Your goal is to extract structured information from the user's input that matches the form described below. When extracting information please make sure it matches the type information exactly. Do not add any attributes that do not appear in the schema shown below.

```TypeScript

 { // Information on the clinical trial characteristics from the abstract
 conditions: string // The condition or disease being treated in the clinical trial
 drug_or_intervention: string // The drug or intervention used in the clinical trial
}
```


Please output the extracted information in JSON format. Do not output anything except for the extracted information. Do not add any clarifying information. Do not add any fields that are not in the schema. If the text contains attributes that do not appear in the schema, please ignore them. All output must be in JSON format and follow the schema specified above. Wrap the JSON in <json> tags.



Input:Plasma cytokine and angiogenic factor profiling identifies markers associated with tumor shrinkage in early-stage non-small cell lung cancer patients treated with pazopanib.  There is an unmet need for pharmacodynamic and predictive biomarkers for antiangiogenic agents. Recent studies have shown that soluble vascular endothelial growth factor receptor 2 (sVEGFR2), VEGF, and several other soluble factors may be modulated by VEGF pathway inhibitors. We conducted a broad profiling of cytokine and angiogenic factors (CAF) to investigate the relationship between baseline CAF levels, CAF changes during treatment, and tumor shrinkage in early-stage non-small cell lung cancer (NSCLC) patients treated with pazopanib, an oral angiogenesis inhibitor targeting VEGFR, platelet-derived growth factor receptor, and c-kit. Plasma samples were collected before treatment and on the last day of therapy from 33 patients with early-stage NSCLC participating in a single-arm phase II trial. Levels of 31 CAFs were measured by suspension bead multiplex assays or ELISA and correlated with change in tumor volume. Pazopanib therapy was associated with significant changes of eight CAFs; sVEGFR2 showed the largest decrease, whereas placental growth factor underwent the largest increase. Increases were also observed in stromal cell-derived factor-1alpha, IP-10, cutaneous T-cell-attracting chemokine, monokine induced by IFN-gamma, tumor necrosis factor-related apoptosis-inducing ligand, and IFN-alpha. Posttreatment changes in plasma sVEGFR2 and interleukin (IL)-4 significantly correlated with tumor shrinkage. Baseline levels of 11 CAFs significantly correlated with tumor shrinkage, with IL-12 showing the strongest association. Using multivariate classification, a baseline CAF signature consisting of hepatocyte growth factor and IL-12 was associated with tumor response to pazopanib and identified responding patients with 81% accuracy. These data suggest that CAF profiling may be useful for identifying patients likely to benefit from pazopanib, and merit further investigation in clinical trials. Output:
{'conditions': 'Non-Small Cell Lung Cancer|Lung Cancer, Non-Small Cell', 'interventions': 'Drug: Pazopanib'}