Next-Generation Sequencing Analysis and Algorithms for PDX and CDX Models

Personalizing non-small cell lung cancer treatment through patient-derived xenograft models: preclinical and clinical factors for consideration

PURPOSE

In the pursuit of creating personalized and more effective treatment strategies for lung cancer patients, Patient-Derived Xenografts (PDXs) have been introduced as preclinical platforms that can recapitulate the specific patient's tumor in an in vivo model. We investigated how well PDX models can preserve the tumor's clinical and molecular characteristics across different generations.

METHODS

A Non-Small Cell Lung Cancer (NSCLC) PDX model was established in NSG-SGM3 mice and clinical and preclinical factors were assessed throughout subsequent passages. Our cohort consisted of 40 NSCLC patients, which were used to create 20 patient-specific PDX models in NSG-SGM3 mice. Histopathological staining and Whole Exome Sequencing (WES) analysis were preformed to understand tumor heterogeneity throughout serial passages.

RESULTS

The main factors that contributed to the growth of the engrafted PDX in mice were a higher grade or stage of disease, in contrast to the long duration of chemotherapy treatment, which was negatively correlated with PDX propagation. Successful PDX growth was also linked to poorer prognosis and overall survival, while growth pattern variability was affected by the tumor aggressiveness, primarily affecting the first passage. Pathology analysis showed preservation of the histological type and grade; however, WES analysis revealed genomic instability in advanced passages, leading to the inconsistencies in clinically relevant alterations between the PDXs and biopsies.

CONCLUSIONS

Our study highlights the impact of multiple clinical and preclinical factors on the engraftment success, growth kinetics, and tumor stability of patient-specific NSCLC PDXs, and underscores the importance of considering these factors when guiding and evaluating prolonged personalized treatment studies for NSCLC patients in these models, as well as signaling the imperative for additional investigations to determine the full clinical potential of this technique.

Representation of genomic intratumor heterogeneity in multi-region non-small cell lung cancer patient-derived xenograft models

Patient-derived xenograft (PDX) models are widely used in cancer research. To investigate the genomic fidelity of non-small cell lung cancer PDX models, we established 48 PDX models from 22 patients enrolled in the TRACERx study. Multi-region tumor sampling increased successful PDX engraftment and most models were histologically similar to their parent tumor. Whole-exome sequencing enabled comparison of tumors and PDX models and we provide an adapted mouse reference genome for improved removal of NOD scid gamma (NSG) mouse-derived reads from sequencing data. PDX model establishment caused a genomic bottleneck, with models often representing a single tumor subclone. While distinct tumor subclones were represented in independent models from the same tumor, individual PDX models did not fully recapitulate intratumor heterogeneity. On-going genomic evolution in mice contributed modestly to the genomic distance between tumors and PDX models. Our study highlights the importance of considering primary tumor heterogeneity when using PDX models and emphasizes the benefit of comprehensive tumor sampling.

Establishment and Thorough Characterization of Xenograft (PDX) Models Derived from Patients with Pancreatic Cancer for Molecular Analyses and Chemosensitivity Testing

Patient-derived xenograft (PDX) tumor models are essential for identifying new biomarkers, signaling pathways and novel targets, to better define key factors of therapy response and resistance mechanisms. Therefore, this study aimed at establishing pancreas carcinoma (PC) PDX models with thorough molecular characterization, and the identification of signatures defining responsiveness toward drug treatment. In total, 45 PC-PDXs were generated from 120 patient tumor specimens and the identity of PDX and corresponding patient tumors was validated. The majority of engrafted PDX models represent ductal adenocarcinomas (PDAC). The PDX growth characteristics were assessed, with great variations in doubling times (4 to 32 days). The mutational analyses revealed an individual mutational profile of the PDXs, predominantly showing alterations in the genes encoding KRAS, TP53, FAT1, KMT2D, MUC4, RNF213, ATR, MUC16, GNAS, RANBP2 and CDKN2A. Sensitivity of PDX toward standard of care (SoC) drugs gemcitabine, 5-fluorouracil, oxaliplatin and abraxane, and combinations thereof, revealed PDX models with sensitivity and resistance toward these treatments. We performed correlation analyses of drug sensitivity of these PDX models and their molecular profile to identify signatures for response and resistance. This study strongly supports the importance and value of PDX models for improvement in therapies of PC.

Plumbagin has an inhibitory effect on the growth of TSCC PDX model and it enhances the anticancer efficacy of cisplatin

BACKGROUND

Head and neck squamous cell carcinomas are the sixth most common malignant tumors worldwide. Tongue squamous cell carcinoma is a common malignant tumor of this type, and it is associated with poor prognosis, a high rate of recurrence and a low survival rate. Plumbagin is derived from Plumbago zeylanica L, several studies report that plumbagin could inhibit cell, tumor metastasis, induce apoptosis in various cancer cells. Patient-derived xenograft (PDX) model can maintain the heterogeneity and microenvironment of human tumors, is a powerful research tool for developing potentially effective therapies for TSCC.

METHODS

Tumor tissues obtained from TSCC patients were implanted into immunodeficient mice to establish TSCC PDX models. Subsequently, the PDX models were used to evaluate the anti-tumor effects of plumbagin on TSCC. Furthermore, we conducted next-generation sequencing (NGS) and explored the mRNA expression profiles between the treatment and control groups. We selected eight mRNAs related to the characteristics and prognosis of TSCC patients for further analysis.

RESULTS

Plumbagin could inhibit the growth of TSCC PDX models and inhibit expression of Akt/mTOR pathway. In addition, plumbagin was shown to increase drug sensitivity to cisplatin. The eight mRNAs selected for further analysis, AXL, SCG5, VOPP1, DCBLD2 and DRAM1 are cancer-promoting genes, DUSP1, AQP5 and BLNK are cancer suppressor genes. And they were related to the diagnosis, growth, prognosis, and immune cell infiltration in TSCC patients.

CONCLUSION

Plumbagin exhibits an inhibitory effect on the growth of the PDX model of TSCC. Moreover, plumbagin enhances the inhibitory effects of cisplatin.

Lineage Plasticity in SCLC Generates Non-Neuroendocrine Cells Primed for Vasculogenic Mimicry

INTRODUCTION

Vasculogenic mimicry (VM), the process of tumor cell transdifferentiation to endow endothelial-like characteristics supporting de novo vessel formation, is associated with poor prognosis in several tumor types, including SCLC. In genetically engineered mouse models (GEMMs) of SCLC, NOTCH, and MYC co-operate to drive a neuroendocrine (NE) to non-NE phenotypic switch, and co-operation between NE and non-NE cells is required for metastasis. Here, we define the phenotype of VM-competent cells and molecular mechanisms underpinning SCLC VM using circulating tumor cell-derived explant (CDX) models and GEMMs.

METHODS

We analyzed perfusion within VM vessels and their association with NE and non-NE phenotypes using multiplex immunohistochemistry in CDX, GEMMs, and patient biopsies. We evaluated their three-dimensional structure and defined collagen-integrin interactions.

RESULTS

We found that VM vessels are present in 23/25 CDX models, 2 GEMMs, and in 20 patient biopsies of SCLC. Perfused VM vessels support tumor growth and only NOTCH-active non-NE cells are VM-competent in vivo and ex vivo, expressing pseudohypoxia, blood vessel development, and extracellular matrix organization signatures. On Matrigel, VM-primed non-NE cells remodel extracellular matrix into hollow tubules in an integrin β1-dependent process.

CONCLUSIONS

We identified VM as an exemplar of functional heterogeneity and plasticity in SCLC and these findings take considerable steps toward understanding the molecular events that enable VM. These results support therapeutic co-targeting of both NE and non-NE cells to curtail SCLC progression and to improve the outcomes of patients with SCLC in the future.

Challenges and Prospects of Patient-Derived Xenografts for Cancer Research

We discuss the importance of the in vivo models in elucidating cancer biology, focusing on the patient-derived xenograft (PDX) models, which are classic and standard functional in vivo platforms for preclinical evaluation. We provide an overview of the most representative models, including cell-derived xenografts (CDX), tumor and metastatic cell-derived xenografts, and PDX models utilizing humanized mice (HM). The orthotopic models, which could reproduce the cancer environment and its progression, similar to human tumors, are particularly common. The standard procedures and rationales of gastric adenocarcinoma (GAC) orthotopic models are addressed. Despite the significant advantages of the PDX models, such as recapitulating key features of human tumors and enabling drug testing in the in vivo context, some challenges must be acknowledged, including loss of heterogeneity, selection bias, clonal evolution, stroma replacement, tumor micro-environment (TME) changes, host cell carryover and contaminations, human-to-host cell oncogenic transformation, human and host viral infections, as well as limitations for immunologic research. To compensate for these limitations, other mouse models, such as syngeneic and humanized mouse models, are currently utilized. Overall, the PDX models represent a powerful tool in cancer research, providing critical insights into tumor biology and potential therapeutic targets, but their limitations and challenges must be carefully considered for their effective use. Lastly, we present an intronic quantitative PCR (qPCR) method to authenticate, detect, and quantify human/murine cells in cell lines and PDX samples.

Towards an optimal model for gastric cancer peritoneal metastasis: current challenges and future directions

Peritoneal metastasis is a challenging aspect of clinical practice for gastric cancer. Animal models are crucial in understanding molecular mechanisms, assessing drug efficacy, and conducting clinical intervention studies, including those related to gastric cancer peritoneal metastasis. Unlike other xenograft models, peritoneal metastasis models should not only present tumor growth at the transplant site, but also recapitulate tumor cell metastasis in the abdominal cavity. Developing a reliable model of gastric cancer peritoneal metastasis involves several technical aspects, such as the selection of model animals, source of xenograft tumors, technology of transplantation, and dynamic monitoring of the tumor progression. To date, challenges remain in developing a reliable model that can completely recapitulate peritoneal metastasis. Thus, this review aims to summarize the techniques and strategies used to establish animal models of gastric cancer peritoneal metastasis, providing a reference for future model establishment.

ACT-Discover: identifying karyotype heterogeneity in pancreatic cancer evolution using ctDNA

BACKGROUND

Liquid biopsies and the dynamic tracking of somatic mutations within circulating tumour DNA (ctDNA) can provide insight into the dynamics of cancer evolution and the intra-tumour heterogeneity that fuels treatment resistance. However, identifying and tracking dynamic changes in somatic copy number alterations (SCNAs), which have been associated with poor outcome and metastasis, using ctDNA is challenging. Pancreatic adenocarcinoma is a disease which has been considered to harbour early punctuated events in its evolution, leading to an early fitness peak, with minimal further subclonal evolution.

METHODS

To interrogate the role of SCNAs in pancreatic adenocarcinoma cancer evolution, we applied whole-exome sequencing of 55 longitudinal cell-free DNA (cfDNA) samples taken from 24 patients (including 8 from whom a patient-derived xenograft (PDX) was derived) with metastatic disease prospectively recruited into a clinical trial. We developed a method, Aneuploidy in Circulating Tumour DNA (ACT-Discover), that leverages haplotype phasing of paired tumour biopsies or PDXs to identify SCNAs in cfDNA with greater sensitivity.

RESULTS

SCNAs were observed within 28 of 47 evaluable cfDNA samples. Of these events, 30% could only be identified by harnessing the haplotype-aware approach leveraged in ACT-Discover. The exceptional purity of PDX tumours enabled near-complete phasing of genomic regions in allelic imbalance, highlighting an important auxiliary function of PDXs. Finally, although the classical model of pancreatic cancer evolution emphasises the importance of early, homogenous somatic events as a key requirement for cancer development, ACT-Discover identified substantial heterogeneity of SCNAs, including parallel focal and arm-level events, affecting different parental alleles within individual tumours. Indeed, ongoing acquisition of SCNAs was identified within tumours throughout the disease course, including within an untreated metastatic tumour.

CONCLUSIONS

This work demonstrates the power of haplotype phasing to study genomic variation in cfDNA samples and reveals undiscovered intra-tumour heterogeneity with important scientific and clinical implications. Implementation of ACT-Discover could lead to important insights from existing cohorts or underpin future prospective studies seeking to characterise the landscape of tumour evolution through liquid biopsy.

A new intronic quantitative PCR method led to the discovery of transformation from human ascites to murine malignancy in a mouse model

Purpose

To establish a fast and accurate detection method for interspecies contaminations in the patient-derived xenograft (PDX) models and cell lines, and to elucidate possible mechanisms if interspecies oncogenic transformation is detected.

Methods

A fast and highly sensitive intronic qPCR method detecting Gapdh intronic genomic copies was developed to quantify if cells were human or murine or a mixture. By this method, we documented that murine stromal cells were abundant in the PDXs; we also authenticated our cell lines to be human or murine.

Results

In one mouse model, GA0825-PDX transformed murine stromal cells into a malignant tumorigenic murine P0825 cell line. We traced the timeline of this transformation and discovered three subpopulations descended from the same GA0825-PDX model: epithelium-like human H0825, fibroblast-like murine M0825, and main passaged murine P0825 displayed differences in tumorigenic capability in vivo. P0825 was the most aggressive and H0825 was weakly tumorigenic. Immunofluorescence (IF) staining revealed that P0825 cells highly expressed several oncogenic and cancer stem cell markers. Whole exosome sequencing (WES) analysis revealed that TP53 mutation in the human ascites IP116-generated GA0825-PDX may have played a role in the human-to-murine oncogenic transformation.

Conclusion

This intronic qPCR is able to quantify human/mouse genomic copies with high sensitivity and within a time frame of a few hours. We are the first to use intronic genomic qPCR for authentication and quantification of biosamples. Human ascites transformed murine stroma into malignancy in a PDX model.

Glioblastoma cells have increased capacity to repair radiation-induced DNA damage after migration to the olfactory bulb

BACKGROUND

The invasive nature of GBM combined with the diversity of brain microenvironments creates the potential for a topographic heterogeneity in GBM radioresponse. Investigating the mechanisms responsible for a microenvironment-induced differential GBM response to radiation may provide insights into the molecules and processes mediating GBM radioresistance.

METHODS

Using a model system in which human GBM stem-like cells implanted into the right striatum of nude mice migrate throughout the right hemisphere (RH) to the olfactory bulb (OB), the radiation-induced DNA damage response was evaluated in each location according to γH2AX and 53BP1 foci and cell cycle phase distribution as determined by flow cytometry and immunohistochemistry. RNAseq was used to compare transcriptomes of tumor cells growing in the OB and the RH. Protein expression and neuron-tumor interaction were defined by immunohistochemistry and confocal microscopy.

RESULTS

After irradiation, there was a more rapid dispersal of γH2AX and 53BP1 foci in the OB versus in the RH, indicative of increased double strand break repair capacity in the OB and consistent with the OB providing a radioprotective niche. With respect to the cell cycle, by 6 h after irradiation there was a significant loss of mitotic tumor cells in both locations suggesting a similar activation of the G2/M checkpoint. However, by 24 h post-irradiation there was an accumulation of G2 phase cells in the OB, which continued out to at least 96 h. Transcriptome analysis showed that tumor cells in the OB had higher expression levels of DNA repair genes involved in non-homologous end joining and genes related to the spindle assembly checkpoint. Tumor cells in the OB were also found to have an increased frequency of soma-soma contact with neurons.

CONCLUSION

GBM cells that have migrated to the OB have an increased capacity to repair radiation-induced double strand breaks and altered cell cycle regulation. These results correspond to an upregulation of genes involved in DNA damage repair and cell cycle control. Because the murine OB provides a source of radioresistant tumor cells not evident in other experimental systems, it may serve as a model for investigating the mechanisms mediating GBM radioresistance.

cfDNA methylome profiling for detection and subtyping of small cell lung cancers

Small cell lung cancer (SCLC) is characterized by morphologic, epigenetic and transcriptomic heterogeneity. Subtypes based upon predominant transcription factor expression have been defined that, in mouse models and cell lines, exhibit potential differential therapeutic vulnerabilities, with epigenetically distinct SCLC subtypes also described. The clinical relevance of these subtypes is unclear, due in part to challenges in obtaining tumor biopsies for reliable profiling. Here we describe a robust workflow for genome-wide DNA methylation profiling applied to both patient-derived models and to patients' circulating cell-free DNA (cfDNA). Tumor-specific methylation patterns were readily detected in cfDNA samples from patients with SCLC and were correlated with survival outcomes. cfDNA methylation also discriminated between the transcription factor SCLC subtypes, a precedent for a liquid biopsy cfDNA-methylation approach to molecularly subtype SCLC. Our data reveal the potential clinical utility of cfDNA methylation profiling as a universally applicable liquid biopsy approach for the sensitive detection, monitoring and molecular subtyping of patients with SCLC.

Whole transcriptome profiling of liquid biopsies from tumour xenografted mouse models enables specific monitoring of tumour-derived extracellular RNA

While cell-free DNA (cfDNA) is widely being investigated, free circulating RNA (extracellular RNA, exRNA) has the potential to improve cancer therapy response monitoring and detection due to its dynamic nature. However, it remains unclear in which blood subcompartment tumour-derived exRNAs primarily reside. We developed a host-xenograft deconvolution framework, exRNAxeno, with mapping strategies to either a combined human-mouse reference genome or both species genomes in parallel, applicable to exRNA sequencing data from liquid biopsies of human xenograft mouse models. The tool enables to distinguish (human) tumoural RNA from (murine) host RNA, to specifically analyse tumour-derived exRNA. We applied the combined pipeline to total exRNA sequencing data from 95 blood-derived liquid biopsy samples from 30 mice, xenografted with 11 different tumours. Tumoural exRNA concentrations are not determined by plasma platelet levels, while host exRNA concentrations increase with platelet content. Furthermore, a large variability in exRNA abundance and transcript content across individual mice is observed. The tumoural gene detectability in plasma is largely correlated with the RNA expression levels in the tumour tissue or cell line. These findings unravel new aspects of tumour-derived exRNA biology in xenograft models and open new avenues to further investigate the role of exRNA in cancer.

Regulation of neuroendocrine plasticity by the RNA-binding protein ZFP36L1

Some small cell lung cancers (SCLCs) are highly sensitive to inhibitors of the histone demethylase LSD1. LSD1 inhibitors are thought to induce their anti-proliferative effects by blocking neuroendocrine differentiation, but the mechanisms by which LSD1 controls the SCLC neuroendocrine phenotype are not well understood. To identify genes required for LSD1 inhibitor sensitivity in SCLC, we performed a positive selection genome-wide CRISPR/Cas9 loss of function screen and found that ZFP36L1, an mRNA-binding protein that destabilizes mRNAs, is required for LSD1 inhibitor sensitivity. LSD1 binds and represses ZFP36L1 and upon LSD1 inhibition, ZFP36L1 expression is restored, which is sufficient to block the SCLC neuroendocrine differentiation phenotype and induce a non-neuroendocrine "inflammatory" phenotype. Mechanistically, ZFP36L1 binds and destabilizes SOX2 and INSM1 mRNAs, two transcription factors that are required for SCLC neuroendocrine differentiation. This work identifies ZFP36L1 as an LSD1 target gene that controls the SCLC neuroendocrine phenotype and demonstrates that modulating mRNA stability of lineage transcription factors controls neuroendocrine to non-neuroendocrine plasticity.

Direct Conversion of Human Endothelial Cells Into Liver Cancer-Forming Cells Using Nonintegrative Episomal Vectors

Liver cancer is an aggressive cancer associated with a poor prognosis. Development of therapeutic strategies for liver cancer requires fundamental research using suitable experimental models. Recent progress in direct reprogramming technology has enabled the generation of many types of cells that are difficult to obtain and provide a cellular resource in experimental models of human diseases. In this study, we aimed to establish a simple one-step method for inducing cells that can form malignant human liver tumors directly from healthy endothelial cells using nonintegrating episomal vectors. To screen for factors capable of inducing liver cancer-forming cells (LCCs), we selected nine genes and one short hairpin RNA that suppresses tumor protein p53 (TP53) expression and introduced them into human umbilical vein endothelial cells (HUVECs), using episomal vectors. To identify the essential factors, we examined the effect of changing the amounts and withdrawing individual factors. We then analyzed the proliferation, gene and protein expression, morphologic and chromosomal abnormality, transcriptome, and tumor formation ability of the induced cells. We found that a set of six factors, forkhead box A3 (FOXA3), hepatocyte nuclear factor homeobox 1A (HNF1A), HNF1B, lin-28 homolog B (LIN28B), MYCL proto-oncogene, bHLH transcription factor (L-MYC), and Kruppel-like factor 5 (KLF5), induced direct conversion of HUVECs into LCCs. The gene expression profile of these induced LCCs (iLCCs) was similar to that of human liver cancer cells, and these cells effectively formed tumors that resembled human combined hepatocellular-cholangiocarcinoma following transplantation into immunodeficient mice. Conclusion: We succeeded in the direct induction of iLCCs from HUVECs by using nonintegrating episomal vectors. iLCCs generated from patients with cancer and healthy volunteers will be useful for further advancements in cancer research and for developing methods for the diagnosis, treatment, and prognosis of liver cancer.

Correlation between Patient-Derived Xenograft Modeling and Prognosis in Osteosarcoma

Objective

To retrospectively analyze and compare the relationship between the success rate of patient‐derived xenograft (PDX) modeling of osteosarcoma and prognosis (3‐year overall survival rate and disease‐free survival rate) and incidence of lung metastasis.

Methods

The sample group consisted of 57 osteosarcoma patients with definite pathological diagnoses from Shanghai General Hospital from 2015–2017. PDX models in 57 patients were analyzed by retrospective analyses. Among the patients currently inoculated, 20 were tumorigenic in the PDX model, and 37 were nontumorigenic. According to the tumorigenicity of PDXs, the corresponding osteosarcoma patients were divided into two groups. The effects of clinically related indicators on the model were retrospectively compared. The patients were followed, and the 3‐year survival, 3‐year disease‐free survival (DFS), and lung metastasis rates were collected. The relationship between the modeling success and patient prognosis was investigated.

Results

In the chemotherapy‐treated group, the PDX modeling success rate was 17.4%, and in the nonchemotherapy group, the success rate was 47.1%. The success of PDX modeling was related to whether patients received chemotherapy. The success rate of PDX modeling is significantly reduced after receiving chemotherapy. The 3‐year overall survival rate of the PDX‐grafted group was 49.23%, and that of the PDX‐nongrafted group was 65.71%. There was a significant difference between the two groups, showing a strong negative correlation between the 3‐year survival rate and the success rate of the PDX model. The 3‐year disease‐free survival rate of the PDX‐grafted group was 29.54%. The 3‐year DFS of the PDX‐nongrafted group was 50.34%. There was a significant difference between the two groups. Lower grafted rates indicate a higher DFS rate. The incidence of lung metastasis in the PDX‐grafted group was 32.4%, and that in the nongrafted group was 13.1%. There was a significant difference between the two groups. The successful establishment of the PDX model indicates that patients are more likely to have lung metastases.

Conclusions

The success of PDX modeling often indicates poor prognosis (low 3‐year overall survival rate and disease‐free survival rate) and a greater possibility of lung metastasis. Therefore, PDX modeling in osteosarcoma patients can accurately predict the prognosis of patients and the risk of lung metastasis in advance to help us develop better therapeutic strategies.

Novel patient-derived xenograft and cell line models for therapeutic screening in NF2-associated schwannoma

Treatment of schwannomas in patients with neurofibromatosis type 2 (NF2) is extremely unsatisfactory, and innovative therapeutic approaches are urgently needed. However, the lack of clinically relevant NF2-associated schwannoma models has severely hampered drug discovery in this rare disease. Here, we report the first establishment and characterization of patient-derived xenograft (PDX) and cell line models of NF2-associated schwannoma, which recapitulate the morphological and histopathological features of patient tumors, retain patient NF2 mutations, and maintain gene expression profiles resembling patient tumor profiles with the preservation of multiple key signaling pathways commonly dysregulated in human schwannomas. Using gene expression profiling, we identified elevated PI3K/AKT/mTOR networks in human NF2-associated vestibular schwannomas. Using high-throughput screening of 157 inhibitors targeting the PI3K/AKT/mTOR pathways in vitro, we identified a dozen inhibitors (such as BEZ235, LY2090314, and AZD8055) with significant growth-suppressive effects. Interestingly, we observed that three cell lines displayed differential therapeutic responses to PI3K/AKT/mTOR inhibitors. Furthermore, we demonstrated two orally bioavailable inhibitors AZD8055 and PQR309 suppressed NF2-associated schwannoma growth both in vitro and in vivo. In conclusion, our novel patient-derived models of NF2-associated schwannoma closely mimic the phenotypes and genotypes of patient tumors, making them reliable preclinical tools for testing novel personalized therapies. This article is protected by copyright. All rights reserved.

Personalized Treatment of Advanced Gastric Cancer Guided by the MiniPDX Model

Background

The morbidity and mortality of gastric cancer are high in China. There are challenges to develop precise and individualized drug regimens for patients with gastric cancer after a standard treatment. Choosing the most appropriate anticancer drug after a patient developing drug resistance is very important to improve the patient's prognosis. MiniPDX has been widely used as a new and reliable preclinical research model to predict the sensitivity of anticancer drugs.

Methods

The OncoVee® MiniPDX system developed by Shanghai LIDE Biotech Co., Ltd. was used to establish the MiniPDX models using specimens of patients with gastric cancer. The cancer tissues were biopsied under endoscopy, and then, the tumor cell suspension was prepared for a drug sensitivity test by subcutaneously implanting into Balb/c-nude mice. The selected optimal regimen obtained from the MiniPDX assay was used to treat patients with drug-resistant gastric cancer.

Results

We successfully established an individualized and sensitive drug screening system for four patients from January 2021 to July 2021. MiniPDX models identified potentially effective drugs for these four patients, with partial remission in two of the patients after treatment and disease progression in the remaining of two patients. Severe side effects from chemotherapy or targeted therapy were not observed in all patients.

Conclusion

Establishing a personalized drug screening system for patients with drug-resistant gastric cancer can guide the selection of clinical drugs, improve the clinical benefit of patients, and avoid ineffective treatments. It can be an effective supplement for treatment options.

Soluble guanylate cyclase signalling mediates etoposide resistance in progressing small cell lung cancer

Small cell lung cancer (SCLC) has a 5-year survival rate of <7%. Rapid emergence of acquired resistance to standard platinum-etoposide chemotherapy is common and improved therapies are required for this recalcitrant tumour. We exploit six paired pre-treatment and post-chemotherapy circulating tumour cell patient-derived explant (CDX) models from donors with extensive stage SCLC to investigate changes at disease progression after chemotherapy. Soluble guanylate cyclase (sGC) is recurrently upregulated in post-chemotherapy progression CDX models, which correlates with acquired chemoresistance. Expression and activation of sGC is regulated by Notch and nitric oxide (NO) signalling with downstream activation of protein kinase G. Genetic targeting of sGC or pharmacological inhibition of NO synthase re-sensitizes a chemoresistant CDX progression model in vivo, revealing this pathway as a mediator of chemoresistance and potential vulnerability of relapsed SCLC.

Evolutionary predictability of genetic versus nongenetic resistance to anticancer drugs in melanoma

Therapy resistance arises from heterogeneous drug-tolerant persister cells or minimal residual disease (MRD) through genetic and nongenetic mechanisms. A key question is whether specific molecular features of the MRD ecosystem determine which of these two distinct trajectories will eventually prevail. We show that, in melanoma exposed to mitogen-activated protein kinase therapeutics, emergence of a transient neural crest stem cell (NCSC) population in MRD concurs with the development of nongenetic resistance. This increase relies on a glial cell line-derived neurotrophic factor-dependent signaling cascade, which activates the AKT survival pathway in a focal adhesion kinase (FAK)-dependent manner. Ablation of the NCSC population through FAK inhibition delays relapse in patient-derived tumor xenografts. Strikingly, all tumors that ultimately escape this treatment exhibit resistance-conferring genetic alterations and increased sensitivity to extracellular signal-regulated kinase inhibition. These findings identify an approach that abrogates the nongenetic resistance trajectory in melanoma and demonstrate that the cellular composition of MRD deterministically imposes distinct drug resistance evolutionary paths.

Co-Clinical Imaging Resource Program (CIRP): Bridging the Translational Divide to Advance Precision Medicine

The National Institutes of Health’s (National Cancer Institute) precision medicine initiative emphasizes the biological and molecular bases for cancer prevention and treatment. Importantly, it addresses the need for consistency in preclinical and clinical research. To overcome the translational gap in cancer treatment and prevention, the cancer research community has been transitioning toward using animal models that more fatefully recapitulate human tumor biology. There is a growing need to develop best practices in translational research, including imaging research, to better inform therapeutic choices and decision-making. Therefore, the National Cancer Institute has recently launched the Co-Clinical Imaging Research Resource Program (CIRP). Its overarching mission is to advance the practice of precision medicine by establishing consensus-based best practices for co-clinical imaging research by developing optimized state-of-the-art translational quantitative imaging methodologies to enable disease detection, risk stratification, and assessment/prediction of response to therapy. In this communication, we discuss our involvement in the CIRP, detailing key considerations including animal model selection, co-clinical study design, need for standardization of co-clinical instruments, and harmonization of preclinical and clinical quantitative imaging pipelines. An underlying emphasis in the program is to develop best practices toward reproducible, repeatable, and precise quantitative imaging biomarkers for use in translational cancer imaging and therapy. We will conclude with our thoughts on informatics needs to enable collaborative and open science research to advance precision medicine.

Chromatin conformation capture (Hi-C) sequencing of patient-derived xenografts: analysis guidelines

BACKGROUND

Sequencing of patient-derived xenograft (PDX) mouse models allows investigation of the molecular mechanisms of human tumor samples engrafted in a mouse host. Thus, both human and mouse genetic material is sequenced. Several methods have been developed to remove mouse sequencing reads from RNA-seq or exome sequencing PDX data and improve the downstream signal. However, for more recent chromatin conformation capture technologies (Hi-C), the effect of mouse reads remains undefined.

RESULTS

We evaluated the effect of mouse read removal on the quality of Hi-C data using in silico created PDX Hi-C data with 10% and 30% mouse reads. Additionally, we generated 2 experimental PDX Hi-C datasets using different library preparation strategies. We evaluated 3 alignment strategies (Direct, Xenome, Combined) and 3 pipelines (Juicer, HiC-Pro, HiCExplorer) on Hi-C data quality.

CONCLUSIONS

Removal of mouse reads had little-to-no effect on data quality as compared with the results obtained with the Direct alignment strategy. Juicer extracted more valid chromatin interactions for Hi-C matrices, regardless of the mouse read removal strategy. However, the pipeline effect was minimal, while the library preparation strategy had the largest effect on all quality metrics. Together, our study presents comprehensive guidelines on PDX Hi-C data processing.

Fast lightweight accurate xenograft sorting

Motivation

With an increasing number of patient-derived xenograft (PDX) models being created and subsequently sequenced to study tumor heterogeneity and to guide therapy decisions, there is a similarly increasing need for methods to separate reads originating from the graft (human) tumor and reads originating from the host species’ (mouse) surrounding tissue. Two kinds of methods are in use: On the one hand, alignment-based tools require that reads are mapped and aligned (by an external mapper/aligner) to the host and graft genomes separately first; the tool itself then processes the resulting alignments and quality metrics (typically BAM files) to assign each read or read pair. On the other hand, alignment-free tools work directly on the raw read data (typically FASTQ files). Recent studies compare different approaches and tools, with varying results.

Results

We show that alignment-free methods for xenograft sorting are superior concerning CPU time usage and equivalent in accuracy. We improve upon the state of the art sorting by presenting a fast lightweight approach based on three-way bucketed quotiented Cuckoo hashing. Our hash table requires memory comparable to an FM index typically used for read alignment and less than other alignment-free approaches. It allows extremely fast lookups and uses less CPU time than other alignment-free methods and alignment-based methods at similar accuracy. Several engineering steps (e.g., shortcuts for unsuccessful lookups, software prefetching) improve the performance even further.

Availability

Our software xengsort is available under the MIT license at http://gitlab.com/genomeinformatics/xengsort. It is written in numba-compiled Python and comes with sample Snakemake workflows for hash table construction and dataset processing.

MiniPDX-guided postoperative anticancer treatment can effectively prolong the survival of patients with hepatocellular carcinoma

BACKGROUND

The recurrence rate of hepatocellular carcinoma (HCC) after partial hepatectomy is still high. How to choose the most appropriate anti-tumor drug in the early postoperative period is crucial to improve the prognosis of patients. Recently, MiniPDX has been widely used as a new and reliable preclinical research model capable of predicting the sensitivities of anti-tumor drugs.

METHODS

Twenty-eight patients with HCC were selected to use the MiniPDX model to screen the most sensitive anti-tumor drugs from five groups of drug regimens for preventive treatment after partial hepatectomy, and another 42 patients with HCC were selected to be treated with Sorafenib during the same period as the control group. The tumor-free survival rate and overall survival rate were analyzed and compared between these two groups. The relationship between drug sensitivity and biomarkers related to HCC was also analyzed.

RESULTS

Kaplan-Meier survival curve analysis showed that the tumor-free survival (DFS) of patients in the MiniPDX group was significantly longer than that in the control group (median DFS: 25.8 months vs. 18.2 months, P = 0.022, HR 2.19, 95% CI 1.17-4.12). The overall survival (OS) of the patients in the MiniPDX group was also longer than that in the control group (median OS: 29.4 months vs. 23.8 months, P = 0.039, HR 2.37, 95% CI 1.12-5.00). The longest follow-up period was 36 months. The relationship analyzed between the efficacy of the five drugs (Regorafenib, Regorafenib, Lenvatinib, Gemcitabine, 5-FU + Oxaliplatin) and AFP, Ki-67, VEGFR, FGFR, P53, and Nrf2 showed different correlations.

CONCLUSION

The use of the MiniPDX model to select drugs to guide anti-tumor treatment after partial hepatectomy could effectively prolong the survival of patients with HCC.

Transcriptomic Hallmarks of Tumor Plasticity and Stromal Interactions in Brain Metastasis

The brain is a major site of relapse for several cancers, yet deciphering the mechanisms of brain metastasis remains a challenge because of the complexity of the brain tumor microenvironment (TME). To define the molecular landscape of brain metastasis from intact tissue in vivo, we employ an RNA-sequencing-based approach, which leverages the transcriptome of xenografts and distinguishes tumor cell and stromal gene expression with improved sensitivity and accuracy. Our data reveal shifts in epithelial and neuronal-like lineage programs in malignant cells as they adapt to the brain TME and the reciprocal neuroinflammatory response of the stroma. We identify several transcriptional hallmarks of metastasis that are specific to particular regions of the brain, induced across multiple tumor types, and confirmed in syngeneic models and patient biopsies. These data may serve as a resource for exploring mechanisms of TME co-adaptation within, as well as across, different subtypes of brain metastasis.

Genetic characterization of a unique neuroendocrine transdifferentiation prostate circulating tumor cell-derived eXplant model

Transformation of castration-resistant prostate cancer (CRPC) into an aggressive neuroendocrine disease (CRPC-NE) represents a major clinical challenge and experimental models are lacking. A CTC-derived eXplant (CDX) and a CDX-derived cell line are established using circulating tumor cells (CTCs) obtained by diagnostic leukapheresis from a CRPC patient resistant to enzalutamide. The CDX and the derived-cell line conserve 16% of primary tumor (PT) and 56% of CTC mutations, as well as 83% of PT copy-number aberrations including clonal TMPRSS2-ERG fusion and NKX3.1 loss. Both harbor an androgen receptor-null neuroendocrine phenotype, TP53, PTEN and RB1 loss. While PTEN and RB1 loss are acquired in CTCs, evolutionary analysis suggest that a PT subclone harboring TP53 loss is the driver of the metastatic event leading to the CDX. This CDX model provides insights on the sequential acquisition of key drivers of neuroendocrine transdifferentiation and offers a unique tool for effective drug screening in CRPC-NE management.

A biobank of small cell lung cancer CDX models elucidates inter- and intratumoral phenotypic heterogeneity

Although small cell lung cancer (SCLC) is treated as a homogeneous disease, biopsies and preclinical models reveal heterogeneity in transcriptomes and morphology. SCLC subtypes were recently defined by neuroendocrine transcription factor (NETF) expression. Circulating-tumor-cell-derived explant models (CDX) recapitulate donor patients' tumor morphology, diagnostic NE marker expression and chemotherapy responses. We describe a biobank of 38 CDX models, including six CDX pairs generated pretreatment and at disease progression revealing complex intra- and intertumoral heterogeneity. Transcriptomic analysis confirmed three of four previously described subtypes based on ASCL1, NEUROD1 and POU2F3 expression and identified a previously unreported subtype based on another NETF, ATOH1. We document evolution during disease progression exemplified by altered MYC and NOTCH gene expression, increased 'variant' cell morphology, and metastasis without strong evidence of epithelial to mesenchymal transition. This CDX biobank provides a research resource to facilitate SCLC personalized medicine.

Radiation Drives the Evolution of Orthotopic Xenografts Initiated from Glioblastoma Stem-like Cells

A consequence of the intratumor heterogeneity (ITH) of glioblastoma (GBM) is the susceptibility to treatment-driven evolution. To determine the potential of radiotherapy to influence GBM evolution, we used orthotopic xenografts initiated from CD133+ GBM stem-like cells (GSC). Toward this end, orthotopic xenografts grown in nude mice were exposed to a fractionated radiation protocol, which resulted in a significant increase in animal survival. Brain tumors from control and irradiated mice were then collected at morbidity and compared in terms of growth pattern, clonal diversity, and genomic architecture. In mice that received fractionated radiation, tumors were less invasive, with more clearly demarcated borders and tumor core hypercellularity as compared with controls, suggesting a fundamental change in tumor biology. Viral integration site analysis indicated a reduction in clonal diversity in the irradiated tumors, implying a decrease in ITH. Changes in clonal diversity were not detected after irradiation of GSCs in vitro, suggesting that the radiation-induced reduction in ITH was dependent on the brain microenvironment. Whole-exome sequencing revealed differences in mutation patterns between control and irradiated tumors, which included modifications in the presence and clonality of driver mutations associated with GBM. Moreover, changes in the distribution of mutations as a function of subpopulation size between control and irradiated tumors were consistent with subclone expansion and contraction, that is, subpopulation evolution. Taken together, these results indicate that radiation drives the evolution of the GSC-initiated orthotopic xenografts and suggest that radiation-driven evolution may have therapeutic implications for recurrent GBM. SIGNIFICANCE: Radiation drives the evolution of glioblastoma orthotopic xenografts; when translated to the clinic, this may have therapeutic implications for recurrent tumors.

Impact of mouse contamination in genomic profiling of patient-derived models and best practice for robust analysis

BACKGROUND

Patient-derived xenograft and cell line models are popular models for clinical cancer research. However, the inevitable inclusion of a mouse genome in a patient-derived model is a remaining concern in the analysis. Although multiple tools and filtering strategies have been developed to account for this, research has yet to demonstrate the exact impact of the mouse genome and the optimal use of these tools and filtering strategies in an analysis pipeline.

RESULTS

We construct a benchmark dataset of 5 liver tissues from 3 mouse strains using human whole-exome sequencing kit. Next-generation sequencing reads from mouse tissues are mappable to 49% of the human genome and 409 cancer genes. In total, 1,207,556 mouse-specific alleles are aligned to the human genome reference, including 467,232 (38.7%) alleles with high sensitivity to contamination, which are pervasive causes of false cancer mutations in public databases and are signatures for predicting global contamination. Next, we assess the performance of 8 filtering methods in terms of mouse read filtration and reduction of mouse-specific alleles. All filtering tools generally perform well, although differences in algorithm strictness and efficiency of mouse allele removal are observed. Therefore, we develop a best practice pipeline that contains the estimation of contamination level, mouse read filtration, and variant filtration.

CONCLUSIONS

The inclusion of mouse cells in patient-derived models hinders genomic analysis and should be addressed carefully. Our suggested guidelines improve the robustness and maximize the utility of genomic analysis of these models.

The fidelity of cancer cells in PDX models: Characteristics, mechanism and clinical significance

Patient-derived xenograft (PDX) models are widely used as preclinical cancer models and are considered better than cell culture models in recapitulating the histological features, molecular characteristics and intratumoral heterogeneity (ITH) of human tumors. While the PDX model is commonly accepted for use in drug discovery and other translational studies, a growing body of evidence has suggested its limitations. Recently, the fidelity of cancer cells within a PDX has been questioned, which may impede the future application of these models. In this review, we will focus the variable phenotypes of xenograft tumors and the genomic instability and molecular inconsistency of PDX tumors after serial transplantation. Next, we will discuss the underlying mechanism of ITH and its clinical relevance. Stochastic selection bias in the sampling process and/or deterministic clonal dynamics due to murine selective pressure may have detrimental effects on the results of personalized medicine and drug screening studies. In addition, we aim to identify a possible solution for the issue of fidelity in current PDX models and to discuss emerging next-generation preclinical models.

Sensitive and specific post-call filtering of genetic variants in xenograft and primary tumors

Motivation

Tumor genome sequencing offers great promise for guiding research and therapy, but spurious variant calls can arise from multiple sources. Mouse contamination can generate many spurious calls when sequencing patient-derived xenografts. Paralogous genome sequences can also generate spurious calls when sequencing any tumor. We developed a BLAST-based algorithm, Mouse And Paralog EXterminator (MAPEX), to identify and filter out spurious calls from both these sources.

Results

When calling variants from xenografts, MAPEX has similar sensitivity and specificity to more complex algorithms. When applied to any tumor, MAPEX also automatically flags calls that potentially arise from paralogous sequences. Our implementation, mapexr, runs quickly and easily on a desktop computer. MAPEX is thus a useful addition to almost any pipeline for calling genetic variants in tumors.

Availability and implementation

The mapexr package for R is available at https://github.com/bmannakee/mapexr under the MIT license.

Contact

mannakee@email.arizona.edu or rgutenk@email.arizona.edu or eknudsen@email.arizona.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Ex vivo culture of cells derived from circulating tumour cell xenograft to support small cell lung cancer research and experimental therapeutics

BACKGROUND AND PURPOSE

Small cell lung cancer (SCLC) is an aggressive disease with median survival of <2 years. Tumour biopsies for research are scarce, especially from extensive-stage patients, with repeat sampling at disease progression rarely performed. We overcame this limitation for relevant preclinical models by developing SCLC circulating tumour cell derived explants (CDX), which mimic the donor tumour pathology and chemotherapy response. To facilitate compound screening and identification of clinically relevant biomarkers, we developed short-term ex vivo cultures of CDX tumour cells.

EXPERIMENTAL APPROACH

CDX tumours were disaggregated, and the human tumour cells derived were cultured for a maximum of 5 weeks. Phenotypic, transcriptomic and pharmacological characterization of these cells was performed.

KEY RESULTS

CDX cultures maintained a neuroendocrine phenotype, and most changes in the expression of protein-coding genes observed in cultures, for up to 4 weeks, were reversible when the cells were re-implanted in vivo. Moreover, the CDX cultures exhibited a similar sensitivity to chemotherapy compared to the corresponding CDX tumour in vivo and were able to predict in vivo responses to therapeutic candidates.

CONCLUSIONS AND IMPLICATIONS

Short-term cultures of CDX provide a tractable platform to screen new treatments, identify predictive and pharmacodynamic biomarkers and investigate mechanisms of resistance to better understand the progression of this recalcitrant tumour.

The Combination of the PARP Inhibitor Olaparib and the WEE1 Inhibitor AZD1775 as a New Therapeutic Option for Small Cell Lung Cancer

Purpose: Introduced in 1987, platinum-based chemotherapy remains standard of care for small cell lung cancer (SCLC), a most aggressive, recalcitrant tumor. Prominent barriers to progress are paucity of tumor tissue to identify drug targets and patient-relevant models to interrogate novel therapies. Following our development of circulating tumor cell patient-derived explants (CDX) as models that faithfully mirror patient disease, here we exploit CDX to examine new therapeutic options for SCLC.Experimental Design: We investigated the efficacy of the PARP inhibitor olaparib alone or in combination with the WEE1 kinase inhibitor AZD1775 in 10 phenotypically distinct SCLC CDX in vivo and/or ex vivo These CDX represent chemosensitive and chemorefractory disease including the first reported paired CDX generated longitudinally before treatment and upon disease progression.Results: There was a heterogeneous depth and duration of response to olaparib/AZD1775 that diminished when tested at disease progression. However, efficacy of this combination consistently exceeded that of cisplatin/etoposide, with cures in one CDX model. Genomic and protein analyses revealed defects in homologous recombination repair genes and oncogenes that induce replication stress (such as MYC family members), predisposed CDX to combined olaparib/AZD1775 sensitivity, although universal predictors of response were not noted.Conclusions: These preclinical data provide a strong rationale to trial this combination in the clinic informed by prevalent, readily accessed circulating tumor cell-based biomarkers. New therapies will be evaluated in SCLC patients after first-line chemotherapy, and our data suggest that the combination of olaparib/AZD1775 should be used as early as possible and before disease relapse. Clin Cancer Res; 24(20); 5153-64. ©2018 AACR.

XenofilteR: computational deconvolution of mouse and human reads in tumor xenograft sequence data

BACKGROUND

Mouse xenografts from (patient-derived) tumors (PDX) or tumor cell lines are widely used as models to study various biological and preclinical aspects of cancer. However, analyses of their RNA and DNA profiles are challenging, because they comprise reads not only from the grafted human cancer but also from the murine host. The reads of murine origin result in false positives in mutation analysis of DNA samples and obscure gene expression levels when sequencing RNA. However, currently available algorithms are limited and improvements in accuracy and ease of use are necessary.

RESULTS

We developed the R-package XenofilteR, which separates mouse from human sequence reads based on the edit-distance between a sequence read and reference genome. To assess the accuracy of XenofilteR, we generated sequence data by in silico mixing of mouse and human DNA sequence data. These analyses revealed that XenofilteR removes > 99.9% of sequence reads of mouse origin while retaining human sequences. This allowed for mutation analysis of xenograft samples with accurate variant allele frequencies, and retrieved all non-synonymous somatic tumor mutations.

CONCLUSIONS

XenofilteR accurately dissects RNA and DNA sequences from mouse and human origin, thereby outperforming currently available tools. XenofilteR is open source and available at https://github.com/PeeperLab/XenofilteR .

EMT- and stroma-related gene expression and resistance to PD-1 blockade in urothelial cancer

Cancers infiltrated with T-cells are associated with a higher likelihood of response to PD-1/PD-L1 blockade. Counterintuitively, a correlation between epithelial-mesenchymal transition (EMT)-related gene expression and T-cell infiltration has been observed across tumor types. Here we demonstrate, using The Cancer Genome Atlas (TCGA) urothelial cancer dataset, that although a gene expression-based measure of infiltrating T-cell abundance and EMT-related gene expression are positively correlated, these signatures convey disparate prognostic information. We further demonstrate that non-hematopoietic stromal cells are a major source of EMT-related gene expression in bulk urothelial cancer transcriptomes. Finally, using a cohort of patients with metastatic urothelial cancer treated with a PD-1 inhibitor, nivolumab, we demonstrate that in patients with T-cell infiltrated tumors, higher EMT/stroma-related gene expression is associated with lower response rates and shorter progression-free and overall survival. Together, our findings suggest a stroma-mediated source of immune resistance in urothelial cancer and provide rationale for co-targeting PD-1 and stromal elements.

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

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

Computational approach to discriminate human and mouse sequences in patient-derived tumour xenografts

BACKGROUND

Patient-Derived Tumour Xenografts (PDTXs) have emerged as the pre-clinical models that best represent clinical tumour diversity and intra-tumour heterogeneity. The molecular characterization of PDTXs using High-Throughput Sequencing (HTS) is essential; however, the presence of mouse stroma is challenging for HTS data analysis. Indeed, the high homology between the two genomes results in a proportion of mouse reads being mapped as human.

RESULTS

In this study we generated Whole Exome Sequencing (WES), Reduced Representation Bisulfite Sequencing (RRBS) and RNA sequencing (RNA-seq) data from samples with known mixtures of mouse and human DNA or RNA and from a cohort of human breast cancers and their derived PDTXs. We show that using an In silico Combined human-mouse Reference Genome (ICRG) for alignment discriminates between human and mouse reads with up to 99.9% accuracy and decreases the number of false positive somatic mutations caused by misalignment by >99.9%. We also derived a model to estimate the human DNA content in independent PDTX samples. For RNA-seq and RRBS data analysis, the use of the ICRG allows dissecting computationally the transcriptome and methylome of human tumour cells and mouse stroma. In a direct comparison with previously reported approaches, our method showed similar or higher accuracy while requiring significantly less computing time.

CONCLUSIONS

The computational pipeline we describe here is a valuable tool for the molecular analysis of PDTXs as well as any other mixture of DNA or RNA species.

Melanoma: Genetic Abnormalities, Tumor Progression, Clonal Evolution and Tumor Initiating Cells

Melanoma is an aggressive neoplasia issued from the malignant transformation of melanocytes, the pigment-generating cells of the skin. It is responsible for about 75% of deaths due to skin cancers. Melanoma is a phenotypically and molecularly heterogeneous disease: cutaneous, uveal, acral, and mucosal melanomas have different clinical courses, are associated with different mutational profiles, and possess distinct risk factors. The discovery of the molecular abnormalities underlying melanomas has led to the promising improvement of therapy, and further progress is expected in the near future. The study of melanoma precursor lesions has led to the suggestion that the pathway of tumor evolution implies the progression from benign naevi, to dysplastic naevi, to melanoma in situ and then to invasive and metastatic melanoma. The gene alterations characterizing melanomas tend to accumulate in these precursor lesions in a sequential order. Studies carried out in recent years have, in part, elucidated the great tumorigenic potential of melanoma tumor cells. These findings have led to speculation that the cancer stem cell model cannot be applied to melanoma because, in this malignancy, tumor cells possess an intrinsic plasticity, conferring the capacity to initiate and maintain the neoplastic process to phenotypically different tumor cells.