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Last Updated: 03/04/21

Validation of Prognostic and Pathway Signatures in Lethal Prostate Cancer

U01 CA157703

Susan Halabi, Ph.D.
Duke University Medical Center, Durham, NC

Dr. Halabi’s program validated established RNA, DNA, and microarray prognostic and pathway signatures in men with high-risk prostate cancer. The program focused on distinguishing causative pathways from those that merely correlate with aggressiveness of disease. The most promising signatures were adapted to formalin-fixed paraffin-embedded tissues with the aim to develop clinically deployable assays.

The data generated was widely shared with the larger investigative community in hopes of accelerating progress toward effective therapies for lethal prostate cancer.

Collaborators:

  • The project team included investigators from UCSF, the NCI Cooperative Group CALGB (now the Alliance), Vancouver Prostate Centre, University of North Carolina, Duke University Medical Center, The Ohio State University Medical Center, Genomic Health, Inc., and Sage Bionetworks.
  • Specimens were supplied by the CALGB, through both established biorepositories and a prospective trial.
  • Statistical support was provided by the CALGB Statistical Center.

Projects:

  • Validate prognostic signatures in localized lethal prostate cancer
  • Validate pathway signatures associated with localized lethal prostate cancer
  • Adapt the prognostic and pathway signatures for clinical deployment

Publications:

Database of genomic biomarkers for cancer drugs and clinical targetability in solid tumors.

Authors: Dienstmann, R., Jang, I.S., Bot, B, et al.

Source: Cancer Discov. 2015 Feb;5(2):118-23

Abstract: SUMMARY: Comprehensive genomic profiling is expected to revolutionize cancer therapy. In this Prospective, we present the prevalence of mutations and copy-number alterations with predictive associations across solid tumors at different levels of stringency for gene-drug targetability. More than 90% of The Cancer Genome Atlas samples have potentially targetable alterations, the majority with multiple events, illustrating the challenges for treatment prioritization given the complexity of the genomic landscape. Nearly 80% of the variants in rarely mutated oncogenes are of uncertain functional significance, reflecting the gap in our understanding of the relevance of many alterations potentially linked to therapeutic actions. Access to targeted agents in early clinical trials could affect treatment decision in 75% of patients with cancer. Prospective implementation of large-scale molecular profiling and standardized reports of predictive biomarkers are fundamental steps for making precision cancer medicine a reality.

Stepwise group sparse regression (SGSR): gene-set-based pharmacogenomic predictive models with stepwise selection of functional priors.

Authors: Jang, I. S., Dienstmann, R., Margolin, AA, and Guinney, J.

Source: Pac Symp Biocomput 2015: 32-43

Abstract: Complex mechanisms involving genomic aberrations in numerous proteins and pathways are believed to be a key cause of many diseases such as cancer. With recent advances in genomics, elucidating the molecular basis of cancer at a patient level is now feasible, and has led to personalized treatment strategies whereby a patient is treated according to his or her genomic profile. However, there is growing recognition that existing treatment modalities are overly simplistic, and do not fully account for the deep genomic complexity associated with sensitivity or resistance to cancer therapies. To overcome these limitations, large-scale pharmacogenomic screens of cancer cell lines--in conjunction with modern statistical learning approaches--have been used to explore the genetic underpinnings of drug response. While these analyses have demonstrated the ability to infer genetic predictors of compound sensitivity, to date most modeling approaches have been data-driven, i.e. they do not explicitly incorporate domain-specific knowledge (priors) in the process of learning a model. While a purely data-driven approach offers an unbiased perspective of the data--and may yield unexpected or novel insights--this strategy introduces challenges for both model interpretability and accuracy. In this study, we propose a novel prior-incorporated sparse regression model in which the choice of informative predictor sets is carried out by knowledge-driven priors (gene sets) in a stepwise fashion. Under regularization in a linear regression model, our algorithm is able to incorporate prior biological knowledge across the predictive variables thereby improving the interpretability of the final model with no loss--and often an improvement--in predictive performance. We evaluate the performance of our algorithm compared to well-known regularization methods such as LASSO, Ridge and Elastic net regression in the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (Sanger) pharmacogenomics datasets, demonstrating that incorporation of the biological priors selected by our model confers improved predictability and interpretability, despite much fewer predictors, over existing state-of-the-art methods.

Sample Size Requirements and Study Duration for Testing Main Effects and Interactions in Completely Randomized Factorial Designs When Time to Event is the Outcome.

Authors: Moser, B. K. and Halabi, S.

Source: Commun Stat Theory Methods. 2015;44(2):275-285

Abstract: In this paper we develop the methodology for designing clinical trials with any factorial arrangement when the primary outcome is time to event. We provide a matrix formulation for calculating the sample size and study duration necessary to test any effect with a pre-specified type I error rate and power. Assuming that a time to event follows an exponential distribution, we describe the relationships between the effect size, the power, and the sample size. We present examples for illustration purposes. We provide a simulation study to verify the numerical calculations of the expected number of events and the duration of the trial. The change in the power produced by a reduced number of observations or by accruing no patients to certain factorial combinations is also described.

A novel test to compare two treatments based on endpoints involving both nonfatal and fatal events.

Authors: Potthoff, R. F. and Halabi, S.

Source: Pharm Stat. 2015 Apr 20

Abstract: In a clinical trial comparing two treatment groups, one commonly-used endpoint is time to death. Another is time until the first nonfatal event (if there is one) or until death (if not). Both endpoints have drawbacks. The wrong choice may adversely affect the value of the study by impairing power if deaths are too few (with the first endpoint) or by lessening the role of mortality if not (with the second endpoint). We propose a compromise that provides a simple test based on the time to death if the patient has died or time since randomization augmented by an increment otherwise. The test applies the ordinary two-sample Wilcoxon statistic to these values. The formula for the increment (the same for experimental and control patients) must be specified before the trial starts. In the simplest (and perhaps most useful) case, the increment assumes only two values, according to whether or not the (surviving) patient had a nonfatal event. More generally, the increment depends on the time of the first nonfatal event, if any, and the time since randomization. The test has correct Type I error even though it does not handle censoring in a customary way. For conditions where investigators would face no easy (advance) choice between the two older tests, simulation results favor the new test. An example using a renal-cancer trial is presented. Copyright © 2015 John Wiley & Sons, Ltd.

Successful whole-exome sequencing from a prostate cancer bone metastasis biopsy.

Authors: Van Allen, E. M., Foye, A., Wagle, N., et al.

Source: Prostate Cancer Prostatic Dis. 2014 Mar;17(1):23-7

Abstract: BACKGROUND: Comprehensive molecular characterization of cancer that has metastasized to bone has proved challenging, which may limit the diagnostic and potential therapeutic opportunities for patients with bone-only metastatic disease. METHODS: We describe successful tissue acquisition, DNA extraction, and whole-exome sequencing from a bone metastasis of a patient with metastatic, castration-resistant prostate cancer (PCa). RESULTS: The resulting high-quality tumor sequencing identified plausibly actionable somatic genomic alterations that dysregulate the phosphoinostide 3-kinase pathway, as well as a theoretically actionable germline variant in the BRCA2 gene. CONCLUSIONS: We demonstrate the feasibility of diagnostic bone metastases profiling and analysis that will be required for the widespread application of prospective 'precision medicine' to men with advanced PCa.

Heterogeneity in the inter-tumor transcriptome of high risk prostate cancer.

Authors: Wyatt, A. W., Mo, F., Wang, K., et al.

Source: Genome Biol. 2014 Aug 26;15(8):426

Abstract: BACKGROUND: Genomic analyses of hundreds of prostate tumors have defined a diverse landscape of mutations and genome rearrangements, but the transcriptomic effect of this complexity is less well understood, particularly at the individual tumor level. We selected a cohort of 25 high-risk prostate tumors, representing the lethal phenotype, and applied deep RNA-sequencing and matched whole genome sequencing, followed by detailed molecular characterization. RESULTS: Ten tumors were exposed to neo-adjuvant hormone therapy and expressed marked evidence of therapy response in all except one extreme case, which demonstrated early resistance via apparent neuroendocrine transdifferentiation. We observe high inter-tumor heterogeneity, including unique sets of outlier transcripts in each tumor. Interestingly, outlier expression converged on druggable cellular pathways associated with cell cycle progression, translational control or immune regulation, suggesting distinct contemporary pathway affinity and a mechanism of tumor stratification. We characterize hundreds of novel fusion transcripts, including a high frequency of ETS fusions associated with complex genome rearrangements and the disruption of tumor suppressors. Remarkably, several tumors express unique but potentially-oncogenic non-ETS fusions, which may contribute to the phenotype of individual tumors, and have significance for disease progression. Finally, one ETS-negative tumor has a striking tandem duplication genotype which appears to be highly aggressive and present at low recurrence in ETS-negative prostate cancer, suggestive of a novel molecular subtype. CONCLUSIONS: The multitude of rare genomic and transcriptomic events detected in a high-risk tumor cohort offer novel opportunities for personalized oncology and their convergence on key pathways and functions has broad implications for precision medicine.