Personalized In Vitro and In Vivo Cancer Models to Guide Precision Medicine

Modeling Human Digestive Diseases With CRISPR-Cas9-Modified Organoids

Insights into the stem cell niche have allowed researchers to cultivate adult tissue stem cells as organoids that display structural and phenotypic features of healthy and diseased epithelial tissues. Organoids derived from patients' tissues are used as models of disease and to test drugs. CRISPR-Cas9 technology can be used to genetically engineer organoids for studies of monogenic diseases and cancer. We review the derivation of organoids from human gastrointestinal tissues and how CRISPR-Cas9 technology can be used to study these organoids. We discuss burgeoning technologies that are broadening our understanding of diseases of the digestive system.

Predict drug sensitivity of cancer cells with pathway activity inference

Background

Predicting cellular responses to drugs has been a major challenge for personalized drug therapy regimen. Recent pharmacogenomic studies measured the sensitivities of heterogeneous cell lines to numerous drugs, and provided valuable data resources to develop and validate computational approaches for the prediction of drug responses. Most of current approaches predict drug sensitivity by building prediction models with individual genes, which suffer from low reproducibility due to biologic variability and difficulty to interpret biological relevance of novel gene-drug associations. As an alternative, pathway activity scores derived from gene expression could predict drug response of cancer cells.

Method

In this study, pathway-based prediction models were built with four approaches inferring pathway activity in unsupervised manner, including competitive scoring approaches (DiffRank and GSVA) and self-contained scoring approaches (PLAGE and Z-score). These unsupervised pathway activity inference approaches were applied to predict drug responses of cancer cells using data from Cancer Cell Line Encyclopedia (CCLE).

Results

Our analysis on all the 24 drugs from CCLE demonstrated that pathway-based models achieved better predictions for 14 out of the 24 drugs, while taking fewer features as inputs. Further investigation on indicated that pathway-based models indeed captured pathways involving drug-related genes (targets, transporters and metabolic enzymes) for majority of drugs, whereas gene-models failed to identify these drug-related genes, in most cases. Among the four approaches, competitive scoring (DiffRank and GSVA) provided more accurate predictions and captured more pathways involving drug-related genes than self-contained scoring (PLAGE and Z-Score). Detailed interpretation of top pathways from the top method (DiffRank) highlights the merit of pathway-based approaches to predict drug response by identifying pathways relevant to drug mechanisms.

Conclusion

Taken together, pathway-based modeling with inferred pathway activity is a promising alternative to predict drug response, with the ability to easily interpret results and provide biological insights into the mechanisms of drug actions.

Electronic supplementary material

The online version of this article (10.1186/s12920-018-0449-4) contains supplementary material, which is available to authorized users.

DRAP: a toolbox for drug response analysis and visualization tailored for preclinical drug testing on patient-derived xenograft models

Background

One of the key reasons for the high failure rate of new agents and low therapeutic benefit of approved treatments is the lack of preclinical models that mirror the biology of human tumors. At present, the optimal cancer model for drug response study to date is patient-derived xenograft (PDX) models. PDX recaptures both inter- and intra-tumor heterogeneity inherent in human cancer, which represent a valuable platform for preclinical drug testing and personalized medicine applications. Building efficient drug response analysis tools is critical but far from adequate for the PDX platform.

Results

In this work, we first classified the emerging PDX preclinical trial designs into four patterns based on the number of tumors, arms, and animal repeats in every arm. Then we developed an R package, DRAP, which implements Drug Response Analyses on PDX platform separately for the four patterns, involving data visualization, data analysis and conclusion presentation. The data analysis module offers statistical analysis methods to assess difference of tumor volume between arms, tumor growth inhibition (TGI) rate calculation to quantify drug response, and drug response level analysis to label the drug response at animal level. In the end, we applied DRAP in two case studies through which the functions and usage of DRAP were illustrated.

Conclusion

DRAP is the first integrated toolbox for drug response analysis and visualization tailored for PDX platform. It would greatly promote the application of PDXs in drug development and personalized cancer treatments.

Electronic supplementary material

The online version of this article (10.1186/s12967-019-1785-7) contains supplementary material, which is available to authorized users.

Immunogenic chemotherapy: Dose and schedule dependence and combination with immunotherapy

Conventional cytotoxic cancer chemotherapy is often immunosuppressive and associated with drug resistance and tumor regrowth after a short period of tumor shrinkage or growth stasis. However, certain cytotoxic cancer chemotherapeutic drugs, including doxorubicin, mitoxantrone, and cyclophosphamide, can kill tumor cells by an immunogenic cell death pathway, which activates robust innate and adaptive anti-tumor immune responses and has the potential to greatly increase the efficacy of chemotherapy. Here, we review studies on chemotherapeutic drug-induced immunogenic cell death, focusing on how the choice of a conventional cytotoxic agent and its dose and schedule impact anti-tumor immune responses. We propose a strategy for effective immunogenic chemotherapy that employs a modified metronomic schedule for drug delivery, which we term medium-dose intermittent chemotherapy (MEDIC). Striking responses have been seen in preclinical cancer models using MEDIC, where an immunogenic cancer chemotherapeutic agent is administered intermittently and at an intermediate dose, designed to impart strong and repeated cytotoxic damage to tumors, and on a schedule compatible with activation of a sustained anti-tumor immune response, thereby maximizing anti-cancer activity. We also discuss strategies for combination chemo-immunotherapy, and we outline approaches to identify new immunogenic chemotherapeutic agents for drug development.

The future of myeloma precision medicine: integrating the compendium of known drug resistance mechanisms with emerging tumor profiling technologies

Multiple myeloma (MM) is a hematologic malignancy that is considered mostly incurable in large part due to the inability of standard of care therapies to overcome refractory disease and inevitable drug-resistant relapse. The post-genomic era has been a productive period of discovery where modern sequencing methods have been applied to large MM patient cohorts to modernize our current perception of myeloma pathobiology and establish an appreciation for the vast heterogeneity that exists between and within MM patients. Numerous pre-clinical studies conducted in the last two decades have unveiled a compendium of mechanisms by which malignant plasma cells can escape standard therapies, many of which have potentially quantifiable biomarkers. Exhaustive pre-clinical efforts have evaluated countless putative anti-MM therapeutic agents and many of these have begun to enter clinical trial evaluation. While the palette of available anti-MM therapies is continuing to expand it is also clear that malignant plasma cells still have mechanistic avenues by which they can evade even the most promising new therapies. It is therefore becoming increasingly clear that there is an outstanding need to develop and employ precision medicine strategies in MM management that harness emerging tumor profiling technologies to identify biomarkers that predict efficacy or resistance within an individual's sub-clonally heterogeneous tumor. In this review we present an updated overview of broad classes of therapeutic resistance mechanisms and describe selected examples of putative biomarkers. We also outline several emerging tumor profiling technologies that have the potential to accurately quantify biomarkers for therapeutic sensitivity and resistance at genomic, transcriptomic and proteomic levels. Finally, we comment on the future of implementation for precision medicine strategies in MM and the clear need for a paradigm shift in clinical trial design and disease management.

Tumor Tissue Explant Culture of Patient-Derived Xenograft as Potential Prioritization Tool for Targeted Therapy

Despite of remarkable progress made in the head and neck cancer (HNC) therapy, the survival rate of this metastatic disease remain low. Tailoring the appropriate therapy to patients is a major challenge and highlights the unmet need to have a good preclinical model that will predict clinical response. Hence, we developed an accurate and time efficient drug screening method of tumor ex vivo analysis (TEVA) system, which can predict patient-specific drug responses. In this study, we generated six patient derived xenografts (PDXs) which were utilized for TEVA. Briefly, PDXs were cut into 2 × 2 × 2 mm3 explants and treated with clinically relevant drugs for 24 h. Tumor cell proliferation and death were evaluated by immunohistochemistry and TEVA score was calculated. Ex vivo and in vivo drug efficacy studies were performed on four PDXs and three drugs side-by-side to explore correlation between TEVA and PDX treatment in vivo. Efficacy of drug combinations was also ventured. Optimization of the culture timings dictated 24 h to be the time frame to detect drug responses and drug penetrates 2 × 2 × 2 mm3 explants as signaling pathways were significantly altered. Tumor responses to drugs in TEVA, significantly corresponds with the drug efficacy in mice. Overall, this low cost, robust, relatively simple and efficient 3D tissue-based method, employing material from one PDX, can bypass the necessity of drug validation in immune-incompetent PDX-bearing mice. Our data provides a potential rationale for utilizing TEVA to predict tumor response to targeted and chemo therapies when multiple targets are proposed.

Constructing and Deconstructing Cancers using Human Pluripotent Stem Cells and Organoids

Cell lines and animal models have provided the foundation of cancer research for many years. However, human pluripotent stem cells (hPSCs) and organoids are increasingly enabling insights into tumor development, progression, and treatment. Here, we review recent studies using hPSCs to elucidate the reciprocal roles played by genetic alterations and cell identity in cancer formation. We also review studies using human organoids as models that recapitulate both intra- and inter-tumoral heterogeneity to gain new insights into tumorigenesis and treatment responses. Finally, we highlight potential opportunities for cancer research using hPSC-derived organoids and genome editing in the future.

Organoid technology in disease modelling, drug development, personalized treatment and regeneration medicine

Organoid technology bridges the gap between conventional two-dimensional cell line culture and in vivo models. The near-physiological technology can virtually recapitulates organ development and human diseases, such as infectious diseases, genetic abnormality and even cancers. In addition, organoids can more accurately predict drug responses, and serve as an excellent platform for drug development, including efficacy evaluation, toxicity testing and pharmacokinetics analysis. Furthermore, organoids can also be exploited to explore the possible optimized treatment strategies for each individual patient. Besides, organoid technology is a promising strategy for regeneration medicine and transplantation use, which can overcome the deficiency in the supply of healthy donor tissues and inherent immunological rejection through establishing isogenic organoids from minuscule amounts of patient biopsies. Collectively, organoids hold enormous potential for clinical applications and bring basic research closer to clinical practice. In this review, we described common organoid lines, summarized the potential clinical applications, and outlined the current limitations.

High-throughput screening in colorectal cancer tissue-originated spheroids

Patient‐derived cancer organoid culture is an important live material that reflects clinical heterogeneity. However, the limited amount of organoids available for each case as well as the considerable amount of time and cost to expand in vitro makes it impractical to perform high‐throughput drug screening using organoid cultures from multiple patients. Here, we report an advanced system for the high‐throughput screening of 2427 drugs using the cancer tissue‐originated spheroid (CTOS) method. In this system, we apply the CTOS method in an ex vivo platform from xenograft tumors, using machines to handle CTOS and reagents, and testing a CTOS reference panel of multiple CTOS lines for the hit drugs. CTOS passages in xenograft tumors resulted in minimal changes of morphological and genomic status, and xenograft tumor generation efficiently expanded the number of CTOS to evaluate multiple drugs. Our panel of colorectal cancer CTOS lines exhibited diverse sensitivities to the hit compounds, demonstrating the usefulness of this system for investigating highly heterogeneous disease.

Prediction of DNA Repair Inhibitor Response in Short-Term Patient-Derived Ovarian Cancer Organoids

Based on genomic analysis, 50% of high-grade serous ovarian cancers (HGSC) are predicted to have DNA repair defects. Whether this substantial subset of HGSCs actually have functional repair defects remains unknown. Here, we devise a platform for functional profiling of DNA repair in short-term patient-derived HGSC organoids. We tested 33 organoid cultures derived from 22 patients with HGSC for defects in homologous recombination (HR) and replication fork protection. Regardless of DNA repair gene mutational status, a functional defect in HR in the organoids correlated with PARP inhibitor sensitivity. A functional defect in replication fork protection correlated with carboplatin and CHK1 and ATR inhibitor sensitivity. Our results indicate that a combination of genomic analysis and functional testing of organoids allows for the identification of targetable DNA damage repair defects. Larger numbers of patient-derived organoids must be analyzed to determine whether these assays can reproducibly predict patient response in the clinic.Significance: Patient-derived ovarian tumor organoids grow rapidly and match the tumors from which they are derived, both genetically and functionally. These organoids can be used for DNA repair profiling and therapeutic sensitivity testing and provide a rapid means of assessing targetable defects in the parent tumor, offering more suitable treatment options. Cancer Discov; 8(11); 1404-21. ©2018 AACR. This article is highlighted in the In This Issue feature, p. 1333.

Head and neck cancer organoids established by modification of the CTOS method can be used to predict in vivo drug sensitivity

OBJECTIVES

Currently there are no standard biomarkers of head and neck squamous cell carcinoma (HNSCC) response to therapy. This is, due to a lack of adequate predictive tumor models. To this end, we established cancer organoid lines from individual patient's tumors, and characterized their growth characteristics and response to different drug treatments with the objective of using these models for prediction of treatment response.

MATERIALS AND METHODS

Forty-three patients' samples were processed to establish organoids. To analyze the character of these organoids, immunohistochemistry, Western blotting, drug sensitivity assays, clonogenic survival assays, and animal experiments were performed. The HPV status and TP53 mutational status were also confirmed in these lines.

RESULTS

HNSCC organoids were successfully established with success rate of 30.2%. Corresponding two-dimensional cell lines were established from HNSCC organoids at higher success rate (53.8%). These organoids showed similar histological features and stem cell, epithelial and mesenchymal marker expression to the original tumors, thus recapitulating many of the characteristics of the original tumor cells. The cisplatin and docetaxel IC50 were determined for HNSCC organoids and the corresponding 2D cell lines using drug sensitivity and clonogenic survival assays. Responses to drug treatment in vivo were found to be similar to the IC₅₀ calculated from organoids by drug sensitivity assays in vitro.

CONCLUSION

We established novel in vitro HNSCC cancer organoid lines retaining many properties of the original tumors from they were derived. These organoids can predict in vivo drug sensitivity and may represent useful tools to develop precision treatments for HNSCC.

Mapping genetic vulnerabilities reveals BTK as a novel therapeutic target in oesophageal cancer

OBJECTIVE

Oesophageal cancer is the seventh most common cause of cancer-related death worldwide. Disease relapse is frequent and treatment options are limited.

DESIGN

To identify new biomarker-defined therapeutic approaches for patients with oesophageal cancer, we integrated the genomic profiles of 17 oesophageal tumour-derived cell lines with drug sensitivity data from small molecule inhibitor profiling, identifying drug sensitivity effects associated with cancer driver gene alterations. We also interrogated recently described RNA interference screen data for these tumour cell lines to identify candidate genetic dependencies or vulnerabilities that could be exploited as therapeutic targets.

RESULTS

By integrating the genomic features of oesophageal tumour cell lines with siRNA and drug screening data, we identified a series of candidate targets in oesophageal cancer, including a sensitivity to inhibition of the kinase BTK in MYC amplified oesophageal tumour cell lines. We found that this genetic dependency could be elicited with the clinical BTK/ERBB2 kinase inhibitor, ibrutinib. In both MYC and ERBB2 amplified tumour cells, ibrutinib downregulated ERK-mediated signal transduction, cMYC Ser-62 phosphorylation and levels of MYC protein, and elicited G1 cell cycle arrest and apoptosis, suggesting that this drug could be used to treat biomarker-selected groups of patients with oesophageal cancer.

CONCLUSIONS

BTK represents a novel candidate therapeutic target in oesophageal cancer that can be targeted with ibrutinib. On the basis of this work, a proof-of-concept phase II clinical trial evaluating the efficacy of ibrutinib in patients with MYC and/or ERBB2 amplified advanced oesophageal cancer is currently underway (NCT02884453).

TRIAL REGISTRATION NUMBER

NCT02884453; Pre-results.

Model organism data evolving in support of translational medicine

Model organism databases (MODs) have been collecting and integrating biomedical research data for 30 years and were designed to meet specific needs of each model organism research community. The contributions of model organism research to understanding biological systems would be hard to overstate. Modern molecular biology methods and cost reductions in nucleotide sequencing have opened avenues for direct application of model organism research to elucidating mechanisms of human diseases. Thus, the mandate for model organism research and databases has now grown to include facilitating use of these data in translational applications. Challenges in meeting this opportunity include the distribution of research data across many databases and websites, a lack of data format standards for some data types, and sustainability of scale and cost for genomic database resources like MODs. The issues of widely distributed data and application of data standards are some of the challenges addressed by FAIR (Findable, Accessible, Interoperable, and Re-usable) data principles. The Alliance of Genome Resources is now moving to address these challenges by bringing together expertly curated research data from fly, mouse, rat, worm, yeast, zebrafish, and the Gene Ontology consortium. Centralized multi-species data access, integration, and format standardization will lower the data utilization barrier in comparative genomics and translational applications and will provide a framework in which sustainable scale and cost can be addressed. This article presents a brief historical perspective on how the Alliance model organisms are complementary and how they have already contributed to understanding the etiology of human diseases. In addition, we discuss four challenges for using data from MODs in translational applications and how the Alliance is working to address them, in part by applying FAIR data principles. Ultimately, combined data from these animal models are more powerful than the sum of the parts.

Characterization of drug responses of mini patient-derived xenografts in mice for predicting cancer patient clinical therapeutic response

Background

Patient-derived organoids and xenografts (PDXs) have emerged as powerful models in functional diagnostics with high predictive power for anticancer drug response. However, limitations such as engraftment failure and time-consuming for establishing and expanding PDX models followed by testing drug efficacy, and inability to subject to systemic drug administration for ex vivo organoid culture hinder realistic and fast decision-making in selecting the right therapeutics in the clinic. The present study aimed to develop an advanced PDX model, namely MiniPDX, for rapidly testing drug efficacy to strengthen its value in personalized cancer treatment.

Methods

We developed a rapid in vivo drug sensitivity assay, OncoVee^® MiniPDX, for screening clinically relevant regimens for cancer. In this model, patient-derived tumor cells were arrayed within hollow fiber capsules, implanted subcutaneously into mice and cultured for 7 days. The cellular activity morphology and pharmacokinetics were systematically evaluated. MiniPDX performance (sensitivity, specificity, positive and negative predictive values) was examined using PDX as the reference. Drug responses were examined by tumor cell growth inhibition rate and tumor growth inhibition rate in PDX models and MiniPDX assays respectively. The results from MiniPDX were also used to evaluate its predictive power for clinical outcomes.

Results

Morphological and histopathological features of tumor cells within the MiniPDX capsules matched those both in PDX models and in original tumors. Drug responses in the PDX tumor graft assays correlated well with those in the corresponding MiniPDX assays using 26 PDX models generated from patients, including 14 gastric cancer, 10 lung cancer and 2 pancreatic cancer. The positive predictive value of MiniPDX was 92%, and the negative predictive value was 81% with a sensitivity of 80% and a specificity of 93%. Through expanding to clinical tumor samples, MiniPDX assay showed potential of wide clinical application.

Conclusions

Fast in vivo MiniPDX assay based on capsule implantation was developed-to assess drug responses of both PDX tumor grafts and clinical cancer specimens. The high correlation between drug responses of paired MiniPDX and PDX tumor graft assay, as well as translational data suggest that MiniPDX assay is an advanced tool for personalized cancer treatment.

Integrated genomic analyses in PDX model reveal a cyclin-dependent kinase inhibitor Palbociclib as a novel candidate drug for nasopharyngeal carcinoma

Background

Patient-derived xenograft (PDX) tumor model has become a new approach in identifying druggable tumor mutations, screening and evaluating personalized cancer drugs based on the mutated targets.

Methods

We established five nasopharyngeal carcinoma (NPC) PDXs in mouse model. Subsequently, whole-exome sequencing (WES) and genomic mutation analyses were performed to search for genetic alterations for new drug targets. Potential drugs were applied in two NPC PDX mice model to assess their anti-cancer activities. RNA sequencing and transcriptomic analysis were performed in one NPC PDX mice to correlate with the efficacy of the anti-cancer drugs.

Results

A relative high incident rate of copy number variations (CNVs) of cell cycle-associated genes. Among the five NPC-PDXs, three had cyclin D1 (CCND1) amplification while four had cyclin-dependent kinase inhibitor CDKN2A deletion. Furthermore, CCND1 overexpression was observed in > 90% FFPE clinical metastatic NPC tumors (87/91) and was associated with poor outcomes. CNV analysis disclosed that plasma CCND1/CDKN2A ratio is correlated with EBV DNA load in NPC patients’ plasma and could serve as a screening test to select potential CDK4/6 inhibitor treatment candidates. Based on our NPC PDX model and RNA sequencing, Palbociclib, a cyclin-dependent kinase inhibitor, proved to have anti-tumor effects by inducing G1 arrest. One NPC patient with liver metastatic was treated with Palbociclib, had stable disease response and a drop in Epstein Barr virus (EBV) EBV titer.

Conclusions

Our integrated information of sequencing-based genomic studies and tumor transcriptomes with drug treatment in NPC-PDX models provided guidelines for personalized precision treatments and revealed a cyclin-dependent kinase inhibitor Palbociclib as a novel candidate drug for NPC.

Electronic supplementary material

The online version of this article (10.1186/s13046-018-0873-5) contains supplementary material, which is available to authorized users.

Organoid technology and applications in cancer research

During the past decade, the three-dimensional organoid technology has sprung up and become more and more popular among researchers. Organoids are the miniatures of in vivo tissues and organs, and faithfully recapitulate the architectures and distinctive functions of a specific organ.

These amazing three-dimensional constructs represent a promising, near-physiological model for human cancers, and tremendously support diverse potential applications in cancer research. Up to now, highly efficient establishment of organoids can be achieved from both normal and malignant tissues of patients. Using this bioengineered platform, the links of infection-cancer progression and mutation-carcinogenesis are feasible to be modeled. Another potential application is that organoid technology facilitates drug testing and guides personalized therapy. Although organoids still fail to model immune system accurately, co-cultures of organoids and lymphocytes have been reported in several studies, bringing hope for further application of this technology in immunotherapy. In addition, the potential value in regeneration medicine might be another paramount branch of organoid technology, which might refine current transplantation therapy through the replacement of irreversibly progressively diseased organs with isogenic healthy organoids.

In conclusion, organoids represent an excellent preclinical model for human tumors, promoting the translation from basic cancer research to clinical practice. In this review, we outline organoid technology and summarize its applications in cancer research.

Development of patient-derived orthotopic xenografts from metastatic colorectal cancer in nude mice

Liver metastasis is the major cause of death for patients with colorectal cancer. Despite treatment with surgery and chemotherapy, patient outcomes are quite unfavourable. Thus, there is an urgent need to develop new treatment strategies with the associated establishment of good animal models. Metastatic disease can be modelled using patient-derived orthotopic xenografts, which accurately replicate intra-tumoral heterogeneity so that various chemotherapeutic agents can be tested on individual tumours to aid in clinical decision-making. The objective of this study was to develop metastatic colorectal tumours in athymic nude mice by implanting fresh tumour fragments into mouse liver parenchyma. Metastatic tumours were successfully propagated in mice following transplantation from human patients, then serially implanted in second and third-generation mice. Morphologic and immunohistochemical characteristics indicate that xenografts recreate the tumour architecture and mismatch repair gene expression for MLH1, MSH2, MSH1, and PMS2. After tumour implantation during the first passage, the time of tumour growth decreased without loss of tumour identity. Post-transplantation lymphoproliferative disease was observed in one case. This pilot study was successful in establishing the institutional PDX preclinical platform to study new therapeutic strategies, disease progression biomarkers, and treatment responsiveness.

The growing role of precision and personalized medicine for cancer treatment

Cancer is a devastating disease that takes the lives of hundreds of thousands of people every year. Due to disease heterogeneity, standard treatments, such as chemotherapy or radiation, are effective in only a subset of the patient population. Tumors can have different underlying genetic causes and may express different proteins in one patient versus another. This inherent variability of cancer lends itself to the growing field of precision and personalized medicine (PPM). There are many ongoing efforts to acquire PPM data in order to characterize molecular differences between tumors. Some PPM products are already available to link these differences to an effective drug. It is clear that PPM cancer treatments can result in immense patient benefits, and companies and regulatory agencies have begun to recognize this. However, broader changes to the healthcare and insurance systems must be addressed if PPM is to become part of standard cancer care.

Personalized Cancer Models for Target Discovery and Precision Medicine

Although cancer research is progressing at an exponential rate, translating this knowledge to develop better cancer drugs and more effectively match drugs to patients is lagging. Genome profiling of tumors provides a snapshot of the genetic complexity of individual tumors, yet this knowledge is insufficient to guide therapy for most patients. Model systems, usually cancer cell lines or mice, have been instrumental in cancer research and drug development, but translation of results to the clinic is inefficient, in part, because these models do not sufficiently reflect the complexity and heterogeneity of human cancer. Here, we discuss the potential of combining genomics with high-throughput functional testing of patient-derived tumor cells to overcome key roadblocks in both drug target discovery and precision medicine.

The Mouse Hospital and Its Integration in Ultra-Precision Approaches to Cancer Care

Precision medicine holds real promise for the treatment of cancer. Adapting therapeutic strategies so patients receive individualized treatment protocols, will transform how diseases like cancer are managed. Already, molecular profiling technologies have provided unprecedented capacity to characterize tumors, yet the ability to translate this to actionable outcome in the clinic is limited. To enable real time translation of personalized therapeutic approaches to patient care in a co-clinical manner will require the adoption and integration of approaches that facilitate modeling of patient disease. The Mouse Hospital represents an approach that is ideally suited to pre- and co-clinical evaluation of novel therapeutic strategies for clinical care. Patient derived xenograft (PDX) technologies and in situ tumor modeling approaches using genetically engineered mouse models (GEMMs) already have a proven capacity to mimic human tumor responses, and their application can deliver invaluable insights into appropriate clinical approaches for individual patients by mirroring human clinical trials using a Co-Clinical Trial project and Mouse Hospital infrastructure. Additionally, the integration of the Mouse Hospital with other emerging technologies for the application of precision medicines, including organoid technologies, provides a platform that enables medical centers to truly reap the benefits that precision medicine has to offer.

Molecular Diagnostics in Clinical Oncology

There are multiple applications of molecular tests in clinical oncology. Mutation analysis is now routinely utilized for the diagnosis of hereditary cancer syndromes. Healthy carriers of cancer-predisposing mutations benefit from tight medical surveillance and various preventive interventions. Cancers caused by germ-line mutations often require significant modification of the treatment strategy. Personalized selection of cancer drugs based on the presence of actionable mutations has become an integral part of cancer therapy. Molecular tests underlie the administration of EGFR, BRAF, ALK, ROS1, PARP inhibitors as well as the use of some other cytotoxic and targeted drugs. Tumors almost always shed their fragments (single cells or their clusters, DNA, RNA, proteins) into various body fluids. So-called liquid biopsy, i.e., the analysis of circulating DNA or some other tumor-derived molecules, holds a great promise for non-invasive monitoring of cancer disease, analysis of drug-sensitizing mutations and early cancer detection. Some tumor- or tissue-specific mutations and expression markers can be efficiently utilized for the diagnosis of cancers of unknown primary origin (CUPs). Systematic cataloging of tumor molecular portraits is likely to uncover a multitude of novel medically relevant DNA- and RNA-based markers.

Tumor-on-a-chip platforms for assessing nanoparticle-based cancer therapy

Cancer has become the most prevalent cause of deaths, placing a huge economic and healthcare burden worldwide. Nanoparticles (NPs), as a key component of nanomedicine, provide alternative options for promoting the efficacy of cancer therapy. Current conventional cancer models have limitations in predicting the effects of various cancer treatments. To overcome these limitations, biomimetic and novel 'tumor-on-a-chip' platforms have emerged with other innovative biomedical engineering methods that enable the evaluation of NP-based cancer therapy. In this review, we first describe cancer models for evaluation of NP-based cancer therapy techniques, and then present the latest advances in 'tumor-on-a-chip' platforms that can potentially facilitate clinical translation of NP-based cancer therapies.

Patient-Derived Xenograft Models for Endometrial Cancer Research

Endometrial cancer (EC) is the most common malignancy of the genital tract among women in developed countries. Recently, a molecular classification of EC has been performed providing a system that, in conjunction with histological observations, reliably improves EC classification and enhances patient management. Patient-derived xenograft models (PDX) represent nowadays a promising tool for translational research, since they closely resemble patient tumour features and retain molecular and histological features. In EC, PDX models have already been used, mainly as an individualized approach to evaluate the efficacy of novel therapies and to identify treatment-response biomarkers; however, their uses in more global or holistic approaches are still missing. As a collaborative effort within the ENITEC network, here we describe one of the most extensive EC PDX cohorts developed from primary tumour and metastasis covering all EC subtypes. Our models are histologically and molecularly characterized and represent an excellent reservoir of EC tumour samples for translational research. This review compiles the information on current methods of EC PDX generation and their utility and provides new perspectives for the exploitation of these valuable tools in order to increase the success ratio for translating results to clinical practice.

The molecularly imprinted polymer essentials: curation of anticancer, ophthalmic, and projected gene therapy drug delivery systems

The development of polymeric materials as drug delivery systems has advanced from systems that rely on classical passive targeting to carriers that can sustain the precisely controlled release of payloads upon physicochemical triggers in desired microenvironment. Molecularly imprinted polymers (MIP), materials designed to capture specific molecules based on their molecular shape and charge distribution, are attractive candidates for fulfilling these purposes. In particular, drug-imprinted polymers coupled with active targeting mechanisms have been explored as potential drug delivery systems. In this review, we have curated important recent efforts in the development of drug-imprinted polymers in a variety of clinical applications, especially oncology and ophthalmology. MIP possesses properties that may complement the traditional delivery systems of these two disciplines, such as passive enhanced permeability and retention effect (EPR) in cancer tumors, and passive drug diffusion in delivering ophthalmic therapeutics. Furthermore, the prospects of MIP integration with the emerging gene therapies will be discussed.

Precision Medicine for Relapsed Multiple Myeloma on the Basis of an Integrative Multiomics Approach

Purpose

Multiple myeloma (MM) is a malignancy of plasma cells, with a median survival of 6 years. Despite recent therapeutic advancements, relapse remains mostly inevitable, and the disease is fatal in the majority of patients. A major challenge in the treatment of patients with relapsed MM is the timely identification of treatment options in a personalized manner. Current approaches in precision oncology aim at matching specific DNA mutations to drugs, but incorporation of genome-wide RNA profiles has not yet been clinically assessed.

Methods

We have developed a novel computational platform for precision medicine of relapsed and/or refractory MM on the basis of DNA and RNA sequencing. Our approach expands on the traditional DNA-based approaches by integrating somatic mutations and copy number alterations with RNA-based drug repurposing and pathway analysis. We tested our approach in a pilot precision medicine clinical trial with 64 patients with relapsed and/or refractory MM.

Results

We generated treatment recommendations in 63 of 64 patients. Twenty-six patients had treatment implemented, and 21 were assessable. Of these, 11 received a drug that was based on RNA findings, eight received a drug that was based on DNA, and two received a drug that was based on both RNA and DNA. Sixteen of the 21 evaluable patients had a clinical response (ie, reduction of disease marker ≥ 25%), giving a clinical benefit rate of 76% and an overall response rate of 66%, with five patients having ongoing responses at the end of the trial. The median duration of response was 131 days.

Conclusion

Our results show that a comprehensive sequencing approach can identify viable options in patients with relapsed and/or refractory myeloma, and they represent proof of principle of how RNA sequencing can contribute beyond DNA mutation analysis to the development of a reliable drug recommendation tool.

DNA Repair Molecular Beacon assay: a platform for real-time functional analysis of cellular DNA repair capacity

Numerous studies have shown that select DNA repair enzyme activities impact response and/or toxicity of genotoxins, suggesting a requirement for enzyme functional analyses to bolster precision medicine or prevention. To address this need, we developed a DNA Repair Molecular Beacon (DRMB) platform that rapidly measures DNA repair enzyme activity in real-time. The DRMB assay is applicable for discovery of DNA repair enzyme inhibitors, for the quantification of enzyme rates and is sufficiently sensitive to differentiate cellular enzymatic activity that stems from variation in expression or effects of amino acid substitutions. We show activity measures of several different base excision repair (BER) enzymes, including proteins with tumor-identified point mutations, revealing lesion-, lesion-context- and cell-type-specific repair dependence; suggesting application for DNA repair capacity analysis of tumors. DRMB measurements using lysates from isogenic control and APE1-deficient human cells suggests the major mechanism of base lesion removal by most DNA glycosylases may be mono-functional base hydrolysis. In addition, development of a microbead-conjugated DRMB assay amenable to flow cytometric analysis further advances its application. Our studies establish an analytical platform capable of evaluating the enzyme activity of select DNA repair proteins in an effort to design and guide inhibitor development and precision cancer therapy options.

Suppression of insulin feedback enhances the efficacy of PI3K inhibitors

Mutations in PIK3CA, which encodes the p110α subunit of the insulin-activated phosphatidylinositol-3 kinase (PI3K), and loss of function mutations in PTEN, which encodes a phosphatase that degrades the phosphoinositide lipids generated by PI3K, are among the most frequent events in human cancers1,2. However, pharmacological inhibition of PI3K has resulted in variable clinical responses, raising the possibility of an inherent mechanism of resistance to treatment. As p110α mediates virtually all cellular responses to insulin, targeted inhibition of this enzyme disrupts glucose metabolism in multiple tissues. For example, blocking insulin signalling promotes glycogen breakdown in the liver and prevents glucose uptake in the skeletal muscle and adipose tissue, resulting in transient hyperglycaemia within a few hours of PI3K inhibition. The effect is usually transient because compensatory insulin release from the pancreas (insulin feedback) restores normal glucose homeostasis3. However, the hyperglycaemia may be exacerbated or prolonged in patients with any degree of insulin resistance and, in these cases, necessitates discontinuation of therapy3-6. We hypothesized that insulin feedback induced by PI3K inhibitors may reactivate the PI3K-mTOR signalling axis in tumours, thereby compromising treatment effectiveness7,8. Here we show, in several model tumours in mice, that systemic glucose-insulin feedback caused by targeted inhibition of this pathway is sufficient to activate PI3K signalling, even in the presence of PI3K inhibitors. This insulin feedback can be prevented using dietary or pharmaceutical approaches, which greatly enhance the efficacy/toxicity ratios of PI3K inhibitors. These findings have direct clinical implications for the multiple p110α inhibitors that are in clinical trials and provide a way to increase treatment efficacy for patients with many types of tumour.

Organoid cultures recapitulate esophageal adenocarcinoma heterogeneity providing a model for clonality studies and precision therapeutics

Esophageal adenocarcinoma (EAC) incidence is increasing while 5-year survival rates remain less than 15%. A lack of experimental models has hampered progress. We have generated clinically annotated EAC organoid cultures that recapitulate the morphology, genomic, and transcriptomic landscape of the primary tumor including point mutations, copy number alterations, and mutational signatures. Karyotyping of organoid cultures has confirmed polyclonality reflecting the clonal architecture of the primary tumor. Furthermore, subclones underwent clonal selection associated with driver gene status. Medium throughput drug sensitivity testing demonstrates the potential of targeting receptor tyrosine kinases and downstream mediators. EAC organoid cultures provide a pre-clinical tool for studies of clonal evolution and precision therapeutics.

A comparison of next-generation sequencing analysis methods for cancer xenograft samples

The application of next-generation sequencing (NGS) technology in cancer is influenced by the quality and purity of tissue samples. This issue is especially critical for patient-derived xenograft (PDX) models, which have proven to be by far the best preclinical tool for investigating human tumor biology, because the sensitivity and specificity of NGS analysis in xenograft samples would be compromised by the contamination of mouse DNA and RNA. This definitely affects downstream analyses by causing inaccurate mutation calling and gene expression estimates. The reliability of NGS data analysis for cancer xenograft samples is therefore highly dependent on whether the sequencing reads derived from the xenograft could be distinguished from those originated from the host. That is, each sequence read needs to be accurately assigned to its original species. Here, we review currently available methodologies in this field, including Xenome, Disambiguate, bamcmp and pdxBlacklist, and provide guidelines for users.

Primary Patient-Derived Cancer Cells and Their Potential for Personalized Cancer Patient Care

Personalized cancer therapy is based on a patient's tumor lineage, histopathology, expression analyses, and/or tumor DNA or RNA analysis. Here, we aim to develop an in vitro functional assay of a patient's living cancer cells that could complement these approaches. We present methods for developing cell cultures from tumor biopsies and identify the types of samples and culture conditions associated with higher efficiency of model establishment. Toward the application of patient-derived cell cultures for personalized care, we established an immunofluorescence-based functional assay that quantifies cancer cell responses to targeted therapy in mixed cell cultures. Assaying patient-derived lung cancer cultures with this method showed promise in modeling patient response for diagnostic use. This platform should allow for the development of co-clinical trial studies to prospectively test the value of drug profiling on tumor-biopsy-derived cultures to direct patient care.

Understanding Disease Biology and Informing the Management of Pancreas Cancer With Preclinical Model Systems

Recent advances in cytotoxic therapies for pancreatic ductal adenocarcinoma (PDA) are overshadowed by stalled clinical progress of more targeted strategies, the vast majority of which have failed in clinical trials. Inability to translate preclinical promise into clinical efficacy derives, in part, from imperfect disease modeling and mismatches between preclinical and clinical study design and execution. Into these gaps fall our patients who enter the clinical trial landscape expectantly and bear the brunt of its inadequacies. If improving patient survival is paramount, then it must be acknowledged that the failure of a phase III trial represents a larger failure of all of the work that preceded it. Repeated failures suggest a need to reappraise the current preclinical-to-clinical apparatus. Exceptional models of PDA are now available to researchers, and the first steps toward a new era of success can begin with improved selection and application of these systems. We discuss the key features of the major preclinical platforms for PDA and propose a paradigm for rigorous interrogation of prospective therapies.

Development of Patient-Derived Gastric Cancer Organoids from Endoscopic Biopsies and Surgical Tissues

BACKGROUND

Organoids are three-dimensional in vitro models of human disease developed from benign and malignant gastrointestinal tissues with tremendous potential for personalized medicine applications. We sought to determine whether gastric cancer patient-derived organoids (PDOs) could be safely established from endoscopic biopsies for rapid drug screening.

METHODS

Patients underwent esophagogastroduodenoscopy (EGD) for surveillance or staging and had additional forceps biopsies taken for PDO creation. Cancer tissues from operative specimens were also used to create PDOs. To address potential tumor heterogeneity, we performed low-coverage whole-genome sequencing of endoscopic-derived PDOs with paired surgical PDOs and whole-tumor lysates. The stability of genomic alterations in endoscopic organoids was assessed by next-generation sequencing and nested polymerase chain reaction (PCR) assay. The feasibility and potential accuracy of drug sensitivity screening with endoscopic-derived PDOs were also evaluated.

RESULTS

Gastric cancer PDOs (n = 15) were successfully established from EGD forceps biopsies (n = 8) and surgical tissues (n = 7) from five patients with gastric adenocarcinoma. Low-coverage whole-genomic profiling of paired EGD and surgical PDOs along with whole-tumor lysates demonstrated absence of tumor heterogeneity. Nested PCR assay identified similar KRAS alterations in primary tumor and paired organoids. Drug sensitivity testing of endoscopic-derived PDOs displayed standard dose-response curves to current gastric cancer cytotoxic therapies.

CONCLUSIONS

Our study results demonstrate the feasibility of developing gastric cancer PDOs from EGD biopsies. These results also indicate that endoscopic-derived PDOs are accurate surrogates of the primary tumor and have the potential for drug sensitivity screening and personalized medicine applications.

A Chemosensitivity Study of Colorectal Cancer Using Xenografts of Patient-Derived Tumor-Initiating Cells

Current genomic and gene expression analyses provide versatile tools to improve cancer chemotherapy. However, it is still difficult to predict whether each patient responds to a particular regimen or not. To predict chemosensitivity in each patient with colorectal cancer, we developed an evaluation method using the primary tumor-initiating cells (TIC, aka cancer stem cells) xenografted in nude mice subcutaneously (patient-derived spheroid xenografts; PDSX). Simultaneously, we also prepared the conventional patient-derived xenografts (PDX) from the same patients' tumors and compared the dosing results with those of PDSXs. We further compared the chemosensitivities of PDSXs with those of 7 patients who had been given regimens such as FOLFOX and FOLFIRI to treat their metastatic lesions. As per the results, the PDSX method provided much more precise and predictable tumor growth with less variance than conventional PDX, although both retained the epithelial characteristics of the primary tumors. Likewise, drug-dosing tests showed essentially the same results in PDXs and PDSXs, with stronger statistical power in PDSXs. Notably, the cancer chemosensitivity in each patient was precisely reflected in that of the PDSX mice along the clinical course until the resistance emerged at the terminal stage. This "paraclinical" xenograft trials using PDSXs may help selection of chemotherapy regimens efficacious for each patient, and, more importantly, avoiding inefficient ones by which the patient can lose precious time and QOL. Furthermore, the PDSX method may be employed for evaluations of off-label uses of cancer chemotherapeutics and compassionate uses of yet-unapproved new drugs in personalized therapies. Mol Cancer Ther; 17(10); 2187-96. ©2018 AACR.

Preclinical optimization of antibody-based radiopharmaceuticals for cancer imaging and radionuclide therapy-Model, vector, and radionuclide selection

Intact antibodies and their truncated counterparts (eg, Fab, scFv fragments) are generally exquisitely specific and selective vectors, enabling recognition of individual cancer-associated molecular phenotypes against a complex and dynamic biomolecular background. Complementary alignment of these advantages with unique properties of radionuclides is a defining paradigm in both radioimmunoimaging and radioimmunotherapy, which remain some of the most adept and promising tools for cancer diagnosis and treatment. This review discusses how translational potency can be maximized through rational selection of antibody-nuclide couples for radioimmunoimaging/therapy in preclinical models.

Precision oncology in liver cancer

With the widespread adoption of molecular profiling in clinical oncology practice, many physicians are faced with making therapeutic decisions based upon isolated genomic alterations. For example, epidermal growth factor receptor tyrosine kinase inhibitors (TKIs) are effective in EGFR-mutant non-small cell lung cancers (NSCLC) while anti-EGFR monoclonal antibodies are ineffective in Ras-mutant colorectal cancers. The matching of mutations with drugs aimed at their respective gene products represents the current state of "precision" oncology. Despite the great expectations of this approach, only a fraction of cancers responds to 'targeted' interventions, and many early responders will ultimately develop resistance to these agents. The underwhelming success of mutation-driven therapies across all cancer types is not due to an inability to detect genetic changes in tumors; rather a deficit in functional insight into the genomic alterations that give rise to each cancer. The Achilles heel of precision oncology thus remains the lack of a robust functional understanding of an individual cancer genome that then allows prediction of the best therapy and resultant outcome for that patient. Current practice focuses on one 'actionable' mutation at a time, while solid cancers typically possess many mutations that involve different cellular sub-populations within a tumor. No method or platform currently exists to guide the interpretation of these complex data, nor to accurately predict response to treatment. This problem is particularly germane to primary liver cancers (PLC), for which only a handful of targeted therapies have been introduced. Here, we will review strategies aimed at overcoming some of these challenges in precision oncology, using liver cancer as an example.

Organoids in cancer research

The recent advances in in vitro 3D culture technologies, such as organoids, have opened new avenues for the development of novel, more physiological human cancer models. Such preclinical models are essential for more efficient translation of basic cancer research into novel treatment regimens for patients with cancer. Wild-type organoids can be grown from embryonic and adult stem cells and display self-organizing capacities, phenocopying essential aspects of the organs they are derived from. Genetic modification of organoids allows disease modelling in a setting that approaches the physiological environment. Additionally, organoids can be grown with high efficiency from patient-derived healthy and tumour tissues, potentially enabling patient-specific drug testing and the development of individualized treatment regimens. In this Review, we evaluate tumour organoid protocols and how they can be utilized as an alternative model for cancer research.

Preclinical models for precision oncology

Precision medicine approaches have revolutionized oncology. Personalized treatments require not only identification of the driving molecular alterations, but also development of targeted therapies and diagnostic tests to identify the appropriate patient populations for clinical trials and subsequent therapeutic implementation. Preclinical in vitro and in vivo models are widely used to predict efficacy of newly developed treatments. Here we discuss whether, and to what extent, preclinical models including cell lines, organoids and tumorgrafts recapitulate key features of human tumors. The potential of preclinical models to anticipate treatment efficacy and clinical benefit is also presented, using examples in different tumor types.

Patient derived organoids to model rare prostate cancer phenotypes

A major hurdle in the study of rare tumors is a lack of existing preclinical models. Neuroendocrine prostate cancer is an uncommon and aggressive histologic variant of prostate cancer that may arise de novo or as a mechanism of treatment resistance in patients with pre-existing castration-resistant prostate cancer. There are few available models to study neuroendocrine prostate cancer. Here, we report the generation and characterization of tumor organoids derived from needle biopsies of metastatic lesions from four patients. We demonstrate genomic, transcriptomic, and epigenomic concordance between organoids and their corresponding patient tumors. We utilize these organoids to understand the biologic role of the epigenetic modifier EZH2 in driving molecular programs associated with neuroendocrine prostate cancer progression. High-throughput organoid drug screening nominated single agents and drug combinations suggesting repurposing opportunities. This proof of principle study represents a strategy for the study of rare cancer phenotypes.

Cancer-predicting transcriptomic and epigenetic signatures revealed for ulcerative colitis in patient-derived epithelial organoids

Ulcerative colitis (UC) is a prevalent form of inflammatory bowel disease (IBD) whose pathogenic mechanisms remain unclear. Elucidating these mechanisms is important to reduce UC symptoms and to prevent UC progression into colitis-associated colon cancer (CAC). Our goal was to develop and validate faithful, human-derived, UC models and analyze them at histologic, transcriptomic and epigenetic levels to allow mechanistic studies of UC and CAC pathogenesis.

We generated patient-derived primary-organoid cultures from UC and non-IBD colonic epithelium. We phenotyped them histologically and used next-generation-sequencing approaches to profile whole transcriptomes and epigenomes of organoids and primary tissues.

Tissue organization and expression of mucin 2 (MUC2) and lysozyme (LYZ) demonstrated histologic faithfulness of organoids to healthy and diseased colonic epithelium. Transcriptomic analyses showed increased expression of inflammatory pathways in UC patient-derived organoids and tissues. Profiling for active enhancers using the H3K27ac histone modification revealed UC-derived organoid enrichment for pathways indicative of gastrointestinal cancer, including S100 calcium-binding protein P (S100P), and revealed novel markers for GI cancer, including both LYZ and neuropeptide S receptor 1 (NPSR1). Immunolocalization showed increased levels of LYZ, S100P, and NPSR1 proteins in UC and CAC.

In conclusion, primary colonic organoid cultures from UC and non-IBD patients can be established that faithfully represent diseased or normal colonic states. These models reveal precancerous molecular pathways that are already activated in UC. The findings demonstrate the suitability of primary organoids for dissecting UC and CAC pathogenic mechanisms and suggest new targets for therapeutic intervention.

Evaluation of anticancer agents using patient-derived tumor organoids characteristically similar to source tissues

Patient-derived tumor xenograft models represent a promising preclinical cancer model that better replicates disease, compared with traditional cell culture; however, their use is low-throughput and costly. To overcome this limitation, patient-derived tumor organoids (PDOs) were established from human lung, ovarian and uterine tumor tissues, among others, to accurately and efficiently recapitulate the tissue architecture and function. PDOs were able to be cultured for >6 months, and formed cell clusters with similar morphologies to their source tumors. Comparative histological and comprehensive gene expression analyses proved that the characteristics of PDOs were similar to those of their source tumors, even following long-term expansion in culture. At present, 53 PDOs have been established by the Fukushima Translational Research Project, and were designated as Fukushima PDOs (F-PDOs). In addition, the in vivo tumorigenesis of certain F-PDOs was confirmed using a xenograft model. The present study represents a detailed analysis of three F-PDOs (termed REME9, 11 and 16) established from endometrial cancer tissues. These were used for cell growth inhibition experiments using anticancer agents. A suitable high-throughput assay system, with 96- or 384-well plates, was designed for each F-PDO, and the efficacy of the anticancer agents was subsequently evaluated. REME9 and 11 exhibited distinct responses and increased resistance to the drugs, as compared with conventional cancer cell lines (AN3 CA and RL95-2). REME9 and 11, which were established from tumors that originated in patients who did not respond to paclitaxel and carboplatin (the standard chemotherapy for endometrial cancer), exhibited high resistance (half-maximal inhibitory concentration >10 µM) to the two agents. Therefore, assay systems using F-PDOs may be utilized to evaluate anticancer agents using conditions that better reflect clinical conditions, compared with conventional methods using cancer cell lines, and to discover markers that identify the pharmacological effects of anticancer agents.

Modelling glioma invasion using 3D bioprinting and scaffold-free 3D culture

Glioma is a highly aggressive form of brain cancer, with some subtypes having 5-year survival rates of less than 5%. Tumour cell invasion into the surrounding parenchyma seems to be the primary driver of these poor outcomes, as most gliomas recur within 2 cm of the original surgically-resected tumour. Many current approaches to the development of anticancer therapy attempt to target genetic weaknesses in a particular cancer, but may not take into account the microenvironment experienced by a tumour and the patient-specific genetic differences in susceptibility to treatment. Here we demonstrate the use of complementary approaches, 3D bioprinting and scaffold-free 3D tissue culture, to examine the invasion of glioma cells into neural-like tissue with 3D confocal microscopy. We found that, while both approaches were successful, the use of 3D tissue culture for organoid development offers the advantage of broad accessibility. As a proof-of-concept of our approach, we developed a system in which we could model the invasion of human glioma cells into mouse neural progenitor cell-derived spheroids. We show that we can follow invasion of human tumour cells using cell-tracking dyes and 3D laser scanning confocal microscopy, both in real time and in fixed samples. We validated these results using conventional cryosectioning. Our scaffold-free 3D approach has broad applicability, as we were easily able to examine invasion using different neural progenitor cell lines, thus mimicking differences that might be observed in patient brain tissue. These results, once applied to iPSC-derived cerebral organoids that incorporate the somatic genetic variability of patients, offer the promise of truly personalized treatments for brain cancer.

Transcriptional changes associated with in vivo growth of muscle-invasive bladder cancer cell lines in nude mice

Cancer cells set in motion transcriptomic programs allowing for adaptation and growth in immunocompromised mice to form xenografts, a frequently used tool in cancer research. 2D cultures may not be representative of tumors growing in a complex host microenvironment. This can result in different responses to the same agent tested in vitro and in vivo which impedes the process of developing novel therapeutics. Understanding the transition cells undergo from 2D cell culture to a 3D host microenvironment will help in developing and choosing appropriate models for pre-clinical studies. Our study characterized the transcriptome of a three frequently used muscle-invasive bladder cancer cell lines HT1376, T24 and UM-UC-3 grown in culture and xenografts in nude mice. We found that bladder cancer cells undergo few transcriptomic changes when transitioned from 2D cell culture to xenografts in nude mice. UM-UC-3 cells have the least transcriptomic alterations followed by T24 and HT1376 cells. Respective xenografts cluster with their parental cell lines rather than other xenografts or cell lines. We applied established bladder cancer molecular subtypes to our data and found that UM-UC-3, containing the least transcriptomic alterations, most closely resembled the basal-like molecular subtype of bladder cancer. HT1376 and T24 have mixed basal and luminal molecular signatures. Our studies suggest this subset of bladder cancer cell lines and derived xenografts maintain similar transcriptomic profiles in both 2D culture and 3D xenografts and can be used interchangeably in pre-clinical studies.

Toward A variable RBE for proton beam therapy

In the clinic, proton beam therapy (PBT) is based on the use of a generic relative biological effectiveness (RBE) of 1.1 compared to photons in human cancers and normal tissues. However, the experimental basis for this RBE lacks any significant number of representative tumor models and clinically relevant endpoints for dose-limiting organs at risk. It is now increasingly appreciated that much of the variations of treatment responses in cancers are due to inter-tumoral genomic heterogeneity. Indeed, recently it has been shown that defects in certain DNA repair pathways, which are found in subsets of many cancers, are associated with a RBE increase in vitro. However, there currently exist little in vivo or clinical data that confirm the existence of similarly increased RBE values in human cancers. Furthermore, evidence for variable RBE values for normal tissue toxicity has been sparse and conflicting to date. If we could predict variable RBE values in patients, we would be able to optimally use and personalize PBT. For example, predictive tumor biomarkers may facilitate selection of patients with proton-sensitive cancers previously ineligible for PBT. Dose de-escalation may be possible to reduce normal tissue toxicity, especially in pediatric patients. Knowledge of increased tumor RBE may allow us to develop biologically optimized therapies to enhance local control while RBE biomarkers for normal tissues could lead to a better understanding and prevention of unusual PBT-associated toxicity. Here, we will review experimental data on the repair of proton damage to DNA that impact both RBE values and biophysical modeling to predict RBE variations. Experimental approaches for studying proton sensitivity in vitro and in vivo will be reviewed as well and recent clinical findings discussed. Ultimately, therapeutically exploiting the understudied biological advantages of protons and developing approaches to limit treatment toxicity should fundamentally impact the clinical use of PBT.

Cancer Drug Response Profile scan (CDRscan): A Deep Learning Model That Predicts Drug Effectiveness from Cancer Genomic Signature

In the era of precision medicine, cancer therapy can be tailored to an individual patient based on the genomic profile of a tumour. Despite the ever-increasing abundance of cancer genomic data, linking mutation profiles to drug efficacy remains a challenge. Herein, we report Cancer Drug Response profile scan (CDRscan) a novel deep learning model that predicts anticancer drug responsiveness based on a large-scale drug screening assay data encompassing genomic profiles of 787 human cancer cell lines and structural profiles of 244 drugs. CDRscan employs a two-step convolution architecture, where the genomic mutational fingerprints of cell lines and the molecular fingerprints of drugs are processed individually, then merged by 'virtual docking', an in silico modelling of drug treatment. Analysis of the goodness-of-fit between observed and predicted drug response revealed a high prediction accuracy of CDRscan (R2 > 0.84; AUROC > 0.98). We applied CDRscan to 1,487 approved drugs and identified 14 oncology and 23 non-oncology drugs having new potential cancer indications. This, to our knowledge, is the first-time application of a deep learning model in predicting the feasibility of drug repurposing. By further clinical validation, CDRscan is expected to allow selection of the most effective anticancer drugs for the genomic profile of the individual patient.

Patient-derived Models Reveal Impact of the Tumor Microenvironment on Therapeutic Response

Background

Androgen deprivation therapy is a first-line treatment for disseminated prostate cancer (PCa). However, virtually all tumors become resistant and recur as castration-resistant PCa, which has no durable cure. One major hurdle in the development of more effective therapies is the lack of preclinical models that adequately recapitulate the heterogeneity of PCa, significantly hindering the ability to accurately predict therapeutic response.

Objective

To leverage the ex vivo culture method termed patient-derived explant (PDE) to examine the impact of PCa therapeutics on a patient-by-patient basis.

Design setting and participants

Fresh PCa tissue from patients who underwent radical prostatectomy was cultured as PDEs to examine therapeutic response.

Outcome measurements and statistical analysis

The impact of genomic and chemical perturbations in PDEs was assessed using various parameters (eg, AR levels, Ki67 staining, and desmoplastic indices).

Results and limitations

PDE maintained the integrity of the native tumor microenvironment (TME), tumor tissue morphology, viability, and endogenous hormone signaling. Tumor cells in this model system exhibited de novo proliferative capacity. Examination of the native TME in the PDE revealed a first-in-field insight into patient-specific desmoplastic stromal indices and predicted responsiveness to AR-directed therapeutics.

Conclusions

The PDE model allows for a comprehensive evaluation of individual tumors in their native TME to ultimately develop more effective therapeutic regimens tailored to individuals. Discernment of novel stromal markers may provide a basis for applying precision medicine in treating advanced PCa, which would have a transformative effect on patient outcomes.

Patient summary

In this study, an innovative model system was used to more effectively mimic human disease. The patient-derived explant (PDE) system can be used to predict therapeutic response and identify novel targets in advanced disease. Thus, the PDE will be an asset for the development of novel metrics for the implementation of precision medicine in prostate cancer.The patient-derived explant (PDE) model allows for a comprehensive evaluation of individual human tumors in their native tumor microenvironment (TME). TME analysis revealed first-in-field insight into predicted tumor responsiveness to AR-directed therapeutics through evaluation of patient-specific desmoplastic stromal indices.

Breast cancer development and progression: Risk factors, cancer stem cells, signaling pathways, genomics, and molecular pathogenesis

As the most commonly occurring cancer in women worldwide, breast cancer poses a formidable public health challenge on a global scale. Breast cancer consists of a group of biologically and molecularly heterogeneous diseases originated from the breast. While the risk factors associated with this cancer varies with respect to other cancers, genetic predisposition, most notably mutations in BRCA1 or BRCA2 gene, is an important causative factor for this malignancy. Breast cancers can begin in different areas of the breast, such as the ducts, the lobules, or the tissue in between. Within the large group of diverse breast carcinomas, there are various denoted types of breast cancer based on their invasiveness relative to the primary tumor sites. It is important to distinguish between the various subtypes because they have different prognoses and treatment implications. As there are remarkable parallels between normal development and breast cancer progression at the molecular level, it has been postulated that breast cancer may be derived from mammary cancer stem cells. Normal breast development and mammary stem cells are regulated by several signaling pathways, such as estrogen receptors (ERs), HER2, and Wnt/β-catenin signaling pathways, which control stem cell proliferation, cell death, cell differentiation, and cell motility. Furthermore, emerging evidence indicates that epigenetic regulations and noncoding RNAs may play important roles in breast cancer development and may contribute to the heterogeneity and metastatic aspects of breast cancer, especially for triple-negative breast cancer. This review provides a comprehensive survey of the molecular, cellular and genetic aspects of breast cancer.