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Conditional Reprogramming for Patient-Derived Cancer Models and Next-Generation Living Biobanks.

ABSTRACT: Traditional cancer models including cell lines and animal models have limited applications in both basic and clinical cancer research. Genomics-based precision oncology only help 2-20% patients with solid cancer. Functional diagnostics and patient-derived cancer models are needed for precision cancer biology. In this review, we will summarize applications of conditional cell reprogramming (CR) in cancer research and next generation living biobanks (NGLB). Together with organoids, CR has been cited in two NCI (National Cancer Institute, USA) programs (PDMR: patient-derived cancer model repository; HCMI: human cancer model initiatives. HCMI will be distributed through ATCC). Briefly, the CR method is a simple co-culture technology with a Rho kinase inhibitor, Y-27632, in combination with fibroblast feeder cells, which allows us to rapidly expand both normal and malignant epithelial cells from diverse anatomic sites and mammalian species and does not require transfection with exogenous viral or cellular genes. Establishment of CR cells from both normal and tumor tissue is highly efficient. The robust nature of the technique is exemplified by the ability to produce 2 × 106 cells in five days from a core biopsy of tumor tissue. Normal CR cell cultures retain a normal karyotype and differentiation potential and CR cells derived from tumors retain their tumorigenic phenotype. CR also allows us to enrich cancer cells from urine (for bladder cancer), blood (for prostate cancer), and pleural effusion (for non-small cell lung carcinoma). The ability to produce inexhaustible cell populations using CR technology from small biopsies and cryopreserved specimens has the potential to transform biobanking repositories (NGLB: next-generation living biobank) and current pathology practice by enabling genetic, biochemical, metabolomic, proteomic, and biological assays, including chemosensitivity testing as a functional diagnostics tool for precision cancer medicine. We discussed analyses of patient-derived matched normal and tumor models using a case with tongue squamous cell carcinoma as an example. Last, we summarized applications in cancer research, disease modeling, drug discovery, and regenerative medicine of CR-based NGLB.

SUBMITTER: Palechor-Ceron N

PROVIDER: S-EPMC6912808 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Conditional Reprogramming for Patient-Derived Cancer Models and Next-Generation Living Biobanks.

Palechor-Ceron Nancy N Krawczyk Ewa E Dakic Aleksandra A Simic Vera V Yuan Hang H Blancato Jan J Wang Weisheng W Hubbard Fleesie F Zheng Yun-Ling YL Dan Hancai H Strome Scott S Cullen Kevin K Davidson Bruce B Deeken John F JF Choudhury Sujata S Ahn Peter H PH Agarwal Seema S Zhou Xuexun X Schlegel Richard R Furth Priscilla A PA Pan Chong-Xian CX Liu Xuefeng X

Cells 20191027 11

Traditional cancer models including cell lines and animal models have limited applications in both basic and clinical cancer research. Genomics-based precision oncology only help 2-20% patients with solid cancer. Functional diagnostics and patient-derived cancer models are needed for precision cancer biology. In this review, we will summarize applications of conditional cell reprogramming (CR) in cancer research and next generation living biobanks (NGLB). Together with organoids, CR has been cit ...[more]

PMID: 31717887

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Project description:BackgroundThe development of secondary resistance (SR) in metastatic colorectal cancer (mCRC) treated with anti-epidermal growth factor receptor (anti-EGFR) antibodies is not fully understood at the molecular level. Here we tested in vivo selection of anti-EGFR SR tumors in CRC patient-derived xenograft (PDX) models as a strategy for a molecular dissection of SR mechanisms.MethodsWe analyzed 21 KRAS, NRAS, BRAF, and PI3K wildtype CRC patient-derived xenograft (PDX) models for their anti-EGFR sensitivity. Furthermore, 31 anti-EGFR SR tumors were generated via chronic in vivo treatment with cetuximab. A multi-omics approach was employed to address molecular primary and secondary resistance mechanisms. Gene set enrichment analyses were used to uncover SR pathways. Targeted therapy of SR PDX models was applied to validate selected SR pathways.ResultsIn vivo anti-EGFR SR could be established with high efficiency. Chronic anti-EGFR treatment of CRC PDX tumors induced parallel evolution of multiple resistant lesions with independent molecular SR mechanisms. Mutations in driver genes explained SR development in a subgroup of CRC PDX models, only. Transcriptional reprogramming inducing anti-EGFR SR was discovered as a common mechanism in CRC PDX models frequently leading to RAS signaling pathway activation. We identified cAMP and STAT3 signaling activation, as well as paracrine and autocrine signaling via growth factors as novel anti-EGFR secondary resistance mechanisms. Secondary resistant xenograft tumors could successfully be treated by addressing identified transcriptional changes by tailored targeted therapies.ConclusionsOur study demonstrates that SR PDX tumors provide a unique platform to study molecular SR mechanisms and allow testing of multiple treatments for efficient targeting of SR mechanisms, not possible in the patient. Importantly, it suggests that the development of anti-EGFR tolerant cells via transcriptional reprogramming as a cause of anti-EGFR SR in CRC is likely more prevalent than previously anticipated. It emphasizes the need for analyses of SR tumor tissues at a multi-omics level for a comprehensive molecular understanding of anti-EGFR SR in CRC.

Project description:Cell-based models of many neurological and psychiatric diseases, established by reprogramming patient somatic cells into human induced pluripotent stem cells (hiPSCs), have now been reported. While numerous reports have demonstrated that neuronal cells differentiated from hiPSCs are electrophysiologically active mature neurons, the âageâ of these cells relative to cells in the human brain remains unresolved. Comparisons of gene expression profiles of hiPSC-derived neural progenitor cells (NPCs) and neurons to the Allen BrainSpan Atlas indicate that hiPSC neural cells most resemble first trimester neural tissue. Consequently, we posit that hiPSC-derived neural cells may most accurately be used to model the early developmental defects that contribute to disease predisposition rather than the late features of the disease. Though the characteristic symptoms of schizophrenia SZ generally appear late in adolescence, it is now thought to be a neurodevelopmental condition, often predated by a prodromal period that can appear in early childhood. Postmortem studies of SZ brain tissue typically describe defects in mature neurons, such as reduced neuronal size and spine density in the prefrontal cortex and hippocampus, but abnormalities of neuronal organization, particularly in the cortex, have also been reported. We postulated that defects in cortical organization in SZ might result from abnormal migration of neural cells. To test this hypothesis, we directly reprogrammed fibroblasts from SZ patients into hiPSCs and subsequently differentiated these disorder-specific hiPSCs into NPCs. SZ hiPSC differentiated into forebrain NPCs have altered expression of a number of cellular adhesion genes and WNT signaling. Methods: We compared global transcription of forebrain NPCs from six control and four SZ patients by RNAseq. Results: Multi-dimensional scaling (MDS) resolved most SZ and control hiPSC NPC samples; 848 genes were significantly differentially expressed (FDR<0.01) Conclusions: The WNT signaling pathway was enriched 2-fold (fisher exact test p-value = 0.031). 1-2 independent differentiations (biological replicates) for each of four control and four schizophrenia patients were analyzed; samples were generated in parallel to neuron RNAseq data.

Dataset Information

Conditional Reprogramming for Patient-Derived Cancer Models and Next-Generation Living Biobanks.

Publications

Conditional Reprogramming for Patient-Derived Cancer Models and Next-Generation Living Biobanks.

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