Cell-free DNA from nail clippings as source of normal control for genomic studies in hematologic malignancies

Abstract

Comprehensive genomic sequencing is becoming a critical component in the assessment of hematologic malignancies, with broad implications for patients' management. In this context, unequivocally discriminating somatic from germline events is challenging but greatly facilitated by matched analysis of tumor:normal pairs of samples. In contrast to solid tumors, in hematologic malignancies conventional sources of normal control material (peripheral blood, buccal swabs, saliva) could be highly involved by the neoplastic process, rendering them unsuitable. In this work we describe our real-world experience using cell-free DNA (cfDNA) isolated from nail clippings as an alternate source of normal control material, through the dedicated review of 2,610 tumor:nail pairs comprehensively sequenced by MSK-IMPACT-heme. Overall, we found that nail cfDNA is a robust germline control for paired genomic studies. In a subset of patients, nail DNA may be contaminated by tumor DNA, reflecting unique attributes of the hematologic disease and transplant history. Contamination is generally low level, but significantly more common among patients with myeloid neoplasms (20.5%; 304/1,482) than among those with lymphoid diseases (5.4%; 61/1,128) and particularly enriched in myeloproliferative neoplasms with marked myelofibrosis. When identified in patients with lymphoid and plasma-cell neoplasms, mutations commonly reflected a myeloid profile and correlated with a concurrent/evolving clonal myeloid neoplasm. Donor DNA was identified in 22% (11/50) of nails collected after allogeneic stem-cell transplantation. In this cohort, an association with a recent history of graft-versus-host disease was identified. These findings should be considered as a potential limitation to the use of nails as a source of normal control DNA but could also provide important diagnostic information regarding the disease process.

Figure 1.

Summary of extraction methods and DNA quality characteristics. (A) Description of the two nail processing methods. Nail clippings were manually cut with scissors into small fragments (method 1) or pulverized (method 2), followed by enzymatic digestion and DNA extraction. Processing by method 1 takes several days for digestion (up to 6 days) whereas method 2 is much quicker (<24 hours, generally overnight). (B) Comparison of fragment sizes obtained from methods 1 and 2 established using the 5300 Fragment Analyzer System with the HS Small Fragment kit. The average sizes were 154 bp and 169 bp for methods 1 and 2, respectively. The difference was not statistically significant (P=0.16). (C) Representative electropherogram showing the DNA fragment distribution obtained from a nail sample analyzed by the Agilent 5300 Fragment Analyzer System with the HS Genomic DNA kit. DNA is primarily composed of short fragments with a main peak centered around 150 bp; highest point at 153 bp. Note that the high molecular weight DNA (arrow) present represents a minimal proportion of the total DNA. The numbers across the x and y axis are relabeled with larger characters over the original electropherogram to facilitate viewing. (D) Comparison of total DNA yields in nanograms (ng) observed across 4,356 nail DNA examples extracted between January 2017 and December 2021, stratified by method type; 1,807 and 2,616 were processed by methods 1 and 2, respectively. Average total DNA yields (Qubit measurements) were significantly lower for method 1, 398.7 ng (minimum: 1.3, median: 184.8, maximum: 6,240.0), compared to method 2, 835.4 ng (minimum: 1.2, median: 470.4, maximum: 9,540.0). The method is labeled on the x axis and total DNA yield denoted on the y axis. The overall distribution for methods 1 and 2 is depicted on the right and zoomed details are shown on the left. Differences are statistically significant (P<2.2x10^-16). (E) Comparison of insert sizes from sequenced libraries (sheared) of samples processed with the two methods. Insert sizes for each sample were divided by size and stratified into quartiles. Overall, samples processed by method 1 had a higher proportion of shorter inserts with statistically significant differences (P<0.001) as compared to samples processed with method 2, suggesting greater degradation. Method 1 (mean 118 bp, range 41-176, standard deviation 17.6) compared to method 2 (mean 126, range 59-200, standard deviation 20.0) (F) Representative insert distribution plot including several clinical samples. Library insert sizes are shown on the x axis and density (proportion of total) on the y axis. Note that nail DNA exhibits a prominent jagged or sawtooth pattern with 10 bp periodicity in fragments below 150 bp. This is seen across all nail samples (sheared and non-sheared) but is most prominent in samples processed with method 1 after several days of digestion, in contrast to the pattern observed on sheared bone marrow DNA. Source data are provided as a Source Data file. **** denotes statistically significant differences. bp: base pairs; NS: no statistical significance; RFU: relative fluorescence unit.

Figure 2.

Distribution of patients and mutations by disease categories. (A) Distribution of patients in the clinical cohort. Patients are categorized by broad disease category (OncoTree Classification System) as specified on the far left (y axis). For each category, the number of patients is denoted inside the parentheses (N=patients with mutations in nail/total number of patients). The percent of patients is shown on the x axis. Yellow bars depict the proportion of patients with mutations in the nail; blue bars represent those patients with mutations identified only in tumor. The highest proportion of patients with mutations in the nail include those with mast cell disease, myelodysplastic/myeloproliferative neoplasms and myeloproliferative neoplasms. (B) Distribution of mutations in the clinical cohort. The number of mutations is categorized according to the same broad OncoTree categories as in (A). For each category, the number of mutations is specified inside the parentheses (N=mutations identified in nail/mutations in tumor only). The percent of mutations is depicted on the x axis. Yellow bars depict the proportion of mutations identified in the nail; blue bars correspond to mutations identified only in the tumor. Diseases with the highest proportion of mutations identified in the nail include myeloproliferative neoplasms, myelodysplastic/myeloproliferative neoplasms and mast cell disease. (C) Distribution of mutations by variant allele frequency (%), binned on the y axis, and number of mutations across the cohort on the x axis. Tumor mutations (blue bars) were distributed over a broad range with VAF averaging 26.7%. By contrast, mutations in nail were identified at significantly lower VAF (P<2.2x10^-16), average 4.4%. The VAF of mutations in nail were significantly lower for lymphoid neoplasms (yellow) compared to myeloid neoplasms (red), at 3.3% versus 4.9%, respectively (P=0.006). (D) Density plot depicting the absolute differences in VAF for mutations detected in the nail, compared to the same mutations in the corresponding tumor sample and stratified based on tumor category (Lymphoid vs. Myeloid). Mutations in nail are present at significantly lower VAF compared to the same mutation in the corresponding tumor. The large absolute differences between the tumor and the nail enables the determination that the variant is somatic in origin. The median absolute differences for mutations in patients with lymphoid malignancies was 29%, compared to 35% for those with myeloid malignancies. Using a non-parametric Wilcoxon rank sum test (Mann-Whitney U test), the differences in distribution of Lymphoid versus Myeloid are statistically significant (P=0.0097). Source data are provided as a Source Data file. TLL: T-lymphoblastic leukemia/lymphoma; PCM: plasma cell myeloma; MTNN: mature T and NK neoplasms; MBN: mature B-cell neoplasms; LATL: lymphoid atypical; HL: Hodgkin lymphoma; BLL: B-lymphoblastic leukemia/lymphoma; MPN Workup: cases with suspected MPN but for whom the work up was incomplete; MPN: myeloproliferative neoplasms; MLNER: myeloid/lymphoid neoplasms with eosinophilia and rearrangement of PDGFRA/PDGFRB or FGFR1 or with PCM1-JAK2; MDS Workup: cases with suspected MDS but for whom the work up was incomplete; MDS/MPN: myelodysplastic/myeloproliferative neoplasms; MDS: myelodysplastic syndromes; MCD: mast cell diseases; HDCN: histiocytic and dendritic cell neoplasms; BPDCN: blastic plasmacytoid dendritic cell neoplasm; AML: acute myeloid leukemias; ALAL: acute leukemias of ambiguous lineage; VAF: variant allele frequency.

Figure 3.

Comparison of variant allele frequencies between tumor DNA and nail DNA. (A-C) Distribution of mutations in patients with tumors in the myeloid category. (A) Bar graph depicting all mutations detected in the myeloid tumors (blue). Each bar represents one mutation. Nail DNA mutations (orange) are plotted next to the corresponding mutation in the tumor DNA. A total of 4,649 mutations were detected in the tumors. Of these, 674 (14.5%) were identified in the corresponding nails. Mutations in the nail are arranged by variant allele frequency (VAF) from highest to lowest (range, 1-59.7%). The subset of cases with nail mutations is expanded and reorganized by the corresponding VAF in the tumor, from highest to lowest, for comparison (zoomed area to the right). In all, 304 patients harbored the 674 mutations in the nail. Of these, 13 (4.3% of patients) had VAF > 20%. Despite tumor contamination in the nail, VAF in the nail were significantly lower than those in the tumor such that the distinction between somatic and germline variants could be made in all cases except in one patient (see details in 3H). Note that rare mutations are identified at slightly higher VAF in nail than in tumor. This corresponded to tumor samples submitted immediately after treatment, at the time of minimal residual disease, while the nail reflected contamination from several months previously when the level of disease was high level. (B) Mutations in myeloid tumors are stratified by their VAF (%) and binned as depicted on the y axis. The number of mutations in each category is denoted on the x axis. (C) The proportion of mutations identified in the nail based on the VAF of the mutation in the corresponding tumors. The VAF (%) of mutations in the tumor is shown on the y axis. The proportion of the corresponding mutations identified in the nail (orange) is given on the x axis. Note that in the myeloid category, the higher the VAF of the mutations in the tumor, the higher the proportion identified in the nail. (D-F) Distribution of mutations in patients with tumors in the lymphoid category. (D) Bar graph depicting all mutations detected in the lymphoid tumors (blue). Each bar represents one mutation. Nail mutations (orange) are plotted next to the corresponding mutation in the tumor. A total of 6,303 mutations were seen in the patients with lymphoid tumors. Of these, 118 mutations (1.9%) were identified in the corresponding nails. Mutations in the nail are arranged by VAF from highest to lowest (range, 1-17.4%). The set of cases with nail mutations is expanded and reorganized by the corresponding VAF in the tumor, from highest to lowest, for comparison (zoomed insert to the right). In all, 61 patients harbored the 118 mutations in the nail. (E) Mutations in lymphoid tumors are stratified by their VAF and binned as depicted on the y axis. The number of mutations in each category is denoted on the x axis. (F) The proportion of mutations identified in the nail based on the VAF of the mutation in the corresponding tumors. VAF (%) of mutations in the tumor is shown on the y axis. The proportion of the corresponding mutations identified in the nail (orange) is denoted on the x axis. Note that, in contrast to the myeloid tumors, mutations with the highest VAF in the tumor are not present in the nail. (G) Distribution of nail samples based on VAF and the timing of sampling relative to the corresponding tumor. The median interval between nail and tumor sampling was 3 days (range, 1,512 days before tumor sampling to 7,042 after tumor sampling). The number of days is displayed on the x axis. Large gaps between collection of the two samples often reflected different disease loads causing diagnostic difficulties. (H) The only case in the cohort of 2,610 patients with mutations in the nail at a VAF of ~60% (similar to that of the tumor), raising the possibility of a germline event. Across the entire cohort, the highest VAF were associated with patients with myeloproliferative neoplasms and marked myelofibrosis. In all cases, except this unique case, the VAF in the tumor were comparatively higher (double), allowing discrimination of the mutation as somatic. In this case, the JAK2 mutation was detected with a VAF of ~60% in both tumor and nail. According to the clinical history, this patient had occupational-related trauma to his hands and upper extremities. Multiple hematomas and ecchymoses on the upper extremities were documented in the weeks prior to the nail collection. The finding of several other mutations at high level and the overall pattern suggested tumor contamination, likely related to trauma. Reticulin staining of a prior bone marrow biopsy showed 3+ fibrosis. The table on the right lists all mutations and corresponding VAF in the tested blood sample, the corresponding nail collected 4 months later and the subsequent blood sample after 2 years.

Figure 4.

Most common alterations identified in nail DNA. (A) Heatmap showing the distribution of mutated genes identified in the nails based on disease categories. Genes included are the most commonly mutated, defined as those altered with a frequency >0.5% among the total mutations in nails or tumor. In nails, this encompassed genes mutated >4 times among the 792 mutations detected; in tumor, this encompassed genes mutated >50 times among the 10,942 mutations. Mutated genes are organized based on frequency in the tumor, with the highest number of mutations at the top. The total number of mutations in each disease category is annotated in the first row, followed by details of the number of mutations in each gene by disease category. Boxes are color coded as indicated by the color scale (red) indicating the percentage of total number of mutations identified in the nail. (B) The numbers of total mutations identified in each gene are listed in columns labeled Total Nail and Total Tumor. In the top row, all mutations detected in the cohort followed by detailed numbers for each gene. The proportions of total tumor mutations identified in the nail are displayed as percentages in the far-right column (plotted as a side bar and labeled). (C) Most frequently mutated genes in nails across all disease categories in order of frequency from left to right (TET2 being most common). Distribution of mutations by variant allele frequency (VAF) (%) on the y axis. Although most mutations in these genes were identified in the nails at VAF <10%, these genes also had the highest number of outliers, often related to the presence of loss of heterozygosity (LOH). (D) Assessment of somatic copy number (CN) alteration profiles of two tumor samples demonstrate CN-LOH involving genes TET2 (chromosome 4) and JAK2 (chromosomes 9). MSK-IMPACT-heme analysis includes the assessment of genome-wide total and allele-specific copy number states which are calculated using the open-source R package FACETS2n (v0.3.0). The mutations were detected in the tumor at VAF of 91% (TET2) and 89% (JAK2), respectively. The corresponding nail samples had the same mutations at VAF of 40% and 36%, respectively. The figure shows the integrated visualization of FACETS analysis. The top panels display total CN log-ratio along genomic positions on chromosome 4 (left) and 9 (right), which are both copy neutral. The middle panels show the allele-specific log-odds-ratio revealing allelic divergence for regions of chromosomes 4 (left) and 9 (right), consistent with LOH events. The bottom panel displays the inferred integer CN with allelic losses of chromosomal segments 4 (left) and 9 (right) in the genomic regions containing TET2 and JAK2, respectively (red lines). The black line corresponds to the total CN. ALAL: acute leukemias of ambiguous lineage; AML: acute myeloid leukemias; HDCN: histiocytic and dendritic cell neoplasms; MCD: mast cell diseases; MDS: myelodysplastic syndromes; MDS/MPN: myelodysplastic/myeloproliferative neoplasms; MDS w/up: cases with suspected MDS but for which work up was incomplete; MPN: myeloproliferative neoplasms; MPN w/up: cases with suspected MPN but for which work up was incomplete; BLL: B-lymphoblastic leukemia/lymphoma; HL: Hodgkin lymphoma; LATL: lymphoid atypical; MBN: mature B-cell neoplasms; MTNN: mature T and NK neoplasms; PCM: plasma cell myeloma; TLL: T-lymphoblastic leukemia/lymphoma.

Figure 5.

Representative cases of lymphoma or plasma cell neoplasms with tumor mutations identified in nail DNA. (A-C) The tables display the mutations detected in each sample sequenced, along with the corresponding variant allele frequencies (VAF) (%), highlighted according to the color scale (top right). Myeloid (M) lineage mutations are highlighted in red, lymphoid/plasma cell (L/PC)-associated mutations are highlighted in blue. Samples appear in order of collection; time in parenthesis is the interval relative to sample 1. (A) An 83-year-old male with a history of chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) for 13 years, on active surveillance (known BRAF, NRAS and KRAS mutations), presents for further assessment of suspected progression of disease (increased lymphocytosis, fatigue, anemia, and worsening thrombocytopenia). The blood sample (Bulk blood, 1) showed evidence of CLL/SLL, 59.4% by flow cytometry (FC). Sequencing revealed 13 mutations – the top eight were consistent with the previously diagnosed CLL/SLL. Other mutations (red) suggested a coexisting myeloproliferative neoplasm. Only the latter subset was detected in the nail (2). A bone marrow (BM) sample (3) was obtained 2 weeks later, demonstrating a genomic profile identical to that in the blood. B and myeloid cell populations were sorted (4 and 5, respectively) and sequenced independently, showing segregation of the mutations into two distinct categories of lymphoid and myeloid origin. Morphologically (bottom left), the BM sample revealed extensive CLL/SLL (insert highlights ~80% involvement by PAX-5 immunostain). At the bottom right there are areas with increased atypical megakaryocytes, severe reticulin fibrosis (insert shows retic stain), and no increase in blasts. The findings are consistent with a JAK2 p.V617F and MPL p.W515L-mutated myeloproliferative neoplasm, fibrotic phase of primary myelofibrosis coexisting with CLL/SLL. Note that despite the known chronicity of the CLL and markedly higher VAF for the lymphoid-derived mutations, only myeloid-derived mutations are identified in the nail. (B) An 80-year-old female with a 2-year history of smoldering myeloma presents with progressive cytopenia. A BM sample was obtained (1) demonstrating patchy involvement by a plasma cell myeloma (PCM), ~15% by aspirate differential (2.9% of white blood cells by FC). No myelodysplasia was identified. Bulk BM sequencing revealed six mutations: one subclonal PRDM1 and five mutations at high VAF (TET2, PHF6, CUX1 and KMT2C), the latter five were also detected in the nail sample (2). A subsequent BM sample (3) 1 year later showed increased patchy involvement by PCM (40-80% on aspirate differential, 20% by FC) and overt dysplastic changes, while sequencing demonstrated the same mutations previously detected. To further assess the myeloma component, plasma cells (PC) were isolated from an aliquot of the same sample (CD138 magnetic bead-based positive selection) and sequenced (4). This enriched population established the myeloma-specific genomic profile, which excluded the five mutations detected in the nails. Further BM sampling 2 years later (5) showed overt multilineage dysplasia with a minimal PC component (<5%). At this time the mutation profile reflected only the myeloid-derived mutations. The table displays the specific mutations detected in each sample. The bottom left picture depicts the findings of the BM sample (3), which is involved by both the PC neoplasm and myelodysplastic syndrome (MDS). Note the dysplastic megakaryocytes among numerous PC. CD138 immunostaining highlights the PC (bottom right). The overall findings are consistent with PC neoplasm with an emerging MDS. Mutations detected in the nail DNA correspond to those detected in the myeloid neoplasm and preceded overt morphological features of dysplasia. All PC lineage mutations were distinctly absent in the nail DNA. (C) A 71-year-old female with a history of angioimmunoblastic T-cell lymphoma (AITL) presented with disease recurrence. Sequencing of DNA from an involved lymph node showed the ten mutations detailed in the table (sample 1). Only a subset was detected in the nail DNA (2), specifically those associated with clonal hematopoiesis (CH) but hallmark mutations of AITL (RHOA, IDH) were distinctly absent. A blood sample (3) was obtained 6 months later, showing minimal involvement by an abnormal T-cell population (0.060% of the white blood cells by FC). However, sequencing demonstrated the same mutations (CH type) identified in the nail, at very high level. A follow up BM sample 1 year later demonstrated minimal residual involvement by AITL (0.0043% of white blood cells by FC); morphologically the BM was markedly hypercellular with mild dyspoiesis. Sequencing revealed the same three mutations and emergence of a JAK2 mutation. In conjunction, the findings were consistent with a new myelodysplastic/myeloproliferative neoplasm (MDS/MPN) emerging in a patient with active AITL. The bottom left picture depicts the involvement by AITL in the lymph node (the insert shows cells highlighted by CD3 immunostaining). On the right, the markedly hypercellular BM can be seen.