Patient-derived xenograft models-the future of personalised cancer treatment

ALK inhibitors suppress HCC and synergize with anti-PD-1 therapy and ABT-263 in preclinical models

Hepatocellular carcinoma (HCC) currently lacks effective therapies, leaving a critical need for new treatment options. A previous study identified the anaplastic lymphoma kinase (ALK) amplification in HCC patients, raising the question of whether ALK inhibitors could be a viable treatment. Here, we showed that both ALK inhibitors and ALK knockout effectively halted HCC growth in cell cultures. Lorlatinib, a potent ALK inhibitor, suppressed HCC tumor growth and metastasis across various mouse models. Additionally, in an advanced immunocompetent humanized mouse model, when combined with an anti-PD-1 antibody, lorlatinib more potently suppressed HCC tumor growth, surpassing individual drug efficacy. Lorlatinib induced apoptosis and senescence in HCC cells, and the senolytic agent ABT-263 enhanced the efficacy of lorlatinib. Additional studies identified that the apoptosis-inducing effect of lorlatinib was mediated via GGN and NRG4. These findings establish ALK inhibitors as promising HCC treatments, either alone or in combination with immunotherapies or senolytic agents.

Assessing Protein Expression in Patient-Derived Xenografts Using Western Blotting

The use of patient-derived xenografts (PDXs) in cancer research is increasing due to their ability to closely mimic the features of patient tumors. The ability to quickly and robustly measure protein expression levels in these tissues is a key methodology required in a broad range of experimental designs. Western blotting (WB) is a cost effective and simple tool that is highly specific and sensitive for detecting and quantifying individual proteins, posttranslational modifications and aberrant signaling pathways. Here, we described a method to assess protein expression in PDX tissues using WB to detect proteins involved in cell growth signaling pathways.

Deciphering the Tumor Microenvironment of Colorectal Cancer and Guiding Clinical Treatment With Patient-Derived Organoid Technology: Progress and Challenges

Colorectal cancer (CRC) is one of the most prevalent malignant tumors of the digestive tract worldwide. Despite notable advancements in CRC treatment, there is an urgent requirement for preclinical model systems capable of accurately predicting drug efficacy in CRC patients, to identify more effective therapeutic options. In recent years, substantial strides have been made in the field of organoid technology, patient-derived organoid models can phenotypically replicate the original intra-tumor and inter-tumor heterogeneity of CRC, reflecting cellular interactions of the tumor microenvironment. Patient-derived organoid models have become an indispensable tool for investigating the pathogenesis of CRC and facilitating translational research. This review focuses on the application of organoid technology in CRC modeling, tumor microenvironment, and guiding clinical treatment, particularly in drug screening and personalized medicine. It also examines the existing challenges encountered in clinical organoid research and provides a prospective outlook on the future development directions of clinical organoid research, encompassing the standardization of organoid culture technology and the application of tissue engineering technology.

Rhabdomyosarcoma: Current Therapy, Challenges, and Future Approaches to Treatment Strategies

Rhabdomyosarcoma is a rare cancer arising in skeletal muscle that typically impacts children and young adults. It is a worldwide challenge in child health as treatment outcomes for metastatic and recurrent disease still pose a major concern for both basic and clinical scientists. The treatment strategies for rhabdomyosarcoma include multi-agent chemotherapies after surgical resection with or without ionization radiotherapy. In this comprehensive review, we first provide a detailed clinical understanding of rhabdomyosarcoma including its classification and subtypes, diagnosis, and treatment strategies. Later, we focus on chemotherapy strategies for this childhood sarcoma and discuss the impact of three mechanisms that are involved in the chemotherapy response including apoptosis, macro-autophagy, and the unfolded protein response. Finally, we discuss in vivo mouse and zebrafish models and in vitro three-dimensional bioengineering models of rhabdomyosarcoma to screen future therapeutic approaches and promote muscle regeneration.

Patient-derived tumor models in cancer research: Evaluation of the oncostatic effects of melatonin

The development of new anticancer therapies tends to be very slow. Although their impact on potential candidates is confirmed in preclinical studies, ∼95 % of these new therapies are not approved when tested in clinical trials. One of the main reasons for this is the lack of accurate preclinical models. In this context, there are different patient-derived models, which have emerged as a powerful oncological tool: patient-derived xenografts (PDXs), patient-derived organoids (PDOs), and patient-derived cells (PDCs). Although all these models are widely applied, PDXs, which are created by engraftment of patient tumor tissues into mice, is considered more reliable. In fundamental research, the PDX model is used to evaluate drug-sensitive markers and, in clinical practice, to select a personalized therapeutic strategy. Melatonin is of particular importance in the development of innovative cancer treatments due to its oncostatic impact and lack of adverse effects. However, the literature regarding the oncostatic effect of melatonin in patient-derived tumor models is scant. This review aims to describe the important role of patient-derived models in the development of anticancer treatments, focusing, in particular, on PDX models, as well as their use in cancer research. This review also summarizes the existing literature on the anti-tumoral effect of melatonin in patient-derived models in order to propose future anti-neoplastic clinical applications.

Cancer treatment therapies: traditional to modern approaches to combat cancers

As far as health issues are concerned, cancer causes one out of every six deaths around the globe. As potent therapeutics are still awaited for the successful treatment of cancer, some unconventional treatments like radiotherapy, surgery, and chemotherapy and some advanced technologies like gene therapy, stem cell therapy, natural antioxidants, targeted therapy, photodynamic therapy, nanoparticles, and precision medicine are available to diagnose and treat cancer. In the present scenario, the prime focus is on developing efficient nanomedicines to treat cancer. Although stem cell therapy has the capability to target primary as well as metastatic cancer foci, it also has the ability to repair and regenerate injured tissues. However, nanoparticles are designed to have such novel therapeutic capabilities. Targeted therapy is also now available to arrest the growth and development of cancer cells without damaging healthy tissues. Another alternative approach in this direction is photodynamic therapy (PDT), which has more potential to treat cancer as it does minimal damage and does not limit other technologies, as in the case of chemotherapy and radiotherapy. The best possible way to treat cancer is by developing novel therapeutics through translational research. In the present scenario, an important event in modern oncology therapy is the shift from an organ-centric paradigm guiding therapy to complete molecular investigations. The lacunae in anticancer therapy may be addressed through the creation of contemporary and pertinent cancer therapeutic techniques. In the meantime, the growth of nanotechnology, material sciences, and biomedical sciences has revealed a wide range of contemporary therapies with intelligent features, adaptable functions, and modification potential. The development of numerous therapeutic techniques for the treatment of cancer is summarized in this article. Additionally, it can serve as a resource for oncology and immunology researchers.

PROBABILISTIC LEARNING OF TREATMENT TREES IN CANCER

Accurate identification of synergistic treatment combinations and their underlying biological mechanisms is critical across many disease domains, especially cancer. In translational oncology research, preclinical systems such as patient-derived xenografts (PDX) have emerged as a unique study design evaluating multiple treatments administered to samples from the same human tumor implanted into genetically identical mice. In this paper, we propose a novel Bayesian probabilistic tree-based framework for PDX data to investigate the hierarchical relationships between treatments by inferring treatment cluster trees, referred to as treatment trees (Rx-tree). The framework motivates a new metric of mechanistic similarity between two or more treatments accounting for inherent uncertainty in tree estimation; treatments with a high estimated similarity have potentially high mechanistic synergy. Building upon Dirichlet Diffusion Trees, we derive a closed-form marginal likelihood encoding the tree structure, which facilitates computationally efficient posterior inference via a new two-stage algorithm. Simulation studies demonstrate superior performance of the proposed method in recovering the tree structure and treatment similarities. Our analyses of a recently collated PDX dataset produce treatment similarity estimates that show a high degree of concordance with known biological mechanisms across treatments in five different cancers. More importantly, we uncover new and potentially effective combination therapies that confer synergistic regulation of specific downstream biological pathways for future clinical investigations. Our accompanying code, data, and shiny application for visualization of results are available at: https://github.com/bayesrx/RxTree.

Analysis of NK-92 cytotoxicity in nasopharyngeal carcinoma cell lines and patient-derived xenografts using impedance-based growth method

Natural killer (NK) cells are innate immune cells that can remove viral-infected tumour cells without antigen priming. This characteristic offers NK cells an edge over other immune cells as a potential therapy for nasopharyngeal carcinoma (NPC). In this study, we report how cytotoxicity was evaluated in target NPC cell lines and patient-derived xenograft (PDX) cells with effector NK-92, a commercially available NK cell line, by using xCELLigence RTCA system (a real-time, label-free impedance-based monitoring platform). Cell viability, proliferation and cytotoxicity were examined by RTCA. Cell morphology, growth and cytotoxicity were also monitored by microscopy. RTCA and microscopy showed that both target and effector cells were able to proliferate normally and to maintain original morphology in co-culture medium as they were in their own respective culture medium. As target and effector (T:E) cell ratios increased, cell viability as measured by arbitrary cell index (CI) values in RTCA decreased in all cell lines and PDX cells. NPC PDX cells were more sensitive to the cytotoxicity effect of NK-92 cells, than the NPC cell lines. These data were substantiated by GFP-based microscopy. We have shown how the RTCA system can be used for a high throughput screening of the effects of NK cells in cancer studies to obtain data such as cell viability, proliferation and cytotoxicity.

A new tumorgraft panel to accelerate precision medicine in prostate cancer

Background

Despite the significant advances in the management of advanced prostate cancer (PCa), metastatic PCa is currently considered incurable. For further investigations in precision treatment, the development of preclinical models representing the complex prostate tumor heterogeneity are mandatory. Accordingly, we aimed to establish a resource of patient-derived xenograft (PDX) models that exemplify each phase of this multistage disease for accurate and rapid evaluation of candidate therapies.

Methods

Fresh tumor samples along with normal corresponding tissues were obtained directly from patients at surgery. To ensure that the established models reproduce the main features of patient's tumor, both PDX tumors at multiple passages and patient's primary tumors, were processed for histological characteristics. STR profile analyses were also performed to confirm patient identity. Finally, the responses of the PDX models to androgen deprivation, PARP inhibitors and chemotherapy were also evaluated.

Results

In this study, we described the development and characterization of 5 new PDX models of PCa. Within this collection, hormone-naïve, androgen-sensitive and castration-resistant (CRPC) primary tumors as well as prostate carcinoma with neuroendocrine differentiation (CRPC-NE) were represented. Interestingly, the comprehensive genomic characterization of the models identified recurrent cancer driver alterations in androgen signaling, DNA repair and PI3K, among others. Results were supported by expression patterns highlighting new potential targets among gene drivers and the metabolic pathway. In addition, in vivo results showed heterogeneity of response to androgen deprivation and chemotherapy, like the responses of patients to these treatments. Importantly, the neuroendocrine model has been shown to be responsive to PARP inhibitor.

Conclusion

We have developed a biobank of 5 PDX models from hormone-naïve, androgen-sensitive to CRPC primary tumors and CRPC-NE. Increased copy-number alterations and accumulation of mutations within cancer driver genes as well as the metabolism shift are consistent with the increased resistance mechanisms to treatment. The pharmacological characterization suggested that the CRPC-NE could benefit from the PARP inhibitor treatment. Given the difficulties in developing such models, this relevant panel of PDX models of PCa will provide the scientific community with an additional resource for the further development of PDAC research.

3D cancer models: One step closer to in vitro human studies

Cancer immunotherapy is the great breakthrough in cancer treatment as it displayed prolonged progression-free survival over conventional therapies, yet, to date, in only a minority of patients. In order to broad cancer immunotherapy clinical applicability some roadblocks need to be overcome, first among all the lack of preclinical models that faithfully depict the local tumor microenvironment (TME), which is known to dramatically affect disease onset, progression and response to therapy. In this review, we provide the reader with a detailed overview of current 3D models developed to mimick the complexity and the dynamics of the TME, with a focus on understanding why the TME is a major target in anticancer therapy. We highlight the advantages and translational potentials of tumor spheroids, organoids and immune Tumor-on-a-Chip models in disease modeling and therapeutic response, while outlining pending challenges and limitations. Thinking forward, we focus on the possibility to integrate the know-hows of micro-engineers, cancer immunologists, pharmaceutical researchers and bioinformaticians to meet the needs of cancer researchers and clinicians interested in using these platforms with high fidelity for patient-tailored disease modeling and drug discovery.

LINC00511, a future star for the diagnosis and therapy of digestive system malignant tumors

The digestive system malignant tumors (DSMTs), mainly consist of digestive tract and digestive gland tumors, become an inescapable culprit to hazard human health worldwide. Due to the huge hysteresis in the cognitive theories of DSMTs occurrence and progression, advances in medical technology have not improved the prognosis. Therefore, more studies on a variety of tumor-associated molecular biomarkers and more detailed disclosure on potential regulatory networks are urgently needed to facilitate the diagnostic and therapeutic strategies of DSMTs. With the development of cancer bioinformatics, a special type of endogenous RNA involved in multi-level cellular function regulation rather than encoding protein, is categorized as non-coding RNAs (ncRNAs) and becomes a hotspot issue in oncology. Among them, long non-coding RNAs (lncRNAs), transcription length > 200 nt, show obvious superiority in both research quantity and dimension compared to microRNAs (miRNAs) and circular RNAs (circRNAs). As a recently discovered lncRNA, LINC00511 has been confirmed to be closely associated with DSMTs and might be exploited as a novel biomarker. In the present review, the comprehensive studies of LINC00511 in DSMTs are summarized, as well as the underlying molecular regulatory networks. In addition, deficiencies in researches are point out and discussed. The Cumulative oncology studies provide a fully credible theoretical basis for identifying the regulatory role of LINC00511 in human DSMTs. LINC00511, proved to be an oncogene in DSMTs, might be defined as a potential biomarker for diagnosis and prognosis evaluation, as well as a rare therapeutic target.

CXCR1/2 dual-inhibitor ladarixin reduces tumour burden and promotes immunotherapy response in pancreatic cancer

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy with few therapeutic options available. Despite immunotherapy has revolutionised cancer treatment, the results obtained in PDAC are still disappointing. Emerging evidence suggests that chemokines/CXCRs-axis plays a pivotal role in immune tumour microenvironment modulation, which may influence immunotherapy responsiveness. Here, we evaluated the effectiveness of CXCR1/2 inhibitor ladarixin, alone or in combination with anti-PD-1, against immunosuppression in PDAC.

METHODS

A set of preclinical models was obtained by engrafting mouse PDAC-derived cells into syngeneic immune-competent mice, as well as by orthotopically transplanting patient-derived PDAC tumour into human immune-system-reconstituted (HIR) mice (HuCD34-NSG-mice). Tumour-bearing mice were randomly assigned to receive vehicles, ladarixin, anti-PD-1 or drugs combination.

RESULTS

CXCR1/2 inhibition by ladarixin reverted in vitro tumour-mediated M2 macrophages polarisation and migration. Ladarixin as single agent reduced tumour burden in cancer-derived graft (CDG) models with high-immunogenic potential and increased the efficacy of ICI in non-immunogenic CDG-resistant models. In a HIR mouse model bearing the immunogenic subtype of human PDAC, ladarixin showed high efficacy increasing the antitumor effect of anti-PD-1.

CONCLUSION

Ladarixin in combination with anti-PD-1 might represent an extremely effective approach for the treatment of immunotherapy refractory PDAC, allowing pro-tumoral to immune-permissive microenvironment conversion.

Patient-derived organoids (PDOs) and PDO-derived xenografts (PDOXs): New opportunities in establishing faithful pre-clinical cancer models

One of the major bottlenecks in advancing basic cancer research and developing novel cancer therapies is the lack of in vitro pre-clinical models that faithfully recapitulate tumor properties in the patients. Monolayer cultures of cancer cell lines usually lose the heterogeneity of the parental tumors, while patient-derived xenograft (PDX) suffers from its time- and resource-intensive nature. The emergence of organoid culture system and its application in cancer research provides a unique opportunity to develop novel in vitro cancer pre-clinical models. Here we review the recent advances in utilizing organoids culture system and other related three-dimensional culture systems in studying cancer biology, performing drug screening, and developing cancer therapies. In particular, we discuss the advantages of applying xenograft initiated from patient-derived organoids (PDOs) as a faithful cancer pre-clinical model in basic cancer research and precision medicine.

Predictive validity in drug discovery: what it is, why it matters and how to improve it

Successful drug discovery is like finding oases of safety and efficacy in chemical and biological deserts. Screens in disease models, and other decision tools used in drug research and development (R&D), point towards oases when they score therapeutic candidates in a way that correlates with clinical utility in humans. Otherwise, they probably lead in the wrong direction. This line of thought can be quantified by using decision theory, in which 'predictive validity' is the correlation coefficient between the output of a decision tool and clinical utility across therapeutic candidates. Analyses based on this approach reveal that the detectability of good candidates is extremely sensitive to predictive validity, because the deserts are big and oases small. Both history and decision theory suggest that predictive validity is under-managed in drug R&D, not least because it is so hard to measure before projects succeed or fail later in the process. This article explains the influence of predictive validity on R&D productivity and discusses methods to evaluate and improve it, with the aim of supporting the application of more effective decision tools and catalysing investment in their creation.

Patient-derived intrafemoral orthotopic xenografts of peripheral blood or bone marrow from acute myeloid and acute lymphoblastic leukemia patients: clinical characterization, methodology, and validation

Acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) are malignant clonal diseases of the hematopoietic system with an unsatisfactory overall prognosis. The main obstacle is the increased resistance of AML and ALL cells to chemotherapy. The development and validation of new therapeutic strategies for acute leukemia require preclinical models that accurately recapitulate the genetic, pathological, and clinical features of acute leukemia. A patient-derived orthotopic xenograft (PDOX) model is established using surgical orthotopic implantation. They closely resemble human tumor progression and microenvironment and are more reliable translational research tools than subcutaneous-transplant models. In this study, we established PDOX models by direct intrafemoral injection of bone marrow and peripheral blood cells from AML and ALL patients, characterized their pathology, cytology, and genetics, and compared the model's characteristics and drug responsiveness with those of the corresponding patients.

Integrated molecular and pharmacological characterization of patient-derived xenografts from bladder and ureteral cancers identifies new potential therapies

Background

Muscle-invasive bladder cancer (MIBC) and upper urinary tract urothelial carcinoma (UTUC) are molecularly heterogeneous. Despite chemotherapies, immunotherapies, or anti-fibroblast growth factor receptor (FGFR) treatments, these tumors are still of a poor outcome. Our objective was to develop a bank of patient-derived xenografts (PDXs) recapitulating the molecular heterogeneity of MIBC and UTUC, to facilitate the preclinical identification of therapies.

Methods

Fresh tumors were obtained from patients and subcutaneously engrafted into immune-compromised mice. Patient tumors and matched PDXs were compared regarding histopathology, transcriptomic (microarrays), and genomic profiles [targeted Next-Generation Sequencing (NGS)]. Several PDXs were treated with chemotherapy (cisplatin/gemcitabine) or targeted therapies [FGFR and epidermal growth factor (EGFR) inhibitors].

Results

A total of 31 PDXs were established from 1 non-MIBC, 25 MIBC, and 5 upper urinary tract tumors, including 28 urothelial (UC) and 3 squamous cell carcinomas (SCCs). Integrated genomic and transcriptomic profiling identified the PDXs of three different consensus molecular subtypes [basal/squamous (Ba/Sq), luminal papillary, and luminal unstable] and included FGFR3-mutated PDXs. High histological and genomic concordance was found between matched patient tumor/PDX. Discordance in molecular subtypes, such as a Ba/Sq patient tumor giving rise to a luminal papillary PDX, was observed (n=5) at molecular and histological levels. Ten models were treated with cisplatin-based chemotherapy, and we did not observe any association between subtypes and the response. Of the three Ba/Sq models treated with anti-EGFR therapy, two models were sensitive, and one model, of the sarcomatoid variant, was resistant. The treatment of three FGFR3-mutant PDXs with combined FGFR/EGFR inhibitors was more efficient than anti-FGFR3 treatment alone.

Conclusions

We developed preclinical PDX models that recapitulate the molecular heterogeneity of MIBCs and UTUC, including actionable mutations, which will represent an essential tool in therapy development. The pharmacological characterization of the PDXs suggested that the upper urinary tract and MIBCs, not only UC but also SCC, with similar molecular characteristics could benefit from the same treatments including anti-FGFR for FGFR3-mutated tumors and anti-EGFR for basal ones and showed a benefit for combined FGFR/EGFR inhibition in FGFR3-mutant PDXs, compared to FGFR inhibition alone.

Patient-derived rectal cancer organoids—applications in basic and translational cancer research

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers and among the leading causes of death in both men and women. Rectal cancer (RC) is particularly challenging compared with colon cancer as the treatment after diagnosis of RC is more complex on account of its narrow anatomical location in the pelvis adjacent to the urogenital organs. More and more existing studies have begun to refine the research on RC and colon cancer separately. Early diagnosis and multiple treatment strategies optimize outcomes for individual patients. However, the need for more accurate and precise models to facilitate RC research is underscored due to the heterogeneity of clinical response and morbidity interrelated with radical surgery. Organoids generated from biopsies of patients have developed as powerful models to recapitulate many aspects of their primary tissue, consisting of 3-D self-organizing structures, which shed great light on the applications in both biomedical and clinical research. As the preclinical research models for RC are usually confused with colon cancer, research on patient-derived RC organoid models enable personalized analysis of cancer pathobiology, organizational function, and tumor initiation and progression. In this review, we discuss the various applications of patient-derived RC organoids over the past two years in basic cancer biology and clinical translation, including sequencing analysis, drug screening, precision therapy practice, tumor microenvironment studies, and genetic engineering opportunities.

Evaluating Translational Methods for Personalized Medicine—A Scoping Review

The introduction of personalized medicine, through the increasing multi-omics characterization of disease, brings new challenges to disease modeling. The scope of this review was a broad evaluation of the relevance, validity, and predictive value of the current preclinical methodologies applied in stratified medicine approaches. Two case models were chosen: oncology and brain disorders. We conducted a scoping review, following the Joanna Briggs Institute guidelines, and searched PubMed, EMBASE, and relevant databases for reports describing preclinical models applied in personalized medicine approaches. A total of 1292 and 1516 records were identified from the oncology and brain disorders search, respectively. Quantitative and qualitative synthesis was performed on a final total of 63 oncology and 94 brain disorder studies. The complexity of personalized approaches highlights the need for more sophisticated biological systems to assess the integrated mechanisms of response. Despite the progress in developing innovative and complex preclinical model systems, the currently available methods need to be further developed and validated before their potential in personalized medicine endeavors can be realized. More importantly, we identified underlying gaps in preclinical research relating to the relevance of experimental models, quality assessment practices, reporting, regulation, and a gap between preclinical and clinical research. To achieve a broad implementation of predictive translational models in personalized medicine, these fundamental deficits must be addressed.

Strategies to mitigate the on- and off-target toxicities of recombinant immunotoxins: an antibody engineering perspective

Targeted cancer therapies using immunotoxins has achieved remarkable efficacies in hematological malignancies. However, the clinical development of immunotoxins is also faced with many challenges like anti-drug antibodies and dose-limiting toxicity issues. Such a poor efficacy/safety ratio is also the major hurdle in the research and development of antibody-drug conjugates. From an antibody engineering perspective, various strategies were summarized/proposed to tackle the notorious on target off tumor toxicity issues, including passive strategy (XTENylation of immunotoxins) and active strategies (modulating the affinity and valency of the targeting moiety of immunotoxins, conditionally activating immunotoxins in the tumor microenvironments and reconstituting split toxin to reduce systemic toxicity etc.). By modulating the functional characteristics of the targeting moiety and the toxic moiety of immunotoxins, selective tumor targeting can be augmented while sparing the healthy cells in normal tissues expressing the same target of interest. If successful, the improved therapeutic index will likely help to address the dose-limiting toxicities commonly observed in the clinical trials of various immunotoxins.

Precision-Cut Tumor Slices (PCTS) as an Ex Vivo Model in Immunotherapy Research

Precision-cut tumor slices (PCTS) have recently emerged as important ex vivo human tumor models, offering the opportunity to study individual patient responses to targeted immunotherapies, including CAR-T cell therapies. In this review, an outline of different human tumor models available in laboratory settings is provided, with a focus on the unique characteristics of PCTS. Standard PCTS generation and maintenance procedures are outlined, followed by an in-depth overview of PCTS utilization in preclinical research aiming to better understand the unique functional characteristics of cytotoxic T cells within human tumors. Furthermore, recent studies using PCTS as an ex vivo model for predicting patient responses to immunotherapies and other targeted therapies against solid tumors are thoroughly presented. Finally, the advantages and limitations of the PCTS models are discussed. PCTS are expected to gain momentum and be fully utilized as a significant tool towards better patient stratification and personalized medicine.

Genomic and Molecular Signatures of Successful Patient-Derived Xenografts for Oral Cavity Squamous Cell Carcinoma

Background

Oral cavity squamous cell carcinoma (OSCC) is an aggressive malignant tumor with high recurrence and poor prognosis in the advanced stage. Patient-derived xenografts (PDXs) serve as powerful preclinical platforms for drug testing and precision medicine for cancer therapy. We assess which molecular signatures affect tumor engraftment ability and tumor growth rate in OSCC PDXs.

Methods

Treatment-naïve OSCC primary tumors were collected for PDX models establishment. Comprehensive genomic analysis, including whole-exome sequencing and RNA-seq, was performed on case-matched tumors and PDXs. Regulatory genes/pathways were analyzed to clarify which molecular signatures affect tumor engraftment ability and the tumor growth rate in OSCC PDXs.

Results

Perineural invasion was found as an important pathological feature related to engraftment ability. Tumor microenvironment with enriched hypoxia, PI3K-Akt, and epithelial–mesenchymal transition pathways and decreased inflammatory responses had high engraftment ability and tumor growth rates in OSCC PDXs. High matrix metalloproteinase-1 (MMP1) expression was found that have a great graft advantage in xenografts and is associated with pooled disease-free survival in cancer patients.

Conclusion

This study provides a panel with detailed genomic characteristics of OSCC PDXs, enabling preclinical studies on personalized therapy options for oral cancer. MMP1 could serve as a biomarker for predicting successful xenografts in OSCC patients.

The next wave of cellular immunotherapies in pancreatic cancer

Pancreatic cancer is an aggressive disease that is predicted to become the second leading cause of cancer-related death worldwide by 2030. The overall 5-year survival rate is around 10%. Pancreatic cancer typically presents late with locally advanced or metastatic disease, and there are limited effective treatments available. Cellular immunotherapy, such as chimeric antigen receptor (CAR) T cell therapy, has had significant success in treating hematological malignancies. However, CAR T cell therapy efficacy in pancreatic cancer has been limited. This review provides an overview of current and ongoing CAR T cell clinical studies of pancreatic cancer and the major challenges and strategies to improve CAR T cell efficacy. These strategies include arming CAR T cells; developing off-the-shelf allogeneic CAR T cells; using other immune CAR cells, like natural killer cells and tumor-infiltrating lymphocytes; and combination therapy. Careful incorporation of preclinical models will enhance management of affected individuals, assisting incorporation of cellular immunotherapies. A multifaceted, personalized approach involving cellular immunotherapy treatment is required to improve pancreatic cancer outcomes.

Opportunities and challenges of patient-derived models in cancer research: patient-derived xenografts, patient-derived organoid and patient-derived cells

Background

As reported, preclinical animal models differ greatly from the human body. The evaluation model may be the colossal obstacle for scientific research and anticancer drug development. Therefore, it is essential to propose efficient evaluation systems similar to clinical practice for cancer research.

Main body

While it has emerged for decades, the development of patient-derived xenografts, patient-derived organoid and patient-derived cell used to be limited. As the requirements for anticancer drug evaluation increases, patient-derived models developed rapidly recently, which is widely applied in basic research, drug development, and clinical application and achieved remarkable progress. However, there still lack systematic comparison and summarize reports for patient-derived models. In the current review, the development, applications, strengths, and challenges of patient-derived models in cancer research were characterized.

Conclusion

Patient-derived models are an indispensable approach for cancer research and human health.

Precision medicine for metastatic colorectal cancer in clinical practice

Globally, metastatic colorectal cancer is one of the leading causes for cancer-related death. Treatment limited to conventional chemotherapeutics extended life for only a few months. However, advances in surgical approaches and medical treatment regimens have greatly increased survival, even leading to long-term remission in selected patients. Advances in multiomics analysis of tumors have built a foundation for molecular-targeted therapies. Furthermore, immunotherapies are on the edge of revolutionizing oncological practice. This review summarizes recent advances in the growing toolbox of personalized treatment for patients with metastatic colorectal cancer. We provide an overview of current multimodal therapy and explain novel immunotherapy and targeted therapy approaches in detail. We emphasize clinically relevant therapies, such as inhibitors of MAPK signaling, and give recommendations for clinical practice. Finally, we describe the potential predictive impact of molecular subtypes and provide an outlook on novel concepts, such as functional precision medicine.

Molecular Targets for Gastric Cancer Treatment and Future Perspectives from a Clinical and Translational Point of View

Gastric cancer is a leading cause of cancer death worldwide. Systemic treatment comprising chemotherapy and targeted therapy is the standard of care in advanced/metastatic gastric cancer. Comprehensive molecular characterization of gastric adenocarcinomas by the TCGA Consortium and ACRG has resulted in the definition of distinct molecular subtypes. These efforts have in parallel built a basis for the development of novel molecularly stratified treatment approaches. Based on this molecular characterization, an increasing number of specific genomic alterations can potentially serve as treatment targets. Consequently, the development of promising compounds is ongoing. In this review, key molecular alterations in gastric and gastroesophageal junction cancers will be addressed. Finally, the current status of the translation of targeted therapy towards clinical applications will be reviewed.

3D Cancer Models: Depicting Cellular Crosstalk within the Tumour Microenvironment

The tumour microenvironment plays a critical role in tumour progression and drug resistance processes. Non-malignant cell players, such as fibroblasts, endothelial cells, immune cells and others, interact with each other and with the tumour cells, shaping the disease. Though the role of each cell type and cell communication mechanisms have been progressively studied, the complexity of this cellular network and its role in disease mechanism and therapeutic response are still being unveiled. Animal models have been mainly used, as they can represent systemic interactions and conditions, though they face recognized limitations in translational potential due to interspecies differences. In vitro 3D cancer models can surpass these limitations, by incorporating human cells, including patient-derived ones, and allowing a range of experimental designs with precise control of each tumour microenvironment element. We summarize the role of each tumour microenvironment component and review studies proposing 3D co-culture strategies of tumour cells and non-malignant cell components. Moreover, we discuss the potential of these modelling approaches to uncover potential therapeutic targets in the tumour microenvironment and assess therapeutic efficacy, current bottlenecks and perspectives.

MCL-1 Inhibition Overcomes Anti-apoptotic Adaptation to Targeted Therapies in B-Cell Precursor Acute Lymphoblastic Leukemia

Multiple targeted therapies are currently explored for pediatric and young adult B-cell precursor acute lymphoblastic leukemia (BCP-ALL) treatment. However, this new armamentarium of therapies faces an old problem: choosing the right treatment for each patient. The lack of predictive biomarkers is particularly worrying for pediatric patients since it impairs the implementation of new treatments in the clinic. In this study, we used the functional assay dynamic BH3 profiling (DBP) to evaluate two new treatments for BCP-ALL that could improve clinical outcome, especially for relapsed patients. We found that the MEK inhibitor trametinib and the multi-target tyrosine kinase inhibitor sunitinib exquisitely increased apoptotic priming in an NRAS-mutant and in a KMT2A-rearranged cell line presenting a high expression of FLT3, respectively. Following these observations, we sought to study potential adaptations to these treatments. Indeed, we identified with DBP anti-apoptotic changes in the BCL-2 family after treatment, particularly involving MCL-1 - a pro-survival strategy previously observed in adult cancers. To overcome this adaptation, we employed the BH3 mimetic S63845, a specific MCL-1 inhibitor, and evaluated its sequential addition to both kinase inhibitors to overcome resistance. We observed that the metronomic combination of both drugs with S63845 was synergistic and showed an increased efficacy compared to single agents. Similar observations were made in BCP-ALL KMT2A-rearranged PDX cells in response to sunitinib, showing an analogous DBP profile to the SEM cell line. These findings demonstrate that rational sequences of targeted agents with BH3 mimetics, now extensively explored in clinical trials, may improve treatment effectiveness by overcoming anti-apoptotic adaptations in BCP-ALL.

Co-delivery systems: hope for clinical application?

Cancer is a multidimensional and challenging disease to handle. Current statistics reveal that we are far from satisfying cancer treatment. Taking advantage of different therapeutic agents that affect multiple pathways has been established as highly productive. Nevertheless, owing to several hindrances to conventional combination therapy, such as lack of tumor targeting, non-uniform pharmacokinetic of the combined drugs, and off-target side effects, it is well documented that this treatment approach is unlikely to address all the difficulties observed in monotherapy. Co-delivery systems could enhance the therapeutic efficacy of the combination therapy by targeting cancer cells and improving the pharmacokinetic and physicochemical properties of the therapeutic agents. Nevertheless, it seems that present knowledge in responding to the challenges in cancer treatment is still inadequate and far from optimal treatment, which highlights the urgent need for systematic studies direct to identify various aspects of co-delivery systems. Accordingly, to gather informative data, save time, and achieve superior results, the following steps are necessary: (1) implementing computational methods to predict drug-drug interactions (DDIs) in vitro and in vivo, (2) meticulous cancer studies at the cellular and molecular levels to obtain specific criteria for selecting preclinical and clinical models, (3) extensive physiological and pharmacokinetic study of nanocarriers behavior in preclinical models, and (4) finding the optimal formulation and analyzing its behavior in cellular and animal models facilitates bridging in vivo models to clinical trials. This review aims to deliver an overview of co-delivery systems, rationales, and suggestions for further studies in this field.

Intestinal multicellular organoids to study colorectal cancer

Modeling colorectal cancer (CRC) using organoids has burgeoned in the last decade, providing enhanced in vitro models to study the development and possible treatment options for this type of cancer. In this review, we describe both normal and CRC intestinal organoid models and their utility in the cancer research field. Besides highlighting studies that develop epithelial CRC organoid models, i.e. organoids without tumor microenvironment (TME) cellular components, we emphasize on the need for TME in CRC modeling, to help reduce translational disparities in this area. Also, we discuss the utilization of CRC organoids derived from pluripotent stem cells, as well as their potential to be used in cancer research. Finally, limitations and challenges in the current CRC organoids field, are discussed.

Machine learning for perturbational single-cell omics

Cell biology is fundamentally limited in its ability to collect complete data on cellular phenotypes and the wide range of responses to perturbation. Areas such as computer vision and speech recognition have addressed this problem of characterizing unseen or unlabeled conditions with the combined advances of big data, deep learning, and computing resources in the past 5 years. Similarly, recent advances in machine learning approaches enabled by single-cell data start to address prediction tasks in perturbation response modeling. We first define objectives in learning perturbation response in single-cell omics; survey existing approaches, resources, and datasets (https://github.com/theislab/sc-pert); and discuss how a perturbation atlas can enable deep learning models to construct an informative perturbation latent space. We then examine future avenues toward more powerful and explainable modeling using deep neural networks, which enable the integration of disparate information sources and an understanding of heterogeneous, complex, and unseen systems.

Exploring the Complementarity of Pancreatic Ductal Adenocarcinoma Preclinical Models

Simple Summary

Pancreatic ductal adenocarcinoma (PDAC) patient care lacks well-established molecular characterization of the tumors, which would allow for better-personalized treatment selection if improved. To overcome this problem, preclinical models are frequent-ly adopted tools used to elucidate the molecular characterization of PDAC tumors. Unfortunately, the vast majority of studies using these preclinical models fail when transferred to patients despite initially promising results. This study presents for the first time a comparison between three preclinical matched models directly derived from patient tumors. We show that their applicability to preclinical studies should be considered with a complementary focus, avoiding tumor-based direct extrapolations, which might generate misleading conclusions and consequently the overlook of clinically relevant features. We finally highlight the importance of validating and refining predictive transcriptomic signatures using a combination of these models.

Abstract

Purpose: Compare pancreatic ductal adenocarcinoma (PDAC), preclinical models, by their transcriptome and drug response landscapes to evaluate their complementarity. Experimental Design: Three paired PDAC preclinical models—patient-derived xenografts (PDX), xenograft-derived pancreatic organoids (XDPO) and xenograft-derived primary cell cultures (XDPCC)—were derived from 20 patients and analyzed at the transcriptomic and chemosensitivity level. Transcriptomic characterization was performed using the basal-like/classical subtyping and the PDAC molecular gradient (PAMG). Chemosensitivity for gemcitabine, irinotecan, 5-fluorouracil and oxaliplatin was established and the associated biological pathways were determined using independent component analysis (ICA) on the transcriptome of each model. The selection criteria used to identify the different components was the chemosensitivity score (CSS) found for each drug in each model. Results: PDX was the most dispersed model whereas XDPO and XDPCC were mainly classical and basal-like, respectively. Chemosensitivity scoring determines that PDX and XDPO display a positive correlation for three out of four drugs tested, whereas PDX and XDPCC did not correlate. No match was observed for each tumor chemosensitivity in the different models. Finally, pathway analysis shows a significant association between PDX and XDPO for the chemosensitivity-associated pathways and PDX and XDPCC for the chemoresistance-associated pathways. Conclusions: Each PDAC preclinical model possesses a unique basal-like/classical transcriptomic phenotype that strongly influences their global chemosensitivity. Each preclinical model is imperfect but complementary, suggesting that a more representative approach of the clinical reality could be obtained by combining them. Translational Relevance: The identification of molecular signatures that underpin drug sensitivity to chemotherapy in PDAC remains clinically challenging. Importantly, the vast majority of studies using preclinical in vivo and in vitro models fail when transferred to patients in a clinical setting despite initially promising results. This study presents for the first time a comparison between three preclinical models directly derived from the same patients. We show that their applicability to preclinical studies should be considered with a complementary focus, avoiding tumor-based direct extrapolations, which might generate misleading conclusions and consequently the overlook of clinically relevant features.

Brain Tumor Biobank Development for Precision Medicine: Role of the Neurosurgeon

Neuro-oncology biobanks are critical for the implementation of a precision medicine program. In this perspective, we review our first year experience of a brain tumor biobank with integrated next generation sequencing. From our experience, we describe the critical role of the neurosurgeon in diagnosis, research, and precision medicine efforts. In the first year of implementation of the biobank, 117 patients (Female: 62; Male: 55) had 125 brain tumor surgeries. 75% of patients had tumors biobanked, and 16% were of minority race/ethnicity. Tumors biobanked were as follows: diffuse gliomas (45%), brain metastases (29%), meningioma (21%), and other (5%). Among biobanked patients, 100% also had next generation sequencing. Eleven patients qualified for targeted therapy based on identification of actionable gene mutations. One patient with a hereditary cancer predisposition syndrome was also identified. An iterative quality improvement process was implemented to streamline the workflow between the operating room, pathology, and the research laboratory. Dedicated tumor bank personnel in the department of neurosurgery greatly improved standard operating procedure. Intraoperative selection and processing of tumor tissue by the neurosurgeon was integral to increasing success with cell culture assays. Currently, our institutional protocol integrates standard histopathological diagnosis, next generation sequencing, and functional assays on surgical specimens to develop precision medicine protocols for our patients. This perspective reviews the critical role of neurosurgeons in brain tumor biobank implementation and success as well as future directions for enhancing precision medicine efforts.

Preclinical In Vivo Modeling of Pediatric Sarcoma-Promises and Limitations

Pediatric sarcomas are an extremely heterogeneous group of genetically distinct diseases. Despite the increasing knowledge on their molecular makeup in recent years, true therapeutic advancements are largely lacking and prognosis often remains dim, particularly for relapsed and metastasized patients. Since this is largely due to the lack of suitable model systems as a prerequisite to develop and assess novel therapeutics, we here review the available approaches to model sarcoma in vivo. We focused on genetically engineered and patient-derived mouse models, compared strengths and weaknesses, and finally explored possibilities and limitations to utilize these models to advance both biological understanding as well as clinical diagnosis and therapy.

Tissues and Tumor Microenvironment (TME) in 3D: Models to Shed Light on Immunosuppression in Cancer

Immunosuppression in cancer has emerged as a major hurdle to immunotherapy efforts. Immunosuppression can arise from oncogene-induced signaling within the tumor as well as from tumor-associated immune cells. Understanding various mechanisms by which the tumor can undermine and evade therapy is critical in improving current cancer immunotherapies. While mouse models have allowed for the characterization of key immune cell types and their role in tumor development, extrapolating these mechanisms to patients has been challenging. There is need for better models to unravel the effects of genetic alterations inherent in tumor cells and immune cells isolated from tumors on tumor growth and to investigate the feasibility of immunotherapy. Three-dimensional (3D) organoid model systems have developed rapidly over the past few years and allow for incorporation of components of the tumor microenvironment such as immune cells and the stroma. This bears great promise for derivation of patient-specific models in a dish for understanding and determining the impact on personalized immunotherapy. In this review, we will highlight the significance of current experimental models employed in the study of tumor immunosuppression and evaluate current tumor organoid-immune cell co-culture systems and their potential impact in shedding light on cancer immunosuppression.

ProteaseGuru: A Tool for Protease Selection in Bottom-Up Proteomics

Bottom-up proteomics is currently the dominant strategy for proteome analysis. It relies critically upon the use of a protease to digest proteins into peptides, which are then identified by liquid chromatography-mass spectrometry (LC-MS). The choice of protease(s) has a substantial impact upon the utility of the bottom-up results obtained. Protease selection determines the nature of the peptides produced, which in turn affects the ability to infer the presence and quantities of the parent proteins and post-translational modifications in the sample. We present here the software tool ProteaseGuru, which provides in silico digestions by candidate proteases, allowing evaluation of their utility for bottom-up proteomic experiments. This information is useful for both studies focused on a single or small number of proteins, and for analysis of entire complex proteomes. ProteaseGuru provides a convenient user interface, valuable peptide information, and data visualizations enabling the comparison of digestion results of different proteases. The information provided includes data tables of theoretical peptide sequences and their biophysical properties, results summaries outlining the numbers of shared and unique peptides per protease, histograms facilitating the comparison of proteome-wide proteolytic data, protein-specific summaries, and sequence coverage maps. Examples are provided of its use to inform analysis of variant-containing proteins in the human proteome, as well as for studies requiring the use of multiple proteomic databases such as a human:mouse xenograft model, and microbiome metaproteomics.

Mouse Models of Peritoneal Carcinomatosis to Develop Clinical Applications

Simple Summary

Peritoneal carcinomatosis mouse models as a platform to test, improve and/or predict the appropriate therapeutic interventions in patients are crucial to providing medical advances. Here, we overview reported mouse models to explore peritoneal carcinomatosis in translational biomedical research.

Abstract

Peritoneal carcinomatosis of primary tumors originating in gastrointestinal (e.g., colorectal cancer, gastric cancer) or gynecologic (e.g., ovarian cancer) malignancies is a widespread type of tumor dissemination in the peritoneal cavity for which few therapeutic options are available. Therefore, reliable preclinical models are crucial for research and development of efficacious treatments for this condition. To date, a number of animal models have attempted to reproduce as accurately as possible the complexity of the tumor microenvironment of human peritoneal carcinomatosis. These include: Syngeneic tumor cell lines, human xenografts, patient-derived xenografts, genetically induced tumors, and 3D scaffold biomimetics. Each experimental model has its own strengths and limitations, all of which can influence the subsequent translational results concerning anticancer and immunomodulatory drugs under exploration. This review highlights the current status of peritoneal carcinomatosis mouse models for preclinical development of anticancer drugs or immunotherapeutic agents.

Breast Cancer Organoids Model Patient-Specific Response to Drug Treatment

Tumor organoids are tridimensional cell culture systems that are generated in vitro from surgically resected patients' tumors. They can be propagated in culture maintaining several features of the tumor of origin, including cellular and genetic heterogeneity, thus representing a promising tool for precision cancer medicine. Here, we established patient-derived tumor organoids (PDOs) from different breast cancer subtypes (luminal A, luminal B, human epidermal growth factor receptor 2 (HER2)-enriched, and triple negative). The established model systems showed histological and genomic concordance with parental tumors. However, in PDOs, the ratio of diverse cell populations was frequently different from that originally observed in parental tumors. We showed that tumor organoids represent a valuable system to test the efficacy of standard therapeutic treatments and to identify drug resistant populations within tumors. We also report that inhibitors of mechanosignaling and of Yes-associated protein 1 (YAP) activation can restore chemosensitivity in drug resistant tumor organoids.

Association of Tumor Budding With Immune Evasion Pathways in Primary Colorectal Cancer and Patient-Derived Xenografts

Tumor budding has been found to be of prognostic significance for several cancers, including colorectal cancer (CRC). Additionally, the molecular classification of CRC has led to the identification of different immune microenvironments linked to distinct prognosis and therapeutic response. However, the association between tumor budding and the different molecular subtypes of CRC and distinct immune profiles have not been fully elucidated. This study focused, firstly, on the validation of derived xenograft models (PDXs) for the evaluation of tumor budding and their human counterparts and, secondly, on the association between tumor budding and the immune tumor microenvironment by the analysis of gene expression signatures of immune checkpoints, Toll-like receptors (TLRs), and chemokine families. Clinical CRC samples with different grades of tumor budding and their corresponding PDXs were included in this study. Tumor budding grade was reliably reproduced in early passages of PDXs, and high-grade tumor budding was intimately related with a poor-prognosis CMS4 mesenchymal subtype. In addition, an upregulation of negative regulatory immune checkpoints (PDL1, TIM-3, NOX2, and IDO1), TLRs (TLR1, TLR3, TLR4, and TLR6), and chemokine receptors and ligands (CXCR2, CXCR4, CXCL1, CXCL2, CXCL6, and CXCL9) was detected in high-grade tumor budding in both human samples and their corresponding xenografts. Our data support a close link between high-grade tumor budding in CRC and a distinctive immune-suppressive microenvironment promoting tumor invasion, which may have a determinant role in the poor prognosis of the CMS4 mesenchymal subtype. In addition, our study demonstrates that PDX models may constitute a robust preclinical platform for the development of novel therapies directed against tumor budding in CRC.

Personalized Medicine: Recent Progress in Cancer Therapy

Translational research has revolutionized how we develop new treatments for cancer patients. The change from an organ-centric concept guiding treatment choice towards deep molecular analysis, driving a personalized approach, is one of the most important advances of modern oncology. Several tools such as next generation sequencing and RNA sequencing have greatly improved the capacity to detect predictive and prognostic molecular alterations. Detection of gene mutations, amplifications, and fusions has therefore altered the history of several diseases in both a localized and metastatic setting. This shift in perspective, in which attention is focused on the specific molecular alterations of the tumor, has opened the door to personalized treatment. This situation is reflected in the increasing number of basket trials selecting specific molecular targets. Nonetheless, some weaknesses need to be addressed. The complexity of cancer cells enriched with concomitant molecular alterations complicates identification of the driver. Moreover, tumor heterogeneity could be responsible for the lack of benefit when targeted agents are used. In light of this, there is growing interest in the role of multidisciplinary committees or molecular tumor boards to try to enhance selection. The aim of this review is to critically analyze the evolution of cancer treatment towards a precision approach, underlining some recent successes and unexpected failures.