Clonal heterogeneity influences drug responsiveness in renal cancer assessed by ex vivo drug testing of multiple patient-derived cancer cells

Predicting drug response from single-cell expression profiles of tumours

BACKGROUND

Intra-tumour heterogeneity (ITH) presents a significant obstacle in formulating effective treatment strategies in clinical practice. Single-cell RNA sequencing (scRNA-seq) has evolved as a powerful instrument for probing ITH at the transcriptional level, offering an unparalleled opportunity for therapeutic intervention.

RESULTS

Drug response prediction at the single-cell level is an emerging field of research that aims to improve the efficacy and precision of cancer treatments. Here, we introduce DREEP (Drug Response Estimation from single-cell Expression Profiles), a computational method that leverages publicly available pharmacogenomic screens from GDSC2, CTRP2, and PRISM and functional enrichment analysis to predict single-cell drug sensitivity from transcriptomic data. We validated DREEP extensively in vitro using several independent single-cell datasets with over 200 cancer cell lines and showed its accuracy and robustness. Additionally, we also applied DREEP to molecularly barcoded breast cancer cells and identified drugs that can selectively target specific cell populations.

CONCLUSIONS

DREEP provides an in silico framework to prioritize drugs from single-cell transcriptional profiles of tumours and thus helps in designing personalized treatment strategies and accelerating drug repurposing studies. DREEP is available at https://github.com/gambalab/DREEP .

Inferring therapeutic vulnerability within tumors through integration of pan-cancer cell line and single-cell transcriptomic profiles

Single-cell RNA sequencing greatly advanced our understanding of intratumoral heterogeneity through identifying tumor subpopulations with distinct biologies. However, translating biological differences into treatment strategies is challenging, as we still lack tools to facilitate efficient drug discovery that tackles heterogeneous tumors. One key component of such approaches tackles accurate prediction of drug response at the single-cell level to offer therapeutic options to specific cell subpopulations. Here, we present a transparent computational framework (nicknamed scIDUC) to predict therapeutic efficacies on an individual-cell basis by integrating single-cell transcriptomic profiles with large, data-rich pan-cancer cell line screening datasets. Our method achieves high accuracy, with predicted sensitivities easily able to separate cells into their true cellular drug resistance status as measured by effect size (Cohen's d > 1.0). More importantly, we examine our method's utility with three distinct prospective tests covering different diseases (rhabdomyosarcoma, pancreatic ductal adenocarcinoma, and castration-resistant prostate cancer), and in each our predicted results are accurate and mirrored biological expectations. In the first two, we identified drugs for cell subpopulations that are resistant to standard-of-care (SOC) therapies due to intrinsic resistance or effects of tumor microenvironments. Our results showed high consistency with experimental findings from the original studies. In the third test, we generated SOC therapy resistant cell lines, used scIDUC to identify efficacious drugs for the resistant line, and validated the predictions with in-vitro experiments. Together, scIDUC quickly translates scRNA-seq data into drug response for individual cells, displaying the potential as a first-line tool for nuanced and heterogeneity-aware drug discovery.

Ex Vivo Drug Testing in Patient-derived Papillary Renal Cancer Cells Reveals EGFR and the BCL2 Family as Therapeutic Targets

BACKGROUND

Immune checkpoint inhibitors and antiangiogenic agents are used for first-line treatment of advanced papillary renal cell carcinoma (pRCC) but pRCC response rates to these therapies are low.

OBJECTIVE

To generate and characterise a functional ex vivo model to identify novel treatment options in advanced pRCC.

DESIGN, SETTING, AND PARTICIPANTS

We established patient-derived cell cultures (PDCs) from seven pRCC samples from patients and characterised them via genomic analysis and drug profiling.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Comprehensive molecular characterisation in terms of copy number analysis and whole-exome sequencing confirmed the concordance of pRCC PDCs with the original tumours. We evaluated their sensitivity to novel drugs by generating drug scores for each PDC.

RESULTS AND LIMITATIONS

PDCs confirmed pRCC-specific copy number variations such as gains in chromosomes 7, 16, and 17. Whole-exome sequencing revealed that PDCs retained mutations in pRCC-specific driver genes. We performed drug screening with 526 novel and oncological compounds. Whereas exposure to conventional drugs showed low efficacy, the results highlighted EGFR and BCL2 family inhibition as the most effective targets in our pRCC PDCs.

CONCLUSIONS

High-throughput drug testing on newly established pRCC PDCs revealed that inhibition of EGFR and BCL2 family members could be a therapeutic strategy in pRCC.

PATIENT SUMMARY

We used a new approach to generate patient-derived cells from a specific type of kidney cancer. We showed that these cells have the same genetic background as the original tumour and can be used as models to study novel treatment options for this type of kidney cancer.

Comparison of two supporting matrices for patient-derived cancer cells in 3D drug sensitivity and resistance testing assay (3D-DSRT)

Central to the success of functional precision medicine of solid tumors is to perform drug testing of patient-derived cancer cells (PDCs) in tumor-mimicking ex vivo conditions. While high throughput (HT) drug screening methods have been well-established for cells cultured in two-dimensional (2D) format, this approach may have limited value in predicting clinical responses. Here, we describe the results of the optimization of drug sensitivity and resistance testing (DSRT) in three-dimensional (3D) growth supporting matrices in a HT mode (3D-DSRT) using the hepatocyte cell line (HepG2) as an example. Supporting matrices included widely used animal-derived Matrigel and cellulose-based hydrogel, GrowDex, which has earlier been shown to support 3D growth of cell lines and stem cells. Further, the sensitivity of ovarian cancer PDCs, from two patients included in the functional precision medicine study, was tested for 52 drugs in 5 different concentrations using 3D-DSRT. Shortly, in the optimized protocol, the PDCs are embedded with matrices and seeded to 384-well plates to allow the formation of the spheroids prior to the addition of drugs in nanoliter volumes with acoustic dispenser. The sensitivity of spheroids to drug treatments is measured with cell viability readout (here, 72 h after addition of drugs). The quality control and data analysis are performed with openly available Breeze software. We show the usability of both matrices in established 3D-DSRT, and report 2D vs 3D growth condition dependent differences in sensitivities of ovarian cancer PDCs to MEK-inhibitors and cytotoxic drugs. This study provides a proof-of-concept for robust and fast screening of drug sensitivities of PDCs in 3D-DSRT, which is important not only for drug discovery but also for personalized ex vivo drug testing in functional precision medicine studies. These findings suggest that comparing results of 2D- and 3D-DSRT is essential for understanding drug mechanisms and for selecting the most effective treatment for the patient.

Protocol for 3D drug sensitivity and resistance testing of patient-derived cancer cells in 384-well plates

Establishment of drug testing of patient-derived cancer cells (PDCs) in physiologically relevant 3-dimensional (3D) culture is central for drug discovery and cancer research, as well as for functional precision medicine. Here, we describe the detailed protocol allowing the 3D drug testing of PDCs - or any type of cells of interest - in Matrigel in 384-well plate format using automation. We also provide an alternative protocol, which does not require supporting matrices. The cancer tissue is obtained directly from clinics (after surgery or biopsy) and processed into single cell suspension. Systematic drug sensitivity and resistance testing (DSRT) is carried out on the PDCs directly after cancer cell isolation from tissue or on cells expanded for a few passages. In the 3D-DSRT assay, the PDCs are plated in 384-well plates in Matrigel, grown as spheroids, and treated with compounds of interest for 72 h. The cell viability is directly measured using a luminescence-based assay. Alternatively, prior to the cell viability measurement, drug-treated cells can be directly subjected to automated high-content bright field imaging or stained for fluorescence (live) cell microscopy for further image analysis. This is followed by the quality control and data analysis. The 3D-DSRT can be performed within a 1-3-week timeframe of the clinical sampling of cancer tissue, depending on the amount of the obtained tissue, growth rate of cancer cells, and the number of drugs being tested. The 3D-DSRT method can be flexibly modified, e.g., to be carried out with or without supporting matrices with U-bottom 384-well plates when appropriate for the PDCs or other cell models used.

Drug response profiles in patient-derived cancer cells across histological subtypes of ovarian cancer: real-time therapy tailoring for a patient with low-grade serous carcinoma

Many efforts are underway to develop novel therapies against the aggressive high-grade serous ovarian cancers (HGSOCs), while our understanding of treatment options for low-grade (LGSOC) or mucinous (MUCOC) of ovarian malignancies is not developing as well. We describe here a functional precision oncology (fPO) strategy in epithelial ovarian cancers (EOC), which involves high-throughput drug testing of patient-derived ovarian cancer cells (PDCs) with a library of 526 oncology drugs, combined with genomic and transcriptomic profiling. HGSOC, LGSOC and MUCOC PDCs had statistically different overall drug response profiles, with LGSOCs responding better to targeted inhibitors than HGSOCs. We identified several subtype-specific drug responses, such as LGSOC PDCs showing high sensitivity to MDM2, ERBB2/EGFR inhibitors, MUCOC PDCs to MEK inhibitors, whereas HGSOCs showed strongest effects with CHK1 inhibitors and SMAC mimetics. We also explored several drug combinations and found that the dual inhibition of MEK and SHP2 was synergistic in MAPK-driven EOCs. We describe a clinical case study, where real-time fPO analysis of samples from a patient with metastatic, chemorefractory LGSOC with a CLU-NRG1 fusion guided clinical therapy selection. fPO-tailored therapy with afatinib, followed by trastuzumab and pertuzumab, successfully reduced tumour burden and blocked disease progression over a five-year period. In summary, fPO is a powerful approach for the identification of systematic drug response differences across EOC subtypes, as well as to highlight patient-specific drug regimens that could help to optimise therapies to individual patients in the future.

Autophagy-related gene LAPTM4B promotes the progression of renal clear cell carcinoma and is associated with immunity

Renal cell carcinoma (RCC) is a common urologic disease. Currently, surgery is the primary treatment for renal cancer; immunotherapy is not as effective a treatment strategy as expected. Hence, understanding the mechanism in the tumor immune microenvironment (TME) and exploring novel immunotherapeutic targets are considered important. Recent studies have demonstrated that autophagy could affect the immune environment of renal cell carcinoma and induce proliferation and apoptosis of cancer cells. By comparing lysosomal genes and regulating autophagy genes, we identified the LAPTM4B gene to be related to RCC autophagy. By analyzing the TCGA-KIRC cohort using bioinformatics, we found M2 macrophages associated with tumor metastasis to be significantly increased in the immune microenvironment of patients with high expression of LAPTM4B. GO/KEGG/GSEA/GSVA results showed significant differences in tumor autophagy- and metastasis-related pathways. Single-cell sequencing was used to compare the expression of LAPTM4B in different cell types and obtain the differences in lysosomal and autophagy pathway activities in different ccRCC cells. Subsequently, we confirmed the differential expression of LAPTM4B in renal cell carcinoma of different Fuhrman grades using western blotting. Downregulation of LAPTM4B expression significantly reduced the proliferation of renal cell carcinoma cells and promoted cell apoptosis through cell experiments. Overall, our study demonstrated that the autophagy-related gene LAPTM4B plays a critical role in the TME of RCC, and suggested that LAPTM4B is a potential therapeutic target for RCC immunotherapy.

Bioinformatics roadmap for therapy selection in cancer genomics

Tumour heterogeneity is one of the main characteristics of cancer and can be categorised into inter- or intratumour heterogeneity. This heterogeneity has been revealed as one of the key causes of treatment failure and relapse. Precision oncology is an emerging field that seeks to design tailored treatments for each cancer patient according to epidemiological, clinical and omics data. This discipline relies on bioinformatics tools designed to compute scores to prioritise available drugs, with the aim of helping clinicians in treatment selection. In this review, we describe the current approaches for therapy selection depending on which type of tumour heterogeneity is being targeted and the available next-generation sequencing data. We cover intertumour heterogeneity studies and individual treatment selection using genomics variants, expression data or multi-omics strategies. We also describe intratumour dissection through clonal inference and single-cell transcriptomics, in each case providing bioinformatics tools for tailored treatment selection. Finally, we discuss how these therapy selection workflows could be integrated into the clinical practice.

Identification and validation of a cigarette smoke-related five-gene signature as a prognostic biomarker in kidney renal clear cell carcinoma

Cigarette smoking greatly promotes the progression of kidney renal clear cell carcinoma (KIRC), however, the underlying molecular events has not been fully established. In this study, RCC cells were exposed to the tobacco specific nitrosamine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK, nicotine-derived nitrosamine) for 120 days (40 passages), and then the soft agar colony formation, wound healing and transwell assays were used to explore characteristics of RCC cells. RNA-seq was used to explore differentially expressed genes. We found that NNK promoted RCC cell growth and migration in a dose-dependent manner, and RNA-seq explored 14 differentially expressed genes. In TCGA-KIRC cohort, Lasso regression and multivariate COX regression models screened and constructed a five-gene signature containing ANKRD1, CYB5A, ECHDC3, MT1E, and AKT1S1. This novel gene signature significantly associated with TNM stage, invasion depth, metastasis, and tumor grade. Moreover, when compared with individual genes, the gene signature contained a higher hazard ratio and therefore had a more powerful value for the prognosis of KIRC. A nomogram was also developed based on clinical features and the gene signature, which showed good application. Finally, AKT1S1, the most crucial component of the gene signature, was significantly induced after NNK exposure and its related AKT/mTOR signaling pathway was dramatically activated. Our findings supported that NNK exposure would promote the KIRC progression, and the novel cigarette smoke-related five-gene signature might serve as a highly efficient biomarker to identify progression of KIRC patients, AKT1S1 might play an important role in cigarette smoke exposure-induced KIRC progression.

Longitudinal Monitoring of Intra-Tumoural Heterogeneity Using Optical Barcoding of Patient-Derived Colorectal Tumour Models

Simple Summary

Colorectal cancer (CRC) is the second most common cancer worldwide. Despite improvements in the clinical management of CRC, outcomes for those with metastatic disease remain extremely poor. One reason for this is tumour heterogeneity, which refers to the observation that each cell within complex tumour cell populations displays different genetic features and biological behaviours. Such tumour heterogeneity is known to impact treatment efficacy and promote tumour recurrence. Here, we present a multi-colour barcoding methodology that allows for different lineages of colorectal cancer cells to be identified and monitored, thus allowing for tumour heterogeneity to be quantified in real-time. We show that discrete cell lineages can be quantified by both fluorescence microscopy and flow cytometry. Using this approach, we show that the cell culture models that are traditionally used in cancer research display limited heterogeneity, whereas patient-derived organoids—which are generated from fresh tumour resections—more faithfully represent the heterogeneity observed in cancer patients.

Abstract

Geno- and phenotypic heterogeneity amongst cancer cell subpopulations are established drivers of treatment resistance and tumour recurrence. However, due to the technical difficulty associated with studying such intra-tumoural heterogeneity, this phenomenon is seldom interrogated in conventional cell culture models. Here, we employ a fluorescent lineage technique termed “optical barcoding” (OBC) to perform simultaneous longitudinal tracking of spatio-temporal fate in 64 patient-derived colorectal cancer subclones. To do so, patient-derived cancer cell lines and organoids were labelled with discrete combinations of reporter constructs, stably integrated into the genome and thus passed on from the founder cell to all its clonal descendants. This strategy enables the longitudinal monitoring of individual cell lineages based upon their unique optical barcodes. By designing a novel panel of six fluorescent proteins, the maximum theoretical subpopulation resolution of 64 discriminable subpopulations was achieved, greatly improving throughput compared with previous studies. We demonstrate that all subpopulations can be purified from complex clonal mixtures via flow cytometry, permitting the downstream isolation and analysis of any lineages of interest. Moreover, we outline an optimized imaging protocol that can be used to image optical barcodes in real-time, allowing for clonal dynamics to be resolved in live cells. In contrast with the limited intra-tumour heterogeneity observed in conventional 2D cell lines, the OBC technique was successfully used to quantify dynamic clonal expansions and contractions in 3D patient-derived organoids, which were previously demonstrated to better recapitulate the heterogeneity of their parental tumour material. In summary, we present OBC as a user-friendly, inexpensive, and high-throughput technique for monitoring intra-tumoural heterogeneity in in vitro cell culture models.

Beyondcell: targeting cancer therapeutic heterogeneity in single-cell RNA-seq data

We present Beyondcell, a computational methodology for identifying tumour cell subpopulations with distinct drug responses in single-cell RNA-seq data and proposing cancer-specific treatments. Our method calculates an enrichment score in a collection of drug signatures, delineating therapeutic clusters (TCs) within cellular populations. Additionally, Beyondcell determines the therapeutic differences among cell populations and generates a prioritised sensitivity-based ranking in order to guide drug selection. We performed Beyondcell analysis in five single-cell datasets and demonstrated that TCs can be exploited to target malignant cells both in cancer cell lines and tumour patients. Beyondcell is available at: https://gitlab.com/bu_cnio/beyondcell .

Application Research of Individualized Conditional Reprogramming System to Guide Treatment of Gastric Cancer

Background

Gastric cancer (GC) is one of the most common causes of malignant tumors in the world. Due to the high heterogeneity of GC and lack of specificity of available chemotherapy regimens, these tumors are prone to resistance, recurrence, and metastasis. Here, we formulated an individualized chemotherapy regimen for GC using a modified individual conditional reprogramming (i-CR) system. We established a primary tumor cell bank of GC cells and completed drug screening in order to realize individualized and accurate GC treatment.

Methods

We collected specimens from 93 surgical or gastroscopy GC cases and established a primary tumor cell bank using the i-CR system and PDX models. We also completed in vitro culture and drug sensitivity screening of the GC cells using the i-CR system. Whole-exome sequencing (WES) of the i-CR cells was performed using P0 and P5. We then chose targeted chemotherapy drugs based on the i-CR system results.

Results

Of the 72 cases that were collected from surgical specimens, 26 cases were successfully cultured with i-CR system, and of the 21 cases collected from gastroscopy specimens, seven were successfully cultured. Among these, 20 cases of the PDX model were established. SRC ± G3 had the highest culture success rate. The i-CR cells of P0 and P5 appeared to be highly conserved. According to drug sensitivity screening, we examined the predictive value of responses of GC patients to chemotherapeutic agents, especially in neoadjuvant patients.

Conclusion

The i-CR system does not only represent the growth characteristics of tumors in vivo, but also provides support for clinical drug use. Drug susceptibility results were relatively consistent with clinical efficacy.

Differential sensitivity of renal carcinoma cells to doxorubicin and epigenetic therapeutics depends on the genetic background

Differential sensitivity to chemotherapeutics is a limitation in chemotherapy of kidney cancer patients. Role of genetic background in chemotherapy is not fully understood. Therefore, this study evaluated the influence of genetic/epigenetic background of renal cancer cells on the sensitivity to chemotherapeutics. Two renal cell carcinoma (RCC) cell lines, Caki-1 and 786-0, with different genetic makeup of p53 and VHL were treated with doxorubicin either alone or in combination with epigenetic therapeutics 5-aza-2-dc and TSA. Sensitivity of RCC cells to these drugs was evaluated by cell viability and cell cycle analysis and was further confirmed by analysis of selected genes expression. Cell viability data revealed that 786-0 cells were more sensitive than Caki-1 to doxorubicin. Combination of doxorubicin with 5-aza-2-dc or TSA was more effective to inhibit growth of Caki-1 cells but not the 786-0. Data of cell cycle analysis and expression of representative genes for tumor suppressor, cell cycle and survival, drug transporter and DNA repair further provided the molecular basis for differential sensitivity of Caki-1 and 786-0 cell lines to doxorubicin. Important findings of this study suggest that doxorubicin is more cytotoxic to primary renal cancer 786-0 cells with mutant VHL and p53 than the metastatic Caki-1 cells with wild-type VHL and p53, and this differential response was independent of p53 expression level. This study suggests that combination of doxorubicin with epigenetic therapeutics could potentially be beneficial in clinical treatment of renal cancer patients with wild-type VHL and p53 but not in patients with mutant VHL and p53.

Ex Vivo Analysis of Primary Tumor Specimens for Evaluation of Cancer Therapeutics

The use of ex vivo drug sensitivity testing to predict drug activity in individual patients has been actively explored for almost 50 years without delivering a generally useful predictive capability. However, extended failure should not be an indicator of futility. This is especially true in cancer research where ultimate success is often preceded by less successful attempts. For example, both immune- and genetic-based targeted therapies for cancer underwent numerous failed attempts before biological understanding, improved targets, and optimized drug development matured to facilitate an arsenal of transformational drugs. Similarly, the concept of directly assessing drug sensitivity of primary tumor biopsies-and the use of this information to help direct therapeutic approaches-has a long history with a definitive learning curve. In this review, we will survey the history of ex vivo testing as well as the current state of the art for this field. We will present an update on methodologies and approaches, describe the use of these technologies to test cutting-edge drug classes, and describe an increasingly nuanced understanding of tumor types and models for which this strategy is most likely to succeed. We will consider the relative strengths and weaknesses of predicting drug activity across the broad biological context of cancer patients and tumor types. This will include an analysis of the potential for ex vivo drug sensitivity testing to accurately predict drug activity within each of the biological hallmarks of cancer pathogenesis.

Conditional cell reprogramming for modeling host-virus interactions and human viral diseases

Conventional cancer and transformed cell lines are widely used in cancer biology and other fields within biology. These cells usually have abnormalities from the original tumor itself, but may also develop abnormalities due to genetic manipulation, or genetic and epigenetic changes during long‐term passages. Primary cultures may maintain lineage functions as the original tissue types, yet they have a very limited life span or population doubling time because of the nature of cellular senescence. Primary cultures usually have very low yields, and the high variability from any original tissue specimens, largely limiting their applications in research. Animal models are often used for studies of virus infections, disease modeling, development of antiviral drugs, and vaccines. Human viruses often need a series of passages in vivo to adapt to the host environment because of variable receptors on the cell surface and may have intracellular restrictions from the cell types or host species. Here, we describe a long‐term cell culture system, conditionally reprogrammed cells (CRCs), and its applications in modeling human viral diseases and drug discovery. Using feeder layer coculture in presence of Y‐27632 (conditional reprogramming, CR), CRCs can be obtained and rapidly propagated from surgical specimens, core or needle biopsies, and other minimally invasive or noninvasive specimens, for example, nasal cavity brushing. CRCs preserve their lineage functions and provide biologically relevant and physiological conditions, which are suitable for studies of viral entry and replication, innate immune responses of host cells, and discovery of antiviral drugs. In this review, we summarize the applications of CR technology in modeling host‐virus interactions and human viral diseases including severe acute respiratory syndrome coronavirus‐2 and coronavirus disease‐2019, and antiviral discovery.

Prognostic significance of proteomics and multi-omics studies in renal carcinoma

INTRODUCTION

Renal carcinoma, and in particular its most common variant, the clear cell subtype, is often diagnosed incidentally through abdominal imaging and frequently, the tumor is discovered at an early stage. However, 20% to 40% of patients undergoing nephrectomy for clinically localized renal cancer, even after accurate histological and clinical classification, will develop metastasis or recurrence, justifying the associated mortality rate. Therefore, even if renal carcinoma is not among the most frequent nor deadly cancers, a better prognostication is needed.

AREAS COVERED

Recently proteomics or other omics combinations have been applied to both cancer tissues, on the neoplasia itself and surrounding microenvironment, cultured cells, and biological fluids (so-called liquid biopsy) generating a list of prognostic molecular tools that will be reviewed in the present paper.

EXPERT OPINION

Although promising, none of the approaches listed above has been yet translated in clinics. This is likely due to the peculiar genetic and phenotypic heterogeneity of this cancer, which makes nearly each tumor different from all the others. Attempts to overcome this issue will be also revised. In particular, we will discuss how the application of omics-integrated approaches could provide the determinants of response to the different targeted drugs.

Conditional reprogramming: Modeling urological cancer and translation to clinics

Patient-derived models, including cell models (organoids and conditionally reprogrammed cells [CRCs]) and patient-derived xenografts, are urgently needed for both basic and translational cancer research. Conditional reprogramming (CR) technique refers to a co-culture system of primary human normal or tumor cells with irradiated murine fibroblasts in the presence of a Rho-associated kinase inhibitor to allow the primary cells to acquire stem cell properties and the ability to proliferate indefinitely in vitro without any exogenous gene or viral transfection. Considering its robust features, the CR technique may facilitate cancer research in many aspects. Under in vitro culturing, malignant CRCs can share certain genetic aberrations and tumor phenotypes with their parental specimens. Thus, tumor CRCs can promisingly be utilized for the study of cancer biology, the discovery of novel therapies, and the promotion of precision medicine. For normal CRCs, the characteristics of normal karyotype maintenance and lineage commitment suggest their potential in toxicity testing and regenerative medicine. In this review, we discuss the applications, limitations, and future potential of CRCs in modeling urological cancer and translation to clinics.

Microbiome within Primary Tumor Tissue from Renal Cell Carcinoma May Be Associated with PD-L1 Expression of the Venous Tumor Thrombus

Objective

To perform a proof of concept microbiome evaluation and PD-L1 expression profiling in clear-cell renal cell carcinoma (cc-RCC) with associated tumor thrombus (TT).

Methods

After IRB approval, six patients underwent radical nephrectomy (RN) with venous tumor thrombectomy (VTT). We collected fresh tissue specimens from normal adjacent, tumor, and thrombus tissues. We utilized RNA sequencing to obtain PD-L1 expression profiles and perform microbiome analysis. Statistical assessment was performed using Student's t-test, chi-square, and spearman rank correlations using SPSS v25.

Results

We noted the tumor thrombus to be mostly devoid of diverse microbiota. A large proportion of Staphylococcus epidermidus was detected and unknown if this is a surgical or postsurgical contaminant; however, it was noted more in the thrombus than other tissues. Microbiome diversity profiles were most abundant in the primary tumor compared to the thrombus or normal adjacent tissue. Differential expression of PD-L1 was examined in the tumor thrombus to the normal background tissue and noted three of the six subjects had a threshold above 2-fold. These three similar subjects had foreign microbiota that are typical residents of the oral microbiome.

Conclusion

Renal tumors have more diverse microbiomes than normal adjacent tissue. Identification of resident oral microbiome profiles in clear-cell renal cancer with tumor thrombus provides a potential biomarker for thrombus response to PD-L1 inhibition.

A new model isolates glioblastoma clonal interactions and reveals unexpected modes for regulating motility, proliferation, and drug resistance

Tumor clonal heterogeneity drives treatment resistance. But robust models are lacking that permit eavesdropping on the basic interaction network of tumor clones. We developed an in vitro, functional model of clonal cooperation using U87MG glioblastoma cells, which isolates fundamental clonal interactions. In this model pre-labeled clones are co-cultured to track changes in their individual motility, growth, and drug resistance behavior while mixed. This highly reproducible system allowed us to address a new class of fundamental questions about clonal interactions. We demonstrate that (i) a single clone can switch off the motility of the entire multiclonal U87MG cell line in 3D culture, (ii) maintenance of clonal heterogeneity is an intrinsic and influential cancer cell property, where clones coordinate growth rates to protect slow growing clones, and (iii) two drug sensitive clones can develop resistance de novo when cooperating. Furthermore, clonal communication for these specific types of interaction did not require diffusible factors, but appears to depend on cell-cell contact. This model constitutes a straightforward but highly reliable tool for isolating the complex clonal interactions that make up the fundamental "hive mind" of the tumor. It uniquely exposes clonal interactions for future pharmacological and biochemical studies.

Cancer Sample Biobanking at the Next Level: Combining Tissue With Living Cell Repositories to Promote Precision Medicine

Biorespositories of formalin-fixed and paraffin-embedded (FFPE) or fresh frozen human tissues from malignant diseases generated as integral part of the diagnostic workup in many pathology departments have been pivotal resources for translational cancer studies. However, such tissue biobanks have traditionally contained only non-viable specimens and thus cannot enable functional assays for the discovery and validation of therapeutic targets or the assessment of drug responses and resistance to treatment. To overcome these limitations, we have developed a next-generation comprehensive biobanking platform that includes the generation of patient-derived in vitro cell models from colorectal, pancreatic and kidney cancers among others. As such patient-derived cell (PDC) models retain important features of the original human tumors, they have emerged as relevant tools for more dynamic clinical and experimental analyses of cancer. Here, we describe details of the complex processes of acquisition and processing of patient-derived samples, propagation, annotation, characterization and distribution of resulting cell models and emphasize the requirements of quality assurance, organizational considerations and investment into resources. Taken together, we show how clinical tissue collections can be taken to the next level thus promising major new opportunities for understanding and treating cancer in the context of precision medicine.

In Silico Drug Prescription for Targeting Cancer Patient Heterogeneity and Prediction of Clinical Outcome

In silico drug prescription tools for precision cancer medicine can match molecular alterations with tailored candidate treatments. These methodologies require large and well-annotated datasets to systematically evaluate their performance, but this is currently constrained by the lack of complete patient clinicopathological data. Moreover, in silico drug prescription performance could be improved by integrating additional tumour information layers like intra-tumour heterogeneity (ITH) which has been related to drug response and tumour progression. PanDrugs is an in silico drug prescription method which prioritizes anticancer drugs combining both biological and clinical evidence. We have systematically evaluated PanDrugs in the Genomic Data Commons repository (GDC). Our results showed that PanDrugs is able to establish an a priori stratification of cancer patients treated with Epidermal Growth Factor Receptor (EGFR) inhibitors. Patients labelled as responders according to PanDrugs predictions showed a significantly increased overall survival (OS) compared to non-responders. PanDrugs was also able to suggest alternative tailored treatments for non-responder patients. Additionally, PanDrugs usefulness was assessed considering spatial and temporal ITH in cancer patients and showed that ITH can be approached therapeutically proposing drugs or combinations potentially capable of targeting the clonal diversity. In summary, this study is a proof of concept where PanDrugs predictions have been correlated to OS and can be useful to manage ITH in patients while increasing therapeutic options and demonstrating its clinical utility.

Tracing Clonal Dynamics Reveals that Two- and Three-dimensional Patient-derived Cell Models Capture Tumor Heterogeneity of Clear Cell Renal Cell Carcinoma

BACKGROUND

Extensive DNA sequencing has led to an unprecedented view of the diversity of individual genomes and their evolution among patients with clear cell renal cell carcinoma (ccRCC).

OBJECTIVE

To understand subclonal architecture and dynamics of patient-derived two-dimensional (2D) and three-dimensional (3D) ccRCC models in vitro, in order to determine whether they mirror ccRCC inter- and intratumor heterogeneity.

DESIGN, SETTING, AND PARTICIPANTS

We have established a comprehensive platform of living renal cancer cell models from ccRCC surgical specimens.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

We confirmed the concordance of 2D and 3D patient-derived cell (PDC) models with the original tumor tissue in terms of histology, biomarker expression, cancer driver mutations, and copy number alterations. We addressed inter- and intrapatient heterogeneity by analyzing clonal dynamics during serial passaging.

RESULTS AND LIMITATIONS

In-depth genetic characterization verified the presence of heterogeneous cell populations, and revealed a high degree of similarity between subclonal compositions of monolayer and organoid cell cultures and the corresponding parental ccRCCs. Clonal dynamics were evident during serial passaging of cells in vitro, suggesting that PDC cultures can offer insights into evolutionary potential and treatment susceptibility of ccRCC subclones in vivo. Proof-of-concept drug profiling using selected ccRCC-targeted therapy agents highlighted patient-specific vulnerabilities in PDC models that could not be anticipated by interrogating commercially available cell lines.

CONCLUSIONS

We demonstrate that PDC models mirror inter- and intratumor heterogeneity of ccRCC in vitro. Based on our findings, we envision that the use of these models will advance our understanding of the trajectories that cause genetic diversity and their consequences for treatment on an individual level.

PATIENT SUMMARY

In this study, we developed two- and three-dimensional patient-derived models from clear cell renal cell carcinoma (ccRCC) as "mini-tumors in a dish." We show that these cell models retain important features of the human ccRCCs such as the profound tumor heterogeneity, thus highlighting their importance for cancer research and precision medicine.

Organoids as a new model for improving regenerative medicine and cancer personalized therapy in renal diseases

The pressure towards innovation and creation of new model systems in regenerative medicine and cancer research has fostered the development of novel potential therapeutic applications. Kidney injuries provoke a high request of organ transplants making it the most demanding system in the field of regenerative medicine. Furthermore, renal cancer frequently threaten patients’ life and aggressive forms still remain difficult to treat. Ethical issues related to the use of embryonic stem cells, has fueled research on adult, patient-specific pluripotent stem cells as a model for discovery and therapeutic development, but to date, normal and cancerous renal experimental models are lacking. Several research groups are focusing on the development of organoid cultures. Since organoids mimic the original tissue architecture in vitro, they represent an excellent model for tissue engineering studies and cancer therapy testing. We established normal and tumor renal cell carcinoma organoids previously maintained in a heterogeneous multi-clone stem cell-like enriching medium. Starting from adult normal kidney specimens, we were able to isolate and propagate organoid 3D-structures composed of both differentiated and undifferentiated cells while expressing nephron specific markers. Furthermore, we were capable to establish organoids derived from cancer tissues although with a success rate inferior to that of their normal counterpart. Cancer cultures displayed epithelial and mesenchymal phenotype while retaining tumor specific markers. Of note, tumor organoids recapitulated neoplastic masses when orthotopically injected into immunocompromised mice. Our data suggest an innovative approach of long-term establishment of normal- and cancer-derived renal organoids obtained from cultures of fleshly dissociated adult tissues. Our results pave the way to organ replacement pioneering strategies as well as to new models for studying drug-induced nephrotoxicity and renal diseases. Along similar lines, deriving organoids from renal cancer patients opens unprecedented opportunities for generation of preclinical models aimed at improving therapeutic treatments.