SMAD4 and NF1 mutations as potential biomarkers for poor prognosis to cetuximab-based therapy in Chinese metastatic colorectal cancer patients.

BACKGROUND: Cetuximab, an anti-EGFR monoclonal antibody, is used in combination with chemotherapy in clinic to enhance the outcome in metastatic colorectal cancer (mCRC) patients with only ~ 20% response rate. To date only activating mutations in KRAS and NRAS have been identified as poor prognosis biomarkers in cetuximab-based treatment, which makes an urgent need for identification of novel prognosis biomarkers to precisely predict patients' response in order to maximize the benefit. METHODS: In this study, we analysed the mutation profiles of 33 Chinese mCRC patients using comprehensive next-generation sequencing (NGS) targeting 416 cancer-relevant genes before cetuximab treatment. Upon receiving cetuximab-based therapy, patients were evaluated for drug response, and the progression-free survival (PFS) was monitored. The association of specific genetic alterations and cetuximab efficacy was analyzed. RESULTS: Patients carrying SMAD4 mutations (SMAD4mut, n = 8) or NF1 mutations (NF1mut, n = 4) had significantly shorter PFS comparing to those carrying wildtype SMAD4 (SMAD4wt, n = 25) (P = 0.0081) or wildtype NF1 (NF1wt, n = 29) (P = 0.0028), respectively. None of the SMAD4mut or NF1mut patients showed response to cetuximab when assessed at 12-week post-treatment. Interestingly, two patients carrying both SMAD4mut and NF1mut showed the shortest PFS among all the patients. CONCLUSIONS: Our results demonstrated that SMAD4 and NF1 mutations can serve as potential biomarkers for poor prognosis to cetuximab-based therapy in Chinese mCRC patients.

Towards Effective Causal Partitioning by Edge Cutting of Adjoint Graph.

Causal partitioning is an effective approach for causal discovery based on the divide-and-conquer strategy. Up to now, various heuristic methods based on conditional independence (CI) tests have been proposed for causal partitioning. However, most of these methods fail to achieve satisfactory partitioning without violating d-separation, leading to poor inference performance. In this work, we transform causal partitioning into an alternative problem that can be more easily solved. Concretely, we first construct a superstructure G of the true causal graph GT by performing a set of low-order CI tests on the observed data D. Then, we leverage point-line duality to obtain a graph GA adjoint to G. We show that the solution of minimizing edge-cut ratio on GA can lead to a valid causal partitioning with smaller causal-cut ratio on G and without violating d-separation. We design an efficient algorithm to solve this problem. Extensive experiments show that the proposed method can achieve significantly better causal partitioning without violating d-separation than the existing methods. The source code and data are available at https://github.com/hzsiat/CPA.

Hybrid Causal Feature Selection for Cancer Biomarker Identification from RNA-seq Data.

The discovery of cancer biomarkers helps to advance medical diagnosis and plays an important role in biomedical applications. Most of the existing data-driven methods identify biomarkers by ranking-based strategies, which generally return a subset or superset of the actual biomarkers, while some other causal-wise feature selection methods are based on Markov Blanket (MB) learning, facing the challenges of high-dimensionality & low-sample. In this work, we propose a novel hybrid causal feature selection method (called CAFES) to support large-scale cancer biomarker discovery from real RNA-seq data. Concretely, CAFES first uses minimal-redundancy & maximal-relevance strategy for dimensionality reduction that returns a set of candidate features. CAFES then learns the causal skeleton w.r.t. those features by CI tests and further obtains an appropriate superset of the MB of the target variable. Finally, CAFES learns the causal structure of this superset by the DAG-GNN algorithm and then obtains the MB of the target variable, which can be treated as the cancer biomarkers. We conduct experiments to evaluate the proposed method on two real well-known RNA-seq datasets that covering both binary and multi-class cases. We compare our method CAFES with seven recent methods including Semi-HITON-MB, STMB, BAMB, FBED, LCS-FS, EEMB, and EAMB. The results show that CAFES can identify dozens of cancer biomarkers, and 1/6  âˆ¼ 1/2 of the discovered biomarkers can be verified by existing works that they are really directly related to the corresponding disease. An advantage of CAFES is that its Recall is significantly higher than those of all the counterparts, indicating that the continuous optimization (DAG-GNN) with the returned causal skeleton after feature selection (that can be treated as a conditional independence-based constraint to the optimization problem) is effective in cancer biomarkers identification under high-dimensional and low-sample RNA-seq data. The source code of CAFES is available at https://github.com/Milkteaww/CFS.

Causal Gene Identification Using Non-Linear Regression-Based Independence Tests.

With the development of biomedical techniques in the past decades, causal gene identification has become one of the most promising applications in human genome-based business, which can help doctors to evaluate the risk of certain genetic diseases and provide further treatment recommendations for potential patients. When no controlled experiments can be applied, machine learning techniques like causal inference-based methods are generally used to identify causal genes. Unfortunately, most of the existing methods detect disease-related genes by ranking-based strategies or feature selection techniques, which generally return a superset of the corresponding real causal genes. There are also some causal inference-based methods that can identify a part of real causal genes from those supersets, but they are just able to return a few causal genes. This is contrary to our knowledge, as many results from controlled experiments have demonstrated that a certain disease, especially cancer, is usually related to dozens or hundreds of genes. In this work, we present an effective approach for identifying causal genes from gene expression data by using a new search strategy based on non-linear regression-based independence tests, which is able to greatly reduce the search space, and simultaneously establish the causal relationships from the candidate genes to the disease variable. Extensive experiments on real-world cancer datasets show that our method is superior to the existing causal inference-based methods in three aspects: 1) our method can identify dozens of causal genes, and 1/3  âˆ¼ 1/2 of the discovered causal genes can be verified by existing works that they are really directly related to the corresponding disease; 2) The discovered causal genes are able to distinguish the status or disease subtype of the target patient; 3) Most of the discovered causal genes are closely relevant to the disease variable.