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Identification of anti-tumour biologics using primary tumour models, 3-D phenotypic screening and image-based multi-parametric profiling.

ABSTRACT: Monolayer cultures of immortalised cell lines are a popular screening tool for novel anti-cancer therapeutics, but these methods can be a poor surrogate for disease states, and there is a need for drug screening platforms which are more predictive of clinical outcome. In this study, we describe a phenotypic antibody screen using three-dimensional cultures of primary cells, and image-based multi-parametric profiling in PC-3 cells, to identify anti-cancer biologics against new therapeutic targets.ScFv Antibodies and designed ankyrin repeat proteins (DARPins) were isolated using phage display selections against primary non-small cell lung carcinoma cells. The selected molecules were screened for anti-proliferative and pro-apoptotic activity against primary cells grown in three-dimensional culture, and in an ultra-high content screen on a 3-D cultured cell line using multi-parametric profiling to detect treatment-induced phenotypic changes. The targets of molecules of interest were identified using a cell-surface membrane protein array. An anti-CUB domain containing protein 1 (CDCP1) antibody was tested for tumour growth inhibition in a patient-derived xenograft model, generated from a stage-IV non-small cell lung carcinoma, with and without cisplatin.Two primary non-small cell lung carcinoma cell models were established for antibody isolation and primary screening in anti-proliferative and apoptosis assays. These assays identified multiple antibodies demonstrating activity in specific culture formats. A subset of the DARPins was profiled in an ultra-high content multi-parametric screen, where 300 morphological features were measured per sample. Machine learning was used to select features to classify treatment responses, then antibodies were characterised based on the phenotypes that they induced. This method co-classified several DARPins that targeted CDCP1 into two sets with different phenotypes. Finally, an anti-CDCP1 antibody significantly enhanced the efficacy of cisplatin in a patient-derived NSCLC xenograft model.Phenotypic profiling using complex 3-D cell cultures steers hit selection towards more relevant in vivo phenotypes, and may shed light on subtle mechanistic variations in drug candidates, enabling data-driven decisions for oncology target validation. CDCP1 was identified as a potential target for cisplatin combination therapy.

SUBMITTER: Sandercock AM

PROVIDER: S-EPMC4521473 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Identification of anti-tumour biologics using primary tumour models, 3-D phenotypic screening and image-based multi-parametric profiling.

Sandercock Alan M AM Rust Steven S Guillard Sandrine S Sachsenmeier Kris F KF Holoweckyj Nick N Hay Carl C Flynn Matt M Huang Qihui Q Yan Kuan K Herpers Bram B Price Leo S LS Soden Jo J Freeth Jim J Jermutus Lutz L Hollingsworth Robert R Minter Ralph R

Molecular cancer 20150731

<h4>Background</h4>Monolayer cultures of immortalised cell lines are a popular screening tool for novel anti-cancer therapeutics, but these methods can be a poor surrogate for disease states, and there is a need for drug screening platforms which are more predictive of clinical outcome. In this study, we describe a phenotypic antibody screen using three-dimensional cultures of primary cells, and image-based multi-parametric profiling in PC-3 cells, to identify anti-cancer biologics against new t ...[more]

PMID: 26227951

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Project description:A novel set of integrated procedures for quantification of fibroblast-rich stroma and vascular characteristics has recently been presented allowing discovery of novel perivascular and stromal biomarkers in colorectal, renal cell, and ovarian cancer. In the present study, data obtained through these procedures from clinically well-annotated collections of these three tumour types have been used to address two novel questions. First, data have been used to investigate if the three tumour types demonstrate significant differences regarding features such as vessel diameter, vessel density, and perivascular marker expression. Second, analyses of the cohorts have been used to explore the prognostic significance of a novel vascular metric, 'vessel distance inter-quartile range (IQR)' that describes intra-case heterogeneity regarding vessel distribution. The comparisons between the three tumour types demonstrated a set of significant differences. Vessel density of renal cell cancer was statistically significantly higher than in colorectal and ovarian cancer. Vessel diameter was statistically significantly higher in ovarian cancer. Concerning perivascular status, colorectal cancer displayed significantly higher levels of perivascular PDGFR-? expression than the other two tumour types. Intra-case heterogeneity of perivascular PDGFR-? expression was also higher in colorectal cancer. Notably, these fibroblast-dominated stroma phenotypes matched previously described experimental tumour stroma characteristics, which have been linked to differential sensitivity to anti-VEGF drugs. High 'vessel distance IQR' was significantly associated with poor survival in both renal cell cancer and colorectal cancer. In renal cell cancer, this characteristic also acted as an independent prognostic marker according to multivariate analyses including standard clinico-pathological characteristics. Explorative subset analyses indicated particularly strong prognostic significance of 'vessel distance IQR' in T stage 4 of this cancer type. Together, these analyses identified tumour-type-specific vascular-stroma phenotypes of possible functional significance, and suggest 'vessel distance IQR' as a novel prognostic biomarker.

Project description:Multi-parametric quantitative MRI (qMRI) of the spinal cord is a promising non-invasive tool to probe early microstructural damage in neurological disorders. It is usually performed in vivo by combining acquisitions with multiple signal readouts, which exhibit different thermal noise levels, geometrical distortions and susceptibility to physiological noise. This ultimately hinders joint multi-contrast modelling and makes the geometric correspondence of parametric maps challenging. We propose an approach to overcome these limitations, by implementing state-of-the-art microstructural MRI of the spinal cord with a unified signal readout in vivo (i.e. with matched spatial encoding parameters across a range of imaging contrasts). We base our acquisition on single-shot echo planar imaging with reduced field-of-view, and obtain data from two different vendors (vendor 1: Philips Achieva; vendor 2: Siemens Prisma). Importantly, the unified acquisition allows us to compare signal and noise across contrasts, thus enabling overall quality enhancement via multi-contrast image denoising methods. As a proof-of-concept, here we provide a demonstration with one such method, known as Marchenko-Pastur (MP) Principal Component Analysis (PCA) denoising. MP-PCA is a singular value (SV) decomposition truncation approach that relies on redundant acquisitions, i.e. such that the number of measurements is large compared to the number of components that are maintained in the truncated SV decomposition. Here we used in vivo and synthetic data to test whether a unified readout enables more efficient MP-PCA denoising of less redundant acquisitions, since these can be denoised jointly with more redundant ones. We demonstrate that a unified readout provides robust multi-parametric maps, including diffusion and kurtosis tensors from diffusion MRI, myelin metrics from two-pool magnetisation transfer, and T1 and T2 from relaxometry. Moreover, we show that MP-PCA improves the quality of our multi-contrast acquisitions, since it reduces the coefficient of variation (i.e. variability) by up to 17% for mean kurtosis, 8% for bound pool fraction (myelin-sensitive), and 13% for T1, while enabling more efficient denoising of modalities limited in redundancy (e.g. relaxometry). In conclusion, multi-parametric spinal cord qMRI with unified readout is feasible and provides robust microstructural metrics with matched resolution and distortions, whose quality benefits from multi-contrast denoising methods such as MP-PCA.

Project description:PurposeTo evaluate the applicability and estimate the radiobiological parameters of linear-quadratic Poisson tumour control probability (TCP) model for primary prostate cancer patients for two relevant target structures (prostate gland and GTV). The TCP describes the dose-response of prostate after definitive radiotherapy (RT). Also, to analyse and identify possible significant correlations between clinical and treatment factors such as planned dose to prostate gland, dose to GTV, volume of prostate and mpMRI-GTV based on multivariate logistic regression model.MethodsThe study included 129 intermediate and high-risk prostate cancer patients (cN0 and cM0), who were treated with image-guided intensity modulated radiotherapy (IMRT)?±?androgen deprivation therapy with a median follow-up period of 81.4 months (range 42.0-149.0) months. Tumour control was defined as biochemical relapse free survival according to the Phoenix definition (BRFS). MpMRI-GTV was delineated retrospectively based on a pre-treatment multi-parametric MR imaging (mpMRI), which was co-registered to the planning CT. The clinical treatment planning procedure was based on prostate gland, delineated on CT imaging modality. Furthermore, we also fitted the clinical data to TCP model for the two considered targets for the 5-year follow-up after radiation treatment, where our cohort was composed of a total number of 108 patients, of which 19 were biochemical relapse (BR) patients.ResultsFor the median follow-up period of 81.4 months (range 42.0-149.0) months, our results indicated an appropriate ?/??=?1.3 Gy for prostate gland and ?/??=?2.9 Gy for mpMRI-GTV. Only for prostate gland, EQD2 and gEUD2Gy were significantly lower in the biochemical relapse (BR) group compared to the biochemical control (BC) group. Fitting results to the linear-quadratic Poisson TCP model for prostate gland and ?/??=?1.3 Gy were D50?=?66.8 Gy with 95% CI [64.6 Gy, 69.0 Gy], and ??=?3.8 with 95% CI [2.6, 5.2]. For mpMRI-GTV and ?/??=?2.9 Gy, D50 was 68.1 Gy with 95% CI [66.1 Gy, 70.0 Gy], and ??=?4.5 with 95% CI [3.0, 6.1]. Finally, for the 5-year follow-up after the radiation treatment, our results for the prostate gland were: D50?=?64.6 Gy [61.6 Gy, 67.4 Gy], ??=?3.1 [2.0, 4.4], ?/??=?2.2 Gy (95% CI was undefined). For the mpMRI-GTV, the optimizer was unable to deliver any reasonable results for the expected clinical D50 and ?/?. The results for the mpMRI-GTV were D50?=?50.1 Gy [44.6 Gy, 56.0 Gy], ??=?0.8 [0.5, 1.2], ?/??=?0.0 Gy (95% CI was undefined). For a follow-up time of 5 years and a fixed ?/??=?1.6 Gy, the TCP fitting results for prostate gland were D50?=?63.9 Gy [60.8 Gy, 67.0 Gy], ??=?2.9 [1.9, 4.1], and for mpMRI-GTV D50?=?56.3 Gy [51.6 Gy, 61.1 Gy], ??=?1.3 [0.8, 1.9].ConclusionThe linear-quadratic Poisson TCP model was better fit when the prostate gland was considered as responsible target than with mpMRI-GTV. This is compatible with the results of the comparison of the dose distributions among BR and BC groups and with the results achieved with the multivariate logistic model regarding gEUD2Gy. Probably limitations of mpMRI in defining the GTV explain these results. Another explanation could be the relatively homogeneous dose prescription and the relatively low number of recurrences. The failure to identify any benefit for considering mpMRI-GTV as the target responsible for the clinical response is confirmed when considering a fixed ?/??=?1.6 Gy, a fixed follow-up time for biochemical response at 5 years or Gleason score differentiation.

Dataset Information

Identification of anti-tumour biologics using primary tumour models, 3-D phenotypic screening and image-based multi-parametric profiling.

Publications

Identification of anti-tumour biologics using primary tumour models, 3-D phenotypic screening and image-based multi-parametric profiling.

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