination of TFs.

• Testing: Introduced the clones of iPS cells into mouse blastocysts.

Outcome evaluation: Four TFs were identified as minimally required factors for re-programming somatic cells.

• Improvement: How are the iPS cells differentiated into specific type of somatic cells?

• Preliminary results: 1. Somatic cells can be reprogrammed by fusion with ES cells. 2. Several transcription factors (TFs) function in the maintenance of pluripotency

UV-induced melanoma mouse model

• Question: How UV at physiological relevant dose can accelerate melanoma occurrence?

• Hypothesis: melanocytes at skin of different age respond to UV differently.

• Model: HGF-tg mice at different ages.

• Testing: The mice of different age groups received UV treatment. Melanoma incidence and pathology were analyzed.

Outcome evaluation: UV at day 3 significantly accelerated melanoma and increased tumor number. This result is consistent with epidemiological data.

• Improvement: Why skin of young age was more sensitive to UV-induced carcinogenesis? Why race is an important factor?

• Preliminary results: 1. Melanoma occurred in 4-20% of HGF-tg mice in 15 months. 2. Chronic UV accelerated melanoma occurrence in adult HGF-tg mice.

• Location of melanocytes• Wavelengths of UVs• Age• Driver oncogenes• Early lesion (e.g. nevi)• Pigmentation• Genetic traits

Lab Invest (2017), 1–8

Akt phosphorylation of p21 and p27

• Question: Can new Akt substrate be identified by it’s a.a. sequences?

• Hypothesis: Akt activates a network of genes by phosphorylating consensus sequences on them.

• Model: Genebank.

• Testing: In silico identified proteins were subjected to Akt phosphorylation.

Outcome evaluation: p21 and p27 were validated as substrates of Akt. Their association with Akt activities was studied in tumor tissues.

• Improvement: What is the function of the network of the genes regulated by Akt?

• Preliminary results: Akt-phosphorylated sites have been shown to be consensus a.a. sequence.

http://sackler.tufts.edu/Faculty-and-Research/Faculty-Research-Pages/Philip-Tsichlis

YOUR example!