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1. The use of natural products to target cancer stem cells.

   Taylor, Wesley; Jabbarzadeh, Ehsan. (July 2017, American journal of cancer research)

   The cancer stem cell hypothesis has been used to explain many cancer complications resulting in poor patient outcomes including induced drug resistance, metastases to distant organs, and tumor recurrence. While the validity of the cancer stem cell model continues to be the cause of much scientific debate, a number of putative cancer stem cell markers have been identified making studies concerning the targeting of cancer stem cells possible. In this review, a number of identifying properties of cancer stem cells have been outlined including properties contributing to the drug resistance and metastatic potential commonly observed in supposed cancer stem cells. Due to cancer stem cells' numerous survival mechanisms, the diversity of cancer stem cell markers between cancer types and tissues, and the prevalence of cancer stem cell markers among healthy stem and somatic cells, it is likely that currently utilized treatments will continue to fail to eradicate cancer stem cells. The successful treatment of cancer stem cells will rely upon the development of anti-neoplastic drugs capable of influencing many cellular mechanisms simultaneously in order to prevent the survival of this evasive subpopulation. Natural compounds represent a historically rich source of novel, biologically active compounds which are able to interact with a large number of cellular targets while limiting the painful side-effects commonly associated with cancer treatment. A brief review of select natural products that have been demonstrated to diminish the clinically devastating properties of cancer stem cells or to induce cancer stem cell death is also presented. 

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2. Pseudotime Based Discovery of Breast Cancer Heterogeneity

   https://doi.org/10.1109/BIBM47256.2019.8983300  

   Aqila, Tasmia; Mamun, Abdullah Al; Mondal, Ananda Mohan (November 2019, 2019 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM))

   Breast cancer is highly sporadic and heterogeneous in nature. Even the patients with same clinical stage do not cluster together in terms of genomic profiles such as mRNA expression. In order to prevent and cure breast cancer completely, it is essential to decipher the detailed heterogeneity of breast cancer at genomic level. Putting the cancer patients on a time scale, which represents the trajectory of cancer development, may help discover the detailed heterogeneity. This in turn would help establish the mechanisms for prevention and complete cure of breast cancer. The goal of this study is to discover the heterogeneity of breast cancer by ordering the cancer patients using pseudotime. This is achieved through two objectives: First, a computational framework is developed to place the cancer patients on a time scale, meaning construct a trajectory of cancer development, by inferring pseudotime from static mRNA expression data; Second, discovering breast cancer heterogeneity at different time periods of the trajectory using statistical and machine learning techniques. In this study, the trajectory of breast cancer progression was constructed using static mRNA expression profiles of 1072 breast cancer patients by inferring pseudotime. Three sets of key genes discovered using supervised machine learning techniques are used to develop the trajectories. The first set of genes are PAM50 genes which is available in literature. The second and third sets of genes were discovered in the present study using the clinical stages of breast cancer (Stage-I, Stage-II, Stage-III, and Stage-IV). The proposed computational framework has the capability of deciphering heterogeneity in breast cancer at a granular level. The results also show the existence of multiple parallel trajectories at different time periods of cancer development or progression. 

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3. Network-based multi-task learning models for biomarker selection and cancer outcome prediction

   https://doi.org/10.1093/bioinformatics/btz809  

   Wang, Zhibo; He, Zhezhi; Shah, Milan; Zhang, Teng; Fan, Deliang; Zhang, Wei; Cowen, Lenore (November 2019, Bioinformatics)

   Abstract Motivation Detecting cancer gene expression and transcriptome changes with mRNA-sequencing (RNA-Seq) or array-based data are important for understanding the molecular mechanisms underlying carcinogenesis and cellular events during cancer progression. In previous studies, the differentially expressed genes were detected across patients in one cancer type. These studies ignored the role of mRNA expression changes in driving tumorigenic mechanisms that are either universal or specific in different tumor types. To address the problem, we introduce two network-based multi-task learning frameworks, NetML and NetSML, to discover common differentially expressed genes shared across different cancer types as well as differentially expressed genes specific to each cancer type. The proposed frameworks consider the common latent gene co-expression modules and gene-sample biclusters underlying the multiple cancer datasets to learn the knowledge crossing different tumor types. Results Large-scale experiments on simulations and real cancer high-throughput datasets validate that the proposed network-based multi-task learning frameworks perform better sample classification compared with the models without the knowledge sharing across different cancer types. The common and cancer specific molecular signatures detected by multi-task learning frameworks on TCGA ovarian cancer, breast cancer, and prostate cancer datasets are correlated with the known marker genes and enriched in cancer relevant KEGG pathways and Gene Ontology terms. Availability and Implementation Source code is available at: https://github.com/compbiolabucf/NetML Supplementary information Supplementary data are available at Bioinformatics 

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4. Comprehensive live-cell imaging analysis of cryptotanshinone and synergistic drug-screening effects in various human and canine cancer cell lines

   https://doi.org/10.1371/journal.pone.0236074  

   Bittner, Michael L.; Lopes, Rosana; Hua, Jianping; Sima, Chao; Datta, Aniruddha; Wilson-Robles, Heather (February 2021, PLOS ONE)

   Mohan, Chakrabhavi Dhananjaya (Ed.)

   Background Several studies have highlighted both the extreme anticancer effects of Cryptotanshinone (CT), a Stat3 crippling component from Salvia miltiorrhiza , as well as other STAT3 inhibitors to fight cancer. Methods Data presented in this experiment incorporates 2 years of in vitro studies applying a comprehensive live-cell drug-screening analysis of human and canine cancer cells exposed to CT at 20 μM concentration, as well as to other drug combinations. As previously observed in other studies, dogs are natural cancer models, given to their similarity in cancer genetics, epidemiology and disease progression compared to humans. Results Results obtained from several types of human and canine cancer cells exposed to CT and varied drug combinations, verified CT efficacy at combating cancer by achieving an extremely high percentage of apoptosis within 24 hours of drug exposure. Conclusions CT anticancer efficacy in various human and canine cancer cell lines denotes its ability to interact across different biological processes and cancer regulatory cell networks, driving inhibition of cancer cell survival. 

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5. Parallel evolution of reduced cancer risk and tumor suppressor duplications in Xenarthra

   https://doi.org/10.7554/eLife.82558  

   Vazquez, Juan Manuel; Pena, Maria T; Muhammad, Baaqeyah; Kraft, Morgan; Adams, Linda B; Lynch, Vincent J (December 2022, eLife)

   The risk of developing cancer is correlated with body size and lifespan within species, but there is no correlation between cancer and either body size or lifespan between species indicating that large, long-lived species have evolved enhanced cancer protection mechanisms. Previously we showed that several large bodied Afrotherian lineages evolved reduced intrinsic cancer risk, particularly elephants and their extinct relatives ( Proboscideans ), coincident with pervasive duplication of tumor suppressor genes (Vazquez and Lynch, 2021). Unexpectedly, we also found that Xenarthrans (sloths, armadillos, and anteaters) evolved very low intrinsic cancer risk. Here, we show that: (1) several Xenarthran lineages independently evolved large bodies, long lifespans, and reduced intrinsic cancer risk; (2) the reduced cancer risk in the stem lineages of Xenarthra and Pilosa coincided with bursts of tumor suppressor gene duplications; (3) cells from sloths proliferate extremely slowly while Xenarthran cells induce apoptosis at very low doses of DNA damaging agents; and (4) the prevalence of cancer is extremely low Xenarthrans , and cancer is nearly absent from armadillos. These data implicate the duplication of tumor suppressor genes in the evolution of remarkably large body sizes and decreased cancer risk in Xenarthrans and suggest they are a remarkably cancer-resistant group of mammals. 

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