Caroline A. Heckman
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View article: Efficacy of melflufen in multiple myeloma with mutated or deleted TP53
Efficacy of melflufen in multiple myeloma with mutated or deleted TP53 Open
Patients with multiple myeloma bearing a deletion of chromosome 17p (del(17p)), mutation of TP53 , or both have poorer prognosis compared to patients without these aberrations. We investigated the activity and mechanism of melflufen (melph…
View article: Efficacy of the JAK2/FLT3 inhibitor pacritinib in NPM1 mutated Acute Myeloid Leukemia
Efficacy of the JAK2/FLT3 inhibitor pacritinib in NPM1 mutated Acute Myeloid Leukemia Open
Background Acute myeloid leukemia (AML) with mutation in NPM1 (NPM1mut) represents a third ofadult AML cases (Falini et al., 2020). When the mutation co-occurs with FLT3 and/or DNMT3A mutations, the prognosis and response to treatment is l…
View article: Targeting acute myeloid leukemia resistance with two novel combinations demonstrate superior efficacy in TP53, HLA-B, MUC4 and FLT3 mutations
Targeting acute myeloid leukemia resistance with two novel combinations demonstrate superior efficacy in TP53, HLA-B, MUC4 and FLT3 mutations Open
Acute myeloid leukemia (AML) is a genetically heterogeneous malignancy characterized by the clonal expansion of myeloid precursor cells. Despite the advent of venetoclax-based regimens, resistance mechanisms remain a major clinical challen…
View article: Digital twin models for predicting venetoclax and azacitidine-induced neutropenia in patients with acute myeloid leukemia
Digital twin models for predicting venetoclax and azacitidine-induced neutropenia in patients with acute myeloid leukemia Open
Therapeutic toxicity, which can be life-threatening, presents a major challenge in treating patients with acute myeloid leukemia (AML). Medical digital twins, which are virtual representations of patient disease, have the potential to fore…
View article: Predictors of response and rational combinations for the novel <scp>MCL</scp> ‐1 inhibitor <scp>MIK665</scp> in acute myeloid leukemia
Predictors of response and rational combinations for the novel <span>MCL</span> ‐1 inhibitor <span>MIK665</span> in acute myeloid leukemia Open
Despite promising anti‐leukemic activity of MCL‐1 inhibitors in preclinical studies of acute myeloid leukemia (AML), clinical progress has been hindered by limited knowledge of target patient subgroups. To stratify patients for MCL‐1 inhib…
View article: NAMPT INHIBITION SELECTIVELY TARGETS MDS MYELOBLASTS IN HIGH-RISK MDS WITH MONOSOMY 7 OR DEL(7Q)
NAMPT INHIBITION SELECTIVELY TARGETS MDS MYELOBLASTS IN HIGH-RISK MDS WITH MONOSOMY 7 OR DEL(7Q) Open
Monosomy 7 and deletion of the q arm of chromosome 7 (-7/-7q) are high-risk markers in myelodysplastic syndromes (MDS). We previously showed that blasts with -7/-7q from patients with acute myeloid leukemia (AML) are exceptionally sensitiv…
View article: Distinct Stem Cell Identities Converge into Shared Erythroid Stress in ERCC6L2 Disease and Shwachman-Diamond Syndrome
Distinct Stem Cell Identities Converge into Shared Erythroid Stress in ERCC6L2 Disease and Shwachman-Diamond Syndrome Open
ERCC6L2 disease (ED) is a rare bone marrow failure syndrome caused by biallelic germline mutations in ERCC6L2 . ED leads to accumulation of somatic TP53 mutations, myelodysplastic syndrome, and acute myeloid leukemia (AML) with erythroid p…
View article: Shifting Beyond Classical Drug Synergy in Combinatorial Therapy through Solubility Alterations
Shifting Beyond Classical Drug Synergy in Combinatorial Therapy through Solubility Alterations Open
Acute myeloid leukemia (AML) remains a formidable clinical challenge due to genetic heterogeneity, high relapse rates, and toxicities associated with conventional chemotherapies. Rationally designed drug combinations offer improved efficac…
View article: Supplementary Information from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia
Supplementary Information from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia Open
Supplementary Tables S1-S3 Supplementary Results Supplementary Discussion
View article: Supplementary Figure S7 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia
Supplementary Figure S7 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia Open
Supplementary Figure S7
View article: Supplementary Figure S15 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia
Supplementary Figure S15 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia Open
Supplementary Figure S15
View article: Supplementary Figure S4 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia
Supplementary Figure S4 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia Open
Supplementary Figure S4
View article: Supplementary Figure S14 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia
Supplementary Figure S14 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia Open
Supplementary Figure S14
View article: Supplementary Figure S1 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia
Supplementary Figure S1 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia Open
Supplementary Figure S1
View article: Supplementary Figure S13 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia
Supplementary Figure S13 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia Open
Supplementary Figure S13
View article: Supplementary Figure S10 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia
Supplementary Figure S10 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia Open
Supplementary Figure S10
View article: Supplementary Figure S8 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia
Supplementary Figure S8 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia Open
Supplementary Figure S8
View article: Supplementary Figure S2 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia
Supplementary Figure S2 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia Open
Supplementary Figure S2
View article: Supplementary Figure S12 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia
Supplementary Figure S12 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia Open
Supplementary Figure S12
View article: Supplementary Figure S3 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia
Supplementary Figure S3 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia Open
Supplementary Figure S3
View article: Supplementary Table S4 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia
Supplementary Table S4 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia Open
Drug testing data for each patient sample across several drug classes and target mechanisms.
View article: Supplementary Figure S5 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia
Supplementary Figure S5 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia Open
Supplementary Figure S5
View article: Supplementary Figure S11 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia
Supplementary Figure S11 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia Open
Supplementary Figure S11
View article: Supplementary Figure S2 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia
Supplementary Figure S2 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia Open
Supplementary Figure S2
View article: Supplementary Figure S6 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia
Supplementary Figure S6 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia Open
Supplementary Figure S6
View article: Supplementary Figure S10 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia
Supplementary Figure S10 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia Open
Supplementary Figure S10
View article: Supplementary Figure S3 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia
Supplementary Figure S3 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia Open
Supplementary Figure S3
View article: Supplementary Figure S15 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia
Supplementary Figure S15 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia Open
Supplementary Figure S15
View article: Supplementary Figure S1 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia
Supplementary Figure S1 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia Open
Supplementary Figure S1
View article: Supplementary Figure S4 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia
Supplementary Figure S4 from A Machine Learning–Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia Open
Supplementary Figure S4