A Small Step, a Giant Leap: Somatic Hypermutation of a Single Amino Acid Leads to Anti-La Autoreactivity

The anti-La mab 312B, which was established by hybridoma technology from human-La transgenic mice after adoptive transfer of anti-human La T cells, immunoprecipitates both native eukaryotic human and murine La protein. Therefore, it represents a true anti-La autoantibody. During maturation, the anti-La mab 312B acquired somatic hypermutations (SHMs) which resulted in the replacement of four aa in the complementarity determining regions (CDR) and seven aa in the framework regions. The recombinant derivative of the anti-La mab 312B in which all the SHMs were corrected to the germline sequence failed to recognize the La antigen. We therefore wanted to learn which SHM(s) is (are) responsible for anti-La autoreactivity. Humanization of the 312B ab by grafting its CDR regions to a human Ig backbone confirms that the CDR sequences are mainly responsible for anti-La autoreactivity. Finally, we identified that a single amino acid replacement (D > Y) in the germline sequence of the CDR3 region of the heavy chain of the anti-La mab 312B is sufficient for anti-La autoreactivity.

Gaining insights into the DICER1 syndrome: An early report from the Italian DICER1 registry.

1519 Background: DICER1 is a key endoribonuclease in the microRNA pathway that modulates gene expression. Germline loss of function variants in DICER1, first found in pleuropulmonary blastoma, have been subsequently linked to a variety of cancerous (and non) conditions referred to as DICER1 syndrome. In 2018, the Italian Society of Human Genetics launched an initiative aimed at establishing a national registry of DICER1 germline sequence variants. Methods: Centers involved in genetic testing for cancer predisposition were asked to report any identified DICER1 germline variants and related clinical information. Five University and/or research institutes filled-in the electronic survey. Informed consent was obtained from patients or their legal guardians prior to DNA testing by NGS and/or Sanger sequencing. Results: Six DICER1 sequence variants were identified in 11 individuals. Three missense variants are secondary results of NGS panels for cancer predisposition and lack definitive categorization in online databases. Three previously unreported variants are predicted to be protein truncating and, hence, likely pathogenic. Of these, DICER1 c.4844delA p.(Lys1615Argfs*5) and c.4886C > G p.(Ser1629*) result from ad hoc testing offered to probands based on a history of early onset follicular thyroid carcinoma and botryoid-type embryonal rhabdomyosarcoma of the cervix and of pleuropulmonary blastoma 2nd type, respectively. DICER1 c.4643T > A p.(Leu1548*), instead, results from whole exome sequencing in two siblings with malignant melanoma who tested non informative for alterations in the CDKN2A and CDK4 melanoma predisposing genes. Further investigation unearthed thyroid disease in the family and identified two other young carrier individuals, one unaffected and one thyroidectomized due to multinodular goiter. A DICER1 somatic hot spot sequence variant was detected in goiter specimens. Conclusions: Via the newly established national registry we uncovered novel DICER1 germline sequence variants and uncommon genotype-phenotype associations. Our joint effort will help us to refine our knowledge of the rare DICER1 syndrome , to inform research studies, and to improve testing and clinical management strategies.

Whole Genome Sequencing in Routine Hematologic Samples: How to Proceed Analyses Best When Germline Controls Are Missing?

Background: The human genome is very heterogeneous on the individual level which challenges interpretation of whole genome sequencing (WGS) data. In order to reduce complexity in tumor genetics WGS of a tumor is performed together with WGS of "normal" tissue from the respective patient (i.e. fingernails, skin biopsy, hair, buccal swaps) which is used as the germline sequence (tumor/matched normal approach, TMNA). This approach allows the extraction of somatic mutations acquired in the tumor through sophisticated algorithms. In routine diagnostics, especially in hematological neoplasms, "normal" tissue representing the germline sequence is usually not available, which prohibits the standard use of somatic tumor/normal variant calling tools. Aims: On the road to implement WGS into routine diagnostics we tested a TMNA in comparison to a tumor/unmatched normal approach (TUNA), where pooled genomic DNA (Promega, Fitchburg, WI) was used instead of a matched normal. Cohorts and Methods: 9 samples from patients with hematological neoplasms (7 AML, 2 ALL) were sequenced at diagnosis on Illumina HiSeqX machines (Illumina, San Diego, CA), along with complete remission samples to serve as matched normals for the TMNA. For comparison, a mixture of genomic DNA from multiple anonymous donors was used as "normal" for the TUNA. Read mapping and somatic variant calling was performed using the tools Isaac3 and Strelka2, respectively. Statistical differences between groups were assessed by two-sided Mann-Whitney tests. Results: The TMNA produced a median of 17,700 somatic variant calls, while the TUNA produced 419,000. This 24-fold disparity is mainly due to residual germline variants missed by the TUNA. A large fraction of TMNA variants (57%) was located in regions of known low confidence variant calling (as defined by the Genome in a Bottle Consortium) and likely contain mostly artifacts. After removing these regions from analysis a median of 7,700 and 331,000 variants remained in the TMNA and TUNA datasets, respectively. In order to eliminate germline variants, the gnomAD population database was queried and any present variants were discarded. As expected, this removed over 95% of all variants from the TUNA dataset, but also 41% from the TMNA dataset. The latter might be attributed to common germline variants falsely being called as somatic by the TMNA and/or somatic mutations occurring at polymorphic sites. After this filtering step a median of 3,770 and 15,500 variants remained in the TMNA and TUNA datasets, respectively. This 4-fold disparity in variant number is most likely caused by rare germline variation remaining in the TUNA dataset. Of the remaining TMNA variants only 65% could be found within the larger TUNA dataset. A major factor governing this observation was variant allele frequency (VAF). Variants that overlapped between both datasets had on average higher VAFs than those unique to the TMNA (p < 2.2x10-16). Further inspection of the VAF distribution among samples revealed a bimodal or nearly bimodal distribution for all samples. All distributions shared a sharp peak centered on a VAF of 10%, which was unexpected given the estimated tumor fractions of the samples predict VAFs of 25% and higher. Variants in this lower part of the distribution (arbitrarily defined as VAFs < 20%) constitute on average 50% of all variants in a TMNA sample, with extremes reaching 95% in 2 samples. These low frequency variants show distinctly lower mapping qualities than variants with VAFs ≥ 20% (p < 2.2x10-16), i.e. they reside in regions of elevated mapping ambiguity which potentially leads to the creation of artefacts. Analyzing the overlap of only the higher VAF variants we find that 97.4% of all TMNA variants can also be found in the TUNA dataset. Conclusions: Comparing tumor samples to matched normal material from the respective patient is the preferred approach for somatic variant calling in WGS data, however even with modern algorithms false positives due to technical artifacts seem to be highly abundant. A deeper understanding of the nature of these artifacts is crucial for developing appropriate filtering schemes and improving variant calling algorithms. In the absence of a matched normal using a TUNA can uncover the vast majority (97.4%) of high-quality variants found in a TMNA, however distinguishing true somatic variants from residual rare germline variation in a TUNA remains a major challenge. Disclosures Hutter: MLL Munich Leukemia Laboratory: Employment. Nadarajah:MLL Munich Leukemia Laboratory: Employment. Meggendorfer:MLL Munich Leukemia Laboratory: Employment. Kern:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership.

Primary hyperparathyroidism in young patients in Russia: high frequency of hyperparathyroidism-jaw tumor syndrome

Background Primary hyperparathyroidism (PHPT) is a relatively rare disorder among children, adolescents and young adults. Its development at an early age is suspicious for hereditary causes, though the need for routine genetic testing remains controversial. Objective To identify and describe hereditary forms of PHPT in patients with manifestation of the disease under 40 years of age. Design We enrolled 65 patients with PHPT diagnosed before 40 years of age. Ten of them had MEN1 mutation, and PHPT in them was the first manifestation of multiple endocrine neoplasia type 1 syndrome. Methods The other fifty-five patients underwent next-generation sequencing (NGS) of a custom-designed panel of genes, associated with PHPT (MEN1, CASR, CDC73, CDKN1A, CDKN1B, CDKN1C, CDKN2A, CDKN2C, CDKN2D). In cases suspicious for gross CDC73 deletions multiplex ligation-dependent probe amplification was performed. Results NGS revealed six pathogenic or likely pathogenic germline sequence variants: four in CDC73 c.271C>T (p.Arg91*), c.496C>T (p.Gln166*), c.685A>T (p.Arg229*) and c.787C>T (p.Arg263Cys); one in CASR c.3145G>T (p.Glu1049*) and one in MEN1 c.784-9G>A. In two patients, MLPA confirmed gross CDC73 deletions. In total, 44 sporadic and 21 hereditary PHPT cases were identified. Parathyroid carcinomas and atypical parathyroid adenomas were present in 8/65 of young patients, in whom CDC73 mutations were found in 5/8. Conclusions Hereditary forms of PHPT can be identified in up to 1/3 of young patients with manifestation of the disease at <40 years of age. Parathyroid carcinomas or atypical parathyroid adenomas in young patients are frequently associated with CDC73 mutations.