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Metastatic CTC clusters remain relatively unexplored due to the lack of optimized and practical technologies for their detection. Here the authors report Cluster-Wells to isolate CTC clusters in whole blood; they show this allows viable cluster retrieval for further molecular and functional analysis.

Summary: Stiffness has been observed to decrease for many cancer cell types as their metastatic potential increases. Although cell mechanics and metastatic potential are related, the underlying molecular factors associated with these phenotypes remain unknown. Therefore, we have developed a workflow to measure the mechanical properties and gene expression of single cells that is used to generate large linked-datasets. The process combines atomic force microscopy to measure the mechanics of individual cells with multiplexed RT-qPCR gene expression analysis on the same single cells. Surprisingly, the genes that most strongly correlated with mechanical properties were not cytoskeletal, but rather were markers of extracellular matrix remodeling, epithelial-to-mesenchymal transition, cell adhesion, and cancer stemness. In addition, dimensionality reduction analysis showed that cell clustering was improved by combining mechanical and gene expression data types. The single cell genomechanics method demonstrates how single cell studies can identify molecular drivers that could affect the biophysical processes underpinning metastasis.

Despite a substantial overall decrease in mortality, disparities among ethnic minorities in developed countries persist. This study investigated mortality disparities and their associated risk factors for the three largest ethnic groups in the United Kingdom: Asian, Black, and White. Study participants were sampled from the UK Biobank (UKB), a prospective cohort enrolled between 2006 and 2010. Genetics, biological samples, and health information and outcomes data of UKB participants were downloaded and data-fields were prioritized based on participants with death registry records. Kaplan-Meier method was used to evaluate survival differences among ethnic groups; survival random forest feature selection followed by Cox proportional-hazard modeling was used to identify and estimate the effects of shared and ethnic group-specific mortality risk factors. The White ethnic group showed significantly worse survival probability than the Asian and Black groups. In all three ethnic groups, endoscopy and colonoscopy procedures showed significant protective effects on overall mortality. Asian and Black women show lower relative risk of mortality than men, whereas no significant effect of sex was seen for the White group. The strongest ethnic group-specific mortality associations were ischemic heart disease for Asians, COVID-19 for Blacks, and cancers of respiratory/intrathoracic organs for Whites. Mental health-related diagnoses, including substance abuse, anxiety, and depression, were a major risk factor for overall mortality in the Asian group. The effect of mental health on Asian mortality, particularly for digestive cancers, was exacerbated by an observed hesitance to answer mental health questions, possibly related to cultural stigma. C-reactive protein (CRP) serum levels were associated with both overall and cause-specific mortality due to COVID-19 and digestive cancers in the Black group, where elevated CRP has previously been linked to psychosocial stress due to discrimination. Our results point to mortality risk factors that are group-specific and modifiable, supporting targeted interventions towards greater health equity.

Genetic variation is a well-known contributor to the onset and progression of cancer. The goal of this study is to provide a comprehensive examination of the nucleotide and chromosomal variation associated with the onset and progression of serous ovarian cancer. Using a variety of computational and statistical methods, we examine the exome sequence profiles of genetic variants present in the primary tumors of 432 ovarian cancer patient samples to compute: (1) the tumor mutational burden for all genes and (2) the chromosomal copy number alterations associated with the onset/progression of ovarian cancer. Tumor mutational burden is reduced in the late vs. early stages, with the highest levels being associated with loss-of-function mutations in DNA-repair genes. Nucleotide variation and copy number alterations associated with known cancer driver genes are selectively favored over ovarian cancer development. The results indicate that genetic variation is a significant contributor to the onset and progression of ovarian cancer. The measurement of the relative levels of genetic variation associated with individual ovarian cancer patient tumors may be a clinically valuable predictor of potential tumor aggressiveness and resistance to chemotherapy. Tumors found to be associated with high levels of genetic variation may help in the clinical identification of high-risk ovarian cancer patients who could benefit from more frequent monitoring.

Summary: We describe a consensus approach for network construction based on fully conserved gene-gene interactions from randomly downsampled data subsets for an unbiased differential analysis of gene co-expression networks. The pipeline allows users to identify network nodes lost, conserved, and acquired in cancer as well as interpret the functional significance of these network changes. For proof of concept, the protocol is used to leverage RNA-seq data of tumor samples from TCGA and healthy tissue samples from the GTEx database.For complete details on the use and execution of this protocol, please refer to Arshad and McDonald (2021). : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Abstract Background Machine learning has been utilized to predict cancer drug response from multi-omics data generated from sensitivities of cancer cell lines to different therapeutic compounds. Here, we build machine learning models using gene expression data from patients’ primary tumor tissues to predict whether a patient will respond positively or negatively to two chemotherapeutics: 5-Fluorouracil and Gemcitabine. Results We focused on 5-Fluorouracil and Gemcitabine because based on our exclusion criteria, they provide the largest numbers of patients within TCGA. Normalized gene expression data were clustered and used as the input features for the study. We used matching clinical trial data to ascertain the response of these patients via multiple classification methods. Multiple clustering and classification methods were compared for prediction accuracy of drug response. Clara and random forest were found to be the best clustering and classification methods, respectively. The results show our models predict with up to 86% accuracy; despite the study’s limitation of sample size. We also found the genes most informative for predicting drug response were enriched in well-known cancer signaling pathways and highlighted their potential significance in chemotherapy prognosis. Conclusions Primary tumor gene expression is a good predictor of cancer drug response. Investment in larger datasets containing both patient gene expression and drug response is needed to support future work of machine learning models. Ultimately, such predictive models may aid oncologists with making critical treatment decisions.

Summary: Recent findings indicate that changes underlying cancer onset and progression are not only attributable to changes in DNA structure and expression of individual genes but to changes in interactions among these genes as well. We examined co-expression changes in gene-network structure occurring during the onset and progression of nine different cancer types. Network complexity is generally reduced in the transition from normal precursor tissues to corresponding primary tumors. Cross-tissue cancer network similarity generally increases in early-stage cancers followed by a subsequent loss in cross-tissue cancer similarity as tumors reacquire cancer-specific network complexity. Gene-gene connections remaining stable through cancer development are enriched for “housekeeping” gene functions, whereas newly acquired interactions are associated with established cancer-promoting functions. Surprisingly, >90% of changes in gene-gene network interactions in cancers are not associated with changes in the expression of network genes relative to normal precursor tissues.

New tools have been created to allow a superconducting design flow for schematic design, verification, and optimization. These tools integrate with the Cadence design environment. In single flux quantum superconducting electronics, individual component values, such as wire inductances, Josephson junction critical currents, and bias currents, must be optimized to allow for maximum deviance from the designer value, which is also known as the device margin. One tool is used to create a description of the proper circuit behavior. Included with this tool is the ability to automatically create the description from a Cadence netlist. The other tool is an automated device margin circuit schematic verification and optimization tool, which widens device margins while maintaining proper circuit behavior derived from the first tool. Additionally, this optimization tool can automatically correct the circuit schematic using the proper circuit behavior description. In this paper, the functionality of the language used to create the description of the proper circuit behavior is presented. Several circuits are then verified and optimized based on their correct behavior.

Abstract Background Gene-fusion or chimeric transcripts have been implicated in the onset and progression of a variety of cancers. Massively parallel RNA sequencing (RNA-Seq) of the cellular transcriptome is a promising approach for the identification of chimeric transcripts of potential functional significance. We report here the development and use of an integrated computational pipeline for the de novo assembly and characterization of chimeric transcripts in 55 primary breast cancer and normal tissue samples. Methods An integrated computational pipeline was employed to screen the transcriptome of breast cancer and control tissues for high-quality RNA-sequencing reads. Reads were de novo assembled into contigs followed by reference genome mapping. Chimeric transcripts were detected, filtered and characterized using our R-SAP algorithm. The relative abundance of reads was used to estimate levels of gene expression. Results De novo assembly allowed for the accurate detection of 1959 chimeric transcripts to nucleotide level resolution and facilitated detailed molecular characterization and quantitative analysis. A number of the chimeric transcripts are of potential functional significance including 79 novel fusion-protein transcripts and many chimeric transcripts with alterations in their un-translated leader regions. A number of chimeric transcripts in the cancer samples mapped to genomic regions devoid of any known genes. Several ‘pro-neoplastic’ fusions comprised of genes previously implicated in cancer are expressed at low levels in normal tissues but at high levels in cancer tissues. Conclusions Collectively, our results underscore the utility of deep sequencing technologies and improved bioinformatics workflows to uncover novel and potentially significant chimeric transcripts in cancer and normal somatic tissues.

Abstract High-throughput technologies have identified significant changes in patterns of mRNA expression over cancer development but the functional significance of these changes often rests upon the assumption that observed changes in levels of mRNA accurately reflect changes in levels of their encoded proteins. We systematically compared the expression of 4436 genes on the RNA and protein levels between discrete tumor samples collected from the ovary and from the omentum of the same OC patient. The overall correlation between global changes in levels of mRNA and their encoding proteins is low (r = 0.38). The majority of differences are on the protein level with no corresponding change on the mRNA level. Indirect and direct evidence indicates that a significant fraction of the differences may be mediated by microRNAs.

We have previously evaluated the feasibility of a serial code accelerator core with 3-D DRAM stacked on the core operating at high frequencies. While operating at such high frequencies (>24 GHz), there are concerns with removing heat from the 3-D stack. We propose the use of thin diamond sheets, which have high thermal conductivity, as a heat spreader by bonding it close to the processor core substrate and memory stacks. We show, through thermal modeling using COMSOL finite-element analysis tools, the feasibility of diamond as an effective heat spreader in a processor-memory 3-D stack.

We present a rigorous electrical and optical analysis of a highly scaled, graded-base, SiGe heterojunction bipolar transistor (HBT) electrooptic (EO) modulator. In this study, we propose a 2-D electrical model and a 3-D optical model for a graded-base SiGe HBT structure that is capable of operating at a data bit rate of 250 Gbit/s or higher. In this structure, apart from a polysilicon/low doped emitter ( width = 90 nm) and a strained SiGe graded base ( depth = 8.5 nm), a selectively implanted collector (SIC) (depth = 26 nm) is introduced. Furthermore, at a base-emitter swing of 0 to 1.0 V, this model predicts a rise time of 3.48 ps and a fall time of 0.55 ps. Optical simulations predict a π phase shift length (Lπ) of 204 μm, with an extinction ratio of 13.2 dB at a wavelength of 1.55 μm.

We present a rigorous electrical and optical analysis of a strained and graded base SiGe Heterojunction Bipolar Transistor (HBT) electrooptic (EO) modulator. In this paper, we propose a 2-D model for a graded base SiGe HBT structure that is capable of operating at a data bit rate of 80 Gbit/s or higher. In this structure, apart from a polysilicon/monosilicon emitter (Width = 0.12 μm) and a strained SiGe graded base (Depth = 40 nm) , a selectively implanted collector (SIC) (Depth = 0.6 μm) is introduced. Furthermore, the terminal characteristics of this new device modeled using MEDICI are closely compared with the SiGe HBT in the IBM production line, suggesting the possibility of fast deployment of the EO modulator using established commercial processing. At a subcollector depth of 0.4 μm and at a base-emitter swing of 0 to 1.1 V, this model predicts a rise time of 5.1 ps and a fall time of 3.6 ps. Optical simulations predict a π phase shift length (Lπ) of 240.8 μm with an extinction ratio of 7.5 dB at a wavelength of 1.55 μm. Additionally, the tradeoff between the switching speed, Lπ and propagation loss with a thinner subcollector is analyzed and reported.