We conducted a single-center, open-label, dose escalation, and expansion phase I trial of the antiangiogenic multikinase inhibitor regorafenib in patients with advanced myeloid neoplasms. We enrolled 16 patients with relapsed/refractory acute myeloid leukemia (AML), myeloproliferative neoplasms (MPN), chronic myelomonocytic leukemia (CMML), or myelodysplastic syndrome (MDS). A 3 + 3 dose escalation design was used with two planned dose levels (120 or 160 mg daily) and one de-escalation level (80 mg daily).
View Article and Find Full Text PDFWe sought to assess the safety of adding ixazomib, an oral proteasome inhibitor, to a multi-agent treatment regimen for older adults with acute lymphoblastic leukemia (ALL). Patients 51 to 75 years of age with newly diagnosed ALL were screened. Induction consisted of prednisone (P), vincristine (V), and doxorubicin (D).
View Article and Find Full Text PDFBackground: Quiescence (G0) is a transient, cell cycle-arrested state. By entering G0, cancer cells survive unfavorable conditions such as chemotherapy and cause relapse. While G0 cells have been studied at the transcriptome level, how post-transcriptional regulation contributes to their chemoresistance remains unknown.
View Article and Find Full Text PDFBackground: Increased aurora A kinase (AAK) expression occurs in acute myeloid leukaemia; AAK inhibition is a promising therapeutic target in this disease. We therefore aimed to assess the activity of alisertib combined with 7 + 3 induction chemotherapy in previously untreated patients with high-risk acute myeloid leukaemia.
Methods: We did a single-arm, phase 2 trial of patients recruited from the Dana-Farber/Harvard Cancer Center in the USA.
Acute myeloid leukemia (AML) is a heterogeneous disease that resides within a complex microenvironment, complicating efforts to understand how different cell types contribute to disease progression. We combined single-cell RNA sequencing and genotyping to profile 38,410 cells from 40 bone marrow aspirates, including 16 AML patients and five healthy donors. We then applied a machine learning classifier to distinguish a spectrum of malignant cell types whose abundances varied between patients and between subclones in the same tumor.
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