Structure-Guided Discovery of PD-1/PD-L1 Interaction Inhibitors: Peptide Design, Screening, and Optimization via Computation-Aided Phage Display Engineering

Cancer immunotherapy harnesses the immune system to combat tumors and has emerged as a major cancer treatment modality. The PD-1/PD-L1 immune checkpoint modulates interactions between tumor cells and T cells and has been extensively targeted in cancer immunotherapy. However, the monoclonal antibodies known to target this immune checkpoint have considerable side effects, and novel PD-1/PD-L1 inhibitors are therefore required. Herein, a peptide inhibitor to disrupt PD-1/PD-L1 interactions was designed through structure-driven phage display engineering coupled to computational modification and optimization. BetaPb, a novel peptide library constructed by using the known structure of PD-1/PD-L, was used to develop inhibitors against the immune checkpoint, and specific peptides with high affinity toward PD-1 were screened through enzyme-linked immunosorbent assays, homogeneous time-resolved fluorescence, and biolayer interferometry. A potential inhibitor, B8, was preliminarily screened through biopanning. The binding affinity of B8 toward PD-1 was confirmed through computation-aided optimization. Assessment of B8 variants (B8.1, B8.2, B8.3, B8.4, and B8.5) demonstrated their attenuation of PD-1/PD-L1 interactions. B8.4 exhibited the strongest attenuation efficiency at a half-maximal effective concentration of 0.1 μM and the strongest binding affinity to PD-1 (equilibrium dissociation constant = 0.1 μM). B8.4 outperformed the known PD-1/PD-L1 interaction inhibitor PL120131 in disrupting PD-1/PD-L1 interactions, revealing that B8.4 has remarkable potential for modification to yield an antitumor agent. This study provides valuable information for the future development of peptide-based drugs, therapeutics, and immunotherapies for cancer.

Production and characterization of single-chain variable fragment antibodies targeting the breast cancer tumor marker nectin-4

Background

Nectin-4 is a novel biomarker overexpressed in various types of cancer, including breast cancer, in which it has been associated with poor prognosis. Current literature suggests that nectin-4 has a role in cancer progression and may have prognostic and therapeutic implications. The present study aims to produce nectin-4-specific single-chain variable fragment (scFv) antibodies and evaluate their applications in breast cancer cell lines and clinical specimens.

Methods

We generated recombinant nectin-4 ectodomain fragments as immunogens to immunize chickens and the chickens' immunoglobulin genes were amplified for construction of anti-nectin-4 scFv libraries using phage display. The binding capacities of the selected clones were evaluated with the recombinant nectin-4 fragments, breast cancer cell lines, and paraffin-embedded tissue sections using various laboratory approaches. The binding affinity and in silico docking profile were also characterized.

Results

We have selected two clones (S21 and L4) from the libraries with superior binding capacity. S21 yielded higher signals when used as the primry antibody for western blot analysis and flow cytometry, whereas clone L4 generated cleaner and stronger signals in immunofluorescence and immunohistochemistry staining. In addition, both scFvs could diminish attachment-free cell aggregation of nectin-4-positive breast cancer cells. As results from ELISA indicated that L4 bound more efficiently to fixed nectin-4 ectodomain, molecular docking analysis was further performed and demonstrated that L4 possesses multiple polar contacts with nectin-4 and diversity in interacting residues.

Conclusion

Overall, the nectin-4-specific scFvs could recognize nectin-4 expressed by breast cancer cells and have the merit of being further explored for potential diagnostic and therapeutic applications.

An auristatin-based antibody-drug conjugate targeting EphA2 in pancreatic cancer treatment

Pancreatic adenocarcinoma, a highly aggressive form of cancer with a poor prognosis, necessitates the development of innovative treatment strategies. Our prior research showcased the growth-inhibiting effects of the anti-EphA2 antibody drug hSD5 on pancreatic cancer tumors. This antibody targets and induces the degradation of the EphA2 receptor while also prompting the antibody's internalization. A deeper dive into the hSD5 Fab crystallographic structure and docking studies revealed that hSD5's CDRH3 drives the primary interaction between hSD5 and the EphA2 active site. In this study, we developed a novel antibody-drug conjugate (ADC)-the auristatin-based hSD5-vedotin specifically targeting EphA2 in pancreatic cancer cells. This ADC aims at the tumor-specific antigen EphA2, triggering endocytosis and releasing the conjugated payload molecule Monomethyl auristatin E (MMAE), amplifying the tumor-killing effect. Upon cellular entry, hSD5-vedotin demonstrated an impressive tumor-killing response, inhibiting tumor cell growth and promoting apoptosis even at lower antibody concentrations. In a pancreatic cancer xenograft animal model, hSD5-vedotin showcased the potential to suppress tumor growth entirely. Notably, potential immune resistance responses were also observed in recurrent pancreatic cancer tumors. Our empirical results underscore the possibility of developing hSD5-vedotin further, which we anticipate will have a broader and more potent therapeutic impact on pancreatic cancer and other EphA2-related cancers.

Single-chain fragment antibody disrupting the EphA4 function as a therapeutic drug for gastric cancer

Studies have shown that the high expression of EphA4 in gastric cancer tissues may correlate with unfavorable clinical pathological characteristics. Therefore, EphA4 may be an effective target for treating gastric cancer in addition to HER-2/neu. In this study, generated scFv S3 can bind endogenous EphA4 of gastric cancer cells and has significant membrane staining. Additionally, scFv S3 binding to EphA4 inhibits the growth and migration of cancer cells and the growth induction that ephrinA1 generates in gastric cancer cells. We found that EphA4 molecules may degrade through antibody treatment of cells, and the increase in LAMP1 and LAMP2 indicates that lysosome is involved in the degradation. The scFv S3 administration leads to the signals pAKT, pERK, and pSTAT3 decrease in cancer cells. The xenograft model of HER-2/neu low expressing gastric cancer cell SNU-16 exhibits better therapeutic effects by scFv S3 than trastuzumab scFv. The scFv S3 administration in vivo can degrade EphA4 molecules in tumor tissues, decreasing Ki67 and increasing cleaved C3 molecule expression. Furthermore, we identified and validated that scFv S3 generates essential ionic bonding with R162 on EphA4. The antibody may provide effective treatment for patients with gastric cancer and abnormal activation or overexpression of EphA4 signaling.

Deep Learning Can Predict Bevacizumab Therapeutic Effect and Microsatellite Instability Directly from Histology in Epithelial Ovarian Cancer

Epithelial ovarian cancer (EOC) remains a significant cause of mortality among gynecologic cancers, with the majority of cases being diagnosed at an advanced stage. Before targeted therapies were available, EOC treatment relied largely on debulking surgery and platinum-based chemotherapy. Vascular endothelial growth factors have been identified as inducing tumor angiogenesis. According to several clinical trials, anti-vascular endothelial growth factor-targeted therapy with bevacizumab was effective in all phases of EOC treatment. However, there are currently no biomarkers accessible for regular therapeutic use despite the importance of patient selection. Microsatellite instability (MSI), caused by a deficiency of the DNA mismatch repair system, is a molecular abnormality observed in EOC associated with Lynch syndrome. Recent evidence suggests that angiogenesis and MSI are interconnected. Developing predictive biomarkers, which enable the selection of patients who might benefit from bevacizumab-targeted therapy or immunotherapy, is critical for realizing personalized precision medicine. In this study, we developed 2 improved deep learning methods that eliminate the need for laborious detailed image-wise annotations by pathologists and compared them with 3 state-of-the-art methods to not only predict the efficacy of bevacizumab in patients with EOC using mismatch repair protein immunostained tissue microarrays but also predict MSI status directly from histopathologic images. In prediction of therapeutic outcomes, the 2 proposed methods achieved excellent performance by obtaining the highest mean sensitivity and specificity score using MSH2 or MSH6 markers and outperformed 3 state-of-the-art deep learning methods. Moreover, both statistical analysis results, using Cox proportional hazards model analysis and Kaplan-Meier progression-free survival analysis, confirm that the 2 proposed methods successfully differentiate patients with positive therapeutic effects and lower cancer recurrence rates from patients experiencing disease progression after treatment (P < .01). In prediction of MSI status directly from histopathology images, our proposed method also achieved a decent performance in terms of mean sensitivity and specificity score even for imbalanced data sets for both internal validation using tissue microarrays from the local hospital and external validation using whole section slides from The Cancer Genome Atlas archive.

Effectiveness of anti-erythropoietin producing Hepatocellular receptor Type-A2 antibody in pancreatic cancer treatment

Erythropoietin-producing hepatocyte receptor type A2 (EphA2) is a tyrosine kinase that binds to ephrins (e.g., ephrin-A1) to initiate bidirectional signaling between cells. The binding of EphA2 and ephrin-A1 leads to the inhibition of Ras-MAPK activity and tumor growth. During tumorigenesis, the normal interaction between EphA2 and ephrin-A1 is hindered, which leads to the overexpression of EphA2 and induces cancer. The overexpression of EphA2 has been identified as a notable tumor marker in diagnosing and treating pancreatic cancer. In this study, we used phage display to isolate specific antibodies against the active site of EphA2 by using a discontinuous recombinant epitope for immunization. The therapeutic efficacy and inhibition mechanism of the generated antibody against pancreatic cancer was validated and clarified. The generated antibodies were bound to the conformational epitope of endogenous EphA2 on cancer cells, thus inducing cellular endocytosis and causing EphA2 degradation. Molecule signals pAKT, pERK, pFAK, and pSTAT3 were weakened, inhibiting the proliferation and migration of pancreatic cancer cells. The humanized antibody hSD5 could effectively inhibit the growth of the xenograft pancreatic cancer tumor cells BxPc-3 and Mia PaCa-2 in mice, respectively. When antibody hSD5 was administered with gemcitabine, significantly improved effects on tumor growth inhibition were observed. Based on the efficacy of the IgG hSD5 antibodies, clinical administration of the hSD5 antibodies is likely to suppress tumors in patients with pancreatic cancer and abnormal activation or overexpression of EphA2 signaling.

Exploring the Benefits of 3D-Printed Bolus in Cone Beam CT for Modified Radical Mastectomy Breast Cancer

PURPOSE/OBJECTIVE(S)

To improve the accuracy of superficial dose coverage for patients undergoing modified radical mastectomy (MRM), boluses are frequently applied to the skin surface of targeted region during radiotherapy. The irregular curvature of MRM breast cases results in discrepancies of dosimetry and poor contact. Additionally, the presence of an air gap causes unnecessary high-dose escalation and uncertainty in dose calculation, making the use of commercial flat bolus problematic. This study evaluated the effectiveness of 3D-printed bolus by comparing it to commercial bolus in setup variations and dosimetric compliance through daily cone-beam computed tomography (CBCT) scans.

MATERIALS/METHODS

Ten patients underwent MRM were divided into 2 groups. 5 patients treated with 0.5 cm commercial bolus as group A while the other 5 patients covered by 3D Bolus as group B. 3D bolus was made of polylactic acid filament (PLA) and contoured in advance with thickness of 0.5 cm on the surface of target. Positional errors were recorded through daily image guidance and compared with the images. Both groups had right-sided breast with neck lymph nodes involved and received 50 Gy in 25 fractions using 4 partial arcs of the volumetric modulated arc therapy (VMAT) technique. The CBCTs were recalculated through treatment planning system (TPS) to assess superficial dose coverage. A two-tailed student's t-test was applied.

RESULTS

Groups A and B pitch angles were -0.203 ± 0.837° and 0.334 ± 0.909° (p = 0.0003). Roll angle were 0.313 ± 0.728° and -0.633 ± 1.286° (p = 0.0000013). Yaw angle were -0.034 ± 0.872° and 0.018 ± 0.883° (p = 0.721). There was a trend of differences in the Z-axis and significant statistical differences in the pitch and roll angles due to aligning gel layers directly with group A's body surface, while 3D bolus rigidly adherence to group B's delineated curve. In the planned target volume (PTV), the CI and HI of group A were 0.9448±0.0208 and 1.2061±0.0448, respectively, while 0.9776±0.0144 and 1.1472±0.0206 (p<0.005) were for group B. As for the superficial region, which is defined from body surface to 0.5cm inside PTV, CI and HI of A were 0.8290±0.0599 and 1.4675±0.2434, while for B, they were 0.9753±0.0214 and 1.1330±0.0126, respectively (p<0.005).

CONCLUSION

The study analyzed the results through image comparison and investigated the CI and HI between two types of boluses. The 3D bolus reduces setup errors and improves dose coverage especially when superficial region is concerned. Better consistency of patient repositioning and dosimetry can be achieved and proved by daily assessment of CBCT scan. The customization of 3D bolus with integration of TPS and CT scans provides a solution to the inadequacies of commercial bolus. The results suggest that the use of 3D bolus is a promising development in radiation therapy for MRM breast cancer patients.

The Effectiveness of Music Interventions for Improving Anxiety Symptoms in Cancer Patients Undergoing Radiation Therapy

PURPOSE/OBJECTIVE(S)

Being alone in the treatment room during radiation therapy (RT) often causes anxiety which may lead to treatment failure. The benefit of music interventions in reducing anxiety among cancer patients during RT simulations is still controversial. The goal of this study is to evaluate the effect of music on anxiety symptoms among patients undergoing initial RT and a randomized trial was conducted.

MATERIALS/METHODS

Patients who received RT for the first time were enrolled and were randomly allocated into groups of experiment and control. The questionnaire of mood thermometer (BSRS-5), distress thermometer (DT), and the Beck Anxiety Inventory (BAI-C) and were given before and after RT. We also measured the changes in physiological symptoms for ten consecutive days since the first day of RT. The experimental group was given music during RT, while the control group was not. The generalized linear mixed model was used to estimate the pre-post difference of music interventions on the BSRS-5, DT, and BAI-C compared with control group after considering the random intercept and the unstructured residual variance-covariance matrix.

RESULTS

A total of 50 patients in the experimental group and 50 patients in the control group were enrolled in this study. The satisfaction degree of treatment in the experimental group was 100%, but it was 96% in control group (p = 0.0048). The pre-test BSRS-5 was 3.4±2.3 for the experimental group versus 2.6±2.3 for the control group (p = 0.0815), the post-test BSRS-5 score was significantly reduced in the experimental group (1.6±1.4 for experimental group versus 2.7±2.2 for the control group, p = 0.0057), the significant pre-post difference of BSRS-5 between group was found (p = 0.0024). When the experimental group withdrew from the music intervention, the BSRS-5 rebounded to 3.7±3.0. The pre-test DT was 4.3±1.5 and 3.4±1.7 in the experimental group and control group (p = 0.0051), and the post-test DT score was significantly reduced in the experimental group (2.7±1.2 for the experimental group and 3.5±1.3 for the control group, p = 0.0021), the significant pre-post difference of DT between group was found (p<.0001). When the experimental group withdrew from the music intervention, the DT rebounded to 3.7±1.4. The pre-test BAI-C was 6.7±11.7 and 7.8±8.5 in the experimental group and control group (p = 0.5878), and the post-test BAI-C score was significantly lower in the experimental group (1.7±3.7 for the experimental group and 7.4±8.0 for the control group, p<.0001), however, the pre-post difference of BAI-C between group was not significant (p = 0.0619). When the experimental group withdrew from the music intervention, the BAI-C rebounded to 6.5±8.7. There is a significantly increased systolic blood pressure in the control group compared with the patients had music intervention.

CONCLUSION

This study's results provide evidence that playing music routinely (music intervention) during RT can be an excellent way to relieve patients' anxiety and improve their comfort.

Stereotactic Radiosurgery for Small Intact Brain Metastasis: A Comparative Evaluation of 3 Different Single Fraction Prescription Doses

PURPOSE/OBJECTIVE(S)

While single-fraction stereotactic radiosurgery (SRS) in the treatment of small brain metastases (SBM, ≤ 2 cm) is well established, prescription dosing varies considerably across institutions and clinical trials. The choice of prescription dose is a delicate balance between local failure (LF) and radiation necrosis (RN) risks. In the modern era, historically-established dosing thresholds may no longer be applicable. To evaluate the impact of prescription dose on outcomes, we performed a comparative analysis of patients with SBM treated with definitive SRS using three different prescriptions, at a single tertiary institution.

MATERIALS/METHODS

Consecutive patients with intact SBM treated with SRS from January 2017 and December 2021 were analyzed. Baseline patient characteristics and dosing parameters were abstracted from the medical record. To limit the integral brain dose when treating multiple brain metastases, the institutional practice was to reduce prescription dose as the total number of lesions increased (i.e., 24 Gy for ≤10 lesions, 22 Gy for 11-20, and 20 Gy for >20). A per lesion analysis, where each lesion was followed from the date of SRS to the last follow-up, was conducted with primary endpoints of LF and RN. Gray's test was used to compare the cumulative incidence of the LF and RN, with death as a competing risk. Factors affecting LF were analyzed using Cox hazard regression analysis.

RESULTS

A total of 1318 SBM in 250 patients received SRS and met the inclusion criteria. The median age was 62 years (range: 18-90), median KPS was 90 (range: 50-100) and 66% were female. The most common primary tumors were lung (55.5%) and breast cancers (26.4%). With a median follow-up of 12 months, 136 (11%) LF in 44 patients and 70 (5.7%) RN events in 46 patients occurred. The actuarial 1-year cumulative rate of LF was lower in lesions treated with 24 Gy (6.4%, 95% CI: 4.7-8.6%) or 22 Gy (5.8%, 95% CI: 3.7-8.7%) compared to 20 Gy (15.4%, 95% CI: 10.9-20.5%) (p<0.01). 22 Gy and 24 Gy prescription doses were associated with a 44% and 52% reduction in risk in LF compared to 20 Gy (HR: 0.56; 95% CI: 0.36-0.9; p = 0.01 and HR: 0.48; 95% CI: 0.31-0.74; p<0.01, respectively). In a subset analysis of radiosensitive tumors, 1-year LF rate was still lower with 24 Gy (7.4%, 95% CI: 5.3-9.9%) and 22 Gy (6.1%, 95% CI: 3.7-9.4%) than 20 Gy (15.7%, 95% CI: 11.2-21%) (p = 0.01). The cumulative 1-year RN rate numerically declined with dose, but was not statistically significantly different, with 3.6% (95% CI: 2.3-5.3%) for 24 Gy, 2.6% (95% CI: 1.3-4.8%) for 22 Gy and 1.4% (95% CI: 0.4-3.7%) for 20 Gy.

CONCLUSION

Patients treated with single fraction SRS to intact SBM were at increased risk of LF with prescription doses of 20 Gy compared to 22-24 Gy, without an increased risk of RN. Even in patients with radiosensitive histologies, higher LF rates were still observed following 20 Gy compared to 22-24 Gy.

Src Kinase Activates Signal Transducer and Activator of Transcription 3 to Enhance Radioresistance in Triple Negative Breast Cancer Cells

PURPOSE/OBJECTIVE(S)

Triple-negative breast cancer (TNBC) is known to be insensitive to radiation therapy, and it requires to development radiosensitizer for TNBC.

MATERIALS/METHODS

We previously established a radioresistant sub-line from MDA-MB-231 cells, called 231-RR, and have demonstrated that 231-RR cells displayed high cancer stem cell (CSC) activity. And 231-RR cells were treated with dasatinib, a Src inhibitor.

RESULTS

In the present study, we discovered that the activation of Src kinase was increased in 231-RR cells. Treatment of dasatinib, an Src inhibitor, sensitized 231-RR cells to radiotherapy, along with the increased p-γH2Axser139, indicated the enhancement of DNA damage. Dasatinib also caused the downregulation of cancer stemness factors, including c-Myc, OCT4, and the Notch intracellular domain, as well as the decrease of phosphorylation of Signal Transducer and Activator of Transcription 3 (STAT3). The treatment of C188-9, a STAT3 inhibitor, also sensitized 231-RR cells toward radiotherapy along with the increased p-γH2Axser139, but without changing the phosphorylation of Src, indicating that STAT3 is a downstream event of Src activation.

CONCLUSION

In conclusion, our data suggests that the inhibitors of Src or STAT3 could function as radiosensitizers or CSC targeting agents for TNBC radiotherapy.

Effect of using G-FAST to recognize emergent large vessel occlusion: A city-wide community experience

BACKGROUND/PURPOSE

A prehospital bypass strategy was suggested for large vessel occlusion. This study aimed to evaluate the effect of a bypass strategy using the gaze-face-arm-speech-time test (G-FAST) implemented in a metropolitan community.

METHODS

Pre-notified patients with positive Cincinnati Prehospital Stroke Scale and symptom onset <3 h from July 2016 to December 2017 (pre-intervention period) and those with positive G-FAST and symptom onset <6 h from July 2019 to December 2020 (intervention period) were included. Patients aged <20 years and those with missing in-hospital data were excluded. The primary outcomes were the rates of receiving endovascular thrombectomy (EVT) and intravenous thrombolysis (IVT). The secondary outcomes were total prehospital time, door-to-computed tomography (CT) time, door-to-needle (DTN) time, and door-to-puncture (DTP) time.

RESULTS

We included 802 and 695 pre-notified patients from the pre-intervention and intervention periods, respectively. The characteristics of the patients in the two periods were similar. In the primary outcomes, pre-notified patients during the intervention period showed higher rates of receiving EVT (4.49% vs. 15.25%, p < 0.001) and IVT (15.34% vs. 21.58%, p = 0.002). In the secondary outcomes, pre-notified patients during intervention period had longer total prehospital time (mean 23.38 vs 25.23 min, p < 0.001), longer door-to-CT time (median 10 vs 11 min, p < 0.001), longer DTN time (median 53 vs 54.5 min, p < 0.001) but shorter DTP time (median 141 vs 139.5 min, p < 0.001).

CONCLUSION

The prehospital bypass strategy with G-FAST showed benefits for stroke patients.

Detection of ERBB2 and CEN17 signals in fluorescent in situ hybridization and dual in situ hybridization for guiding breast cancer HER2 target therapy

The overexpression of the human epidermal growth factor receptor 2 (HER2) is a predictive biomarker in therapeutic effects for metastatic breast cancer. Accurate HER2 testing is critical for determining the most suitable treatment for patients. Fluorescent in situ hybridization (FISH) and dual in situ hybridization (DISH) have been recognized as FDA-approved methods to determine HER2 overexpression. However, analysis of HER2 overexpression is challenging. Firstly, the boundaries of cells are often unclear and blurry, with large variations in cell shapes and signals, making it challenging to identify the precise areas of HER2-related cells. Secondly, the use of sparsely labeled data, where some unlabeled HER2-related cells are classified as background, can significantly confuse fully supervised AI learning and result in unsatisfactory model outcomes. In this study, we present a weakly supervised Cascade R-CNN (W-CRCNN) model to automatically detect HER2 overexpression in HER2 DISH and FISH images acquired from clinical breast cancer samples. The experimental results demonstrate that the proposed W-CRCNN achieves excellent results in identification of HER2 amplification in three datasets, including two DISH datasets and a FISH dataset. For the FISH dataset, the proposed W-CRCNN achieves an accuracy of 0.970±0.022, precision of 0.974±0.028, recall of 0.917±0.065, F1-score of 0.943±0.042 and Jaccard Index of 0.899±0.073. For DISH datasets, the proposed W-CRCNN achieves an accuracy of 0.971±0.024, precision of 0.969±0.015, recall of 0.925±0.020, F1-score of 0.947±0.036 and Jaccard Index of 0.884±0.103 for dataset 1, and an accuracy of 0.978±0.011, precision of 0.975±0.011, recall of 0.918±0.038, F1-score of 0.946±0.030 and Jaccard Index of 0.884±0.052 for dataset 2, respectively. In comparison with the benchmark methods, the proposed W-CRCNN significantly outperforms all the benchmark approaches in identification of HER2 overexpression in FISH and DISH datasets (p<0.05). With the high degree of accuracy, precision and recall , the results show that the proposed method in DISH analysis for assessment of HER2 overexpression in breast cancer patients has significant potential to assist precision medicine.

CW-NET for Multi-type Cell Detection and Classification in Bone Marrow Examination and Mitotic Figure Examination

MOTIVATION

Bone marrow examination is one of the most important indicators in diagnosing hematologic disorders and is typically performed under the microscope via oil-immersion objective lens with a total 100X objective magnification. On the other hand, mitotic detection and identification is critical not only for accurate cancer diagnosis and grading but also for predicting therapy success and survival. Fully automated bone marrow examination and mitotic figure examination from whole-slide images is highly demanded but challenging and poorly explored. Firstly, the complexity and poor reproducibility of microscopic image examination are due to the cell type diversity, delicate intra-lineage discrepancy within the multi-type cell maturation process, cells overlapping, lipid interference and stain variation. Secondly, manual annotation on whole-slide images is tedious, laborious and subject to intra-observer variability, which causes the supervised information restricted to limited, easily identifiable and scattered cells annotated by humans. Thirdly, when the training data is sparsely labeled, many unlabeled objects of interest are wrongly defined as background, which severely confuses AI learners.

RESULTS

This paper presents an efficient and fully automatic CW-Net approach to address the three issues mentioned above and demonstrates its superior performance on both bone marrow examination and mitotic figure examination. The experimental results demonstrate the robustness and generalizability of the proposed CW-Net on a large bone marrow WSI dataset with 16,456 annotated cells of 19 bone marrow cell types and a large-scale WSI dataset for mitotic figure assessment with 262,481 annotated cells of five cell types.

AVAILABILITY AND IMPELMENTATION

An online web-based system of the proposed method has been created for demonstration. (see https://youtu.be/MRMR25Mls1A).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Development of anti-aflatoxin B1 nanobodies from a novel mutagenesis-derived synthetic library for traditional Chinese medicine and foods safety testing

BACKGROUND

The main commercially available methods for detecting small molecules of mycotoxins in traditional Chinese medicine (TCM) and functional foods are enzyme-linked immunosorbent assay and mass spectrometry. Regarding the development of diagnostic antibody reagents, effective methods for the rapid preparation of specific monoclonal antibodies are inadequate.

METHODS

In this study, a novel synthetic phage-displayed nanobody Golden Glove (SynaGG) library with a glove-like cavity configuration was established using phage display technology in synthetic biology. We applied this unique SynaGG library on the small molecule aflatoxin B1 (AFB1), which has strong hepatotoxicity, to isolate specific nanobodies with high affinity for AFB1.

RESULT

These nanobodies exhibit no cross-reactivity with the hapten methotrexate, which is recognized by the original antibody template. By binding to AFB1, two nanobodies can neutralize AFB1-induced hepatocyte growth inhibition. Using molecular docking, we found that the unique non-hypervariable complementarity-determining region 4 (CDR4) loop region of the nanobody was involved in the interaction with AFB1. Specifically, the CDR4's positively charged amino acid arginine directed the binding interaction between the nanobody and AFB1. We then rationally optimized the interaction between AFB1 and the nanobody by mutating serine at position 2 into valine. The binding affinity of the nanobody to AFB1 was effectively improved, and this result supported the use of molecular structure simulation for antibody optimization.

CONCLUSION

In summary, this study revealed that the novel SynaGG library, which was constructed through computer-aided design, can be used to isolate nanobodies that specifically bind to small molecules. The results of this study could facilitate the development of nanobody materials to detect small molecules for the rapid screening of TCM materials and foods in the future.

Interpretable attention-based deep learning ensemble for personalized ovarian cancer treatment without manual annotations

Inhibition of pathological angiogenesis has become one of the first FDA approved targeted therapies widely tested in anti-cancer treatment, i.e. VEGF-targeting monoclonal antibody bevacizumab, in combination with chemotherapy for frontline and maintenance therapy for women with newly diagnosed ovarian cancer. Identification of the best predictive biomarkers of bevacizumab response is necessary in order to select patients most likely to benefit from this therapy. Hence, this study investigates the protein expression patterns on immunohistochemical whole slide images of three angiogenesis related proteins, including Vascular endothelial growth factor, Angiopoietin 2 and Pyruvate kinase isoform M2, and develops an interpretable and annotation-free attention based deep learning ensemble framework to predict the bevacizumab therapeutic effect on patients with epithelial ovarian cancer or peritoneal serous papillary carcinoma using tissue microarrays (TMAs). In evaluation with five-fold cross validation, the proposed ensemble model using the protein expressions of both Pyruvate kinase isoform M2 and Angiopoietin 2 achieves a notably high F-score (0.99±0.02), accuracy (0.99±0.03), precision (0.99±0.02), recall (0.99±0.02) and AUC (1.00±0). Kaplan-Meier progression free survival analysis confirms that the proposed ensemble is able to identify patients in the predictive therapeutic sensitive group with low cancer recurrence (p<0.001), and the Cox proportional hazards model analysis further confirms the above statement (p=0.012). In conclusion, the experimental results demonstrate that the proposed ensemble model using the protein expressions of both Pyruvate kinase isoform M2 and Angiopoietin 2 can assist treatment planning of bevacizumab targeted therapy for patients with ovarian cancer.

The Blockade of Mitogen-Activated Protein Kinase 14 Activation by Marine Natural Product Crassolide Triggers ICD in Tumor Cells and Stimulates Anti-Tumor Immunity

Immunogenic cell death (ICD) refers to a type of cell death that stimulates immune responses. It is characterized by the surface exposure of damage-associated molecular patterns (DAMPs), which can facilitate the uptake of antigens by dendritic cells (DCs) and stimulate DC activation, resulting in T cell immunity. The activation of immune responses through ICD has been proposed as a promising approach for cancer immunotherapy. The marine natural product crassolide, a cembranolide isolated from the Formosan soft coral Lobophytum michaelae, has been shown to have cytotoxic effects on cancer cells. In this study, we investigated the effects of crassolide on the induction of ICD, the expression of immune checkpoint molecules and cell adhesion molecules, as well as tumor growth in a murine 4T1 mammary carcinoma model. Immunofluorescence staining for DAMP ectolocalization, Western blotting for protein expression and Z′-LYTE kinase assay for kinase activity were performed. The results showed that crassolide significantly increased ICD and slightly decreased the expression level of CD24 on the surface of murine mammary carcinoma cells. An orthotopic tumor engraftment of 4T1 carcinoma cells indicated that crassolide-treated tumor cell lysates stimulate anti-tumor immunity against tumor growth. Crassolide was also found to be a blocker of mitogen-activated protein kinase 14 activation. This study highlights the immunotherapeutic effects of crassolide on the activation of anticancer immune responses and suggests the potential clinical use of crassolide as a novel treatment for breast cancer.

Annotation-Free Deep Learning-Based Prediction of Thyroid Molecular Cancer Biomarker BRAF (V600E) from Cytological Slides

Thyroid cancer is the most common endocrine cancer. Papillary thyroid cancer (PTC) is the most prevalent form of malignancy among all thyroid cancers arising from follicular cells. Fine needle aspiration cytology (FNAC) is a non-invasive method regarded as the most cost-effective and accurate diagnostic method of choice in diagnosing PTC. Identification of BRAF (V600E) mutation in thyroid neoplasia may be beneficial because it is specific for malignancy, implies a worse prognosis, and is the target for selective BRAF inhibitors. To the authors' best knowledge, this is the first automated precision oncology framework effectively predict BRAF (V600E) immunostaining result in thyroidectomy specimen directly from Papanicolaou-stained thyroid fine-needle aspiration cytology and ThinPrep cytological slides, which is helpful for novel targeted therapies and prognosis prediction. The proposed deep learning (DL) framework is evaluated on a dataset of 118 whole slide images. The results show that the proposed DL-based technique achieves an accuracy of 87%, a precision of 94%, a sensitivity of 91%, a specificity of 71% and a mean of sensitivity and specificity at 81% and outperformed three state-of-the-art deep learning approaches. This study demonstrates the feasibility of DL-based prediction of critical molecular features in cytological slides, which not only aid in accurate diagnosis but also provide useful information in guiding clinical decision-making in patients with thyroid cancer. With the accumulation of data and the continuous advancement of technology, the performance of DL systems is expected to be improved in the near future. Therefore, we expect that DL can provide a cost-effective and time-effective alternative tool for patients in the era of precision oncology.

On the firefighter problem with spreading vaccination for maximizing the number of saved nodes: the IP model and LP rounding algorithms

When an infectious disease spreads, how to quickly vaccinate with a limited budget per time step to reduce the impact of the virus is very important. Specifically, vaccination will be carried out in every time step, and vaccinated nodes will no longer be infected. Meanwhile, the protection from vaccination can spread to the neighbors of a vaccinated node. Our goal is to efficiently find optimal and approximation solutions to our problem with various algorithms. In this paper, we first design an integer linear program to solve this problem. We then propose approximation algorithms of (1) Linear programming (LP) deterministic threshold rounding, (2) LP dependent randomized rounding, and (3) LP independent randomized rounding. We prove that the LP independent randomized rounding algorithm has a high probability of finding a feasible solution that gives an approximation ratio of ( 1 - δ ) , where a small constant δ between 0 and 1 reduces the lower bound on the feasibility probability. We also provide experimental results for three different rounding algorithms to show that they perform numerically well in terms of approximation ratios. These analytical and numerical studies allow each individual to adopt the most appropriate approximation algorithm to efficiently resolve the vaccination problem when her reliance on commercial optimization solvers is costly.

Fast cross-staining alignment of gigapixel whole slide images with application to prostate cancer and breast cancer analysis

Joint analysis of multiple protein expressions and tissue morphology patterns is important for disease diagnosis, treatment planning, and drug development, requiring cross-staining alignment of multiple immunohistochemical and histopathological slides. However, cross-staining alignment of enormous gigapixel whole slide images (WSIs) at single cell precision is difficult. Apart from gigantic data dimensions of WSIs, there are large variations on the cell appearance and tissue morphology across different staining together with morphological deformations caused by slide preparation. The goal of this study is to build an image registration framework for cross-staining alignment of gigapixel WSIs of histopathological and immunohistochemical microscopic slides and assess its clinical applicability. To the authors' best knowledge, this is the first study to perform real time fully automatic cross staining alignment of WSIs with 40× and 20× objective magnification. The proposed WSI registration framework consists of a rapid global image registration module, a real time interactive field of view (FOV) localization model and a real time propagated multi-level image registration module. In this study, the proposed method is evaluated on two kinds of cancer datasets from two hospitals using different digital scanners, including a dual staining breast cancer data set with 43 hematoxylin and eosin (H&E) WSIs and 43 immunohistochemical (IHC) CK(AE1/AE3) WSIs, and a triple staining prostate cancer data set containing 30 H&E WSIs, 30 IHC CK18 WSIs, and 30 IHC HMCK WSIs. In evaluation, the registration performance is measured by not only registration accuracy but also computational time. The results show that the proposed method achieves high accuracy of 0.833 ± 0.0674 for the triple-staining prostate cancer data set and 0.931 ± 0.0455 for the dual-staining breast cancer data set, respectively, and takes only 4.34 s per WSI registration on average. In addition, for 30.23% data, the proposed method takes less than 1 s for WSI registration. In comparison with the benchmark methods, the proposed method demonstrates superior performance in registration accuracy and computational time, which has great potentials for assisting medical doctors to identify cancerous tissues and determine the cancer stage in clinical practice.

Weakly supervised deep learning for prediction of treatment effectiveness on ovarian cancer from histopathology images

Despite the progress made during the last two decades in the surgery and chemotherapy of ovarian cancer, more than 70 % of advanced patients are with recurrent cancer and decease. Surgical debulking of tumors following chemotherapy is the conventional treatment for advanced carcinoma, but patients with such treatment remain at great risk for recurrence and developing drug resistance, and only about 30 % of the women affected will be cured. Bevacizumab is a humanized monoclonal antibody, which blocks VEGF signaling in cancer, inhibits angiogenesis and causes tumor shrinkage, and has been recently approved by FDA as a monotherapy for advanced ovarian cancer in combination with chemotherapy. Considering the cost, potential toxicity, and finding that only a portion of patients will benefit from these drugs, the identification of new predictive method for the treatment of ovarian cancer remains an urgent unmet medical need. In this study, we develop weakly supervised deep learning approaches to accurately predict therapeutic effect for bevacizumab of ovarian cancer patients from histopathological hematoxylin and eosin stained whole slide images, without any pathologist-provided locally annotated regions. To the authors' best knowledge, this is the first model demonstrated to be effective for prediction of the therapeutic effect of patients with epithelial ovarian cancer to bevacizumab. Quantitative evaluation of a whole section dataset shows that the proposed method achieves high accuracy, 0.882 ± 0.06; precision, 0.921 ± 0.04, recall, 0.912 ± 0.03; F-measure, 0.917 ± 0.07 using 5-fold cross validation and outperforms two state-of-the art deep learning approaches Coudray et al. (2018), Campanella et al. (2019). For an independent TMA testing set, the three proposed methods obtain promising results with high recall (sensitivity) 0.946, 0.893 and 0.964, respectively. The results suggest that the proposed method could be useful for guiding treatment by assisting in filtering out patients without positive therapeutic response to suffer from further treatments while keeping patients with positive response in the treatment process. Furthermore, according to the statistical analysis of the Cox Proportional Hazards Model, patients who were predicted to be invalid by the proposed model had a very high risk of cancer recurrence (hazard ratio = 13.727) than patients predicted to be effective with statistical signifcance (p < 0.05).

A Weakly Supervised Deep Learning Method for Guiding Ovarian Cancer Treatment and Identifying an Effective Biomarker

Ovarian cancer is a common malignant gynecological disease. Molecular target therapy, i.e., antiangiogenesis with bevacizumab, was found to be effective in some patients of epithelial ovarian cancer (EOC). Although careful patient selection is essential, there are currently no biomarkers available for routine therapeutic usage. To the authors’ best knowledge, this is the first automated precision oncology framework to effectively identify and select EOC and peritoneal serous papillary carcinoma (PSPC) patients with positive therapeutic effect. From March 2013 to January 2021, we have a database, containing four kinds of immunohistochemical tissue samples, including AIM2, c3, C5 and NLRP3, from patients diagnosed with EOC and PSPC and treated with bevacizumab in a hospital-based retrospective study. We developed a hybrid deep learning framework and weakly supervised deep learning models for each potential biomarker, and the experimental results show that the proposed model in combination with AIM2 achieves high accuracy 0.92, recall 0.97, F-measure 0.93 and AUC 0.97 for the first experiment (66% training and 34%testing) and high accuracy 0.86 ± 0.07, precision 0.9 ± 0.07, recall 0.85 ± 0.06, F-measure 0.87 ± 0.06 and AUC 0.91 ± 0.05 for the second experiment using five-fold cross validation, respectively. Both Kaplan-Meier PFS analysis and Cox proportional hazards model analysis further confirmed that the proposed AIM2-DL model is able to distinguish patients gaining positive therapeutic effects with low cancer recurrence from patients with disease progression after treatment (p < 0.005).

Histopathological whole slide image dataset for classification of treatment effectiveness to ovarian cancer

Ovarian cancer is the leading cause of gynecologic cancer death among women. Regardless of the development made in the past two decades in the surgery and chemotherapy of ovarian cancer, most of the advanced-stage patients are with recurrent cancer and die. The conventional treatment for ovarian cancer is to remove cancerous tissues using surgery followed by chemotherapy, however, patients with such treatment remain at great risk for tumor recurrence and progressive resistance. Nowadays, new treatment with molecular-targeted agents have become accessible. Bevacizumab as a monotherapy in combination with chemotherapy has been recently approved by FDA for the treatment of epithelial ovarian cancer (EOC). Prediction of therapeutic effects and individualization of therapeutic strategies are critical, but to the authors' best knowledge, there are no effective biomarkers that can be used to predict patient response to bevacizumab treatment for EOC and peritoneal serous papillary carcinoma (PSPC). This dataset helps researchers to explore and develop methods to predict the therapeutic effect of patients with EOC and PSPC to bevacizumab.

Host Genetic Associations with Salivary Microbiome in Oral Cancer

Despite the growing recognition of a host genetic effect on shaping gut microbiota composition, the genetic determinants of oral microbiota remain largely unexplored, especially in the context of oral diseases. Here, we performed a microbiome genome-wide association study in 2 independent cohorts of patients with oral squamous cell carcinoma (OSCC, n = 144 and 67) and an additional group of noncancer individuals (n = 104). Besides oral bacterial dysbiosis and signatures observed in OSCC, associations of 3 loci with the abundance of genus-level taxa and 4 loci with β diversity measures were detected (q < 0.05) at the discovery stage. The most significant hit (rs10906082 with the genus Lachnoanaerobaculum, P = 3.55 × 10^-9 at discovery stage) was replicated in a second OSCC cohort. Moreover, the other 2 taxonomical associations, rs10973953 with the genus Kingella (P = 1.38 × 10^-9) and rs4721629 with the genus Parvimonas (P = 3.53 × 10^-8), were suggestive in the meta-analysis combining 2 OSCC cohorts. Further pathway analysis revealed that these loci were enriched for genes in regulation of oncogenic and angiogenic responses, implicating a genetic anchor to the oral microbiome in estimation of casual relationships with OSCC. Our findings delineate the role of host genotypes in influencing the structure of oral microbial communities.

A Worldwide Bibliometric Analysis of Publications on Coronavirus in the Past 3 Decades: A Bibliometric Article

Background:

Coronaviruses caused three pandemics and impact public health globally in the 21st century. However, limited data were for the evaluation of the trend of coronavirus researches. We aimed to analyze quantitatively, qualitatively, and visually evaluate global scientific publications on coronavirus by using bibliometric analysis.

Methods:

Coronavirus-related research from 1990–2019 was retrieved from the Web of Science Core Collection database (WoS). Microsoft Excel and VOS viewer software were used to assess the characteristics of publications.

Results:

Overall, 9,553 publications on coronavirus were retrieved on 12 Mar 2020. The United States took a leading position in coronavirus-related research and accounted for more than one-thirds (36.7%) of all publications. The most productive journal in this field was Journal of Virology (1,056, 11.1%), and the most productive institution was University of Hong Kong (394, 4.1%). The main hot topics in coronavirus field were virus infection and protein. Active collaborations between countries were observed.

Conclusion:

Over the past three decades, coronavirus research has gradually increased due to two global outbreaks. Through this global bibliometric evaluation, some relevant evidence could be provided. Corresponding to the impact of novel coronavirus (COVID-19), a large number of articles can be expected to appear in the next few years, and international cooperation should be strengthened to solve the problem.

Deep learning for bone marrow cell detection and classification on whole-slide images

Bone marrow (BM) examination is an essential step in both diagnosing and managing numerous hematologic disorders. BM nucleated differential count (NDC) analysis, as part of BM examination, holds the most fundamental and crucial information. However, there are many challenges to perform automated BM NDC analysis on whole-slide images (WSIs), including large dimensions of data to process, complicated cell types with subtle differences. To the authors best knowledge, this is the first study on fully automatic BM NDC using WSIs with 40x objective magnification, which can replace traditional manual counting relying on light microscopy via oil-immersion 100x objective lens with a total 1000x magnification. In this study, we develop an efficient and fully automatic hierarchical deep learning framework for BM NDC WSI analysis in seconds. The proposed hierarchical framework consists of (1) a deep learning model for rapid localization of BM particles and cellular trails generating regions of interest (ROI) for further analysis, (2) a patch-based deep learning model for cell identification of 16 cell types, including megakaryocytes, mitotic cells, and four stages of erythroblasts which have not been demonstrated in previous studies before, and (3) a fast stitching model for integrating patch-based results and producing final outputs. In evaluation, the proposed method is firstly tested on a dataset with a total of 12,426 annotated cells using cross validation, achieving high recall and accuracy of 0.905 ± 0.078 and 0.989 ± 0.006, respectively, and taking only 44 seconds to perform BM NDC analysis for a WSI. To further examine the generalizability of our model, we conduct an evaluation on the second independent dataset with a total of 3005 cells, and the results show that the proposed method also obtains high recall and accuracy of 0.842 and 0.988, respectively. In comparison with the existing small-image-based benchmark methods, the proposed method demonstrates superior performance in recall, accuracy and computational time.

Using the Goldilocks Principle to model coral ecosystem engineering

The occurrence and proliferation of reef-forming corals is of vast importance in terms of the biodiversity they support and the ecosystem services they provide. The complex three-dimensional structures engineered by corals are comprised of both live and dead coral, and the function, growth and stability of these systems will depend on the ratio of both. To model how the ratio of live : dead coral may change, the ‘Goldilocks Principle’ can be used, where organisms will only flourish if conditions are ‘just right’. With data from particle imaging velocimetry and numerical smooth particle hydrodynamic modelling with two simple rules, we demonstrate how this principle can be applied to a model reef system, and how corals are effectively optimizing their own local flow requirements through habitat engineering. Building on advances here, these approaches can be used in conjunction with numerical modelling to investigate the growth and mortality of biodiversity supporting framework in present-day and future coral reef structures.

Expanding resources of endovascular thrombectomy: An optimization model

BACKGROUND/PURPOSE

Recently optimized models for selecting the locations of hospitals capable of providing endovascular thrombectomy (EVT) did not consider the accuracy of the prehospital stroke scale assessment and possibility of secondary transport. Our study aimed to propose a new model for selecting existing hospitals with intravenous thrombolysis capability to become EVT-capable hospitals.

METHODS

A sequential order was provided to upgrade hospitals providing intravenous thrombolysis, using a mixed integer programming model based on current medical resource allocation. In addition, we drafted a centralized plan to redistribute existing EVT resources by redetermining locations of EVT-capable hospitals. Using historical data of 7679 on-scene patients with suspected stroke, the model was implemented to determine the hospital that maximizes the number of patients receiving EVT treatment within call-to-definitive-treatment time.

RESULTS

All suspected stroke patients were sent to EVT-capable hospitals directly under the current medical resource allocation model. After upgrading one additional hospital to become an EVT-capable hospital, the percentage of patients receiving definitive treatment within the standard call-to-definitive-treatment time was elevated from 68.82% to 72.97%. In the model, assuming that there is no hospital providing EVT, all patients suspected of stroke will be sent to EVT-capable hospitals directly after upgrading three or more hospitals to be able to provide treatment.

CONCLUSION

All patients eligible for acute stroke treatment are sent to EVT-capable hospitals in the simulation under the current medical resource allocation model. This model can be utilized to provide insights for capacity redistribution in other regions.

Comprehensive Deep Mutational Scanning Reveals the Immune-Escaping Hotspots of SARS-CoV-2 Receptor-Binding Domain Targeting Neutralizing Antibodies

The rapid spread of SARS-CoV-2 has caused the COVID-19 pandemic, resulting in the collapse of medical care systems and economic depression worldwide. To combat COVID-19, neutralizing antibodies have been investigated and developed. However, the evolutions (mutations) of the receptor-binding domain (RBD) of SARS-CoV-2 enable escape from neutralization by these antibodies, further impairing recognition by the human immune system. Thus, it is critical to investigate and predict the putative mutations of RBD that escape neutralizing immune responses. Here, we employed computational analyses to comprehensively investigate the mutational effects of RBD on binding to neutralizing antibodies and angiotensin-converting enzyme 2 (ACE2) and demonstrated that the RBD residues K417, L452, L455, F456, E484, G485, F486, F490, Q493, and S494 were consistent with clinically emerging variants or experimental observations of attenuated neutralizations. We also revealed common hotspots, Y449, L455, and Y489, that exerted comparable destabilizing effects on binding to both ACE2 and neutralizing antibodies. Our results provide valuable information on the putative effects of RBD variants on interactions with neutralizing antibodies. These findings provide insights into possible evolutionary hotspots that can escape recognition by these antibodies. In addition, our study results will benefit the development and design of vaccines and antibodies to combat the newly emerging variants of SARS-CoV-2.

A real-time camera-based adaptive breathing monitoring system

Breathing is one of the vital signs used to assess the physical health of a subject. Non-contact-based measurements of both breathing rate and changes in breathing rate help monitor health condition of subjects more flexibly. In this paper, we present an improved real-time camera-based adaptive breathing monitoring system, which includes real time (1) adaptive breathing motion detection, (2) adaptive region of interest detection to eliminate environmental noise, (3) breathing and body movement classification, (4) respiration rate estimation, (5) monitor change in respiration rate to examine overall health of an individual, and (6) online adaptation to lighting. The proposed system does not pose any positional and postural constraint. For evaluation, 30 videos of 15 animals are tested with drugs to simulate various medical conditions and breathing patterns, and the results from the proposed system are compared with the outputs of an existing FDA-approved invasive medical system for patient monitoring. The results show that the proposed method performs significantly correlated RR results to the reference medical device with the correlation coefficient equal to 0.92 and p-value less than 0.001, and more importantly the proposed video-based method is demonstrated to produce alarms 10 to 20 s earlier than the benchmark medical device. Graphical abstract The proposed system flowchart to extract the respiratory pattern from video.

EGFL6 promotes colorectal cancer cell growth and mobility and the anti-cancer property of anti-EGFL6 antibody

BACKGROUND

The availability of a reliable tumor target for advanced colorectal cancer (CRC) therapeutic approaches is critical since current treatments are limited. Epidermal growth factor-like domain 6 (EGFL6) has been reported to be associated with cancer development. Here, we focused on the role of EGFL6 in CRC progression and its clinical relevance. In addition, an anti-EGFL6 antibody was generated by phage display technology to investigate its potential therapeutic efficacy in CRC.

RESULTS

EGFL6 expression significantly increased in the colon tissues from CRC patients and mice showing spontaneous tumorigenesis, but not in normal tissue. Under hypoxic condition, EGFL6 expression was enhanced at both protein and transcript levels. Moreover, EGFL6 could promote cancer cell migration invasion, and proliferation of CRC cells via up-regulation of the ERK/ AKT pathway. EGFL6 also regulated cell migration, invasion, proliferation, and self-renewal through EGFR/αvβ3 integrin receptors. Treatment with the anti-EGFL6 antibody EGFL6-E5-IgG showed tumor-inhibition and anti-metastasis abilities in the xenograft and syngeneic mouse models, respectively. Moreover, EGFL6-E5-IgG treatment had no adverse effect on angiogenesis and wound healing CONCLUSIONS: We demonstrated that EGFL6 plays a role in CRC tumorigenesis and tumor progression, indicating that EGFL6 is a potential therapeutic target worth further investigation.

Generation of avian-derived anti-B7-H4 antibodies exerts a blockade effect on the immunosuppressive response

For highly conserved mammalian protein, chicken is a suitable immune host to generate antibodies. Monoclonal antibodies have been successfully targeted with immunity checkpoint proteins as a means of cancer treatment; this treatment enhances tumor-specific immunity responses through immunoregulation. Studies have identified the importance of B7-H4 in immunoregulation and its use as a potential target for cancer treatment. High levels of B7-H4 expression are found in tumor tissues and are associated with adverse clinical and pathological characteristics. Using the phage display technique, this study isolated specific single-chain antibody fragments (scFvs) against B7-H4 from chickens. Our experiment proved that B7-H4 clearly induced the inhibition of T-cell activation. Therefore, use of anti-B7-H4 scFvs can effectively block the exhaustion of immunity cells and also stimulate and activate T-cells in peripheral blood mononuclear cells. Sequence analysis revealed that two isolated scFv S2 and S4 have the same VH complementarity-determining regions (CDRs) sequence. Molecule docking was employed to simulate the complex structures of scFv with B7-H4 to analyze the interaction. Our findings revealed that both scFvs employed CDR-H1 and CDR-H3 as main driving forces and had strong binding effects with the B7-H4. The affinity of scFv S2 was better because the CDR-L2 loop of the scFv S2 had three more hydrogen bond interactions with B7-H4. The results of this experiment suggest the usefulness of B7-H4 as a target for immunity checkpoints; the isolated B7-H4-specific chicken antibodies have the potential for use in future cancer immunotherapy applications.

External validation of prehospital stroke scales for emergent large vessel occlusion

BACKGROUND

It is suggested that a prehospital scale should be utilized to identify patients with emergent large vessel occlusion (ELVO). We aimed to perform external validation of nine ELVO scales.

METHODS

This single center retrospective observational study included patients with ischemic stroke visiting the emergency department (ED) within 6 h of symptom onset. Participants were excluded if individual items of the National Institute of Health Stroke Scale scores were not recorded or they did not receive brain computed tomography angiography or magnetic resonance imaging before intravenous thrombolysis or endovascular thrombectomy, and within 24 h of ED admission. The first definition of ELVO was emergent occlusion of the internal carotid artery (ICA) and middle cerebral artery segment 1 (M1). The second definition was emergent occlusion of ICA, M1, basilar artery, middle cerebral artery segment 2, anterior cerebral artery segment 1, and posterior cerebral artery segment 1. Area under the receiver operating characteristic curve (AUROC) was constructed to examine discrimination. The sensitivity, specificity, positive predictive value, and negative predictive value of the nine scales under the two ELVO definitions were calculated.

RESULTS

A total of 1231 patients were included in the study. No significant differences were observed in the AUROC under the two ELVO definitions. However, sensitivity values of these scales were largely different, ranging from 44.56% to 93.68% under the first ELVO definition. The sensitivity values among scales were also different under the second ELVO definition.

CONCLUSION

Stakeholders in the community should choose suitable scales according to their own system conditions.

Tipping the immunostimulatory and inhibitory DAMP balance to harness immunogenic cell death

Induction of tumor cell death is the therapeutic goal for most anticancer drugs. Yet, a mode of drug-induced cell death, known as immunogenic cell death (ICD), can propagate antitumoral immunity to augment therapeutic efficacy. Currently, the molecular hallmark of ICD features the release of damage-associated molecular patterns (DAMPs) by dying cancer cells. Here, we show that gemcitabine, a standard chemotherapy for various solid tumors, triggers hallmark immunostimualtory DAMP release (e.g., calreticulin, HSP70, and HMGB1); however, is unable to induce ICD. Mechanistic studies reveal gemcitabine concurrently triggers prostaglandin E2 release as an inhibitory DAMP to counterpoise the adjuvanticity of immunostimulatory DAMPs. Pharmacological blockade of prostaglandin E2 biosythesis favors CD103+ dendritic cell activation that primes a Tc1-polarized CD8+ T cell response to bolster tumor rejection. Herein, we postulate that an intricate balance between immunostimulatory and inhibitory DAMPs could determine the outcome of drug-induced ICD and pose COX-2/prostaglandin E2 blockade as a strategy to harness ICD.

Chicken antibodies against venom proteins of Trimeresurus stejnegeri in Taiwan

BACKGROUND

The venom of bamboo vipers (Trimeresurus stejnegeri - TS), commonly found in Taiwan, contains deadly hemotoxins that cause severe envenomation. Equine-derived antivenom is a specific treatment against snakebites, but its production costs are high and there are some inevitable side effects. The aim of the present work is to help in the development of an affordable and more endurable therapeutic strategy for snakebites.

METHODS

T. stejnegeri venom proteins were inactivated by glutaraldehyde in order to immunize hens for polyclonal immunoglobulin (IgY) antibodies production. After IgY binding assays, two antibody libraries were constructed expressing single-chain variable fragment (scFv) antibodies joined by the short or long linker for use in phage display antibody technology. Four rounds of biopanning were carried out. The selected scFv antibodies were then further tested for their binding activities and neutralization assays to TS proteins.

RESULTS

Purified IgY from egg yolk showed the specific binding ability to TS proteins. The dimensions of these two libraries contain 2.4 × 107 and 6.8 × 107 antibody clones, respectively. An increase in the titers of eluted phage indicated anti-TS clones remarkably enriched after 2nd panning. The analysis based on the nucleotide sequences of selected scFv clones indicated that seven groups of short linkers and four groups of long linkers were identified. The recombinant scFvs showed significant reactivity to TS venom proteins and a cross-reaction to Trimeresurus mucrosquamatus venom proteins. In in vivo studies, the data demonstrated that anti-TS IgY provided 100% protective effects while combined scFvs augmented partial survival time of mice injected with a lethal amount of TS proteins.

CONCLUSION

Chickens were excellent hosts for the production of neutralization antibodies at low cost. Phage display technology is available for generation of monoclonal antibodies against snake venom proteins. These antibodies could be applied in the development of diagnostic kits or as an alternative for snakebite envenomation treatment in the near future.

Patterns of COVID-19 on computed tomography imaging

PURPOSE

As the designated tertiary referral centre for infectious diseases in Hong Kong, our hospital received the city's first group of patients diagnosed with coronavirus disease 2019 (COVID-19). Herein, we studied the earliest patients admitted to our centre in order to clarify the typical radiological findings, particularly computed tomography (CT) findings, associated with COVID-19.

METHODS

From 22 January 2020 to 29 February 2020, 19 patients with confirmed COVID-19 underwent high-resolution or conventional CT scans of the thorax in our centre. The CT imaging findings of these patients with confirmed COVID-19 in Hong Kong were reviewed in this study.

RESULTS

Ground-glass opacities (GGO) with peripheral subpleural distribution were found in all patients (100%). No specific zonal predominance was observed. All lobes were involved in 16 (84.2%) patients, focal subsegmental consolidations were observed in 14 (73.7%) patients, and interlobular septal thickening was present in 12 (63.2%) patients. No mediastinal lymph node enlargement, centrilobular nodule, or pleural effusion was detected in any of the patients. Other imaging features present in several patients include bronchial dilatation, bronchial wall thickening, and crazy-paving patterns.

CONCLUSIONS

Peripheral subpleural GGO without zonal predominance in the absence of centrilobular nodule, pleural effusion, and lymph node enlargement were consistent findings in patients with confirmed COVID-19. The observed radiological patterns on CT scans can help identify COVID-19 and assess affected patients in the context of the ongoing outbreak.

Generation and Characterization of Single Chain Variable Fragment against Alpha-Enolase of Candida albicans

Candida albicans (C. albicans) is an opportunistic human pathogen responsible for approximately a half of clinical candidemia. The emerging Candida spp. with resistance to azoles is a major challenge in clinic, suggesting an urgent demand for new drugs and therapeutic strategies. Alpha–enolase (Eno1) is a multifunctional protein and represents an important marker for invasive candidiasis. Thus, C. albicans Eno1 (CaEno1) is believed to be an important target for the development of therapeutic agents and antibody drugs. Recombinant CaEno1 (rCaEno1) was first used to immunize chickens. Subsequently, we used phage display technology to construct two single chain variable fragment (scFv) antibody libraries. A novel biopanning procedure was carried out to screen anti-rCaEno1 scFv antibodies, whose specificities were further characterized. The polyclonal IgY antibodies showed binding to rCaEno1 and native CaEno1. A dominant scFv (CaS1) and its properties were further characterized. CaS1 attenuated the growth of C. albicans and inhibited the binding of CaEno1 to plasminogen. Animal studies showed that CaS1 prolonged the survival rate of mice and zebrafish with candidiasis. The fungal burden in kidney and spleen, as well as level of inflammatory cytokines were significantly reduced in CaS1-treated mice. These results suggest CaS1 has potential of being immunotherapeutic drug against C. albicans infections.

Poly(acrylamide-co-acrylic acid) gel polymer electrolyte incorporating with water-soluble sodium sulfide salt for quasi-solid-state quantum dot-sensitized solar cell

Rapid decay of photoanode, leakage from sealant, and evaporation of electrolyte are always the major concerns of quantum dot-sensitized solar cells (QDSCs) based on liquid electrolyte. Subsequently, gel polymer electrolyte (GPE) appears as an attractive solution in addition to lower cost, lighter weight, and flexibility. Poly(acrylamide- co-acrylic acid) (PAAm-PAA) is of special interest to act as a polymer host to entrap liquid electrolyte because it provides high transparency, good gelatinizing properties, and excellent compatibility with the liquid electrolyte. In this work, the electrical and transport properties of PAAm-PAA GPE incorporating with water-soluble sodium sulfide were characterized by impedance spectroscopy. An increment of ionic conductivity was observed with the incorporation of ethylene carbonate (EC) and potassium chloride (KCl). The highest room temperature ionic conductivity of PAAm-PAA GPE is 70.82 mS·cm−1. QDSC based on PAAm-PAA GPE with the composition of 1.3 wt% of KCl, 0.9 wt% of EC, 55.3 wt% of PAAm-PAA, 38.5 wt% of sodium sulfide, and 4.0 wt% of sulfur can present up to 1.80% of light-to-electricity conversion efficiency.

PSPC1-interchanged interactions with PTK6 and β-catenin synergize oncogenic subcellular translocations and tumor progression

Hepatocellular carcinoma (HCC) is one of the most lethal cancers worldwide due to metastasis. Paraspeckle component 1 (PSPC1) upregulation has been identified as an HCC pro-metastatic activator associated with poor patient prognosis, but with a lack of targeting strategy. Here, we report that PSPC1, a nuclear substrate of PTK6, sequesters PTK6 in the nucleus and loses its metastasis driving capability. Conversely, PSPC1 upregulation or PSPC1-Y523F mutation promotes epithelial-mesenchymal transition, stemness, and metastasis via cytoplasmic translocation of active PTK6 and nuclear translocation of β-catenin, which interacts with PSPC1 to augment Wnt3a autocrine signaling. The aberrant nucleocytoplasmic shuttling of active PTK6/β-catenin is reversed by expressing the PSPC1 C-terminal interacting domain (PSPC1-CT131), thereby suppressing PSPC1/PTK6/β-catenin-activated metastasis to prolong the survival of HCC orthotopic mice. Thus, PSPC1 is the contextual determinant of the oncogenic switch of PTK6/β-catenin subcellular localizations, and PSPC1-CT131 functions as a dual inhibitor of PSPC1 and PTK6 with potential for improving cancer therapy.

PSPC1 has a critical role in promoting EMT and metastasis. Here, the authors demonstrate that PSPC1 is the contextual determinant of the oncogenic switch of PTK6/β-catenin subcellular localizations to drive metastasis of hepatocellular carcinoma cells via a PSPC1/PTK6/β-catenin signaling.

Development and clinical applications of cancer immunotherapy against PD-1 signaling pathway

Dramatic advances in immune therapy have emerged as a promising strategy in cancer therapeutics. In addition to chemotherapy and radiotherapy, inhibitors targeting immune-checkpoint molecules such as cytotoxic T-lymphocyte antigen-4 (CTLA-4), programmed cell death receptor-1 (PD-1) and its ligand (PD-L1) demonstrate impressive clinical benefits in clinical trials. In this review, we present background information about therapies involving PD-1/PD-L1 blockade and provide an overview of current clinical trials. Furthermore, we present recent advances involving predictive biomarkers associated with positive therapeutic outcomes in cancer immunotherapy.

Using G-FAST to recognize emergent large vessel occlusion: a training program for a prehospital bypass strategy

INTRODUCTION

The shorter the time between the onset of symptoms and reperfusion using endovascular thrombectomy, the better the functional outcome of patients. A training program was designed for emergency medical technicians (EMTs) to learn the gaze-face-arm-speech-time test (G-FAST) score for initiating a prehospital bypass strategy in an urban city. This study aimed to evaluate the effect of the training program on EMTs.

METHODS

All EMTs in the city were invited to join the training program. The program consisted of a 30 min lecture and a 20 min video which demonstrated the G-FAST evaluation. The participants underwent tests before and after the program. The tests included (1) a questionnaire of knowledge, attitudes, confidence, and behaviors towards stroke care; and (2) watching 10 different scenarios in a video and answering questions, including eight sub-questions of G-FAST parameters, and choosing a suitable receiving hospital.

RESULTS

In total, 1058 EMTs completed the training program. After the program, significant improvement was noted in knowledge, attitudes, and confidence, as well as scenario judgement. The performance of the EMTs in evaluating G-FAST criteria in comatose patients was relatively poor in the pre-test and improved significantly after the training course. Although the participants answered the G-FAST items correctly, they tended to overtriage the patients and refer them to higher-level hospitals.

CONCLUSIONS

A short training program can improve the ability to identify stroke patients and choose a suitable receiving hospital. A future training program could put further emphasis on how to evaluate comatose patients and choose a suitable receiving hospital.

A benchmark for comparing precision medicine methods in thyroid cancer diagnosis using tissue microarrays

Motivation

The aim of precision medicine is to harness new knowledge and technology to optimize the timing and targeting of interventions for maximal therapeutic benefit. This study explores the possibility of building AI models without precise pixel-level annotation in prediction of the tumor size, extrathyroidal extension, lymph node metastasis, cancer stage and BRAF mutation in thyroid cancer diagnosis, providing the patients' background information, histopathological and immunohistochemical tissue images.

Results

A novel framework for objective evaluation of automatic patient diagnosis algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2017- A Grand Challenge for Tissue Microarray Analysis in Thyroid Cancer Diagnosis. Here, we present the datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark. The main contributions of the challenge include the creation of the data repository of tissue microarrays; the creation of the clinical diagnosis classification data repository of thyroid cancer; and the definition of objective quantitative evaluation for comparison and ranking of the algorithms. With this benchmark, three automatic methods for predictions of the five clinical outcomes have been compared, and detailed quantitative evaluation results are presented in this paper. Based on the quantitative evaluation results, we believe automatic patient diagnosis is still a challenging and unsolved problem.

Availability and implementation

The datasets and the evaluation software will be made available to the research community, further encouraging future developments in this field. (http://www-o.ntust.edu.tw/cvmi/ISBI2017/).

Contact

cweiwang@mail.ntust.edu.tw.

Supplementary information

Supplementary data are available at Bioinformatics online.