The Oncology Models Forum An Interactive Platform for Translational Research Cheryl Marks Division of Cancer Biology NCI

Oncology Models Forum

What are the major goals of the Forum? Deploy and regularly refresh the knowledge base about animal models; Convene annual meetings and workshops and webinars; Use HUBzero® capabilities to support collaborations.

How will the NCI support the goals of the Forum? An FOA for U24 applications; Projects from two new FOAs: Collaborative R01s and R01s/competing revisions; Projects from the NCI Informatics Technologies for Cancer Research (ITCR) FOAs; Connections to other NCI or NIH networks and consortia; International participation.

What are the goals of the two Forum-related FOAs? FOA for Collaborative R01s Cross-disciplinary teams to address the technical and experimental parameters that ensure effective translational use of mouse models; Collaborators with differing expertise to identify and propose the means to address unmet translational requirements; Extend the range of cross-disciplinary insights and approaches that address translational oncology modeling needs; Awardees will be required to be active participants in the Oncology Models Forum.

What are the goals of the two Forum-related FOAs? FOA for R01s and competing revisions to R01s Narrower scope than a Collaborative R01 team; Address one or more of the technical and experimental parameters that ensure effective translational use of mouse models; Identify and propose the way to address an unmet translational requirement; If possible, take advantage of the Oncology Models Forum HUBzero® platform to facilitate collaborations.

What projects could the ITCR FOAs support? Develop data capture tools to improve workflow and reduce transcription errors; Re-purpose existing bioinformatics tools to support cross-species comparisons; Update and augment existing models’ databases and link to other related resources; Develop new bioinformatics tools to enable novel analyses of integrated human and mouse data.

What cross-species projects may require HUBzero® support to enable collaborations?

Prostatic intraepithelial neoplasia (mPIN) in GEM mouse models. Representative microscopic images of patient-derived prostate carcinoma xenografts. Prostate malignancies in GEM models of prostate cancer.

Fig. 1. Co-activation of PI3-kinase and Ras signaling pathways is associated with adverse patient outcome. Correlation of PI3-kinase and Ras signaling in human prostate cancer from the Taylor et al. (6) dataset. Shown is a heat map in which each clinical sample (primary tumors of the indicated Gleason scores and metastases) was evaluated for PI3-kinase and Ras signaling. The color key indicates relative expression levels of the pathway. (B) Kaplan–Meier analyses showing the association of activation of both PI3-kinase and Ras signaling with adverse patient outcome in two independent cohorts using biochemical recurrence-free (Left) and prostate cancer-specific survival (Right) as clinical endpoints

Fig. 2. Cooperation of PI3-kinase and Ras signaling pathways in advanced prostate cancer V) Summary of the histo-pathological phenotype (S) Survival curve showing the percent of mice of each genotype remaining alive in number of days after tumors are induced. mPIN only Corresponds to PI3K patients Corresponds to PI3K and K-ras patients (D–F) MRI images with the prostate indicated by the red lines. (G–I) Representative whole-slide pathology images of anterior prostate. (J–L) Representative high-power pathology images of anterior prostate. (M–O) Immuno-histochemical staining for androgen receptor. (P–R) Immunofluorescence images show staining for cytokeratin 8, which stains luminal epithelium, and cytokeratin 5, which stains basal cells.

Fig. 3. Signaling pathway activation in NPK mouse prostate tumors. Pathway enrichment analyses. Summary of GSEA analyses comparing NPK and NP mouse prostate tumors. Cross-species GSEA analyses showing the enrichment of an expression signature from the NPK vs. NP mouse prostate tumors compared with a reference signature of malignant vs. indolent human prostate cancer. GSEA analyses of biological pathways comparing pathways that are differentially activated in the NPK vs. NP mouse prostate tumors with pathways enriched in a reference signature of malignant vs. indolent human prostate cancer. (D-O Immuno-histochemical staining of mouse prostate tissues using the indicated phospho-antibodies.

Schematic representation of the bioinformatics workflow for the repositioning approach used to identify potential candidate drugs for the treatment of SCLC To identify novel therapeutic strategies for patients with SCLC, their bioinformatics approach evaluated the therapeutic potential of FDA-approved drugs for a given disease by comparing gene expression profiles in response to these drugs in multiple cell types across multiple diseases. From this drug-repositioning approach, they computed a list of candidate drugs with predicted efficacy against SCLC. Rather than screen a large number of candidate drugs in cells, they first annotated the known targets of the top-scoring candidates, as well as the pathways enriched in these drug targets. This analysis led to a focus on drugs targeting molecules in the “Neuroactive ligand receptor interaction” and “Calcium signaling” pathways, the top two most significant pathways. SCLC cells are known to express molecules in these pathways, including neuro-hormonal ligands, channels, and receptors.

Inhibitory effects of imipramine, promethazine, and bepridil on SCLC allografts and xenografts A, strategy used for the treatment of mice growing SCLC allograft or xenograft tumors under their skin. NSG immunocompromised mice were subcutaneously implanted with one mouse SCLC cell line (Kp1), one human SCLC cell line (H187), and one primary patient-derived xenograft (PDX) human SCLC tumor (NJH29.

Targeted therapies have demonstrated efficacy against specific subsets of molecularly defined cancers. Therapy choices for most patients with lung cancer are based on a single oncogene. But lung cancers that have identical oncogene mutations show large variations in their responses to the same targeted therapy. The biology responsible for this heterogeneity is not well understood, nor is the impact of co-existing genetic mutations, especially the loss of genes that are tumor suppressors. In this publication the authors used genetically engineered mouse models to conduct a ‘co-clinical’ trial that mirrored an ongoing human clinical trial in patients with KRAS-mutant lung cancers. Their goal was to determine if an inhibitor of MEKinase increased the efficacy of docetaxel, a standard of care chemotherapy.

Docetaxel and selumetinib combination therapy is more efficacious than docetaxel monotherapy in Kras and Kras/p53 lung cancers. Box plot showing tumor response for different genotypes with either docetaxel monotherapy (D) or combination treatment (DS). Lines depict median response, small circles indicate outliers. Waterfall plot showing tumor response after 2 weeks of docetaxel treatment. Each column represents one individual mouse, with data expressed relative to the pre-treatment tumor volume. Waterfall plot showing tumor response after 2 weeks of docetaxel treatment in combination with daily selumetinib. Response rate of docetaxel and selumetinib combination therapy and docetaxel only in mice bearing tumors with different genotypes.

FDG-PET predicts treatment response FDG-PET signal intensity (SUVmax) in Kras, Kras/p53 and Kras/Lkb1 mutant mice. FDG-PET signal intensity in patients with KRAS or KRAS/LKB1 mutant tumors. Comparisons of changes in FDG uptake by PET imaging after 1 day of treatment. Representative FDG-PET/CT images of mice from different genotypes at baseline and 1 day after initiation of treatment.

Docetaxel and selumetinib combination therapy is more efficacious than docetaxel monotherapy in Kras and Kras/p53 lung cancers Tumor volume was longitudinally assessed by MRI imaging in Kras and Kras/p53 mice treated with either docetaxel or docetaxel plus selumetinib. Data points represent median tumor volume relative to start of treatment for all available data at the indicated time point. Progression-free survival for Kras mice treated with either docetaxel or docetaxel plus selumetinib. Median survival for single and combination treatments was 6 weeks and 12 weeks, respectively. Progression-free survival for Kras/p53 mice treated with either docetaxel or docetaxel plus selumetinib. Median survival for single and combination treatments was 2 weeks and 4 weeks, respectively.

These studies showed that loss of either p53 and Lkb1, two clinically relevant tumor suppressors, impaired the response of Kras-mutant lung cancers to docetaxel monotherapy. Adding a MEKinase inhibitor (selumetinib) substantially benefited mice with lung cancer caused by Kras, and Kras and p53 mutations, but mice with Kras and Lkb1 mutations had primary resistance to the combination. Positron-emission tomography (PET) and computed tomography (CT) identified biological markers in mice and patients that suggest why there is differential efficacy in the different genotypes. The co-clinical results identified predictive genetic biomarkers that should be validated by interrogating samples from patients enrolled on the concurrent clinical trial. The studies highlighted the rationale for synchronous co-clinical trials, not only to anticipate the results of ongoing human clinical trials, but also to generate clinically relevant hypotheses that can inform the analysis and design of human studies.

Oncology Models Forum ITCR Projects Focus Groups Collaborative R01s Educational projects International connections Standards & protocols R01s Collaborative projects Resources Meetings