Du, Feiyu

Abstract Introduction Ph-like B-ALL is a poor-risk B-ALL subtype defined by gene expression signatures that resemble the BCR::ABL1-associated transcriptional program in the absence of the BCR-ABL1 oncoprotein. Recent studies have demonstrated that up to 80% of Ph-like B-ALLs have recurrent gene fusions involving cytokine receptors or tyrosine kinases that can be identified via cytogenetics, fluorescence in situ hybridization (FISH) or RNA sequencing. However, the inability to rapidly and accurately identify these alterations in the clinical setting is a major impediment to developing therapeutic strategies that may have efficacy in this B-ALL subtype. Here we used 88 patient (pt) samples to validate a rapid, streamlined clinical WGS assay (ChromoSeq) for B-ALL and assessed the diagnostic yield for Ph-like-associated SVs in 136 pts enrolled in the Alliance A041501 clinical trial. Methods ChromoSeq is a clinical WGS assay for hematologic malignancies that uses rapid laboratory workflows and focused analysis methods to identify clinically relevant SVs, copy number alterations and gene-level mutations. We updated the ChromoSeq pipeline for use in B-ALL pts and validated it using 88 retrospective B-ALL samples (80 adult/young adult and 8 pediatric from Washington University School of Medicine and St. Louis Children's Hospital), 54 of which were analyzed via RNAseq for gene fusions and a 6-gene Ph-like B-ALL signature (CA6, SPATS2L, MUC4, JCHAIN, BMPR1B, ADGRF1, NRXN3 and CRLF2). The “ChromoSeqV2” assay was then used to analyze 136 pretreatment blood or bone marrow samples from the Alliance A041501 (trial NCT03150693). These results were compared to data from RNAseq obtained for research purposes (N=35), clinical cytogenetics (N=69) and Ph-like classification via low-density microarray (LDA) card analysis (all pts). Results ChromoSeqV2 was validated in a CLIA-licensed setting using cohort of 88 retrospective B-ALL samples, 45 of which had ICC and WHO class-defining SVs based on G-banded karyotyping, FISH or RNAseq. ChromoSeqV2 detected 100% of the non-Ph-like SVs (37/37), including BCR::ABL1 (N=20), KMT2Ar (N=9), ETV6::RUNX1 (N=3), TCF3::PBX1 (N=1), and rearrangements involving MYC (N=2) or MEF2D (N=2). Fifteen patients met criteria for Ph-like B-ALL via cytogenetics or RNAseq either due to rearrangements of CRLF2 (N=5), JAK2 (N=2) or ABL2 (N=1) or high expression of a 6-gene Ph-like signature (N=7). ChromoSeqV2 detected a Ph-like SV in 93% of these cases (14/15) with 100% specificity in samples negative by RNAseq (N=23). ChromoSeqV2 was then used to profile 136 samples from the Alliance A041501 trial, where it identified ICC and WHO class-defining SVs in 87 pts (64%; 95% CI: 55-72%), including Ph-like SVs (56/136, 41%), KMT2A rearrangements (N=5), TCF3::PBX1 (N=3) and SVs involving MYC (N=3), MEF2D (N=2), DUX4 (N=2), and ZNF384 (N=1). Ph-like SVs were dominated by CRLF2 rearrangements (N=45) and included SVs involving EPOR (N=4), JAK2 (N=3), ABL1/ABL2 (N=2) and PDGFRB (N=1). IGH was the most common CRLF2 partner (N=32), followed by P2RY8::CRLF2 (N=7) and other genes (N=5). Multiple CRLF2 SVs were detected in 4 pts, and 33% (15/45) of pts with a CRLF2 rearrangement had a co-occurring CRLF2 gene mutation. The yield of ChromoSeqV2 for SVs in pts with the Ph-like expression signature via LDA card analysis was 66% (54/82; 95% CI 55%-76%) compared to 24% (11/45; 95% CI 13%-40%) by cytogenetics when these studies were successful and 70% (12/17; 95% CI 44%-90%) vs RNAseq in pts with available data. The sensitivity of ChromoSeqV2 for Ph-like SVs identified by either cytogenetics, FISH or RNAseq was 100% (21/21; 95% CI 84%-100%) and the positive predictive value of Ph-like SVs for the Ph-like expression signature by the LDA card assay was 96% (54/56; 95% CI 88%-99%). Conclusions Genomic assessment of 136 B-ALL pts from the Alliance A041501 trial using the ChromoSeqV2 rapid clinical WGS assay was 100% sensitive for Ph-like SVs identified by other molecular methods and had a 96% PPV for the Ph-like expression signature via LDA card analysis. The diagnostic yield of SV pts with the Ph-like signature was 66% using ChromoSeqV2, which was similar to RNAseq and superior to cytogenetics. These results demonstrate that ChromoSeqV2 is an effective approach for identifying potentially targetable rearrangements in B-ALL pts suspected of having the Ph-like phenotype. Support: U10CA180821, U10CA180882; . Servier; Pfizer.

Abstract Background: Estrogen receptors are over-expressed in around 70% of breast cancer cases. The genetic changes that occur during aromatase inhibitor (AI) treatment are not well understood and may differ depending upon the patient's response phenotype. Methods: We performed whole genome sequencing (WGS) of matched blood, pre-treatment, and post-treatment biopsy samples from 22 estrogen receptor positive breast cancer patients treated with neoadjuvant aromatase inhibitors. For 5 cases, we performed the whole genome sequencing (WGS) on patients’ matched normal, two pre AI-treatment, and two post AI-treatment DNA isolates from biopsy samples. We validated all putative coding and non-coding somatic mutations using deep sequencing. By comparing the validated somatic mutations from pre- and post- AI treatment biopsy samples, we were able to determine the alterations in the tumor genomes. In every case we defined the clonal architecture of each pair of pre-treatment and post-treatment biopsy samples by comparing the variant allele frequencies from thousands of validated somatic mutations. Results: Comparisons of the two pre AI-treatment biopsy samples from the same patient indicates that the variant allele frequencies of mutations showed high concordances in all 5 cases, 0.74 to 0.95 range of correlation coefficient. Only a small percentage of somatic mutations were detected in one pre-treatment sample and not the other (4.65% overall). In comparing the somatic variations between pre-treatment and matched post-treatment biopsy samples in 22 cases, we found that patients with good clinical response to AI treatment retained known driver mutations only in their pre-treatment tumors. Conversely, those patients with poor clinical response presented new driver mutations in their post-treatment samples. Furthermore, the variant allele frequency for most mutated genes decreased in post AI treatment samples for patients with good AI treatment response; on the contrary, the variant allele frequency increased for patients with poor clinical response. Conclusions: From WGS of matched normal, pre-treatment, and post-treatment biopsy samples, we identified new driver genes mutated in patients with poor clinical response, while patients with good clinical response had lost mutated driver genes in their post-treatment biopsy samples. The genetic landscape revealed by WGS of pre-treatment and post-treatment biopsy samples reveals mutational repertoires are remodeled by AI therapy. This finding suggests deep sequencing of AI treated samples will be necessary to reveal the complete complement of mutations present in a patient's tumor. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr LB-423. doi:1538-7445.AM2012-LB-423