CausNet-partial : 'Partial Generational Orderings' based search for optimal sparse Bayesian networks via dynamic programming with parent set constraints.

Background: In our recent work, we developed a novel dynamic programming algorithm to find optimal Bayesian networks (BNs) with parent set constraints. This 'generational orderings' based dynamic programming search algorithm - CausNet - efficiently searches the space of possible BNs given the possible parent sets. The algorithm supports both continuous and categorical data, as well as continuous, binary and survival outcomes. In the present work, we develop a variant of CausNet - CausNet-partial - which searches the space of 'partial generational orderings', which further reduces the search space and is suited for finding smaller sparse optimal Bayesian networks; and can be applied to 1000s of variables. Results: We test this method both on synthetic and real data. Our algorithm performs better than three state-of-art algorithms that are currently used extensively to find optimal BNs. We apply it to simulated continuous data and also to a benchmark discrete Bayesian network ALARM, a Bayesian network designed to provide an alarm message system for patient monitoring. We first apply the original CausNet and then CausNet-partial varying the partial order from 5 to 2. CausNet-partial discovers small sparse networks with drastically reduced runtime as expected from theory. Conclusions: Our partial generational orderings based search for small optimal networks, is both an efficient and highly scalable approach for finding optimal sparse and small Bayesian Networks and can be applied to 1000s of variables. Using specifiable parameters - correlation, FDR cutoffs, in-degree, and partial order - one can increase or decrease the number of nodes and density of the networks. Availability of two scoring option - BIC and Bge - and implementation for survival outcomes and mixed data types makes our algorithm very suitable for many types of high dimensional data in a variety of fields.

Predictive value of CDC37 gene expression for targeted therapy in metastatic colorectal cancer.

BACKGROUND: CDC37 is a key determinant of client kinase recruitment to the HSP90 chaperoning system. We hypothesized that kinase-specific dependency on CDC37 alters the efficacy of targeted therapies for metastatic colorectal cancer (mCRC). MATERIAL AND METHODS: Two independent mCRC cohorts were analyzed to compare the survival outcomes between CDC37-high and CDC37-low patients (stratified by the median cutoff values): the CALGB/SWOG 80405 trial (226 and 207 patients receiving first-line bevacizumab- and cetuximab-containing chemotherapies, respectively) and Japanese retrospective (50 refractory patients receiving regorafenib) cohorts. A dataset of specimens submitted to a commercial CLIA-certified laboratory was utilized to characterize molecular profiles of CDC37-high (top quartile, N = 5055) and CDC37-low (bottom quartile, N = 5055) CRCs. RESULTS: In the bevacizumab-treated group, CDC37-high patients showed significantly better progression-free survival (PFS) (median 13.3 vs 9.6 months, hazard ratio [HR] 0.59, 95% confidence interval [CI] 0.44-0.79, p < 0.01) than CDC37-low patients. In the cetuximab-treated group, CDC37-high and CDC37-low patients had similar outcomes. In the regorafenib-treated group, CDC37-high patients showed significantly better overall survival (median 11.3 vs 6.0 months, HR 0.24, 95% CI 0.11-0.54, p < 0.01) and PFS (median 3.5 vs 1.9 months, HR 0.51, 95% CI 0.28-0.94, p = 0.03). Comprehensive molecular profiling revealed that CDC37-high CRCs were associated with higher VEGFA, FLT1, and KDR expressions and activated hypoxia signature. CONCLUSIONS: CDC37-high mCRC patients derived more benefit from anti-VEGF therapies, including bevacizumab and regorafenib, but not from cetuximab. Molecular profiles suggested that such tumors were dependent on angiogenesis-relating pathways.

CCR5 and CCL5 gene expression in colorectal cancer: comprehensive profiling and clinical value.

BACKGROUND: The C-C motif chemokine receptor 5 (CCR5)/C-C motif chemokine ligand 5 (CCL5) axis plays a major role in colorectal cancer (CRC). We aimed to characterize the molecular features associated with CCR5/CCL5 expression in CRC and to determine whether CCR5/CCL5 levels could impact treatment outcomes. METHODS: 7604 CRCs tested with NextGen Sequencing on DNA and RNA were analyzed. Molecular features were evaluated according to CCR5 and CCL5 tumor gene expression quartiles. The impact on treatment outcomes was assessed in two cohorts, including 6341 real-world patients and 429 patients from the Cancer and Leukemia Group B (CALGB)/SWOG 80405 trial. RESULTS: CCR5/CCL5 expression was higher in right-sided versus left-sided tumors, and positively associated with consensus molecular subtypes 1 and 4. Higher CCR5/CCL5 expression was associated with higher tumor mutational burden, deficiency in mismatch repair and programmed cell death ligand 1 (PD-L1) levels. Additionally, high CCR5/CCL5 were associated with higher immune cell infiltration in the tumor microenvironment (TME) of MMR proficient tumors. Ingenuity pathway analysis revealed upregulation of the programmed cell death protein 1 (PD-1)/PD-L1 cancer immunotherapy pathway, phosphatase and tensin homolog (PTEN) and peroxisome proliferator-activated receptors (PPAR) signaling, and cytotoxic T-lymphocyte antigen 4 (CTLA-4) signaling in cytotoxic T lymphocytes, whereas several inflammation-related pathways were downregulated. Low CCR5/CCL5 expression was associated with increased benefit from cetuximab-FOLFOX treatment in the CALGB/SWOG 80405 trial, where significant treatment interaction was observed with biologic agents and chemotherapy backbone. CONCLUSIONS: Our data show a strong association between CCR5/CCL5 gene expression and distinct molecular features, gene expression profiles, TME cell infiltration, and treatment benefit in CRC. Targeting the CCR5/CCL5 axis may have clinical applications in selected CRC subgroups and may play a key role in developing and deploying strategies to modulate the immune TME for CRC treatment.