Concurrent upregulation of BCL-XL and BCL2A1 induces ~1000-fold resistance to ABT-737 in chronic lymphocytic leukemia.

ABT-737 and its orally active analog, ABT-263, are rationally designed inhibitors of BCL2 and BCL-XL. ABT-263 shows promising activity in early phase 1 clinical trials in B-cell malignancies, particularly chronic lymphocytic leukemia (CLL). In vitro, peripheral blood CLL cells are extremely sensitive to ABT-737 (EC50 7 nM), with rapid induction of apoptosis in all 60 patients tested, independent of parameters associated with disease progression and chemotherapy resistance. In contrast to data from cell lines, ABT-737–induced apoptosis in CLL cells was largely MCL1-independent. Because CLL cells within lymph nodes are more resistant to apoptosis than those in peripheral blood, CLL cells were cultured on CD154-expressing fibroblasts in the presence of interleukin-4 (IL-4) to mimic the lymph node microenvironment. CLL cells thus cultured developed an approximately 1000-fold resistance to ABT-737 within 24 hours. Investigations of the underlying mechanism revealed that this resistance occurred upstream of mitochondrial perturbation and involved de novo synthesis of the antiapoptotic proteins BCL-XL and BCL2A1, which were responsible for resistance to low and high ABT-737 concentrations, respectively. Our data indicate that after therapy with ABT-737–related inhibitors, resistant CLL cells might develop in lymph nodes in vivo and that treatment strategies targeting multiple BCL2 antiapoptotic members simultaneously may have synergistic activity

Patient-Derived Tumor Explants As a "Live" Preclinical Platform for Predicting Drug Resistance in Patients

An understanding of drug resistance and the development of novel strategies to sensitize highly resistant cancers rely on the availability of suitable preclinical models that can accurately predict patient responses. One of the disadvantages of existing preclinical models is the inability to contextually preserve the human tumor microenvironment (TME) and accurately represent intratumoral heterogeneity, thus limiting the clinical translation of data. By contrast, by representing the culture of live fragments of human tumors, the patient-derived explant (PDE) platform allows drug responses to be examined in a three-dimensional (3D) context that mirrors the pathological and architectural features of the original tumors as closely as possible. Previous reports with PDEs have documented the ability of the platform to distinguish chemosensitive from chemoresistant tumors, and it has been shown that this segregation is predictive of patient responses to the same chemotherapies. Simultaneously, PDEs allow the opportunity to interrogate molecular, genetic, and histological features of tumors that predict drug responses, thereby identifying biomarkers for patient stratification as well as novel interventional approaches to sensitize resistant tumors. This paper reports PDE methodology in detail, from collection of patient samples through to endpoint analysis. It provides a detailed description of explant derivation and culture methods, highlighting bespoke conditions for particular tumors, where appropriate. For endpoint analysis, there is a focus on multiplexed immunofluorescence and multispectral imaging for the spatial profiling of key biomarkers within both tumoral and stromal regions. By combining these methods, it is possible to generate quantitative and qualitative drug response data that can be related to various clinicopathological parameters and thus potentially be used for biomarker identification

Predicting efficiency of Drosophila-trained ComiR on various datasets.

<p>(A) Self-test on the Drosophila Ago1-IP dataset consist of Set I (positive examples) and equal number of negative examples (from Set IV). (B) Performance on an external Drosophila Ago1-IP dataset consisting of Set III (positive examples) and the remaining of Set IV (negative examples). This Drosophila dataset was not used in training ComiR. (C) SN <i>vs.</i> threshold on an external <i>C. elegans</i> AIN-IP dataset (not an ROC curve due to inability to define a negative dataset). (D) Performance on an external human PAR-CLIP dataset. In all cases, TargetScan was used without the evolutionary conservation feature resulting in a binary outcome. For the human dataset the reader can find a continuous TargetScan ROC curve in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002830#pcbi.1002830.s003" target="_blank">Figure S3B</a>, plotted using the <i>context score</i>.</p

The effect of Fermi-Dirac model in miRNA target prediction.

<p>(A) Overlap of predicted targets from PITA and miRanda using a naïve combination of energy scores. (B) Target overlap between PITA and miRanda using the Fermi-Dirac energy score combination. (C) Receiver-operating Characteristic (ROC) curves of PITA and miRanda predictions with naïve (solid lines) and Fermi-Dirac (broken lines) energy score combination. AUC: area under the curve. Positive and negative sets were derived from the Ago1 IP data (Materials and Methods).</p

ComiR schema.

<p>ComiR schema.</p

<i>Comparison of SVM models for multiple miRNA targets</i>.

<p>Multiple miRNA target scores are combined using the naïve model (red dots) or the ComiR model (FD score or WSUM score). The comparison has been performed on the same datasets as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002830#pcbi-1002830-g003" target="_blank">Figure 3</a> with the exception of the <i>C. elegans</i> dataset, which has no proper ROC curve. Results are arranged by the difference the ComiR combination models offers over the naïve combination model. <i>P</i>: PITA, <i>M</i>: miRanda, <i>T</i>: TargetScan, <i>S</i>: mirSVR. AUC: area under the curve.</p

An optimised patient-derived explant platform for breast cancer reflects clinical responses to chemotherapy and antibody-directed therapy

Breast Cancer is the most common cancer among women globally. Despite significant improvements in overall survival, many tumours are refractory to therapy and so novel approaches are required to improve patient outcomes. We have evaluated patient-derived explants (PDEs) as a novel preclinical platform for breast cancer (BC) and implemented cutting-edge digital pathology and multi-immunofluorescent approaches for investigating biomarker changes in both tumour and stromal areas at endpoint. Short-term culture of intact fragments of BCs as PDEs retained an intact immune microenvironment, and tumour architecture was augmented by the inclusion of autologous serum in the culture media. Cell death/proliferation responses to FET chemotherapy in BC-PDEs correlated significantly with BC patient progression-free survival (p = 0.012 and p = 0.0041, respectively) and cell death responses to the HER2 antibody therapy trastuzumab correlated significantly with HER2 status (p = 0.018). These studies show that the PDE platform combined with digital pathology is a robust preclinical approach for informing clinical responses to chemotherapy and antibody-directed therapies in breast cancer. Furthermore, since BC-PDEs retain an intact tumour architecture over the short-term, they facilitate the preclinical testing of anti-cancer agents targeting the tumour microenvironment.</p