Development and Validation of Biopsy-Free Genotyping for Molecular Subtyping of Diffuse Large B-Cell Lymphoma

Abstract

Background: Molecular characterization of tumors currently relies in large part on invasive tissue biopsies, which can often be unavailable or limiting. For example, while biological heterogeneity between diffuse large B-cell lymphoma (DLBCL) patients can be stratified using classification of cell-of-origin (COO), current methods relying on tumor gene expression profiles (GEP) or multi-parametric immunohistochemistry (IHC) require adequate tissue, and can be hampered by modest accuracy and reproducibility. Circulating tumor DNA (ctDNA) is an emerging noninvasive biomarker for tumor detection with potential for DLBCL disease monitoring. Here, we evaluated the utility ofctDNA for COO classification of patients with DLBCL. Methods: We previously built and trained a novel DLBCL COO Naive Bayesian classifier, using somatic alterations in 32 genes and associated with COO subtypes that were previously classified by Affymetrix mRNA GEP of frozen tumors (n=142 Training; Scherer et al., ASH Annual Meeting 2015 / ASCO Annual Meeting 2016). Here, we applied CAPP-Seq, a capture-based targeted high-throughput sequencing (HTS) method (Newman et al., Nat Med 2014), to genetically profile and classify 146 DLBCL patients in independent cohorts. We employed a test/validation framework (n=76 Test, n=70 Validation) to classify patients diagnosed and treated at Stanford University, MD Anderson Cancer Center, or at the University of Eastern Piedmont, Italy. We compared COO classification from pre-treatment plasma samples (n=119), tumor biopsy tissues (n=82, of which 71% were from FFPE), or both (n=41) against the current clinical standard immunohistochemistry method (Hans algorithm, n=117). Kaplan Meier analyses and Cox proportional-hazard models were used to assess clinical utility of biopsy-free and tumor genotyping for prediction of clinical outcomes from time of DLBCL diagnosis. Results: Genotyping of both test and validation cohorts using either tumor or plasma samples allowed COO classification in 100% of patients (GCB=77 / non-GCB=69) with 77% overall concordance to blinded and centralized Hans IHC classification. As expected, histologically transformed (tFL/DLBCL) and double-hit DLBCLs (MYC and BCL2/BCL6 translocated) were strongly biased toward GCB DNA subtype (26/29, 90%), while primary central nervous system lymphomas were almost invariably classified as ABC/non-GCB by ctDNA (6/7, 87%). The concordance between DNA-based COO classification from tumor vs. plasma was 88% (36/41). In the test cohort, COO classification by genotyping from either tissue or plasma was significantly associated with PFS (P=0.003 and 0.02, respectively, Cox proportional-hazard), while classification by Hans criteria failed to show a significant survival difference (P=0.25). The superior outcome of patients with GCB DNA COO subtype was validated when extended to the full cohort (P=0.02, Cox proportional-hazard; P=0.03, log-rank (Figure 1)). We observed a similar, albeit not significant, trend for overall survival. Conclusions: DLBCL COO subtypes can be accurately classified using somatic alterations detectable as circulating tumor DNA. We envision that this approach might help overcome some of the limitations of mRNA GEP and protein/IHC-based methods, including the requirement for invasive biopsies, tissue availability, and suboptimal assay performance. Furthermore, this strategy could support future clinical trials in helping to identify poor-risk groups at diagnosis, to guide future therapy selection, and to improve treatment decisions for patients with DLBCL. Figure 1. Figure 1. Disclosures Newman: Roche: Consultancy. Lovejoy:Roche: Employment. Levy:Kite Pharma: Consultancy; Five Prime Therapeutics: Consultancy; Innate Pharma: Consultancy; Beigene: Consultancy; Corvus: Consultancy; Dynavax: Research Funding; Pharmacyclics: Research Funding. Gaidano:Janssen: Consultancy, Honoraria, Speakers Bureau; Novartis: Consultancy, Honoraria, Speakers Bureau; Gilead: Consultancy, Honoraria, Speakers Bureau; Morphosys: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria; Roche: Consultancy, Honoraria, Speakers Bureau. Diehn:Roche: Consultancy; Novartis: Consultancy; Quanticel Pharmaceuticals: Consultancy; Varian Medical Systems: Research Funding. Alizadeh:Gilead: Consultancy; Celgene: Consultancy; Genentech: Consultancy; Roche: Consultancy.

Minimal residual disease in non‐Hodgkin lymphoma – current applications and future directions

“…We will refer henceforth to these methods as IgNGS approaches (TCR‐based MRD detection is outside the scope of this review). Alternatively, one can look for somatic mutations or other genetic abnormalities that are present in a given patient's tumour and that can be used to track ctDNA (Newman et al , ; Simonsen et al , ; Scherer et al , ). We will refer to these methods as mutNGS approaches.…”

“…We will refer to these methods as mutNGS approaches. Both avenues are now possible using NGS‐based methods (Scherer et al , ). Below are brief descriptions of the NGS‐based assays in current use or development, for which the salient characteristics are summarized in Table .…”

“…This method can simultaneously assay for all important classes of mutations, including single nucleotide variants (SNV's), insertion/deletions (indels), copy number alterations and rearrangements. CAPPseq has allowed highly promising studies in both solid and haematological malignancies (Newman et al , ; Kurtz et al , ; Scherer et al , ).…”

“…Based on their high sensitivity and ability to reliably identify patient‐specific target sequences, mutNGS techniques have proven valuable in the characterization of novel mutations within already identified pathways of lymphomagenesis in DLBCL (Jiang et al , ; Bohers et al , ; Dubois et al , ). Targeted sequencing of proteins known to be involved in B‐cell signalling, toll‐like receptor signalling, nuclear factor‐κB, immunity, apoptoticand epigenetic regulation pathways can yield a library of novel SNV's which are readily detectable by CAPPseq in DLBCL (Jiang et al , ; Bohers et al , ; Kurtz et al , ; Scherer et al , ).…”

Liquid biopsy in lymphoma

“…Ultra-deep next-generation sequencing (NGS) methods can overcome the limitations of assays covering single somatic variants by detecting a large spectrum of genetic alterations, including single nucleotide variants, insertions/deletions, chromosomal rearrangements, and copy number changes. 18–20 The Cancer Personalized Profiling by Deep Sequencing (CAPP-seq) is a targeted capture ultra-deep NGS method for ctDNA detection and quantification in molecular heterogeneous tumors (Figure 1). 18,19 CAPP-seq utilizes a disease-specific “selector”, which is a set of exonic and intronic targets chosen to cover regions of known recurrent mutations for a particular cancer type.…”