In 2018, 3.9 million new cancer cases arose, and 1.9 million cancer deaths occurred in Europe. Leukemia accounts for more than 500.000 cases worldwide per year. To provide these patients the most efficient therapy, it is crucial to identify biomarkers to select the best treatment option for each patient. In current clinical practice, cancer patients are treated according to their stratification in risk groups, with intensity increasing proportionally to the assigned risk. Leukemia cells are characterized based on different readout parameters, such as cell morphology, expression of certain surface molecules and chromosomal aberrations. Currently, correct stratification is very costly - from several hundred to few thousand Euros per patient - leading to a global hematology diagnostics market size of 6 billion USD per year.
Unfortunately, risk group stratification is often unspecific and has limited benefits for patients which is a major disadvantage of current clinical routine. As all patients within the same risk group are treated with the same drugs, numerous patients receive at least partially ineffective agents. Moreover, these drugs might be toxic resulting in avoidable adverse effects for the patients.
The identification of novel biomarkers is therefore essential to improve matching of the individual patient’s tumor with the most efficient drug. Two main strategies are followed: multi-omics sequencing enables drug selection based on descriptive genomic, epigenomic, transcriptomic and proteomic data, while ex vivo drug testing in cell or organoid cultures is used for drug selection on a functional level in preclinical settings. However, although being tested for several years or even decades, these techniques did not yet enter clinical routine. Despite major investments into ex vivo drug screening pipelines, their predictive value is limited, failing to correlate ex vivo drug data with their effectivity in vivo. Similarly, sequencing data are used by the Molecular Tumor Board to choose targeted therapies for individual patients. Regrettably, this approach proved little successful so far, suggesting that the genetic alteration alone is insufficient for response prediction. In contrast to the previous examples, measuring antigen expression levels proved to be predictive and clinically relevant for antibody-directed therapies, but is obviously limited to a subset of treatments. In general, currently used biomarkers are both highly resource-intensive and fail to effectively match a broad spectrum of antitumor therapies to individual patients who would most benefit from the treatment in terms of improved quality of life and extended survival.
As an example, the specific BCL-2 inhibitor Venetoclax is used for the treatment of several types of leukemia and various patient subgroups including elderly acute myeloid leukemia (AML) patients. Yearly costs for Venetoclax treatment sum up to 100.000€ per patient. Routinely, every patient within this group of elderly AML patients receives this drug. However, only a subset of patients benefits from this expensive treatment, while the rest of patients suffer from unnecessary side effects. To address the urgent need for a robust biomarker, several tests have been developed, such as BH3 profiling or the MACS-Score. Still, these tests are specific to this gene/drug pair and cannot be broadly applied to further genes or drugs.
