The functional components of bone marrow (i.e., the hematopoietic and stromal populations) and the adjacent bone have traditionally been evaluated incompletely as distinct entities rather than the integrated system. We perturbed this system in vivo using a medically relevant radiation model in the presence or absence of ovarian function to understand integrated tissue interaction.

Patients with metastatic or recurrent and refractory sarcomas have a dismal prognosis. Therefore, new targeted therapies are urgently needed. This study was designed to evaluate chimeric antigen receptor (CAR) T cells targeting the type I insulin-like growth factor receptor (IGF1R) or tyrosine kinase-like orphan receptor 1 (ROR1) molecules for their therapeutic potential against sarcomas. Here, we report that IGF1R (15/15) and ROR1 (11/15) were highly expressed in sarcoma cell lines including Ewing sarcoma, osteosarcoma, alveolar or embryonal rhabdomyosarcoma, and fibrosarcoma. IGF1R and ROR1 CAR T cells derived from eight healthy donors using the Sleeping Beauty (SB) transposon system were cytotoxic against sarcoma cells and produced high levels of IFN-γ, TNF-α and IL-13 in an antigen-specific manner. IGF1R and ROR1 CAR T cells generated from three sarcoma patients released significant amounts of IFN-γ in response to sarcoma stimulation. The adoptive transfer of IGF1R and ROR1 CAR T cells derived from a sarcoma patient significantly reduced tumor growth in pre-established, systemically disseminated and localized osteosarcoma xenograft models in NSG mice. Infusion of IGF1R and ROR1 CAR T cells also prolonged animal survival in a localized sarcoma model using NOD/scid mice. Our data indicate that both IGF1R and ROR1 can be effectively targeted by SB modified CAR T cells and that such CAR T cells may be useful in the treatment of high risk sarcoma patients.

Many studies have established gene expression-based prognostic signatures for lung cancer. All of these signatures were built from training data sets by learning the correlation of gene expression with the patients' survival time. They require all new sample data to be normalized to the training data, ultimately resulting in common problems of low reproducibility and impracticality. To overcome these problems, we propose a new signature model which does not involve data training. We hypothesize that the imbalance of two opposing effects in lung cancer cells, represented by Yin and Yang genes, determines a patient's prognosis. We selected the Yin and Yang genes by comparing expression data from normal lung and lung cancer tissue samples using both unsupervised clustering and pathways analyses. We calculated the Yin and Yang gene expression mean ratio (YMR) as patient risk scores. Thirty-one Yin and thirty-two Yang genes were identified and selected for the signature development. In normal lung tissues, the YMR is less than 1.0; in lung cancer cases, the YMR is greater than 1.0. The YMR was tested for lung cancer prognosis prediction in four independent data sets and it significantly stratified patients into high- and low-risk survival groups (p = 0.02, HR = 2.72; p = 0.01, HR = 2.70; p = 0.007, HR = 2.73; p = 0.005, HR = 2.63). It also showed prediction of the chemotherapy outcomes for stage II & III. In multivariate analysis, the YMR risk factor was more successful at predicting clinical outcomes than other commonly used clinical factors, with the exception of tumor stage. The YMR can be measured in an individual patient in the clinic independent of gene expression platform. This study provided a novel insight into the biology of lung cancer and shed light on the clinical applicability.

Ovarian carcinosarcoma is a rare subtype of ovarian cancer with poor clinical outcomes. The low incidence of this disease makes accrual to large clinical trials challenging. However, studies have shown that treatment responses in patient-derived xenograft (PDX) models correlate with matched-patient responses in the clinic, supporting their use for preclinical testing of standard and novel therapies. An ovarian carcinosarcoma PDX is presented herein and showed resistance to carboplatin and paclitaxel (similar to the patient) but exhibited significant sensitivity to ifosfamide and paclitaxel. The PDX demonstrated overexpression of EGFR mRNA and gene amplification by array comparative genomic hybridization (log2 ratio 0.399). EGFR phosphorylation was also detected. Angiogensis and insulin-like growth factor pathways were also implicated by overexpression of VEGFC and IRS1. In order to improve response to chemotherapy, the PDX was treated with carboplatin/paclitaxel with or without a pan-HER and VEGF inhibitor (BMS-690514) but there was no tumor growth inhibition or improved animal survival, which may be explained by a KRAS mutation. Resistance was also observed when the IGF-1R inhibitor BMS-754807 was combined with carboplatin/paclitaxel. Because poly (ADP-ribose) polymerase inhibitors have activity in ovarian cancer patients, with and without BRCA mutations, ABT-888 was also tested but found to have no activity. Pathogenic mutations were also detected in TP53 and PIK3CA. In conclusion, ifosfamide/paclitaxel was superior to carboplatin/paclitaxel in this ovarian carcinosarcoma PDX and gene overexpression or amplification alone was not sufficient to predict response to targeted therapy. Better predictive markers of response are needed.

Therapies targeting the type I insulin-like growth factor receptor (IGF-1R) have not been developed with predictive biomarkers to identify tumors with receptor activation. We have previously shown that the insulin receptor substrate (IRS) adaptor proteins are necessary for linking IGF1R to downstream signaling pathways and the malignant phenotype in breast cancer cells. The purpose of this study was to identify gene expression profiles downstream of IGF1R and its two adaptor proteins. IRS-null breast cancer cells (T47D-YA) were engineered to express IRS-1 or IRS-2 alone and their ability to mediate IGF ligand-induced proliferation, motility, and gene expression determined. Global gene expression signatures reflecting IRS adaptor specific and primary vs. secondary ligand response were derived (Early IRS-1, Late IRS-1, Early IRS-2 and Late IRS-2) and functional pathway analysis examined. IRS isoforms mediated distinct gene expression profiles, functional pathways, and breast cancer subtype association. For example, IRS-1/2-induced TGFb2 expression and blockade of TGFb2 abrogated IGF-induced cell migration. In addition, the prognostic value of IRS proteins was significant in the luminal B breast tumor subtype. Univariate and multivariate analyses confirmed that IRS adaptor signatures correlated with poor outcome as measured by recurrence-free and overall survival. Thus, IRS adaptor protein expression is required for IGF ligand responses in breast cancer cells. IRS-specific gene signatures represent accurate surrogates of IGF activity and could predict response to anti-IGF therapy in breast cancer.

Inhibition of pregnancy-associated plasma protein-A (PAPP-A), an upstream activator of the insulin-like growth factor (IGF) pathway, is known to augment sensitivity to platinum-based chemotherapy. This study further tests the efficacy of PAPP-A inhibition with a monoclonal antibody inhibitor (mAb-PA) in ovarian cancer (OC) platinum-resistant patient-derived xenograft (PDX) models.