Genomic profiling for clinical decision making in lymphoid neoplasms

Abstract

With the introduction of large-scale molecular profiling methods and high-throughput sequencing technologies, the genomic features of most lymphoid neoplasms have been characterized at an unprecedented scale. Although the principles for the classification and diagnosis of these disorders, founded on a multidimensional definition of disease entities, have been consolidated over the past 25 years, novel genomic data have markedly enhanced our understanding of lymphomagenesis and enriched the description of disease entities at the molecular level. Yet, the current diagnosis of lymphoid tumors is largely based on morphological assessment and immunophenotyping, with only few entities being defined by genomic criteria. This paper, which accompanies the International Consensus Classification of mature lymphoid neoplasms, will address how established assays and newly developed technologies for molecular testing already complement clinical diagnoses and provide a novel lens on disease classification. More specifically, their contributions to diagnosis refinement, risk stratification, and therapy prediction will be considered for the main categories of lymphoid neoplasms. The potential of whole-genome sequencing, circulating tumor DNA analyses, single-cell analyses, and epigenetic profiling will be discussed because these will likely become important future tools for implementing precision medicine approaches in clinical decision making for patients with lymphoid malignancies.

Figure 1.

Detection capacity of genomic aberrations with different technologies.¹Includes various technologies that may interrogate single nucleotide changes through to the sequence of the entire gene (AS-PCR, fragment analysis, Sanger sequencing, and others). ²Includes gene expression arrays, NanoString, and RT-MLPA assays. ³Most technologies, except FISH, cannot detect subclonal CNAs (<20%) with high confidence. ⁴Including gene fusions. Ticks indicate good capacity to determine a certain aberration/feature, whereas an inverted red triangle indicates a limited/insufficient detection capacity. AS-PCR, allele-specific oligonucleotide polymerase chain reaction; CNA, copy number aberration; IG, immunoglobulin; indel, insertion-deletion; RT-MLPA, reverse transcriptase multiplex ligation–dependent probe amplification; TR, T-cell receptor locus. Created with BioRender.com.

Figure 2.

The molecular classification of MM. Data from the COMMpass study (clinical trial identifier: NCT0145297) are summarized, showing the 5 nonoverlapping subgroups and their associated gene expression, CNVs, SVs, and SNVs. The pinwheels show the expression of CCND, MAF, NSD2, and FGFR3 for individual patients in each group. Gains of chromosomes (or arms) are shown in blue (1 copy) or purple (>1 copy). For illustration, the hyperdiploid subgroup is further subdivided into those with (HRD11-positive) and without (HRD11-negative) trisomy 11, and the patients without translocations or hyperdiploidy are labeled nHRD2 (MM, NOS). Mutations of FGFR3 (black) and PRKD2 (gray) are common in NSD2, whereas mutations of CCND1 (black) and IRF4 (gray) are common in CCND. A variety of different mutations can activate NF-κB (TRAF3, BIRC2/3, and others). Adverse secondary events include biallelic inactivation of CDKN2C, TP53, or RB1. MYC SVs are most common in hyperdiploid MM.

Figure 3.

Genetic subgroups of DLBCL illustrated using the LymphGen algorithm. The relationships between COO and the probabilistic assignments to genetics-based subgroups are shown. The size of the subgroup circles approximates the proportions of patients in each group, with the prevalence based on Schmitz et al, adjusted for a population-based distribution of COO subgroups. Tumors assigned with high confidence to ≥2 subgroups are assigned to the composite group, while ∼37% of tumors are not assigned to any subgroup with sufficient confidence (other). The hallmark genetic features are those frequent within that subgroup but are not required for that assignment. OS following R-CHOP chemoimmunotherapy along with inferred drug targets are shown. GCB, germinal center B-cell–like.

Figure 4.

Approach to diagnosing HGBCL. Lymphomas that potentially fall into the HGBCL categories can have high-grade (blastoid or intermediate [between BL and large-cell]) morphology or resemble DLBCL. Tumors with morphology resembling BL and other HGBCL are assigned to the provisional entity LBCL with 11q aberration (LBCL-11q) if they lack MYC rearrangement and have 11q aberration. The full morphological spectrum of cases with this aberration requires further study. Other cases in this category present with large-cell morphology. Tumors should not be assigned to LBCL-11q if they harbor concurrent MYC and BCL2 or MYC and BCL6 rearrangements. Tumors with morphology resembling BL and an immunophenotype consistent with BL, lacking both MYC rearrangement and 11q aberration, are likely diagnosed as HGBCL, NOS, acknowledging that rare MYC rearrangements cryptic to FISH have been observed.

Figure 5.

Recurrent genetic lesions in mature NK-cell and T-cell neoplasms with potential therapeutic intervention. Representative histology of entities with frequent genetic lesions potentially amenable to therapeutic intervention are shown on the left. The genetic lesions are presented according to functional groups related to TcR signaling, JAK/STAT pathway, epigenetics, or others. Therapeutic efficacy is supported by clinical trial (a); case reports, small case series, or retrospective analyses (b); or experimental or in silico data (c). AITL, Angioimmunoblastic T-cell lymphoma; ATLL, adult T-leukemia/lymphoma; CTCL, cutaneous T-cell lymphoma; ITLPD-GI, indolent clonal T-cell LPD of the gastrointestinal tract; TFHL-F, TFHL, follicular type; T-PLL, T-cell prolymphocytic leukemia. Sources referenced: , , , , , , , , , , , , , , , , , , , , , , , .

Figure 6.

Applications of ctDNA in lymphoma. Schematic illustrates the potential applications of liquid biopsy assessment, as used for the identification of clinically actionable adverse-risk features in lymphomas at different disease milestones. A lymphoid tumor (left of vessel) is imagined as being accessible through blood plasma by analysis of ctDNA fragments. ctDNA is represented by purple double-stranded DNA molecules, and yellow double strands represent non–tumor-derived cell-free DNA molecules. The patient is evaluated by ctDNA profiling during various disease milestones over time (diagnosis, treatment, and relapse). During this temporal sequence, ctDNA can inform risk at diagnosis, during therapy, immediately after induction therapy, in surveillance of disease, and at progression or disease transformation, as illustrated in the panels associated with each milestone. At diagnosis, profiling of tumor DNA obtained from either tissue biopsies or noninvasively through genotyping of plasma (depicted as blood collection tubes), allows for the identification of patients with high tumor burden,^, histological subtypes, and prediction of outcomes. Assessment of ctDNA during and after lymphoma treatment facilitates the detection of both emerging resistance mutations and MRD before progression, with potential for noninvasive prediction of relapse and transformation. Tumor evolution in an anecdotal patient with DLBCL is illustrated, showing tumor response and clonal evolution over the course of the disease (detectable subclones at diagnosis are shown in blue/yellow; an emergent subclone after therapy is shown in red). EMR, early molecular response; MMR, major molecular response; TMTV, total metabolic tumor volume.