John Morlan

Tumor-cell resistance to death receptor-induced apoptosis through mutational inactivation of the proapoptotic Bcl-2 homolog Bax

The importance of Bax for induction of tumor apoptosis through death receptors remains unclear. Here we show that Bax can be essential for death receptor–mediated apoptosis in cancer cells. Bax-deficient human colon carcinoma cells were resistant to death-receptor ligands, whereas Bax-expressing sister clones were sensitive. Bax was dispensable for apical death-receptor signaling events including caspase-8 activation, but crucial for mitochondrial changes and downstream caspase activation. Treatment of colon tumor cells deficient in DNA mismatch repair with the death-receptor ligand apo2 ligand (Apo2L)/tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) selected in vitro or in vivo for refractory subclones with Bax frameshift mutations including deletions at a novel site. Chemotherapeutic agents upregulated expression of the Apo2L/TRAIL receptor DR5 and the Bax homolog Bak in Bax−/− cells, and restored Apo2L/TRAIL sensitivity in vitro and in vivo. Thus, Bax mutation in mismatch repair–deficient tumors can cause resistance to death receptor–targeted therapy, but pre-exposure to chemotherapy rescues tumor sensitivity.

Mutation Detection by Real-Time PCR: A Simple, Robust and Highly Selective Method

Background Molecular tests for diagnosis of disease, particularly cancer, are gaining increased acceptance by physicians and their patients for disease prognosis and selection of treatment options. Gene expression profiles and genetic mutations are key parameters used for the molecular characterization of tumors. A variety of methods exist for mutation analysis but the development of assays with high selectivity tends to require a process of trial and error, and few are compatible with real-time PCR. We sought to develop a real-time PCR-based mutation assay methodology that successfully addresses these issues. Methodology/Principal Findings The method we describe is based on the widely used TaqMan® real-time PCR technology, and combines Allele-Specific PCR with a Blocking reagent (ASB-PCR) to suppress amplification of the wildype allele. ASB-PCR can be used for detection of germ line or somatic mutations in either DNA or RNA extracted from any type of tissue, including formalin-fixed paraffin-embedded tumor specimens. A set of reagent design rules was developed enabling sensitive and selective detection of single point substitutions, insertions, or deletions against a background of wild-type allele in thousand-fold or greater excess. Conclusions/Significance ASB-PCR is a simple and robust method for assaying single nucleotide mutations and polymorphisms within the widely used TaqMan® protocol for real time RT-PCR. The ASB-PCR design rules consistently produce highly selective mutation assays while obviating the need for redesign and optimization of the assay reagents. The method is compatible with formalin-fixed tissue and simultaneous analysis of gene expression by RT-PCR on the same plate. No proprietary reagents other than those for TaqMan chemistry are required, so the method can be performed in any research laboratory with real-time PCR capability.

Selective Depletion of rRNA Enables Whole Transcriptome Profiling of Archival Fixed Tissue

We report a method for Selective Depletion of abundant RNA (SDRNA) species from Human total RNA isolated from formalin-fixed, paraffin-embedded (FFPE) tissue, here demonstrating removal of ribosomal and mitochondrial transcripts from clinical FFPE tissue RNA archived up to 20 years. Importantly, SDRNA removes 98% of targeted RNAs while preserving relative abundance profiles of non-targeted RNAs, enabling routine whole transcriptome analysis of clinically valuable archival tissue specimens by Next-Generation Sequencing.

Whole Transcriptome RNA-Seq Analysis of Breast Cancer Recurrence Risk Using Formalin-Fixed Paraffin-Embedded Tumor Tissue

RNA biomarkers discovered by RT-PCR-based gene expression profiling of archival formalin-fixed paraffin-embedded (FFPE) tissue form the basis for widely used clinical diagnostic tests; however, RT-PCR is practically constrained in the number of transcripts that can be interrogated. We have developed and optimized RNA-Seq library chemistry as well as bioinformatics and biostatistical methods for whole transcriptome profiling from FFPE tissue. The chemistry accommodates low RNA inputs and sample multiplexing. These methods both enable rediscovery of RNA biomarkers for disease recurrence risk that were previously identified by RT-PCR analysis of a cohort of 136 patients, and also identify a high percentage of recurrence risk markers that were previously discovered using DNA microarrays in a separate cohort of patients, evidence that this RNA-Seq technology has sufficient precision and sensitivity for biomarker discovery. More than two thousand RNAs are strongly associated with breast cancer recurrence risk in the 136 patient cohort (FDR <10%). Many of these are intronic RNAs for which corresponding exons are not also associated with disease recurrence. A number of the RNAs associated with recurrence risk belong to novel RNA networks. It will be important to test the validity of these novel associations in whole transcriptome RNA-Seq screens of other breast cancer cohorts.

Selective Depletion of Abundant RNAs to Enable Transcriptome Analysis of Low‐Input and Highly Degraded Human RNA

Ribosomal RNAs (rRNAs) are extremely abundant, often constituting 80% to 90% of total RNA. Since rRNA sequences are often not of interest in genomic RNA sequencing experiments, rRNAs can be removed from the sample before the library preparation step, in order to prevent the majority of the library and the majority of sequencing reads from being rRNA. Removal of rRNA can be especially challenging for low quality and formalin‐fixed paraffin‐embedded (FFPE) RNA samples due to the fragmented nature of these RNA molecules. The NEBNext rRNA Depletion Kit (Human/Mouse/Rat) depletes both cytoplasmic (5 S rRNA, 5.8 S rRNA, 18 S rRNA, and 28 S rRNA) and mitochondrial rRNA (12 S rRNA and 16 S rRNA) from total RNA preparations from human, mouse, and rat samples. Due to the high similarity among mammalian rRNA sequences, it is likely that rRNA depletion can also be achieved for other mammals but has not been empirically tested. This product is compatible with both intact and degraded RNA (e.g., FFPE RNA). The resulting rRNA‐depleted RNA is suitable for RNA‐seq, random‐primed cDNA synthesis, or other downstream RNA analysis applications. Regardless of the quality or amount of input RNA, this method efficiently removes rRNA, while retaining non‐coding and other non‐poly(A) RNAs. The NEBNext rRNA Depletion Kit thus provides a more complete picture of the transcript repertoire than oligo d(T) poly(A) mRNA enrichment methods.

Abstract P4-02-08: Global quantitative measures using next-generation sequencing for breast cancer presence outperform individual tumor markers in plasma

Background: Analytically and clinically validated non-invasive blood tests that quantify breast cancer burden and clinical drug response/resistance are greatly needed. Many groups have successfully detected tumor markers in blood using a variety of technologies, including next generation sequencing (NGS). We performed a comprehensive NGS study on a small number of patients to evaluate the value of global versus individual markers for the quantitation of tumor-derived cell free DNA (cfDNA) in plasma. Methods: DNA isolated from formalin-fixed primary tumor, buffy coat cells, and plasma from 2 patients with metastatic breast cancer were characterized simultaneously for copy number aberrations (CNAs) and differentially methylated regions (DMRs) using whole genome bisulfite sequencing (WBGS), and targeted sequencing-based genotyping of 346 cancer-associated single nucleotide variations (SNVs). CNA and DMR regions were identified from log normalized, GC content corrected counts and DMR data using Poisson and binomial distribution theory and false discovery rate controlling methods. Percent tumor in cfDNA was estimated from the normalized ratio (plasma: primary tumor) of CNA or DMR compared to buffy coat, aggregating over genomic regions. Sample sets from 8 non-metastatic patients were also profiled using the targeted SNV panel in order to compare SNVs between samples and estimate percent tumor cfDNA. Results: WGBS detected tumor specific alterations in each primary tumor compared to buffy coat. By analyzing the genome using 100 Kb bins, we observed over 1000 bins with detectable CNA signal and, among 56 million CpG sites, over 30,000 DMRs. As expected, 5 or fewer informative somatic SNVs were detected in each patient. Analysis of these somatic changes in plasma revealed that the tumor fraction estimated from SNV detected in cfDNA varied widely between sites originally discovered in the patient’s primary tumor. In contrast, similar estimates of tumor fraction in cfDNA were obtained using CNA and DMR profiles within each patient; both methods yielded similar estimates of over 50% in one patient and less than 10% in the other. For the patient with high tumor fraction, both CNA and DMR profiles contained examples of individual large genomic regions that displayed additional clear aberrations in the plasma compared to the original tumor, such as a striking loss of a >25 Mb region of chromosome 4. Conclusions: Although individual somatic SNV in cfDNA can be detected in metastatic disease, calculated allelic fraction based on individual SNVs varies greatly within the same patient. Measuring and integrating CNA or DMR across the genome provided more consistent and reliable estimates of tumor DNA fraction in plasma, and also revealed alterations in plasma from patients with metastatic disease that were not prominent in the primary tumor. Citation Format: Ellen M Beasley, Richard D Abramson, Gregory E Alexander, David Chan, Kristen Bradley, Francois Collin, Michael Crager, Andrew Dei Rossi, Joseph Dorado, Adam Friedman, William J Gibb, Jennie Jeong, Col Jones, C J Ku, Yan Ma, John Morlan, Kunbin Qu, Aibing Rao, Aaron Scott, Haluk Tezcan. Global quantitative measures using next-generation sequencing for breast cancer presence outperform individual tumor markers in plasma [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P4-02-08.

Abstract 4859: Tumor and normal classification of formalin-fixed, paraffin-embedded (FFPE) specimens by transcriptome RNA-seq

We have used RNA-seq to profile and compare normal and cancerous human breast tissue. FFPE breast specimens from a total of 24 patients, 12 normal (N) and 12 tumor (T) specimens from surgical resections, were analyzed on an Illumina9s GA IIx sequencer. Whole transcriptome RNA-Seq libraries were prepared after depletion of ribosomal RNA by a protocol developed at Genomic Health Inc. (GHI). The analysis was multiplexed across two flow cells using barcoding, with two specimens per sequencing lane (1 T and 1 closely age-matched N library from a different patient). FFPE tissue archive times ranged from 10 to 13 years and they were also closely matched within each lane. To evaluate reproducibility, triplicate libraries were created from 4 of the specimens and analyzed within and across flow cells. Libraries yielded, on average, 19 million 51 bp sequences. R 2 values obtained from replicate libraries prepared from the same patient RNA were > 0.9 within and between flow cells. More than 80% of known genes in the human genome were detected in all patients. Several thousand intergenic transcripts were identified by an algorithm developed at GHI. A negative binomial model with tag-wise estimates of dispersion was applied to the known genes and intergenic regions. Inter-patient count variance is generally higher in the set of intergenic sequences than in the set of gene (RefSeq) sequences. Thousands of gene (RefSeq) and intergenic sequences were found to be differentially expressed between T and N tissues. We sought to build classifiers based on flow cell #1 data that could stratify T and N tissues when applied to flow cell #2 data. Sets of genes and intergenic regions were selected for analysis based on high inter-patient count variance. Support vector machine classifiers were trained and then applied to the data from flow cell #2, and also to another GHI tumor/normal RNA-Seq study. Either a set of 100 genes (RefSeq), or a set of 70 intergenic sequences accurately distinguished tumor and normal tissues. Our results offer further evidence of the potential of RNA-Seq for discovery of biomarkers. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4859. doi:10.1158/1538-7445.AM2011-4859