Characterization of Genomic Rearrangements Involving CIITA and SOCS1 Using Targeted Capture Sequencing of Archival Tissue Specimens
Bibliographic record
Abstract
Abstract Introduction: Malignant lymphomas account for 5% of all newly diagnosed cancer cases per year and affect patients of all ages. Genomic rearrangements represent a pathogenic hallmark of most B-cell lymphoma entities and some are associated with an unfavorable clinical outcome. Recurrent structural genomic aberrations involving the MHC class II transactivator CIITA (located on chromosome 16p13) have been identified in multiple lymphoma subtypes in which they contribute to an immune escape phenotype. Moreover, inactivating mutations of the tumor suppressor gene SOCS1, located in close proximity to CIITA on chromosome 16, result in constitutively active JAK-STAT-signaling across a spectrum of lymphomas. Preliminary data from our group indicate that SOCS1 is also involved in recurrent rearrangements. However, more detailed study of these rearrangements was hampered due to the lack of analytic methods and platforms applicable to archival formalin-fixed and paraffin-embedded tissue (FFPET) specimens. Here, using FFPET biopsies, we sought to characterize the comprehensive landscape of CIITA and SOCS1rearrangement partner genes, determine the exact breakpoint anatomy, and study the functional impact of individual alterations. Methods: In order to select cases for DNA extraction and subsequent sequencing analysis we revisited the results of previously conducted fluorescence in-situ hybridization (FISH) experiments performed on lymphoma cases arranged on tissue microarrays. Cases were included based on the presence of rearrangements in CIITA, SOCS1 and the 9p24.1 locus. 92 specimens met these criteria and DNA was extracted using the Qiagen AllPrep DNA/RNA FFPE kit. An optimized 96-well-plate protocol was used for whole genome library construction (FFPET small gap protocol) and 16 to 18 libraries were multiplexed and pooled prior to capture using a custom Agilent SureSelect design targeting the aforementioned loci. Sequencing was performed on an Illumina HiSeq 2500. Predicted structural variants (SV) were generated with the computational tools DELLY and deStruct, and subsequently filtered based on read support. High confidence predictions were validated by either PCR-amplification of the genomic DNA spanning the breakpoint sequence followed by Sanger Sequencing, or customized FISH assays. Results: Library construction was successful in 68 cases (74%) and the mean coverage across the target regions for these libraries was above 600x with 97.7% of bases having an average depth of > 100x. After filtering, a list of 82 rearrangement events was generated consisting of 16 translocations (12 affecting CIITA and 4 affecting SOCS1), 44 deletions, 19 inversions and 3 duplications. Mapping of the translocation breakpoints revealed two distinct breakpoint cluster regions within intron 1 of CIITA and exon 2 of the SOCS1 gene. None of the exact breakpoints were recurrent and identification of rearrangement partner genes confirmed that CIITA and SOCS1 rearrange promiscuously. Furthermore, we demonstrate that some translocation events result in the generation of novel in-frame fusion transcripts (e.g. CIITA-PRDM16), warranting further functional characterization. Consistent with previous studies, intra-chromosomal alterations were detected in intron 1 of CIITA in48% of the cases while SOCS1 was shown to be frequently deleted (20% of cases).Overall, the concordance between FISH and targeted capture sequencing was 63%, reflecting the differences in sensitivity to detect large- and small-scale genomic events. Conclusions: Targeted capture sequencing, in conjunction with FISH, represents a valuable tool to explore the spectrum of genomic structural variants and rearrangement partner genes in archival lymphoma FFPET specimens. Future studies are required to address the functional impact of specific alterations and the utility of capture-sequencing-based assays for clinical decision-making. Disclosures Scott: NanoString Technologies: Patents & Royalties: named inventor on a patent for molecular subtyping of DLBCL that has been licensed to NanoString Technologies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".