Systems biology analysis identifies molecular determinants of chemotherapy-induced diarrhoea.

Chemotherapy-induced diarrhoea (CID) is a common dose-limiting adverse event in patients with cancer. Here, we hypothesise that chemotherapy evokes apoptosis in normal gut epithelium, contributes to CID and that patients with increased risk of CID can be identified using a systems model of BCL-2 protein interactions (DR_MOMP) that calculates the sensitivity of cells to undergo apoptosis. Normal adjacent gut epithelium tissue was collected during resection surgery from a cohort of 35 patients with stage II-III colorectal cancer (CRC) who were subsequently treated with capecitabine, XELOX or FOLFOX. Clinical follow-up, type and grade of adverse events during adjuvant chemotherapy were recorded. The level of five BCL-2 proteins required for the calculation of the DR_MOMP score was quantified together with 62 additional signalling proteins related to apoptotic pathways. Odds ratios for the occurrence of diarrhoea were determined using multinomial logistic regression (MLR). Patients treated with capecitabine who had a DR_MOMP score equal or higher than the mean had a significantly lower frequency of diarrhoea significantly compared to patients below the mean. High DR_MOMP scores indicate high apoptosis resistance. No statistical difference was observed in patients treated with XELOX or FOLFOX. Using MLR, we found that levels of apoptosis-related proteins caspase-8, p53 and XIAP statistically interacted with the DR_MOMP stress dose. Markers of MAPK signalling were prognostic for diarrhoea independently of DR_MOMP. In conclusion, apoptosis sensitivity and MAPK signalling status of the adjacent normal gut epithelium of chemotherapy-naïve patients represent promising biomarkers to identify patients with CRC with increased risk of CID.

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.

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. Clin Cancer Res; 23(5); 1200-12. ©2016 AACR.

BCL-2 system analysis identifies high-risk colorectal cancer patients.

OBJECTIVE: The mitochondrial apoptosis pathway is controlled by an interaction of multiple BCL-2 family proteins, and plays a key role in tumour progression and therapy responses. We assessed the prognostic potential of an experimentally validated, mathematical model of BCL-2 protein interactions (DR_MOMP) in patients with stage III colorectal cancer (CRC). DESIGN: Absolute protein levels of BCL-2 family proteins were determined in primary CRC tumours collected from n=128 resected and chemotherapy-treated patients with stage III CRC. We applied DR_MOMP to categorise patients as high or low risk based on model outputs, and compared model outputs with known prognostic factors (T-stage, N-stage, lymphovascular invasion). DR_MOMP signatures were validated on protein of n=156 patients with CRC from the Cancer Genome Atlas (TCGA) project. RESULTS: High-risk stage III patients identified by DR_MOMP had an approximately fivefold increased risk of death compared with patients identified as low risk (HR 5.2, 95% CI 1.4 to 17.9, p=0.02). The DR_MOMP signature ranked highest among all molecular and pathological features analysed. The prognostic signature was validated in the TCGA colon adenocarcinoma (COAD) cohort (HR 4.2, 95% CI 1.1 to 15.6, p=0.04). DR_MOMP also further stratified patients identified by supervised gene expression risk scores into low-risk and high-risk categories. BCL-2-dependent signalling critically contributed to treatment responses in consensus molecular subtypes 1 and 3, linking for the first time specific molecular subtypes to apoptosis signalling. CONCLUSIONS: DR_MOMP delivers a system-based biomarker with significant potential as a prognostic tool for stage III CRC that significantly improves established histopathological risk factors.

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.

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 COX-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 tag SNPs in PTGS1 (COX-1) and PTGS2 (COX-2) in a population-based case­control study (Diet, Activity and Lifestyle Study, DALS) of colon cancer (n = 1,470 cases/1,837 controls) and rectal cancer (n = 583/775), and independently among cases and controls from the Colon Cancer Family Registry (CCFR; colon n = 959/1,535, rectal n = 505/839). RESULTS: In PTGS2, a functional polymorphism (-765G[C; rs20417) was associated with a twofold increased rectal cancer risk (p = 0.05) in the DALS. This association replicated with a significant nearly fivefold increased risk of rectal cancer in the CCFR study (ORCC vs. GG = 4.88; 95 % CI 1.54­15.45; ORGC vs. GG = 1.36; 95 %CI 0.95­1.94). Genotype­NSAID interactions were observed in the DALS 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.

Genetic variation in TLR or NFkappaB pathways and the risk of breast cancer: a case-control study.

BACKGROUND: Toll-like receptors (TLRs) and the transcription factor nuclear factor-κB (NFκB) are important in inflammation and cancer. METHODS: We examined the association between breast cancer risk and 233 tagging single nucleotide polymorphisms within 31 candidate genes involved in TLR or NFκB pathways. This population-based study in the Seattle area included 845 invasive breast cancer cases, diagnosed between 1997 and 1999, and 807 controls aged 65-79. RESULTS: Variant alleles in four genes were associated with breast cancer risk based on gene-level tests: MAP3K1, MMP9, TANK, and TLR9. These results were similar when the risk of breast cancer was examined within ductal and luminal subtypes. Subsequent exploratory pathway analyses using the GRASS algorithm found no associations for genes in TLR or NFκB pathways. Using publicly available CGEMS GWAS data to validate significant findings (N = 1,145 cases, N = 1,142 controls), rs889312 near MAP3K1 was confirmed to be associated with breast cancer risk (P = 0.04, OR 1.15, 95% CI 1.01-1.30). Further, two SNPs in TANK that were significant in our data, rs17705608 (P = 0.05) and rs7309 (P = 0.04), had similar risk estimates in the CGEMS data (rs17705608 OR 0.83, 95% CI 0.72-0.96; CGEMS OR 0.90, 95% CI 0.80-1.01 and rs7309 OR 0.83, 95% CI 0.73-0.95; CGEMS OR 0.91, 95% CI 0.81-1.02). CONCLUSIONS: Our findings suggest plausible associations between breast cancer risk and genes in TLR or NFκB pathways. Given the few suggestive associations in our data and the compelling biologic rationale for an association between genetic variation in these pathways and breast cancer risk, further studies are warranted that examine these effects.

Genetic variation in proinflammatory cytokines IL6, IL6R, TNF-region, and TNFRSF1A and risk of breast cancer.

Proinflammatory cytokines are associated with age-related diseases including arthritis and heart disease. IL6 and TNF also play key roles in estrogen modulation in older women. We explored whether variation in IL6 and TNF genes influenced the risk of breast cancer in samples that differed by age group: <44 years (228 cases and 271 controls), 45-64 years (426 cases and 396 controls), and 65+ years (228 cases and 239 controls). Samples were drawn from population-based case-control studies conducted in Seattle. Age-adjusted odds ratios (ORs) were calculated to evaluate the risk associated with variants in IL6, IL6R, TNF, and TNFRSF1A. There was a significantly increased risk of breast cancer associated with one or more C>T alleles at IL6 rs2069861 among subjects in the oldest age group (OR 1.8, 95% CI 1.1-2.9), but no overall increased risk of breast cancer associated with any IL6 or IL6R variants in the combined data. There were significantly elevated risks of breast cancer among women 45-64 years old associated with a UTR 5' flanking SNP LTA rs2009658 C>G allele (OR 1.5, 95% CI 1.1-1.9) and a nonsynonomous coding SNP TNFRSF1A rs767455 T>C allele (OR 1.3, 95% CI 1.1-1.6); these two variants were also elevated in the combined data (OR 1.3, 95% CI 1.1-1.5 and OR 1.2, 95% CI 1.1-1.4, respectively). This study supports a modest association between a variant in IL6 and breast cancer among older women and TNF-related variants and breast cancer among middle-aged women. Further evaluation of these genes in other studies is warranted.