Paclitaxel With and Without Pazopanib for Persistent or Recurrent Ovarian Cancer

Richardson, Debra L.; Sill, Michael W.; Coleman, Robert L.; Sood, Anil K.; Pearl, Michael L.; Kehoe, Siobhan M.; Carney, Michael E.; Hanjani, Parviz; Le, Linda Van; Zhou, Xun; Secord, Angeles Alvarez; Gray, Heidi J.; Landrum, Lisa M.; Lankes, Heather A.; Hu, Wei; Aghajanian, Carol

doi:10.1001/jamaoncol.2017.4218

Cited by 64 publications

(48 citation statements)

References 33 publications

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“…Ovarian cancer (OC) is the leading cause of gynecologic cancer deaths and high-grade serous ovarian cancer (HGSOC) is the most common and most lethal histological type [1,2]. The lethality of HGSOC mainly comes from its high risk of recurrence [3,4].…”

mentioning

confidence: 99%

Deep learning provides a new computed tomography-based prognostic biomarker for recurrence prediction in high-grade serous ovarian cancer

Wang

Liu

et al. 2019

Radiotherapy and Oncology

131

114

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Background and purpose: Recurrence is the main risk for high-grade serous ovarian cancer (HGSOC) and few prognostic biomarkers were reported. In this study, we proposed a novel deep learning (DL) method to extract prognostic biomarkers from preoperative computed tomography (CT) images, aiming at providing a non-invasive recurrence prediction model in HGSOC. Materials and methods: We enrolled 245 patients with HGSOC from two hospitals, which included a feature-learning cohort (n = 102), a primary cohort (n = 49) and two independent validation cohorts from two hospitals (n = 49 and n = 45). We trained a novel DL network in 8917 CT images from the featurelearning cohort to extract the prognostic biomarkers (DL feature) of HGSOC. Afterward, a DL-CPH model incorporating the DL feature and Cox proportional hazard (Cox-PH) regression was developed to predict the individual recurrence risk and 3-year recurrence probability of patients. Results: In the two validation cohorts, the concordance-index of the DL-CPH model was 0.713 and 0.694. Kaplan-Meier's analysis clearly identified two patient groups with high and low recurrence risk (p = 0.0038 and 0.0164). The 3-year recurrence prediction was also effective (AUC = 0.772 and 0.825), which was validated by the good calibration and decision curve analysis. Moreover, the DL feature demonstrated stronger prognostic value than clinical characteristics. Conclusions: The DL method extracts effective CT-based prognostic biomarkers for HGSOC, and provides a non-invasive and preoperative model for individualized recurrence prediction in HGSOC. In addition, the DL-CPH model provides a new prognostic analysis method that can utilize CT data without follow-up for prognostic biomarker extraction.

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confidence: 99%

Deep learning provides a new computed tomography-based prognostic biomarker for recurrence prediction in high-grade serous ovarian cancer

Wang

Liu

et al. 2019

Radiotherapy and Oncology

131

114

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“…Representing a cutting‐edge breakthrough in drug discovery, allosteric PPI modulators are widely accepted as a possible complement and alternative to orthosteric PPI ligands due to their potential for improved physiochemical and pharmacological properties. Several allosteric PPI modulators have already reached the clinic, such as paclitaxel ( 19 ) and its derivatives targeting α‐ and β‐tubulin . Nevertheless, problems such as suboptimal pharmacokinetic properties still exist .…”

Section: Discussionmentioning

confidence: 99%

“…Several allosteric PPI modulators have already reached the clinic, such as paclitaxel (19) and its derivatives targeting αand β-tubulin. 6,8,193,195,[206][207][208][209][210][211][212][213] Nevertheless, problems such as suboptimal pharmacokinetic properties still exist. 6 One of the major obstacles encountered during the development of allosteric PPI modulators is the difficulty in carrying out rational drug design.…”

Section: Discussionmentioning

confidence: 99%

Emerging roles of allosteric modulators in the regulation of protein‐protein interactions (PPIs): A new paradigm for PPI drug discovery

Zhang

2019

Medicinal Research Reviews

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Protein‐protein interactions (PPIs) are closely implicated in various types of cellular activities and are thus pivotal to health and disease states. Given their fundamental roles in a wide range of biological processes, the modulation of PPIs has enormous potential in drug discovery. However, owing to the general properties of large, flat, and featureless interfaces of PPIs, previous attempts have demonstrated that the generation of therapeutic agents targeting PPI interfaces is challenging, rendering them almost “undruggable” for decades. To date, rapid progress in chemical and structural biology techniques has promoted the exploitation of allostery as a novel approach in drug discovery. By attaching to allosteric sites that are topologically and spatially distinct from PPI interfaces, allosteric modulators can achieve improved physiochemical properties. Thus, allosteric modulators may represent an alternative strategy to target intractable PPIs and have attracted intense pharmaceutical interest. In this review, we first briefly introduce the characteristics of PPIs and then present different approaches for investigating PPIs, as well as the latest methods for modulating PPIs. Importantly, we comprehensively review the recent progress in the development of allosteric modulators to inhibit or stabilize PPIs. Finally, we conclude with future perspectives on the discovery of allosteric PPI modulators, especially the application of computational methods to aid in allosteric PPI drug discovery.

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“…Numerous studies have considered the predictive relationship between circulating levels of pro-angiogenic cytokines and response to anti-angiogenics including pazopanib [59][60][61]. The prognostic value of VEGF-A is not clear, and more recently SNPs in VEGF-A and VEGFR1 have shown promise in randomized trials [15,62,63]. We found that only baseline levels of circulating levels of angiopoietin-1 correlated with a reduction in tumor size in keeping with published literature; however, no relationship was observed with PKs [64].…”

Section: Discussionmentioning

confidence: 99%

“…Pazopanib is a pan-VEGF tyrosine kinase inhibitor that has additional inhibitory effects on α and β platelet-derived growth factor (PDGF) receptors, and the proto-oncogene receptor tyrosine kinase (c-KIT) [13]. Two large studies investigated the clinical utility of adding pazopanib to weekly paclitaxel, with conflicting results; with the MITO 11 study reporting a 3-month PFS benefit in patients with platinumresistant disease and the study by Richardson and colleagues reporting no benefit, albeit in a mixed platinum-resistant and platinum-sensitive population [14,15]. Du Bois and colleagues demonstrated an improvement in PFS following the addition of maintenance pazopanib after completion of chemotherapy in platinum-sensitive disease [16].…”

Section: Introductionmentioning

confidence: 99%

[18F]Fluciclatide PET as a biomarker of response to combination therapy of pazopanib and paclitaxel in platinum-resistant/refractory ovarian cancer

Sharma

Valls

Inglese

et al. 2019

Eur J Nucl Med Mol Imaging

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Background Angiogenesis is a driver of platinum resistance in ovarian cancer. We assessed the effect of combination pazopanib and paclitaxel followed by maintenance pazopanib in patients with platinum-resistant/refractory ovarian cancer. Integrins α v β 3 and α v β 5 are both upregulated in tumor-associated vasculature. [ 18 F]Fluciclatide is a novel PET tracer that has high affinity for integrins α v β 3/5 , and was used to assess the anti-angiogenic effect of pazopanib. Patients and methodsWe conducted an open-label, phase Ib study in patients with platinum-resistant/refractory ovarian cancer. Patients received 1 week of single-agent pazopanib (800 mg daily) followed by combination therapy with weekly paclitaxel (80 mg/m 2 ). Following completion of 18 weeks of combination therapy, patients continued with single-agent pazopanib until disease progression. Dynamic [ 18 F]fluciclatide-PET imaging was conducted at baseline and after 1 week of pazopanib. Response (RECIST 1.1), toxicities, and survival outcomes were recorded. Circulating markers of angiogenesis were assessed with therapy. Results Fourteen patients were included in the intention-to-treat analysis. Complete and partial responses were seen in seven patients (54%). Median progression-free survival (PFS) was 10.63 months, and overall survival (OS) was 18.5 months. Baseline [ 18 F]fluciclatide uptake was predictive of long PFS. Elevated baseline circulating angiopoietin and fibroblast growth factor (FGF) were predictive of greater reduction in SUV 60,mean following pazopanib. Kinetic modeling of PET data indicated a reduction in K 1 and K i following pazopanib indicating reduced radioligand delivery and retention. Conclusions Combination therapy followed by maintenance pazopanib is effective and tolerable in platinum-resistant/refractory ovarian cancer. [ 18 F]Fluciclatide-PET uptake parameters predict clinical outcome with pazopanib therapy indicating an antiangiogenic response.

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Paclitaxel With and Without Pazopanib for Persistent or Recurrent Ovarian Cancer

Abstract: clinicaltrials.gov Identifier: NCT01468909.

Cited by 64 publications

References 33 publications

Deep learning provides a new computed tomography-based prognostic biomarker for recurrence prediction in high-grade serous ovarian cancer

Deep learning provides a new computed tomography-based prognostic biomarker for recurrence prediction in high-grade serous ovarian cancer

Emerging roles of allosteric modulators in the regulation of protein‐protein interactions (PPIs): A new paradigm for PPI drug discovery

[18F]Fluciclatide PET as a biomarker of response to combination therapy of pazopanib and paclitaxel in platinum-resistant/refractory ovarian cancer

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