Metastasis is the leading cause of death in patients with breast, lung, and head and neck cancers. However, the molecular mechanisms underlying metastases in these cancers remain unclear. We found that the p90 ribosomal S6 kinase 2 (RSK2)- cAMP response element-binding protein (CREB) pathway is commonly activated in diverse metastatic human cancer cells, leading to up-regulation of a CREB transcription target Fascin- 1. We also observed that the protein expression patterns of RSK2 and Fascin-1 correlate in primary human tumor tissue samples from head and neck squamous cell carcinoma patients. Moreover, knockdown of RSK2 disrupts filopodia formation and bundling in highly invasive cancer cells, leading to attenuated cancer cell invasion in vitro and tumor metastasis in vivo, whereas expression of Fascin-1 significantly rescues these phenotypes. Furthermore, targeting RSK2 with the small molecule RSK inhibitor FMK-MEA effectively attenuated the invasive and metastatic potential of cancer cells in vitro and in vivo, respectively. Taken together, our findings for the first time link RSK2-CREB signaling to filopodia formation and bundling through the up-regulation of Fascin-1, providing a proinvasive and prometastatic advantage to human cancers. Therefore, protein effectors of the RSK2-CREB-Fascin-1 pathway represent promising biomarkers and therapeutic targets in the clinical prognosis and treatment of metastatic human cancers.

Early detection of oral cancer and its curable precursors can improve patient survival and quality of life. Hyperspectral imaging (HSI) holds the potential for noninvasive early detection of oral cancer. The quantification of tissue chromophores by spectral unmixing of hyperspectral images could provide insights for evaluating cancer progression. In this study, non-negative matrix factorization has been applied for decomposing hyperspectral images into physiologically meaningful chromophore concentration maps. The approach has been validated by computer-simulated hyperspectral images and in vivo tumor hyperspectral images from a head and neck cancer animal model.

Hyperspectral imaging is a developing modality for cancer detection. The rich information associated with hyperspectral images allow for the examination between cancerous and healthy tissue. This study focuses on a new method that incorporates support vector machines into a minimum spanning forest algorithm for differentiating cancerous tissue from normal tissue. Spectral information was gathered to test the algorithm. Animal experiments were performed and hyperspectral images were acquired from tumor-bearing mice. In vivo imaging experimental results demonstrate the applicability of the proposed classification method for cancer tissue classification on hyperspectral images. © 2014 SPIE.

Complete surgical removal of tumor tissue is essential for postoperative prognosis after surgery. Intraoperative tumor imaging and visualization are an important step in aiding surgeons to evaluate and resect tumor tissue in real time, thus enabling more complete resection of diseased tissue and better conservation of healthy tissue. As an emerging modality, hyperspectral imaging (HSI) holds great potential for comprehensive and objective intraoperative cancer assessment. In this paper, we explored the possibility of intraoperative tumor detection and visualization during surgery using HSI in the wavelength range of 450 nm - 900 nm in an animal experiment. We proposed a new algorithm for glare removal and cancer detection on surgical hyperspectral images, and detected the tumor margins in five mice with an average sensitivity and specificity of 94.4% and 98.3%, respectively. The hyperspectral imaging and quantification method have the potential to provide an innovative tool for image-guided surgery.

Platinum-based chemotherapeutics represent a mainstay of cancer therapy, but resistance limits their curative potential. Through a kinome RNAi screen, we identified microtubule-associated serine/threonine kinase 1 (MAST1) as a main driver of cisplatin resistance in human cancers. Mechanistically, cisplatin but no other DNA-damaging agents inhibit the MAPK pathway by dissociating cRaf from MEK1, while MAST1 replaces cRaf to reactivate the MAPK pathway in a cRaf-independent manner. We show clinical evidence that expression of MAST1, both initial and cisplatin-induced, contributes to platinum resistance and worse clinical outcome. Targeting MAST1 with lestaurtinib, a recently identified MAST1 inhibitor, restores cisplatin sensitivity, leading to the synergistic attenuation of cancer cell proliferation and tumor growth in human cancer cells and patient-derived xenograft models. Jin et al. show that cisplatin dissociates cRaf from MEK1 to inhibit the MAPK pathway and identify MAST1 as a main cisplatin resistance driver that replaces cRaf to reactivate the MAPK pathway. They further show that inhibition of MAST1 by the multi-kinase inhibitor lestaurtinib restores cisplatin sensitivity.

Gold nanorods (AuNRs)-assisted plasmonic photothermal therapy (AuNRs-PPTT) is a promising strategy for combating cancer in which AuNRs absorb near-infrared light and convert it into heat, causing cell death mainly by apoptosis and/or necrosis. Developing a valid PPTT that induces cancer cell apoptosis and avoids necrosis in vivo and exploring its molecular mechanism of action is of great importance. Furthermore, assessment of the long-term fate of the AuNRs after treatment is critical for clinical use. We first optimized the size, surface modification [rifampicin (RF) conjugation], and concentration (2.5 nM) of AuNRs and the PPTT laser power (2 W/cm(2)) to achieve maximal induction of apoptosis. Second, we studied the potential mechanism of action of AuNRs-PPTT using quantitative proteomic analysis in mouse tumor tissues. Several death pathways were identified, mainly involving apoptosis and cell death by releasing neutrophil extracellular traps (NETs) (NETosis), which were more obvious upon PPTT using RF-conjugated AuNRs (AuNRs@RF) than with polyethylene glycol thiol-conjugated AuNRs. Cytochrome c and p53-related apoptosis mechanisms were identified as contributing to the enhanced effect of PPTT with AuNRs@RF. Furthermore, Pin1 and IL18-related signaling contributed to the observed perturbation of the NETosis pathway by PPTT with AuNRs@RF. Third, we report a 15-month toxicity study that showed no long-term toxicity of AuNRs in vivo. Together, these data demonstrate that our AuNRs-PPTT platform is effective and safe for cancer therapy in mouse models. These findings provide a strong framework for the translation of PPTT to the clinic.

The EGFR monoclonal antibody cetuximab is the only approved targeted agent for treating head and neck squamous cell carcinoma (HNSCC). Yet resistance to cetuximab has hindered its activity in this disease. Intrinsic or compensatory HER3 signaling may contribute to cetuximab resistance. To investigate the therapeutic benefit of combining MM-121/SAR256212, an anti-HER3 monoclonal antibody, with cetuximab in HNSCC, we initially screened 12 HNSCC cell lines for total and phosphorylated levels of the four HER receptors. We also investigated the combination of MM-121 with cetuximab in preclinical models of HNSCC. Our results revealed that HER3 is widely expressed and activated in HNSCC cell lines. MM-121 strongly inhibited phosphorylation of HER3 and AKT. When combined with cetuximab, MM-121 exerted a more potent antitumor activity through simultaneously inhibiting the activation of HER3 and EGFR and consequently the downstream PI3K/AKT and ERK pathways in vitro. Both high and low doses of MM-121 in combination with cetuximab significantly suppressed tumor growth in xenograft models and inhibited activations of HER3, EGFR, AKT, and ERK in vivo. Our work is the first report on this new combination in HNSCC and supports the concept that HER3 inhibition may play an important role in future therapy of HNSCC. Our results open the door for further mechanistic studies to better understand the role of HER3 in resistance to EGFR inhibitors in HNSCC.

MEK inhibition is potentially valuable in targeting KRAS-mutant non-small cell lung cancer (NSCLC). Here, we analyzed whether concomitant LKB1 mutation alters sensitivity to the MEK inhibitor selumetinib, and whether the metabolism drug phenformin can enhance the therapeutic effect of selumetinib in isogenic cell lines with different LKB1 status. Isogenic pairs of KRAS-mutant NSCLC cell lines A549, H460 and H157, each with wild-type and null LKB1, as well as genetically engineered mouse-derived cell lines 634 (kras(G12D/wt)/p53(-/-)/lkb1(wt/wt)) and t2 (kras(G12D/wt)/p53(-/-)/lkb1(-/-)) were used in vitro to analyze the activities of selumetinib, phenformin and their combination. Synergy was measured and potential mechanisms investigated. The in vitro findings were then confirmed in vivo using xenograft models. The re-expression of wild type LKB1 increased phospho-ERK level, suggesting that restored dependency on MEK->ERK->MAPK signaling might have contributed to the enhanced sensitivity to selumetinib. In contrast, the loss of LKB1 sensitized cells to phenformin. At certain combination ratios, phenformin and selumetinib showed synergistic activity regardless of LKB1 status. Their combination reduced phospho-ERK and S6 levels and induced potent apoptosis, but was likely through different mechanisms in cells with different LKB1 status. Finally, in xenograft models bearing isogenic A549 cells, we confirmed that loss of LKB1 confers resistance to selumetinib, and phenformin significantly enhances the therapeutic effect of selumetinib. Irrespective of LKB1 status, phenformin may enhance the anti-tumor effect of selumetinib in KRAS-mutant NSCLC. The dual targeting of MEK and cancer metabolism may provide a useful strategy to treat this subset of lung cancer.

It can be challenging to detect tumor margins during surgery for complete resection. The purpose of this work is to develop a novel learning method that learns the difference between the tumor and benign tissue adaptively for cancer detection on hyperspectral images in an animal model. Specifically, an auto-encoder network is trained based on the wavelength bands on hyperspectral images to extract the deep information to create a pixel-wise prediction of cancerous and benign pixel. According to the output hypothesis of each pixel, the misclassified pixels would be reclassified in the right prediction direction based on their adaptive weights. The auto-encoder network is again trained based on these updated pixels. The learner can adaptively improve the ability to identify the cancer and benign tissue by focusing on the misclassified pixels, and thus can improve the detection performance. The adaptive deep learning method highlighting the tumor region proved to be accurate in detecting the tumor boundary on hyperspectral images and achieved a sensitivity of 92.32% and a specificity of 91.31% in our animal experiments. This adaptive learning method on hyperspectral imaging has the potential to provide a noninvasive tool for tumor detection, especially, for the tumor whose margin is indistinct and irregular.

Hyperspectral imaging (HSI) is a noninvasive optical modality that holds promise for early detection of tongue lesions. Spectral signatures generated by HSI contain important diagnostic information that can be used to predict the disease status of the examined biological tissue. However, the underlying pathophysiology for the spectral difference between normal and neoplastic tissue is not well understood. Here, we propose to leverage digital pathology and predictive modeling to select the most discriminative features from digitized histological images to differentiate tongue neoplasia from normal tissue, and then correlate these discriminative pathological features with corresponding spectral signatures of the neoplasia. We demonstrated the association between the histological features quantifying the architectural features of neoplasia on a microscopic scale, with the spectral signature of the corresponding tissue measured by HSI on a macroscopic level. This study may provide insight into the pathophysiology underlying the hyperspectral dataset.