Causal Modeling of CALGB 80405 (Alliance) Identifies ... · • A molecular pathway between 1 oside...
Transcript of Causal Modeling of CALGB 80405 (Alliance) Identifies ... · • A molecular pathway between 1 oside...
• Bayesian causal modeling identified clinical and molecular causal drivers (prognostic biomarkers) of OS for mCRC. The molecular drivers were validated in independent cohorts.
• 1o side, ECOG score, AST, LDH, HGB, and metastases (intra-abdominal, and liver) were the top clinical drivers of OS.
• BRAF & RAS mutations, CMS4, and angiogenesis/ ECM remodeling signature were top molecular drivers of OS.
• Consistent with previous studies, ALOX5 and CDX2 were identified as causal driver genes of OS.
• A molecular pathway between 1o side and OS was identified. Investigation into the molecular underpinnings of sidedness in driving OS is currently in progress.
• The availability of the measures for the drivers at baseline will allow better risk stratification at initiation of treatment.
• Additional research, including prospective studies, is necessary to confirm these findings.
Support: U10CA180821, U10CA180882, U10CA180888; Eli Lilly and Company, Genentech, Pfizer. ClinicalTrials.gov Identifier: NCT00265850
• CALGB 80405 is a recently-completed phase III clinical trial of FOLFOX and FOLFIRI with randomly assigned cetuximab (cet) or bevacizumab (bev) in metastatic CRC (mCRC) patients.
• Hypothesis-free machine learning approaches to this study dataset can provide valuable insights into mCRC prognosis and management of mCRC progression.
• Causal modeling identifies the set of conditional dependencies between variables leading to outcomes.
• We built multivariate causal models of mCRC and examined the network drivers of mCRC survival.
• Using our Bayesian causal machine learning platform REFSTM, an ensemble of 128 network models were built for overall survival (OS) of mCRC.
• The ensemble enables estimation of model uncertainty and identification of key drivers by model consensus.
• Simulations were performed on the ensemble to identify causal drivers of OS after accounting for confounders. Causal effect was quantified by median hazard ratio (HR). For continuous variables, 3rd & 1st quartile values were used to compute HR.
• Analysis of NanoString data:• Consensus molecular subtypes (CMS) were computed using published code
(Guinney et al., Nat. Med. 2015) on GitHub. • Molecular clusters were computed using consensus clustering.
• Patients with both KRAS wild-type and mutant tumors were included and those who received both cet and bev treatments were excluded. Molecular data from primary tumors were included.
• Two independent cohorts (N=117 for mutations, N=206 for nanostring data) were withheld and used for causal drivers validation.
CAUSAL MODELS• Model1: Clinical variables only (N=1463, 68 variables)• Model2: Clinical+molecular variables without raw nanostring data (N=430, 84 vars)• Model3: Clinical+all molecular variables (N=430, 900 vars)
BACKGROUND RESULTS
METHODS
CONCLUSIONS
Causal Modeling of CALGB 80405 (Alliance) Identifies Network drivers of Metastatic Colorectal Cancer Rahul K. Das,1 Leon Furchtgott,1 Daniel Cunha,1 Fang-Shu Ou,2 Federico Innocenti,3 Heinz-Josef Lenz,4 Jeffrey Meyerhardt,5 Kelly Rich,1 Jeanne Latourelle,1 Donna
Niedzwiecki,6 Andrew Nixon,7 Eileen M. O’Reilly,8 Diane Wuest,1 Boris Hayete,1 Iya Khalil,1 Alan Venook,91GNS Healthcare, Cambridge, MA; 2Alliance Statistics and Data Center, Mayo Clinic, Rochester, MN; 3University of North Carolina, Chapel Hill, NC; 4University of Southern California, Los Angeles, CA; 5Dana-Farber Cancer
Institute/Partners Cancer Care, Boston, MA; 6Alliance Statistics and Data Center, Duke University, Durham NC; Duke Cancer Institute, 7Duke University Medical Center, Durham, NC; 8Memorial Sloan Kettering Cancer Center, New York, NY; 9University of California San Francisco, San Francisco, CA
Model 1: Clinical Causal Drivers of OS • 1o side, ECOG performance score, concentrations of aspartate aminotransferase (AST), hemoglobin (HGB),
absolute neutrophil counts (ANC), lactate dehydrogenase (LDH) and metastases at intra-abdominal, lung, and liver were the strongest causal drivers of OS.
• Clustering of NanoString data revealed three molecular clusters with upregulation of different signatures: (1) WNT-signaling, (2) Angiogenesis & ECM remodeling, (3) Immune infiltration.
• BRAF mutation, RAS mutation, CMS4, and angiogenesis signature were the top molecular drivers of OS.
• Causal effects of 10 side on OS was found to be driven by a molecular pathway.
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●axon guidance
leukocyte cell−cell adhesion
positive regulation of peptidyl−tyrosine phosphorylation
cell−cell signaling
positive regulation of endothelial cell migration
motor neuron axon guidance
positive regulation of apoptotic process
angiogenesis
patterning of blood vessels
response to cytokine
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response to mechanical stimulus
cellular response to extracellular stimulus
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chemokine−mediated signaling pathway
response to hypoxia
cell adhesion
extracellular matrix organization
platelet degranulation
positive regulation of monocyte chemotaxis
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Fig. 2: Reverse Engineering: Consensus Subnetwork to OS Fig. 3: Top Causal Drivers from Simulations
Fig. 6: Consensus Subnetwork to OS Fig. 7: Top Causal Drivers from Simulations
Fig. 5: Over-represented GO Biological Processes in Angiogenesis Cluster
Model 3: Causal Driver Genes of OS • ALOX5 and CDX2 were among the top causal driver genes of OS. • The causal genes in the molecular pathways leading to OS are involved in ECM remodeling and angiogenesis, thereby
corroborating the findings from Model 2.
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positive regulation of transcription, DNA−templated
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epidermal growth factor receptor signaling pathway
osteoblast differentiation
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embryonic digit morphogenesis
fibroblast growth factor receptor signaling pathway
negative regulation of apoptotic process
neurotrophin TRK receptor signaling pathway
positive regulation of collagen biosynthetic process
positive regulation of stem cell proliferation
proximal/distal pattern formation
Fc−epsilon receptor signaling pathway
positive regulation of interferon−gamma production
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Fig. 9: Consensus Subnetwork to OSFig. 8: GO Biological Processes where Causal Driver Genes are Over-represented
Fig. 10: Top Causal Drivers from Simulations
Validation of Causal Drivers of OS• Identified causal drivers were validated in independent cohorts
using univariate Cox proportional hazard model. HR, 95% CI, and p-value are shown in the plots below.
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HR = 3.28 (1.67, 6.43)p = 0.0002
HR = 2.01 (1.35, 2.99)p = 0.0004
HR = 0.56 (0.41, 0.77)p = 0.0002
HR = 1.87 (1.22, 2.89)p = 0.0038
Fig. 11: Kaplan Meier Survival Curves
Model 2: Molecular Causal Drivers of OS Fig.4: Molecular Clusters
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Fig. 1: Schematic of REFSTM Reverse Engineering & Forward Simulation Workflow
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