Collagen is the major component of the tumor microenvironment and participates in cancer fibrosis. Collagen biosynthesis can be regulated by cancer cells through mutated genes, transcription factors, signaling pathways and receptors; furthermore, collagen can influence tumor cell behavior through integrins, discoidin domain receptors, tyrosine kinase receptors, and some signaling pathways. Exosomes and microRNAs are closely associated with collagen in cancer. Hypoxia, which is common in collagen-rich conditions, intensifies cancer progression, and other substances in the extracellular matrix, such as fibronectin, hyaluronic acid, laminin, and matrix metalloproteinases, interact with collagen to influence cancer cell activity. Macrophages, lymphocytes, and fibroblasts play a role with collagen in cancer immunity and progression. Microscopic changes in collagen content within cancer cells and matrix cells and in other molecules ultimately contribute to the mutual feedback loop that influences prognosis, recurrence, and resistance in cancer. Nanoparticles, nanoplatforms, and nanoenzymes exhibit the expected gratifying properties. The pathophysiological functions of collagen in diverse cancers illustrate the dual roles of collagen and provide promising therapeutic options that can be readily translated from bench to bedside. The emerging understanding of the structural properties and functions of collagen in cancer will guide the development of new strategies for anticancer therapy.

/pdf/the-role-of-collagen-in-cancer-from-bench-to-bedside-2lesd2isdl.pdf

The role of collagen in cancer: from bench to bedside

Innovations in Next-Generation Sequencing are enabling generation of DNA sequence data at ever faster rates and at very low cost. Large sequencing centers typically employ hundreds of such systems. Such high-throughput and low-cost generation of data underscores the need for commensurate acceleration in downstream computational analysis of the sequencing data. A fundamental step in downstream analysis is mapping of the reads to a long reference DNA sequence, such as a reference human genome. Sequence mapping is a compute-intensive step that accounts for more than 30% of the overall time of the GATK workflow. BWA-MEM is one of the most widely used tools for sequence mapping and has tens of thousands of users. 
In this work, we focus on accelerating BWA-MEM through an efficient architecture aware implementation, while maintaining identical output. The volume of data requires distributed computing environment, usually deploying multicore processors. Since the application can be easily parallelized for distributed memory systems, we focus on performance improvements on a single socket multicore processor. BWA-MEM run time is dominated by three kernels, collectively responsible for more than 85% of the overall compute time. We improved the performance of these kernels by 1) improving cache reuse, 2) simplifying the algorithms, 3) replacing small fragmented memory allocations with a few large contiguous ones, 4) software prefetching, and 5) SIMD utilization wherever applicable - and massive reorganization of the source code enabling these improvements. 
As a result, we achieved nearly 2x, 183x, and 8x speedups on the three kernels, respectively, resulting in up to 3.5x and 2.4x speedups on end-to-end compute time over the original BWA-MEM on single thread and single socket of Intel Xeon Skylake processor. To the best of our knowledge, this is the highest reported speedup over BWA-MEM.

Efficient Architecture-Aware Acceleration of BWA-MEM for Multicore Systems

Innovations in Next-Generation Sequencing are enabling generation of DNA sequence data at ever faster rates and at very low cost. For example, the Illumina NovaSeq 6000 sequencer can generate 6 Terabases of data in less than two days, sequencing nearly 20 Billion short DNA fragments called reads at the low cost of $1000 per human genome. Large sequencing centers typically employ hundreds of such systems. Such highthroughput and low-cost generation of data underscores the need for commensurate acceleration in downstream computational analysis of the sequencing data. A fundamental step in downstream analysis is mapping of the reads to a long reference DNA sequence, such as a reference human genome. Sequence mapping is a compute-intensive step that accounts for more than 30% of the overall time of the GATK (Genome Analysis ToolKit) best practices workflow. BWA-MEM is one of the most widely used tools for sequence mapping and has tens of thousands of users. In this work, we focus on accelerating BWA-MEM through an efficient architecture aware implementation, while maintaining identical output. The volume of data requires distributed computing and is usually processed on clusters or cloud deployments with multicore processors usually being the platform of choice. Since the application can be easily parallelized across multiple sockets (even across distributed memory systems) by simply distributing the reads equally, we focus on performance improvements on a single socket multicore processor. BWA-MEM run time is dominated by three kernels, collectively responsible for more than 85% of the overall compute time. We improved the performance of the three kernels by 1) using techniques to improve cache reuse, 2) simplifying the algorithms, 3) replacing many small memory allocations with a few large contiguous ones to improve hardware prefetching of data, 4) software prefetching of data, and 5) utilization of SIMD wherever applicable and massive reorganization of the source code to enable these improvements. As a result, we achieved nearly 2×, 183×, and 8× speedups on the three kernels, respectively, resulting in up to 3:5× and 2:4× speedups on end-to-end compute time over the original BWA-MEM on single thread and single socket of Intel Xeon Skylake processor. To the best of our knowledge, this is the highest reported speedup over BWA-MEM (running on a single CPU) while using a single CPU or a single CPU-single GPGPU/FPGA combination. Source-code: https://github.com/bwa-mem2/bwa-mem2

Therapeutic resistance continues to be an indominable foe in our ambition for curative cancer treatment. Recent insights into the molecular determinants of acquired treatment resistance in the clinical and experimental setting have challenged the widely held view of sequential genetic evolution as the primary cause of resistance and brought into sharp focus a range of non-genetic adaptive mechanisms. Notably, the genetic landscape of the tumour and the non-genetic mechanisms used to escape therapy are frequently linked. Remarkably, whereas some oncogenic mutations allow the cancer cells to rapidly adapt their transcriptional and/or metabolic programme to meet and survive the therapeutic pressure, other oncogenic drivers convey an inherent cellular plasticity to the cancer cell enabling lineage switching and/or the evasion of anticancer immunosurveillance. The prevalence and diverse array of non-genetic resistance mechanisms pose a new challenge to the field that requires innovative strategies to monitor and counteract these adaptive processes. In this Perspective we discuss the key principles of non-genetic therapy resistance in cancer. We provide a perspective on the emerging data from clinical studies and sophisticated cancer models that have studied various non-genetic resistance pathways and highlight promising therapeutic avenues that may be used to negate and/or counteract the non-genetic adaptive pathways.

Non-genetic mechanisms of therapeutic resistance in cancer.

Droplet microfluidics has revolutionized the study of single cells. The ability to compartmentalize cells within picoliter droplets in microfluidic devices has opened up a wide range of strategies to extract information at the genomic, transcriptomic, proteomic, or metabolomic level from large numbers of individual cells. Studying the different molecular landscapes at single-cell resolution has provided the authors with a detailed picture of intracellular heterogeneity and the resulting changes in cellular phenotypes. In addition, these technologies have aided in the discovery of rare cells in tumors or in the immune system, and left the authors with a deeper understanding of the fundamental biological processes that determine cell fate. This review aims to provide a detailed overview of the various droplet microfluidic strategies reported in the literature, taking into account the sometimes subtle differences in workflow or reagents that enable or improve certain protocols. Specifically, approaches to targeted- and whole-genome analysis, as well as whole-transcriptome profiling techniques, are reviewed. In addition, an up-to-date overview of new methods to characterize and quantify single-cell protein levels, and of developments to screen secreted molecules such as antibodies, cytokines, or metabolites at the single-cell level, is provided.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/adbi.201900188

Single-Cell Analysis Using Droplet Microfluidics.

Molecular analysis of circulating tumor cells (CTCs) at single-cell resolution offers great promise for cancer diagnostics and therapeutics from simple liquid biopsy. Recent development of massively parallel single-cell RNA-sequencing (scRNA-seq) provides a powerful method to resolve the cellular heterogeneity from gene expression and pathway regulation analysis. However, the scarcity of CTCs and the massive contamination of blood cells limit the utility of currently available technologies. Here, we present Hydro-Seq, a scalable hydrodynamic scRNA-seq barcoding technique, for high-throughput CTC analysis. High cell-capture efficiency and contamination removal capability of Hydro-Seq enables successful scRNA-seq of 666 CTCs from 21 breast cancer patient samples at high throughput. We identify breast cancer drug targets for hormone and targeted therapies and tracked individual cells that express markers of cancer stem cells (CSCs) as well as of epithelial/mesenchymal cell state transitions. Transcriptome analysis of these cells provides insights into monitoring target therapeutics and processes underlying tumor metastasis.

/pdf/hydro-seq-enables-contamination-free-high-throughput-single-2eez4syl58.pdf

Hydro-Seq enables contamination-free high-throughput single-cell RNA-sequencing for circulating tumor cells

The Smith-Waterman (S-W) algorithm is widely adopted by the state-of-the-art DNA sequence aligners Existing wave front-based methods ignored the fact that the S-W algorithm is fed with significantly varied-size inputs in modern aligners, in which the S-W algorithm is further optimized by exerting extensive pruning In this paper, we propose an architecture, tailored for varied input sizes as well as harnessing software pruning strategies, to accelerate S-W Our implementation demonstrates a 264x speedup over a 24-thread Intel Has well Xeon server, and outperforms wave front-based implementations by up to 6x with the same FPGA resource

/pdf/a-novel-high-throughput-acceleration-engine-for-read-4cbrdq7ukq.pdf

A Novel High-Throughput Acceleration Engine for Read Alignment

The recent development of non-volatile memory (NVM), such as spin-torque transfer magnetoresistive RAM (STT-RAM) and phase-change RAM (PRAM), with the advantage of low leakage and high density, provides an energy-efficient alternative to traditional SRAM in cache systems. We propose a novel reconfigurable hybrid cache architecture (RHC), in which NVM is incorporated in the last-level cache together with SRAM. RHC can be reconfigured by powering on/off SRAM/NVM arrays in a way-based manner. In this work, we discuss both the architecture and circuit design issues for RHC. Furthermore, we provide hardware-based mechanisms to dynamically reconfigure RHC on-the-fly based on the cache demand. Experimental results on a wide range of benchmarks show that the proposed RHC achieves an average 63%, 48% and 25% energy saving over non-reconfigurable SRAM-based cache, non-reconfigurable hybrid cache, and reconfigurable SRAM-based cache, while maintaining the system performance (at most 4% performance overhead).

Dynamically reconfigurable hybrid cache: an energy-efficient last-level cache design

FPGA-enabled datacenters have shown great potential for providing performance and energy efficiency improvement, and captured a great amount of attention from both academia and industry. In this paper we aim to answer one key question: how can we efficiently integrate FPGAs into state-of-the-art big-data computing frameworks? Although very important, this problem has not been well studied, especially for the integration of fine-grained FPGA accelerators that have short execution time but will be invoked many times. To provide a generalized methodology and insight for efficient integration, we conduct an in-depth analysis of challenges and corresponding solutions of integration at single-thread, single-node multi-thread, and multi-node levels. With a step-by-step case study for the next-generation DNA sequencing application, we demonstrate how a straightforward integration with 1000x slowdown can be tuned into an efficient integration with 2.6x overall system speedup and 2.4x energy efficiency improvement.

/pdf/when-spark-meets-fpgas-a-case-study-for-next-generation-dna-28klcc4sez.pdf

When Spark Meets FPGAs: A Case Study for Next-Generation DNA Sequencing Acceleration

FPGA-enabled datacenters have shown great potential for providing performance and energy efficiency improvement. In this paper we aim to answer one key question: how can we efficiently integrate FPGAs into state-of-the-art big-data computing frameworks like Apache Spark? To provide a generalized methodology and insights for efficient integration, we conduct an in-depth analysis of challenges at single-thread, single-node multi-thread, and multi-node levels, and propose solutions including batch processing and the FPGA-as-a-Service framework to address them. With a step-by-step case study for the next-generation DNA sequencing application, we demonstrate how a straightforward integration with 1,000x slowdown can be tuned into an efficient integration with 2.6x overall system speedup and 2.4x energy efficiency improvement.

Yu-Ting Chen

Papers

Hydro-Seq enables contamination-free high-throughput single-cell RNA-sequencing for circulating tumor cells

A Novel High-Throughput Acceleration Engine for Read Alignment

Dynamically reconfigurable hybrid cache: an energy-efficient last-level cache design

When Spark Meets FPGAs: A Case Study for Next-Generation DNA Sequencing Acceleration

When apache spark meets FPGAs: a case study for next-generation DNA sequencing acceleration