How Genetics Can Drive Initial Therapy Choices for Older Patients with Acute Myeloid Leukemia.

OPINION STATEMENT: Treatment of older adults with acute myeloid leukemia (AML) is challenging. Therapy decisions must be guided by multiple factors including aging-related conditions (e.g., comorbidities, functional impairment), therapy benefits and risks, patient preferences, and disease characteristics. Balancing these factors requires understanding the unique, and frequently higher-risk cytogenetic and molecular characteristics of AML in older adult populations, which should caution providers not to reduce therapy intensity on the basis of age alone. Instead, geriatric assessments should be employed to determine fitness for therapy. Treatment options in AML are increasingly targeted to specific mutations or recognized to have differential benefits on the basis of genomics, and representation of older adults and geriatric outcome reporting in clinical trials is improving. Additionally, newer studies have begun to explore personalized therapy strategies on the basis of initial genetic testing. Review and refinement of practice guidelines for older patients on the basis of these advances is needed and is anticipated to remain an important topic in ongoing hematology/oncology clinical education.

Synchronous B-Cell Acute Lymphoblastic Leukemia and Serous Ovarian Carcinoma: A Case Report.

Adult patients with B-cell acute lymphoblastic leukemia (ALL) have higher rates of antecedent and subsequent malignancies. However, synchronous identification of ALL and ovarian cancer is exceedingly rare. We report the unique case of a 65-year-old woman with synchronous B-cell ALL and low-grade serous ovarian carcinoma diagnosed after surgical intervention for a small bowel obstruction. Treatment with inotuzumab ozogamicin followed by adnexal mass resection and postoperative letrozole was successful in achieving complete remission for both her leukemia and ovarian cancer.

Common variants in signaling transcription-factor-binding sites drive phenotypic variability in red blood cell traits.

Genome-wide association studies identify genomic variants associated with human traits and diseases. Most trait-associated variants are located within cell-type-specific enhancers, but the molecular mechanisms governing phenotypic variation are less well understood. Here, we show that many enhancer variants associated with red blood cell (RBC) traits map to enhancers that are co-bound by lineage-specific master transcription factors (MTFs) and signaling transcription factors (STFs) responsive to extracellular signals. The majority of enhancer variants reside on STF and not MTF motifs, perturbing DNA binding by various STFs (BMP/TGF-ß-directed SMADs or WNT-induced TCFs) and affecting target gene expression. Analyses of engineered human blood cells and expression quantitative trait loci verify that disrupted STF binding leads to altered gene expression. Our results propose that the majority of the RBC-trait-associated variants that reside on transcription-factor-binding sequences fall in STF target sequences, suggesting that the phenotypic variation of RBC traits could stem from altered responsiveness to extracellular stimuli.

CHD7 and Runx1 interaction provides a braking mechanism for hematopoietic differentiation.

Hematopoietic stem and progenitor cell (HSPC) formation and lineage differentiation involve gene expression programs orchestrated by transcription factors and epigenetic regulators. Genetic disruption of the chromatin remodeler chromodomain-helicase-DNA-binding protein 7 (CHD7) expanded phenotypic HSPCs, erythroid, and myeloid lineages in zebrafish and mouse embryos. CHD7 acts to suppress hematopoietic differentiation. Binding motifs for RUNX and other hematopoietic transcription factors are enriched at sites occupied by CHD7, and decreased RUNX1 occupancy correlated with loss of CHD7 localization. CHD7 physically interacts with RUNX1 and suppresses RUNX1-induced expansion of HSPCs during development through modulation of RUNX1 activity. Consequently, the RUNX1:CHD7 axis provides proper timing and function of HSPCs as they emerge during hematopoietic development or mature in adults, representing a distinct and evolutionarily conserved control mechanism to ensure accurate hematopoietic lineage differentiation.

RNA helicase DDX21 mediates nucleotide stress responses in neural crest and melanoma cells.

The availability of nucleotides has a direct impact on transcription. The inhibition of dihydroorotate dehydrogenase (DHODH) with leflunomide impacts nucleotide pools by reducing pyrimidine levels. Leflunomide abrogates the effective transcription elongation of genes required for neural crest development and melanoma growth in vivo1. To define the mechanism of action, we undertook an in vivo chemical suppressor screen for restoration of neural crest after leflunomide treatment. Surprisingly, we found that alterations in progesterone and progesterone receptor (Pgr) signalling strongly suppressed leflunomide-mediated neural crest effects in zebrafish. In addition, progesterone bypasses the transcriptional elongation block resulting from Paf complex deficiency, rescuing neural crest defects in ctr9 morphant and paf1(alnz24) mutant embryos. Using proteomics, we found that Pgr binds the RNA helicase protein Ddx21. ddx21-deficient zebrafish show resistance to leflunomide-induced stress. At a molecular level, nucleotide depletion reduced the chromatin occupancy of DDX21 in human A375 melanoma cells. Nucleotide supplementation reversed the gene expression signature and DDX21 occupancy changes prompted by leflunomide. Together, our results show that DDX21 acts as a sensor and mediator of transcription during nucleotide stress.

Machine learning approach to literature mining for the genetics of complex diseases.

To generate a parsimonious gene set for understanding the mechanisms underlying complex diseases, we reasoned it was necessary to combine the curation of public literature, review of experimental databases and interpolation of pathway-associated genes. Using this strategy, we previously built the following two databases for reproductive disorders: The Database for Preterm Birth (dbPTB) and The Database for Preeclampsia (dbPEC). The completeness and accuracy of these databases is essential for supporting our understanding of these complex conditions. Given the exponential increase in biomedical literature, it is becoming increasingly difficult to manually maintain these databases. Using our curated databases as reference data sets, we implemented a machine learning-based approach to optimize article selection for manual curation. We used logistic regression, random forests and neural networks as our machine learning algorithms to classify articles. We examined features derived from abstract text, annotations and metadata that we hypothesized would best classify articles with genetically relevant content associated to the disorder of interest. Combinations of these features were used build the classifiers and the performance of these feature sets were compared to a standard 'Bag-of-Words'. Several combinations of these genetic based feature sets outperformed 'Bag-of-Words' at a threshold such that 95% of the curated gene set obtained from the original manual curation of all articles were extracted from the articles classified by machine learning as 'considered'. The performance was superior in terms of the reduction of required manual curation and two measures of the harmonic mean of precision and recall. The reduction in workload ranged from 0.814 to 0.846 for the dbPTB and 0.301 to 0.371 for the dbPEC. Additionally, a database of metadata and annotations is generated which allows for rapid query of individual features. Our results demonstrate that machine learning algorithms can identify articles with relevant data for databases of genes associated with complex diseases.