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Engineered Niches to Analyze Mechanisms of Metastasis and Guide Precision Medicine.

ABSTRACT: Cancer metastasis poses a challenging problem both clinically and scientifically, as the stochastic nature of metastatic lesion formation introduces complexity for both early detection and the study of metastasis in preclinical models. Engineered metastatic niches represent an emerging approach to address this stochasticity by creating bioengineered sites where cancer can preferentially metastasize. As the engineered niche captures the earliest metastatic cells at a nonvital location, both noninvasive and biopsy-based monitoring of these sites can be performed routinely to detect metastasis early and monitor alterations in the forming metastatic niche. The engineered metastatic niche also provides a new platform technology that serves as a tunable site to molecularly dissect metastatic disease mechanisms. Ultimately, linking the engineered niches with advances in sensor development and synthetic biology can provide enabling tools for preclinical cancer models and fosters the potential to impact the future of clinical cancer care.

SUBMITTER: Morris AH

PROVIDER: S-EPMC7501202 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Engineered Niches to Analyze Mechanisms of Metastasis and Guide Precision Medicine.

Morris Aaron H AH Orbach Sophia M SM Bushnell Grace G GG Oakes Robert S RS Jeruss Jacqueline S JS Shea Lonnie D LD

Cancer research 20200514 18

Cancer metastasis poses a challenging problem both clinically and scientifically, as the stochastic nature of metastatic lesion formation introduces complexity for both early detection and the study of metastasis in preclinical models. Engineered metastatic niches represent an emerging approach to address this stochasticity by creating bioengineered sites where cancer can preferentially metastasize. As the engineered niche captures the earliest metastatic cells at a nonvital location, both nonin ...[more]

PMID: 32409307

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Project description:Large-scale screening of drug sensitivity on cancer cell models can mimic in vivo cellular behavior providing wider scope for biological research on cancer. Since the therapeutic effect of a single drug or drug combination depends on the individual patient's genome characteristics and cancer cells integration reaction, the identification of an effective agent in an in vitro model by using large number of cancer cell models is a promising approach for the development of targeted treatments. Precision cancer medicine is to select the most appropriate treatment or treatments for an individual patient. However, it still lacks the tools to bridge the gap between conventional in vitro cancer cell models and clinical patient response to inhibitors. An optimal two-layer decision system model is developed to identify the cancer cells that most closely resemble an individual tumor for optimum therapeutic interventions in precision cancer medicine. Accordingly, an optimal grid parameters selection is designed to seek the highest accordance for treatment selection to the patient's preference for drug response and in vitro cancer cell drug screening. The optimal two-layer decision system model overcomes the challenge of heterology data comparison between the tumor and the cancer cells, as well as between the continual variation of drug responses in vitro and the discrete ones in clinical practice. We simulated the model accuracy using 681 cancer cells' mRNA and associated 481 drug screenings and validated our results on 315 breast cancer patients drug selection across seven drugs (docetaxel, doxorubicin, fluorouracil, paclitaxel, tamoxifen, cyclophosphamide, lapitinib). Comparing with the real response of a drug in clinical patients, the novel model obtained an overall average accordance over 90.8% across the seven drugs. At the same time, the optimal cancer cells and the associated optimal therapeutic efficacy of cancer drugs are recommended. The novel optimal two-layer decision system model was used on 1097 patients with breast cancer in guiding precision medicine for a recommendation of their optimal cancer cells (30 cancer cells) and associated efficacy of certain cancer drugs. Our model can detect the most similar cancer cells for each individual patient. A successful clinical translation model (optimal two-layer decision system model) was developed to bridge in-vitro basic science to clinical practice in a therapeutic intervention application for the first time. The novel tool kills two birds with one stone. It can help basic science to seek optimal cancer cell models for an individual tumor, while prioritizing clinical drugs' recommendations in practice. Tool associated platform website: We extended the breast cancer research to 32 more types of cancers across 45 therapy predictions. The website is set up and can be accessed by the link: https://pcm2019.shinyapps.io/drug_response_prediction/.

Project description:BackgroundWith rapid advances in genomic medicine, the complexity of delivering precision medicine to oncology patients across a university health system demanded the creation of a Molecular Tumor Board (MTB) for patient selection and assessment of treatment options. The objective of this report is to analyze our progress to date and discuss the importance of the MTB in the implementation of personalized medicine.Materials and methodsPatients were reviewed in the MTB for appropriateness for comprehensive next generation sequencing (NGS) cancer gene set testing based on set criteria that were in place. Because profiling of stage IV lung cancer, colon cancer, and melanoma cancers were standard of care, these cancer types were excluded from this process. We subsequently analyzed the types of cases referred for testing and approved with regards to their results.Results191 cases were discussed at the MTB and 132 cases were approved for testing. Forty-six cases (34.8%) had driver mutations that were associated with an active targeted therapeutic agent, including BRAF, PIK3CA, IDH1, KRAS, and BRCA1. An additional 56 cases (42.4%) had driver mutations previously reported in some type of cancer. Twenty-two cases (16.7%) did not have any clinically significant mutations. Eight cases did not yield adequate DNA. 15 cases were considered for targeted therapy, 13 of which received targeted therapy. One patient experienced a near complete response. Seven of 13 had stable disease or a partial response.ConclusionsMTB at University of Alabama-Birmingham is unique because it reviews the appropriateness of NGS testing for patients with recurrent cancer and serves as a forum to educate our physicians about the pathways of precision medicine. Our results suggest that our detection of actionable mutations may be higher due to our careful selection. The application of precision medicine and molecular genetic testing for cancer patients remains a continuous educational process for physicians.

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Engineered Niches to Analyze Mechanisms of Metastasis and Guide Precision Medicine.

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Engineered Niches to Analyze Mechanisms of Metastasis and Guide Precision Medicine.

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