Gene expression profiling for targeted cancer treatment

Comprehensive description of the current breast cancer microenvironment advancements via single-cell analysis

Breast cancer is a heterogeneous disease with a complex microenvironment consisting of tumor cells, immune cells, fibroblasts and vascular cells. These cancer-associated cells shape the tumor microenvironment (TME) and influence the progression of breast cancer and the therapeutic responses in patients. The exact composition of the intra-tumoral cells is mixed as the highly heterogeneous and dynamic nature of the TME. Recent advances in single-cell technologies such as single-cell DNA sequencing (scDNA-seq), single-cell RNA sequencing (scRNA-seq) and mass cytometry have provided new insights into the phenotypic and functional diversity of tumor-infiltrating cells in breast cancer. In this review, we have outlined the recent progress in single-cell characterization of breast tumor ecosystems, and summarized the phenotypic diversity of intra-tumoral cells and their potential prognostic relevance.

Digital Multiplexed Gene Expression Analysis of mRNA and miRNA from Routinely Processed and Stained Cytological Smears: A Proof-of-Principle Study

OBJECTIVE

Although transcriptomic assessments of small samples using high-throughput techniques are usually performed on fresh or frozen tissues, there is a growing demand for those performed on stained cellular specimens already used for diagnostic purposes.

STUDY DESIGN

The possibility of detecting mRNAs and microRNAs (miRNAs) from routinely processed cytological samples using nCounter® technology was explored. Fresh samples from pleural and peritoneal effusions were analyzed using 2 parallel methods: samples were smeared and routinely stained using the May-Grünwald-Giemsa or Diff-Quik® method and mounted using conventional methods, and they were also studied following a snap freezing method, in which samples were maintained at -80°C until use. mRNAs and miRNAs were assessed and compared after total RNA extraction from both routinely processed samples and their matched frozen controls.

RESULTS

A good concordance was found between the gene expression measured in routinely processed samples and their matched frozen controls for the majority of mRNAs and miRNAs tested. However, the standard deviation of low-expressed miRNA was high.

CONCLUSIONS

Although nCounter® technology is a robust method to measure and characterize both mRNAs and miRNAs from routinely processed cytological samples, caution is recommended for the interpretation of low-expressed miRNA.

Single-cell RNA sequencing in breast cancer: Understanding tumor heterogeneity and paving roads to individualized therapy

Single‐cell RNA sequencing (scRNA‐seq) is a novel technology that allows transcriptomic analyses of individual cells. During the past decade, scRNA‐seq sensitivity, accuracy, and efficiency have improved due to innovations including more sensitive, automated, and cost‐effective single‐cell isolation methods with higher throughput as well as ongoing technological development of scRNA‐seq protocols. Among the variety of current approaches with distinct features, researchers can choose the most suitable method to carry out their research. By profiling single cells in a complex population mix, scRNA‐seq presents great advantages over traditional sequencing methods in dissecting heterogeneity in cell populations hidden in bulk analysis and exploring rare cell types associated with tumorigenesis and metastasis. scRNA‐seq studies in recent years in the field of breast cancer research have clustered breast cancer cell populations with different molecular subtypes to identify distinct populations that may correlate with poor prognosis and drug resistance. The technology has also been used to explain tumor microenvironment heterogeneity by identifying distinct immune cell subsets that may be associated with immunosurveillance and are potential immunotherapy targets. Moreover, scRNA‐seq has diverse applications in breast cancer research besides exploring heterogeneity, including the analysis of cell‐cell communications, regulatory single‐cell states, immune cell distributions, and more. scRNA‐seq is also a promising tool that can facilitate individualized therapy due to its ability to define cell subsets with potential treatment targets. Although scRNA‐seq studies of therapeutic selection in breast cancer are currently limited, the application of this technology in this field is prospective. Joint efforts and original ideas are needed to better implement scRNA‐seq technologies in breast cancer research to pave the way for individualized treatment management. This review provides a brief introduction on the currently available scRNA‐seq approaches along with their corresponding strengths and weaknesses and may act as a reference for the selection of suitable methods for research. We also discuss the current applications of scRNA‐seq in breast cancer research for tumor heterogeneity analysis, individualized therapy, and the other research directions mentioned above by reviewing corresponding published studies. Finally, we discuss the limitations of current scRNA‐seq technologies and technical problems that remain to be overcome.

BCL9 provides multi-cellular communication properties in colorectal cancer by interacting with paraspeckle proteins

Colorectal cancer (CRC) is the third most commonly diagnosed cancer, which despite recent advances in treatment, remains incurable due to molecular heterogeneity of tumor cells. The B-cell lymphoma 9 (BCL9) oncogene functions as a transcriptional co-activator of the Wnt/β-catenin pathway, which plays critical roles in CRC pathogenesis. Here we have identified a β-catenin-independent function of BCL9 in a poor-prognosis subtype of CRC tumors characterized by expression of stromal and neural associated genes. In response to spontaneous calcium transients or cellular stress, BCL9 is recruited adjacent to the interchromosomal regions, where it stabilizes the mRNA of calcium signaling and neural associated genes by interacting with paraspeckle proteins. BCL9 subsequently promotes tumor progression and remodeling of the tumor microenvironment (TME) by sustaining the calcium transients and neurotransmitter-dependent communication among CRC cells. These data provide additional insights into the role of BCL9 in tumor pathogenesis and point towards additional avenues for therapeutic intervention.

H3K4me3, H3K9ac, H3K27ac, H3K27me3 and H3K9me3 Histone Tags Suggest Distinct Regulatory Evolution of Open and Condensed Chromatin Landmarks

Background: Transposons are selfish genetic elements that self-reproduce in host DNA. They were active during evolutionary history and now occupy almost half of mammalian genomes. Close insertions of transposons reshaped structure and regulation of many genes considerably. Co-evolution of transposons and host DNA frequently results in the formation of new regulatory regions. Previously we published a concept that the proportion of functional features held by transposons positively correlates with the rate of regulatory evolution of the respective genes. Methods: We ranked human genes and molecular pathways according to their regulatory evolution rates based on high throughput genome-wide data on five histone modifications (H3K4me3, H3K9ac, H3K27ac, H3K27me3, H3K9me3) linked with transposons for five human cell lines. Results: Based on the total of approximately 1.5 million histone tags, we ranked regulatory evolution rates for 25075 human genes and 3121 molecular pathways and identified groups of molecular processes that showed signs of either fast or slow regulatory evolution. However, histone tags showed different regulatory patterns and formed two distinct clusters: promoter/active chromatin tags (H3K4me3, H3K9ac, H3K27ac) vs. heterochromatin tags (H3K27me3, H3K9me3). Conclusion: In humans, transposon-linked histone marks evolved in a coordinated way depending on their functional roles.

Simplicity DiffExpress: A Bespoke Cloud-Based Interface for RNA-seq Differential Expression Modeling and Analysis

One of the key challenges for transcriptomics-based research is not only the processing of large data but also modeling the complexity of features that are sources of variation across samples, which is required for an accurate statistical analysis. Therefore, our goal is to foster access for wet lab researchers to bioinformatics tools, in order to enhance their ability to explore biological aspects and validate hypotheses with robust analysis. In this context, user-friendly interfaces can enable researchers to apply computational biology methods without requiring bioinformatics expertise. Such bespoke platforms can improve the quality of the findings by allowing the researcher to freely explore the data and test a new hypothesis with independence. Simplicity DiffExpress is a data-driven software platform dedicated to enabling non-bioinformaticians to take ownership of the differential expression analysis (DEA) step in a transcriptomics experiment while presenting the results in a comprehensible layout, which supports an efficient results exploration, information storage, and reproducibility. Simplicity DiffExpress’ key component is the bespoke statistical model validation that guides the user through any necessary alteration in the dataset or model, tackling the challenges behind complex data analysis. The software utilizes edgeR, and it is implemented as part of the Simplicity^TM platform, providing a dynamic interface, with well-organized results that are easy to navigate and are shareable. Computational biologists and bioinformaticians can also benefit from its use since the data validation is more informative than the usual DEA resources. Wet-lab collaborators can benefit from receiving their results in an organized interface. Simplicity DiffExpress is freely available for academic use, and it is cloud-based (https://simplicity.nsilico.com/dea).

Identification of gene expression levels in primary melanoma associated with clinically meaningful characteristics

Factors influencing melanoma survival include sex, age, clinical stage, lymph node involvement, as well as Breslow thickness, presence of tumor-infiltrating lymphocytes based on histological analysis of primary melanoma, mitotic rate, and ulceration. Identification of genes whose expression in primary tumors is associated with these key tumor/patient characteristics can shed light on molecular mechanisms of melanoma survival. Here, we show results from a gene expression analysis of formalin-fixed paraffin-embedded primary melanomas with extensive clinical annotation. The Cancer Genome Atlas data on primary melanomas were used for validation of nominally significant associations. We identified five genes that were significantly associated with the presence of tumor-infiltrating lymphocytes in the joint analysis after adjustment for multiple testing: IL1R2, PPL, PLA2G3, RASAL1, and SGK2. We also identified two genes significantly associated with melanoma metastasis to the regional lymph nodes (PIK3CG and IL2RA), and two genes significantly associated with sex (KDM5C and KDM6A). We found that LEF1 was significantly associated with Breslow thickness and CCNA2 and UBE2T with mitosis. RAD50 was the gene most significantly associated with survival, with a higher level of expression associated with worse survival.

Retroelement-Linked Transcription Factor Binding Patterns Point to Quickly Developing Molecular Pathways in Human Evolution

Background: Retroelements (REs) are transposable elements occupying ~40% of the human genome that can regulate genes by providing transcription factor binding sites (TFBS). RE-linked TFBS profile can serve as a marker of gene transcriptional regulation evolution. This approach allows for interrogating the regulatory evolution of organisms with RE-rich genomes. We aimed to characterize the evolution of transcriptional regulation for human genes and molecular pathways using RE-linked TFBS accumulation as a metric. Methods: We characterized human genes and molecular pathways either enriched or deficient in RE-linked TFBS regulation. We used ENCODE database with mapped TFBS for 563 transcription factors in 13 human cell lines. For 24,389 genes and 3124 molecular pathways, we calculated the score of RE-linked TFBS regulation reflecting the regulatory evolution rate at the level of individual genes and molecular pathways. Results: The major groups enriched by RE regulation deal with gene regulation by microRNAs, olfaction, color vision, fertilization, cellular immune response, and amino acids and fatty acids metabolism and detoxication. The deficient groups were involved in translation, RNA transcription and processing, chromatin organization, and molecular signaling. Conclusion: We identified genes and molecular processes that have characteristics of especially high or low evolutionary rates at the level of RE-linked TFBS regulation in human lineage.

Predicting responses to platin chemotherapy agents with biochemically-inspired machine learning

The selection of effective genes that accurately predict chemotherapy responses might improve cancer outcomes. We compare optimized gene signatures for cisplatin, carboplatin, and oxaliplatin responses in the same cell lines and validate each signature using data from patients with cancer. Supervised support vector machine learning is used to derive gene sets whose expression is related to the cell line GI₅₀ values by backwards feature selection with cross-validation. Specific genes and functional pathways distinguishing sensitive from resistant cell lines are identified by contrasting signatures obtained at extreme and median GI₅₀ thresholds. Ensembles of gene signatures at different thresholds are combined to reduce the dependence on specific GI₅₀ values for predicting drug responses. The most accurate gene signatures for each platin are: cisplatin: BARD1, BCL2, BCL2L1, CDKN2C, FAAP24, FEN1, MAP3K1, MAPK13, MAPK3, NFKB1, NFKB2, SLC22A5, SLC31A2, TLR4, and TWIST1; carboplatin: AKT1, EIF3K, ERCC1, GNGT1, GSR, MTHFR, NEDD4L, NLRP1, NRAS, RAF1, SGK1, TIGD1, TP53, VEGFB, and VEGFC; and oxaliplatin: BRAF, FCGR2A, IGF1, MSH2, NAGK, NFE2L2, NQO1, PANK3, SLC47A1, SLCO1B1, and UGT1A1. Data from The Cancer Genome Atlas (TCGA) patients with bladder, ovarian, and colorectal cancer were used to test the cisplatin, carboplatin, and oxaliplatin signatures, resulting in 71.0%, 60.2%, and 54.5% accuracies in predicting disease recurrence and 59%, 61%, and 72% accuracies in predicting remission, respectively. One cisplatin signature predicted 100% of recurrence in non-smoking patients with bladder cancer (57% disease-free; N = 19), and 79% recurrence in smokers (62% disease-free; N = 35). This approach should be adaptable to other studies of chemotherapy responses, regardless of the drug or cancer types.

Predicting responses to platin chemotherapy agents with biochemically-inspired machine learning

The selection of effective genes that accurately predict chemotherapy responses might improve cancer outcomes. We compare optimized gene signatures for cisplatin, carboplatin, and oxaliplatin responses in the same cell lines and validate each signature using data from patients with cancer. Supervised support vector machine learning is used to derive gene sets whose expression is related to the cell line GI₅₀ values by backwards feature selection with cross-validation. Specific genes and functional pathways distinguishing sensitive from resistant cell lines are identified by contrasting signatures obtained at extreme and median GI₅₀ thresholds. Ensembles of gene signatures at different thresholds are combined to reduce the dependence on specific GI₅₀ values for predicting drug responses. The most accurate gene signatures for each platin are: cisplatin: BARD1, BCL2, BCL2L1, CDKN2C, FAAP24, FEN1, MAP3K1, MAPK13, MAPK3, NFKB1, NFKB2, SLC22A5, SLC31A2, TLR4, and TWIST1; carboplatin: AKT1, EIF3K, ERCC1, GNGT1, GSR, MTHFR, NEDD4L, NLRP1, NRAS, RAF1, SGK1, TIGD1, TP53, VEGFB, and VEGFC; and oxaliplatin: BRAF, FCGR2A, IGF1, MSH2, NAGK, NFE2L2, NQO1, PANK3, SLC47A1, SLCO1B1, and UGT1A1. Data from The Cancer Genome Atlas (TCGA) patients with bladder, ovarian, and colorectal cancer were used to test the cisplatin, carboplatin, and oxaliplatin signatures, resulting in 71.0%, 60.2%, and 54.5% accuracies in predicting disease recurrence and 59%, 61%, and 72% accuracies in predicting remission, respectively. One cisplatin signature predicted 100% of recurrence in non-smoking patients with bladder cancer (57% disease-free; N = 19), and 79% recurrence in smokers (62% disease-free; N = 35). This approach should be adaptable to other studies of chemotherapy responses, regardless of the drug or cancer types.

Predicting responses to platin chemotherapy agents with biochemically-inspired machine learning

The selection of effective genes that accurately predict chemotherapy responses might improve cancer outcomes. We compare optimized gene signatures for cisplatin, carboplatin, and oxaliplatin responses in the same cell lines and validate each signature using data from patients with cancer. Supervised support vector machine learning is used to derive gene sets whose expression is related to the cell line GI₅₀ values by backwards feature selection with cross-validation. Specific genes and functional pathways distinguishing sensitive from resistant cell lines are identified by contrasting signatures obtained at extreme and median GI₅₀ thresholds. Ensembles of gene signatures at different thresholds are combined to reduce the dependence on specific GI₅₀ values for predicting drug responses. The most accurate gene signatures for each platin are: cisplatin: BARD1, BCL2, BCL2L1, CDKN2C, FAAP24, FEN1, MAP3K1, MAPK13, MAPK3, NFKB1, NFKB2, SLC22A5, SLC31A2, TLR4, and TWIST1; carboplatin: AKT1, EIF3K, ERCC1, GNGT1, GSR, MTHFR, NEDD4L, NLRP1, NRAS, RAF1, SGK1, TIGD1, TP53, VEGFB, and VEGFC; and oxaliplatin: BRAF, FCGR2A, IGF1, MSH2, NAGK, NFE2L2, NQO1, PANK3, SLC47A1, SLCO1B1, and UGT1A1. Data from The Cancer Genome Atlas (TCGA) patients with bladder, ovarian, and colorectal cancer were used to test the cisplatin, carboplatin, and oxaliplatin signatures, resulting in 71.0%, 60.2%, and 54.5% accuracies in predicting disease recurrence and 59%, 61%, and 72% accuracies in predicting remission, respectively. One cisplatin signature predicted 100% of recurrence in non-smoking patients with bladder cancer (57% disease-free; N = 19), and 79% recurrence in smokers (62% disease-free; N = 35). This approach should be adaptable to other studies of chemotherapy responses, regardless of the drug or cancer types.

Machine learning has identified genetic signatures that predict how patients will respond to three of the most widely used cancer drugs. Chemotherapy regimens are usually based on how groups of people with similar cancers respond to them, but genetic differences can render the drugs more or less effective in individual patients. Machine learning provides a way of sifting through large amounts of data to identify patterns—in this case, in gene signatures associated with cancer recurrence and remission. The authors investigated cellular responses to cisplatin, carboplatin, and oxaliplatin and identified signatures in 11–15 genes which were the most predictive for each drug. The compositions of these signatures are also tailored to how well these therapies prevent growth of cancer cells. Accuracy varied, but one cisplatin signature was able to predict all instances of disease recurrence in non-smokers with bladder cancer.

Cancer; an induced disease of twentieth century! Induction of tolerance, increased entropy and 'Dark Energy': loss of biorhythms (Anabolism v. Catabolism)

Maintenance of health involves a synchronized network of catabolic and anabolic signals among organs/tissues/cells that requires differential bioenergetics from mitochondria and glycolysis (biological laws or biorhythms). We defined biological circadian rhythms as Yin (tumoricidal) and Yang (tumorigenic) arms of acute inflammation (effective immunity) involving immune and non-immune systems. Role of pathogens in altering immunity and inducing diseases and cancer has been documented for over a century. However, in 1955s decision makers in cancer/medical establishment allowed public (current baby boomers) to consume million doses of virus-contaminated polio vaccines. The risk of cancer incidence and mortality sharply rose from 5% (rate of hereditary/genetic or innate disease) in 1900s, to its current scary status of 33% or 50% among women and men, respectively. Despite better hygiene, modern detection technologies and discovery of antibiotics, baby boomers and subsequent 2–3 generations are sicker than previous generations at same age. American health status ranks last among other developed nations while America invests highest amount of resources for healthcare. In this perspective we present evidence that cancer is an induced disease of twentieth century, facilitated by a great deception of cancer/medical establishment for huge corporate profits. Unlike popularized opinions that cancer is 100, 200 or 1000 diseases, we demonstrate that cancer is only one disease; the severe disturbances in biorhythms (differential bioenergetics) or loss of balance in Yin and Yang of effective immunity. Cancer projects that are promoted and funded by decision makers are reductionist approaches, wrong and unethical and resulted in loss of millions of precious lives and financial toxicity to society. Public vaccination with pathogen-specific vaccines (e.g., flu, hepatitis, HPV, meningitis, measles) weakens, not promotes, immunity. Results of irresponsible projects on cancer sciences or vaccines are increased population of drug-dependent sick society. Outcome failure rates of claimed ‘targeted’ drugs, ‘precision’ or ‘personalized’ medicine are 90% (± 5) for solid tumors. We demonstrate that aging, frequent exposures to environmental hazards, infections and pathogen-specific vaccines and ingredients are ‘antigen overload’ for immune system, skewing the Yin and Yang response profiles and leading to induction of ‘mild’, ‘moderate’ or ‘severe’ immune disorders. Induction of decoy or pattern recognition receptors (e.g., PRRs), such as IRAK-M or IL-1dRs (‘designer’ molecules) and associated genomic instability and over-expression of growth promoting factors (e.g., pyruvate kinases, mTOR and PI3Ks, histamine, PGE2, VEGF) could lead to immune tolerance, facilitating cancer cells to hijack anabolic machinery of immunity (Yang) for their increased growth requirements. Expression of constituent embryonic factors would negatively regulate differentiation of tumor cells through epithelial–mesenchymal-transition and create “dual negative feedback loop” that influence tissue metabolism under hypoxic conditions. It is further hypothesized that induction of tolerance creates ‘dark energy’ and increased entropy and temperature in cancer microenvironment allowing disorderly cancer proliferation and mitosis along with increased glucose metabolism via Crabtree and Pasteur Effects, under mitophagy and ribophagy, conditions that are toxic to host survival. Effective translational medicine into treatment requires systematic and logical studies of complex interactions of tumor cells with host environment that dictate clinical outcomes. Promoting effective immunity (biological circadian rhythms) are fundamental steps in correcting host differential bioenergetics and controlling cancer growth, preventing or delaying onset of diseases and maintaining public health. The author urges independent professionals and policy makers to take a closer look at cancer dilemma and stop the ‘scientific/medical ponzi schemes’ of a powerful group that control a drug-dependent sick society before all hopes for promoting public health evaporate.

Toward biotechnology in space: High-throughput instruments for in situ biological research beyond Earth

Space biotechnology is a nascent field aimed at applying tools of modern biology to advance our goals in space exploration. These advances rely on our ability to exploit in situ high throughput techniques for amplification and sequencing DNA, and measuring levels of RNA transcripts, proteins and metabolites in a cell. These techniques, collectively known as "omics" techniques have already revolutionized terrestrial biology. A number of on-going efforts are aimed at developing instruments to carry out "omics" research in space, in particular on board the International Space Station and small satellites. For space applications these instruments require substantial and creative reengineering that includes automation, miniaturization and ensuring that the device is resistant to conditions in space and works independently of the direction of the gravity vector. Different paths taken to meet these requirements for different "omics" instruments are the subjects of this review. The advantages and disadvantages of these instruments and technological solutions and their level of readiness for deployment in space are discussed. Considering that effects of space environments on terrestrial organisms appear to be global, it is argued that high throughput instruments are essential to advance (1) biomedical and physiological studies to control and reduce space-related stressors on living systems, (2) application of biology to life support and in situ resource utilization, (3) planetary protection, and (4) basic research about the limits on life in space. It is also argued that carrying out measurements in situ provides considerable advantages over the traditional space biology paradigm that relies on post-flight data analysis.