Improved cancer prognosis is a central goal for precision health medicine. Though many models can predict differential survival from data, there is a strong need for sophisticated algorithms that can aggregate and filter relevant predictors from increasingly complex data inputs. In turn, these models should provide deeper insight into which types of data are most relevant to improve prognosis. Deep Learning-based neural networks offer a potential solution for both problems because they are highly flexible and account for data complexity in a non-linear fashion. In this study, we implement Deep Learning-based networks to determine how gene expression data predicts Cox regression survival in breast cancer. We accomplish this through an algorithm called SALMON (Survival Analysis Learning with Multi-Omics Neural Networks), which aggregates and simplifies gene expression data and cancer biomarkers to enable prognosis prediction. The results revealed improved performance when more omics data were used in model construction. Rather than use raw gene expression values as model inputs, we innovatively use eigengene modules from the result of gene co-expression network analysis. The corresponding high impact co-expression modules and other omics data are identified by feature selection technique, then examined by conducting enrichment analysis and exploiting biological functions, escalated the interpretation of input feature from gene level to co-expression modules level. Our study shows the feasibility of discovering breast cancer related co-expression modules, sketch a blueprint of future endeavors on Deep Learning-based survival analysis. SALMON source code is available at https://github.com/huangzhii/SALMON/.

Senescent cells within the tumor microenvironment (TME) adopt a pro-inflammatory, senescence-associated secretory phenotype (SASP) that promotes cancer initiation, progression and therapeutic resistance. Here, exposure to Palbociclib (PD-0332991), a CDK4/6 inhibitor, induces senescence and a robust SASP in normal fibroblasts. Senescence caused by prolonged CDK4/6 inhibition is DNA damage-independent and associated with Mdm2 downregulation, whereas the SASP elicited by these cells is largely reliant upon NF-κB activation. Based upon these observations, it was hypothesized that the exposure of non-transformed stromal cells to PD-0332991 would promote tumor growth. Ongoing clinical trials of CDK4/6 inhibitors in melanoma prompted a validation of this hypothesis using a suite of genetically defined melanoma cells (i.e. Ras mutant, Braf mutant, and Ras/Braf wild type). When cultured in the presence of CDK4/6i–induced senescent fibroblasts, melanoma cell lines exhibited genotype-dependent proliferative responses. However, in vivo, PD-0332991-treated fibroblasts enhanced the growth of all melanoma lines tested and promoted the recruitment of Gr-1-positive immune cells. These data indicate that prolonged CDK4/6 inhibitor treatment causes normal fibroblasts to enter senescence and adopt a robust SASP. Such senescent cells suppress the anti-tumor immune response and promote melanoma growth in immunocompetent, in vivo models. Implications The ability of prolonged CDK4/6 inhibitor treatment to induce cellular senescence and a robust SASP in primary cells may hinder therapeutic efficacy and promote long-term, gerontogenic consequences that should be considered in clinical trials aiming to treat melanoma and other cancer types.

Background: Recent advances in kernel-based Deep Learning models have introduced a new era in medical research. Originally designed for pattern recognition and image processing, Deep Learning models are now applied to survival prognosis of cancer patients. Specifically, Deep Learning versions of the Cox proportional hazards models are trained with transcriptomic data to predict survival outcomes in cancer patients. Methods: In this study, a broad analysis was performed on TCGA cancers using a variety of Deep Learning-based models, including Cox-nnet, DeepSurv, and a method proposed by our group named AECOX (AutoEncoder with Cox regression network). Concordance index and p-value of the log-rank test are used to evaluate the model performances. Results: All models show competitive results across 12 cancer types. The last hidden layers of the Deep Learning approaches are lower dimensional representations of the input data that can be used for feature reduction and visualization. Furthermore, the prognosis performances reveal a negative correlation between model accuracy, overall survival time statistics, and tumor mutation burden (TMB), suggesting an association among overall survival time, TMB, and prognosis prediction accuracy. Conclusions: Deep Learning based algorithms demonstrate superior performances than traditional machine learning based models. The cancer prognosis results measured in concordance index are indistinguishable across models while are highly variable across cancers. These findings shedding some light into the relationships between patient characteristics and survival learnability on a pan-cancer level.