A machine learning platform to optimize the translation of personalized network models to the clinic

PURPOSE Dynamic network models predict clinical prognosis and inform therapeutic intervention by elucidating disease-driven aberrations at the systems level. However, the personalization of model predictions requires the profiling of multiple model inputs, which hampers clinical translation. PATIENTS AND METHODS We applied APOPTO-CELL, a prognostic model of apoptosis signaling, to showcase the establishment of computational platforms that require a reduced set of inputs. We designed two distinct and complementary pipelines: a probabilistic approach to exploit a consistent subpanel of inputs across the whole cohort (Ensemble) and a machine learning approach to identify a reduced protein set tailored for individual patients (Tree). Development was performed on a virtual cohort of 3,200,000 patients, with inputs estimated from clinically relevant protein profiles. Validation was carried out in an in-house stage III colorectal cancer cohort, with inputs profiled in surgical resections by reverse phase protein array (n = 120) and/or immunohistochemistry (n = 117). RESULTS Ensemble and Tree reproduced APOPTO-CELL predictions in the virtual patient cohort with 92% and 99% accuracy while decreasing the number of inputs to a consistent subset of three proteins (40% reduction) or a personalized subset of 2.7 proteins on average (46% reduction), respectively. Ensemble and Tree retained prognostic utility in the in-house colorectal cancer cohort. The association between the Ensemble accuracy and prognostic value (Spearman ρ = 0.43; P = .02) provided a rationale to optimize the input composition for specific clinical settings. Comparison between profiling by reverse phase protein array (gold standard) and immunohistochemistry (clinical routine) revealed that the latter is a suitable technology to quantify model inputs. CONCLUSION This study provides a generalizable framework to optimize the development of network-based prognostic assays and, ultimately, to facilitate their integration in the routine clinical workflow

COX-1 (PTGS1) and COX-2 (PTGS2) polymorphisms, NSAID interactions, and risk of colon and rectal cancers in two independent populations

Nonsteroidal anti-inflammatory drugs (NSAIDs) target the prostaglandin H synthase enzymes, cyclooxygenase (COX)-1 and -2, and reduce colorectal cancer risk. Genetic variation in the genes encoding these enzymes may be associated with changes in colon and rectal cancer risk and in NSAID efficacy

Genetic variation in prostaglandin synthesis and related pathways, NSAID use and colorectal cancer risk in the Colon Cancer Family Registry

Although use of non-steroidal anti-inflammatory drugs (NSAIDs) generally decreases colorectal cancer (CRC) risk, inherited genetic variation in inflammatory pathways may alter their potential as preventive agents. We investigated whether variation in prostaglandin synthesis and related pathways influences CRC risk in the Colon Cancer Family Registry by examining associations between 192 single nucleotide polymorphisms (SNPs) and two variable nucleotide tandem repeats (VNTRs) within 17 candidate genes and CRC risk. We further assessed interactions between these polymorphisms and NSAID use on CRC risk. Using a case-unaffected-sibling-control design, this study included 1621 primary invasive CRC cases and 2592 sibling controls among Caucasian men and women aged 18–90. After adjustment for multiple comparisons, two intronic SNPs were associated with rectal cancer risk: rs11571364 in ALOX12 [ORhet/hzv = 1.87, 95% confidence interval (CI) = 1.19–2.95, P = 0.03] and rs45525634 in PTGER2 (ORhet/hzv = 0.49, 95% CI = 0.29–0.82, P = 0.03). Additionally, there was an interaction between NSAID use and the intronic SNP rs2920421 in ALOX12 on risk of CRC (P = 0.03); among those with heterozygous genotypes, risk was reduced for current NSAID users compared with never or former users (ORhet = 0.60, 95% CI = 0.45–0.80), though not among those with homozygous wild-type or variant genotypes. The results of this study suggest that genetic variation in ALOX12 and PTGER2 may affect the risk of rectal cancer. In addition, this study suggests plausible interactions between NSAID use and variants in ALOX12 on CRC risk. These results may aid in the development of genetically targeted cancer prevention strategies with NSAIDs

AMPK‐regulated miRNA‐210‐3p is activated during ischaemic neuronal injury and modulates PI3K‐p70S6K signalling

AbstractProgressive neuronal injury following ischaemic stroke is associated with glutamate‐induced depolarization, energetic stress and activation of AMP‐activated protein kinase (AMPK). We here identify a molecular signature associated with neuronal AMPK activation, as a critical regulator of cellular response to energetic stress following ischaemia. We report a robust induction of microRNA miR‐210‐3p both in vitro in primary cortical neurons in response to acute AMPK activation and following ischaemic stroke in vivo. Bioinformatics and reverse phase protein array analysis of neuronal protein expression changes in vivo following administration of a miR‐210‐3p mimic revealed altered expression of phosphatase and tensin homolog (PTEN), 3‐phosphoinositide‐dependent protein kinase 1 (PDK1), ribosomal protein S6 kinase (p70S6K) and ribosomal protein S6 (RPS6) signalling in response to increasing miR‐210‐3p. In vivo, we observed a corresponding reduction in p70S6K activity following ischaemic stroke. Utilizing models of glutamate receptor over‐activation in primary neurons, we demonstrated that induction of miR‐210‐3p was accompanied by sustained suppression of p70S6K activity and that this effect was reversed by miR‐210‐3p inhibition. Collectively, these results provide new molecular insight into the regulation of cell signalling during ischaemic injury, and suggest a novel mechanism whereby AMPK regulates miR‐210‐3p to control p70S6K activity in ischaemic stroke and excitotoxic injury. imag

Genetic variation in prostaglandin synthesis and related pathways, NSAID use and colorectal cancer risk in the Colon Cancer Family Registry

Although use of non-steroidal anti-inflammatory drugs (NSAIDs) generally decreases colorectal cancer (CRC) risk, inherited genetic variation in inflammatory pathways may alter their potential as preventive agents. We investigated whether variation in prostaglandin synthesis and related pathways influences CRC risk in the Colon Cancer Family Registry by examining associations between 192 single nucleotide polymorphisms (SNPs) and two variable nucleotide tandem repeats (VNTRs) within 17 candidate genes and CRC risk. We further assessed interactions between these polymorphisms and NSAID use on CRC risk. Using a case-unaffected-sibling-control design, this study included 1621 primary invasive CRC cases and 2592 sibling controls among Caucasian men and women aged 18–90. After adjustment for multiple comparisons, two intronic SNPs were associated with rectal cancer risk: rs11571364 in ALOX12 [OR(het/hzv) = 1.87, 95% confidence interval (CI) = 1.19–2.95, P = 0.03] and rs45525634 in PTGER2 (OR(het/hzv) = 0.49, 95% CI = 0.29–0.82, P = 0.03). Additionally, there was an interaction between NSAID use and the intronic SNP rs2920421 in ALOX12 on risk of CRC (P = 0.03); among those with heterozygous genotypes, risk was reduced for current NSAID users compared with never or former users (OR(het) = 0.60, 95% CI = 0.45–0.80), though not among those with homozygous wild-type or variant genotypes. The results of this study suggest that genetic variation in ALOX12 and PTGER2 may affect the risk of rectal cancer. In addition, this study suggests plausible interactions between NSAID use and variants in ALOX12 on CRC risk. These results may aid in the development of genetically targeted cancer prevention strategies with NSAIDs

A stepwise integrated approach to personalized risk predictions in stage III colorectal cancer

Purpose: Apoptosis is essential for chemotherapy responses. In this discovery and validation study, we evaluated the suitability of a mathematical model of apoptosis execution (APOPTO-CELL) as a stand-alone signature and as a constituent of further refined prognostic stratification tools. Experimental Design: Apoptosis competency of primary tumor samples from patients with stage III colorectal cancer (n = 120) was calculated by APOPTO-CELL from measured protein concentrations of Procaspase-3, Procaspase-9, SMAC, and XIAP. An enriched APOPTO-CELL signature (APOPTO-CELL-PC3) was synthesized to capture apoptosome-independent effects of Caspase-3. Furthermore, a machine learning Random Forest approach was applied to APOPTO-CELL-PC3 and available molecular and clinicopathologic data to identify a further enhanced signature. Association of the signature with prognosis was evaluated in an independent colon adenocarcinoma cohort (TCGA COAD, n = 136). Results: We identified 3 prognostic biomarkers (P = 0.04, P = 0.006, and P = 0.0004 for APOPTO-CELL, APOPTO-CELL-PC3, and Random Forest signatures, respectively) with increasing stratification accuracy for patients with stage III colorectal cancer. The APOPTO-CELL-PC3 signature ranked highest among all features. The prognostic value of the signatures was independently validated in stage III TCGA COAD patients (P = 0.01, P = 0.04, and P = 0.02 for APOPTO-CELL, APOPTO-CELL-PC3, and Random Forest signatures, respectively). The signatures provided further stratification for patients with CMS1-3 molecular subtype. Conclusions: The integration of a systems-biology–based biomarker for apoptosis competency with machine learning approaches is an appealing and innovative strategy toward refined patient stratification. The prognostic value of apoptosis competency is independent of other available clinicopathologic and molecular factors, with tangible potential of being introduced in the clinical management of patients with stage III colorectal cancer

COX-1 (PTGS1) and COX-2 (PTGS2) polymorphisms, NSAID interactions, and risk of colon and rectal cancers in two independent populations

PURPOSE: Nonsteroidal anti-inflammatory drugs (NSAIDs) target the prostaglandin H synthase enzymes, cyclooxygenase (COX)-1 and -2, and reduce colorectal cancer risk. Genetic variation in the genes encoding these enzymes may be associated with changes in colon and rectal cancer risk and in NSAID efficacy. METHODS: We genotyped candidate polymorphisms and tagSNPs in PTGS1 (COX-1) and PTGS2 (COX-2) in a population-based case-control study (Diet, Activity and Lifestyle Study, DALS) of colon cancer (n=1470 cases/1837 controls) and rectal cancer (n=583/775), and independently among cases and controls from the Colon Cancer Family Registry (CCFR; colon n= 959/1535, rectal n= 505/839). RESULTS: In PTGS2, a functional polymorphism (−765G>C; rs20417) was associated with a 2-fold increased rectal cancer risk (p=0.05) in the DALS study. This association replicated with a significant nearly 5-fold increased risk of rectal cancer in the CCFR study (OR(CC vs GG)=4.88; 95%CI=1.54–15.45; OR(GC vs GG)=1.36; 95%CI: 0.95–1.94). Genotype-NSAID interactions were observed in the DALS study for PTGS1 and rectal cancer risk, and for PTGS2 and colon cancer risk, but were no longer significant after correcting for multiple comparisons and did not replicate in the CCFR. No significant associations between PTGS1 polymorphisms and colon or rectal cancer risk were observed. CONCLUSIONS: These findings suggest that polymorphisms in PTGS2 may be associated with rectal cancer risk and impact the protective effects of NSAIDs