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The leading challenge and limiting factor in finding cures to cancer is drug resistance (Vasan et al. 2019). However, the causes behind drug resistance are numerous, but the approach Ianevski et al. take focuses on resistance due to tumor heterogeneity through their usage of acute myeloid leukemia (ALM) patient cells. ALM is a heterogeneous disease, meaning its causes can come from numerous sources. This makes treatment difficult because no particular cause contributes to the severity of the diseases. This is why even with an increase of molecularly targeted treatment options both primary and acquired drug resistance occur. Primary drug resistance is from a predisposition, most likely genetic, that prevents successful outcomes of therapies while acquired resistance develops after routine treatment (Ianevski et al. 2021). One example in AML is that mutations like FLT3 can cause 60% of ALM patients to have resistance to monotherapy, a treatment only using one drug for cell inhibition (Ianevski et al.). Cell inhibition aims to inhibit the division and proliferation of mainly cancerous cells, but often, the effects can be counteracted by other cellular pathways to maintain cell life. These realities are what caused Ianevski et al. to search for a way to find multitargeted therapies that inhibit mechanisms of resistance. Also, they aimed to find combinatorial therapies that would also minimize toxic effects to the patients because older patients tend to be more vulnerable to drug-induced toxicity, and ALM is a disease that has an average presentation age in the late 60s (Burnett et al. 2011). The solution was a computational-experimental approach that aimed to capture two things: cell subpopulation responses and molecular heterogeneity of disease progression. Through those data, they hoped to find combination therapies that are patient-specific by honoring their specific mutations and subsequent gene expression.
The study aimed to treat two diagnosis stages (no treatment) and two refractory stages (resistant to treatment) patients. In order to test whether combination therapy would be an effective method of treatment, the researchers had to find which therapies would be both effective and safe. They first took bone marrow samples from all four patients. Bone marrow samples are important as they are the foundation for the production of red blood cells and hold their genetic baseline. Single-cell RNA sequencing (scRNA-seq) was then performed on the cell samples to determine both the baseline of gene expression and that of various disease stages within the patient. The technique scRNA-seq measures gene expression within a single cell by isolating cells and sequencing their RNA. Then the sequences of the RNA are reverse transcribed to the genome to determine read counts that correspond to DNA sequences and show expression. After scRNA-seq, patients’ overall gene expression was combined with their response viability to single-drug treatment to gauge collaborative combinations with a low possibility for toxic effects. In order to prioritize patient-specific combinations, the researchers used gene set variation analysis which compressed each cell’s transcriptomic profile (gene expression) and turned each cell into an enrichment score based on their compound-target expression. Enrichment scores represent the degree to which genes are under or overrepresented in the cell. The GSVA results not only in enrichment scores, but it develops protein targets for the therapies to be tested in an ex vivo, outside the organism, whole-well viability assay. Before a whole-well viability assay can measure the effectiveness of drug combos, the drugs that pose the most potential must be determined. Ianevski et al. trained an XGBoost Machine (software library) learning model with conformal prediction to determine the most synergistic drug combos. The model uses target expression levels of compounds in malignant (non-lymphocytes) and nonmalignant (lymphocytes) cells and allows for control over the overall synergistic effects and varying co-inhibition effects between AML and nonmalignant cells. Whole-well viability assay was the ex vivo measure to confirm that the drugs can work in a collaborative manner, but flow cytometry measured the effects the patient-specific drugs had on their cells. Flow cytometry is a quantitative technique that analyzes the size, shape, and properties of heterogeneous cell mixtures (Mitedustar 2015). Overall, they found that the combinatorial drug treatment allowed for selective co-inhibition of malignant cells while minimizing the inhibition on nonmalignant cells which would result in toxicity. Although the results varied slightly amongst the patients, the takeaways were that patient-tailored drug combination therapy can find ways to address the current stage of a patient’s disease and that continuous testing and alterations to treatment may be necessary to prevent future resistance at progressed stages.
Ianevski et al.’s study show a very promising future for molecular targeted therapies in cancer treatment. However, the sample size is extremely small for application to the entirety of patients within this cancer type let alone other cancers. The next steps should aim to not only further understand ALM combination therapy but should incorporate a collaborative effort across cancer types. The promising part of this study is that the research began with answering drug resistance which affects all cancers not just ALM, meaning co-inhibition of malignant cells across cancers with combination therapy may be possible. While these advances are in their beginning stages, it would also be interesting to see innovative ways to reduce cost as multiple tests are performed to achieve the optimal combinations. By increasing applications of this study and accessibility through cost reduction, this study can change the ways in which cancer treatment progresses.
Kamryn Graham is a sophomore biology major at Davidson College. Contact her at kagraham@davidson.edu.
References
1.Vasan, N., Baselga, J. & Hyman, D. M. A view on drug resistance in cancer. Nature575, 299–309 (2019).
2.Ianevski, A. et al. Patient-tailored design for selective co-inhibition of leukemic cell subpopulations. Science Advances7, eabe4038 (2021).
3.Burnett, A., Wetzler, M. & Löwenberg, B. Therapeutic Advances in Acute Myeloid Leukemia. JCO29, 487–494 (2011).
4. Mitedustar. Flow Cytometry Animation. (2015).
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