Bibliographic record
Abstract
The unusual incidence patterns for nasopharyngeal carcinoma (NPC) in China, Northeast India, Arctic Inuit, Peninsular and island Southeast Asia, Polynesian Islanders, and North Africans indicate a role for NPC risk genes in Chinese, Chinese-related, and not-obviously Chinese-related populations. Renewed interest in NPC genetic risk has been stimulated by a hypothesis that NPC population patterns originated in Bai-Yue / pre-Austronesian-speaking aborigines and were dispersed during the last glacial maximum by Sundaland submersion. Five articles in this issue of the Chinese Journal of Cancer, first presented at a meeting on genetic aspects of NPC [National Cancer Center of Singapore (NCCS), February 20-21, 2010], are directed towards incidence patterns, to early detection of affected individuals within risk populations, and to the application of genetic technology advances to understanding the nature of high risk. Turnbull presents a general framework for understanding population migrations that underlie NPC and similar complex diseases, including other viral cancers. Trejaut et al. apply genetic markers to detail migration from East Asia through Taiwan to the populating of Island Polynesia. Migration dispersal in a westward direction took mongoloid peoples to modern day Northeast India adjacent to Western China (Xinjiang). NPC incidence in mongoloid Nagas ranks amongst the highest in the world, whereas elsewhere in India NPC is uncommon. Cao et al. detail incidence patterns in Southeast China that have occurred over recent decades. Finally, Ji et al. describe the utility of Epstein-Barr virus serostatus in early NPC detection. While genetic risk factors still remain largely unknown, human leukocyte antigen (HLA) genes have been a focus of attention since the discovery of an HLA association with NPC in 1973 and, two years later, that NPC susceptibility in highest-risk Cantonese involved the co-occurrence of multi-HLA locus combinations of HLA genes as chromosome combinations, or haplotypes (e.g. HLA-A2-B46), whereas in relatively lower-risk non-Cantonese Chinese (Hokkiens, Teochews) they appeared to act independently, a strength of association reflecting the 30-50-fold difference in incidence between highest risk Cantonese and lowest-risk Indians. The prototypic haplotype HLA-A2-B46 extends over megabases. An upstream DNA segment (near HLA-DPA1), has close similarity to Gorilla, with no obvious homology to Chimpanzee in current databases, suggesting that a reticulate model of primate evolution may be more appropriate than simple phylogeny. The DNA variation level in this segment is high enough for it to be a hominin remnant. HLA-B46 arose in mongoloids and remains largely limited to Chinese so the question arises as to whether the hominin candidate segment indicates an eastward trek of Homo neanderthalensis or the survival of much earlier Homo erectus? In 2011 sequencing technologies have finally caught up with the requirement to separate parental haplotypes. Recently achieved chromosome separation for whole genome di-haploid genetic and epigenetic analysis of parental inheritance in single individuals will reveal interacting patterns of multi-locus haplotypes as humans move in and through successive environments, thus providing definitive information on the genetic affinities between extant populations, and of the migrations that have led to the global distribution of modern Homo. The challenge can now be met of seeking HLA-associated locations both within and outside the HLA complex on each of the pair of chromosomes. More broadly, for every disease, genetic risk detection will require resolution of the diploid genome as a di-haplome. In the context of NPC, HLA genetic risk complete autosomal di-haplomic sequencing will enable testing of the Wee unitary origin hypothesis of NPC risk even among populations with no apparent mongoloid affinity.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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".