Advancing Precision Medicine:
MicroRNA Prognostic Signatures and
Prediction Models for Breast Cancer
By Natalie Ng
Ingenui...
Motivation
● In the United States alone, there are over 500 new cases of breast cancer
and more than 100 breast cancer rel...
Research Outline
1. Phase 1: Create prognostic models predictive of distant
metastasis-free survival (time to metastasis) ...
Why miRNAs?
● What are miRNAs?
○ Small, conserved RNA, 19-25 nucleotides long
○ Bind to both DNA and RNA to inhibit both t...
Dataset Selection
Phase 1: In Silico Discovery
● GSE22220: Superseries dataset that comprises of linked mRNA
and miRNA exp...
In Silico Discovery Workflow
● Goal: to select miRNAs that are not only differentially expressed
but also able to regulate...
ER+ Output
Phase 1: In Silico Discovery
● Differentially expressed mRNAs used to predict miRNAs.
● Confidence Settings: Ex...
ER- Output
Phase 1: In Silico Discovery
● Differentially expressed mRNAs used to predict miRNAs.
● Confidence Settings: Ex...
Phase 1: In Silico Discovery
Cox Regression & Model Selection
● Two Strategies of Cox Regression
○ Forward Step Wise Selec...
Phase 1: In Silico Discovery
Cox Regression & Model Selection
● Two Strategies of Cox Regression
○ Forward Step Wise Selec...
ER+ Model
Clear separation
between a high
risk and low risk
group for
metastasis
Coefficient for Cox Regression: The large...
ER- Model
Also, a clear
separation
between a high
risk and low risk
group for
metastasis
Coefficient for Cox Regression: T...
Computational Validation
Phase 1: In Silico Discovery
● Models were validated with independent patient samples by
computin...
Network Maps
Phase 1: In Silico Discovery
Many of the downstream targets are related to metastasis.
This confirms the vali...
Network Maps
Phase 1: In Silico Discovery
Many of the downstream targets are related to metastasis.
This confirms the vali...
Experimental Validation In Vitro
Phase 2: In Vitro Validation
● Determine the correlation between miRNA expressions and in...
Experimental Design
Phase 2: In Vitro Validation
1. Measure metastatic characteristics (migration, invasion, proliferation...
Migration and Invasion
Phase 2: In Vitro Validation
MDA-231 had the greatest ability to
migrate and invade, with a respons...
Proliferation
Phase 2: In Vitro Validation
All experiments repeated twice.
● Yellow MTT is reduced to Purple Formazan in
d...
miRNA Expression Profiling
Phase 2: In Vitro Validation
● Of the 12 miRNAs, 5 were detected in significant amounts.
● The ...
Correlation
Phase 2: In Vitro Validation
In the highly metastatic cell line MDA-231, 4 of the 5
detectable miRNAs match mo...
Major Accomplishments
I developed a novel in silico discovery
workflow to identify a unique
combination of miRNAs that can...
Further Research
1. Could miR-210 be an independent indicator of metastasis? miR-210 is
the only difference between MDA-23...
Acknowledgements
● Chen Lab, Stanford University (supported experimental
studies by providing laboratory access and traini...
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miRNA Breast Cancer Prognosis -- Ingenuity Systems

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miRNA Breast Cancer Prognosis -- Ingenuity Systems

  1. 1. Advancing Precision Medicine: MicroRNA Prognostic Signatures and Prediction Models for Breast Cancer By Natalie Ng Ingenuity Systems and Stanford University, School of Medicine
  2. 2. Motivation ● In the United States alone, there are over 500 new cases of breast cancer and more than 100 breast cancer related deaths each day. ● One in every eight women will develop breast cancer in their lifetime. ● Today, 85-90% of breast cancer patients are given chemotherapy, while only 30% need it and benefit from it. The other 70% experience the unnecessary, severe side-effects of chemotherapy. Goals 1. Develop a tool that advances precision medicine by enabling doctors to predict which patients will develop cancer recurrence or metastasis, evaluate potential benefits, and screen candidates for chemotherapy. 2. Design the tool to have maximum accessibility in the clinic, by using as few biomarkers as possible without sacrificing predictability.
  3. 3. Research Outline 1. Phase 1: Create prognostic models predictive of distant metastasis-free survival (time to metastasis) for breast cancer patients a. Use microRNAs (miRNA) instead of traditional protein coding genes. b. Integrate both miRNA and mRNA data, to increase predictability by drawing from two sets of genomic information c. Computationally validate the signatures. 2. Phase 2: Experimentally validate the prognostic signature in vitro a. Measure miRNA expressions and metastatic characteristics (migration, invasion, proliferation) in breast cancer cell lines. b. Determine if there is a correlation between metastatic characteristics and miRNA expressions.
  4. 4. Why miRNAs? ● What are miRNAs? ○ Small, conserved RNA, 19-25 nucleotides long ○ Bind to both DNA and RNA to inhibit both transcription and translation ● I hypothesize that miRNAs can provide better prediction with fewer biomarkers. ○ miRNAs act as regulators of networks in cells ○ One miRNA can bind to as many as 200 targets. The dysregulation of even one miRNA can change the phenotype of cells in drastic ways. ● 50% of miRNAs originate from unstable regions of the genome that are involved in cancer tumorigenesis.
  5. 5. Dataset Selection Phase 1: In Silico Discovery ● GSE22220: Superseries dataset that comprises of linked mRNA and miRNA expression profiles of 207 breast cancer patients. ○ Has both ER+ and ER- patients ● Survival Curves generated using Kaplan Meier analysis ○ Represent percentage of patients who are metastasis-free after a given number of years ● ER- has a significantly poorer prognosis than ER+ Create two signatures, one for ER+ and another for ER-
  6. 6. In Silico Discovery Workflow ● Goal: to select miRNAs that are not only differentially expressed but also able to regulate downstream mRNAs First demonstration of integrating mRNA and miRNA data through a knowledge based tool. Phase 1: In Silico Discovery
  7. 7. ER+ Output Phase 1: In Silico Discovery ● Differentially expressed mRNAs used to predict miRNAs. ● Confidence Settings: Experimental and Highly Predicted
  8. 8. ER- Output Phase 1: In Silico Discovery ● Differentially expressed mRNAs used to predict miRNAs. ● Confidence Settings: Experimental and Highly Predicted
  9. 9. Phase 1: In Silico Discovery Cox Regression & Model Selection ● Two Strategies of Cox Regression ○ Forward Step Wise Selection: Employs a combination of univariate and multivariate analysis. ○ Penalized Cox Regression (L1, L2, L1+L2): Used to create more parsimonious models and tune parameters. ● Model Selection based on two criteria ○ Maximize Area Under Curve (AUC) of a Receiver Operating Characteristic (ROC) ○ Most parsimonious model What does an ROC Curve Represent? ● Every test is a balance of sensitivity and specificity. ● Only in an ideal test is the area under an ROC curve 1 ● Goal of model fitting is to maximize the area under the ROC curve.
  10. 10. Phase 1: In Silico Discovery Cox Regression & Model Selection ● Two Strategies of Cox Regression ○ Forward Step Wise Selection: Employs a combination of univariate and multivariate analysis. ○ Penalized Cox Regression (L1, L2, L1+L2): Used to create more parsimonious models and tune parameters. ● Model Selection based on two criteria ○ Maximize Area Under Curve (AUC) of a Receiver Operating Characteristic (ROC) ○ Most parsimonious model What does an ROC Curve Represent? ● Every test is a balance of sensitivity and specificity. ● Only in an ideal test is the area under an ROC curve 1 ● Goal of model fitting is to maximize the area under the ROC curve. Formulations of Area Under Curve (AUC) ● L1 Penalized Cox Regression was selected because the models maximized the area under the ROC curve (shown by AUC-WGE and AUC-RS) and were the most parsimonious (fewest covariates).
  11. 11. ER+ Model Clear separation between a high risk and low risk group for metastasis Coefficient for Cox Regression: The larger the Coefficient, the more that particular miRNA drives the model. Phase 1: In Silico Discovery
  12. 12. ER- Model Also, a clear separation between a high risk and low risk group for metastasis Coefficient for Cox Regression: The larger the Coefficient, the more that particular miRNA drives the model. Phase 1: In Silico Discovery
  13. 13. Computational Validation Phase 1: In Silico Discovery ● Models were validated with independent patient samples by computing the Area Under Curve (AUC) of a ROC curve Models still showed predictive value when extended to new data, especially the prognostic model for ER- breast cancer.
  14. 14. Network Maps Phase 1: In Silico Discovery Many of the downstream targets are related to metastasis. This confirms the validity of the proposed workflow. Not only are the miRNAs differentially expressed, but they are also able to create regulated expression in downstream mRNAs related to metasasis.
  15. 15. Network Maps Phase 1: In Silico Discovery Many of the downstream targets are related to metastasis. This confirms the validity of the proposed workflow. Not only are the miRNAs differentially expressed, but they are also able to create regulated expression in downstream mRNAs related to metasasis.
  16. 16. Experimental Validation In Vitro Phase 2: In Vitro Validation ● Determine the correlation between miRNA expressions and in vitro metastatic characteristics ○ For now, I focused on the ER- model because it is the more invasive form of breast cancer. ● Cell Lines that represented a range of metastatic potential based on prior publications were selected. MCF10, non-malignant cells, were used as a control.
  17. 17. Experimental Design Phase 2: In Vitro Validation 1. Measure metastatic characteristics (migration, invasion, proliferation) in vitro. 2. Measure miRNA expression using qPCR. 3. Determine if miRNA expressions correlate to in vitro metastatic characteristics.
  18. 18. Migration and Invasion Phase 2: In Vitro Validation MDA-231 had the greatest ability to migrate and invade, with a response specific to the chemoattractant. All experiments repeated twice. Transwell Assay Results ● Over time, starved cells migrate through the transwell towards the chemoattractant. ● Greater the number of cells below the transwell, the greater the migration/invasion. ● In the invasion assay, transwell is coated with matrigel, an analog of the extra-cellular matrix.
  19. 19. Proliferation Phase 2: In Vitro Validation All experiments repeated twice. ● Yellow MTT is reduced to Purple Formazan in dividing and viable cells by mitochondrial reductace. ● The greater the absorbance at 540 nm, which corresponds to the color of purple formazan, the greater the proliferation. MTT Assay Results Among the cancer cell lines, SKBR-3, followed by MDA-231 and MDA- 436 had abilities to proliferate.
  20. 20. miRNA Expression Profiling Phase 2: In Vitro Validation ● Of the 12 miRNAs, 5 were detected in significant amounts. ● The reason that not more were detected is that cell lines are not comprehensive representations of actual tumors, which have a much wider variety of cell types. All experiments repeated twice.
  21. 21. Correlation Phase 2: In Vitro Validation In the highly metastatic cell line MDA-231, 4 of the 5 detectable miRNAs match model prediction. Indicates that model prediction holds up experimentally.
  22. 22. Major Accomplishments I developed a novel in silico discovery workflow to identify a unique combination of miRNAs that can be used to predict metastasis. I validated the prognostic signatures in vitro. Correlation was observed between miRNA expressions in the ER- signature and in vitro metastatic characteristics, indicating that model prediction holds up experimentally. 1 2
  23. 23. Further Research 1. Could miR-210 be an independent indicator of metastasis? miR-210 is the only difference between MDA-231 and MDA-453. I will investigate the effects of miR-210 upregulation and knockdown through lentiviral infection. 2. Perform validation using in vivo models. 3. Invesitage pathways regulated by the miRNAs to identify their mechanism of action.
  24. 24. Acknowledgements ● Chen Lab, Stanford University (supported experimental studies by providing laboratory access and training) ○ Professor Chang-Zheng Chen ○ Dr. Rita Fragoso ● Ingenuity Systems (provided summer internship and computational resources) ○ Dr. Stuart Tugendreich ○ Dr. Debra Toburen I conducted this independent research from June 2012 to April 2013. Part of this research has been presented at the 2012 Personal Genome and Medical Genomics Meeting, Cold Spring Harbor Laboratory.

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