Random survival forest model identifies novel biomarkers of event-free survival in high-risk pediatric acute lymphoblastic leukemia.

High-risk pediatric B-ALL patients experience 5-year negative event rates up to 25%. Although some biomarkers of relapse are utilized in the clinic, their ability to predict outcomes in high-risk patients is limited. Here, we propose a random survival forest (RSF) machine learning model utilizing interpretable genomic inputs to predict relapse/death in high-risk pediatric B-ALL patients. We utilized whole exome sequencing profiles from 156 patients in the TARGET-ALL study (with samples collected at presentation) further stratified into training and test cohorts (109 and 47 patients, respectively). To avoid overfitting and facilitate the interpretation of machine learning results, input genomic variables were engineered using a stepwise approach involving univariable Cox models to select variables directly associated with outcomes, genomic coordinate-based analysis to select mutational hotspots, and correlation analysis to eliminate feature co-linearity. Model training identified 7 genomic regions most predictive of relapse/death-free survival. The test cohort error rate was 12.47%, and a polygenic score based on the sum of the top 7 variables effectively stratified patients into two groups, with significant differences in time to relapse/death (log-rank P = 0.001, hazard ratio = 5.41). Our model outperformed other EFS modeling approaches including an RSF using gold-standard prognostic variables (error rate = 24.35%). Validation in 174 standard-risk patients and 3 patients who failed to respond to induction therapy confirmed that our RSF model and polygenic score were specific to high-risk disease. We propose that our feature selection/engineering approach can increase the clinical interpretability of RSF, and our polygenic score could be utilized for enhance clinical decision-making in high-risk B-ALL.

Calling Variants in the Clinic: Informed Variant Calling Decisions Based on Biological, Clinical, and Laboratory Variables.

Deep sequencing genomic analysis is becoming increasingly common in clinical research and practice, enabling accurate identification of diagnostic, prognostic, and predictive determinants. Variant calling, distinguishing between true mutations and experimental errors, is a central task of genomic analysis and often requires sophisticated statistical, computational, and/or heuristic techniques. Although variant callers seek to overcome noise inherent in biological experiments, variant calling can be significantly affected by outside factors including those used to prepare, store, and analyze samples. The goal of this review is to discuss known experimental features, such as sample preparation, library preparation, and sequencing, alongside diverse biological and clinical variables, and evaluate their effect on variant caller selection and optimization.

Preclinical activity of FF-10501-01, a novel inosine-5'-monophosphate dehydrogenase inhibitor, in acute myeloid leukemia.

BACKGROUND: FF-10501-01 is a selective inosine monophosphate dehydrogenase (IMPDH) inhibitor that has shown activity in cancer cell lines. We studied whether FF-10501-01 is effective in targeting a variety of hypomethylating agent (HMA)-sensitive and -resistant acute myelogenous leukemia (AML) cell lines. METHODS: We treated multiple cell lines (including HMA-resistant cells) with FF-10501-01 and analyzed proliferation, apoptosis, and cell cycle status. We also assessed HMA-FF-10501-01 combinations and the ability of extracellular guanosine to rescue cell proliferation in FF-10501-01-treated cells. We performed high-performance liquid chromatography (HPLC) to study guanine nucleotide levels in treated and untreated cells. Finally, we studied the effects of FF-10501-01 in fresh peripheral blood cells taken from AML patients. RESULTS: FF-10501-01 showed a strong dose-dependent effect on proliferation and induced apoptosis at approximately 30µM. The effects of FF-10501-01 treatment on cell cycle status were variable, with no statistically significant trends. Guanosine rescued proliferation in FF-10501-01-treated cells, and HPLC results showed significant decreases in phosphorylated guanosine levels in MOLM13 cells. FF-10501-01 effectively reduced proliferation at concentrations of 300µM and above in 3 primary AML samples. CONCLUSIONS: FF-10501-01 effectively induces AML cell death and reduces AML peripheral blood cell proliferation by targeting guanine nucleotide biosynthesis regardless of HMA resistance status.

PDE4 Differential Expression Is a Potential Prognostic Factor and Therapeutic Target in Patients With Myelodysplastic Syndrome and Chronic Myelomonocytic Leukemia.

BACKGROUND (OR PURPOSE): Inflammation has an essential role in the pathogenesis of myelodysplastic syndromes (MDS). Its expression is controlled by phosphodiesterase 4 (PDE4). Thus, PDE4 inhibitors might be useful therapeutic targets for MDS. PATIENTS (OR MATERIALS) AND METHODS: We evaluated the expression of each isoform of PDE4 (A, B, C, and D) using transcriptomic profiling and examined the potential impact on the outcome of patients with MDS in terms of survival and response to hypomethylating agents. Total RNA was extracted from CD34(+) bone marrow hematopoietic cells from healthy individuals (n = 10) and patients with MDS (n = 24) or chronic myelomonocytic leukemia (n = 19). RESULTS: The study cohort had a median follow-up period of 21.2 months (range, 0.2-68 months) and a median overall survival of 17.6 months (95% confidence interval, 9.6-25.6). The main finding of the present study was that PDE4 mean expression was generally higher in patients with MDS than in healthy individuals. Also, upregulated PDE4 expression seemed to have a possible negative effect on survival (P > .05). Moreover, lower, compared with higher, mean PDE4A and PDE4C expression is indicative of a response to a hypomethylating agent (0.09 and 0.03 vs. 0.54 and 0.49, respectively; P > .05). CONCLUSION: These results should be confirmed in a larger patient cohort. PDE4 expression could be an effective potential prognostic factor and therapeutic target for patients with MDS and chronic myelomonocytic leukemia. The role of PDE4 inhibitors should be explored in vitro against MDS cell lines and in preclinical mouse models of MDS.

Unraveling Myelodysplastic Syndromes: Current Knowledge and Future Directions.

Myelodysplastic syndromes (MDS) affect more than 30,000 patients in the USA per year, most of whom are elderly, and these diseases are associated with dismal prognoses. The main features of MDS are ineffective hematopoiesis and aberrant myeloid differentiation. Furthermore, MDS are heterogeneous, both clinically and molecularly. This heterogeneity and the frequent occurrence of age-related comorbidities make the management of these diseases challenging. In fact, there have been no new drug approvals for MDS in the USA in the last 9 years, and few currently available investigational drugs are likely to be approved in the near future. Novel targeted treatment based on better understanding of the pathogenesis of MDS is needed to maximize patient outcomes. Here, we discuss new insights into diagnostic accuracy, prognostic assessment, pathogenic mechanisms, and effective treatments for MDS.