Individualized Outcome Predictions for High Risk DLBCL Patients
Diffuse large-B-cell lymphoma (DLBCL) represents the most frequent
lymphoma in adults. Between 35 to 40 % of patients are cured by
anthracyclin therapy. The relatively high therapeutic failure
may be explained by the existence of lymphoma subgroups with
different responsivenes to chemotherapeutic agents.
- The analysis of gene-expression profiles from RNA-expression chip arrays by correlative classification (heatmaps) leads to the distinction of several patient groups with different outcome prognosis (group future) in this patient stratification effort ( L1 L2).
Although the calculated gene signatures are useful for patient stratification (Kaplan-Meier) and for the potential identification of malignancy associated metabolic pathways, the so identified patient groups are inhomogeneus because they consist in variable proportion of survivors and non survivors. Stratification does therefore not permit individualized outcome predictions for patients as important prerequisite for individualized therapy planning at diagnosis.
It was investigated, whether discriminatory data pattern classification by a data sieving algorithm permits the individualized pretherapeutic identification (>95% correct) and outcome prediction for high risk DLBCL patients.
3. Results: The predictive identification of individual high risk patients is possible for the data of both studies (fig.1) (L3 L4). Predictive (fig.2) and prognostic (stratified patient groups) (fig.3) classification patterns are different, as was expected.
4. Conclusion: The metaanalysis of reported gene expression data by discriminating instead of correlating analysis permits the individualized pretherapeutic identification of the majority of high risk patients at diagnosis.
L1. Rosenwald A, Wright G, Chan WC, Connors JM, Campo E, Fisher RI, Gascoyne RD, Müller-Hermelink HK, Smeland EB, Staudt LM. The use of molecular profiling to predict survival after chemotherapy for diffuse large B-cell lymphoma. NEJM 346:1937-47(2002), ( chip data, patient data)
L2. Grau M, Lenz G, Lenz P. Dissection of gene expressiondata sets into clinically relevant interaction signatures via high-dimensional correlation maximation. Nat Comm 10:5417(2019) ( chip data, patient data)
L3. Valet G, Höffkes HG. Data pattern analysis for the individualised pretherapeutic identification of high-risk diffues large B-cell lymphoma (DLBCL) patients by cytomics. Cytometry Part A 59A:232-236(2004)
L4. Valet G. Individualized outcome prediction for high risk diffuse large B-cell lymphoma (DLBCL) patients (2021).
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