Allele quantification using molecular inversion probes (MIP)

Detection of genomic copy number changes has been an important research area, especially in cancer. Several high-throughput technologies have been developed to detect these changes. Features that are important for the utility of technologies assessing copy number changes include the ability to interrogate regions of interest at the desired density as well as the ability to differentiate the two homologs. In addition, assessing formaldehyde fixed and paraffin embedded (FFPE) samples allows the utilization of the vast majority of cancer samples. To address these points we demonstrate the use of molecular inversion probe (MIP) technology to the study of copy number. MIP is a high-throughput genotyping technology capable of interrogating >20 000 single nucleotide polymorphisms in the same tube. We have shown the ability of MIP at this multiplex level to provide copy number measurements while obtaining the allele information. In addition we have demonstrated a proof of principle for copy number analysis in FFPE samples

Antigen receptor sequencing of paired bone marrow samples shows homogeneous distribution of acute lymphoblastic leukemia subclones

In B-cell precursor acute lymphoblastic leukemia, the initial leukemic cells share the same antigen receptor gene rearrangements. However, due to ongoing rearrangement processes, leukemic cells with different gene rearrangement patterns can develop, resulting in subclone formation. We studied leukemic subclones and their distribution in the bone marrow and peripheral blood at diagnosis

Immunoglobulin and T Cell Receptor Gene High-Throughput Sequencing Quantifies Minimal Residual Disease in Acute Lymphoblastic Leukemia and Predicts Post-Transplantation Relapse and Survival

AbstractMinimal residual disease (MRD) quantification is an important predictor of outcome after treatment for acute lymphoblastic leukemia (ALL). Bone marrow ALL burden ≥ 10−4 after induction predicts subsequent relapse. Likewise, MRD ≥ 10−4 in bone marrow before initiation of conditioning for allogeneic (allo) hematopoietic cell transplantation (HCT) predicts transplantation failure. Current methods for MRD quantification in ALL are not sufficiently sensitive for use with peripheral blood specimens and have not been broadly implemented in the management of adults with ALL. Consensus-primed immunoglobulin (Ig), T cell receptor (TCR) amplification and high-throughput sequencing (HTS) permit use of a standardized algorithm for all patients and can detect leukemia at 10−6 or lower. We applied the LymphoSIGHT HTS platform (Sequenta Inc., South San Francisco, CA) to quantification of MRD in 237 samples from 29 adult B cell ALL patients before and after allo-HCT. Using primers for the IGH-VDJ, IGH-DJ, IGK, TCRB, TCRD, and TCRG loci, MRD could be quantified in 93% of patients. Leukemia-associated clonotypes at these loci were identified in 52%, 28%, 10%, 35%, 28%, and 41% of patients, respectively. MRD ≥ 10−4 before HCT conditioning predicted post-HCT relapse (hazard ratio [HR], 7.7; 95% confidence interval [CI], 2.0 to 30; P = .003). In post-HCT blood samples, MRD ≥10−6 had 100% positive predictive value for relapse with median lead time of 89 days (HR, 14; 95% CI, 4.7 to 44, P < .0001). The use of HTS-based MRD quantification in adults with ALL offers a standardized approach with sufficient sensitivity to quantify leukemia MRD in peripheral blood. Use of this approach may identify a window for clinical intervention before overt relapse

RB but not R-HCVAD is a feasible induction regimen prior to auto-HCT in frontline MCL: results of SWOG Study S1106

Aggressive induction chemotherapy followed by autologous haematopoietic stem cell transplant (auto-HCT) is effective for younger patients with mantle cell lymphoma (MCL). However, the optimal induction regimen is widely debated. The Southwesterm Oncology Group S1106 trial was designed to assess rituximab plushyperCVAD/MTX/ARAC (hyperfractionated cyclophosphamide, vincristine, doxorubicin and dexamethasone, alternating with high dose cytarabine and methotrexate) (RH) versus rituximab plus bendamustine (RB) in a randomized phase II trial to select a pre-transplant induction regimen for future development. Patients had previously untreated stage III, IV, or bulky stage II MCL and received either 4 cycles of RH or 6 cycles of RB, followed by auto-HCT. Fifty-three of a planned 160 patients were accrued; an unacceptably high mobilization failure rate (29%) on the RH arm prompted premature study closure. The estimated 2-year progression-free survival (PFS) was 81% vs. 82% and overall survival (OS) was 87% vs. 88% for RB and RH, respectively. RH is not an ideal platform for future multi-centre transplant trials in MCL. RB achieved a 2-year PFS of 81% and a 78% MRD negative rate. Premature closure of the study limited the sample size and the precision of PFS estimates and MRD rates. However, RB can achieve a deep remission and could be a platform for future trials in MCL

RB but not R‐HCVAD is a feasible induction regimen prior to auto‐HCT in frontline MCL: results of SWOG Study S1106

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136294/1/bjh14480_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136294/2/bjh14480.pd

Prediction of epigenetically regulated genes in breast cancer cell lines

Methylation of CpG islands within the DNA promoter regions is one mechanism that leads to aberrant gene expression in cancer. In particular, the abnormal methylation of CpG islands may silence associated genes. Therefore, using high-throughput microarrays to measure CpG island methylation will lead to better understanding of tumor pathobiology and progression, while revealing potentially new biomarkers. We have examined a recently developed high-throughput technology for measuring genome-wide methylation patterns called mTACL. Here, we propose a computational pipeline for integrating gene expression and CpG island methylation profles to identify epigenetically regulated genes for a panel of 45 breast cancer cell lines, which is widely used in the Integrative Cancer Biology Program (ICBP). The pipeline (i) reduces the dimensionality of the methylation data, (ii) associates the reduced methylation data with gene expression data, and (iii) ranks methylation-expression associations according to their epigenetic regulation. Dimensionality reduction is performed in two steps: (i) methylation sites are grouped across the genome to identify regions of interest, and (ii) methylation profles are clustered within each region. Associations between the clustered methylation and the gene expression data sets generate candidate matches within a fxed neighborhood around each gene. Finally, the methylation-expression associations are ranked through a logistic regression, and their significance is quantified through permutation analysis. Our two-step dimensionality reduction compressed 90% of the original data, reducing 137,688 methylation sites to 14,505 clusters. Methylation-expression associations produced 18,312 correspondences, which were used to further analyze epigenetic regulation. Logistic regression was used to identify 58 genes from these correspondences that showed a statistically signifcant negative correlation between methylation profles and gene expression in the panel of breast cancer cell lines. Subnetwork enrichment of these genes has identifed 35 common regulators with 6 or more predicted markers. In addition to identifying epigenetically regulated genes, we show evidence of differentially expressed methylation patterns between the basal and luminal subtypes. Our results indicate that the proposed computational protocol is a viable platform for identifying epigenetically regulated genes. Our protocol has generated a list of predictors including COL1A2, TOP2A, TFF1, and VAV3, genes whose key roles in epigenetic regulation is documented in the literature. Subnetwork enrichment of these predicted markers further suggests that epigenetic regulation of individual genes occurs in a coordinated fashion and through common regulators

High quality copy number and genotype data from FFPE samples using Molecular Inversion Probe (MIP) microarrays

BACKGROUND:A major challenge facing DNA copy number (CN) studies of tumors is that most banked samples with extensive clinical follow-up information are Formalin-Fixed Paraffin Embedded (FFPE). DNA from FFPE samples generally underperforms or suffers high failure rates compared to fresh frozen samples because of DNA degradation and cross-linking during FFPE fixation and processing. As FFPE protocols may vary widely between labs and samples may be stored for decades at room temperature, an ideal FFPE CN technology should work on diverse sample sets. Molecular Inversion Probe (MIP) technology has been applied successfully to obtain high quality CN and genotype data from cell line and frozen tumor DNA. Since the MIP probes require only a small (~40 bp) target binding site, we reasoned they may be well suited to assess degraded FFPE DNA. We assessed CN with a MIP panel of 50,000 markers in 93 FFPE tumor samples from 7 diverse collections. For 38 FFPE samples from three collections we were also able to asses CN in matched fresh frozen tumor tissue.RESULTS:Using an input of 37 ng genomic DNA, we generated high quality CN data with MIP technology in 88% of FFPE samples from seven diverse collections. When matched fresh frozen tissue was available, the performance of FFPE DNA was comparable to that of DNA obtained from matched frozen tumor (genotype concordance averaged 99.9%), with only a modest loss in performance in FFPE.CONCLUSION:MIP technology can be used to generate high quality CN and genotype data in FFPE as well as fresh frozen samples.This item is part of the UA Faculty Publications collection. For more information this item or other items in the UA Campus Repository, contact the University of Arizona Libraries at [email protected]

Functional single nucleotide polymorphism-based association studies

Abstract Association studies hold great promise for the elucidation of the genetic basis of diseases. Studies based on functional single nucleotide polymorphisms (SNPs) or on linkage disequilibrium (LD) represent two main types of designs. LD-based association studies can be comprehensive for common causative variants, but they perform poorly for rare alleles. Conversely, functional SNP-based studies are efficient because they focus on the SNPs with the highest a priori chance of being associated. Our poor ability to predict the functional effect of SNPs, however, hampers attempts to make these studies comprehensive. Recent progress in comparative genomics, and evidence that functional elements tend to lie in conserved regions, promises to change the landscape, permitting functional SNP association studies to be carried out that comprehensively assess common and rare alleles. SNP genotyping technologies are already sufficient for such studies, but studies will require continued genomic sequencing of multiple species, research on the functional role of conserved sequences and additional SNP discovery and validation efforts (including targeted SNP discovery to identify the rare alleles in functional regions). With these resources, we expect that comprehensive functional SNP association studies will soon be possible.</p