Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies

Yadav, Bhagwan; Pemovska, Tea; Szwajda, Agnieszka; Kulesskiy, Evgeny; Kontro, Mika; Karjalainen, Reijo; Majumder, Muntasir Mamun; Malani, Disha; Murumägi, Astrid; Knowles, Jonathan; Porkka, Kimmo; Heckman, Caroline A.; Kallioniemi, Olli; Wennerberg, Krister; Aittokallio, Tero

doi:10.1038/srep05193

Cited by 271 publications

(364 citation statements)

References 22 publications

(56 reference statements)

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“…Drug efficacy was estimated as a drug sensitivity score (DSS) according to the model proposed by Yadav et al . (2014). Prior to testing for subtype associations, 24/69 conventional chemotherapeutics were filtered out due to low cross‐sample variance (either having high or no effect across all cell lines).…”

Section: Methodsmentioning

confidence: 99%

Prognostic, predictive, and pharmacogenomic assessments of CDX2 refine stratification of colorectal cancer

et al. 2018

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We aimed to refine the value of CDX2 as an independent prognostic and predictive biomarker in colorectal cancer (CRC) according to disease stage and chemotherapy sensitivity in preclinical models. CDX2 expression was evaluated in 1045 stage I–IV primary CRCs by gene expression (n = 403) or immunohistochemistry (n = 642) and in relation to 5‐year relapse‐free survival (RFS), overall survival (OS), and chemotherapy. Pharmacogenomic associations between CDX2 expression and 69 chemotherapeutics were assessed by drug screening of 35 CRC cell lines. CDX2 expression was lost in 11.6% of cases and showed independent poor prognostic value in multivariable models. For individual stages, CDX2 was prognostic only in stage IV, independent of chemotherapy. Among stage I–III patients not treated in an adjuvant setting, CDX2 loss was associated with a particularly poor survival in the BRAF‐mutated subgroup, but prognostic value was independent of microsatellite instability status and the consensus molecular subtypes. In stage III, the 5‐year RFS rate was higher among patients with loss of CDX2 who received adjuvant chemotherapy than among patients who did not. The CDX2‐negative cell lines were significantly more sensitive to chemotherapeutics than CDX2‐positive cells, and the multidrug resistance genes MDR1 and CFTR were significantly downregulated both in CDX2‐negative cells and in patient tumors. Loss of CDX2 in CRC is an adverse prognostic biomarker only in stage IV disease and appears to be associated with benefit from adjuvant chemotherapy in stage III. Early‐stage patients not qualifying for chemotherapy might be reconsidered for such treatment if their tumor has loss of CDX2 and mutated BRAF.

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Section: Methodsmentioning

confidence: 99%

Prognostic, predictive, and pharmacogenomic assessments of CDX2 refine stratification of colorectal cancer

et al. 2018

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“…Concordant subtypes were assigned for three of the four matching PDX-patient tumor pairs (Supplementary Table S13). Although with a lower sample number, the in vivo models also recapitulated important features (37) were calculated for each drug based on cell viability after treatment at five different concentrations, and quality control showed strong reproducibility of the DSS values between independent drug screens of the same cell line (RKO; Pearson correlation 0.99, standard deviation of difference between repeated screens 1.36). Furthermore, drug screen reproducibility between paired cell lines from each of four patients was associated with their pair-wise similarity in gene expression (Supplementary Fig.…”

Section: Cms Classification Of Preclinical Modelsmentioning

confidence: 99%

“…A drug sensitivity score (DSS; ref. 37) was calculated per drug and cell line relative to a negative and a positive control, based on cell viability after drug treatment at five different concentrations over a 10,000-fold concentration range. Drugs (n ¼ 218) with low efficacy (DSS values above 7 in less than three cell lines) and low variation in DSS values (crosssample range below 7) were excluded from further analyses.…”

Section: Drug Screening In Colorectal Cancer Cell Linesmentioning

confidence: 99%

Colorectal Cancer Consensus Molecular Subtypes Translated to Preclinical Models Uncover Potentially Targetable Cancer Cell Dependencies

Sveen

Bruun

Eide

et al. 2018

Clinical Cancer Research

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Purpose: Response to standard oncologic treatment is limited in colorectal cancer. The gene expression-based consensus molecular subtypes (CMS) provide a new paradigm for stratified treatment and drug repurposing; however, drug discovery is currently limited by the lack of translation of CMS to preclinical models.Experimental Design: We analyzed CMS in primary colorectal cancers, cell lines, and patient-derived xenografts (PDX). For classification of preclinical models, we developed an optimized classifier enriched for cancer cell-intrinsic gene expression signals, and performed high-throughput in vitro drug screening (n ¼ 459 drugs) to analyze subtype-specific drug sensitivities.Results: The distinct molecular and clinicopathologic characteristics of each CMS group were validated in a single-hospital series of 409 primary colorectal cancers. The new, cancer cell-adapted classifier was found to perform well in primary tumors, and applied to a panel of 148 cell lines and 32 PDXs, these colorectal cancer models were shown to recapitulate the biology of the CMS groups. Drug screening of 33 cell lines demonstrated subtype-dependent response profiles, confirming strong response to EGFR and HER2 inhibitors in the CMS2 epithelial/canonical group, and revealing strong sensitivity to HSP90 inhibitors in cells with the CMS1 microsatellite instability/immune and CMS4 mesenchymal phenotypes. This association was validated in vitro in additional CMS-predicted cell lines. Combination treatment with 5-fluorouracil and luminespib showed potential to alleviate chemoresistance in a CMS4 PDX model, an effect not seen in a chemosensitive CMS2 PDX model.Conclusions: We provide translation of CMS classification to preclinical models and uncover a potential for targeted treatment repurposing in the chemoresistant CMS4 group.

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“…This information can be used to systematically train computational models of the molecular signaling pathways contributing to drug sensitivity and resistance in various cancer settings, and to propose novel drug targets and combination approaches. Cell line screens have provided some success in explaining or predicting drug responses by driver gene mutations (2)(3)(4); however, in many cases the true mechanism of resistance remains elusive or more complex. Most predictive methods routinely used today use correlative statistics or feature-based learning techniques such as machine learning, while network methods remain scarce despite their potential for extracting mechanistic insights and actionable biomarkers.…”

Section: Introductionmentioning

confidence: 99%

Cell-Specific Computational Modeling of the PIM Pathway in Acute Myeloid Leukemia

et al. 2017

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Personalized therapy is a major goal of modern oncology, as patient responses vary greatly even within a histologically defined cancer subtype. This is especially true in acute myeloid leukemia (AML), which exhibits striking heterogeneity in molecular segmentation. When calibrated to cell-specific data, executable network models can reveal subtle differences in signaling that help explain differences in drug response. Furthermore, they can suggest drug combinations to increase efficacy and combat acquired resistance. Here, we experimentally tested dynamic proteomic changes and phenotypic responses in diverse AML cell lines treated with pan-PIM kinase inhibitor and fms-related tyrosine kinase 3 (FLT3) inhibitor as single agents and in combination. We constructed cell-specific executable models of the signaling axis, connecting genetic aberrations in FLT3, tyrosine kinase 2 (TYK2), platelet-derived growth factor receptor alpha (PDGFRA), and fibroblast growth factor receptor 1 (FGFR1) to cell proliferation and apoptosis via the PIM and PI3K kinases. The models capture key differences in signaling that later enabled them to accurately predict the unique proteomic changes and phenotypic responses of each cell line. Furthermore, using cell-specific models, we tailored combination therapies to individual cell lines and successfully validated their efficacy experimentally. Specifically, we showed that cells mildly responsive to PIM inhibition exhibited increased sensitivity in combination with PIK3CA inhibition. We also used the model to infer the origin of PIM resistance engineered through prolonged drug treatment of MOLM16 cell lines and successfully validated experimentally our prediction that this resistance can be overcome with AKT1/2 inhibition.

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Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies

Cited by 271 publications

References 22 publications

Prognostic, predictive, and pharmacogenomic assessments of CDX2 refine stratification of colorectal cancer

Prognostic, predictive, and pharmacogenomic assessments of CDX2 refine stratification of colorectal cancer

Colorectal Cancer Consensus Molecular Subtypes Translated to Preclinical Models Uncover Potentially Targetable Cancer Cell Dependencies

Cell-Specific Computational Modeling of the PIM Pathway in Acute Myeloid Leukemia

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