Precision Medicine in Oncology: In Vitro Drug Sensitivity and Resistance Test (DSRT) for Selection of Personalized Anticancer Therapy

Drug-Induced Differential Gene Expression Analysis on Nanoliter Droplet Microarrays: Enabling Tool for Functional Precision Oncology

Drug-induced differential gene expression analysis (DGEA) is essential for uncovering the molecular basis of cell phenotypic changes and understanding individual tumor responses to anticancer drugs. Performing high throughput DGEA is challenging due to the high cost and labor-intensive multi-step sample preparation protocols. In particular, performing drug-induced DGEA on cancer cells derived from patient biopsies is even more challenging due to the scarcity of available cells. A novel, miniaturized, nanoliter-scale method for drug-induced DGEA is introduced, enabling high-throughput and parallel analysis of patient-derived cell drug responses, overcoming the limitations and laborious nature of traditional protocols. The method is based on the Droplet Microarray (DMA), a microscope glass slide with hydrophilic spots on a superhydrophobic background, facilitating droplet formation for cell testing. DMA allows microscopy-based phenotypic analysis, cDNA extraction, and DGEA. The procedure includes cell lysis for mRNA isolation and cDNA conversion followed by droplet pooling for qPCR analysis. In this study, the drug-induced DGEA protocol on the DMA platform is demonstrated using patient-derived chronic lymphocytic leukemia (CLL) cells. This methodology is critical for DGEA with limited cell numbers and promise for applications in functional precision oncology. This method enables molecular profiling of patient-derived samples after drug treatment, crucial for understanding individual tumor responses to anticancer drugs.

Optimized patient-specific immune checkpoint inhibitor therapies for cancer treatment based on tumor immune microenvironment modeling

Enhancing patient response to immune checkpoint inhibitors (ICIs) is crucial in cancer immunotherapy. We aim to create a data-driven mathematical model of the tumor immune microenvironment (TIME) and utilize deep reinforcement learning (DRL) to optimize patient-specific ICI therapy combined with chemotherapy (ICC). Using patients' genomic and transcriptomic data, we develop an ordinary differential equations (ODEs)-based TIME dynamic evolutionary model to characterize interactions among chemotherapy, ICIs, immune cells, and tumor cells. A DRL agent is trained to determine the personalized optimal ICC therapy. Numerical experiments with real-world data demonstrate that the proposed TIME model can predict ICI therapy response. The DRL-derived personalized ICC therapy outperforms predefined fixed schedules. For tumors with extremely low CD8 + T cell infiltration ('extremely cold tumors'), the DRL agent recommends high-dosage chemotherapy alone. For tumors with higher CD8 + T cell infiltration ('cold' and 'hot tumors'), an appropriate chemotherapy dosage induces CD8 + T cell proliferation, enhancing ICI therapy outcomes. Specifically, for 'hot tumors', chemotherapy and ICI are administered simultaneously, while for 'cold tumors', a mid-dosage of chemotherapy makes the TIME 'hotter' before ICI administration. However, in several 'cold tumors' with rapid resistant tumor cell growth, ICC eventually fails. This study highlights the potential of utilizing real-world clinical data and DRL algorithm to develop personalized optimal ICC by understanding the complex biological dynamics of a patient's TIME. Our ODE-based TIME dynamic evolutionary model offers a theoretical framework for determining the best use of ICI, and the proposed DRL agent may guide personalized ICC schedules.

A novel approach to generate enzyme-free single cell suspensions from archived tissues for miRNA sequencing

Obtaining high-quality omics data at the single-cell level from archived human tissue samples is crucial for gaining insights into cellular heterogeneity and pushing the field of personalized medicine forward. In this technical brief we present a comprehensive methodological framework for the efficient enzyme-free preparation of tissue-derived single cell suspensions and their conversion into single-cell miRNA sequencing libraries. The resulting data from this study have the potential to deepen our understanding of miRNA expression at the single-cell level and its relevance in the context of the examined tissues. The workflow encompasses tissue collection, RNALater immersion, storage, thawing, TissueGrinder-mediated dissociation, miRNA lysis, library preparation, sequencing, and data analysis. Quality control measures ensure reliable miRNA data, with specific attention to sample quality. The UMAP analysis reveals tissue-specific cell clustering, while miRNA diversity reflects tissue variations. The presented workflow effectively processes preserved tissues, extending opportunities for retrospective analysis and biobank utilization.

BMC3PM: bioinformatics multidrug combination protocol for personalized precision medicine and its application in cancer treatment

BACKGROUND

In recent years, drug screening has been one of the most significant challenges in the field of personalized medicine, particularly in cancer treatment. However, several new platforms have been introduced to address this issue, providing reliable solutions for personalized drug validation and safety testing. In this study, we developed a personalized drug combination protocol as the primary input to such platforms.

METHODS

To achieve this, we utilized data from whole-genome expression profiles of 6173 breast cancer patients, 312 healthy individuals, and 691 drugs. Our approach involved developing an individual pattern of perturbed gene expression (IPPGE) for each patient, which was used as the basis for drug selection. An algorithm was designed to extract personalized drug combinations by comparing the IPPGE and drug signatures. Additionally, we employed the concept of drug repurposing, searching for new benefits of existing drugs that may regulate the desired genes.

RESULTS

Our study revealed that drug combinations obtained from both specialized and non-specialized cancer medicines were more effective than those extracted from only specialized medicines. Furthermore, we observed that the individual pattern of perturbed gene expression (IPPGE) was unique to each patient, akin to a fingerprint.

CONCLUSIONS

The personalized drug combination protocol developed in this study offers a methodological interface between drug repurposing and combination drug therapy in cancer treatment. This protocol enables personalized drug combinations to be extracted from hundreds of drugs and thousands of drug combinations, potentially offering more effective treatment options for cancer patients.

Ex vivo drug response profiling for response and outcome prediction in hematologic malignancies: the prospective non-interventional SMARTrial

Ex vivo drug response profiling is a powerful tool to study genotype-drug response associations and is being explored as a tool set for precision medicine in cancer. Here we conducted a prospective non-interventional trial to investigate feasibility of ex vivo drug response profiling for treatment guidance in hematologic malignancies (SMARTrial, NCT03488641 ). The primary endpoint to provide drug response profiling reports within 7 d was met in 91% of all study participants (N = 80). Secondary endpoint analysis revealed that ex vivo resistance to chemotherapeutic drugs predicted chemotherapy treatment failure in vivo. We confirmed the predictive value of ex vivo response to chemotherapy in a validation cohort of 95 individuals with acute myeloid leukemia treated with daunorubicin and cytarabine. Ex vivo drug response profiles improved ELN-22 risk stratification in individuals with adverse risk. We conclude that ex vivo drug response profiling is clinically feasible and has the potential to predict chemotherapy response in individuals with hematologic malignancies beyond clinically established genetic markers.

Treatment for a primary multidrug-resistant B-cell acute lymphoblastic leukemia patient carrying a SSBP2-CSF1R fusion gene: a case report

SSBP2-CSF1R is an important biomarker for clinical diagnosis and prognosis of Philadelphia chromosome-like acute lymphoblastic leukemia (Ph-like ALL). This case report presents a pediatric Ph-like ALL patient carrying the SSBP2-CSF1R fusion gene. The patient was resistant to most conventional chemotherapy regimens and to dasatinib, an inhibitor that has been reported to have a therapeutic effect on SSBP2-CSF1R fusion Ph-like ALL, as she remained minimal residual disease (MRD) positive (detection by flow cytometry) and SSBP2-CSF1R fusion gene (detection by RT-PCR) positive after five rounds of such regimens. We thus conducted a large-scale in vitro screening to assess the sensitivity of the patient's leukemic cells to anti-cancer drugs. Based on the susceptibility results, we chose to combine cytarabine, homoharringtonine, dexamethasone, fludarabine, vindesine, and epirubicin for treatment. Clinical results showed that after a course of treatment, both MRD and SSBP2-CSF1R fusion gene turned negative, and there was no recurrence during an 18-month follow-up. In conclusion, our study suggests that the SSBP2-CSF1R fusion gene may be an important biomarker of primary drug resistance in Ph-like ALL, and indicate that the combination of cytarabine, homoharringtonine, dexamethasone, fludarabine, vindesine, and epirubicin can achieve optimal therapeutic results in this category of patients.

3D bioprinting tumor models mimic the tumor microenvironment for drug screening

Cancer is a severe threat to human life and health and represents the main cause of death globally. Drug therapy is one of the primary means of treating cancer; however, most anticancer medications do not proceed beyond preclinical testing because the conditions of actual human tumors are not effectively mimicked by traditional tumor models. Hence, bionic in vitro tumor models must be developed to screen for anticancer drugs. Three-dimensional (3D) bioprinting technology can produce structures with built-in spatial and chemical complexity and models with accurately controlled structures, a homogeneous size and morphology, less variation across batches, and a more realistic tumor microenvironment (TME). This technology can also rapidly produce such models for high-throughput anticancer medication testing. This review describes 3D bioprinting methods, the use of bioinks in tumor models, and in vitro tumor model design strategies for building complex tumor microenvironment features using biological 3D printing technology. Moreover, the application of 3D bioprinting in vitro tumor models in drug screening is also discussed.

Case report: Successful management of a refractory double-expressor diffuse large B-cell lymphoma patient under the guidance of in vitro high-throughput drug sensitivity test

Introduction

Double-expressor diffuse large B-cell lymphoma (DEL), harboring double expression of MYC and BCL2, has an inferior prognosis following standard first-line therapy with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisolone (R-CHOP). We initiated a clinical trial to treat newly diagnosed DEL with R-CHOP plus Bruton's tyrosine kinase (BTK) inhibitor (BTKi) zanubrutinib (ZR-CHOP) and achieved a high complete response (CR) rate while four patients progressed during therapy, one of them carrying ATM and CD58 mutations. We applied an in vitro high-throughput drug sensitivity test for the prediction of clinical responses to different drugs in this patient.

Case presentation

We report a 30-year-old female patient diagnosed with stage III (DEL), with ATM and CD58 mutations. The patient achieved partial response (PR) after two cycles of ZR-CHOP and remained PR after four cycles of ZR-CHOP, while the disease progressed after six cycles of ZR-CHOP. High-throughput drug screening using a panel of 117 compounds identified a range of therapies with efficacy for this patient. The primary tumor cells showed moderate sensitivity to bortezomib, thalidomide, and gemcitabine as a single agent and bortezomib, thalidomide, and dexamethasone (VTD) as a combined regimen. The patient was treated with two cycles of VTD regimen (bortezomib 1.3 mg/m², d1, 4, 8, 11; thalidomide 100 mg, d1-21; dexamethasone 20 mg, d1, 2, 4, 5, 8, 9) and achieved PR with only a small lesion left. Another two cycles of VTD plus gemcitabine were then administered, and the patient achieved CR. Stem cells were mobilized, and autologous hematopoietic stem cell transplantation was carried out afterward. The patient remained CR for more than 3 months after transplantation.

Conclusion

In this article, we present a first-line chemoresistant DEL patient with ATM and CD58 mutations who was treated successfully with VTD plus gemcitabine under the guidance of in vitro high-throughput drug sensitivity test.

Drug Sensitivity Testing for Cancer Therapy, Technique Analysis and Trends

The techniques and qualities of drug sensitivity testing (DST) for anticancer treatment have grown rapidly in the past two decades worldwide. Much of DST progress came from advanced systems of technical versatility (faster, highly-throughput, highly-sensitive, and smaller in tumor quantity). As the earliest drug selective system, biomedical knowledge and technical advances for DST are mutually supported. More importantly, many pharmacological controversies are resolved by these technical advances. With this technical stride, the clinical landscape of DST entered into a new phase (>500 samples per testing and extremely low quantity of tumor cells). As a forerunner of the drug selection system, DST awaits a new version that can adapt to complicated therapeutic situations and diverse tumor categories in the clinic. By upholding this goal of pathogenic and therapeutic diversity, DST could eventually cure more cancer patients by establishing high-quality drug selection systems. To smoothen DST development, there is a need to increase the understanding of cancer biology, pathology and pharmacology (cancer heterogeneity, plasticity, metastasis and drug resistance) with well-informative parameters before chemotherapy. In this article, medicinal and technical insights into DST are especially highlighted.

Advances in 3D Bioprinting for Cancer Biology and Precision Medicine: From Matrix Design to Application

The tumor microenvironment is highly complex owing to its heterogeneous composition and dynamic nature. This makes tumors difficult to replicate using traditional 2D cell culture models that are frequently used for studying tumor biology and drug screening. This often leads to poor translation of results between in vitro and in vivo and is reflected in the extremely low success rates of new candidate drugs delivered to the clinic. Therefore, there has been intense interest in developing 3D tumor models in the laboratory that are representative of the in vivo tumor microenvironment and patient samples. 3D bioprinting is an emerging technology that enables the biofabrication of structures with the virtue of providing accurate control over distribution of cells, biological molecules, and matrix scaffolding. This technology has the potential to bridge the gap between in vitro and in vivo by closely recapitulating the tumor microenvironment. Here, a brief overview of the tumor microenvironment is provided and key considerations in biofabrication of tumor models are discussed. Bioprinting techniques and choice of bioinks for both natural and synthetic polymers are also outlined. Lastly, current bioprinted tumor models are reviewed and the perspectives of how clinical applications can greatly benefit from 3D bioprinting technologies are offered.

Drug Sensitivity Testing for Cancer Therapy, Key Areas

AIMS

Cancer is a high-mortality disease (9.6 million deaths in 2018 worldwide). Given various anticancer drugs, drug selection plays a key role in patient survival in clinical trials.

METHODS

Drug Sensitivity Testing (DST), one of the leading drug selective systems, was widely practiced for therapeutic promotion in the clinic. Notably, DSTs assist in drug selection that benefits drug responses against cancer from 20-22% to 30-35% over the past two decades. The relationship between drug resistance in vitro and drug treatment benefits was associated with different tumor origins and subtypes. Medical theory and underlying DST mechanisms remain poorly understood until now. The study of the clinical scenario, sustainability and financial support for mechanism and technical promotions is indispensable.

RESULTS

Despite the great technical advance, therapeutic prediction and drug selection by DST needs to be miniature, versatility and cost-effective in the clinic. Multi-parameters and automation of DST should be a future trend. Advanced biomedical knowledge and clinical approaches to translating oncologic profiles into drug selection were the main focuses of DST developments. With a great technical stride, the clinical architecture of the DST platform was entering higher levels (drug response testing at any stage of cancer patients and miniaturization of tumor samples).

DISCUSSION

The cancer biology and pharmacology for drug selection mutually benefit the clinic. New proposals to reveal more therapeutic information and drug response prediction at genetic, molecular and omics levels should be estimated overall.

CONCLUSION

By upholding this goal of non-invasive, versatility and automation, DST could save the life of several thousand annually worldwide. In this article, new insights into DST novelty and development are highlighted.

Modelling acute myeloid leukemia (AML): What's new? A transition from the classical to the modern

Acute myeloid leukemia (AML) is a heterogeneous malignancy affecting myeloid cells in the bone marrow (BM) but can spread giving rise to impaired hematopoiesis. AML incidence increases with age and is associated with poor prognostic outcomes. There has been a disconnect between the success of novel drug compounds observed in preclinical studies of hematological malignancy and less than exceptional therapeutic responses in clinical trials. This review aims to provide a state-of-the-art overview on the different preclinical models of AML available to expand insights into disease pathology and as preclinical screening tools. Deciphering the complex physiological and pathological processes and developing predictive preclinical models are key to understanding disease progression and fundamental in the development and testing of new effective drug treatments. Standard scaffold-free suspension models fail to recapitulate the complex environment where AML occurs. To this end, we review advances in scaffold/matrix-based 3D models and outline the most recent advances in on-chip technology. We also provide an overview of clinically relevant animal models and review the expanding use of patient-derived samples, which offer the prospect to create more "patient specific" screening tools either in the guise of 3D matrix models, microphysiological "organ-on-chip" tools or xenograft models and discuss representative examples.

Circulating tumour cell gene expression and chemosensitivity analyses: predictive accuracy for response to multidisciplinary treatment of patients with unresectable refractory recurrent rectal cancer or unresectable refractory colorectal cancer liver metastases

Background

Patients with unresectable recurrent rectal cancer (RRC) or colorectal cancer (CRC) with liver metastases, refractory to at least two lines of traditional systemic therapy, may receive third line intraarterial chemotherapy (IC) and targeted therapy (TT) using drugs selected by chemosensitivity and tumor gene expression analyses of liquid biopsy-derived circulating tumor cells (CTCs).

Methods

In this retrospective study, 36 patients with refractory unresectable RRC or refractory unresectable CRC liver metastases were submitted for IC and TT with agents selected by precision oncotherapy chemosensitivity assays performed on liquid biopsy-derived CTCs, transiently cultured in vitro, and by tumor gene expression in the same CTC population, as a ratio to tumor gene expression in peripheral mononuclear blood cells (PMBCs) from the same individual. The endpoint was to evaluate the predictive accuracy of a specific liquid biopsy precision oncotherapy CTC purification and in vitro culture methodology for a positive RECIST 1.1 response to the therapy selected.

Results

Our analyses resulted in evaluations of 94.12% (95% CI 0.71–0.99) for sensitivity, 5.26% (95% CI 0.01–0.26) for specificity, a predictive value of 47.06% (95% CI 0.29–0.65) for a positive response, a predictive value of 50% (95% CI 0.01–0.98) for a negative response, with an overall calculated predictive accuracy of 47.22% (95% CI 0.30–0.64).

Conclusions

This is the first reported estimation of predictive accuracy derived from combining chemosensitivity and tumor gene expression analyses on liquid biopsy-derived CTCs, transiently cultured in vitro which, despite limitations, represents a baseline and benchmark which we envisage will be improve upon by methodological and technological advances and future clinical trials.

CDK6 Immunophenotype Implicates Potential Therapeutic Application of CDK4/6 Inhibitors in Urothelial Carcinoma

Background

Infiltrating bladder urothelial carcinoma is the most common bladder malignancy with limited therapeutic options and poor prognosis. Identifying new therapeutic targets or strategies has important clinical significance. The data from public sources indicate poor prognosis in urothelial carcinoma cases with high CDK6 mRNA levels. Furthermore, studies have shown that CDK6 expression is elevated in urothelial carcinoma tissue compared to the surrounding urothelium, thus presenting a case for performing CDK4/6 inhibitor targeted research in urothelial carcinoma. However, a phase II trial showed that CDK4/6 inhibitors are not effective for advanced urothelial carcinoma, suggesting that case screening is important for targeted therapy.

Objective

Immunohistochemistry (IHC) is simple and easy to perform and can be used to screen urothelial carcinoma cases with high CDK6 expression in clinical practice. The aim of this study was to determine the CDK6 expression threshold for positive cases.

Methods

We evaluated the correlation between the H-score of CDK6 protein expression and survival or CDK6 mRNA level using RNA sequencing. The effects of different CDK4/6 inhibitors were tested on bladder carcinoma cell lines with different CDK6 expression levels.

Results

The H-score, which predicts poor prognosis and reflects a high CDK6 mRNA level, was determined as the selection criterion for positive cases. Furthermore, we found that urothelial carcinoma cell lines with higher CDK6 expression levels displayed greater sensitivity to CDK4/6 inhibitors than cells with lower expression levels.

Conclusions

IHC staining for CDK6 protein in urothelial carcinoma is proposed as a promising screening platform for CDK4/6 inhibitor targeted therapy.

Interrogating and Quantifying In Vitro Cancer Drug Pharmacodynamics via Agent-Based and Bayesian Monte Carlo Modelling

The effectiveness of chemotherapy in cancer cell regression is often limited by drug resistance, toxicity, and neoplasia heterogeneity. However, due to the significant complexities entailed by the many cancer growth processes, predicting the impact of interference and symmetry-breaking mechanisms is a difficult problem. To quantify and understand more about cancer drug pharmacodynamics, we combine in vitro with in silico cancer models. The anti-proliferative action of selected cytostatics is interrogated on human colorectal and breast adenocarcinoma cells, while an agent-based computational model is employed to reproduce experiments and shed light on the main therapeutic mechanisms of each chemotherapeutic agent. Multiple drug administration scenarios on each cancer cell line are simulated by varying the drug concentration, while a Bayesian-based method for model parameter optimisation is employed. Our proposed procedure of combining in vitro cancer drug screening with an in silico agent-based model successfully reproduces the impact of chemotherapeutic drugs in cancer growth behaviour, while the mechanisms of action of each drug are characterised through model-derived probabilities of cell apoptosis and division. We suggest that our approach could form the basis for the prospective generation of experimentally-derived and model-optimised pharmacological variables towards personalised cancer therapy.

Label-free sensing of cells with fluorescence lifetime imaging: The quest for metabolic heterogeneity

Molecular, morphological, and physiological heterogeneity is the inherent property of cells which governs differences in their response to external influence. Tumor cell metabolic heterogeneity is of a special interest due to its clinical relevance to tumor progression and therapeutic outcomes. Rapid, sensitive, and noninvasive assessment of metabolic heterogeneity of cells is a great demand for biomedical sciences. Fluorescence lifetime imaging (FLIM), which is an all-optical technique, is an emerging tool for sensing and quantifying cellular metabolism by measuring fluorescence decay parameters of endogenous fluorophores, such as NAD(P)H. To achieve accurate discrimination between metabolically diverse cellular subpopulations, appropriate approaches to FLIM data collection and analysis are needed. In this paper, the unique capability of FLIM to attain the overarching goal of discriminating metabolic heterogeneity is demonstrated. This has been achieved using an approach to data analysis based on the nonparametric analysis, which revealed a much better sensitivity to the presence of metabolically distinct subpopulations compared to more traditional approaches of FLIM measurements and analysis. The approach was further validated for imaging cultured cancer cells treated with chemotherapy. These results pave the way for accurate detection and quantification of cellular metabolic heterogeneity using FLIM, which will be valuable for assessing therapeutic vulnerabilities and predicting clinical outcomes.

Pharmacometabolomics Applied to Personalized Medicine in Urological Cancers

Prostate cancer (PCa), bladder cancer (BCa), and renal cell carcinoma (RCC) are the most common urological cancers, and their incidence has been rising over time. Surgery is the standard treatment for these cancers, but this procedure is only effective when the disease is localized. For metastatic disease, PCa is typically treated with androgen deprivation therapy, while BCa is treated with chemotherapy, and RCC is managed primarily with targeted therapies. However, response rates to these therapeutic options remain unsatisfactory due to the development of resistance and treatment-related toxicity. Thus, the discovery of biomarkers with prognostic and predictive value is needed to stratify patients into different risk groups, minimizing overtreatment and the risk of drug resistance development. Pharmacometabolomics, a branch of metabolomics, is an attractive tool to predict drug response in an individual based on its own metabolic signature, which can be collected before, during, and after drug exposure. Hence, this review focuses on the application of pharmacometabolomic approaches to identify the metabolic responses to hormone therapy, targeted therapy, immunotherapy, and chemotherapy for the most prevalent urological cancers.

Pump-free microfluidic magnetic levitation approach for density-based cell characterization

Magnetic levitation (MagLev) provides a simple but promising method for density-based analysis and detection down to the individual cell level. However, each existing MagLev configuration for the single-cell density measurement, mainly consisting of a capillary (∼50 mm) placed between two magnets, yields a fairly low sample utilization because of no knowledge about the sample cells in the regions other than the limited microscope vision. Moreover, the quantitative analysis may be affected due to the unclearly defined measurement area, which is specifically associated with the uneven magnetization of magnets, cell size, degree of aggregation. In this work, we explore a pump-free microfluidic magnetic levitation approach for density-based cell characterization, enabling sensitive and effective cellular density measurement on small sample volumes. The microfluidic MagLev comprises a pump-free microfluidic chip placed between two ring magnets with like poles facing. With no external pumps, connectors or control facility, much smaller amounts of fluids (∼4 μL) could be driven automatically in the entire microchannel in 16 s. Based on the pump-free mechanism, unique density signatures of cells from different lineages (ARPE-19, HCT116, HeLa, HT1080, Huh7) are characterized by monitoring the levitation profiles. Furthermore, variation in density of A549 lung cancer cells subjected to a drug treatment are observed in our platform, allowing evaluation of the efficacy of the drug treatment at the individual cell level. Thereby, the proposed pump-free microfluidic MagLev platform, a low-cost, fully automatic and portable design for label-free density-based cell characterization, provides a universal detection tool that operates efficiently within small-volume environments.

Spheroid Culture Differentially Affects Cancer Cell Sensitivity to Drugs in Melanoma and RCC Models

2D culture as a model for drug testing often turns to be clinically futile. Therefore, 3D cultures (3Ds) show potential to better model responses to drugs observed in vivo. In preliminary studies, using melanoma (B16F10) and renal (RenCa) cancer, we confirmed that 3Ds better mimics the tumor microenvironment. Here, we evaluated how the proposed 3D mode of culture affects tumor cell susceptibility to anti-cancer drugs, which have distinct mechanisms of action (everolimus, doxorubicin, cisplatin). Melanoma spheroids showed higher resistance to all used drugs, as compared to 2D. In an RCC model, such modulation was only observed for doxorubicin treatment. As drug distribution was not affected by the 3D shape, we assessed the expression of MDR1 and mTor. Upregulation of MDR1 in RCC spheroids was observed, in contrast to melanoma. In both models, mTor expression was not affected by the 3D cultures. By NGS, 10 genes related with metabolism of xenobiotics by cytochrome p450 were deregulated in renal cancer spheroids; 9 of them were later confirmed in the melanoma model. The differences between 3D models and classical 2D cultures point to the potential to uncover new non-canonical mechanisms to explain drug resistance set by the tumor in its microenvironment.

Hydrogel based tissue engineering and its future applications in personalized disease modeling and regenerative therapy

BACKGROUND

Evolution in the in vitro cell culture from conventional 2D to 3D technique has been a significant accomplishment. The 3D culture models have provided a close and better insight into the physiological study of the human body. The increasing demand for organs like liver, kidney, and pancreas for transplantation, rapid anti-cancer drug screening, and the limitations associated with the use of animal models have attracted the interest of researchers to explore 3D organ culture.

MAIN BODY

Natural, synthetic, and hybrid material-based hydrogels are being used as scaffolds in 3D culture and provide 'close-to-in vivo' structures. Organoids: the stem cell-derived small size 3D culture systems are now favored due to their ability to mimic the in-vivo conditions of organ or tissue and this characteristic has made it eligible for a variety of clinical applications, drug discovery and regenerative medicine are a few of the many areas of application. The use of animal models for clinical applications has been a long-time ethical and biological challenge to get accurate outcomes. 3D bioprinting has resolved the issue of vascularization in organoid culture to a great extent by its layer-by-layer construction approach. The 3D bioprinted organoids have a popular application in personalized disease modeling and rapid drug development and therapeutics.

SHORT CONCLUSIONS

This review paper, focuses on discussing the novel organoid culture approach, its advantages and limitations, and potential applications in a variety of life science areas namely cancer research, cell therapy, tissue engineering, and personalized medicine and drug discovery.

GRAPHICAL ABSTRACT

In Vitro Anticancer Drug Sensitivity Sensing through Single-Cell Raman Spectroscopy

Traditional in vitro anticancer drug sensitivity testing at the population level suffers from lengthy procedures and high false positive rates. To overcome these defects, we built a confocal Raman microscopy sensing system and proposed a single-cell approach via Raman-deuterium isotope probing (Raman-DIP) as a rapid and reliable in vitro drug efficacy evaluation method. Raman-DIP detected the incorporation of deuterium into the cell, which correlated with the metabolic activity of the cell. The human non-small cell lung cancer cell line HCC827 and human breast cancer cell line MCF-7 were tested against eight different anticancer drugs. The metabolic activity of cancer cells could be detected as early as 12 h, independent of cell growth. Incubation of cells in 30% heavy water (D₂O) did not show any negative effect on cell viability. Compared with traditional methods, Raman-DIP could accurately determine the drug effect, meanwhile, it could reduce the testing period from 72–144 h to 48 h. Moreover, the heterogeneity of cells responding to anticancer drugs was observed at the single-cell level. This proof-of-concept study demonstrated the potential of Raman-DIP to be a reliable tool for cancer drug discovery and drug susceptibility testing.

The Extracellular Matrix-Derived Biomarkers for Diagnosis, Prognosis, and Personalized Therapy of Malignant Tumors

The tumor biomarkers already have proven clinical value and have become an integral part in cancer management and modern translational oncology. The tumor tissue microenvironment (TME), which includes extracellular matrix (ECM), signaling molecules, immune and stromal cells, and adjacent non-tumorous tissue, contributes to cancer pathogenesis. Thus, TME-derived biomarkers have many clinical applications. This review is predominately based on the most recent publications (manuscripts published in a last 5 years, or seminal publications published earlier) and fills a gap in the current literature on the cancer biomarkers derived from the TME, with particular attention given to the ECM and products of its processing and degradation, ECM-associated extracellular vesicles (EVs), biomechanical characteristics of ECM, and ECM-derived biomarkers predicting response to the immunotherapy. We discuss the clinical utility of the TME-incorporating three-dimensional in vitro and ex vivo cell culture models for personalized therapy. We conclude that ECM is a critical driver of malignancies and ECM-derived biomarkers should be included in diagnostics and prognostics panels of markers in the clinic.

Early precursor T-cell acute lymphoblastic leukemia: current paradigms and evolving concepts

Early precursor T cell-acute lymphoblastic leukemia (ETP-ALL) is a rare entity characterized by chemo-resistance and a paucity of data regarding optimal management. We review here the literature regarding the management of ETP-ALL and focus on the recent, emerging data, regarding the potential role of molecularly targeted approaches with a focus on venetoclax.

A Pilot Study of the Predictive Potential of Chemosensitivity and Gene Expression Assays Using Circulating Tumour Cells from Patients with Recurrent Ovarian Cancer

Circulating tumour cells (CTCs) from liquid biopsies are under current investigation in several cancers, including epithelial ovarian cancer (EOC) but face significant drawbacks in terms of non-standardised methodology, low viable cell numbers and accuracy of CTC identification. In this pilot study, we report that chemosensitivity assays using liquid biopsy-derived metastatic EOC CTCs, from 10 patients, nine with stage IIIC and one with stage IV disease, in progression after systemic chemotherapy, submitted for hypoxic isolated abdominal perfusion (HAP), are both feasible and useful in predicting response to therapy. Viable metastatic EOC CTCs (>5 cells/mL for all 10 blood samples), enriched by transient culture and identified by reverse transcription polymerase chain reaction (RT-PCR) and indirect immunofluorescence (IF), were subjected to flow cytometry-based Annexin V-PE assays for chemosensitivity to several chemotherapeutic agents and by RT-PCR for tumour gene expression profiling. Using a cut-off value of >80% cell death, CTC chemosensitivity tests were predictive of patient RECIST 1.1 responses to HAP therapy associated with 100% sensitivity, 50% specificity, 33% positive predictive, 100% negative predictive and 60% accuracy values. We propose that the methodology employed in this study is feasible and has the potential to predict response to therapy, setting the stage for a larger study.