scholarly journals Inferring tumor-specific cancer dependencies through integrating ex-vivo drug response assays and drug-protein profiling

2022 ◽  
Author(s):  
Alina Batzilla ◽  
Junyan Lu ◽  
Jarno Kivioja ◽  
Kerstin Putzker ◽  
Joe Lewis ◽  
...  

The development of cancer therapies may be improved by the discovery of tumor-specific molecular dependencies. The requisite tools include genetic and chemical perturbations, each with its strengths and limitations. Drug perturbations can be readily applied to primary cancer samples at a large scale, but mechanistic understanding of hits and further pharmaceutical development is often complicated by the fact that a small compound has a range of affinities to multiple proteins. To computationally infer the molecular dependencies of individual cancers from their ex-vivo drug sensitivity profiles, we developed a mathematical model that deconvolutes these data using measurements of protein-drug affinity profiles. Our method, DepInfeR, correctly identified known dependencies, including EGFR dependence in Her2+ breast cancer cell line, FLT3 dependence in AML tumors with FLT3-ITD mutations, and the differential dependencies on the B-cell receptor pathway in two major subtypes of chronic lymphocytic leukemia (CLL). Furthermore, our method uncovered new subgroup-specific dependencies, including a previously unreported dependence of high-risk CLL on Checkpoint kinase 1 (CHEK1). The method also produced a more accurate map of the molecular dependencies in a heterogeneous set of 117 CLL samples. The ability to deconvolute polypharmacological phenotypes into underlying causal molecular dependencies should increase the utility of high-throughput drug response assays for functional precision oncology.

Cancers ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 354
Author(s):  
Subir Roy Chowdhury ◽  
Cheryl Peltier ◽  
Sen Hou ◽  
Amandeep Singh ◽  
James B. Johnston ◽  
...  

Mitochondrial respiration is becoming more commonly used as a preclinical tool and potential biomarker for chronic lymphocytic leukemia (CLL) and activated B-cell receptor (BCR) signaling. However, respiration parameters have not been evaluated with respect to dose of ibrutinib given in clinical practice or the effect of progression on ibrutinib treatment on respiration of CLL cells. We evaluated the impact of low and standard dose ibrutinib on CLL cells from patients treated in vivo on mitochondrial respiration using Oroboros oxygraph. Cytokines CCL3 and CCL4 were evaluated using the Mesoscale. Western blot analysis was used to evaluate the BCR and apoptotic pathways. We observed no difference in the mitochondrial respiration rates or levels of plasma chemokine (C-C motif) ligands 3 and 4 (CCL3/CCL4), β-2 microglobulin (β-2 M) and lactate dehydrogenase (LDH) between low and standard doses of ibrutinib. This may confirm why clinical observations of the safety and efficacy of low dose ibrutinib are observed in practice. Of interest, we also observed that the mitochondrial respiration of CLL cells paralleled the increase in β-2 M and LDH at progression. Our study further supports mitochondrial respiration as a biomarker for response and progression on ibrutinib in CLL cells and a valuable pre-clinical tool.


2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Chayaporn Suphavilai ◽  
Shumei Chia ◽  
Ankur Sharma ◽  
Lorna Tu ◽  
Rafael Peres Da Silva ◽  
...  

AbstractWhile understanding molecular heterogeneity across patients underpins precision oncology, there is increasing appreciation for taking intra-tumor heterogeneity into account. Based on large-scale analysis of cancer omics datasets, we highlight the importance of intra-tumor transcriptomic heterogeneity (ITTH) for predicting clinical outcomes. Leveraging single-cell RNA-seq (scRNA-seq) with a recommender system (CaDRReS-Sc), we show that heterogeneous gene-expression signatures can predict drug response with high accuracy (80%). Using patient-proximal cell lines, we established the validity of CaDRReS-Sc’s monotherapy (Pearson r>0.6) and combinatorial predictions targeting clone-specific vulnerabilities (>10% improvement). Applying CaDRReS-Sc to rapidly expanding scRNA-seq compendiums can serve as in silico screen to accelerate drug-repurposing studies. Availability: https://github.com/CSB5/CaDRReS-Sc.


2019 ◽  
Vol 116 (44) ◽  
pp. 22020-22029 ◽  
Author(s):  
Aritro Nath ◽  
Eunice Y. T. Lau ◽  
Adam M. Lee ◽  
Paul Geeleher ◽  
William C. S. Cho ◽  
...  

Large-scale cancer cell line screens have identified thousands of protein-coding genes (PCGs) as biomarkers of anticancer drug response. However, systematic evaluation of long noncoding RNAs (lncRNAs) as pharmacogenomic biomarkers has so far proven challenging. Here, we study the contribution of lncRNAs as drug response predictors beyond spurious associations driven by correlations with proximal PCGs, tissue lineage, or established biomarkers. We show that, as a whole, the lncRNA transcriptome is equally potent as the PCG transcriptome at predicting response to hundreds of anticancer drugs. Analysis of individual lncRNAs transcripts associated with drug response reveals nearly half of the significant associations are in fact attributable to proximal cis-PCGs. However, adjusting for effects of cis-PCGs revealed significant lncRNAs that augment drug response predictions for most drugs, including those with well-established clinical biomarkers. In addition, we identify lncRNA-specific somatic alterations associated with drug response by adopting a statistical approach to determine lncRNAs carrying somatic mutations that undergo positive selection in cancer cells. Lastly, we experimentally demonstrate that 2 lncRNAs, EGFR-AS1 and MIR205HG, are functionally relevant predictors of anti-epidermal growth factor receptor (EGFR) drug response.


2019 ◽  
Vol 20 (5) ◽  
pp. 1734-1753 ◽  
Author(s):  
Raziur Rahman ◽  
Saugato Rahman Dhruba ◽  
Kevin Matlock ◽  
Carlos De-Niz ◽  
Souparno Ghosh ◽  
...  

Abstract Recent years have seen an increase in the availability of pharmacogenomic databases such as Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) that provide genomic and functional characterization information for multiple cell lines. Studies have alluded to the fact that specific characterizations may be inconsistent between different databases. Analysis of the potential discrepancies in the different databases is highly significant, as these sources are frequently used to analyze and validate methodologies for personalized cancer therapies. In this article, we review the recent developments in investigating the correspondence between different pharmacogenomics databases and discuss the potential factors that require attention when incorporating these sources in any modeling analysis. Furthermore, we explored the consistency among these databases using copulas that can capture nonlinear dependencies between two sets of data.


Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 5023-5023
Author(s):  
Y. Lynn Wang ◽  
Zibo Song ◽  
Pin Lu ◽  
John P. Leonard ◽  
Morton Coleman ◽  
...  

Abstract B cell receptor (BCR) signaling plays an essential role in the pathogenesis of chronic lymphocytic leukemia. In a subset of patients with a poor clinical outcome, BCR ligation leads to increased cell metabolism and cell survival (Cancer Research66, 7158–66, 2006). Based on these findings, we tested whether targeting BCR signaling with dasatinib, an inhibitor of Src kinase, would interfere with the signaling cascade and cause death of CLL B cells. CLL leukemic cells were isolated from 34 patients and were incubated with or without dasatinib at a low dose of 128 nM. Among 34 cases, viability of leukemic cells was reduced by 2% to 90%, with an average of ~50% reduction on day 4 of ex vivo culture. Further study showed that CLL B cells undergo death by apoptosis via the intrinsic pathway which involves the generation of reactive oxygen species. Analysis of the Src family kinases showed that phosphorylation of Src, Lyn and Hck was inhibited by dasatinib not only in those cases that responded to dasatinib with apoptosis, but also in those that did not respond well (<20% apoptosis). Further analysis revealed that suppressed activity of two downstream molecules, Syk and PLC Statistical analysis showed a significant correlation between CLL dasatinib response and their IgVH mutation and ZAP70 status. Cases with worse prognoses by these criteria have a better response to the kinase inhibitor. Lastly, we have also found that ZAP70 positive cases showed a greater degree of PLC


Cancers ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1519 ◽  
Author(s):  
Kost ◽  
Saleh ◽  
Mejia ◽  
Mostafizar ◽  
Bouchard ◽  
...  

: The phosphatidyl-inositol 3 kinase (PI3K) δ inhibitor, idelalisib (IDE), is a potent inhibitor of the B-cell receptor pathway and a novel and highly effective agent for the treatment of chronic lymphocytic leukemia (CLL). We evaluated the activities of IDE in comparison to bendamusine (BEN), a commonly used alkylating agent, in primary CLL cells ex vivo. In contrast to BEN, IDE was cytotoxic to cells from extensively-treated patients, including those with a deletion (del)17p. Cross-resistance was not observed between BEN and IDE, confirming their different modes of cytotoxicity. Marked synergy was seen between BEN and IDE, even in cases that were resistant to BEN or IDE individually, and those with deletion (del) 17p. CD40L/interleukin 4 (IL4) co-treatment mimicking the CLL microenvironment increased resistance to IDE, but synergy was retained. PI3Kδ-deficient murine splenic B cells were more resistant to IDE and showed reduced synergy with BEN, thus confirming the importance of functional PI3Kδ protein. Although IDE was observed to induce γH2AX, IDE did not enhance activation of the DNA damage response nor DNA repair activity. Interestingly, IDE decreased global RNA synthesis and was antagonistic with 5,6-Dichlorobenzimidazole 1-b-D-ribofuranoside (DRB), an inhibitor of transcription. These findings add to the increasingly complex cellular effects of IDE, and B cell receptor (BCR) inhibitors in general, in CLL.


2017 ◽  
Vol 114 (36) ◽  
pp. E7554-E7563 ◽  
Author(s):  
Stephen E. Kurtz ◽  
Christopher A. Eide ◽  
Andy Kaempf ◽  
Vishesh Khanna ◽  
Samantha L. Savage ◽  
...  

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.


Leukemia ◽  
2021 ◽  
Author(s):  
Cedric Schleiss ◽  
Raphael Carapito ◽  
Luc-Matthieu Fornecker ◽  
Leslie Muller ◽  
Nicodème Paul ◽  
...  

AbstractB-cell receptor (BCR) signaling is crucial for the pathophysiology of most mature B-cell lymphomas/leukemias and has emerged as a therapeutic target whose effectiveness remains limited by the occurrence of mutations. Therefore, deciphering the cellular program activated downstream this pathway has become of paramount importance for the development of innovative therapies. Using an original ex vivo model of BCR-induced proliferation of chronic lymphocytic leukemia cells, we generated 108 temporal transcriptional and proteomic profiles from 1 h up to 4 days after BCR activation. This dataset revealed a structured temporal response composed of 13,065 transcripts and 4027 proteins, comprising a leukemic proliferative signature consisting of 430 genes and 374 proteins. Mathematical modeling of this complex cellular response further highlighted a transcriptional network driven by 14 early genes linked to proteins involved in cell proliferation. This group includes expected genes (EGR1/2, NF-kB) and genes involved in NF-kB signaling modulation (TANK, ROHF) and immune evasion (KMO, IL4I1) that have not yet been associated with leukemic cells proliferation. Our study unveils the BCR-activated proliferative genetic program in primary leukemic cells. This approach combining temporal measurements with modeling allows identifying new putative targets for innovative therapy of lymphoid malignancies and also cancers dependent on ligand–receptor interactions.


2019 ◽  
Author(s):  
Sumana Srivatsa ◽  
Hesam Montazeri ◽  
Gaia Bianco ◽  
Mairene Coto-Llerena ◽  
Charlotte KY Ng ◽  
...  

Despite the progress in precision oncology, development of cancer therapies is limited by the dearth of suitable drug targets1. Novel candidate drug targets can be identified based on the concept of synthetic lethality (SL), which refers to pairs of genes for which an aberration in either gene alone is non-lethal, but co-occurrence of the aberrations is lethal to the cell. We developed SLIdR (Synthetic Lethal Identification in R), a statistical framework for identifying SL pairs from large-scale perturbation screens. SLIdR successfully predicts SL pairs even with small sample sizes while minimizing the number of false positive targets. We applied SLIdR to Project DRIVE data2 and found both established and novel pan-cancer and cancer type-specific SL pairs. We identified and experimentally validated a novel SL interaction between AXIN1 and URI1 in hepatocellular carcinoma, thus corroborating the potential of SLIdR to identify new SL-based drug targets.


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