Brennan, Timothy

Abstract Introduction Detecting genetic abnormalities is mandatory during diagnostic workup and for potential individualization of therapy selection in multiple myeloma (MM) and its precursor conditions. At present, this requires invasive bone marrow (BM) biopsies, severely limiting early detection, patient monitoring, and the precise selection of therapy. The current standard for detecting genetic alterations in MM is fluorescence in situ hybridization (FISH), which cannot detect point mutations and other clinically relevant alterations. Consequently, the IMS-IMWG guidelines were recently updated to require next-generation sequencing for the classification of high-risk MM. Moreover, the importance of identifying alterations in therapeutic targets (e.g., BCMA, GPRC5D) for guiding immunotherapies is becoming increasingly recognized. To address this need, we recently launched GenoPredicta, a CLIA-approved Laboratory Developed Test that enables routine monitoring, informing diagnosis, and treatment selection by comprehensively and robustly characterizing MM genomes with whole genome sequencing (WGS) from a minimum of ~50 circulating tumor cells (CTCs) isolated from peripheral blood (PB) or tumor cells from BM. Here, we describe the characterization of clinical and research samples with GenoPredicta, highlighting resistance-conferring genetic alterations that can only be detected by sequencing and that are consistent with patients' clinical histories, while further demonstrating the complete concordance of GenoPredicta with prior FISH results. Methods Tumor cells were isolated from BM and/or PB samples using fluorescence-activated cell sorting (FACS) (after CD138+ enrichment for PB) and subjected to whole genome sequencing (WGS), from which we identified copy number alterations, structural variants, and single nucleotide variants and indels. For a subset of samples, the BM was also characterized by FISH, using a standard clinical panel for characterizing MM; for other samples prior FISH results were available from patients' clinical records. Results The analytical validation of GenoPredicta, conducted on a retrospective cohort of >60 viably frozen samples from >30 patients, showed complete concordance between BM and PB for detected genomic abnormalities, with all clinically relevant events (11 translocations and 125 CNVs) observed in BM recapitulated in PB (100% recall). All events identified by FISH in 14 samples (8 translocations and 30 CNVs) were also identified in the BM (100% recall), with 50 translocations and 30 CNVs not detected by FISH also absent in the WGS data (100% precision). The abnormalities detected in these samples also included multiple high-risk events not assayed by FISH, including biallelic TP53 loss (Del 17p coupled with a deleterious TP53 mutation, observed in 4 patients) and MYC::IGL translocations (2 patients). Moreover, for all fresh peripheral blood clinical samples with FISH results available from patients' histories, GenoPredicta recalled 100% of reported events. Among these clinical samples, we notably observed a case with both a biallelic GPRC5D deletion and a TNFRSF17 (BCMA) deletion combined with a truncating mutation on the remaining allele. This was consistent with the patient's clinical history showing progression after therapy with talquetamab (anti-GPRC5D bispecific), and previous nonresponse to teclistamab (anti-BCMA bispecific) after prior therapy with Cilta-cel (BCMA-directed CAR T). GenoPredicta also detected other mutations likely to confer resistance to immunotherapies, including loss-of-function mutations in IKZF3 and deletions of FCRL5 in frozen peripheral blood research samples. Conclusion We demonstrate that WGS-based characterization of MM from BM or CTCs is a viable replacement for FISH for clinical diagnosis and monitoring, with blood-based measurements enabling comprehensive profiling of the myeloma cancer genome. Importantly, this includes genetic alterations that confer resistance to therapy, allowing for both early detection of such alterations and more precise selection of therapy.

Abstract Acute Lymphoblastic Leukemia (ALL) is the most common malignancy observed in the pediatric population. Recent genomic studies have made considerable strides in improving our understanding of the molecular pathogenesis of pediatric B-cell ALL. This includes the discovery of recurrent somatic alterations targeting B-cell transcription factors, mutations in epigenetic regulators, and oncogenic mutations which activate signaling effectors, including in Ph-like B-ALL. However, the molecular pathogenesis of adult B-cell ALL has been less well studied, and there is a need to identify novel therapeutic targets in adult B-ALL. We therefore performed detailed genomic profiling in adults with B-cell ALL. We performed targeted DNA/RNA capture and sequencing of 189 patients with B-cell ALL enrolled on the ECOG E2993 clinical trial. All samples were taken at the time of diagnosis before patients were treated with multi-agent chemotherapy and followed for clinical outcomes including overall survival and remission/relapse duration and kinetics. We used the FoundationOne® Heme assay for our genomics study. This platform allows us to identify short events (point substitutions, short insertions/deletions) copy number and fusion events present in DNA and/or RNA. Of the 203 samples profiled, 189 had sufficient quality genomic data with a median coverage of 435 on DNA sequencing and 7.85 million on target distinct reads per case on RNA sequencing. In all, we identified cancer-related mutations in 108 genes, 55 of these genes being mutated in more than one sample. We identified a median of 2.98 genomic alterations per sample (standard deviation of 1.78) in the target gene set, which included all genes known to be altered in B-ALL and in other hematologic malignancies. The most common genomic events were the BCR-ABL1 fusion (69 samples, 36.5%), followed by CDKN2A loss (57 samples, 30%), CDKN2B loss (35 samples, 18%), NRAS and KRAS point mutations (32 samples, 16.9%), and MLL fusion genes with a spectrum of partners (25 samples, 13.2%). Notably, we found that BCR-ABL1 fusion are mutually exclusive with both RAS mutations (p<10^-7) and MLL mutations (p<5*10^-4). We next investigated the spectrum and frequency of alterations observed in different classes of oncogenes/tumor suppressors. We identified mutations which result in activation of oncogenic signaling pathways in 130 cases or 68.7% of our cohort. This included known mutations/fusions, such as BCR-ABL, RAS-MAPK pathway mutations, and JAK-STAT pathway alterations, and included missense alleles, insertion/deletion events, and fusion genes targeting a spectrum of genes including ABL, PDGFRB, and CRLF2. In aggregate, 33.8% of the cohort had mutations which activate signaling exclusive of BCR-ABL, demonstrating the high prevalence of the Ph-like B-ALL subtype in adult B-ALL. We identified mutations affecting key transcription factors, including PAX5 and IKZF1, in 31.2% of our cohort including missense, nonsense and deletion events. To confirm our ability to detect small deletions of PAX5 and IKZF1, we performed array CGH of 50 cases and compared copy number calls using high-throughput sequencing and CGH. Of note, all deletions targeting PAX5 and IKZF1 identified by NGS were confirmed by CGH. We also identified mutations in epigenetic regulators in 30.6% of cases, including MLL fusions, mutations in the DNA methylation/hydroxymethylation the pathway (DNMT3A, TET1, TET2), and mutations targeted chromatin modulators. Detailed analyses of the mutational spectra and their relationship to clinical outcome will be presented at the meeting. Taken together, these data show we can use a clinically validated sequencing assay to identify known and novel somatic mutations in adult B-ALL, including sequence alterations, rearrangements and copy number aberrations. These data demonstrate the feasibility of combined DNA and RNA study to identify a complex landscape of fusions and short mutations with prognostic and therapeutic relevance, and inform the use of genomic profiling in B-ALL in the clinical context. Figure 1. Figure 1. Disclosures He: Foundation Medicine, Inc.: Employment, Equity Ownership. Brennan:Foundation Medicine, Inc.: Employment, Equity Ownership. Nahas:Foundation Medicine, Inc.: Employment, Equity Ownership. Balassubramanian:Foundation Medicine, Inc.: Employment, Equity Ownership. Otto:Foundation Medicine, Inc.: Employment, Equity Ownership. Lipson:Foundation Medicine, Inc.: Employment, Equity Ownership. Stephens:Foundation Medicine, Inc.: Employment, Equity Ownership. Miller:Foundation Medicine, Inc.: Employment, Equity Ownership. van den Brink:Boehringer Ingelheim: Consultancy, Other: Advisory board attendee; Merck: Honoraria; Regeneron: Honoraria; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees; Tobira Therapeutics: Other: Advisory board attendee. Rowe:Amgen: Consultancy; BioSight Ltd.: Consultancy, Membership on an entity's Board of Directors or advisory committees; BioLineRx Ltd.: Consultancy. Levine:Foundation Medicine: Consultancy; CTI BioPharma: Membership on an entity's Board of Directors or advisory committees; Loxo Oncology: Membership on an entity's Board of Directors or advisory committees.