Novel tumor markers provide improved prediction of survival after diagnosis of human immunodeficiency virus (HIV)-related diffuse large B-cell lymphoma.
Chun R ChaoMichael J SilverbergLie-Hong ChenLanfang XuOtoniel Martínez-MazaDonald I AbramsHongbin D ZhaReina HaqueJonathan SaidPublished in: Leukemia & lymphoma (2017)
Existing prognostic tools for HIV + diffuse large B-cell lymphoma (DLBCL) fail to accurately predict patient outcomes. To develop a novel prognostic algorithm incorporating molecular tumor characteristics and HIV disease factors, we included 80 patients with HIV-related DLBCL diagnosed between 1996 and 2007. Immunohistochemistry staining was used to analyze the expression of 26 tumor markers. Clinical data were collected from medical records. Logistic regression and bootstrapping were used to select and assess stability of the prognostic model, respectively. Of the tumor markers examined, expression of cMYC, Ki 67, CD44, EBV, SKP2, BCL6, p53, CD20 and IgM were associated with two-year mortality. The final prognostic model, confirmed in bootstrapped samples, included IPI, circulating CD4 cell count, history of clinical AIDS, and expression of CD44, p53, IgM and EBV. This model incorporating HIV disease history and tumor markers, achieved better prediction for two-year mortality [AUC = 0.87, 95% CI: 0.78-0.96] compared with IPI alone [AUC = 0.63 (0.51-0.75)].
Keyphrases
- diffuse large b cell lymphoma
- human immunodeficiency virus
- antiretroviral therapy
- hiv infected
- hiv positive
- epstein barr virus
- hepatitis c virus
- hiv aids
- hiv testing
- poor prognosis
- men who have sex with men
- south africa
- cardiovascular disease
- risk factors
- machine learning
- healthcare
- single cell
- neoadjuvant chemotherapy
- cardiovascular events
- big data
- bone marrow
- type diabetes
- squamous cell carcinoma
- electronic health record
- deep learning
- rectal cancer
- mesenchymal stem cells
- long non coding rna
- stem cells