Understanding the therapeutic effect of drug dose and scheduling is critical to inform the design and implementation of clinical trials. The increasing complexity of both mono, and particularly combination therapies presents a substantial challenge in the clinical stages of drug development for oncology. Using a systems pharmacology approach, we have extended an existing PK-PD model of tumor growth with a mechanistic model of the cell cycle, enabling simulation of mono and combination treatment with the ATR inhibitor AZD6738 and ionizing radiation. Using AZD6738, we have developed multi-parametric cell based assays measuring DNA damage and cell cycle transition, providing quantitative data suitable for model calibration. Our in vitro calibrated cell cycle model is predictive of tumor growth observed in in vivo mouse xenograft studies. The model is being used for phase I clinical trial designs for AZD6738, with the aim of improving patient care through quantitative dose and scheduling prediction.

A single click ensemble segmentation (SCES) approach based on an existing “Click&Grow” algorithm is presented. The SCES approach requires only one operator selected seed point as compared with multiple operator inputs, which are typically needed. This facilitates processing large numbers of cases. Evaluation on a set of 129 CT lung tumor images using a similarity index (SI) was done. The average SI is above 93% using 20 different start seeds, showing stability. The average SI for 2 different readers was 79.53%. We then compared the SCES algorithm with the two readers, the level set algorithm and the skeleton graph cut algorithm obtaining an average SI of 78.29%, 77.72%, 63.77% and 63.76% respectively. We can conclude that the newly developed automatic lung lesion segmentation algorithm is stable, accurate and automated.

Our findings indicate that measurement of changes in tumor volumes is adequately reproducible. Using tumor volumes as the basis for response assessments could have a positive impact on both patient management and clinical trials. More authoritative work to qualify or discard changes in volume as the basis for response assessments should proceed.

The weight of the evidence indicates there are circumstances in which volumetric image analysis adds value to clinical trial science and the practice of medicine.

Purpose KRAS wild-type status is an imperfect predictor of sensitivity to anti-EGFR monoclonal antibodies in colorectal cancer (CRC), motivating efforts to identify novel molecular aberrations driving RAS. This study aimed to build a quantitative readout of RAS pathway activity to: (1) uncover molecular surrogates of RAS activity specific to CRC; (2) improve the prediction of cetuximab response in patients; (3) suggest new treatment strategies. Methods A model of RAS pathway activity was trained in a large CRC dataset and validated in three independent CRC patient datasets. Novel molecular traits were inferred from the TCGA CRC data. The ability of the RAS model to predict resistance to cetuximab was tested in mouse xenografts and three independent patient cohorts. Drug sensitivity correlations between our model and large cell line compendiums were performed. Results The performance of the RAS model was remarkably robust across 3 validation datasets. (1) Our model confirmed the heterogeneity of the RAS phenotype in KRAS wild-type patients, and suggests novel molecular traits driving its phenotype (e.g. MED12 loss, GBXW7 mutation, MAP2K4 mutation). (2) It improved the prediction of response and progression free survival (HR=2.0; p<.01) to cetuximab compared to KRAS mutation (xenograft and patient cohorts). (3) Our model consistently predicted sensitivity to MEK inhibitors (p<.01) in 2 cell panel screens. Conclusions Modeling the RAS phenotype in CRC allows for the robust interrogation of RAS pathway activity across cell lines, xenografts, and patient cohorts. It demonstrates clinical utility in predicting response to anti-EGFR agents and MEK inhibitors.

Canine cancers occur with an incidence similar to that of humans and share many features with human malignancies including histological appearance, tumor genetics, biological behavior, and response to conventional therapies. As observed in humans, the telomerase reverse transcriptase (TERT) activity is largely confined to tumor tissues and absent in the majority of normal dog tissues. Therefore, dog TERT (dTERT) can constitute a valid target for translational cancer immunotherapy. We have evaluated the ability of adenovirus serotype 6 (Ad6) and DNA electroporation (DNA-EP) to induce immune responses against dTERT in dogs affected by malignant lymphoma (ML). The vaccine was combined with standard chemotherapy regimen [cyclophosphamide, vincristine, prednisone (COP)]. dTERT-specific immune response was induced in 13 out of 14 treated animals (93%) and remained detectable and long-lasting with the absence of autoimmunity or other side effects. Most interestingly, the survival time of vaccine/Chemo-treated dogs was significantly increased over historic controls of Chemo-treated animals (>97.8 versus 37 weeks, respectively, P = 0.001). Our results show that Ad6/DNA-EP-based cancer vaccine against dTERT overcomes host immune tolerance, should be combined with chemotherapy, induces long-lasting immune responses, and significantly prolongs the survival of ML canine patients. These data support further evaluation of this approach in human clinical trials.

No abstract

Background: Moderate concentrations of γ-secretase inhibitor increase Aβ production in different scenarios from cell lines to humans.Results: A mathematical model, including α-, β-, and γ-secretases, is proposed describing Aβ rise.Conclusion: The Aβ rise is decided by the interplay between the three secretases and not γ-secretase alone.Significance: This has important implications for the development of drugs targeting Aβ production in Alzheimer disease.