Initiation and Characterization of Small Cell Lung Cancer Patient-Derived Xenografts from Ultrasound-Guided Transbronchial Needle Aspirates

Small cell lung cancer (SCLC) is a devastating disease with limited treatment options. Due to its early metastatic nature and rapid growth, surgical resection is rare. Standard of care treatment regimens remain largely unchanged since the 1980’s, and five-year survival lingers near 5%. Patient-derived xenograft (PDX) models have been established for other tumor types, amplifying material for research and serving as models for preclinical experimentation; however, limited availability of primary tissue has curtailed development of these models for SCLC. The objective of this study was to establish PDX models from commonly collected fine needle aspirate biopsies of primary SCLC tumors, and to assess their utility as research models of primary SCLC tumors. These transbronchial needle aspirates efficiently engrafted as xenografts, and tumor histomorphology was similar to primary tumors. Resulting tumors were further characterized by H&E and immunohistochemistry, cryopreserved, and used to propagate tumor-bearing mice for the evaluation of standard of care chemotherapy regimens, to assess their utility as models for tumors in SCLC patients. When treated with Cisplatin and Etoposide, tumor-bearing mice responded similarly to patients from whom the tumors originated. Here, we demonstrate that PDX tumor models can be efficiently established from primary SCLC transbronchial needle aspirates, even after overnight shipping, and that resulting xenograft tumors are similar to matched primary tumors in cancer patients by both histology and chemo-sensitivity. This method enables physicians at non-research institutions to collaboratively contribute to the rapid establishment of extensive PDX collections of SCLC, enabling experimentation with clinically relevant tissues and development of improved therapies for SCLC patients.     

Machine-learning aided in situ drug sensitivity screening predicts treatment outcomes in ovarian PDX tumors

Long-term treatment outcomes for patients with high grade ovarian cancers have not changed despite innovations in therapies. There is no recommended assay for predicting patient response to second-line therapy, thus clinicians must make treatment decisions based on each individual patient. Patient-derived xenograft (PDX) tumors have been shown to predict drug sensitivity in ovarian cancer patients, but the time frame for intraperitoneal (IP) tumor generation, expansion, and drug screening is beyond that for tumor recurrence and platinum resistance to occur, thus results do not have clinical utility. We describe a drug sensitivity screening assay using a drug delivery microdevice implanted for 24 h in subcutaneous (SQ) ovarian PDX tumors to predict treatment outcomes in matched IP PDX tumors in a clinically relevant time frame. The SQ tumor response to local microdose drug exposure was found to be predictive of the growth of matched IP tumors after multi-week systemic therapy using significantly fewer animals (10 SQ vs 206 IP). Multiplexed immunofluorescence image analysis of phenotypic tumor response combined with a machine learning classifier could predict IP treatment outcomes against three second-line cytotoxic therapies with an average AUC of 0.91.     

Establishment and evaluation of four different types of patient-derived xenograft models

Background

Patient-derived xenografts (PDX) have a biologically stable in tumor architecture, drug responsiveness, mutational status and global gene-expression patterns. Numerous PDX models have been established to date, however their thorough characterization regarding the tumor formation and rates of tumor growth in the established models remains a challenging task. Our study aimed to provide more detailed information for establishing the PDX models successfully and effectively.

Methods

We transplanted four different types of solid tumors from 108 Chinese patients, including 21 glioblastoma (GBM), 11 lung cancers (LC), 54 gastric cancers (GC) and 21 colorectal cancers (CRC), and took tumor tissues passaged for three successive generations. Here we report the rate of tumor formation, tumor-forming times, tumor growth curves and mortality of mice in PDX model. We also report H&E staining and immunohistochemistry for HLA-A, CD45, Ki67, GFAP, and CEA protein expression between patient cancer tissues and PDX models.

Results

Tumor formation rate increased significantly in subsequent tumor generations. Also, the survival rates of GC and CRC were remarkably higher than GBM and LC. As for the time required for the formation of tumors, which reflects the tumor growth rate, indicated that tumor growth rate always increased as the generation number increased. The tumor growth curves also illustrate this law. Similarly, the survival rate of PDX mice gradually improved with the increased generation number in GC and CRC. And generally, there was more proliferation (Ki67+) in the PDX models than in the patient tumors, which was in accordance with the results of tumor growth rate. The histological findings confirm similar histological architecture and degrees of differentiation between patient cancer tissues and PDX models with statistical analysis by GraphPad Prism 5.0.

Conclusion

We established four different types of PDX models successfully, and our results add to the current understanding of the establishment of PDX models and may contribute to the extension of application of different types of PDX models.

     

Whole Exome Sequencing of Rapid Autopsy Tumors and Xenograft Models Reveals Possible Driver Mutations Underlying Tumor Progression

Pancreatic Ductal Adenocarcinoma (PDAC) is a highly lethal malignancy due to its propensity to invade and rapidly metastasize and remains very difficult to manage clinically. One major hindrance towards a better understanding of PDAC is the lack of molecular data sets and models representative of end stage disease. Moreover, it remains unclear how molecularly similar patient-derived xenograft (PDX) models are to the primary tumor from which they were derived. To identify potential molecular drivers in metastatic pancreatic cancer progression, we obtained matched primary tumor, metastases and normal (peripheral blood) samples under a rapid autopsy program and performed whole exome sequencing (WES) on tumor as well as normal samples. PDX models were also generated, sequenced and compared to tumors. Across the matched data sets generated for three patients, there were on average approximately 160 single-nucleotide mutations in each sample. The majority of mutations in each patient were shared among the primary and metastatic samples and, importantly, were largely retained in the xenograft models. Based on the mutation prevalence in the primary and metastatic sites, we proposed possible clonal evolution patterns marked by functional mutations affecting cancer genes such as KRAS, TP53 and SMAD4 that may play an important role in tumor initiation, progression and metastasis. These results add to our understanding of pancreatic tumor biology, and demonstrate that PDX models derived from advanced or end-stage likely closely approximate the genetics of the disease in the clinic and thus represent a biologically and clinically relevant pre-clinical platform that may enable the development of effective targeted therapies for PDAC.     

Harnessing genetically engineered mouse models for preclinical testing

Recent studies cast doubt on the value of traditionally used models as tools for testing therapies for human cancer. Although the standard practice of xenografting tumors into immunocompromised mice generates reproducible tumors, drug testing in these models has low predictive power when compared to the clinical responses in Phase II trials. The use of tumor-bearing genetically engineered mouse models holds promise for improving preclinical testing. These models recapitulate specific molecular pathways in tumor initiation or progression and provide a biological system in which to study the disease process for assessing efficacy of new therapies and proof-of-principle for testing molecularly targeted drugs. In this review, we discuss the advantages and limitations of genetically engineered mice and plausible solutions for adapting these valuable tumors for wider use in preclinical testing by transplantation into na?ve recipients. We also provide examples of comparative molecular analysis of mammary tumors from MMTV-Polyoma Middle-T antigen and MMTV-wnt1 models as tools for finding clinical correlates, validating existing models and guiding the development of new genetically engineered mouse models for cancer.     

STRN-ALK rearranged pediatric malignant peritoneal mesothelioma – Functional testing of 527 cancer drugs in patient-derived cancer cells

Genetic rearrangements involving the anaplastic lymphoma kinase (ALK) gene create oncogenic drivers for several cancers, including malignant peritoneal mesothelioma (MPeM). Here, we report genomic and functional precision oncology profiling on a rare case of a 5-year old patient diagnosed with wide-spread and aggressive MPeM, driven by STRN-ALK rearrangement. We established genomically representative patient-derived cancer cells (PDCs) from the tumor sample and performed high-throughput drug sensitivity testing with 527 oncology compounds to identify potent inhibitors. As expected, the PDCs were overall sensitive to the ALK inhibitors, although the eight different inhibitors tested had variable efficacy. We also discovered other effective inhibitors, such as MEK/ERK inhibitors and those targeting pathways downstream of ALK as well as Bcl-xl inhibitors. In contrast, most cytotoxic drugs were not very effective. ALK inhibitors synergized with MEK and PI3K/mTOR inhibitors, highlighting potential combinatorial strategies to enhance drug efficacy and tackle drug resistance. Based on genomic data and associated functional validation, the patient was treated with the ALK inhibitor crizotinib in combination with conventional chemotherapy (cisplatin and gemcitabine). A complete disease remission was reached, lasting now for over 3 years. Our results illustrate a rare pediatric cancer case, and highlight the potential of functional precision oncology to discover pathogenetic drivers, validate dependency on driver signals, compare different inhibitors against each other and potentially enhance targeted treatments by drug combinations. Such real-time implementation of functional precision oncology could pave the way towards safer and more effective personalized cancer therapies for individual pediatric cancer patients with rare tumors.     

Metformin Treatment Does Not Inhibit Growth of Pancreatic Cancer Patient-Derived Xenografts

There is currently tremendous interest in developing anti-cancer therapeutics targeting cell signaling pathways important for both cancer cell metabolism and growth. Several epidemiological studies have shown that diabetic patients taking metformin have a decreased incidence of pancreatic cancer. This has prompted efforts to evaluate metformin, a drug with negligible toxicity, as a therapeutic modality in pancreatic cancer. Preclinical studies in cell line xenografts and one study in patient-derived xenograft (PDX) models were promising, while recently published clinical trials showed no benefit to adding metformin to combination therapy regimens for locally advanced and metastatic pancreatic cancer. PDX models in which patient tumors are directly engrafted into immunocompromised mice have been shown to be excellent preclinical models for biomarker discovery and therapeutic development. We evaluated the response of four PDX tumor lines to metformin treatment and found that all four of our PDX lines were resistant to metformin. We found that the mechanisms of resistance may occur through lack of sustained activation of adenosine monophosphate-activated protein kinase (AMPK) or downstream reactivation of the mammalian target of rapamycin (mTOR). Moreover, combined treatment with metformin and mTOR inhibitors failed to improve responses in cell lines, which further indicates that metformin alone or in combination with mTOR inhibitors will be ineffective in patients, and that resistance to metformin may occur through multiple pathways. Further studies are required to better understand these mechanisms of resistance and inform potential combination therapies with metformin and existing or novel therapeutics.     

Development and Characterization of Bladder Cancer Patient-Derived Xenografts for Molecularly Guided Targeted Therapy

Background

The overarching goal of this project is to establish a patient-derived bladder cancer xenograft (PDX) platform, annotated with deep sequencing and patient clinical information, to accelerate the development of new treatment options for bladder cancer patients. Herein, we describe the creation, initial characterization and use of the platform for this purpose.

Methods and Findings

Twenty-two PDXs with annotated clinical information were established from uncultured unselected clinical bladder cancer specimens in immunodeficient NSG mice. The morphological fidelity was maintained in PDXs. Whole exome sequencing revealed that PDXs and parental patient cancers shared 92–97% of genetic aberrations, including multiple druggable targets. For drug repurposing, an EGFR/HER2 dual inhibitor lapatinib was effective in PDX BL0440 (progression-free survival or PFS of 25.4 days versus 18.4 days in the control, p = 0.007), but not in PDX BL0269 (12 days versus 13 days in the control, p = 0.16) although both expressed HER2. To screen for the most effective MTT, we evaluated three drugs (lapatinib, ponatinib, and BEZ235) matched with aberrations in PDX BL0269; but only a PIK3CA inhibitor BEZ235 was effective (p<0.0001). To study the mechanisms of secondary resistance, a fibroblast growth factor receptor 3 inhibitor BGJ398 prolonged PFS of PDX BL0293 from 9.5 days of the control to 18.5 days (p<0.0001), and serial biopsies revealed that the MAPK/ERK and PIK3CA-AKT pathways were activated upon resistance. Inhibition of these pathways significantly prolonged PFS from 12 day of the control to 22 days (p = 0.001). To screen for effective chemotherapeutic drugs, four of the first six PDXs were sensitive to the cisplatin/gemcitabine combination, and chemoresistance to one drug could be overcome by the other drug.

Conclusion

The PDX models described here show good correlation with the patient at the genomic level and known patient response to treatment. This supports further evaluation of the PDXs for their ability to accurately predict a patient’s response to new targeted and combination strategies for bladder cancer.     

Synergistic effect of PARP inhibitor and BRD4 inhibitor in multiple models of ovarian cancer

Ovarian cancer has the highest facility rate among gynaecological tumours. Current therapies including PARP inhibitors have a defect that ovarian tumour is easy to recurrent and become resistant to therapy. To solve this problem, we found that BRD4 inhibitor AZD5153 and PARP inhibitor olaparib had a widespread synergistic effect in multiple models with different gene backgrounds. AZD5153 sensitizes cells to olaparib and reverses the acquired resistance by down-regulating PTEN expression levels to destabilize hereditary materials. In this study, we used the following multiple ovarian cancer models PDX, PDO and 3D/2D cell lines to elucidate the co-effect of AZD5153 and olaparib in vivo and in vitro. The similar results of these models further proved that the mechanism identified was consistent with the biological process occurring in ovarian cancer patients after drug treatment. This consistency between the results of different models suggests the possibility of translating these laboratory research findings into clinical studies towards developing treatments.     

Conventional Chemotherapy and Oncogenic Pathway Targeting in Ovarian Carcinosarcoma Using a Patient-Derived Tumorgraft

Ovarian carcinosarcoma is a rare subtype of ovarian cancer with poor clinical outcomes. The low incidence of this disease makes accrual to large clinical trials challenging. However, studies have shown that treatment responses in patient-derived xenograft (PDX) models correlate with matched-patient responses in the clinic, supporting their use for preclinical testing of standard and novel therapies. An ovarian carcinosarcoma PDX is presented herein and showed resistance to carboplatin and paclitaxel (similar to the patient) but exhibited significant sensitivity to ifosfamide and paclitaxel. The PDX demonstrated overexpression of EGFR mRNA and gene amplification by array comparative genomic hybridization (log2 ratio 0.399). EGFR phosphorylation was also detected. Angiogensis and insulin-like growth factor pathways were also implicated by overexpression of VEGFC and IRS1. In order to improve response to chemotherapy, the PDX was treated with carboplatin/paclitaxel with or without a pan-HER and VEGF inhibitor (BMS-690514) but there was no tumor growth inhibition or improved animal survival, which may be explained by a KRAS mutation. Resistance was also observed when the IGF-1R inhibitor BMS-754807 was combined with carboplatin/paclitaxel. Because poly (ADP-ribose) polymerase inhibitors have activity in ovarian cancer patients, with and without BRCA mutations, ABT-888 was also tested but found to have no activity. Pathogenic mutations were also detected in TP53 and PIK3CA. In conclusion, ifosfamide/paclitaxel was superior to carboplatin/paclitaxel in this ovarian carcinosarcoma PDX and gene overexpression or amplification alone was not sufficient to predict response to targeted therapy. Better predictive markers of response are needed.     

Tumor models for prostate cancer exemplified by fibroblast growth factor 8-induced tumorigenesis and tumor progression

Prostate cancer is a very common malignancy among Western males. Although most tumors are indolent and grow slowly, some grow and metastasize aggressively. Because prostate cancer growth is usually androgen-dependent, androgen ablation offers a therapeutic option to treat post-resection tumor recurrence or primarily metastasized prostate cancer. However, patients often relapse after the primary response to androgen ablation therapy, and there is no effective cure for cases of castration-resistant prostate cancer (CRPC). The mechanisms of tumor growth in CRPC are poorly understood. Although the androgen receptors (ARs) remain functional in CRPC, other mechanisms are clearly activated (e.g., disturbed growth factor signaling). Results from our laboratory and others have shown that dysregulation of fibroblast growth factor (FGF) signaling, including FGF receptor 1 (FGFR1) activation and FGF8b overexpression, has an important role in prostate cancer growth and progression. Several experimental models have been developed for prostate tumorigenesis and various stages of tumor progression. These models include genetically engineered mice and rats, as well as induced tumors and xenografts in immunodeficient mice. The latter was created using parental and genetically modified cell lines. All of these models greatly helped to elucidate the roles of different genes in prostate carcinogenesis and tumor progression. Recently, patient-derived xenografts have been studied for possible use in testing individual, specific responses of tumor tissue to different treatment options. Feasible and functional CRPC models for drug responsiveness analysis and the development of effective therapies targeting the FGF signaling pathway and other pathways in prostate cancer are being actively investigated.     

Establishment of peripheral blood mononuclear cell-derived humanized lung cancer mouse models for studying efficacy of PD-L1/PD-1 targeted immunotherapy

ABSTRACT

Animal models used to evaluate efficacies of immune checkpoint inhibitors are insufficient or inaccurate. We thus examined two xenograft models used for this purpose, with the aim of optimizing them. One method involves the use of peripheral blood mononuclear cells and cell line-derived xenografts (PBMCs-CDX model). For this model, we implanted human lung cancer cells into NOD-scid-IL2Rg?/? (NSI) mice, followed by injection of human PBMCs. The second method involves the use of hematopoietic stem and progenitor cells and CDX (HSPCs-CDX model). For this model, we first reconstituted the human immune system by transferring human CD34+ hematopoietic stem and progenitor cells (HSPCs-derived humanized model) and then transplanted human lung cancer cells. We found that the PBMCs-CDX model was more accurate in evaluating PD-L1/PD-1 targeted immunotherapies. In addition, it took only four weeks with the PBMCs-CDX model for efficacy evaluation, compared to 10–14 weeks with the HSPCs-CDX model. We then further established PBMCs-derived patient-derived xenografts (PDX) models, including an auto-PBMCs-PDX model using cancer and T cells from the same tumor, and applied them to assess the antitumor efficacies of anti-PD-L1 antibodies. We demonstrated that this PBMCs-derived PDX model was an invaluable tool to study the efficacies of PD-L1/PD-1 targeted cancer immunotherapies. Overall, we found our PBMCs-derived models to be excellent preclinical models for studying immune checkpoint inhibitors.     

A pharmacogenomic method for individualized prediction of drug sensitivity

Identifying the best drug for each cancer patient requires an efficient individualized strategy. We present MATCH (M erging genomic and pharmacologic A nalyses for T herapy CH oice), an approach using public genomic resources and drug testing of fresh tumor samples to link drugs to patients. Valproic acid (VPA) is highlighted as a proof‐of‐principle. In order to predict specific tumor types with high probability of drug sensitivity, we create drug response signatures using publically available gene expression data and assess sensitivity in a data set of >40 cancer types. Next, we evaluate drug sensitivity in matched tumor and normal tissue and exclude cancer types that are no more sensitive than normal tissue. From these analyses, breast tumors are predicted to be sensitive to VPA. A meta‐analysis across breast cancer data sets shows that aggressive subtypes are most likely to be sensitive to VPA, but all subtypes have sensitive tumors. MATCH predictions correlate significantly with growth inhibition in cancer cell lines and three‐dimensional cultures of fresh tumor samples. MATCH accurately predicts reduction in tumor growth rate following VPA treatment in patient tumor xenografts. MATCH uses genomic analysis with in vitro testing of patient tumors to select optimal drug regimens before clinical trial initiation.     

Genomic Characterisation of Small Cell Lung Cancer Patient-Derived Xenografts Generated from Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration Specimens

Patient-derived xenograft (PDX) models generated from surgical specimens are gaining popularity as preclinical models of cancer. However, establishment of PDX lines from small cell lung cancer (SCLC) patients is difficult due to very limited amount of available biopsy material. We asked whether SCLC cells obtained from endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) could generate PDX lines that maintained the phenotypic and genetic characteristics of the primary tumor. Following successful EBUS-TBNA sampling for diagnostic purposes, we obtained an extra sample for cytologic analysis and implantation into the flanks of immunodeficient mice. Animals were monitored for engraftment for up to 6 months. Histopathologic and immunohistochemical analysis, and targeted next-generation re-sequencing, were then performed in both the primary sample and the derivative PDX line. A total of 12 patients were enrolled in the study. EBUS-TBNA aspirates yielded large numbers of viable tumor cells sufficient to inject between 18,750 and 1,487,000 cells per flank, and to yield microgram quantities of high-quality DNA. Of these, samples from 10 patients generated xenografts (engraftment rate 83%) with a mean latency of 104 days (range 63–188). All but one maintained a typical SCLC phenotype that closely matched the original sample. Identical mutations that are characteristic of SCLC were identified in both the primary sample and xenograft line. EBUS-TBNA has the potential to be a powerful tool in the development of new targeting strategies for SCLC patients by providing large numbers of viable tumor cells suitable for both xenografting and complex genomic analysis.     

Drug screening model meets cancer organoid technology

Tumor organoids inherit the genomic and molecular characteristics of the donor tumor, which not only bridge the gap between genome and phenotype but also circumvent the disadvantages such as genetic information change by using 2D cell lines and the mouse-specific tumor evolution in patient-derived xenograft (PDX). So, cancer organoid has been widely applied to preclinical drug evaluation, biomarker identification, biological research, and individualized therapy. Besides, cancer organoid can be preserved, resuscitated, passed infinitely, and mechanically cultured on a chip for drug screening; it has become one of the partial models for low/high-throughput drug screening in the preclinical trial in vitro. Therefore, this review presents the recent developments of tumor organoids for drug screening, which will introduce from four aspects, including the stability/credibility, types, application, deficiency and prospect of the tumor organoids model for drug screening.     

Epigenetic cancer therapy: Proof of concept and remaining challenges

Over the past few years several drugs that target epigenetic modifications have shown clinical benefits, thus seemingly validating epigenetic cancer therapy. More recently, however, it has become clear that these drugs are either characterized by low specificity or that their target enzymes have low substrate specificity. As such, clinical proof-of-concept for epigenetic cancer therapies remains to be established. Human cancers are characterized by widespread changes in their genomic DNA methylation and histone modification patterns. Epigenetic cancer therapy aims to restore normal epigenetic modification patterns through the inhibition of epigenetic modifier enzymes. In this review, we provide an overview about the known functional roles of DNA methyltransferases, histone deacetylases, histone methyltransferases, and demethylases in cancer development. The available data identify several examples that warrant further consideration as drug targets. Future research should be directed toward targeted enzyme inhibition and toward exploring interactions between epigenetic pathways to maximize cancer specificity.     

A triple-drug nanotherapy to target breast cancer cells,cancer stem cells,and tumor vasculature

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, accounting for the majority of breast cancer-related death. Due to the lack of specific therapeutic targets, chemotherapeutic agents (e.g., paclitaxel) remain the mainstay of systemic treatment, but enrich a subpopulation of cells with tumor-initiating capacity and stem-like characteristics called cancer stem cells (CSCs); thus development of a new and effective strategy for TNBC treatment is an unmet medical need. Cancer nanomedicine has transformed the landscape of cancer drug development, allowing for a high therapeutic index. In this study, we developed a new therapy by co-encapsulating clinically approved drugs, such as paclitaxel, verteporfin, and combretastatin (CA4) in polymer-lipid hybrid nanoparticles (NPs) made of FDA-approved biomaterials. Verteporfin is a drug used in the treatment of macular degeneration and has recently been found to inhibit the Hippo/YAP (Yes-associated protein) pathway, which is known to promote the progression of breast cancer and the development of CSCs. CA4 is a vascular disrupting agent and has been tested in phase II/III of clinical trials. We found that our new three drug-NP not only effectively inhibited TNBC cell viability and cell migration, but also significantly diminished paclitaxel-induced and/or CA4-induced CSC enrichment in TNBC cells, partially through inhibiting the upregulated Hippo/YAP signaling. Combination of verteporfin and CA4 was also more effective in suppressing angiogenesis in an in vivo zebrafish model than single drug alone. The efficacy and application potential of our triple drug-NPs were further assessed by using clinically relevant patient-derived xenograft (PDX) models. Triple drug-NP effectively inhibited the viability of PDX organotypic slide cultures ex vivo and stopped the growth of PDX tumors in vivo. This study developed an approach capable of simultaneously inhibiting bulk cancer cells, CSCs, and angiogenesis.Subject terms: Breast cancer, Cancer stem cells     

Tissue-specific cancer stem/progenitor cells: Therapeutic implications

Surgical resection, chemotherapy, and radiation are the standard therapeutic modalities for treating cancer. These approaches are intended to target the more mature and rapidly dividing cancer cells. However, they spare the relatively quiescent and intrinsically resistant cancer stem cells (CSCs) subpopulation residing within the tumor tissue. Thus, a temporary eradication is achieved and the tumor bulk tends to revert supported by CSCs' resistant features. Based on their unique expression profile, the identification, isolation, and selective targeting of CSCs hold great promise for challenging treatment failure and reducing the risk of cancer recurrence. Yet, targeting CSCs is limited mainly by the irrelevance of the utilized cancer models. A new era of targeted and personalized anti-cancer therapies has been developed with cancer patient-derived organoids (PDOs) as a tool for establishing pre-clinical tumor models. Herein, we discuss the updated and presently available tissue-specific CSC markers in five highly occurring solid tumors. Additionally, we highlight the advantage and relevance of the three-dimensional PDOs culture model as a platform for modeling cancer, evaluating the efficacy of CSC-based therapeutics, and predicting drug response in cancer patients.