Genetic risk of chronic lymphocytic leukemia: a tale of two cities
Bibliographic record
Abstract
Chronic lymphocytic leukemia (CLL) has one of the highest familial risks of any cancer, with fi rst-degree relatives having an 8.5-fold increase in their risk of the disease [1]. CLL families are defi ned as having two or more blood-related family members with CLL, and a familial CLL case is a person with a blood-related relative with CLL. Th e genetic basis of familial CLL is not yet fully defi ned. Currently, 25 inherited genetic variants have been identifi ed to be associated with risk of CLL through well-designed genomewide association studies [2]. Th ese genetic variants are common, with allele frequencies greater than 5%. A number of these variants are mapped in or near genes involved in apoptosis, a key biological pathway. Although these variants themselves do not cause CLL, they provide direction for future functional studies. Taken together, these variants explain ∼ 17% of the genetic heritability of incident CLL, and mathematical models suggest that more genetically related variants are yet to be identifi ed with larger studies or studies using newer technology. Th ese genetic variants may include rare variants, epigenomic changes or structural variants. Of interest, the currently known 25 variants are associated with CLL risk regardless of the familial status of the CLL cases. Th at is, familial CLL cases improve the ability to locate the genetic variants but the known genetic variants are not specifi c to familial CLL risk. As a result, the population risk of CLL refl ects a major genetic component rather than an environmental one, given the limited evidence for a role for environmental factors in CLL risk. In this issue of Leukemia and Lymphoma , Mak et al . [3] report their incidence fi ndings of CLL in people of Chinese descent living in the Canadian province of British Columbia (BC) and Hong Kong during the period 1983 – 2008. It is well known that CLL incidence varies by ethnicity, with the highest incidence seen in individuals of European descent and the lowest rate among Asians [4]. Prior migration studies [5,6] have shown that Asians retain the lower CLL incidence rates characteristic of their country of origin when they migrate to the West. Th e study by Mak et al . supports these fi ndings using two population-based cancer registries and census data from BC and Hong Kong. Th e authors identifi ed all cases of CLL in these regions during the period of investigation. Th e ethnicity of the Chinese BC cases was verifi ed from individual patient charts. Th e number of individuals with CLL who were non-Chinese BC, Chinese BC or Hong Kong cases were compared to their respective census data, resulting in clear diff erences in incidence between the Chinese and non-Chinese populations and a similarity of incidence between the BC-Chinese and HK-Chinese populations. Th e validity of these conclusions could be limited because of structural problems with this study. A minor concern is that the authors did not specify what ethnic groups were included in the non-Chinese BC cases of CLL, but given the ethnic distribution of BC, a majority would be expected to be of European descent. Of greater concern is the authors ’ failure to consider the several major changes in the methods of detection and diagnostic defi nitions of CLL during the period of investigation of their study. Th e current defi nition of CLL as the presence of a circulating clonal population of B-cell lymphocytes of more than 5 10 9 cells/L that have a characteristic immunophenotype [7] dates to 2008, and would thus not have been used for the majority of cases reported in this study. Th e changes in diagnostic methods and criteria for CLL are the most likely cause for the apparent increase in the incidence of CLL over time, regardless of ethnic group or age group in this study, which is likely erroneous. Although this is a major fl aw of the manuscript, it does not detract from the take-home message of supporting a strong genetic component to the risk of CLL for all patients with the disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".