Abstract 2755: Germline copy number variations in melanoma families with/without CDKN2A/CDK4 mutations
Bibliographic record
Abstract
Abstract Cutaneous malignant melanoma (CMM) is an etiologically heterogeneous disease with genetic, host, environmental factors, and their interactions contributing to its development. CDKN2A and CDK4 are the two established major susceptibility genes for melanoma identified so far. Recent evidence suggests that copy number variations (CNVs) may contribute to disease susceptibility in several inherited diseases including cancer. The goals of this study were 1) to assess whether the frequency of CNVs varied by CMM or CDKN2A/CDK4 mutation status; and 2) to identify rare CNVs that were related to melanoma predisposition in these high-risk families. We used genome-wide tiling CGH arrays (Nimblegen 720K exon-focused) to investigate characteristics of CNVs in 174 CMM cases, 44 high-risk unaffected family members (with dysplastic nevi/large number of moles or germline CDKN2A mutations), and 48 unrelated spouses from 50 American melanoma-prone families (21 CDKN2A+, 2 CDK4+, 27 mutation negative). We used the Nexus Copy Number™ built-in FASST2 algorithm to identify significant CNVs (significant threshold = 0.000001; minimal number of probes per segment = 5; log2 ratio>0.3 for gains and -0.3 for losses). We found that the median number of total CNVs, or gains or losses separately, did not show significant differences in CMM cases, high-risk unaffected family members, and unrelated controls. Among CMM cases, CNV frequencies were not significantly associated with germline CDKN2A/CDK4 mutation status, age at melanoma diagnosis, or number of melanomas. Similar results were obtained when number of genes and lengths of DNA segments affected by CNVs were analyzed. Restriction to large CNVs (>10 kb or >100 kb) or rare CNVs (not reported in the Toronto CNV database) did not change results significantly. On the other hand, we identified several rare large CNVs (>10 kb, not reported in unrelated controls) that either involved known melanoma genes or co-segregated with melanoma (observed in multiple CMM cases) within families. These included a 1.3Mb deletion in PARP1 in a single CMM case, a 10 kb deletion in CDKN2A in 4 of 5 CMM cases and two obligate gene carriers in a large family that was negative for CDKN2A mutations by sequencing, a 175 kb deletion in LINGO2 in 2 of 3 CMM cases in one family, a 10 kb deletion in 8q24 in 3 of 4 CMM cases in one family, a 110 kb deletion in 2q22.1 in all three cases in a family, and a 57 kb duplication in 4q32.2 in all five cases in a CDKN2A mutation positive family. The role of these CNVs, particularly those involving genes that have unknown function related to CMM development, in CMM susceptibility remain to be investigated. Citation Format: Xiaohong (Rose) Yang, Jianxin Shi, Hunter Bennett, Laura Burke, Casey Dagnall, Laurie Burdette, Belynda Hicks, Margaret Tucker, Alisa Goldstein. Germline copy number variations in melanoma families with/without CDKN2A/CDK4 mutations. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2755. doi:10.1158/1538-7445.AM2015-2755
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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) | 0.004 | 0.000 |
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".