Clinical Utility of Patient-Derived Xenografts to Determine Biomarkers of Prognosis and Map Resistance Pathways in <i>EGFR</i>-Mutant Lung Adenocarcinoma

Stewart, Erin; Mascaux, Céline; Pham, Nhu-An; Sakashita, Shingo; Sykes, Jenna; Kim, Lucia; Yanagawa, Naoki; Allo, Ghassan; Ishizawa, Kota; Wang, Dennis; Zhu, Chang-Qi; Li, Ming; Ng, Christine; Liu, Geoffrey; Pintilie, Melania; Martin, Petra; John, Thomas; Jurišica, Igor; Leighl, Natasha B.; Neel, Benjamin G.; Waddell, Thomas K.; Shepherd, Frances A.; Tsao, Ming‐Sound

doi:10.1200/jco.2014.60.1492

Cited by 94 publications

(87 citation statements)

References 43 publications

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“…Some resistance mechanisms are thus reversible in the absence of drug, as shown for melanoma 64,65 and lung adenocarcinoma 66 . This suggests that treatmentresistant PDXs should be exposed to continuous treatment immediately after implantation, although this is a cost-and labour-intensive approach.…”

Section: Pdx Models Of Treatment-resistant Diseasementioning

confidence: 99%

“…Alternatively, models can be developed from pretreatment tumour samples, and resistance can be recapitulated in the PDX Although PDXs generally retain drug-sensitivity profiles that are similar to those of the corresponding patient tumour 30,38,62,63 , PDX models derived from treatment-resistant tumours can become sensitive again upon xenografting, owing to the effect of the so-called "drug holiday" in which treatment is discontinued after tumour implantation to facilitate engraftment. Some 13 resistance mechanisms are thus reversible in the absence of drug, as shown for melanoma 64,65 and lung adenocarcinoma 66 . This suggests that treatmentresistant PDXs should be exposed to continuous treatment immediately after implantation, although this is a cost-and labour-intensive approach.…”

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Interrogating open issues in cancer precision medicine with patient-derived xenografts

et al. 2017

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Patient-derived xenografts (PDXs) have emerged as an important platform to elucidate new treatments and biomarkers in oncology. PDX models are used to address clinically relevant questions, including the contribution of tumour heterogeneity to therapeutic responsiveness, the patterns of cancer evolutionary dynamics during tumour progression and under drug pressure, and the mechanisms of resistance to treatment. The ability of PDX models to predict clinical outcomes is being improved through mouse humanization strategies and implementation of co-clinical trials, within which patients and PDXs reciprocally inform therapeutic decisions. This Opinion article discusses aspects of PDX modelling that are relevant to these questions and highlights the merits of shared PDX resources to advance cancer medicine from the 6 perspective of EurOPDX, an international initiative devoted to PDX-based research.Response to anticancer therapies varies owing to the substantial molecular heterogeneity of human tumours and to poorly defined mechanisms of drug efficacy and resistance 1 . Immortalized cancer cell lines, either cultured in vitro or grown as xenografts, cannot interrogate the complexity of human tumours, and only provide determinate insights into human disease, as they are limited in number and diversity, and have been cultured on plastic over decades 2 .This disconnection in scale and biological accuracy contributes considerably to attrition in drug development [3][4][5] .Surgically derived clinical tumour samples that are implanted in mice (known as patient-derived xenografts (PDXs)) are expected to better inform therapeutic development strategies. As intact tissue -in which the tumour architecture and the relative proportion of cancer cells and stromal cells are both maintained -is directly implanted into recipient animals, the alignment with human disease is enhanced. More importantly, PDXs retain the idiosyncratic characteristics of different tumours from different patients; hence, they can effectively recapitulate the intra-tumour and inter-tumour heterogeneity that typifies human cancer 6-9 . 7 Exhaustive information on the key characteristics and the practical applications of PDXs can be found in recent reviews [10][11][12][13] . In this Opinion article, we discuss basic methodological concepts, as well as challenges and opportunities in developing "next-generation" models to improve the reach of PDXs as preclinical tools for in vivo studies (TABLE 1). We also elaborate on the merits of PDXs for exploring the intrinsic heterogeneity and subclonal genetic evolution of individual tumours, and discuss how this may influence therapeutic resistance. Finally, we examine the utility of PDXs in navigating complex variables in clinical decision-making, such as the discovery of predictive and prognostic biomarkers, and the categorization of genotype-drug response correlations in high-throughput formats. Being primarily co-authored by leading members of the EurOPDX Consortium (see Further information), we provide...

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Section: Pdx Models Of Treatment-resistant Diseasementioning

confidence: 99%

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Interrogating open issues in cancer precision medicine with patient-derived xenografts

et al. 2017

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“…These patient-derived tumor xenografts (PDTXs) are currently a valuable resource for study (Tentler et al 2012) and increasingly performing as predictors of response to targeted therapy (Stewart et al 2015). Because TP53 is ubiquitously altered in cancers, TP53 mutations are a tool to confirm the provenance of PDTX (Park et al 2013;Walters et al 2013;Dodbiba et al 2015).…”

Section: Tp53 At the Crossroads Of Clinical Genomics And Cancer Therapymentioning

confidence: 99%

Clinical Outcomes of TP53 Mutations in Cancers

Robles

Jen

Harris

2016

Cold Spring Harb Perspect Med

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High-throughput sequencing of cancer genomes is increasingly becoming an essential tool of clinical oncology that facilitates target identification and targeted therapy within the context of precision medicine. The cumulative profiles of somatic mutations in cancer yielded by comprehensive molecular studies also constitute a fingerprint of historical exposures to exogenous and endogenous mutagens, providing insight into cancer evolution and etiology. Mutational signatures that were first established by inspection of the TP53 gene somatic landscape have now been confirmed and expanded by comprehensive sequencing studies. Further, the degree of granularity achieved by deep sequencing allows detection of low-abundance mutations with clinical relevance. In tumors, they represent the emergence of small aggressive clones; in normal tissues, they signal a mutagenic exposure related to cancer risk; and, in blood, they may soon become effective surveillance tools for diagnostic purposes and for monitoring of cancer prognosis and recurrence.

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“…With the heightened interest in PDXs, 23,24 especially for the testing of conventional and targeted therapies, it becomes important to establish large banks of the different tumor types, so as to capture the genetic diversity and heterogeneity of human tumors as well as the range of drug sensitivities. Because of the cost and effort of establishing such banks, they are best performed by consortiums that pool resources and make them available to the scientific public.…”

Section: Resultsmentioning

confidence: 99%

From Mice to Men and Back: An Assessment of Preclinical Model Systems for the Study of Lung Cancers

Gazdar

Hirsch

Minna

2016

Journal of Thoracic Oncology

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Clinical Utility of Patient-Derived Xenografts to Determine Biomarkers of Prognosis and Map Resistance Pathways in EGFR-Mutant Lung Adenocarcinoma

Abstract: PDX models closely recapitulate primary tumor biology and clinical outcome. They may serve as important laboratory models to investigate mechanisms of resistance to targeted therapies, and for preclinical testing of novel treatment strategies.

Cited by 94 publications

References 43 publications

Interrogating open issues in cancer precision medicine with patient-derived xenografts

Interrogating open issues in cancer precision medicine with patient-derived xenografts

Clinical Outcomes of TP53 Mutations in Cancers

From Mice to Men and Back: An Assessment of Preclinical Model Systems for the Study of Lung Cancers

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