Integrative analysis of drug response and clinical outcome in acute myeloid leukemia.

Acute myeloid leukemia (AML) is a cancer of myeloid-lineage cells with limited therapeutic options. We previously combined ex vivo drug sensitivity with genomic, transcriptomic, and clinical annotations for a large cohort of AML patients, which facilitated discovery of functional genomic correlates. Here, we present a dataset that has been harmonized with our initial report to yield a cumulative cohort of 805 patients (942 specimens). We show strong cross-cohort concordance and identify features of drug response. Further, deconvoluting transcriptomic data shows that drug sensitivity is governed broadly by AML cell differentiation state, sometimes conditionally affecting other correlates of response. Finally, modeling of clinical outcome reveals a single gene, PEAR1, to be among the strongest predictors of patient survival, especially for young patients. Collectively, this report expands a large functional genomic resource, offers avenues for mechanistic exploration and drug development, and reveals tools for predicting outcome in AML.

Functional genomic landscape of acute myeloid leukaemia.

The implementation of targeted therapies for acute myeloid leukaemia (AML) has been challenging because of the complex mutational patterns within and across patients as well as a dearth of pharmacologic agents for most mutational events. Here we report initial findings from the Beat AML programme on a cohort of 672 tumour specimens collected from 562 patients. We assessed these specimens using whole-exome sequencing, RNA sequencing and analyses of ex vivo drug sensitivity. Our data reveal mutational events that have not previously been detected in AML. We show that the response to drugs is associated with mutational status, including instances of drug sensitivity that are specific to combinatorial mutational events. Integration with RNA sequencing also revealed gene expression signatures, which predict a role for specific gene networks in the drug response. Collectively, we have generated a dataset-accessible through the Beat AML data viewer (Vizome)-that can be leveraged to address clinical, genomic, transcriptomic and functional analyses of the biology of AML.

Targeting of colony-stimulating factor 1 receptor (CSF1R) in the CLL microenvironment yields antineoplastic activity in primary patient samples.

In many malignancies, the tumor microenvironment includes CSF1R-expressing supportive monocyte/macrophages that promote tumor cell survival. For chronic lymphocytic leukemia (CLL), these supportive monocyte/macrophages are known as nurse-like cells (NLCs), although the potential effectiveness of selective small-molecule inhibitors of CSF1R against CLL is understudied. Here, we demonstrate the preclinical activity of two inhibitors of CSF1R, GW-2580 and ARRY-382, in primary CLL patient samples. We observed at least 25% of CLL samples showed sub-micromolar sensitivity to CSF1R inhibitors. This sensitivity was observed in samples with varying genetic and clinical backgrounds, although higher white cell count and monocyte cell percentage was associated with increased sensitivity. Depleting CD14-expressing monocytes preferentially decreased viability in samples sensitive to CSF1R inhibitors, and treating samples with CSF1R inhibitors eliminated the presence of NLCs in long-term culture conditions. These results indicate that CSF1R small-molecule inhibitors target CD14-expressing monocytes in the CLL microenvironment, thereby depriving leukemia cells of extrinsic support signals. In addition, significant synergy was observed combining CSF1R inhibitors with idelalisib or ibrutinib, two current CLL therapies that disrupt tumor cell intrinsic B-cell receptor signaling. These findings support the concept of simultaneously targeting supportive NLCs and CLL cells and demonstrate the potential clinical utility of this combination.

Molecularly targeted drug combinations demonstrate selective effectiveness for myeloid- and lymphoid-derived hematologic malignancies.

Translating the genetic and epigenetic heterogeneity underlying human cancers into therapeutic strategies is an ongoing challenge. Large-scale sequencing efforts have uncovered a spectrum of mutations in many hematologic malignancies, including acute myeloid leukemia (AML), suggesting that combinations of agents will be required to treat these diseases effectively. Combinatorial approaches will also be critical for combating the emergence of genetically heterogeneous subclones, rescue signals in the microenvironment, and tumor-intrinsic feedback pathways that all contribute to disease relapse. To identify novel and effective drug combinations, we performed ex vivo sensitivity profiling of 122 primary patient samples from a variety of hematologic malignancies against a panel of 48 drug combinations. The combinations were designed as drug pairs that target nonoverlapping biological pathways and comprise drugs from different classes, preferably with Food and Drug Administration approval. A combination ratio (CR) was derived for each drug pair, and CRs were evaluated with respect to diagnostic categories as well as against genetic, cytogenetic, and cellular phenotypes of specimens from the two largest disease categories: AML and chronic lymphocytic leukemia (CLL). Nearly all tested combinations involving a BCL2 inhibitor showed additional benefit in patients with myeloid malignancies, whereas select combinations involving PI3K, CSF1R, or bromodomain inhibitors showed preferential benefit in lymphoid malignancies. Expanded analyses of patients with AML and CLL revealed specific patterns of ex vivo drug combination efficacy that were associated with select genetic, cytogenetic, and phenotypic disease subsets, warranting further evaluation. These findings highlight the heuristic value of an integrated functional genomic approach to the identification of novel treatment strategies for hematologic malignancies.