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BackgroundTo predict treatment response and 2 years overall survival (OS) of radio-chemotherapy in patients with esophageal cancer (EC) by radiomics based on the computed tomography (CT) images.MethodsThis study retrospectively collected 171 nonsurgical EC patients treated with radio-chemotherapy from Jan 2010 to Jan 2019. 80 patients were randomly divided into training (n=64) and validation (n=16) cohorts to predict the radiochemotherapy response. The models predicting treatment response were established by Lasso and logistic regression. A total of 156 patients were allocated into the training cohort (n=110), validation cohort (n=23) and test set (n=23) to predict 2-year OS. The Lasso Cox model and Cox proportional hazards model established the models predicting 2-year OS.ResultsTo predict the radiochemotherapy response, WFK as a radiomics feature, and clinical stages and clinical M stages (cM) as clinical features were selected to construct the clinical-radiomics model, achieving 0.78 and 0.75 AUC (area under the curve) in the training and validation sets, respectively. Furthermore, radiomics features called WFI and WGI combined with clinical features (smoking index, pathological types, cM) were the optimal predictors to predict 2-year OS. The AUC values of the clinical-radiomics model were 0.71 and 0.70 in the training set and validation set, respectively.ConclusionsThis study demonstrated that planning CT-based radiomics showed the predictability of the radiochemotherapy response and 2-year OS in nonsurgical esophageal carcinoma. The predictive results prior to treatment have the potential to assist physicians in choosing the optimal therapeutic strategy to prolong overall survival.

Background: Sialic acid-binding immunoglobulin-type lectin (SIGLEC) family members are involved in regulating immune-cell activation, proliferation, and apoptosis, and they play an important role in tumor development. However, their expression and correlation with immune molecules in lung adenocarcinoma (LUAD) remain unclear.Methods: We utilized Gene Expression Profiling Interactive Analysis, Kaplan–Meier analysis, the limma package in R/Bioconductor, the University of California Santa Cruz Cancer Genome Browser, cBioPortal, STRING, Cytoscape, DAVID, and the Tumor Immune Estimation Resource for gene and protein profiling and analyses.Results: The results showed that SIGLEC10 and SIGLEC15 levels were upregulated in LUAD, whereas SIGLEC1, CD22 (SIGLEC2), CD33, myelin-associated glycoprotein (SIGLEC4), SIGLEC5, SIGLEC6, SIGLEC7, SIGLEC8, SIGLEC11, and SIGLEC14 levels were significantly downregulated, with their low expression associated with poor overall survival. Moreover, we observed high SIGLEC-mutation rates (22%) in LUAD patients, with SIGLEC functions determined as primarily involved in regulating the immune response, signal transduction, inflammatory response, and cell adhesion. Furthermore, we found that SIGLEC expression was significantly correlated with immune-cell infiltration, especially macrophages, neutrophils, and dendritic cells, and highly associated with immune molecules such as CD80, CD86, CD28, B-cell-activating factor, programmed cell death 1 ligand 2, and colony stimulating factor 1 receptor.Conclusion: These results provide insight into the potential molecular mechanism associated with SIGLEC-related development of LUAD, as well as clues for screening biomarkers and therapeutic targets.

Background and PurposeTo develop an artificial intelligence-based full-process solution for rectal cancer radiotherapy.Materials and MethodsA full-process solution that integrates autosegmentation and automatic treatment planning was developed under a single deep-learning framework. A convolutional neural network (CNN) was used to generate segmentations of the target and the organs at risk (OAR) as well as dose distribution. A script in Pinnacle that simulates the treatment planning process was used to execute plan optimization. A total of 172 rectal cancer patients were used for model training, and 18 patients were used for model validation. Another 40 rectal cancer patients were used for an end-to-end evaluation for both autosegmentation and treatment planning. The PTV and OAR segmentation was compared with manual segmentation. The planning results was evaluated by both objective and subjective assessment.ResultsThe total time for full-process planning without contour modification was 7 min, and an additional 15 min may require for contour modification and re-optimization. The PTV DICE similarity coefficient was greater than 0.85 for all 40 patients in the evaluation dataset while the DICE indices of the OARs also indicated good performance. There were no significant differences between the auto plans and manual plans. The physician accepted 80% of the auto plans without any further operation.ConclusionWe developed a deep learning-based automatic solution for rectal cancer treatment that can improve the efficiency of treatment planning.

Background: Gastric cancer (GC) remains a refractory cancer particularly in Eastern Asia. Large tumor suppressor kinases 1/2 (LATS1/2) are core members of the Hippo pathway. The role of LATS1/2 in the prognosis of different subtypes of advanced gastric cancer and its relationship with the tumor immune microenvironment in GC remain unknown. Exploring the role of LATS1/2 in GC might provide potential immunotherapeutic approaches for treating GC.Methods: Four hundred and ninety surgically resected primary GC samples were assessed for LATS1/2, CD8, FOXP3, and CD163. Correlations between LATS1/2 expression and immune-related markers were investigated and the prognoses of patients with different GC subtypes were analyzed.Results: CD8 and CD163 appeared to be favorable and adverse prognostic factors, respectively. LATS1/2 and FOXP3 did not predict patients' overall survival. However, in microsatellite-stable GC patients, high LATS1/2 and FOXP3 expression and low CD8 expression predicted poor prognoses. Furthermore, high LATS1/2 expression was significantly correlated with decreased CD8 and increased FOXP3. Combined analysis of LATS1/2, CD8, and FOXP3 had better prognostic accuracy than did each marker individually.Conclusions: Different biological molecules can predict the prognoses of different types of GC patients. LATS1/2, core kinases in the Hippo pathway, are closely related to CD8 and FOXP3. Further understanding the mechanisms of LATS1/2 in CD8+ T cells and FOXP3+ Treg cells provides further theoretical basis and potential targets for GC immunotherapy.

Taxanes and CDK4/6 inhibitors (CDK4/6i) are two families of successful anti-mitotic drugs used in the treatment of solid tumors. Paclitaxel, representing taxane compounds, has been used either alone or in combination with other agents (commonly carboplatin/cisplatin) in the treatment of many solid tumors including ovarian, breast, lung, prostate cancers, and Kaposi’s sarcoma. Paclitaxel has been routinely prescribed in cancer treatment since the 1990s, and its prominent role is unlikely to be replaced in the foreseeable future. Paclitaxel and other taxanes work by binding to and stabilizing microtubules, causing mitotic arrest, aberrant mitosis, and cell death. CDK4/6i (palbociclib, ribociclib, abemaciclib) are relatively new cell cycle inhibitors that have been found to be effective in breast cancer treatment, and are currently being developed in other solid tumors. CDK4/6i blocks cell cycle progression at the G1 phase, resulting in cell death by mechanisms not yet fully elucidated. At first glance, paclitaxel and CDK4/6i are unlikely synergistic agents as both are cell cycle inhibitors that work at different phases of the cell cycle, and few clinical trials have yet considered adding CDK4/6i to existing paclitaxel chemotherapy. However, recent findings suggest the importance of a non-mitotic mechanism of paclitaxel in cancer cell death and pre-clinical data support rationale for a strategic paclitaxel and CDK4/6i combination. In mouse tumor model studies, drug sequencing resulted in differential efficacy, indicating complex biological interactions of the two drugs. This article reviews the rationales of combining paclitaxel with CDK4/6i as a potential therapeutic option in recurrent ovarian cancer.

Aneuploidy, loss or gain of whole chromosomes, is a prominent feature of carcinomas, and is generally considered to play an important role in the initiation and progression of cancer. In high-grade serous ovarian cancer, the only common gene aberration is the p53 point mutation, though extensive genomic perturbation is common due to severe aneuploidy, which presents as a deviant karyotype. Several mechanisms for the development of aneuploidy in cancer cells have been recognized, including chromosomal non-disjunction during mitosis, centrosome amplification, and more recently, nuclear envelope rupture at interphase. Many cancer types including ovarian cancer have lost or reduced expression of Lamin A/C, a structural component of the lamina matrix that underlies the nuclear envelope in differentiated cells. Several recent studies suggest that a nuclear lamina defect caused by the loss or reduction of Lamin A/C leads to failure in cytokinesis and formation of tetraploid cells, transient nuclear envelope rupture, and formation of nuclear protrusions and micronuclei during the cell cycle gap phase. Thus, loss and reduction of Lamin A/C underlies the two common features of cancer—aberrations in nuclear morphology and aneuploidy. We discuss here and emphasize the newly recognized mechanism of chromosomal instability due to the rupture of a defective nuclear lamina, which may account for the rapid genomic changes in carcinogenesis.