Association of Germline <i>CHEK2</i> Gene Variants with Risk and Prognosis of Non-Hodgkin Lymphoma

<div><p>The checkpoint kinase 2 gene (<i>CHEK2</i>) codes for the CHK2 protein, an important mediator of the DNA damage response pathway. The <i>CHEK2</i> gene has been recognized as a multi-cancer susceptibility gene; however, its role in non-Hodgkin lymphoma (NHL) remains unclear. We performed mutation analysis of the entire <i>CHEK2</i> coding sequence in 340 NHL patients using denaturing high-performance liquid chromatography (DHPLC) and multiplex ligation-dependent probe amplification (MLPA). Identified hereditary variants were genotyped in 445 non-cancer controls. The influence of <i>CHEK2</i> variants on disease risk was statistically evaluated. Identified <i>CHEK2</i> germline variants included four truncating mutations (found in five patients and no control; P = 0.02) and nine missense variants (found in 21 patients and 12 controls; P = 0.02). Carriers of non-synonymous variants had an increased risk of NHL development [odds ratio (OR) 2.86; 95% confidence interval (CI) 1.42–5.79] and an unfavorable prognosis [hazard ratio (HR) of progression-free survival (PFS) 2.1; 95% CI 1.12–4.05]. In contrast, the most frequent intronic variant c.319+43dupA (identified in 22% of patients and 31% of controls) was associated with a decreased NHL risk (OR = 0.62; 95% CI 0.45–0.86), but its positive prognostic effect was limited to NHL patients with diffuse large B-cell lymphoma (DLBCL) treated by conventional chemotherapy without rituximab (HR-PFS 0.4; 94% CI 0.17–0.74). Our results show that germ-line <i>CHEK2</i> mutations affecting protein coding sequence confer a moderately-increased risk of NHL, they are associated with an unfavorable NHL prognosis, and they may represent a valuable predictive biomarker for patients with DLBCL.</p></div

Germline alterations of the <i>CHEK2</i> gene changing the CHK2 protein structure identified in NHL patients and controls with their frequencies and related odds ratios (OR).

<p>^a New alterations</p><p>^b Two alterations of the <i>CHEK2</i> coding sequence were identified in one patient (c.470T>C and c.1259+1G>C); <i>OR</i>–odds ratio; <i>CI</i>–confidence interval.</p><p>Note: The nomenclature of <i>CHEK2</i> alterations was based on NCBI <i>CHEK2</i> Reference Sequences NG_008150.1 (gene) and NM_007194.3 (mRNA).</p><p>Germline alterations of the <i>CHEK2</i> gene changing the CHK2 protein structure identified in NHL patients and controls with their frequencies and related odds ratios (OR).</p

Overall survival (OS; upper panels) and progression-free survival (PFS; lower panels) in all NHL patients (regardless of histology subtype) classified according to the type of <i>CHEK2</i> alterations.

<p>Panels show: <b>A.</b> the influence of all alterations affecting the CHK2 coding sequence (cds; HR_OS = 1.6; 95% CI 0.79–3.24 and HR_PFS = 2.1; 95% CI 1.12–4.05); <b>B.</b> the influence of the I157T mutation (HR_OS = 1.5; 95% CI 0.62–3.70 and HR_PFS = 3.7; 95% CI 1.42–9.43) and <b>C.</b> the influence of the c.319+43dupA variant (HR_OS = 0.8; 95% CI 0.50–1.15 and HR_PFS = 0.6; 95% CI 0.44–0.89).</p

Germline intronic and silent alterations in the <i>CHEK2</i> gene in NHL patients and controls with their frequencies and related odds ratios (OR).

<p>^a New alterations</p><p>^bThe c.319+43dupA alteration also did not show a statistically significant deviation from the Hardy-Weinberg equilibrium in any of the analyzed groups (all p > 0.05).</p><p>Germline intronic and silent alterations in the <i>CHEK2</i> gene in NHL patients and controls with their frequencies and related odds ratios (OR).</p

Overall survival (OS; upper panels) and progression-free survival (PFS; lower panels) in DLBCL patients.

<p>Panels show: <b>A.</b> the influence of all alterations affecting the CHK2 coding sequence (cds; HR_OS = 2.3; 95% CI 0.77–6.97 and HR_PFS = 2.6; 95% CI 0.91–7.44); <b>B.</b> the influence of the I157T mutation (HR_OS = 2.9; 95% CI 0.70–12.00 and HR_PFS = 5.2; 95% CI 1.25–22.16) and <b>C.</b> the influence of the c.319+43dupA variant (HR_OS = 0.6; 95% CI 0.32–0.97 and HR_PFS = 0.5; 95% CI 0.32–0.86).</p

A schematic diagram showing individual coding exons and flanking intronic sequences affected by the identified <i>CHEK2</i> sequence variants.

<p>The most important structural/functional domains of CHK2 kinase are depicted by color bars [SQ/TQ domain (amino acid (aa) 19–69) in blue, FHA domain (aa 112–175) in yellow, and kinase domain (aa 220–486) in violet]. The left-hand side shows synonymous and intronic <i>CHEK2</i> variants (italicized) while the right-hand side shows CHK2 protein structure-altering variants (frame-shift and missense) that were described in the NHL patients group (in red), controls (green) or in both populations (in black).</p

cDNA analysis of patient No.1273 uncovered with an intronic variant c.1026+5_1026+7delGTA causing RAD51C out-of-frame exon 8 skipping.

<p>The electrophoresis (left) of PCR products amplified with primers located in exon 5 and 3’ UTR sequence (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127711#pone.0127711.s001" target="_blank">S1 Table</a>) shows two products in a patient No.1273 compared to a wild-type control (C) sample. Sequencing chromatogram of the patient’s PCR product shows the presence of aberrantly spliced mRNA with exon 8 skipping.</p

Criteria for the enrollment of high-risk <i>BRCA1</i>- and <i>BRCA2</i>-negative individuals in this study and number of identified mutations in each group.

<p>BC—breast cancer; HBOC—hereditary breast and ovarian cancer; HOC—hereditary ovarian cancer; OC—ovarian cancer; y/o—years old. Number of rare missense variants are in brackets.</p><p>Criteria for the enrollment of high-risk <i>BRCA1</i>- and <i>BRCA2</i>-negative individuals in this study and number of identified mutations in each group.</p

Validation of CZECANCA (CZEch CAncer paNel for Clinical Application) for targeted NGS-based analysis of hereditary cancer syndromes

<div><p>Background</p><p>Carriers of mutations in hereditary cancer predisposition genes represent a small but clinically important subgroup of oncology patients. The identification of causal germline mutations determines follow-up management, treatment options and genetic counselling in patients’ families. Targeted next-generation sequencing-based analyses using cancer-specific panels in high-risk individuals have been rapidly adopted by diagnostic laboratories. While the use of diagnosis-specific panels is straightforward in typical cases, individuals with unusual phenotypes from families with overlapping criteria require multiple panel testing. Moreover, narrow gene panels are limited by our currently incomplete knowledge about possible genetic dispositions.</p><p>Methods</p><p>We have designed a multi-gene panel called CZECANCA (CZEch CAncer paNel for Clinical Application) for a sequencing analysis of 219 cancer-susceptibility and candidate predisposition genes associated with frequent hereditary cancers.</p><p>Results</p><p>The bioanalytical and bioinformatics pipeline was validated on a set of internal and commercially available DNA controls showing high coverage uniformity, sensitivity, specificity and accuracy. The panel demonstrates a reliable detection of both single nucleotide and copy number variants. Inter-laboratory, intra- and inter-run replicates confirmed the robustness of our approach.</p><p>Conclusion</p><p>The objective of CZECANCA is a nationwide consolidation of cancer-predisposition genetic testing across various clinical indications with savings in costs, human labor and turnaround time. Moreover, the unified diagnostics will enable the integration and analysis of genotypes with associated phenotypes in a national database improving the clinical interpretation of variants.</p></div

Validation of CZECANCA (CZEch CAncer paNel for Clinical Application) for targeted NGS-based analysis of hereditary cancer syndromes - Fig 7

<p><b>Comparison of variant detection (shown as values of variant allelic fraction; AF) in DNA reference standards</b> (NA12878, NA24149, NA24385, NA24631 and NA24143) obtained from CZECANCA analysis (x-axis) and AF from VCF files for these standards downloaded from <a href="http://jimb.stanford.edu/giab/" target="_blank">http://jimb.stanford.edu/giab/</a> (y-axis). The graph shows all variants with GATK quality >100 reached in CZECANCA analysis (including FP variants) and undetected (FN) variants. Heterozygote variants clustered in the center, while homozygote variants in right upper corner. Variant distribution was partially influenced by the differences in mean sequencing coverage targeting 100X and 300X in CZECANCA and DNA reference standards VCFs, respectively. The number of TP, TN, FP, FN, and total number of variant (= CZECANCA target) was used to calculate of sensitivity, specificity, and accuracy of CZECANCA analysis.</p