A comprehensive characterization of the cell-free transcriptome reveals tissue- and subtype-specific biomarkers for cancer detection.

Cell-free RNA (cfRNA) is a promising analyte for cancer detection. However, a comprehensive assessment of cfRNA in individuals with and without cancer has not been conducted. We perform the first transcriptome-wide characterization of cfRNA in cancer (stage III breast [n = 46], lung [n = 30]) and non-cancer (n = 89) participants from the Circulating Cell-free Genome Atlas (NCT02889978). Of 57,820 annotated genes, 39,564 (68%) are not detected in cfRNA from non-cancer individuals. Within these low-noise regions, we identify tissue- and cancer-specific genes, defined as "dark channel biomarker" (DCB) genes, that are recurrently detected in individuals with cancer. DCB levels in plasma correlate with tumor shedding rate and RNA expression in matched tissue, suggesting that DCBs with high expression in tumor tissue could enhance cancer detection in patients with low levels of circulating tumor DNA. Overall, cfRNA provides a unique opportunity to detect cancer, predict the tumor tissue of origin, and determine the cancer subtype.

Alignment-free filtering for cfNA fusion fragments.

MOTIVATION: Cell-free nucleic acid (cfNA) sequencing data require improvements to existing fusion detection methods along multiple axes: high depth of sequencing, low allele fractions, short fragment lengths and specialized barcodes, such as unique molecular identifiers. RESULTS: AF4 was developed to address these challenges. It uses a novel alignment-free kmer-based method to detect candidate fusion fragments with high sensitivity and orders of magnitude faster than existing tools. Candidate fragments are then filtered using a max-cover criterion that significantly reduces spurious matches while retaining authentic fusion fragments. This efficient first stage reduces the data sufficiently that commonly used criteria can process the remaining information, or sophisticated filtering policies that may not scale to the raw reads can be used. AF4 provides both targeted and de novo fusion detection modes. We demonstrate both modes in benchmark simulated and real RNA-seq data as well as clinical and cell-line cfNA data. AVAILABILITY AND IMPLEMENTATION: AF4 is open sourced, licensed under Apache License 2.0, and is available at: https://github.com/grailbio/bio/tree/master/fusion.

Ancient hot and cold genes and chemotherapy resistance emergence.

We use a microfabricated ecology with a doxorubicin gradient and population fragmentation to produce a strong Darwinian selective pressure that drives forward the rapid emergence of doxorubicin resistance in multiple myeloma (MM) cancer cells. RNA sequencing of the resistant cells was used to examine (i) emergence of genes with high de novo substitution densities (i.e., hot genes) and (ii) genes never substituted (i.e., cold genes). The set of cold genes, which were 21% of the genes sequenced, were further winnowed down by examining excess expression levels. Both the most highly substituted genes and the most highly expressed never-substituted genes were biased in age toward the most ancient of genes. This would support the model that cancer represents a revision back to ancient forms of life adapted to high fitness under extreme stress, and suggests that these ancient genes may be targets for cancer therapy.

Single-cell analyses of transcriptional heterogeneity during drug tolerance transition in cancer cells by RNA sequencing.

The acute cellular response to stress generates a subpopulation of reversibly stress-tolerant cells under conditions that are lethal to the majority of the population. Stress tolerance is attributed to heterogeneity of gene expression within the population to ensure survival of a minority. We performed whole transcriptome sequencing analyses of metastatic human breast cancer cells subjected to the chemotherapeutic agent paclitaxel at the single-cell and population levels. Here we show that specific transcriptional programs are enacted within untreated, stressed, and drug-tolerant cell groups while generating high heterogeneity between single cells within and between groups. We further demonstrate that drug-tolerant cells contain specific RNA variants residing in genes involved in microtubule organization and stabilization, as well as cell adhesion and cell surface signaling. In addition, the gene expression profile of drug-tolerant cells is similar to that of untreated cells within a few doublings. Thus, single-cell analyses reveal the dynamics of the stress response in terms of cell-specific RNA variants driving heterogeneity, the survival of a minority population through generation of specific RNA variants, and the efficient reconversion of stress-tolerant cells back to normalcy.

Compartmental genomics in living cells revealed by single-cell nanobiopsy.

The ability to study the molecular biology of living single cells in heterogeneous cell populations is essential for next generation analysis of cellular circuitry and function. Here, we developed a single-cell nanobiopsy platform based on scanning ion conductance microscopy (SICM) for continuous sampling of intracellular content from individual cells. The nanobiopsy platform uses electrowetting within a nanopipette to extract cellular material from living cells with minimal disruption of the cellular milieu. We demonstrate the subcellular resolution of the nanobiopsy platform by isolating small subpopulations of mitochondria from single living cells, and quantify mutant mitochondrial genomes in those single cells with high throughput sequencing technology. These findings may provide the foundation for dynamic subcellular genomic analysis.

Distinctive microRNA expression signatures in proton-irradiated mice.

Proton particles comprise the most abundant ionizing radiation (IR) in outer space. These high energy particles are known to cause frequent double- and single-stranded DNA lesions that can lead to cancer and tumor formation. Understanding the mechanism of cellular response to proton-derived IR is vital for determining health risks to astronauts during space missions. Our understanding of the consequences of these high energy charged particles on microRNA (miRNA) regulation is still in infancy. miRNAs are non-coding, single-stranded RNAs of ~22 nucleotides that constitute a novel class of gene regulators. They regulate diverse biological processes, and each miRNA can control hundreds of gene targets. To investigate the effect of proton radiation on these master regulators, we examined the miRNA expression in selected mice organs that had been exposed to whole-body proton irradiation (2 Gy), and compared this to control mice (0 Gy exposure). RNA was isolated from three tissues (testis, brain, and liver) from treated and control mice and subjected to high-throughput small RNA sequencing. Bioinformatics analysis of small RNA sequencing data revealed dysregulation of (p < 0.05; 20 up- and 10 down-regulated) 14 mouse testis, 8 liver, and 8 brain miRNAs. The statistically significant and unique miRNA expression pattern found among three different proton-treated mouse tissues indicates a tissue-specific response to proton radiation. In addition to known miRNAs, sequencing revealed differential expression of 11 miRNAs in proton-irradiated mice that have not been previously reported in association with radiation exposure and cancer. The dysregulation of miRNAs on exposure to proton radiation suggest a possible mechanism of proton particles involvement in the onset of cell tumorgenesis. In summary, we have established that specific miRNAs are vulnerable to proton radiation, that such differential expression profile may depend upon the tissue, and that there are more miRNAs affected by proton radiation than have been previously observed.

HLA typing from RNA-seq data using hierarchical read weighting [corrected].

Correctly matching the HLA haplotypes of donor and recipient is essential to the success of allogenic hematopoietic stem cell transplantation. Current HLA typing methods rely on targeted testing of recognized antigens or sequences. Despite advances in Next Generation Sequencing, general high throughput transcriptome sequencing is currently underutilized for HLA haplotyping due to the central difficulty in aligning sequences within this highly variable region. Here we present the method, HLAforest, that can accurately predict HLA haplotype by hierarchically weighting reads and using an iterative, greedy, top down pruning technique. HLAforest correctly predicts >99% of allele group level (2 digit) haplotypes and 93% of peptide-level (4 digit) haplotypes of the most diverse HLA genes in simulations with read lengths and error rates modeling currently available sequencing technology. The method is very robust to sequencing error and can predict 99% of allele-group level haplotypes with substitution rates as high as 8.8%. When applied to data generated from a trio of cell lines, HLAforest corroborated PCR-based HLA haplotyping methods and accurately predicted 16/18 (89%) major class I genes for a daughter-father-mother trio at the peptide level. Major class II genes were predicted with 100% concordance between the daughter-father-mother trio. In fifty HapMap samples with paired end reads just 37 nucleotides long, HLAforest predicted 96.5% of allele group level HLA haplotypes correctly and 83% of peptide level haplotypes correctly. In sixteen RNAseq samples with limited coverage across HLA genes, HLAforest predicted 97.7% of allele group level haplotypes and 85% of peptide level haplotypes correctly.

Non-enzymatic transfer of sequence information under plausible prebiotic conditions.

We simulated in our laboratory a prebiotic environment where dry and wet periods were cycled. Under anhydrous conditions, lipid molecules present in the medium could form fluid lamellar matrices and work as organizing agents for the condensation of nucleic acid monomers into polymers. We exposed a mixture of 2'-deoxyribonucleoside 5'-monophosphates and a ssDNA oligomer template to this dry environment at 90 °C under a continuous gentle stream of CO(2) and we followed it with rehydration periods. After five dry/wet cycles we were able to detect the presence of a product that was complementary to the template. The reaction had a 0.5% yield with respect to the template, as measured by staining with the Pico Green(®) fluorescent probe. Absent initial template, the product of the reaction remained below the detection limit. In order to characterize the fidelity of replication, the synthesized strand was ligated to adapters, amplified by PCR, and sequenced. The alignment of the sequenced DNA to the expected complementary sequence revealed that the misincorporation rate was 9.9%. We present these results as a proof of concept for the possibility of having non-enzymatic transfer of sequence information in a prebiotically plausible environment.