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.

A Distributed Network for Intensive Longitudinal Monitoring in Metastatic Triple-Negative Breast Cancer.

Accelerating cancer research is expected to require new types of clinical trials. This report describes the Intensive Trial of OMics in Cancer (ITOMIC) and a participant with triple-negative breast cancer metastatic to bone, who had markedly elevated circulating tumor cells (CTCs) that were monitored 48 times over 9 months. A total of 32 researchers from 14 institutions were engaged in the patient's evaluation; 20 researchers had no prior involvement in patient care and 18 were recruited specifically for this patient. Whole-exome sequencing of 3 bone marrow samples demonstrated a novel ROS1 variant that was estimated to be present in most or all tumor cells. After an initial response to cisplatin, a hypothesis of crizotinib sensitivity was disproven. Leukapheresis followed by partial CTC enrichment allowed for the development of a differential high-throughput drug screen and demonstrated sensitivity to investigational BH3-mimetic inhibitors of BCL-2 that could not be tested in the patient because requests to the pharmaceutical sponsors were denied. The number and size of CTC clusters correlated with clinical status and eventually death. Focusing the expertise of a distributed network of investigators on an intensively monitored patient with cancer can generate high-resolution views of the natural history of cancer and suggest new opportunities for therapy. Optimization requires access to investigational drugs.

RADIA: RNA and DNA integrated analysis for somatic mutation detection.

The detection of somatic single nucleotide variants is a crucial component to the characterization of the cancer genome. Mutation calling algorithms thus far have focused on comparing the normal and tumor genomes from the same individual. In recent years, it has become routine for projects like The Cancer Genome Atlas (TCGA) to also sequence the tumor RNA. Here we present RADIA (RNA and DNA Integrated Analysis), a novel computational method combining the patient-matched normal and tumor DNA with the tumor RNA to detect somatic mutations. The inclusion of the RNA increases the power to detect somatic mutations, especially at low DNA allelic frequencies. By integrating an individual's DNA and RNA, we are able to detect mutations that would otherwise be missed by traditional algorithms that examine only the DNA. We demonstrate high sensitivity (84%) and very high precision (98% and 99%) for RADIA in patient data from endometrial carcinoma and lung adenocarcinoma from TCGA. Mutations with both high DNA and RNA read support have the highest validation rate of over 99%. We also introduce a simulation package that spikes in artificial mutations to patient data, rather than simulating sequencing data from a reference genome. We evaluate sensitivity on the simulation data and demonstrate our ability to rescue back mutations at low DNA allelic frequencies by including the RNA. Finally, we highlight mutations in important cancer genes that were rescued due to the incorporation of the RNA.