Chemoresponse assay for evaluating response to sunitinib in primary cultures of breast cancer

Suchy, Sarah L.; Hancher, L. M.; Wang, Dakun; Ervin, Paul R.; Brower, Stacey L.

doi:10.4161/cbt.11.12.15710

Cited by 5 publications

(4 citation statements)

References 44 publications

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“…Interestingly, short-term cultivation of primary explants of human tumours had been explored previously, for example, growing the specimens in plasma clots 43 or using engineered 3D explant cultures 20 25 44 45 46 47 48 . While these explant models did capture the heterogenous cancer and stromal cell population to certain degree, and were used to study tumour heterogeneity, invasiveness and response to treatment 15 16 17 21 25 49 , these attempts did not show full viability at diverse functional levels. While extracellular matrix support was shown to help preserve and recreate many important morphologic and phenotypic properties in these 3D spheroids and organotypic cultures, these studies did not elucidate the importance of conserving tumour type- and grade-matched matrix factors in maintaining functional organization and dynamics.…”

Section: Discussionmentioning

confidence: 99%

Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity

et al. 2015

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Predicting clinical response to anticancer drugs remains a major challenge in cancer treatment. Emerging reports indicate that the tumour microenvironment and heterogeneity can limit the predictive power of current biomarker-guided strategies for chemotherapy. Here we report the engineering of personalized tumour ecosystems that contextually conserve the tumour heterogeneity, and phenocopy the tumour microenvironment using tumour explants maintained in defined tumour grade-matched matrix support and autologous patient serum. The functional response of tumour ecosystems, engineered from 109 patients, to anticancer drugs, together with the corresponding clinical outcomes, is used to train a machine learning algorithm; the learned model is then applied to predict the clinical response in an independent validation group of 55 patients, where we achieve 100% sensitivity in predictions while keeping specificity in a desired high range. The tumour ecosystem and algorithm, together termed the CANScript technology, can emerge as a powerful platform for enabling personalized medicine.

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Section: Discussionmentioning

confidence: 99%

Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity

et al. 2015

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“…We anticipate applications in drug evaluation for this novel disease-on-a-chip model. Testing patient tumor samples for their response to anticancer drugs is an emerging direction for individualized therapy, [23][24][25] and disease-on-chips models might help determine tumor responses to therapies with increased accuracy by placing cancer cells within a proper architectural context. Future developments of disease-on-chips will include compartmentalization with microfluidics to permit simultaneous testing of multiple tumors or of different drug regimens.…”

mentioning

confidence: 99%

Disease-on-a-chip: mimicry of tumor growth in mammary ducts

Vidi¹,

Maleki

Ochoa

et al. 2014

Lab Chip

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We present a disease-on-a-chip model in which cancer grows within phenotypically normal breast luminal epithelium on semicircular acrylic support mimicking portions of mammary ducts. The cells from tumor nodules developing within these hemichannels are morphologically distinct from their counterparts cultured on flat surfaces. Moreover, tumor nodules cocultured with the luminal epithelium in hemichannels display a different anticancer drug sensitivity compared to nodules cocultured with the luminal epithelium on a flat surface and to monocultures of tumor nodules. The mimicry of tumor development within the epithelial environment of mammary ducts provides a framework for the design and test of anticancer therapies.

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“…In the recent past, chemotherapy testing has been performed on cancer cells from patients without prior separation and enrichment of the CSLCs from the bulk of tumor cells [30], [36]–[45].…”

Section: Discussionmentioning

confidence: 99%

Chemo-Predictive Assay for Targeting Cancer Stem-Like Cells in Patients Affected by Brain Tumors

et al. 2014

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Administration of ineffective anticancer therapy is associated with unnecessary toxicity and development of resistant clones. Cancer stem-like cells (CSLCs) resist chemotherapy, thereby causing relapse of the disease. Thus, development of a test that identifies the most effective chemotherapy management offers great promise for individualized anticancer treatments. We have developed an ex vivo chemotherapy sensitivity assay (ChemoID), which measures the sensitivity of CSLCs as well as the bulk of tumor cells to a variety of chemotherapy agents. Two patients, a 21-year old male (patient 1) and a 5-month female (patient 2), affected by anaplastic WHO grade-III ependymoma were screened using the ChemoID assay. Patient 1 was found sensitive to the combination of irinotecan and bevacizumab, which resulted in a prolonged disease progression free period of 18 months. Following recurrence, the combination of various chemotherapy drugs was tested again with the ChemoID assay. We found that benzyl isothiocyanate (BITC) greatly increased the chemosensitivity of the ependymoma cells to the combination of irinotecan and bevacizumab. After patient 1 was treated for two months with irinotecan, bevacizumab and supplements of cruciferous vegetable extracts containing BITC, we observed over 50% tumoral regression in comparison with pre-ChemoID scan as evidenced by MRI. Patient 2 was found resistant to all treatments tested and following 6 cycles of vincristine, carboplatin, cyclophosphamide, etoposide, and cisplatin in various combinations, the tumor of this patient rapidly progressed and proton beam therapy was recommended. As expected animal studies conducted with patient derived xenografts treated with ChemoID screened drugs recapitulated the clinical observation. This assay demonstrates that patients with the same histological stage and grade of cancer may vary considerably in their clinical response, suggesting that ChemoID testing which measures the sensitivity of CSLCs as well as the bulk of tumor cells to a variety of chemotherapy agents could lead to more effective and personalized anticancer treatments in the future.

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Chemoresponse assay for evaluating response to sunitinib in primary cultures of breast cancer

Cited by 5 publications

References 44 publications

Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity

Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity

Disease-on-a-chip: mimicry of tumor growth in mammary ducts

Chemo-Predictive Assay for Targeting Cancer Stem-Like Cells in Patients Affected by Brain Tumors

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