Gene-expression patterns in peripheral blood classify familial breast cancer susceptibility

Systems profiling reveals recurrently dysregulated cytokine signaling responses in ER+ breast cancer patients' blood

Cytokines operate in concert to maintain immune homeostasis and coordinate immune responses. In cases of ER+ breast cancer, peripheral immune cells exhibit altered responses to several cytokines, and these alterations are correlated strongly with patient outcomes. To develop a systems-level understanding of this dysregulation, we measured a panel of cytokine responses and receptor abundances in the peripheral blood of healthy controls and ER+ breast cancer patients across immune cell types. Using tensor factorization to model this multidimensional data, we found that breast cancer patients exhibited widespread alterations in response, including drastically reduced response to IL-10 and heightened basal levels of pSmad2/3 and pSTAT4. ER+ patients also featured upregulation of PD-L1, IL6Rα, and IL2Rα, among other receptors. Despite this, alterations in response to cytokines were not explained by changes in receptor abundances. Thus, tensor factorization helped to reveal a coordinated reprogramming of the immune system that was consistent across our cohort.

Systems profiling reveals recurrently dysregulated cytokine signaling responses in ER+ breast cancer patients' blood

Cytokines mediate cell-to-cell communication across the immune system and therefore are critical to immunosurveillance in cancer and other diseases. Several cytokines show dysregulated abundance or signaling responses in breast cancer, associated with the disease and differences in survival and progression. Cytokines operate in a coordinated manner to affect immune surveillance and regulate one another, necessitating a systems approach for a complete picture of this dysregulation. Here, we profiled cytokine signaling responses of peripheral immune cells from breast cancer patients as compared to healthy controls in a multidimensional manner across ligands, cell populations, and responsive pathways. We find alterations in cytokine responsiveness across pathways and cell types that are best defined by integrated signatures across dimensions. Alterations in the abundance of a cytokine's cognate receptor do not explain differences in responsiveness. Rather, alterations in baseline signaling and receptor abundance suggesting immune cell reprogramming are associated with altered responses. These integrated features suggest a global reprogramming of immune cell communication in breast cancer.

Blood-Based mRNA Tests as Emerging Diagnostic Tools for Personalised Medicine in Breast Cancer

Molecular diagnostic tests help clinicians understand the underlying biological mechanisms of their patients' breast cancer (BC) and facilitate clinical management. Several tissue-based mRNA tests are used routinely in clinical practice, particularly for assessing the BC recurrence risk, which can guide treatment decisions. However, blood-based mRNA assays have only recently started to emerge. This review explores the commercially available blood mRNA diagnostic assays for BC. These tests enable differentiation of BC from non-BC subjects (Syantra DX, BCtect), detection of small tumours <10 mm (early BC detection) (Syantra DX), detection of different cancers (including BC) from a single blood sample (multi-cancer blood test Aristotle), detection of BC in premenopausal and postmenopausal women and those with high breast density (Syantra DX), and improvement of diagnostic outcomes of DNA testing (variant interpretation) (+RNAinsight). The review also evaluates ongoing transcriptomic research on exciting possibilities for future assays, including blood transcriptome analyses aimed at differentiating lymph node positive and negative BC, distinguishing BC and benign breast disease, detecting ductal carcinoma in situ, and improving early detection further (expression changes can be detected in blood up to eight years before diagnosing BC using conventional approaches, while future metastatic and non-metastatic BC can be distinguished two years before BC diagnosis).

Construction and Validation of a m7G-Related Gene-Based Prognostic Model for Gastric Cancer

Background

Gastric cancer (GC) is one of the most common malignant tumors of the digestive system. Chinese cases of GC account for about 40% of the global rate, with approximately 1.66 million people succumbing to the disease each year. Despite the progress made in the treatment of GC, most patients are diagnosed at an advanced stage due to the lack of obvious clinical symptoms in the early stages of GC, and their prognosis is still very poor. The m7G modification is one of the most common forms of base modification in post-transcriptional regulation, and it is widely distributed in the 5′ cap region of tRNA, rRNA, and eukaryotic mRNA.

Methods

RNA sequencing data of GC were downloaded from The Cancer Genome Atlas. The differentially expressed m7G-related genes in normal and tumour tissues were determined, and the expression and prognostic value of m7G-related genes were systematically analysed. We then built models using the selected m7G-related genes with the help of machine learning methods.The model was then validated for prognostic value by combining the receiver operating characteristic curve (ROC) and forest plots. The model was then validated on an external dataset. Finally, quantitative real-time PCR (qPCR) was performed to detect gene expression levels in clinical gastric cancer and paraneoplastic tissue.

Results

The model is able to determine the prognosis of GC samples quantitatively and accurately. The ROC analysis of model has an AUC of 0.761 and 0.714 for the 3-year overall survival (OS) in the training and validation sets, respectively. We determined a correlation between risk scores and immune cell infiltration and concluded that immune cell infiltration affects the prognosis of GC patients. NUDT10, METTL1, NUDT4, GEMIN5, EIF4E1B, and DCPS were identified as prognostic hub genes and potential therapeutic agents were identified based on these genes.

Conclusion

The m7G-related gene-based prognostic model showed good prognostic discrimination. Understanding how m7G modification affect the infiltration of the tumor microenvironment (TME) cells will enable us to better understand the TME’s anti-tumor immune response, and hopefully guide more effective immunotherapy methods.

Effects of germline and somatic events in candidate BRCA-like genes on breast-tumor signatures

Mutations in BRCA1 and BRCA2 cause deficiencies in homologous recombination repair (HR), resulting in repair of DNA double-strand breaks by the alternative non-homologous end-joining pathway, which is more error prone. HR deficiency of breast tumors is important because it is associated with better responses to platinum salt therapies and PARP inhibitors. Among other consequences of HR deficiency are characteristic somatic-mutation signatures and gene-expression patterns. The term "BRCA-like" (or "BRCAness") describes tumors that harbor an HR defect but have no detectable germline mutation in BRCA1 or BRCA2. A better understanding of the genes and molecular events associated with tumors being BRCA-like could provide mechanistic insights and guide development of targeted treatments. Using data from The Cancer Genome Atlas (TCGA) for 1101 breast-cancer patients, we identified individuals with a germline mutation, somatic mutation, homozygous deletion, and/or hypermethylation event in BRCA1, BRCA2, and 59 other cancer-predisposition genes. Based on the assumption that BRCA-like events would have similar downstream effects on tumor biology as BRCA1/BRCA2 germline mutations, we quantified these effects based on somatic-mutation signatures and gene-expression profiles. We reduced the dimensionality of the somatic-mutation signatures and expression data and used a statistical resampling approach to quantify similarities among patients who had a BRCA1/BRCA2 germline mutation, another type of aberration in BRCA1 or BRCA2, or any type of aberration in one of the other genes. Somatic-mutation signatures of tumors having a non-germline aberration in BRCA1/BRCA2 (n = 80) were generally similar to each other and to tumors from BRCA1/BRCA2 germline carriers (n = 44). Additionally, somatic-mutation signatures of tumors with germline or somatic events in ATR (n = 16) and BARD1 (n = 8) showed high similarity to tumors from BRCA1/BRCA2 carriers. Other genes (CDKN2A, CTNNA1, PALB2, PALLD, PRSS1, SDHC) also showed high similarity but only for a small number of events or for a single event type. Tumors with germline mutations or hypermethylation of BRCA1 had relatively similar gene-expression profiles and overlapped considerably with the Basal-like subtype; but the transcriptional effects of the other events lacked consistency. Our findings confirm previously known relationships between molecular signatures and germline or somatic events in BRCA1/BRCA2. Our methodology represents an objective way to identify genes that have similar downstream effects on molecular signatures when mutated, deleted, or hypermethylated.

ShinyLearner: A containerized benchmarking tool for machine-learning classification of tabular data

BACKGROUND

Classification algorithms assign observations to groups based on patterns in data. The machine-learning community have developed myriad classification algorithms, which are used in diverse life science research domains. Algorithm choice can affect classification accuracy dramatically, so it is crucial that researchers optimize the choice of which algorithm(s) to apply in a given research domain on the basis of empirical evidence. In benchmark studies, multiple algorithms are applied to multiple datasets, and the researcher examines overall trends. In addition, the researcher may evaluate multiple hyperparameter combinations for each algorithm and use feature selection to reduce data dimensionality. Although software implementations of classification algorithms are widely available, robust benchmark comparisons are difficult to perform when researchers wish to compare algorithms that span multiple software packages. Programming interfaces, data formats, and evaluation procedures differ across software packages; and dependency conflicts may arise during installation.

FINDINGS

To address these challenges, we created ShinyLearner, an open-source project for integrating machine-learning packages into software containers. ShinyLearner provides a uniform interface for performing classification, irrespective of the library that implements each algorithm, thus facilitating benchmark comparisons. In addition, ShinyLearner enables researchers to optimize hyperparameters and select features via nested cross-validation; it tracks all nested operations and generates output files that make these steps transparent. ShinyLearner includes a Web interface to help users more easily construct the commands necessary to perform benchmark comparisons. ShinyLearner is freely available at https://github.com/srp33/ShinyLearner.

CONCLUSIONS

This software is a resource to researchers who wish to benchmark multiple classification or feature-selection algorithms on a given dataset. We hope it will serve as example of combining the benefits of software containerization with a user-friendly approach.

A pan-cancer analysis of progression mechanisms and drug sensitivity in cancer cell lines

Biomarker discovery involves identifying genetic abnormalities within a tumor. However, one of the main challenges in defining such therapeutic targets is accounting for the molecular heterogeneity of cancer. By integrating somatic mutation and gene expression data from hundreds of heterogeneous cell lines from the Cancer Cell Line Encyclopedia (CCLE), we identify sequences of genetic events that may help explain common patterns of oncogenesis across 22 tumor types, and evaluate the general effect of late-stage mutations on drug sensitivity and resistance mechanisms. Through gene enrichment analysis, we find several cancer-specific and immune pathways that are significantly enriched in each of our three proposed phases of cancer progression. By further analyzing the drug activity area associated with compounds that target the BRAF oncogene, a known predictor of drug sensitivity for several compounds used in cancer treatment, we verify that the acquisition of new driver mutations interferes with the targeted drug mechanism, meaning that cells without late-stage mutations generally respond better to drugs.

Review and Meta-Analyses of TAAR1 Expression in the Immune System and Cancers

Since its discovery in 2001, the major focus of TAAR1 research has been on its role in monoaminergic regulation, drug-induced reward and psychiatric conditions. More recently, TAAR1 expression and functionality in immune system regulation and immune cell activation has become a topic of emerging interest. Here, we review the immunologically-relevant TAAR1 literature and incorporate open-source expression and cancer survival data meta-analyses. We provide strong evidence for TAAR1 expression in the immune system and cancers revealed through NCBI GEO datamining and discuss its regulation in a spectrum of immune cell types as well as in numerous cancers. We discuss connections and logical directions for further study of TAAR1 in immunological function, and its potential role as a mediator or modulator of immune dysregulation, immunological effects of psychostimulant drugs of abuse, and cancer progression.

Curated compendium of human transcriptional biomarker data

One important use of genome-wide transcriptional profiles is to identify relationships between transcription levels and patient outcomes. These translational insights can guide the development of biomarkers for clinical application. Data from thousands of translational-biomarker studies have been deposited in public repositories, enabling reuse. However, data-reuse efforts require considerable time and expertise because transcriptional data are generated using heterogeneous profiling technologies, preprocessed using diverse normalization procedures, and annotated in non-standard ways. To address this problem, we curated 45 publicly available, translational-biomarker datasets from a variety of human diseases. To increase the data's utility, we reprocessed the raw expression data using a uniform computational pipeline, addressed quality-control problems, mapped the clinical annotations to a controlled vocabulary, and prepared consistently structured, analysis-ready data files. These data, along with scripts we used to prepare the data, are available in a public repository. We believe these data will be particularly useful to researchers seeking to perform benchmarking studies—for example, to compare and optimize machine-learning algorithms' ability to predict biomedical outcomes.

A Comparative Study of the Molecular Characteristics of Familial Gliomas and Other Cancers

BACKGROUND

Familial cancers are those that co-occur among first-degree relatives without showing Mendelian patterns of inheritance.

MATERIALS AND METHODS

In this analysis, we compare the genomic characteristics of familial and sporadic cancers, with a focus on low-grade gliomas (LGGs) using sequence and expression data from the Cancer Genome Atlas.

RESULTS

Familial cancers show similar genomic and molecular biomarker profiles to sporadic cancers, consistent with the similarity in their clinical features. There are no statistically significant differences among somatic mutation, copy number variant, or gene expression patterns between familial and sporadic cancers; methylation profiles are the only class of molecular data to show significant differences.

CONCLUSION

Familial cancers are likely driven by multiple, individually weak contributions to familiality (i.e. large numbers of alleles and/or shared environmental risks). Consequently, these risk factors tend to be obscured by stronger confounding variables such as clinical or molecular variation among cancer subtypes.