Molecular subtyping of cancer is a critical step towards more individualized therapy and provides important biological insights into cancer heterogeneity. Although gene expression signature-based classification has been widely demonstrated to be an effective approach in the last decade, the widespread implementation has long been limited by platform differences, batch effects, and the difficulty to classify individual patient samples. Here, we describe a novel supervised cancer classification framework, deep cancer subtype classification (DeepCC), based on deep learning of functional spectra quantifying activities of biological pathways. In two case studies about colorectal and breast cancer classification, DeepCC classifiers and DeepCC single sample predictors both achieved overall higher sensitivity, specificity, and accuracy compared with other widely used classification methods such as random forests (RF), support vector machine (SVM), gradient boosting machine (GBM), and multinomial logistic regression algorithms. Simulation analysis based on random subsampling of genes demonstrated the robustness of DeepCC to missing data. Moreover, deep features learned by DeepCC captured biological characteristics associated with distinct molecular subtypes, enabling more compact within-subtype distribution and between-subtype separation of patient samples, and therefore greatly reduce the number of unclassifiable samples previously. In summary, DeepCC provides a novel cancer classification framework that is platform independent, robust to missing data, and can be used for single sample prediction facilitating clinical implementation of cancer molecular subtyping.

Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths worldwide. Similar to many other malignancies, CRC is a heterogeneous disease, making it a clinical challenge for optimization of treatment modalities in reducing the morbidity and mortality associated with this disease. A more precise understanding of the biological properties that distinguish patients with colorectal tumors, especially in terms of their clinical features, is a key requirement towards a more robust, targeted-drug design, and implementation of individualized therapies. In the recent decades, extensive studies have reported distinct CRC subtypes, with a mutation-centered view of tumor heterogeneity. However, more recently, the paradigm has shifted towards transcriptome-based classifications, represented by six independent CRC taxonomies. In 2015, the colorectal cancer subtyping consortium reported the identification of four consensus molecular subtypes (CMSs), providing thus far the most robust classification system for CRC. In this review, we summarize the historical timeline of CRC classification approaches; discuss their salient features and potential limitations that may require further refinement in near future. In other words, in spite of the recent encouraging progress, several major challenges prevent translation of molecular knowledge gleaned from CMSs into the clinic. Herein, we summarize some of these potential challenges and discuss exciting new opportunities currently emerging in related fields. We believe, close collaborations between basic researchers, bioinformaticians and clinicians are imperative for addressing these challenges, and eventually paving the path for CRC subtyping into routine clinical practice as we usher into the era of personalized medicine.

Aim To systematically evaluate the safety and tolerability of calcitonin-gene-related peptide binding monoclonal antibodies from the results of randomized controlled trials. Methods Online databases were searched on calcitonin-gene-related peptide binding monoclonal antibodies for the prevention of episodic migraine. Overall withdrawal, withdrawal due to adverse events, adverse events, serious adverse events and specific adverse events were extracted from the included studies. A meta-analysis was performed with Revman 5.3.0 software. Results Ten studies that investigated four drugs (galcanezumab, erenumab, fremanezumab and eptinezumab) with 5817 participants were included in this study. Serious adverse events, overall withdrawals, withdrawal due to adverse events and any adverse events were not significantly associated with monoclonal antibody treatment. Injection site pain and erythema were significantly higher in the calcitonin-gene-related peptide binding monoclonal antibodies treatment group than in the placebo group. The rates of serious adverse events were significantly higher in the galcanezumab 120 mg group. Injection site erythema was associated with galcanezumab 120 mg and 240 mg. Injection site pain and nasopharyngitis were associated with galcanezumab 150 mg and 5 mg, respectively. Overall adverse events were significantly higher with erenumab 70 mg and 140 mg. Treatment-related adverse events were significantly higher with fremanezumab 225 mg/month and 675 mg/quarter. Conclusions This study provides data on the safety and tolerability profiles of calcitonin-gene-related peptide binding monoclonal antibodies and confirms their potential use as preventive treatments for episodic migraine. In addition to the acceptable withdrawal rates, serious adverse events were rare, and the severity of most adverse events was mild to moderate. Injection site reaction may be the major adverse event associated with galcanezumab.

Sexual differences have been observed in the onset and prognosis of human cardiovascular diseases, but the underlying mechanisms are not clear. Here, we found that zebrafish heart regeneration is faster in females, can be accelerated by estrogen and is suppressed by the estrogen-antagonist tamoxifen. Injuries to the zebrafish heart, but not other tissues, increased plasma estrogen levels and the expression of estrogen receptors, especially esr2a. The resulting endocrine disruption induces the expression of the female-specific protein vitellogenin in male zebrafish. Transcriptomic analyses suggested heart injuries triggered pronounced immune and inflammatory responses in females. These responses, previously shown to elicit heart regeneration, could be enhanced by estrogen treatment in males and reduced by tamoxifen in females. Furthermore, a prior exposure to estrogen preconditioned the zebrafish heart for an accelerated regeneration. Altogether, this study reveals that heart regeneration is modulated by an estrogen-inducible inflammatory response to cardiac injury. These findings elucidate a previously unknown layer of control in zebrafish heart regeneration and provide a new model system for the study of sexual differences in human cardiac repair.

It is necessary to develop prognostic tools of metastatic pancreatic cancer (MPC) for optimizing therapeutic strategies. Thus, we tried to develop and validate a prognostic nomogram of MPC. Data from 3 clinical trials (NCT00844649, NCT01124786, and NCT00574275) and 133 Chinese MPC patients were used for analysis. The former 2 trials were taken as the training cohort while NCT00574275 was used as the validation cohort. In addition, 133 MPC patients treated in China were taken as the testing cohort. Cox regression model was used to investigate prognostic factors in the training cohort. With these factors, we established a nomogram and verified it by Harrell's concordance index (C‐index) and calibration plots. Furthermore, the nomogram was externally validated in the validation cohort and testing cohort. In the training cohort (n = 445), performance status, liver metastasis, Carbohydrate antigen 19‐9 (CA19‐9) log‐value, absolute neutrophil count (ANC), and albumin were independent prognostic factors for overall survival (OS). A nomogram was established with these factors to predict OS and survival probabilities. The nomogram showed an acceptable discrimination ability (C‐index: .683) and good calibration, and was further externally validated in the validation cohort (n = 273, C‐index: .699) and testing cohort (n = 133, C‐index: .653).The nomogram total points (NTP) had the potential to stratify patients into 3‐risk groups with median OS of 11.7, 7.0 and 3.7 months (P < .001), respectively. In conclusion, the prognostic nomogram with NTP can predict OS for patients with MPC with considerable accuracy.