Using Whole Genome Sequencing (WGS) of 3256 Patients with Hematologic Malignancies to Determine Genome Instability

Background:

Genome instability is a hallmark of cancer. Mutations in DNA repair pathway genes are frequent in a number of solid tumors. Defects in DNA repair or damage response can weaken response to conventional chemotherapy and are frequently regarded as poor prognostic markers. However, a high tumor mutation burden (TMB, number of somatic mutations per mega base) was recently found to correlate with better response to immune checkpoint inhibitors e.g. in colon cancer. Patients with defects in the DNA mismatch repair (MMR) pathway in solid tumors are among the cases with the highest TMB. Hematological malignancies are generally expected on the lower end of the TMB spectrum. We used whole genome sequencing (WGS) for 3256 patients with hematological malignancies (lymphatic and myeloid) to determine factors of genetic instability across all entities.

Aim:

1. To determine the number of known mutations in genes from the DNA repair pathway

2. To estimate TMB using WGS and identify cases with high TMB in hematologic malignancies

Methods:

We investigated a cohort of 3256 patients with hematological malignancies, who were analyzed according to WHO diagnostic gold standards for routine purposes (incl. 584 acute myeloid leukemia [AML] and 635 myelodysplastic syndromes [MDS] samples). We performed amplification-free library preparation and sequencing on HiseqX and NovaSeq 6000 with a median coverage of 106x. Mapping and variant calling was performed with standard pipelines via BaseSpace (all Illumina, San Diego, CA). A pool of gender-matched genomic DNA (Promega, Madison, WI) was used for a tumor-unmatched normal variant calling. (a) In detail we evaluated 180 genes involved in DNA repair. We filtered on (likely) pathogenic variants from ClinVar and for TP53 on protein-truncating variants and (likely, possibly) pathogenic variants from the IARC database. (b) For TBM calculation we determined protein-altering changes and then subtracted all gnomAD listed variants in order to eliminate most germline variants.

Results:

We found 479 of 3256 (15%) patients with at least one pathogenic variant according to current database annotations in DNA repair or damage response genes. Most pathogenic variants were found in TP53 (330/3256; 10%) and ATM (25/3256, 1%), however, this is probably the effect of the already available systematic database annotation for both genes. For routine diagnostic purposes TP53 mutation status had been analyzed for 1184 patients with Sanger sequencing (7%) or amplicon next-generation sequencing (93%). A 98% and 99% concordance of the pathogenic and non-pathogenic TP53 status was found in comparison to WGS. Mutations in genes from the DNA double-strand break repair (and/or homologous DNA pairing and strand exchange) pathway were found in 93 patients (3%). Pathogenic and potentially germline MMR gene mutations were found in only 3 patients (0.1%, 2 MLH1, 1 MSH6), which equals the expected frequency in the Western population (0.05-0.3%).

Next, we calculated TMB. The average was 2.4 [range: 0.4-39.2]. Only samples above the 95^th percentile were defined as "TMB^hi" (TMB ≥5). TMB was lowest in chronic myeloid leukemia (CML) and essential thrombocythemia (ET) (< 2) and no ET or CML patient was found among the TMB^hi. We then focused on MDS, which is our largest subcohort: 56 of 635 (9%) patients were in TMB^hi. Furthermore, among MDS patients, a significantly higher TMB was found in MDS-EB-2 (average 3.3 vs. 2.3 for non EB-2, p<.001) and a significantly lower TMB in MDS with isolated del(5q) (1.7 vs. 2.6 in all other MDS, p<.001).

There was no association of high TMB and annotated DNA repair gene mutations. The three MMR deficient patients had an average TMB (1.8-2.2) in the hematologic sample. However, TMB was positively correlated with age (Spearman's rank correlation, p<.001) and average TMB was lower in patients under 60 years (2.3 vs. 2.5, p=0.007, t-test).

Conclusions:

High TMB is rare in hematological neoplasms, and our data across all entities suggest that the acquisition of mutations over age should be a major contributing factor.

While TP53 and a few other factors are well studied, using genome-wide datasets in the near future will allow to deeply understand the broad patterns or signatures of genome stability (incl. copy number or structural variants) and their prognostic or predictive value.