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

The OVAREX study: Establishment of ex vivo ovarian cancer models to validate innovative therapies and to identify predictive biomarkers

BACKGROUND

Ovarian cancer is the first cause of death from gynecological malignancies mainly due to development of chemoresistance. Despite the emergence of PARP inhibitors, which have revolutionized the therapeutic management of some of these ovarian cancers, the 5-year overall survival rate remains around 45%. Therefore, it is crucial to develop new therapeutic strategies, to identify predictive biomarkers and to predict the response to treatments. In this context, functional assays based on patient-derived tumor models could constitute helpful and relevant tools for identifying efficient therapies or to guide clinical decision making.

METHOD

The OVAREX study is a single-center non-interventional study which aims at investigating the feasibility of establishing in vivo and ex vivo models and testing ex vivo models to predict clinical response of ovarian cancer patients. Patient-Derived Xenografts (PDX) will be established from tumor fragments engrafted subcutaneously into immunocompromised mice. Explants will be generated by slicing tumor tissues and Ascites-Derived Spheroids (ADS) will be isolated following filtration of ascites. Patient-derived tumor organoids (PDTO) will be established after dissociation of tumor tissues or ADS, cell embedding into extracellular matrix and culture in specific medium. Molecular and histological characterizations will be performed to compare tumor of origin and paired models. Response of ex vivo tumor-derived models to conventional chemotherapy and PARP inhibitors will be assessed and compared to results of companion diagnostic test and/or to the patient's response to evaluate their predictive value.

DISCUSSION

This clinical study aims at generating PDX and ex vivo models (PDTO, ADS, and explants) from tumors or ascites of ovarian cancer patients who will undergo surgical procedure or paracentesis. We aim at demonstrating the predictive value of ex vivo models for their potential use in routine clinical practice as part of precision medicine, as well as establishing a collection of relevant ovarian cancer models that will be useful for the evaluation of future innovative therapies.

TRIAL REGISTRATION

The clinical trial has been validated by local research ethic committee on January 25th 2019 and registered at ClinicalTrials.gov with the identifier NCT03831230 on January 28th 2019, last amendment v4 accepted on July 18, 2023.

An optimised patient-derived explant platform for breast cancer reflects clinical responses to chemotherapy and antibody-directed therapy

Breast Cancer is the most common cancer among women globally. Despite significant improvements in overall survival, many tumours are refractory to therapy and so novel approaches are required to improve patient outcomes. We have evaluated patient-derived explants (PDEs) as a novel preclinical platform for breast cancer (BC) and implemented cutting-edge digital pathology and multi-immunofluorescent approaches for investigating biomarker changes in both tumour and stromal areas at endpoint. Short-term culture of intact fragments of BCs as PDEs retained an intact immune microenvironment, and tumour architecture was augmented by the inclusion of autologous serum in the culture media. Cell death/proliferation responses to FET chemotherapy in BC-PDEs correlated significantly with BC patient progression-free survival (p = 0.012 and p = 0.0041, respectively) and cell death responses to the HER2 antibody therapy trastuzumab correlated significantly with HER2 status (p = 0.018). These studies show that the PDE platform combined with digital pathology is a robust preclinical approach for informing clinical responses to chemotherapy and antibody-directed therapies in breast cancer. Furthermore, since BC-PDEs retain an intact tumour architecture over the short-term, they facilitate the preclinical testing of anti-cancer agents targeting the tumour microenvironment.

Ex vivo therapeutic screening of metastatic cSCC: A review of methodological considerations for clinical implementation

Cutaneous squamous cell carcinoma (cSCC) is the second most common malignancy worldwide, with most deaths caused by locally advanced and metastatic disease. Treatment of resectable metastases is typically limited to invasive surgery with adjuvant radiotherapy; however, many patients fail to respond and there is minimal data to predict response or propose effective alternatives. Precision medicine could improve this, though genomic biomarkers remain elusive in the high mutational background and genomic complexity of cSCC. A phenotypic approach to precision medicine using patient-derived ex vivo tumour models is gaining favour for its capacity to directly assess biological responses to therapeutics as a functional, predictive biomarker. However, the use of ex vivo models for guiding therapeutic selection has yet to be employed for metastatic cSCC. This review will therefore evaluate the existing experimental models of metastatic cSCC and discuss how ex vivo methods could overcome the shortcomings of these existing models. Disease-specific considerations for a prospective methodological pipeline will also be discussed in the context of precision medicine.

Bioengineered hydrogels enhance ex vivo preservation of patient-derived tumor explants for drug evaluation

Ex vivo patient-derived tumor slices (PDTS) are currently limited by short-term viability in culture. Here, we show how bioengineered hydrogels enable the identification of key matrix parameters that significantly enhance PDTS viability compared to conventional culture systems. As demonstrated using single-cell RNA sequencing and high-dimensional flow cytometry, hydrogel-embedded PDTS tightly preserved cancer, cancer-associated fibroblast, and various immune cell populations and subpopulations in the corresponding original tumor. Cell-cell communication networks within the tumor microenvironment, including immune checkpoint ligand-receptor interactions, were also maintained. Remarkably, our results from a co-clinical trial suggest hydrogel-embedded PDTS may predict sensitivity to immune checkpoint inhibitors (ICIs) in head and neck cancer patients. Further, we show how these longer term-cultured tumor explants uniquely enable the sampling and detection of temporal evolution in molecular readouts when treated with ICIs. By preserving the compositional heterogeneity and complexity of patient tumors, hydrogel-embedded PDTS provide a valuable tool to facilitate experiments targeting the tumor microenvironment.

Rebuilding the microenvironment of primary tumors in humans: a focus on stroma

Conventional tumor models have critical shortcomings in that they lack the complexity of the human stroma. The heterogeneous stroma is a central compartment of the tumor microenvironment (TME) that must be addressed in cancer research and precision medicine. To fully model the human tumor stroma, the deconstruction and reconstruction of tumor tissues have been suggested as new approaches for in vitro tumor modeling. In this review, we summarize the heterogeneity of tumor-associated stromal cells and general deconstruction approaches used to isolate patient-specific stromal cells from tumor tissue; we also address the effect of the deconstruction procedure on the characteristics of primary cells. Finally, perspectives on the future of reconstructed tumor models are discussed, with an emphasis on the essential prerequisites for developing authentic humanized tumor models.

Urine-derived bladder cancer organoids (urinoids) as a tool for cancer longitudinal response monitoring and therapy adaptation

BACKGROUND

Bladder cancer is one of the most common cancer types worldwide. Generally, research relies on invasive sampling strategies.

METHODS

Here, we generate bladder cancer organoids directly from urine (urinoids). In this project, we establish 12 urinoid lines from 22 patients with non-muscle and muscle-invasive bladder tumours, with an efficiency of 55%.

RESULTS

The histopathological features of the urinoids accurately resemble those of the original bladder tumours. Genetically, there is a high concordance of single nucleotide polymorphisms (92.56%) and insertions & deletions (91.54%) between urinoids and original tumours from patient 4. Furthermore, these urinoids show sensitivity to bladder cancer drugs, similar to their tissue-derived organoid counterparts. Genetic analysis of longitudinally generated tumoroids and urinoids from one patient receiving systemic immunotherapy, identify alterations that may guide the choice for second-line therapy. Successful treatment adaptation was subsequently demonstrated in the urinoid setting.

CONCLUSION

Therefore, urinoids can advance precision medicine in bladder cancer as a non-invasive platform for tumour pathogenesis, longitudinal drug-response monitoring, and therapy adaptation.

Multiplex miRNA reporting platform for real-time profiling of living cells

Accurately characterizing cell types within complex cell structures provides invaluable information for comprehending the cellular status during biological processes. In this study, we have developed an miRNA-switch cocktail platform capable of reporting and tracking the activities of multiple miRNAs (microRNAs) at the single-cell level, while minimizing disruption to the cell culture. Drawing on the principles of traditional miRNA-sensing mRNA switches, our platform incorporates subcellular tags and employs intelligent engineering to segment three subcellular regions using two fluorescent proteins. These designs enable the quantification of multiple miRNAs within the same cell. Through our experiments, we have demonstrated the platform's ability to track marker miRNA levels during cell differentiation and provide spatial information of heterogeneity on outlier cells exhibiting extreme miRNA levels. Importantly, this platform offers real-time and in situ miRNA reporting, allowing for multidimensional evaluation of cell profile and paving the way for a comprehensive understanding of cellular events during biological processes.

Differential Expression of SRY-Related HMG-Box Transcription Factor 2, Oligodendrocyte Lineage Transcription Factor 2, and Zinc Finger E-Box Binding Homeobox 1 in Serum-Derived Extracellular Vesicles: Implications for Mithramycin Sensitivity and Targeted Therapy in High-Grade Glioma

Glioblastoma multiforme (GBM) is the most aggressive type of glioma and is often resistant to traditional therapies. Evidence suggests that glioma stem cells (GSCs) contribute to this resistance. Mithramycin (Mit-A) targets GSCs and exhibits antitumor activity in GBM by affecting transcriptional targets such as SRY-related HMG-box transcription factor 2 (SOX2), oligodendrocyte lineage transcription factor 2 (OLIG2), and zinc finger E-box binding homeobox 1 (ZEB1). However, its clinical use has been limited by toxicity. This study explored the diagnostic potential of serum extracellular vesicles (EVs) to identify Mit-A responders. Serum EVs were isolated from 70 glioma patients, and targeted gene expression was analyzed using qRT-PCR. Using chemosensitivity assay, we identified 8 Mit-A responders and 17 nonresponders among 25 glioma patients. The M-score showed a significant correlation (p = 0.045) with isocitrate dehydrogenase 1 mutation but not other clinical variables. The genes SOX2 (p = 0.005), OLIG2 (p = 0.003), and ZEB1 (p = 0.0281) were found to be upregulated in the responder EVs. SOX2 had the highest diagnostic potential (AUC = 0.875), followed by OLIG2 (AUC = 0.772) and ZEB1 (AUC = 0.632).The combined gene panel showed significant diagnostic efficacy (AUC = 0.956) through logistic regression analysis. The gene panel was further validated in the serum EVs of 45 glioma patients. These findings highlight the potential of Mit-A as a targeted therapy for high-grade glioma based on differential gene expression in serum EVs. The gene panel could serve as a diagnostic tool to predict Mit-A sensitivity, offering a promising approach for personalized treatment strategies and emphasizing the role of GSCs in therapeutic resistance.

Lupeol synergizes with 5-fluorouracil to combat c-MET/EphA2 mediated chemoresistance in triple negative breast cancer

Triple-negative breast cancer (TNBC) is the most elusive subtype of breast cancer that encounters treatment dilemmas owing to the paucity of druggable targets. We found hyperactivation of c-MET and ephrin type-A receptor 2 (EphA2) in patients treated with 5FU driven chemotherapy which correlated with lower disease-free survival. However, silencing of both these genes resulted in a marked decrease in the invasive, migratory, and tumorigenic potential of TNBC cells, indicating that a dual target strategy is actionable. Lupeol is a phytochemical, with potent anticancer efficacy and minimal side effects in preclinical studies. A synergistic strategy with 5FU and Lupeol elicited promising anticancer responses in vitro, in vivo, and in patient-derived ex vivo tumor culture models. This synergistic regimen is effective, even in the presence of HGF, which mechanistically orchestrates the activation of c-MET and EphA2. These data lay the foundation for the clinical validation of this combination therapy for TNBC patients.

Breast Cancer Tissue Explants: An Approach to Develop Personalized Therapy in Public Health Services

Breast cancer is one of the main causes of death worldwide. Lately, there is great interest in developing methods that assess individual sensitivity and/or resistance of tumors to antineoplastics to provide personalized therapy for patients. In this study we used organotypic culture of human breast tumor slices to predict the experimental effect of antineoplastics on the viability of tumoral tissue. Samples of breast tumor were taken from 27 patients with clinically advanced breast cancer; slices were obtained and incubated separately for 48 h with paclitaxel, docetaxel, epirubicin, 5-fluorouracil, cyclophosphamide, and cell culture media (control). We determined an experimental tumor sensitivity/resistance (S/R) profile by evaluating tissue viability using the Alamar Blue® metabolic test, and by structural viability (histopathological analyses, necrosis, and inflammation). These parameters were related to immunohistochemical expression of the estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2. The predominant histological type found was infiltrating ductal carcinoma (85.2%), followed by lobular carcinoma (7.4%) and mixed carcinoma (7.4%). Experimental drug resistance was related to positive hormone receptor status in 83% of samples treated with cyclophosphamide (p = 0.027). Results suggest that the tumor S/R profile can help to predict personalized therapy or optimize chemotherapeutic treatments in breast cancer.

Use of patient-derived explants as a preclinical model for precision medicine in colorectal cancer: A scoping review

PURPOSE

Whilst the treatment paradigm for colorectal cancer has evolved significantly over time, there is still a lack of reliable biomarkers of treatment response. Treatment decisions are based on high-risk features such as advanced TNM stage and histology. The role of the tumour microenvironment, which can influence tumour progression and treatment response, has generated considerable interest. Patient-derived explant cultures allow preservation of native tissue architecture and tumour microenvironment. The aim of the scoping review is to evaluate the utility of patient-derived explant cultures as a preclinical model in colorectal cancer.

METHODS

A search was conducted using Ovid MEDLINE, EMBASE, Web of Science, and Cochrane databases from start of database records to September 1, 2022. We included all peer-reviewed human studies in English language which used patient-derived explants as a preclinical model in primary colorectal cancer. Eligible studies were grouped into the following categories: assessing model feasibility; exploring tumour microenvironment; assessing ex vivo drug responses; discovering and validating biomarkers.

RESULTS

A total of 60 studies were eligible. Fourteen studies demonstrated feasibility of using patient-derived explants as a preclinical model. Ten studies explored the tumour microenvironment. Thirty-eight studies assessed ex vivo drug responses of chemotherapy agents and targeted therapies. Twenty-four studies identified potential biomarkers of treatment response.

CONCLUSIONS

Given the preservation of tumour microenvironment and tumour heterogeneity, patient-derived explants has the potential to identify reliable biomarkers, treatment resistance mechanisms, and novel therapeutic agents. Further validation studies are required to characterise, refine and standardise this preclinical model before it can become a part of precision medicine in colorectal cancer.

A clinical evaluation of an ex vivo organ culture system to predict patient response to cancer therapy

Introduction

Ex vivo organ cultures (EVOC) were recently optimized to sustain cancer tissue for 5 days with its complete microenvironment. We examined the ability of an EVOC platform to predict patient response to cancer therapy.

Methods

A multicenter, prospective, single-arm observational trial. Samples were obtained from patients with newly diagnosed bladder cancer who underwent transurethral resection of bladder tumor and from core needle biopsies of patients with metastatic cancer. The tumors were cut into 250 μM slices and cultured within 24 h, then incubated for 96 h with vehicle or intended to treat drug. The cultures were then fixed and stained to analyze their morphology and cell viability. Each EVOC was given a score based on cell viability, level of damage, and Ki67 proliferation, and the scores were correlated with the patients' clinical response assessed by pathology or Response Evaluation Criteria in Solid Tumors (RECIST).

Results

The cancer tissue and microenvironment, including endothelial and immune cells, were preserved at high viability with continued cell division for 5 days, demonstrating active cell signaling dynamics. A total of 34 cancer samples were tested by the platform and were correlated with clinical results. A higher EVOC score was correlated with better clinical response. The EVOC system showed a predictive specificity of 77.7% (7/9, 95% CI 0.4-0.97) and a sensitivity of 96% (24/25, 95% CI 0.80-0.99).

Conclusion

EVOC cultured for 5 days showed high sensitivity and specificity for predicting clinical response to therapy among patients with muscle-invasive bladder cancer and other solid tumors.

Preclinical studies performed in appropriate models could help identify optimal timing of combined chemotherapy and immunotherapy

Immune checkpoint inhibitors (ICI) have been revolutionary in the field of cancer therapy. However, their success is limited to specific indications and cancer types. Recently, the combination treatment of ICI and chemotherapy has gained more attention to overcome this limitation. Unfortunately, many clinical trials testing these combinations have provided limited success. This can partly be attributed to an inadequate choice of preclinical models and the lack of scientific rationale to select the most effective immune-oncological combination. In this review, we have analyzed the existing preclinical evidence on this topic, which is only limitedly available. Furthermore, this preclinical data indicates that besides the selection of a specific drug and dose, also the sequence or order of the combination treatment influences the study outcome. Therefore, we conclude that the success of clinical combination trials could be enhanced by improving the preclinical set up, in order to identify the optimal treatment combination and schedule to enhance the anti-tumor immunity.

Combination of Genomic Landsscape and 3D Culture Functional Assays Bridges Sarcoma Phenotype to Target and Immunotherapy

Genomic-based precision medicine has not only improved tumour therapy but has also shown its weaknesses. Genomic profiling and mutation analysis have identified alterations that play a major role in sarcoma pathogenesis and evolution. However, they have not been sufficient in predicting tumour vulnerability and advancing treatment. The relative rarity of sarcomas and the genetic heterogeneity between subtypes also stand in the way of gaining statistically significant results from clinical trials. Personalized three-dimensional tumour models that reflect the specific histologic subtype are emerging as functional assays to test anticancer drugs, complementing genomic screening. Here, we provide an overview of current target therapy for sarcomas and discuss functional assays based on 3D models that, by recapitulating the molecular pathways and tumour microenvironment, may predict patient response to treatments. This approach opens new avenues to improve precision medicine when genomic and pathway alterations are not sufficient to guide the choice of the most promising treatment. Furthermore, we discuss the aspects of the 3D culture assays that need to be improved, such as the standardisation of growth conditions and the definition of in vitro responses that can be used as a cut-off for clinical implementation.

Lupeol and Paclitaxel cooperate in hindering hypoxia induced vasculogenic mimicry via suppression of HIF-1α-EphA2-Laminin-5γ2 network in human oral cancer

Vasculogenic mimicry (VM), defined as an endothelial cell independent alternative mechanism of blood and nutrient supply by dysregulated tumor cells, is associated with poor prognosis in oral squamous cell carcinoma (OSCC). Here we aim to investigate the underlying molecular mechanism of the synergistic effect of phytochemical Lupeol and standard microtubule inhibitor Paclitaxel in reversing the hypoxia induced VM formation in OSCC. The results demonstrated that the hypoxia induced upregulation of HIF-1α led to augmentation of signaling cascade associated with extracellular matrix remodeling and EMT phenotypes that are mechanistically linked to VM. Induction of HIF-1α altered the expression of EMT/CSC markers (E-Cadherin, Vimentin, Snail, Twist and CD133) and enhanced the ability of cell migration/invasion and spheroid formation. Subsequently, the targeted knockdown of HIF-1α by siRNA led to the perturbation of matrigel mediated tube formation as well as of Laminin-5γ2 expression with the down-regulation of VE-Cadherin, total and phosphorylated (S-897) EphA2, pERK1/2 and MMP2. We also observed that Lupeol in association with Paclitaxel resulted to apoptosis and the disruption of VM associated phenotypes in vitro. We further validated the impact of this novel interventional approach in a patient derived tumor explant culture model of oral malignancy. The ex vivo tumor model mimicked the in vitro anti-VM potential of Lupeol-Paclitaxel combination through down-regulating HIF-1α/EphA2/Laminin-5γ2 cascade. Together, our findings elucidated mechanistic underpinning of hypoxia induced Laminin-5γ2 driven VM formation highlighting that Lupeol-Paclitaxel combination may serve as novel therapeutic intervention in perturbation of VM in human OSCC.

Patient-Derived Ex Vivo Cultures and Endpoint Assays with Surrogate Biomarkers in Functional Testing for Prediction of Therapeutic Response

Prediction of therapeutic outcomes is important for cancer patients in order to reduce side effects and improve the efficacy of anti-cancer drugs. Currently, the most widely accepted method for predicting the efficacy of anti-cancer drugs is gene panel testing based on next-generation sequencing. However, gene panel testing has several limitations. For example, only 10% of cancer patients are estimated to have druggable mutations, even if whole-exome sequencing is applied. Additionally, even if optimal drugs are selected, a significant proportion of patients derive no benefit from the indicated drug treatment. Furthermore, most of the anti-cancer drugs selected by gene panel testing are molecularly targeted drugs, and the efficacies of cytotoxic drugs remain difficult to predict. Apart from gene panel testing, attempts to predict chemotherapeutic efficacy using ex vivo cultures from cancer patients have been increasing. Several groups have retrospectively demonstrated correlations between ex vivo drug sensitivity and clinical outcome. For ex vivo culture, surgically resected tumor tissue is the most abundant source. However, patients with recurrent or metastatic tumors do not usually undergo surgery, and chemotherapy may be the only option for those with inoperable tumors. Therefore, predictive methods using small amounts of cancer tissue from diagnostic materials such as endoscopic, fine-needle aspirates, needle cores and liquid biopsies are needed. To achieve this, various types of ex vivo culture and endpoint assays using effective surrogate biomarkers of drug sensitivity have recently been developed. Here, we review the variety of ex vivo cultures and endpoint assays currently available.

Organ-on-a-chip meets artificial intelligence in drug evaluation

Drug evaluation has always been an important area of research in the pharmaceutical industry. However, animal welfare protection and other shortcomings of traditional drug development models pose obstacles and challenges to drug evaluation. Organ-on-a-chip (OoC) technology, which simulates human organs on a chip of the physiological environment and functionality, and with high fidelity reproduction organ-level of physiology or pathophysiology, exhibits great promise for innovating the drug development pipeline. Meanwhile, the advancement in artificial intelligence (AI) provides more improvements for the design and data processing of OoCs. Here, we review the current progress that has been made to generate OoC platforms, and how human single and multi-OoCs have been used in applications, including drug testing, disease modeling, and personalized medicine. Moreover, we discuss issues facing the field, such as large data processing and reproducibility, and point to the integration of OoCs and AI in data analysis and automation, which is of great benefit in future drug evaluation. Finally, we look forward to the opportunities and challenges faced by the coupling of OoCs and AI. In summary, advancements in OoCs development, and future combinations with AI, will eventually break the current state of drug evaluation.

Activating Inducible T-cell Costimulator Yields Antitumor Activity Alone and in Combination with Anti-PD-1 Checkpoint Blockade

In recent years, there has been considerable interest in mAb-based induction of costimulatory receptor signaling as an approach to combat cancer. However, promising nonclinical data have yet to translate to a meaningful clinical benefit. Inducible T-cell costimulator (ICOS) is a costimulatory receptor important for immune responses. Using a novel clinical-stage anti-ICOS immunoglobulin G4 mAb (feladilimab), which induces but does not deplete ICOS+ T cells and their rodent analogs, we provide an end-to-end evaluation of the antitumor potential of antibody-mediated ICOS costimulation alone and in combination with programmed cell death protein 1 (PD-1) blockade. We demonstrate, consistently, that ICOS is expressed in a range of cancers, and its induction can stimulate growth of antitumor reactive T cells. Furthermore, feladilimab, alone and with a PD-1 inhibitor, induced antitumor activity in mouse and humanized tumor models. In addition to nonclinical evaluation, we present three patient case studies from a first-time-in-human, phase I, open-label, dose-escalation and dose-expansion clinical trial (INDUCE-1; ClinicalTrials.gov: NCT02723955), evaluating feladilimab alone and in combination with pembrolizumab in patients with advanced solid tumors. Preliminary data showing clinical benefit in patients with cancer treated with feladilimab alone or in combination with pembrolizumab was reported previously; with example cases described here. Additional work is needed to further validate the translation to the clinic, which includes identifying select patient populations that will benefit from this therapeutic approach, and randomized data with survival endpoints to illustrate its potential, similar to that shown with CTLA-4 and PD-1 blocking antibodies.

Significance

Stimulation of the T-cell activation marker ICOS with the anti-ICOS agonist mAb feladilimab, alone and in combination with PD-1 inhibition, induces antitumor activity across nonclinical models as well as select patients with advanced solid tumors.

Gynecologic and Breast Cancers: What's New in Chemoresistance and Chemosensitivity Tests?

Gynecological and breast cancers affect women's health worldwide. Although chemotherapy is one of the principal treatments for cancer, it also has limitations owing to toxicity and tumor resistance to the drugs used. Thus, individualized treatment based on personal tumor characteristics is essential for improving therapeutic outcomes and patient survival. Chemoresistance and chemosensitivity tests can be useful for predicting tumor response and guiding chemotherapy choices. This methodology has already been applied to breast, ovarian, cervical, and endometrial cancers, identifying successfully which drugs cause resistance and sensitivity responses for each individual person, influencing their progression-free survival and overall response. In addition, more recent techniques, such as organoids and patient-derived xenografts, can also recapitulate patients' tumor characteristics and contribute to chemo response evaluation. Therefore, this review compiles information on chemoresistance and chemosensitivity tests performed in gynecologic and breast cancers and their main results for women's health improvement.

A living ex vivo platform for functional, personalized brain cancer diagnosis

Functional precision medicine platforms are emerging as promising strategies to improve pre-clinical drug testing and guide clinical decisions. We have developed an organotypic brain slice culture (OBSC)-based platform and multi-parametric algorithm that enable rapid engraftment, treatment, and analysis of uncultured patient brain tumor tissue and patient-derived cell lines. The platform has supported engraftment of every patient tumor tested to this point: high- and low-grade adult and pediatric tumor tissue rapidly establishes on OBSCs among endogenous astrocytes and microglia while maintaining the tumor's original DNA profile. Our algorithm calculates dose-response relationships of both tumor kill and OBSC toxicity, generating summarized drug sensitivity scores on the basis of therapeutic window and allowing us to normalize response profiles across a panel of U.S. Food and Drug Administration (FDA)-approved and exploratory agents. Summarized patient tumor scores after OBSC treatment show positive associations to clinical outcomes, suggesting that the OBSC platform can provide rapid, accurate, functional testing to ultimately guide patient care.

Organotypic Models for Functional Drug Testing of Human Cancers

In the era of personalized oncology, there have been accelerated efforts to develop clinically relevant platforms to test drug sensitivities of individual cancers. An ideal assay will serve as a diagnostic companion to inform the oncologist of the various treatments that are sensitive and insensitive, thus improving outcome while minimizing unnecessary toxicities and costs. To date, no such platform exists for clinical use, but promising approaches are on the horizon that take advantage of improved techniques in creating human cancer models that encompass the entire tumor microenvironment, alongside technologies for assessing and analyzing tumor response. This review summarizes a number of current strategies that make use of intact human cancer tissues as organotypic cultures in drug sensitivity testing.

Making In Vitro Tumor Models Whole Again

As a reductionist approach, patient-derived in vitro tumor models are inherently still too simplistic for personalized drug testing as they do not capture many characteristics of the tumor microenvironment (TME), such as tumor architecture and stromal heterogeneity. This is especially problematic for assessing stromal-targeting drugs such as immunotherapies in which the density and distribution of immune and other stromal cells determine drug efficacy. On the other end, in vivo models are typically costly, low-throughput, and time-consuming to establish. Ex vivo patient-derived tumor explant (PDE) cultures involve the culture of resected tumor fragments that potentially retain the intact  TME of the original tumor. Although developed decades ago, PDE cultures have not been widely adopted likely because of their low-throughput and poor long-term viability. However, with growing recognition of the importance of patient-specific TME in mediating drug response, especially in the field of immune-oncology, there is an urgent need to resurrect these holistic cultures. In this Review, the key limitations of patient-derived tumor explant cultures are outlined and technologies that have been developed or could be employed to address these limitations are discussed. Engineered holistic tumor explant cultures may truly realize the concept of personalized medicine for cancer patients.

Mechanobiology of cancer cell responsiveness to chemotherapy and immunotherapy: Mechanistic insights and biomaterial platforms

Mechanical forces are central to how cancer treatments such as chemotherapeutics and immunotherapies interact with cells and tissues. At the simplest level, electrostatic forces underlie the binding events that are critical to therapeutic function. However, a growing body of literature points to mechanical factors that also affect whether a drug or an immune cell can reach a target, and to interactions between a cell and its environment affecting therapeutic efficacy. These factors affect cell processes ranging from cytoskeletal and extracellular matrix remodeling to transduction of signals by the nucleus to metastasis of cells. This review presents and critiques the state of the art of our understanding of how mechanobiology impacts drug and immunotherapy resistance and responsiveness, and of the in vitro systems that have been of value in the discovery of these effects.

Organoids as an Enabler of Precision Immuno-Oncology

Since the dawn of the past century, landmark discoveries in cell-mediated immunity have led to a greater understanding of the innate and adaptive immune systems and revolutionised the treatment of countless diseases, including cancer. Today, precision immuno-oncology (I/O) involves not only targeting immune checkpoints that inhibit T-cell immunity but also harnessing immune cell therapies. The limited efficacy in some cancers results mainly from a complex tumour microenvironment (TME) that, in addition to adaptive immune cells, comprises innate myeloid and lymphoid cells, cancer-associated fibroblasts, and the tumour vasculature that contribute towards immune evasion. As the complexity of TME has called for more sophisticated human-based tumour models, organoids have allowed the dynamic study of spatiotemporal interactions between tumour cells and individual TME cell types. Here, we discuss how organoids can study the TME across cancers and how these features may improve precision I/O. We outline the approaches to preserve or recapitulate the TME in tumour organoids and discuss their potential, advantages, and limitations. We will discuss future directions of organoid research in understanding cancer immunology in-depth and identifying novel I/O targets and treatment strategies.

Functional precision oncology using patient-derived assays: bridging genotype and phenotype

Genomics-based precision medicine has revolutionized oncology but also has inherent limitations. Functional precision oncology is emerging as a complementary approach that aims to bridge the gap between genotype and phenotype by modelling individual tumours in vitro. These patient-derived ex vivo models largely preserve several tumour characteristics that are not captured by genomics approaches and enable the functional dissection of tumour vulnerabilities in a personalized manner. In this Review, we discuss several examples of personalized functional assays involving tumour organoids, spheroids and explants and their potential to predict treatment responses and drug-induced toxicities in individual patients. These developments have opened exciting new avenues for precision oncology, with the potential for successful clinical applications in contexts in which genomic data alone are not informative. To implement these assays into clinical practice, we outline four key barriers that need to be overcome: assay success rates, turnaround times, the need for standardized conditions and the definition of in vitro responders. Furthermore, we discuss novel technological advances such as microfluidics that might reduce sample requirements, assay times and labour intensity and thereby enable functional precision oncology to be implemented in routine clinical practice.

Perfusion Air Culture of Precision-Cut Tumor Slices: An Ex Vivo System to Evaluate Individual Drug Response under Controlled Culture Conditions

Precision-cut tumor slices (PCTS) maintain tissue heterogeneity concerning different cell types and preserve the tumor microenvironment (TME). Typically, PCTS are cultured statically on a filter support at an air-liquid interface, which gives rise to intra-slice gradients during culture. To overcome this problem, we developed a perfusion air culture (PAC) system that can provide a continuous and controlled oxygen medium, and drug supply. This makes it an adaptable ex vivo system for evaluating drug responses in a tissue-specific microenvironment. PCTS from mouse xenografts (MCF-7, H1437) and primary human ovarian tumors (primary OV) cultured in the PAC system maintained the morphology, proliferation, and TME for more than 7 days, and no intra-slice gradients were observed. Cultured PCTS were analyzed for DNA damage, apoptosis, and transcriptional biomarkers for the cellular stress response. For the primary OV slices, cisplatin treatment induced a diverse increase in the cleavage of caspase-3 and PD-L1 expression, indicating a heterogeneous response to drug treatment between patients. Immune cells were preserved throughout the culturing period, indicating that immune therapy can be analyzed. The novel PAC system is suitable for assessing individual drug responses and can thus be used as a preclinical model to predict in vivo therapy responses.

Advancements in melanoma cancer metastasis models

Metastatic melanoma is a complex and deadly disease. Due to its complexity, the development of novel therapeutic strategies to inhibit metastatic melanoma remains an outstanding challenge. Our ability to study metastasis is advanced with the development of in vitro and in vivo models that better mimic the different steps of the metastatic cascade beginning from primary tumor initiation to final metastatic seeding. In this review, we provide a comprehensive summary of in vitro models, in vivo models, and in silico platforms to study the individual steps of melanoma metastasis. Furthermore, we highlight the advantages and limitations of each model and discuss the challenges of how to improve current models to enhance translation for melanoma cancer patients and future therapies.

Pixelated Microfluidics for Drug Screening on Tumour Spheroids and Ex Vivo Microdissected Tumour Explants

Anticancer drugs have the lowest success rate of approval in drug development programs. Thus, preclinical assays that closely predict the clinical responses to drugs are of utmost importance in both clinical oncology and pharmaceutical research. 3D tumour models preserve the tumoral architecture and are cost- and time-efficient. However, the short-term longevity, limited throughput, and limitations of live imaging of these models have so far driven researchers towards less realistic tumour models such as monolayer cell cultures. Here, we present an open-space microfluidic drug screening platform that enables the formation, culture, and multiplexed delivery of several reagents to various 3D tumour models, namely cancer cell line spheroids and ex vivo primary tumour fragments. Our platform utilizes a microfluidic pixelated chemical display that creates isolated adjacent flow sub-units of reagents, which we refer to as fluidic 'pixels', over tumour models in a contact-free fashion. Up to nine different treatment conditions can be tested over 144 samples in a single experiment. We provide a proof-of-concept application by staining fixed and live tumour models with multiple cellular dyes. Furthermore, we demonstrate that the response of the tumour models to biological stimuli can be assessed using the platform. Upscaling the microfluidic platform to larger areas can lead to higher throughputs, and thus will have a significant impact on developing treatments for cancer.

Artificial intelligence aids in development of nanomedicines for cancer management

Over the last decade, the nanomedicine has experienced unprecedented development in diagnosis and management of diseases. A number of nanomedicines have been approved in clinical use, which has demonstrated the potential value of clinical transition of nanotechnology-modified medicines from bench to bedside. The application of artificial intelligence (AI) in development of nanotechnology-based products could transform the healthcare sector by realizing acquisition and analysis of large datasets, and tailoring precision nanomedicines for cancer management. AI-enabled nanotechnology could improve the accuracy of molecular profiling and early diagnosis of patients, and optimize the design pipeline of nanomedicines by tuning the properties of nanomedicines, achieving effective drug synergy, and decreasing the nanotoxicity, thereby, enhancing the targetability, personalized dosing and treatment potency of nanomedicines. Herein, the advances in AI-enabled nanomedicines in cancer management are elaborated and their application in diagnosis, monitoring and therapy as well in precision medicine development is discussed.

Artificial intelligence-based locoregional markers of brain peritumoral microenvironment

In malignant primary brain tumors, cancer cells infiltrate into the peritumoral brain structures which results in inevitable recurrence. Quantitative assessment of infiltrative heterogeneity in the peritumoral region, the area where biopsy or resection can be hazardous, is important for clinical decision making. Here, we derive a novel set of Artificial intelligence (AI)-based markers capturing the heterogeneity of tumor infiltration, by characterizing free water movement restriction in the peritumoral region using Diffusion Tensor Imaging (DTI)-based free water volume fraction maps. We leverage the differences in the peritumoral region of metastasis and glioblastomas, the former consisting of vasogenic versus the latter containing infiltrative edema, to extract a voxel-wise deep learning-based peritumoral microenvironment index (PMI). Descriptive characteristics of locoregional hubs of uniformly high PMI values are then extracted as AI-based markers to capture distinct aspects of infiltrative heterogeneity. The proposed markers are utilized to stratify patients' survival and IDH1 mutation status on a population of 275 adult-type diffuse gliomas (CNS WHO grade 4). Our results show significant differences in the proposed markers between patients with different overall survival and IDH1 mutation status (t test, Wilcoxon rank sum test, linear regression; p < 0.01). Clustering of patients using the proposed markers reveals distinct survival groups (logrank; p < 10-5, Cox hazard ratio = 1.82; p < 0.005). Our findings provide a panel of markers as surrogates of infiltration that might capture novel insight about underlying biology of peritumoral microstructural heterogeneity, providing potential biomarkers of prognosis pertaining to survival and molecular stratification, with applicability in clinical decision making.

Optimizing culturing conditions in patient derived 3D primary slice cultures of head and neck cancer

Background

Three-dimensional primary slice cultures (SC) of head and neck squamous cell carcinomas (HNC) are realistic preclinical models. Until now, preserving structure and viability ex vivo for several days has been difficult. The aim of this study was to optimize cultivation conditions for HNC SC and analyze the added effects of platelet rich fibrin (PRF) on these conditions.

Methods

SC were prepared from the tumor biopsies of 9 HNC patients. Cultures were incubated for 1 and 7 days in three different media- Keratinocyte serum-free medium (SFM), RPMI-1640i, and 1:1 mix of both, with and without addition of PRF. After culturing, SC were fixated, embedded, and stained with Hematoxylin-Eosin (HE) and cleaved caspase-3. In addition, triple immune fluorescence staining for cytokeratin, vimentin and CD45 was performed. Outcome parameters were cell count and cell density, viability and apoptosis, SC total area and proportions of keratinocytes, mesenchymal and immune cells. The effects of culture time, medium, and addition of PRF were calculated in an SPSS generalized linear model and using the Wald Chi-Squared test.

Results

Ninety-four slice cultures were analyzed. Viability remained stable for 7 days in culture. After addition of PRF, cell viability increased (p=0.05). SC total area decreased (0.44 ± 0.04 mm² on day 1 (95% CI: 0.35 to 0.56) to 0.29 ± 0.03 mm² on day 7 (95% CI: 0.22 to 0.36), but cell density and cell proportions remained stable. Differences in cultivation media had no significant impact on outcome parameters.

Conclusion

HNC SC can be preserved for up to 7 days using the tested cultivation media. Cell viability was best preserved with addition of PRF. HNC SC are a versatile experimental tool to study physiology and drug actions. Autologous PRF can help simulate realistic conditions in vitro.

Breast cancer patient-derived explant cultures recapitulate in vivo drug responses

Assessment of drug sensitivity in tumor tissue ex vivo may significantly contribute to functional diagnostics to guide personalized treatment of cancer. Tumor organoid- and explant-cultures have become attractive tools towards this goal, although culturing conditions for breast cancer (BC) tissue have been among the most challenging to develop. Validation of possibilities to detect concordant responses in individual tumors and their respective cultures ex vivo is still needed. Here we employed BC patient-derived xenografts (PDXs) with distinct drug sensitivity, to evaluate different conditions for tissue dissociation, culturing and monitoring of treatment efficacy ex vivo, aiming to recapitulate the in vivo drug responses. The common challenge of discriminating between tumor and normal cells in the cultured tissue was also addressed. Following conventional enzymatic dissociation of BC tissue, the tumor cells stayed within the non-disrupted tissue fragments, while the single cells represented mostly normal host cells. By culturing such fragments as explants, viable tumor tissue could be maintained and treated ex vivo, providing representative indications on efficacy of the tested treatment. Thus, drug sensitivity profiles, including acquired chemoresistance seen in the PDXs, were recapitulated in the respective explants. To detect the concordant responses, however, the effect monitoring had to be harmonized with the characteristics of the cultured tissue. In conclusion, we present the feasibility of BC explants ex vivo to capture differences in drug sensitivity of individual tumors. The established protocols will aid in setting up an analogous platform for BC patient biopsies with the aim to facilitate functional precision medicine.

A systematic literature review for the prediction of anticancer drug response using various machine-learning and deep-learning techniques

Computational methods have gained prominence in healthcare research. The accessibility of healthcare data has greatly incited academicians and researchers to develop executions that help in prognosis of cancer drug response. Among various computational methods, machine-learning (ML) and deep-learning (DL) methods provide the most consistent and effectual approaches to handle the serious aftermaths of the deadly disease and drug administered to the patients. Hence, this systematic literature review has reviewed researches that have investigated drug discovery and prognosis of anticancer drug response using ML and DL algorithms. Fot this purpose, PRISMA guidelines have been followed to choose research papers from Google Scholar, PubMed, and Sciencedirect websites. A total count of 105 papers that align with the context of this review were chosen. Further, the review also presents accuracy of the existing ML and DL methods in the prediction of anticancer drug response. It has been found from the review that, amidst the availability of various studies, there are certain challenges associated with each method. Thus, future researchers can consider these limitations and challenges to develop a prominent anticancer drug response prediction method, and it would be greatly beneficial to the medical professionals in administering non-invasive treatment to the patients.

Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review

Background

The impact and utility of machine learning (ML)-based prediction tools for cancer outcomes including assistive diagnosis, risk stratification, and adjunctive decision-making have been largely described and realized in the high income and upper-middle-income countries. However, statistical projections have estimated higher cancer incidence and mortality risks in low and lower-middle-income countries (LLMICs). Therefore, this review aimed to evaluate the utilization, model construction methods, and degree of implementation of ML-based models for cancer outcomes in LLMICs.

Methods

PubMed/Medline, Scopus, and Web of Science databases were searched and articles describing the use of ML-based models for cancer among local populations in LLMICs between 2002 and 2022 were included. A total of 140 articles from 22,516 citations that met the eligibility criteria were included in this study.

Results

ML-based models from LLMICs were often based on traditional ML algorithms than deep or deep hybrid learning. We found that the construction of ML-based models was skewed to particular LLMICs such as India, Iran, Pakistan, and Egypt with a paucity of applications in sub-Saharan Africa. Moreover, models for breast, head and neck, and brain cancer outcomes were frequently explored. Many models were deemed suboptimal according to the Prediction model Risk of Bias Assessment tool (PROBAST) due to sample size constraints and technical flaws in ML modeling even though their performance accuracy ranged from 0.65 to 1.00. While the development and internal validation were described for all models included (n=137), only 4.4% (6/137) have been validated in independent cohorts and 0.7% (1/137) have been assessed for clinical impact and efficacy.

Conclusion

Overall, the application of ML for modeling cancer outcomes in LLMICs is increasing. However, model development is largely unsatisfactory. We recommend model retraining using larger sample sizes, intensified external validation practices, and increased impact assessment studies using randomized controlled trial designs

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=308345, identifier CRD42022308345.

Les organoïdes dérivés de tumeurs (ou tumoroïdes), des outils de choix pour la médecine de précision en oncologie

Il est désormais possible d’établir des tumoroïdes à partir de presque tout type de tumeur, notamment en vue de la mise en place de tests fonctionnels prédictifs et/ou de l’identification de signatures moléculaires prédictives. Bien que l’optimisation des conditions de culture ou la complexification du micro-environnement des tumoroïdes soit encore nécessaire, de nombreuses applications sont déjà envisageables dans le domaine de la prédiction de la réponse aux traitements et de l’orientation de la décision thérapeutique. Par l’introduction de leur utilisation en clinique, l’oncologie de précision pourrait bien entrer dans une nouvelle ère dans le courant de la décennie à venir.

Precision oncology using ex vivo technology: a step towards individualised cancer care?

Despite advances in cancer genomics and the increased use of genomic medicine, metastatic cancer is still mostly an incurable and fatal disease. With diminishing returns from traditional drug discovery strategies, and high clinical failure rates, more emphasis is being placed on alternative drug discovery platforms, such as ex vivo approaches. Ex vivo approaches aim to embed biological relevance and inter-patient variability at an earlier stage of drug discovery, and to offer more precise treatment stratification for patients. However, these techniques also have a high potential to offer personalised therapies to patients, complementing and enhancing genomic medicine. Although an array of approaches are available to researchers, only a minority of techniques have made it through to direct patient treatment within robust clinical trials. Within this review, we discuss the current challenges to ex vivo approaches within clinical practice and summarise the contemporary literature which has directed patient treatment. Finally, we map out how ex vivo approaches could transition from a small-scale, predominantly research based technology to a robust and validated predictive tool. In future, these pre-clinical approaches may be integrated into clinical cancer pathways to assist in the personalisation of therapy choices and to hopefully improve patient experiences and outcomes.

Functional precision cancer medicine: drug sensitivity screening enabled by cell culture models

Functional precision medicine is a new, emerging area that can guide cancer treatment by capturing information from direct perturbations of tumor-derived, living cells, such as by drug sensitivity screening. Precision cancer medicine as currently implemented in clinical practice has been driven by genomics, and current molecular tumor boards rely extensively on genomic characterization to advise on therapeutic interventions. However, genomic biomarkers can only guide treatment decisions for a fraction of the patients. In this review we provide an overview of the current state of functional precision medicine, highlight advances for drug-sensitivity screening enabled by cell culture models, and discuss how artificial intelligence (AI) can be coupled to functional precision medicine to guide patient stratification.

Molecular Biosimilarity—An AI-Driven Paradigm Shift

Scientific, technical, and bioinformatics advances have made it possible to establish analytics-based molecular biosimilarity for the approval of biosimilars. If the molecular structure is identical and other product- and process-related attributes are comparable within the testing limits, then a biosimilar candidate will have the same safety and efficacy as its reference product. Classical testing in animals and patients is much less sensitive in terms of identifying clinically meaningful differences, as is reported in the literature. The recent artificial intelligence (AI)-based protein structure prediction model, AlphaFold-2, has confirmed that the primary structure of proteins always determines their 3D structure; thus, we can deduce that a biosimilar with an identical primary structure will have the same efficacy and safety. Further confirmation of the thesis has been established using technologies that are now much more sensitive. For example, mass spectrometry (MS) is thousands of times more sensitive and accurate when compared to any form of biological testing. While regulatory agencies have begun waiving animal testing and, in some cases, clinical efficacy testing, the removal of clinical pharmacology profiling brings with it a dramatic paradigm shift, reducing development costs without compromising safety or efficacy. A list of 160+ products that are ready to enter as biosimilars has been shared. Major actions from regulatory agencies and developers are required to facilitate this paradigm shift.

Patient-derived head and neck tumor slice cultures: a versatile tool to study oncolytic virus action

Head and neck cancer etiology and architecture is quite diverse and complex, impeding the prediction whether a patient could respond to a particular cancer immunotherapy or combination treatment. A concomitantly arising caveat is obviously the translation from pre-clinical, cell based in vitro systems as well as syngeneic murine tumor models towards the heterogeneous architecture of the human tumor ecosystems. To bridge this gap, we have established and employed a patient-derived HNSCC (head and neck squamous cell carcinoma) slice culturing system to assess immunomodulatory effects as well as permissivity and oncolytic virus (OV) action. The heterogeneous contexture of the human tumor ecosystem including tumor cells, cancer-associated fibroblasts and immune cells was preserved in our HNSCC slice culturing approach. Importantly, the immune cell compartment remained to be functional and cytotoxic T-cells could be activated by immunostimulatory antibodies. In addition, we uncovered that a high proportion of the patient-derived HNSCC slice cultures were susceptible to the OV VSV-GP. More specifically, VSV-GP infects a broad spectrum of tumor-associated lineages including epithelial and stromal cells and can induce apoptosis. In sum, this human tumor ex vivo platform might complement pre-clinical studies to eventually propel cancer immune-related drug discovery and ease the translation to the clinics.

Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis

The integration of artificial intelligence in precision medicine has revolutionized healthcare delivery. Precision medicine identifies the phenotype of particular patients with less-common responses to treatment. Recent studies have demonstrated that translational research exploring the convergence between artificial intelligence and precision medicine will help solve the most difficult challenges facing precision medicine. Here, we discuss different aspects of artificial intelligence in precision medicine that improve healthcare delivery. First, we discuss how artificial intelligence changes the landscape of precision medicine and the evolution of artificial intelligence in precision medicine. Second, we highlight the synergies between artificial intelligence and precision medicine and promises of artificial intelligence and precision medicine in healthcare delivery. Third, we briefly explain the promise of big data analytics and the integration of nanomaterials in precision medicine. Last, we highlight the challenges and opportunities of artificial intelligence in precision medicine.

Drug-induced tumor-specific cytotoxicity in a whole tissue ex vivo model of human pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer. PDAC has a dismal prognosis and an inherent resistance to cytostatic drugs. The lack of reliable experimental models is a severe limitation for drug development targeting PDAC. We have employed a whole tissue ex vivo culture model to explore the effect of redox-modulation by sodium selenite on the viability and growth of PDAC. Drug-resistant tumors are more vulnerable to redox-active selenium compounds because of high metabolic activity and redox imbalance. Sodium selenite efficiently and specifically reduced PDAC cell viability (p <0.02) (n=8) and decreased viable de novo tumor cell outgrowth (p<0.05) while preserving non-neoplastic tissues. Major cellular responses (damaged tumor cells > 90%, tumor regression grades III-IV according to Evans) were observed for sodium selenite concentrations between 15-30 µM. Moreover, selenium levels used in this study were significantly below the previously reported maximum tolerated dose for humans. Transcriptome data analysis revealed decreased expression of genes known to drive PDAC growth and metastatic potential (CEMIP, DDR2, PLOD2, P4HA1) while the cell death-inducing genes (ATF3, ACHE) were significantly upregulated (p<0.0001). In conclusion, we report that sodium selenite has an extraordinary efficacy and specificity against drug-resistant pancreatic cancer in an organotypic slice culture model. Our ex vivo organotypic tissue slice culture model can be used to test a variety of drug candidates for swift and reliable drug responses to individual PDAC cases.

Patient-derived cancer models: Valuable platforms for anticancer drug testing

Molecularly targeted treatments and immunotherapy are cornerstones in oncology, with demonstrated efficacy across different tumor types. Nevertheless, the overwhelming majority metastatic disease is incurable due to the onset of drug resistance. Preclinical models including genetically engineered mouse models, patient-derived xenografts and two- and three-dimensional cell cultures have emerged as a useful resource to study mechanisms of cancer progression and predict efficacy of anticancer drugs. However, variables including tumor heterogeneity and the complexities of the microenvironment can impair the faithfulness of these platforms. Here, we will discuss advantages and limitations of these preclinical models, their applicability for drug testing and in co-clinical trials and potential strategies to increase their reliability in predicting responsiveness to anticancer medications.

Next-Generation Personalized Medicine: Implementation of Variability Patterns for Overcoming Drug Resistance in Chronic Diseases

Chronic diseases are a significant healthcare problem. Partial or complete non-responsiveness to chronic therapies is a significant obstacle to maintaining the long-term effect of drugs in these patients. A high degree of intra- and inter-patient variability defines pharmacodynamics, drug metabolism, and medication response. This variability is associated with partial or complete loss of drug effectiveness. Regular drug dosing schedules do not comply with physiological variability and contribute to resistance to chronic therapies. In this review, we describe a three-phase platform for overcoming drug resistance: introducing irregularity for improving drug response; establishing a deep learning, closed-loop algorithm for generating a personalized pattern of irregularity for overcoming drug resistance; and upscaling the algorithm by implementing quantified personal variability patterns along with other individualized genetic and proteomic-based ways. The closed-loop, dynamic, subject-tailored variability-based machinery can improve the efficacy of existing therapies in patients with chronic diseases.

Interpretable Machine Learning Models to Predict the Resistance of Breast Cancer Patients to Doxorubicin from Their microRNA Profiles

Doxorubicin is a common treatment for breast cancer. However, not all patients respond to this drug, which sometimes causes life-threatening side effects. Accurately anticipating doxorubicin-resistant patients would therefore permit to spare them this risk while considering alternative treatments without delay. Stratifying patients based on molecular markers in their pretreatment tumors is a promising approach to advance toward this ambitious goal, but single-gene gene markers such as HER2 expression have not shown to be sufficiently predictive. The recent availability of matched doxorubicin-response and diverse molecular profiles across breast cancer patients permits now analysis at a much larger scale. 16 machine learning algorithms and 8 molecular profiles are systematically evaluated on the same cohort of patients. Only 2 of the 128 resulting models are substantially predictive, showing that they can be easily missed by a standard-scale analysis. The best model is classification and regression tree (CART) nonlinearly combining 4 selected miRNA isoforms to predict doxorubicin response (median Matthew correlation coefficient (MCC) and area under the curve (AUC) of 0.56 and 0.80, respectively). By contrast, HER2 expression is significantly less predictive (median MCC and AUC of 0.14 and 0.57, respectively). As the predictive accuracy of this CART model increases with larger training sets, its update with future data should result in even better accuracy.

Network-based machine learning approach to predict immunotherapy response in cancer patients

Immune checkpoint inhibitors (ICIs) have substantially improved the survival of cancer patients over the past several years. However, only a minority of patients respond to ICI treatment (~30% in solid tumors), and current ICI-response-associated biomarkers often fail to predict the ICI treatment response. Here, we present a machine learning (ML) framework that leverages network-based analyses to identify ICI treatment biomarkers (NetBio) that can make robust predictions. We curate more than 700 ICI-treated patient samples with clinical outcomes and transcriptomic data, and observe that NetBio-based predictions accurately predict ICI treatment responses in three different cancer types—melanoma, gastric cancer, and bladder cancer. Moreover, the NetBio-based prediction is superior to predictions based on other conventional ICI treatment biomarkers, such as ICI targets or tumor microenvironment-associated markers. This work presents a network-based method to effectively select immunotherapy-response-associated biomarkers that can make robust ML-based predictions for precision oncology.

Identifying biomarkers for response to immunotherapy in cancer remains challenging. Here, the authors develop an approach based on network biology and machine learning -NetBio- to identify molecular biomarkers of response to immunotherapy across different cancer types and cohorts.

Agent-based computational modeling of glioblastoma predicts that stromal density is central to oncolytic virus efficacy

Oncolytic viruses (OVs) are emerging cancer immunotherapy. Despite notable successes in the treatment of some tumors, OV therapy for central nervous system cancers has failed to show efficacy. We used an ex vivo tumor model developed from human glioblastoma tissue to evaluate the infiltration of herpes simplex OV rQNestin (oHSV-1) into glioblastoma tumors. We next leveraged our data to develop a computational, model of glioblastoma dynamics that accounts for cellular interactions within the tumor. Using our computational model, we found that low stromal density was highly predictive of oHSV-1 therapeutic success, suggesting that the efficacy of oHSV-1 in glioblastoma may be determined by stromal-to-tumor cell regional density. We validated these findings in heterogenous patient samples from brain metastatic adenocarcinoma. Our integrated modeling strategy can be applied to suggest mechanisms of therapeutic responses for central nervous system cancers and to facilitate the successful translation of OVs into the clinic.

Biomarkers and 3D models predicting response to immune checkpoint blockade in head and neck cancer (Review)

Immunotherapy has evolved into a powerful tool in the fight against a number of types of cancer, including head and neck squamous cell carcinomas (HNSCC). Although checkpoint inhibition (CPI) has definitely enriched the treatment options for advanced stage HNSCC during the past decade, the percentage of patients responding to treatment is widely varying between 14-32% in second-line setting in recurrent or metastatic HNSCC with a sporadic durability. Clinical response and, consecutively, treatment success remain unpredictable in most of the cases. One potential factor is the expression of target molecules of the tumor allowing cancer cells to acquire therapy resistance mechanisms. Accordingly, analyzing and modeling the complexity of the tumor microenvironment (TME) is key to i) stratify subgroups of patients most likely to respond to CPI and ii) to define new combinatorial treatment regimens. Particularly in a heterogeneous disease such as HNSCC, thoroughly studying the interactions and crosstalking between tumor and TME cells is one of the biggest challenges. Sophisticated 3D models are therefore urgently needed to be able to validate such basic science hypotheses and to test novel immuno-oncologic treatment regimens in consideration of the individual biology of each tumor. The present review will first summarize recent findings on immunotherapy, predictive biomarkers, the role of the TME and signaling cascades eliciting during CPI. Second, it will highlight the significance of current promising approaches to establish HNSCC 3D models for new immunotherapies. The results are encouraging and indicate that data obtained from patient-specific tumors in a dish might be finally translated into personalized immuno-oncology.

Precision Medicine in Head and Neck Cancers: Genomic and Preclinical Approaches

Head and neck cancers (HNCs) represent the sixth most widespread malignancy worldwide. Surgery, radiotherapy, chemotherapeutic and immunotherapeutic drugs represent the main clinical approaches for HNC patients. Moreover, HNCs are characterised by an elevated mutational load; however, specific genetic mutations or biomarkers have not yet been found. In this scenario, personalised medicine is showing its efficacy. To study the reliability and the effects of personalised treatments, preclinical research can take advantage of next-generation sequencing and innovative technologies that have been developed to obtain genomic and multi-omic profiles to drive personalised treatments. The crosstalk between malignant and healthy components, as well as interactions with extracellular matrices, are important features which are responsible for treatment failure. Preclinical research has constantly implemented in vitro and in vivo models to mimic the natural tumour microenvironment. Among them, 3D systems have been developed to reproduce the tumour mass architecture, such as biomimetic scaffolds and organoids. In addition, in vivo models have been changed over the last decades to overcome problems such as animal management complexity and time-consuming experiments. In this review, we will explore the new approaches aimed to improve preclinical tools to study and apply precision medicine as a therapeutic option for patients affected by HNCs.

Development of a microfluidic platform to maintain viability of micro-dissected tumor slices in culture

One of the issues limiting the development of personalized medicine is the absence of realistic models that reflect the nature and complexity of tumor tissues. We described a new tissue culture approach that combines a microfluidic chip with the microdissected breast cancer tumor. "Tumor-on-a-chip" devices are suitable for precision medicine since the viability of tissue samples is maintained during the culture period by continuously feeding fresh media and eliminating metabolic wastes from the tissue. However, the mass transport of oxygen, which arguably is the most critical nutrient, is rarely assessed. According to our results, transportation of oxygen provides satisfactory in vivo oxygenation within the system. A high level of dissolved oxygen, around 98%-100% for every 24 h, was measurable in the outlet medium. The microfluidic chip system developed within the scope of this study allows living and testing tumor tissues under laboratory conditions. In this study, tumors were generated in CD-1 mice using MDA-MB-231 and SKBR-3 cell lines. Microdissected tumor tissues were cultured both in the newly developed microfluidic chip system and in conventional 24-well culture plates. Two systems were compared for two different types of tumors. The confocal microscopy analyses, lactate dehydrogenase release, and glucose consumption values showed that the tissues in the microfluidic system remained more viable with respect to the conventional well plate culturing method, up to 96 h. The new culturing technique described here may be superior to conventional culturing techniques for developing new treatment strategies, such as testing chemotherapeutics on tumor samples from individual patients.

Novel Ex Vivo Models of Epithelial Ovarian Cancer: The Future of Biomarker and Therapeutic Research

Epithelial ovarian cancer (EOC) is a heterogenous disease associated with variations in presentation, pathology and prognosis. Advanced EOC is typified by frequent relapse and a historical 5-year survival of less than 30% despite improvements in surgical and systemic treatment. The advent of next generation sequencing has led to notable advances in the field of personalised medicine for many cancer types. Success in achieving cure in advanced EOC has however been limited, although significant prolongation of survival has been demonstrated. Development of novel research platforms is therefore necessary to address the rapidly advancing field of early diagnostics and therapeutics, whilst also acknowledging the significant tumour heterogeneity associated with EOC. Within available tumour models, patient-derived organoids (PDO) and explant tumour slices have demonstrated particular promise as novel ex vivo systems to model different cancer types including ovarian cancer. PDOs are organ specific 3D tumour cultures that can accurately represent the histology and genomics of their native tumour, as well as offer the possibility as models for pharmaceutical drug testing platforms, offering timing advantages and potential use as prospective personalised models to guide clinical decision-making. Such applications could maximise the benefit of drug treatments to patients on an individual level whilst minimising use of less effective, yet toxic, therapies. PDOs are likely to play a greater role in both academic research and drug development in the future and have the potential to revolutionise future patient treatment and clinical trial pathways. Similarly, ex vivo tumour slices or explants have also shown recent renewed promise in their ability to provide a fast, specific, platform for drug testing that accurately represents in vivo tumour response. Tumour explants retain tissue architecture, and thus incorporate the majority of tumour microenvironment making them an attractive method to re-capitulate in vivo conditions, again with significant timing and personalisation of treatment advantages for patients. This review will discuss the current treatment landscape and research models for EOC, their development and new advances towards the discovery of novel biomarkers or combinational therapeutic strategies to increase treatment options for women with ovarian cancer.