Distilling coarse-grained representations of molecular electronic structure with continuously gated message passing.

Bottom-up methods for coarse-grained (CG) molecular modeling are critically needed to establish rigorous links between atomistic reference data and reduced molecular representations. For a target molecule, the ideal reduced CG representation is a function of both the conformational ensemble of the system and the target physical observable(s) to be reproduced at the CG resolution. However, there is an absence of algorithms for selecting CG representations of molecules from which complex properties, including molecular electronic structure, can be accurately modeled. We introduce continuously gated message passing (CGMP), a graph neural network (GNN) method for atomically decomposing molecular electronic structure sampled over conformational ensembles. CGMP integrates 3D-invariant GNNs and a novel gated message passing system to continuously reduce the atomic degrees of freedom accessible for electronic predictions, resulting in a one-shot importance ranking of atoms contributing to a target molecular property. Moreover, CGMP provides the first approach by which to quantify the degeneracy of "good" CG representations conditioned on specific prediction targets, facilitating the development of more transferable CG representations. We further show how CGMP can be used to highlight multiatom correlations, illuminating a path to developing CG electronic Hamiltonians in terms of interpretable collective variables for arbitrarily complex molecules.

Two-Step Machine Learning Approach for Charge-Transfer Coupling with Structurally Diverse Data.

Electronic coupling is important in determining charge-transfer rates and dynamics. Coupling strength is sensitive to both intermolecular, e.g., orientation or distance, and intramolecular degrees of freedom. Hence, it is challenging to build an accurate machine learning model to predict electronic coupling of molecular pairs, especially for those derived from the amorphous phase, for which intermolecular configurations are much more diverse than those derived from crystals. In this work, we devise a new prediction algorithm that employs two consecutive KRR models. The first model predicts molecular orbitals (MOs) from structural variation for each fragment, and coupling is further predicted by using the overlap integral included in a second model. With our two-step procedure, we achieved mean absolute errors of 0.27 meV for an ethylene dimer and 1.99 meV for a naphthalene pair, much improved accuracy amounting to 14-fold and 3-fold error reductions, respectively. In addition, MOs from the first model can also be the starting point to obtain other quantum chemical properties from atomistic structures. This approach is also compatible with a MO predictor with sufficient accuracy.

Identifying Coarse-Grained Representations for Electronic Predictions.

Coarse-grained (CG) simulations are an important computational tool in chemistry and materials science. Recently, systematic "bottom-up" CG models have been introduced to capture electronic structure variations of molecules and polymers at the CG resolution. However, the performance of these models is limited by the ability to select reduced representations that preserve electronic structure information, which remains a challenge. We propose two methods for (i) identifying important electronically coupled atomic degrees of freedom and (ii) scoring the efficacy of CG representations used in conjunction with CG electronic predictions. The first method is a physically motivated approach that incorporates nuclear vibrations and electronic structure derived from simple quantum chemical calculations. We complement this physically motivated approach with a machine learning technique based on the marginal contribution of nuclear degrees of freedom to electronic prediction accuracy using an equivariant graph neural network. By integrating these two approaches, we can both identify critical electronically coupled atomic coordinates and score the efficacy of arbitrary CG representations for making electronic predictions. We leverage this capability to make a connection between optimized CG representations and the future potential for "bottom-up" development of simplified model Hamiltonians incorporating nonlinear vibrational modes.

A 3-gene signature comprising CDH4, STAT4 and EBV-encoded LMP1 for early diagnosis and predicting disease progression of nasopharyngeal carcinoma.

PURPOSE: Nasopharyngeal carcinoma is highly metastatic but difficult to detect in its early stages. It is critical to develop a simple and highly efficient molecular diagnostic method for early detection of NPC in clinical biopsies. METHODS: The transcriptomic data of primary NPC cell strains were used as a discovery tool. Linear regression approach was used to define signatures distinctive between early and late stage of NPC. Expressions of candidates were validated with an independent set of biopsies (n = 39). Leave-one-out cross-validation technique was employed to estimate the prediction accuracy on stage classification. The clinical relevance of marker genes was verified using NPC bulk RNA sequencing data and IHC analysis. RESULTS: Three genes comprising CDH4, STAT4, and CYLD were found to have a significant differentiating power to separate NPC from normal nasopharyngeal samples and predicting disease malignancy. IHC analyses showed stronger CDH4, STAT4, and CYLD immunoreactivity in adjacent basal epithelium compared with that in tumor cells (p < 0.001). EBV-encoded LMP1 was exclusively expressed in NPC tumors. Using an independent set of biopsies, we showed that a model combining CDH4, STAT4, and LMP1 had a 92.86% of diagnostic accuracy, whereas a combination of STAT4 and LMP1 had a 70.59% accuracy for predicting advanced disease. Mechanistic studies suggested that promoter methylation, loss of DNA allele, and LMP1 contributed to the suppressive expression of CDH4, CYLD, and STAT4, respectively. CONCLUSION: A model combining CDH4 and STAT4 and LMP1 was proposed to be a feasible model for diagnosing NPC and predicting late stage of NPC.

Machine-learned dynamic disorder of electron transfer coupling.

Electron transfer (ET) is a fundamental process in chemistry and biochemistry, and electronic coupling is an important determinant of the rate of ET. However, the electronic coupling is sensitive to many nuclear degrees of freedom, particularly those involved in intermolecular movements, making its characterization challenging. As a result, dynamic disorder in electron transfer coupling has rarely been investigated, hindering our understanding of charge transport dynamics in complex chemical and biological systems. In this work, we employed molecular dynamic simulations and machine-learning models to study dynamic disorder in the coupling of hole transfer between neighboring ethylene and naphthalene dimer. Our results reveal that low-frequency modes dominate these dynamics, resulting primarily from intermolecular movements such as rotation and translation. Interestingly, we observed an increasing contribution of translational motion as temperature increased. Moreover, we found that coupling is sub-Ohmic in its spectral density character, with cut-off frequencies in the range of 102 cm-1. Machine-learning models allow direct study of dynamics of electronic coupling in charge transport with sufficient ensemble trajectories, providing further new insights into charge transporting dynamics.

MFI2 upregulation promotes malignant progression through EGF/FAK signaling in oral cavity squamous cell carcinoma.

Oral squamous cell carcinoma (OSCC) is the predominant histological type of the head and neck squamous cell carcinoma (HNSCC). By comparing the differentially expressed genes (DEGs) in OSCC-TCGA patients with copy number variations (CNVs) that we identify in OSCC-OncoScan dataset, we herein identified 37 dysregulated candidate genes. Among these potential candidate genes, 26 have been previously reported as dysregulated proteins or genes in HNSCC. Among 11 novel candidates, the overall survival analysis revealed that melanotransferrin (MFI2) is the most significant prognostic molecular in OSCC-TCGA patients. Another independent Taiwanese cohort confirmed that higher MFI2 transcript levels were significantly associated with poor prognosis. Mechanistically, we found that knockdown of MFI2 reduced cell viability, migration and invasion via modulating EGF/FAK signaling in OSCC cells. Collectively, our results support a mechanistic understanding of a novel role for MFI2 in promoting cell invasiveness in OSCC.

Anterior gradient 2 induces resistance to sorafenib via endoplasmic reticulum stress regulation in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) accounts for almost 80% of all liver cancer cases and is the sixth most common cancer and the second most common cause of cancer-related death worldwide. The survival rate of sorafenib-treated advanced HCC patients is still unsatisfactory. Unfortunately, no useful biomarkers have been verified to predict sorafenib efficacy in HCC. RESULTS: We assessed a sorafenib resistance-related microarray dataset and found that anterior gradient 2 (AGR2) is highly associated with overall and recurrence-free survival and with several clinical parameters in HCC. However, the mechanisms underlying the role of AGR2 in sorafenib resistance and HCC progression remain unknown. We found that sorafenib induces AGR2 secretion via posttranslational modification and that AGR2 plays a critical role in sorafenib-regulated cell viability and endoplasmic reticulum (ER) stress and induces apoptosis in sorafenib-sensitive cells. In sorafenib-sensitive cells, sorafenib downregulates intracellular AGR2 and conversely induces AGR2 secretion, which suppresses its regulation of ER stress and cell survival. In contrast, AGR2 is highly intracellularly expressed in sorafenib-resistant cells, which supports ER homeostasis and cell survival. We suggest that AGR2 regulates ER stress to influence HCC progression and sorafenib resistance. CONCLUSIONS: This is the first study to report that AGR2 can modulate ER homeostasis via the IRE1α-XBP1 cascade to regulate HCC progression and sorafenib resistance. Elucidation of the predictive value of AGR2 and its molecular and cellular mechanisms in sorafenib resistance could provide additional options for HCC treatment.

Cisplatin or Doxorubicin Reduces Cell Viability via the PTPIVA3-JAK2-STAT3 Cascade in Hepatocellular Carcinoma.

Introduction: Hepatocellular carcinoma (HCC) accounts for 80% of all liver cancers and is the 2nd leading cause of cancer-related death in Taiwan. Various factors, including rapid cell growth, a high recurrence rate and drug resistance, make HCC difficult to cure. Moreover, the survival rate of advanced HCC patients treated with systemic chemotherapy remains unsatisfactory. Hence, the identification of novel molecular targets and the underlying mechanisms of chemoresistance in HCC and the development more effective therapeutic regimens are desperately needed. Methods: An MTT assay was used to determine the cell viability after cisplatin or doxorubicin treatment. Western blotting, qRT‒PCR and immunohistochemistry were utilized to examine the protein tyrosine phosphatase IVA3 (PTP4A3) level and associated signaling pathways. ELISA was utilized to analyze the levels of the inflammatory cytokine IL-6 influenced by cisplatin, doxorubicin and PTP4A3 silencing. Results: In this study, we found that PTP4A3 in the cisplatin/doxorubicin-resistant microarray was closely associated with the overall and recurrence-free survival rates of HCC patients. Cisplatin or doxorubicin significantly reduced cell viability and decreased PTP4A3 expression in hepatoma cells. IL-6 secretion increased with cisplatin or doxorubicin treatment and after PTP4A3 silencing. Furthermore, PTP4A3 was highly expressed in tumor tissues versus adjacent normal tissues from HCC patients. In addition, we evaluated the IL-6-associated signaling pathway involving STAT3 and JAK2, and the levels of p-STAT3, p-JAK2, STAT3 and JAK2 were obviously reduced with cisplatin or doxorubicin treatment in HCC cells using Western blotting and were also decreased after silencing PTP4A3. Collectively, we suggest that cisplatin or doxorubicin decreases HCC cell viability via downregulation of PTP4A3 expression through the IL-6R-JAK2-STAT3 cascade. Discussion: Therefore, emerging evidence provides a deep understanding of the roles of PTP4A3 in HCC cisplatin/doxorubicin chemoresistance, which can be applied to develop early diagnosis strategies and reveal prognostic factors to establish novel targeted therapeutics to specifically treat HCC.

Genomic and Molecular Signatures of Successful Patient-Derived Xenografts for Oral Cavity Squamous Cell Carcinoma.

Background: Oral cavity squamous cell carcinoma (OSCC) is an aggressive malignant tumor with high recurrence and poor prognosis in the advanced stage. Patient-derived xenografts (PDXs) serve as powerful preclinical platforms for drug testing and precision medicine for cancer therapy. We assess which molecular signatures affect tumor engraftment ability and tumor growth rate in OSCC PDXs. Methods: Treatment-naïve OSCC primary tumors were collected for PDX models establishment. Comprehensive genomic analysis, including whole-exome sequencing and RNA-seq, was performed on case-matched tumors and PDXs. Regulatory genes/pathways were analyzed to clarify which molecular signatures affect tumor engraftment ability and the tumor growth rate in OSCC PDXs. Results: Perineural invasion was found as an important pathological feature related to engraftment ability. Tumor microenvironment with enriched hypoxia, PI3K-Akt, and epithelial-mesenchymal transition pathways and decreased inflammatory responses had high engraftment ability and tumor growth rates in OSCC PDXs. High matrix metalloproteinase-1 (MMP1) expression was found that have a great graft advantage in xenografts and is associated with pooled disease-free survival in cancer patients. Conclusion: This study provides a panel with detailed genomic characteristics of OSCC PDXs, enabling preclinical studies on personalized therapy options for oral cancer. MMP1 could serve as a biomarker for predicting successful xenografts in OSCC patients.

Enhancing Singlet Fission Coupling with Nonbonding Orbitals.

Singlet fission (SF) is a process where a singlet exciton is split into a pair of triplet excitons. The increase in the excitonic generation can be exploited to enhance the efficiency of solar cells. Molecules with conjugated π bonds are commonly developed for optoelectronic applications including SF, due to their low energy gaps. The electronic coupling for SF in such well-stacked π-conjugated molecule pairs can be rather limited due to the orthogonal π and π* orbital overlaps that are involved in the coupling elements, leading to a large cancellation in the coupling. In the present work, we show that such limits can be removed by involving triplet states of different origins, such as those with nonbonding n orbitals. We demonstrate such an effect for formaldehyde and methylenimine dimers, with a low-lying n-π* triplet state (T1) in addition to the π-π* triplet (T2). We show that the coupling can be enhanced by 40 times or more for the formaldehyde dimer, and 15 times or more for the methylenimine dimer, with the T1-T2 state as the end product of SF. With 1759 randomly oriented pairs of formaldehyde derived from a molecular dynamics simulation, the coupling from a singlet exciton to this T1-T2 state is, on an average, almost two times larger than that for a regular T1-T1 state. We investigated a few families that have been shown to be prospective candidates for SF, using our proposed strategy. However, our unfavorable results indicate that there are clear difficulties in fulfilling the ES1 ≳ ET1 + ET2 energy criterion. Nevertheless, our results provide a new molecular design concept for better SF (and triplet-triplet annihilation, TTA) materials that allows future development.

Nuclear accumulation of KPNA2 impacts radioresistance through positive regulation of the PLSCR1-STAT1 loop in lung adenocarcinoma.

Lung adenocarcinoma (ADC) is the predominant histological type of lung cancer, and radiotherapy is one of the current therapeutic strategies for lung cancer treatment. Unfortunately, biological complexity and cancer heterogeneity contribute to radioresistance development. Karyopherin α2 (KPNA2) is a member of the importin α family that mediates the nucleocytoplasmic transport of cargo proteins. KPNA2 overexpression is observed across cancer tissues of diverse origins. However, the role of KPNA2 in lung cancer radioresistance is unclear. Herein, we demonstrated that high expression of KPNA2 is positively correlated with radioresistance and cancer stem cell (CSC) properties in lung ADC cells. Radioresistant cells exhibited nuclear accumulation of KPNA2 and its cargos (OCT4 and c-MYC). Additionally, KPNA2 knockdown regulated CSC-related gene expression in radioresistant cells. Next-generation sequencing and bioinformatic analysis revealed that STAT1 activation and nuclear phospholipid scramblase 1 (PLSCR1) are involved in KPNA2-mediated radioresistance. Endogenous PLSCR1 interacting with KPNA2 and PLSCR1 knockdown suppressed the radioresistance induced by KPNA2 expression. Both STAT1 and PLSCR1 were found to be positively correlated with dysregulated KPNA2 in radioresistant cells and ADC tissues. We further demonstrated a potential positive feedback loop between PLSCR1 and STAT1 in radioresistant cells, and this PLSCR1-STAT1 loop modulates CSC characteristics. In addition, AKT1 knockdown attenuated the nuclear accumulation of KPNA2 in radioresistant lung cancer cells. Our results collectively support a mechanistic understanding of a novel role for KPNA2 in promoting radioresistance in lung ADC cells.

Chemotherapeutic Drug-Regulated Cytokines Might Influence Therapeutic Efficacy in HCC.

Hepatocellular carcinoma (HCC), the most common type of liver cancer, is the second leading cause of cancer-related mortality worldwide. Processes involved in HCC progression and development, including cell transformation, proliferation, metastasis, and angiogenesis, are inflammation-associated carcinogenic processes because most cases of HCC develop from chronic liver damage and inflammation. Inflammation has been demonstrated to be a crucial factor inducing tumor development in various cancers, including HCC. Cytokines play critical roles in inflammation to accelerate tumor invasion and metastasis by mediating the migration of immune cells into damaged tissues in response to proinflammatory stimuli. Currently, surgical resection followed by chemotherapy is the most common curative therapeutic regimen for HCC. However, after chemotherapy, drug resistance is clearly observed, and cytokine secretion is dysregulated. Various chemotherapeutic agents, including cisplatin, etoposide, and 5-fluorouracil, demonstrate even lower efficacy in HCC than in other cancers. Tumor resistance to chemotherapeutic drugs is the key limitation of curative treatment and is responsible for treatment failure and recurrence, thus limiting the ability to treat patients with advanced HCC. Therefore, the capability to counteract drug resistance would be a major clinical advancement. In this review, we provide an overview of links between chemotherapeutic agents and inflammatory cytokine secretion in HCC. These links might provide insight into overcoming inflammatory reactions and cytokine secretion, ultimately counteracting chemotherapeutic resistance.

Characterization of Recurrent Relevant Genes Reveals a Novel Role of RPL36A in Radioresistant Oral Squamous Cell Carcinoma.

Radioresistance is one of the major factors that contributes to radiotherapy failure in oral cavity squamous cell carcinoma (OSCC). By comparing the prognostic values of 20,502 genes expressed in patients in The Cancer Genome Atlas (TCGA)-OSCC cohort with (n = 162) and without radiotherapy (n = 118), herein identified 297 genes positively correlated with poor disease-free survival in OSCC patients with radiotherapy as the potential radioresistance-associated genes. Among the potential radioresistance-associated genes, 36 genes were upregulated in cancerous tissues relative to normal tissues. The bioinformatics analysis revealed that 60S ribosomal protein L36a (RPL36A) was the most frequently detected gene involved in radioresistance-associated gene-mediated biological pathways. Then, two independent cohorts (n = 162 and n = 136) were assessed to confirm that higher RPL36A transcript levels were significantly associated with a poor prognosis only in OSCC patients with radiotherapy. Mechanistically, we found that knockdown of RPL36A increased radiosensitivity via sensitizing cells to DNA damage and promoted G2/M cell cycle arrest followed by augmenting the irradiation-induced apoptosis pathway in OSCC cells. Taken together, our study supports the use of large-scale genomic data for identifying specific radioresistance-associated genes and suggests a regulatory role for RPL36A in the development of radioresistance in OSCC.

Irradiation Suppresses IFNγ-Mediated PD-L1 and MCL1 Expression in EGFR-Positive Lung Cancer to Augment CD8+ T Cells Cytotoxicity.

Tumor cells express immune checkpoints to exhaust CD8+ T cells. Irradiation damages tumor cells and augments tumor immunotherapy in clinical applications. However, the radiotherapy-mediated molecular mechanism affecting CD8+ T cell activity remains elusive. We aimed to uncover the mechanism of radiotherapy augmenting cytotoxic CD8+ T cells in non-small-cell lung cancer (NSCLC). EGFR-positive NSCLC cell lines were co-cultured with CD8+ T cells from healthy volunteers. Tumor cell viability and apoptosis were consequently measured. IFNγ was identified secreted by CD8+ T cells and PBMCs. Therefore, RNAseq was used to screen the IFNγ-mediated gene expression in A549 cells. The irradiation effect to IFNγ-mediated gene expression was investigated using qPCR and western blots. We found that the co-culture of tumor cells stimulated the increase of granzyme B and IFNγ in CD8+ T, but A549 exhibited resistance against CD8+ T cytotoxicity compared to HCC827. Irradiation inhibited A549 proliferation and enhanced apoptosis, augmenting PBMCs-mediated cytotoxicity against A549. We found that IFNγ simultaneously increased phosphorylation on STAT1 and STAT3 in EGFR-positive lung cancer, resulting in overexpression of PD-L1 (p < 0.05). In RNAseq analysis, MCL1 was identified and increased by the IFNγ-STAT3 axis (p < 0.05). We demonstrated that irradiation specifically inhibited phosphorylation on STAT1 and STAT3 in IFNγ-treated A549, resulting in reductions of PD-L1 and MCL1 (both p < 0.05). Moreover, knockdowns of STAT3 and MCL1 increased the PBMCs-mediated anti-A549 effect. This study demonstrated that A549 expressed MCL1 to resist CD8+ T cell-mediated tumor apoptosis. In addition, we found that irradiation suppressed IFNγ-mediated STAT3 phosphorylation and PD-L1 and MCL1 expression, revealing a potential mechanism of radiotherapy augmenting immune surveillance.

Increase in Akkermansiaceae in Gut Microbiota of Prostate Cancer-Bearing Mice.

Gut microbiota are reported to be associated with many diseases, including cancers. Several bacterial taxa have been shown to be associated with cancer development or response to treatment. However, longitudinal microbiota alterations during the development of cancers are relatively unexplored. To better understand how microbiota changes, we profiled the gut microbiota composition from prostate cancer-bearing mice and control mice at five different time points. Distinct gut microbiota differences were found between cancer-bearing mice and control mice. Akkermansiaceae was found to be significantly higher in the first three weeks in cancer-bearing mice, which implies its role in the early stage of cancer colonization. We also found that Bifidobacteriaceae and Enterococcaceae were more abundant in the second and last sampling week, respectively. The increments of Akkermansiaceae, Bifidobacteriaceae and Enterococcaceae were previously found to be associated with responses to immunotherapy, which suggests links between these bacteria families and cancers. Additionally, our function analysis showed that the bacterial taxa carrying steroid biosynthesis and butirosin and neomycin biosynthesis were increased, whereas those carrying naphthalene degradation decreased in cancer-bearing mice. Our work identified the bacteria taxa altered during prostate cancer progression and provided a resource of longitudinal microbiota profiles during cancer development in a mouse model.

Effects of solvation shell relaxation on chain association mechanisms in poly(3-hexylthiophene) solutions.

Using poly(3-hexylthiophene) (P3HT) as a model conjugated polymer and atomistic molecular dynamics simulations with carefully verified force fields, we performed in-depth investigations of solvation shell properties of P3HT chains (15 repeating units per chain) in two representative groups of non-polar (or aprotic) organic solvents (better solvents: ortho-dichlorobenzene, bromobenzene, and chlorobenzene; poorer solvents: chloroform, para-xylene, and toluene). We demonstrated that solvation shell relaxation properties in P3HT solutions dictate the formation of regular π-π associations and, hence, crystallinity through the initial chain association and subsequent chain sliding. In contrast, the mean features of polymer-solvent interactions, including solvation free energy and radial distribution function, present little or no difference for all solvent media investigated. Better-solvent media were revealed to bear relatively large values of the first solvation shell relaxation time (τ1 ≫ 100 ps) as well as larger ratios of relaxation times for the first two solvation shells (τ1/τ2 > 2), and vice versa for poorer-solvent media (τ1 ≪ 100 ps and τ1/τ2 < 2). The linear hexyl side-chain unit was noted to substantially enlarge both quantities while notably reducing the solvation free energy as well. As discussed herein, these findings shed new light on the mechanistic features by which solvent quality impacts the degree of π-π association crucial for modern applications with crystalline conjugated polymers.

Low-Cost Hole-Transporting Materials Based on Carbohelicene for High-Performance Perovskite Solar Cells.

Two hole-transporting materials (HTMs) based on carbohelicene cores, CH1 and CH2, are developed and used in fabricating efficient and stable perovskite solar cells (PSCs). Owing to the rigid conformation of the helicene core, both compounds possess unique CH-π interactions in the crystalline packing pattern and good phase stability, which are distinct from the π-π intermolecular interactions of conventional planar and spiro-type molecules. PSCs based on CH1 and CH2 as HTMs deliver excellent device efficiencies of 19.36 and 18.71%, respectively, outperforming the control device fabricated with spiro-OMeTAD (18.45%). Furthermore, both PSCs exhibit better ambient stability, with 90% of initial performance retained after aging with a 50-60% relative humidity at 25 °C for 500 h. Due to the low production cost of both compounds, these newly designed carbohelicene-type HTMs have the potential for the future commercialization of PSCs.

Understanding the impact of high-risk human papillomavirus on oropharyngeal squamous cell carcinomas in Taiwan: A retrospective cohort study.

BACKGROUND AND OBJECTIVES: Human papillomavirus (HPV)-driven oropharyngeal squamous cell carcinoma (OPSCC) is increasing globally. In Taiwan, HPV-positive OPSCC is obscured by tobacco, alcohol, and betel quid use. We investigated the role of high-risk HPV (hrHPV) in a large retrospective Taiwan OPSCC cohort. METHODS AND RESULTS: The cohort of 541 OPSCCs treated at Chang Gung Memorial Hospital from 1998-2016 consisted of 507 men (94%) and 34 women (6%). Most used tobacco (81%), alcohol (51%), and betel quid (65%). Formalin-fixed, paraffin-embedded tissue was used for p16 staining (a surrogate marker for HPV) and testing for HPV DNA presence and type by Multiplex HPV PCR-MassArray. HPV DNA and/or p16 staining (HPV-positive) was found in 28.4% (150/528) tumors. p16 and HPV DNA were strongly correlated (F < 0.0001). HPV16 was present in 82.8%, and HPV58 in 7.5% of HPV-positive tumors. HPV was associated with higher age (55.5 vs. 52.7 years, p = 0.004), lower T-stage (p = 0.008) better overall survival (OS) (hazard ratio [HR] 0.58 [95% CI 0.42-0.81], p = 0.001), and disease-free survival (DFS) (HR 0.54 [95% CI 0.40-0.73], p < 0.0001). Alcohol was strongly associated with recurrence and death (OS: HR 2.06 [95% CI 1.54-2.74], p < 0.0001; DFS: HR 1.72 [95% CI 1.33-2.24], p < 0.0001). OS and DFS in HPV-positive cases decreased for alcohol users (p < 0.0001). Obscured by the strong alcohol effect, predictive associations were not found for tobacco or betel quid. CONCLUSIONS: As with HPV-positive OPSCC globally, HPV is an increasingly important etiological factor in Taiwanese OPSCC. HPV-positive OPSCC has considerable survival benefit, but this is reduced by alcohol, tobacco, and betel quid use. hrHPV is a cancer risk factor in males and females. Vaccinating both sexes with a multivalent vaccine including HPV58, combined with alcohol and tobacco cessation policies will be effective cancer-prevention public health strategies in Taiwan.

Artificial neural networks for predicting charge transfer coupling.

Quantum chemistry calculations have been very useful in providing many key detailed properties and enhancing our understanding of molecular systems. However, such calculation, especially with ab initio models, can be time-consuming. For example, in the prediction of charge-transfer properties, it is often necessary to work with an ensemble of different thermally populated structures. A possible alternative to such calculations is to use a machine-learning based approach. In this work, we show that the general prediction of electronic coupling, a property that is very sensitive to intermolecular degrees of freedom, can be obtained with artificial neural networks, with improved performance as compared to the popular kernel ridge regression method. We propose strategies for optimizing the learning rate and batch size, improving model performance, and further evaluating models to ensure that the physical signatures of charge-transfer coupling are well reproduced. We also address the effect of feature representation as well as statistical insights obtained from the loss function and the data structure. Our results pave the way for designing a general strategy for training such neural-network models for accurate prediction.

Cotargeting CHK1 and PI3K Synergistically Suppresses Tumor Growth of Oral Cavity Squamous Cell Carcinoma in Patient-Derived Xenografts.

Oral cavity squamous cell carcinomas (OSCCs) are aggressive tumors, and their recurrence leads to poor prognosis and reduced survival rates. This study aimed to identify therapeutic targets and to evaluate the efficacy of targeted inhibitors in OSCC patient-derived xenograft (PDX) models. Herein, we reported that OSCC PDXs recapitulated the genomic signatures of their paired primary tumors and the expression of CHEK1, PIK3CA, and PIK3CD was significantly upregulated in OSCC. The antitumor efficacy of CHK1 inhibitors (PF477736, AZD7762, LY2606368) and PI3K inhibitors (BYL719, GDC0941, GSK1059615) was investigated in OSCC cell lines and PDX models. Targeting either CHK1 or PI3K effectively inhibited cell proliferation and colony formation by inducing cell cycle arrest and apoptosis in in vitro cell-based assays. Cisplatin-based chemotherapy combined with CHK1 inhibitor treatment synergistically inhibited cell proliferation by suppressing CHK1 phosphorylation and inducing PARP cleavage. Furthermore, compared with monotherapy, cotreatment with CHK1 and PI3K inhibitors exerted synergistic anticancer effects by suppressing CHK1, AKT, and 4E-BP1 phosphorylation. In summary, our study identified CHK1 and PI3K as promising targets, especially in a dual treatment strategy combining a CHK1 inhibitor with cisplatin or a PI3K inhibitor as a novel therapeutic approach for OSCC patients with aberrant cell cycle regulation and PI3K signaling activation.