Diversity spectrum analysis identifies mutation-specific effects of cancer driver genes

Advances in predicting breast cancer driver mutations: Tools for precision oncology (Review)

In the modern era of medicine, prognosis and treatment, options for a number of cancer types including breast cancer have been improved by the identification of cancer‑specific biomarkers. The availability of high‑throughput sequencing and analysis platforms, the growth of publicly available cancer databases and molecular and histological profiling facilitate the development of new drugs through a precision medicine approach. However, only a fraction of patients with breast cancer with few actionable mutations typically benefit from the precision medicine approach. In the present review, the current development in breast cancer driver gene identification, actionable breast cancer mutations, as well as the available therapeutic options, challenges and applications of breast precision oncology are systematically described. Breast cancer driver mutation‑based precision oncology helps to screen key drivers involved in disease development and progression, drug sensitivity and the genes responsible for drug resistance. Advances in precision oncology will provide more targeted therapeutic options for patients with breast cancer, improving disease‑free survival and potentially leading to significant successes in breast cancer treatment in the near future. Identification of driver mutations has allowed new targeted therapeutic approaches in combination with standard chemo‑ and immunotherapies in breast cancer. Developing new driver mutation identification strategies will help to define new therapeutic targets and improve the overall and disease‑free survival of patients with breast cancer through efficient medicine.

NetCellMatch: Multiscale Network-Based Matching of Cancer Cell Lines to Patients Using Graphical Wavelets

Cancer cell lines serve as model in vitro systems for investigating therapeutic interventions. Recent advances in high-throughput genomic profiling have enabled the systematic comparison between cell lines and patient tumor samples. The highly interconnected nature of biological data, however, presents a challenge when mapping patient tumors to cell lines. Standard clustering methods can be particularly susceptible to the high level of noise present in these datasets and only output clusters at one unknown scale of the data. In light of these challenges, we present NetCellMatch, a robust framework for network-based matching of cell lines to patient tumors. NetCellMatch first constructs a global network across all cell line-patient samples using their genomic similarity. Then, a multi-scale community detection algorithm integrates information across topologically meaningful (clustering) scales to obtain Network-Based Matching Scores (NBMS). NBMS are measures of cluster robustness which map patient tumors to cell lines. We use NBMS to determine representative "avatar" cell lines for subgroups of patients. We apply NetCellMatch to reverse-phase protein array data obtained from The Cancer Genome Atlas for patients and the MD Anderson Cell Line Project for cell lines. Along with avatar cell line identification, we evaluate connectivity patterns for breast, lung, and colon cancer and explore the proteomic profiles of avatars and their corresponding top matching patients. Our results demonstrate our framework's ability to identify both patient-cell line matches and potential proteomic drivers of similarity. Our methods are general and can be easily adapted to other'omic datasets.

Developmental heterogeneity of embryonic neuroendocrine chromaffin cells and their maturation dynamics

During embryonic development, nerve-associated Schwann cell precursors (SCPs) give rise to chromaffin cells of the adrenal gland via the "bridge" transient stage, according to recent functional experiments and single cell data from humans and mice. However, currently existing data do not resolve the finest heterogeneity of developing chromaffin populations. Here we took advantage of deep SmartSeq2 transcriptomic sequencing to expand our collection of individual cells from the developing murine sympatho-adrenal anlage and uncover the microheterogeneity of embryonic chromaffin cells and their corresponding developmental paths. We discovered that SCPs on the splachnic nerve show a high degree of microheterogeneity corresponding to early biases towards either Schwann or chromaffin terminal fates. Furthermore, we found that a post-"bridge" population of developing chromaffin cells gives rise to persisting oxygen-sensing chromaffin cells and the two terminal populations (adrenergic and noradrenergic) via diverging differentiation paths. Taken together, we provide a thorough identification of novel markers of adrenergic and noradrenergic populations in developing adrenal glands and report novel differentiation paths leading to them.

ParsVNN: parsimony visible neural networks for uncovering cancer-specific and drug-sensitive genes and pathways

Prediction of cancer-specific drug responses as well as identification of the corresponding drug-sensitive genes and pathways remains a major biological and clinical challenge. Deep learning models hold immense promise for better drug response predictions, but most of them cannot provide biological and clinical interpretability. Visible neural network (VNN) models have emerged to solve the problem by giving neurons biological meanings and directly casting biological networks into the models. However, the biological networks used in VNNs are often redundant and contain components that are irrelevant to the downstream predictions. Therefore, the VNNs using these redundant biological networks are overparameterized, which significantly limits VNNs' predictive and explanatory power. To overcome the problem, we treat the edges and nodes in biological networks used in VNNs as features and develop a sparse learning framework ParsVNN to learn parsimony VNNs with only edges and nodes that contribute the most to the prediction task. We applied ParsVNN to build cancer-specific VNN models to predict drug response for five different cancer types. We demonstrated that the parsimony VNNs built by ParsVNN are superior to other state-of-the-art methods in terms of prediction performance and identification of cancer driver genes. Furthermore, we found that the pathways selected by ParsVNN have great potential to predict clinical outcomes as well as recommend synergistic drug combinations.

Transcriptomic heterogeneity of driver gene mutations reveals novel mutual exclusivity and improves exploration of functional associations

Background

Lung adenocarcinoma (LUAD), as the most common subtype of lung cancer, is the leading cause of cancer deaths in the world. The accumulation of driver gene mutations enables cancer cells to gradually acquire growth advantage. Therefore, it is important to understand the functions and interactions of driver gene mutations in cancer progression.

Methods

We obtained gene mutation data and gene expression profile of 506 LUAD tumors from The Cancer Genome Atlas (TCGA). The subtypes of tumors with driver gene mutations were identified by consensus cluster analysis.

Results

We found 21 significantly mutually exclusive pairs consisting of 20 genes among 506 LUAD patients. Because of the increased transcriptomic heterogeneity of mutations, we identified subtypes among tumors with non‐silent mutations in driver genes. There were 494 mutually exclusive pairs found among driver gene mutations within different subtypes. Furthermore, we identified functions of mutually exclusive pairs based on the hypothesis of functional redundancy of mutual exclusivity. These mutually exclusive pairs were significantly enriched in nuclear division and humoral immune response, which played crucial roles in cancer initiation and progression. We also found 79 mutually exclusive triples among subtypes of tumors with driver gene mutations, which were key roles in cell motility and cellular chemical homeostasis. In addition, two mutually exclusive triples and one mutually exclusive triple were associated with the overall survival and disease‐specific survival of LUAD patients, respectively.

Conclusions

We revealed novel mutual exclusivity and generated a comprehensive functional landscape of driver gene mutations, which could offer a new perspective to understand the mechanisms of cancer development and identify potential biomarkers for LUAD therapy.

Predicting clinical outcomes using cancer progression associated signatures

Somatic mutation signatures are an informative facet of cancer aetiology, however they are rarely useful for predicting patient outcome. The aim of this study is to evaluate the utility of a panel of 142 mutation-signature–associated metrics (P142) for predicting cancer progression in patients from a ‘TCGA PanCancer Atlas’ cohort. The P142 metrics are comprised of AID/APOBEC and ADAR deaminase associated SNVs analyzed for codon context, strand bias, and transitions/transversions. TCGA tumor-normal mutation data was obtained for 10,437 patients, representing 31 of the most prevalent forms of cancer. Stratified random sampling was used to split patients into training, tuning and validation cohorts for each cancer type. Cancer specific machine learning (XGBoost) models were built using the output from the P142 panel to predict patient Progression Free Survival (PFS) status as either “High PFS” or “Low PFS”. Predictive performance of each model was evaluated using the validation cohort. Models accurately predicted PFS status for several cancer types, including adrenocortical carcinoma, glioma, mesothelioma, and sarcoma. In conclusion, the P142 panel of metrics successfully predicted cancer progression status in patients with some, but not all cancer types analyzed. These results pave the way for future studies on cancer progression associated signatures.

p53 Is Regulated in a Biphasic Manner in Hypoxic Human Papillomavirus Type 16 (HPV16)-Positive Cervical Cancer Cells

Although the effect of hypoxia on p53 in human papillomavirus (HPV)-positive cancer cells has been studied for decades, the impact of p53 regulation on downstream targets and cellular adaptation processes during different periods under hypoxia remains elusive. Here, we show that, despite continuous repression of HPV16 E6/E7 oncogenes, p53 did not instantly recover but instead showed a biphasic regulation marked by further depletion within 24 h followed by an increase at 72 h. Of note, during E6/E7 oncogene suppression, lysosomal degradation antagonizes p53 reconstitution. Consequently, the transcription of p53 responsive genes associated with senescence (e.g., PML and YPEL3) cannot be upregulated. In contrast, downstream genes involved in autophagy (e.g., DRAM1 and BNIP3) were activated, allowing the evasion of senescence under hypoxic conditions. Hence, dynamic regulation of p53 along with its downstream network of responsive genes favors cellular adaptation and enhances cell survival, although the expression of the viral E6/E7-oncogenes as drivers for proliferation remained inhibited under hypoxia.

The Role of Peroxisome Proliferator-Activated Receptors (PPARs) in Pan-Cancer

Peroxisome proliferator-activated receptors (PPARs) are members of nuclear transcription factors. The functions of the PPAR family (PPARA, PPARD, and PPARG) and their coactivators (PPARGC1A and PPARGC1B) in maintenance of lipid and glucose homeostasis have been unveiled. However, the roles of PPARs in cancer development remain elusive. In this work, we made use of 11,057 samples across 33 TCGA tumor types to analyze the relationship between PPAR transcriptional expression and tumorigenesis as well as drug sensitivity. We performed multidimensional analyses on PPARA, PPARG, PPARD, PPARGC1A, and PPARGC1B, including differential expression analysis in pan-cancer, immune subtype analysis, clinical analysis, tumor purity analysis, stemness correlation analysis, and drug responses. PPARs and their coactivators expressed differently in different types of cancers, in different immune subtypes. This analysis reveals various expression patterns of the PPAR family at a level of pan-cancer and provides new clues for the therapeutic strategies of cancer.