Distinct germline genetic susceptibility profiles identified for common non-Hodgkin lymphoma subtypes

Comparison of the Ways in Which Nitidine Chloride and Bufalin Induce Programmed Cell Death in Hematological Tumor Cells

The objective of this work to study the programmed cell death (PCD) in hematological tumor cells induced by nitidine chloride (NC) and bufalin (BF). Hematological tumor cells were exposed to various doses of NC and BF to measure the level of growth inhibition. While inverted microscope is used to observe cell morphology, western blot technique is used to detect apoptosis-related protein expression levels. The effects of NC and BF on hematological tumor cells were different. Although abnormal cell morphology could be seen under the inverted microscope, the western blot results showed that the two medicines induced PCD through different pathways. Drug resistance varied in intensity across distinct cells. THP-1, Jurkat, and RPMI-8226 each had half maximum inhibitory concentrations (IC50) of 36.23 nM, 26.71 nM, and 40.46 nM in BF, and 9.24 µM, 4.33 µM, and 28.18 µM in NC, respectively. Different hematopoietic malignancy cells exhibit varying degrees of drug resistance, and the mechanisms by which apoptosis of hematologic tumor cells is triggered by NC and BF are also distinct.

Epidemiology and etiology of diffuse large B-cell lymphoma

As the most common non-Hodgkin lymphoma subtype, diffuse large B-cell lymphoma (DLBCL) incidence patterns generally parallel that for NHL overall. Globally, DLBCL accounts for a third of all NHLs, ranging between 20% and 50% by country. Based on United States (U.S.) cancer registry data, age-standardized incidence rate for DLBCL was 7.2 per 100,000. DLBCL incidence rises with age and is generally higher in males than females; in the U.S., incidence is highest among non-Hispanic whites (9.2/100,000). Like NHL incidence, DLBCL incidence rose in the first half of the 20th century but has largely plateaued. However, there is some evidence that incidence rates are rising in areas of historically low rates, such as Asia; there are also estimates for rising DLBCL incidence in the near future due to the changing demographics in developed countries whose aging population is growing. Established risk factors for DLBCL include those that result in severe immune deficiency such as HIV/AIDS, inherited immunodeficiency syndromes, and organ transplant recipients. Factors that lead to chronic immune dysregulations are also established risk factors, and include a number of autoimmune conditions (eg, Sjögren syndrome, systemic lupus erythematosus, rheumatoid arthritis), viral infections (eg, HIV, KSHV/HHV8, HCV, EBV), and obesity. Family history of NHL/DLBCL, personal history of cancer, and multiple genetic susceptibility loci are also well-established risk factors for DLBCL. There is strong evidence for multiple environmental exposures in DLBCL etiology, including exposure to trichloroethylene, benzene, and pesticides and herbicides, with recent associations noted with glyphosate. There is also strong evidence for associations with other viruses, such as HBV. Recent estimates suggest that obesity accounts for nearly a quarter of DLBCLs that develop, but despite recent gains in the understanding of DLBCL etiology, the majority of disease remain unexplained. An understanding of the host and environmental contributions to disease etiology, and concerted efforts to expand our understanding to multiple race/ethnic groups, will be essential for constructing clinically relevant risk prediction models and develop effective strategies for disease prevention.

Improve the model of disease subtype heterogeneity by leveraging external summary data

Researchers are often interested in understanding the disease subtype heterogeneity by testing whether a risk exposure has the same level of effect on different disease subtypes. The polytomous logistic regression (PLR) model provides a flexible tool for such an evaluation. Disease subtype heterogeneity can also be investigated with a case-only study that uses a case-case comparison procedure to directly assess the difference between risk effects on two disease subtypes. Motivated by a large consortium project on the genetic basis of non-Hodgkin lymphoma (NHL) subtypes, we develop PolyGIM, a procedure to fit the PLR model by integrating individual-level data with summary data extracted from multiple studies under different designs. The summary data consist of coefficient estimates from working logistic regression models established by external studies. Examples of the working model include the case-case comparison model and the case-control comparison model, which compares the control group with a subtype group or a broad disease group formed by merging several subtypes. PolyGIM efficiently evaluates risk effects and provides a powerful test for disease subtype heterogeneity in situations when only summary data, instead of individual-level data, is available from external studies due to various informatics and privacy constraints. We investigate the theoretic properties of PolyGIM and use simulation studies to demonstrate its advantages. Using data from eight genome-wide association studies within the NHL consortium, we apply it to study the effect of the polygenic risk score defined by a lymphoid malignancy on the risks of four NHL subtypes. These results show that PolyGIM can be a valuable tool for pooling data from multiple sources for a more coherent evaluation of disease subtype heterogeneity.

The inherited genetic contribution and polygenic risk score for risk of CLL and MBL: a narrative review

Chronic lymphocytic leukemia (CLL) is a neoplasm of B-cells in the blood and monoclonal B-cell lymphocytosis (MBL) is a precursor state to CLL. This narrative review provides an overview of the genetic studies that identified 43 common variants associated with risk of CLL among individuals of European ancestry. Emerging studies found that ∼50% of these variants are associated with MBL risk. Moreover, the polygenic risk score (PRS) calculated from these CLL variants has been shown to be a robust predictor for both CLL and MBL risk among European ancestry individuals but a weak predictor among African ancestry individuals. By summarizing these genetic studies, we conclude that additional studies are needed in other race/ethnic populations to identify race-specific susceptibility variants, that functional studies are needed to validate the biological mechanisms of the variants, and that the clinical utility of the PRS is limited until preventive strategies for CLL are developed.