MUC16 as a novel target for cancer therapy

Screening of key genes associated with R‑CHOP immunochemotherapy and construction of a prognostic risk model in diffuse large B‑cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is a common subtype of non-Hodgkin lymphoma, which is curable in the majority of patients treated with rituximab plus cyclophosphamide, doxorubicin, vincristine and prednisone (R-CHOP) immunochemotherapy. However, the therapeutic mechanism of R-CHOP has not been elucidated. The GSE32918 and GSE57611 datasets were retrieved from The Gene Expression Omnibus database. The differentially expressed genes (DEGs) associated with R-CHOP therapy were identified using limma. Combined with prognostic information in GSE32918, DEGs found to be significantly associated with prognosis were selected using univariate Cox regression analysis and a risk prediction model was constructed. Based on this model, the samples in the training set (GSE32918) were divided into high and low risk score groups according to the median risk score. A total of 801 DEGs were identified between the R-CHOP treated DLBCL and primary DLBCL samples, from this 116 prognosis-associated genes were selected. Using Cox proportional hazards model, an optimal combination of 12 genes [including calcium/calmodulin dependent protein kinase I (CAMK1), hippocalcin like 4 (HPCAL4) and ephrin A5 (EFNA5)] was selected, and the sample risk score prediction model was constructed and validated. The DEGs between high risk score and low risk score groups were significantly enriched in functions associated with ‘response to DNA damage stimulus’, and pathways including ‘cytokine-cytokine receptor interaction’ and ‘cell cycle’. The optimal combination of the 12 genes, including CAMK1, HPCAL4 and EFNA5, was found to be useful in predicting the prognosis of patients with DLBCL after R-CHOP treatment. Therefore, these genes may be affected by R-CHOP in DLBCL.

MUC16 in non-small cell lung cancer patients affected by familial lung cancer and indoor air pollution: clinical characteristics and cell behaviors

Background

Inherited susceptibility and environmental carcinogens are crucial players in lung cancer etiology, and both exhibit population heterogeneity. MUC16 is overexpressed in various cancers and often associated with poor prognosis. Present work was to investigate the clinical significance of MUC16 in non-small cell lung cancer patients affected by familial lung cancer (FLC) and indoor air pollution caused by coal use.

Methods

Clinicopathologic characteristics and MUC16 expression were analyzed and evaluated in our subject population. Vectors were constructed for MUC16 gene knockout and overexpression, then we examined how MUC16 affected lung cancer cell behaviors, including proliferation, migration, invasion and chemoresistance.

Results

FLC showed significant association with early-onset (P<0.01) and later stage (P<0.01). Indoor air pollution was associated with younger age (P<0.01), later stage (P<0.05) and AD histology type (P<0.05). Interestingly, two age peaks were observed in our FLC and sporadic group respectively, possibly suggesting multiple major contributors to lung cancer in our subject population. MUC16 overexpression was significantly associated with FLC (P<0.05), indoor air pollution (P<0.01) and later stage (P<0.01), additionally more metastasis cases were observed in patients with up-regulated MUC16 (18.1% vs. 10.3%). Taken together, elevated MUC16 may potentially be one molecular character of FLC in local residents. Intriguingly, patients with more MUC16 up-regulation seemed to have a lower number of white blood cells, especially neutrophils, this reflected MUC16's role in immune regulation. In cell behavior experiments, high MUC16 level could contribute to lung cancer cell proliferation, migration, invasion and chemoresistance, but there were variations among cell lines.

Conclusions

MUC16 plays crucial roles in lung cancer pathogenesis, progression and chemoresistance. Interestingly, its association with FLC and indoor air pollution highlights the complexity of lung cancer etiology. Our findings provide useful information to study the intricate interaction between environmental carcinogens and population genetic background.

In silico learning of tumor evolution through mutational time series

Cancer is caused by the effects of somatic mutations known as drivers. Although a number of major cancer drivers have been identified, it is suspected that many more comparatively rare and conditional drivers exist, and the interactions between different cancer-associated mutations that might be relevant for tumor progression are not well understood. We applied an advanced neural network approach to learn the sequence of mutations and the mutational burden in colon and lung cancers and to identify mutations that are associated with individual drivers. A significant ordering of driver mutations is demonstrated, and numerous, previously undetected conditional drivers are identified. These findings broaden the existing understanding of the mechanisms of tumor progression and have implications for therapeutic strategies.

Cancer arises through the accumulation of somatic mutations over time. Understanding the sequence of mutation occurrence during cancer progression can assist early and accurate diagnosis and improve clinical decision-making. Here we employ long short-term memory (LSTM) networks, a class of recurrent neural network, to learn the evolution of a tumor through an ordered sequence of mutations. We demonstrate the capacity of LSTMs to learn complex dynamics of the mutational time series governing tumor progression, allowing accurate prediction of the mutational burden and the occurrence of mutations in the sequence. Using the probabilities learned by the LSTM, we simulate mutational data and show that the simulation results are statistically indistinguishable from the empirical data. We identify passenger mutations that are significantly associated with established cancer drivers in the sequence and demonstrate that the genes carrying these mutations are substantially enriched in interactions with the corresponding driver genes. Breaking the network into modules consisting of driver genes and their interactors, we show that these interactions are associated with poor patient prognosis, thus likely conferring growth advantage for tumor progression. Thus, application of LSTM provides for prediction of numerous additional conditional drivers and reveals hitherto unknown aspects of cancer evolution.