The design, analysis and application of mouse clinical trials in oncology drug development

Statistical classification of treatment responses in mouse clinical trials for stratified medicine in oncology drug discovery

Translational oncology research strives to explore a new aspect: identifying subgroups that exhibit treatment response even during pre-clinical phases. In this study, we focus on PDX models and their implementation in mouse clinical trials (MCT). Our primary objective was to identify subgroups with different treatment responses using Latent Class Mixed Model (LCMM).We used a public dataset and focused on one treatment, encorafenib, and two indications, melanoma and colorectal cancer, for which efficacy depends on a specific mutation BRAF V600E. One LCMM per indication was implemented to classify treatment responses at the PDX level, analyzing the growth kinetics of treated tumors and matched controls within the PDX models. A simulation study was carried out to explore the performance of LCMM in this context. For both applications, LCMM identified classes for which the higher the proportion of mutated BRAF V600E PDX models the greater the treatment effect, which is aligned with encorafenib use recommendations. The simulation study showed that LCMM could identify classes with large differences in treatment effects. LCMM is a suitable tool for MCT to explore treatment response subgroups of PDX. Once these subgroups are defined, characterization of their phenotypes/genotypes could be performed to explore treatment response predictors.

Statistical Assessment of Drug Synergy from In Vivo Combination Studies Using Mouse Tumor Models

Drug combination therapy is a promising strategy for treating cancer; however, its efficacy and synergy require rigorous evaluation in preclinical studies before going to clinical trials. Existing methods have limited power to detect synergy in animal studies. Here, we introduce a novel approach to assess in vivo drug synergy with high sensitivity and low false discovery rate. It can accurately estimate combination index and synergy score under the Bliss independence model and the highest single agent (HSA) model without any assumption on tumor growth kinetics, study duration, data completeness and balance for tumor volume measurement. We show that our method can effectively validate in vitro drug synergy discovered from cell line assays in in vivo xenograft experiments, and can help to elucidate the mechanism of action for immune checkpoint inhibitors in syngeneic mouse models by combining an anti-PD-1 antibody and several tumor-infiltrating leukocytes depletion treatments. We provide a unified view of in vitro and in vivo synergy by presenting a parallelism between the fixed-dose in vitro and the 4-group in vivo combination studies, so they can be better designed, analyzed, and compared. We emphasize that combination index, when defined here via relative survival of tumor cells, is both dose and time dependent, and give guidelines on designing informative in vivo combination studies. We explain how to interpret and apply Bliss and HSA synergies. Finally, we provide an open-source software package named invivoSyn that enables automated analysis of in vivo synergy using our method and several other existing methods.

SIGNIFICANCE

This work presents a general solution to reliably determine in vivo drug synergy in single-dose 4-group animal combination studies.

Anaplerotic nutrient stress drives synergy of angiogenesis inhibitors with therapeutics targeting tumor metabolism

Tumor angiogenesis is a cancer hallmark, and its therapeutic inhibition has provided meaningful, albeit limited, clinical benefit. While anti-angiogenesis inhibitors deprive the tumor of oxygen and essential nutrients, cancer cells activate metabolic adaptations to diminish therapeutic response. Despite these adaptations, angiogenesis inhibition incurs extensive metabolic stress, prompting us to consider such metabolic stress as an induced vulnerability to therapies targeting cancer metabolism. Metabolomic profiling of angiogenesis-inhibited intracranial xenografts showed universal decrease in tricarboxylic acid cycle intermediates, corroborating a state of anaplerotic nutrient deficit or stress. Accordingly, we show strong synergy between angiogenesis inhibitors (Avastin, Tivozanib) and inhibitors of glycolysis or oxidative phosphorylation through exacerbation of anaplerotic nutrient stress in intracranial orthotopic xenografted gliomas. Our findings were recapitulated in GBM xenografts that do not have genetically predisposed metabolic vulnerabilities at baseline. Thus, our findings cement the central importance of the tricarboxylic acid cycle as the nexus of metabolic vulnerabilities and suggest clinical path hypothesis combining angiogenesis inhibitors with pharmacological cancer interventions targeting tumor metabolism for GBM tumors.

SGN-B7H4V, an investigational vedotin ADC directed to the immune checkpoint ligand B7-H4, shows promising activity in preclinical models

BACKGROUND

SGN-B7H4V is a novel investigational vedotin antibody-drug conjugate (ADC) comprising a B7-H4-directed human monoclonal antibody conjugated to the cytotoxic payload monomethyl auristatin E (MMAE) via a protease-cleavable maleimidocaproyl valine citrulline (mc-vc) linker. This vedotin linker-payload system has been clinically validated in multiple Food and Drug Administration approved agents including brentuximab vedotin, enfortumab vedotin, and tisotumab vedotin. B7-H4 is an immune checkpoint ligand with elevated expression on a variety of solid tumors, including breast, ovarian, and endometrial tumors, and limited normal tissue expression. SGN-B7H4V is designed to induce direct cytotoxicity against target cells by binding to B7-H4 on the surface of target cells and releasing the cytotoxic payload MMAE upon internalization of the B7-H4/ADC complex.

METHODS

B7-H4 expression was characterized by immunohistochemistry across multiple solid tumor types. The ability of SGN-B7H4V to kill B7-H4-expressing tumor cells in vitro and in vivo in a variety of xenograft tumor models was also evaluated. Finally, the antitumor activity of SGN-B7H4V as monotherapy and in combination with an anti-programmed cell death-1 (PD-1) agent was evaluated using an immunocompetent murine B7-H4-expressing Renca tumor model.

RESULTS

Immunohistochemistry confirmed B7-H4 expression across multiple solid tumors, with the highest prevalence in breast, endometrial, and ovarian tumors. In vitro, SGN-B7H4V killed B7-H4-expressing tumor cells by MMAE-mediated direct cytotoxicity and antibody-mediated effector functions including antibody-dependent cellular cytotoxicity and antibody-dependent cellular phagocytosis. In vivo, SGN-B7H4V demonstrated strong antitumor activity in multiple xenograft models of breast and ovarian cancer, including xenograft tumors with heterogeneous B7-H4 expression, consistent with the ability of vedotin ADCs to elicit a bystander effect. In an immunocompetent murine B7-H4-expressing tumor model, SGN-B7H4V drove robust antitumor activity as a monotherapy that was enhanced when combined with an anti-PD-1 agent.

CONCLUSION

The immune checkpoint ligand B7-H4 is a promising molecular target expressed by multiple solid tumors. SGN-B7H4V demonstrates robust antitumor activity in preclinical models through multiple potential mechanisms. Altogether, these preclinical data support the evaluation of SGN-B7H4V as a monotherapy in the ongoing phase 1 study of SGN-B7H4V in advanced solid tumors (NCT05194072) and potential future clinical combinations with immunotherapies.

Whole-cage randomization for animal studies with unequal cage or group sizes

Following good statistical practice, in vivo study investigators allocate animals into two or more treatment groups using a randomization routine to eliminate selection bias and balance known and unknown confounding factors. For some studies, however, randomization at the individual animal level cannot be implemented. For example, for studies that involve co-housed male mice, an animal-level randomization can place unfamiliar mice together in the same cage, which can trigger fighting. To meet the ethical obligations to enhance the welfare of an animal used in science, the experimental procedures are, therefore, often modified, and male mice, possibly from the same brood, may be housed together. It follows that animal allocation into groups must proceed at the whole-cage level. Given the small sample sizes in animal studies, controlling baseline variables can be quite challenging. The difficulty greatly increases with a whole-cage randomization restriction. When the number of animals per cage or the treatment group sizes are unequal, there is no algorithm in the literature to perform the task. We propose a novel, fast, and reliable algorithm to provide a whole-cage randomization that balances one or more baseline variables across groups. The algorithm was applied to a realistic example dataset.

Immunotherapy targeting different immune compartments in combination with radiation therapy induces regression of resistant tumors

Radiation therapy (RT) increases tumor response to CTLA-4 inhibition (CTLA4i) in mice and in some patients, yet deep responses are rare. To identify rational combinations of immunotherapy to improve responses we use models of triple negative breast cancer highly resistant to immunotherapy in female mice. We find that CTLA4i promotes the expansion of CD4+ T helper cells, whereas RT enhances T cell clonality and enriches for CD8+ T cells with an exhausted phenotype. Combination therapy decreases regulatory CD4+ T cells and increases effector memory, early activation and precursor exhausted CD8+ T cells. A combined gene signature comprising these three CD8+ T cell clusters is associated with survival in patients. Here we show that targeting additional immune checkpoints expressed by intratumoral T cells, including PD1, is not effective, whereas CD40 agonist therapy recruits resistant tumors into responding to the combination of RT and CTLA4i, indicating the need to target different immune compartments.

Proteomics in Childhood Acute Lymphoblastic Leukemia: Challenges and Opportunities

Acute lymphoblastic leukemia (ALL) is the most common cancer in children and one of the success stories in cancer therapeutics. Risk-directed therapy based on clinical, biologic and genetic features has played a significant role in this accomplishment. Despite the observed improvement in survival rates, leukemia remains one of the leading causes of cancer-related deaths. Implementation of next-generation genomic and transcriptomic sequencing tools has illustrated the genomic landscape of ALL. However, the underlying dynamic changes at protein level still remain a challenge. Proteomics is a cutting-edge technology aimed at deciphering the mechanisms, pathways, and the degree to which the proteome impacts leukemia subtypes. Advances in mass spectrometry enable high-throughput collection of global proteomic profiles, representing an opportunity to unveil new biological markers and druggable targets. The purpose of this narrative review article is to provide a comprehensive overview of studies that have utilized applications of proteomics in an attempt to gain insight into the pathogenesis and identification of biomarkers in childhood ALL.

Bcl-2 family inhibitors sensitize human cancer models to therapy

BH3 mimetics, targeting the Bcl-2 family anti-apoptotic proteins, represent a promising therapeutic opportunity in cancers. ABT-199, the first specific Bcl-2 inhibitor, was approved by FDA for the treatment of several hematological malignancies. We have recently discovered IS21, a novel pan BH3 mimetic with preclinical antitumor activity in several tumor types. Here, we evaluated the efficacy of IS21 and other BH3 mimetics, both as single agents and combined with the currently used antineoplastic agents in T-cell acute lymphoblastic leukemia, ovarian cancer, and melanoma. IS21 was found to be active in T-cell acute lymphoblastic leukemia, melanoma, lung, pancreatic, and ovarian cancer cell lines. Bcl-xL and Mcl-1 protein levels predicted IS21 sensitivity in melanoma and ovarian cancer, respectively. Exploring IS21 mechanism of action, we found that IS21 activity depends on the presence of BAX and BAK proteins: complexes between Bcl-2 and Bcl-xL proteins and their main binding partners were reduced after IS21 treatment. In combination experiments, BH3 mimetics sensitized leukemia cells to chemotherapy, ovarian cancer cells and melanoma models to PARP and MAPK inhibitors, respectively. We showed that this enhancing effect was related to the potentiation of the apoptotic pathway, both in hematologic and solid tumors. In conclusion, our data suggest the use of inhibitors of anti-apoptotic proteins as a therapeutic strategy to enhance the efficacy of anticancer treatment.

Patient-derived models facilitate precision medicine in liver cancer by remodeling cell-matrix interaction

Liver cancer is an aggressive tumor originating in the liver with a dismal prognosis. Current evidence suggests that liver cancer is the fifth most prevalent cancer worldwide and the second most deadly type of malignancy. Tumor heterogeneity accounts for the differences in drug responses among patients, emphasizing the importance of precision medicine. Patient-derived models of cancer are widely used preclinical models to study precision medicine since they preserve tumor heterogeneity ex vivo in the study of many cancers. Patient-derived models preserving cell-cell and cell-matrix interactions better recapitulate in vivo conditions, including patient-derived xenografts (PDXs), induced pluripotent stem cells (iPSCs), precision-cut liver slices (PCLSs), patient-derived organoids (PDOs), and patient-derived tumor spheroids (PDTSs). In this review, we provide a comprehensive overview of the different modalities used to establish preclinical models for precision medicine in liver cancer.

Diverse rescue potencies of p53 mutations to ATO are predetermined by intrinsic mutational properties

Tumor suppressor p53 is inactivated by thousands of heterogeneous mutations in cancer, but their individual druggability remains largely elusive. Here, we evaluated 800 common p53 mutants for their rescue potencies by the representative generic rescue compound arsenic trioxide (ATO) in terms of transactivation activity, cell growth inhibition, and mouse tumor-suppressive activities. The rescue potencies were mainly determined by the solvent accessibility of the mutated residue, a key factor determining whether a mutation is a structural one, and the temperature sensitivity, the ability to reassemble the wild-type DNA binding surface at a low temperature, of the mutant protein. A total of 390 p53 mutants were rescued to varying degrees and thus were termed as type 1, type 2a, and type 2b mutations, depending on the degree to which they were rescued. The 33 type 1 mutations were rescued to amounts comparable to the wild type. In PDX mouse trials, ATO preferentially inhibited growth of tumors harboring type 1 and type 2a mutants. In an ATO clinical trial, we report the first-in-human mutant p53 reactivation in a patient harboring the type 1 V272M mutant. In 47 cell lines derived from 10 cancer types, ATO preferentially and effectively rescued type 1 and type 2a mutants, supporting the broad applicability of ATO in rescuing mutant p53. Our study provides the scientific and clinical communities with a resource of the druggabilities of numerous p53 mutations (www.rescuep53.net) and proposes a conceptual p53-targeting strategy based on individual mutant alleles rather than mutation type.

HX009, a novel BsAb dual targeting PD1 x CD47, demonstrates potent anti-lymphoma activity in preclinical models

Both PD1/PD-L1 and CD47 blockades have demonstrated limited activity in most subtypes of NHL save NK/T-cell lymphoma. The hemotoxicity with anti-CD47 agents in the clinic has been speculated to account for their limitations. Herein we describe a first-in-class and rationally designed bispecific antibody (BsAb), HX009, targeting PD1 and CD47 but with weakened CD47 binding, which selectively hones the BsAb for tumor microenvironment through PD1 interaction, potentially reducing toxicity. In vitro characterization confirmed: (1) Both receptor binding/ligand blockade, with lowered CD47 affinity; (2) functional PD1/CD47 blockades by reporter assays; (3) T-cell activation in Staphylococcal-enterotoxin-B-pretreated PBMC and mixed-lymphocyte-reaction. In vivo modeling demonstrated antitumor activity in Raji-B and Karpass-229-T xenograft lymphomas. In the humanized mouse syngeneic A20 B-lymphoma (huCD47-A20) HuGEMM model, which has quadruple knocked-in hPD1xhPD-L1xhCD47xhSIRPα genes and an intact autologous immune-system, a contribution of effect is demonstrated for each targeted biologic (HX008 targeting PD1 and SIRPα-Fc targeting CD47), which is clearly augmented by the dual targeting with HX009. Lastly, the expression of the immune-checkpoints PD-L1/L2 and CD47 seemed co-regulated among a panel of lymphoma-derived-xenografts, where HX009 maybe more effective in those with upregulated CD47. Our data warrants HX009's further clinical development for treating NHLs.

Improving the predictive power of xenograft and syngeneic anti-tumour studies using mice humanised for pathways of drug metabolism

Drug development is an expensive and time-consuming process, with only a small fraction of drugs gaining regulatory approval from the often many thousands of candidates identified during target validation. Once a lead compound has been identified and optimised, they are subject to intensive pre-clinical research to determine their pharmacodynamic, pharmacokinetic and toxicological properties, procedures which inevitably involve significant numbers of animals - mainly mice and rats, but also dogs and monkeys in much smaller numbers and for specific types of drug candidates. Many compounds that emerge from this process, having been shown to be safe and efficacious in pre-clinical studies, subsequently fail to replicate this outcome in clinical trials, therefore wasting time, money and, most importantly, animals. Due to high rates of metabolism and a differing spectrum of metabolites (some pharmacologically active) in rodents, species differences in drug metabolism can be a major impediment to drug discovery programmes and confound the extrapolation of animal data to humans. To circumvent this, we have developed a complex transgenic mouse model – 8HUM - which faithfully replicates human Phase I drug metabolism (and its regulation), and which will generate more human-relevant data from fewer animals in a pre-clinical setting and reduce attrition in the clinic. One key area for the pre-clinical application of animals in an oncology setting – almost exclusively mice - is their use in anti-tumour studies. We now further demonstrate the utility of the 8HUM mouse using a murine melanoma cell line as a syngeneic tumour and also present an immunodeficient version 8HUM_Rag2 -/- - for use in xenograft studies. These models will be of significant benefit not only to Pharma for pre-clinical drug development work, but also throughout the drug efficacy, toxicology, pharmacology, and drug metabolism communities, where fewer animals will be needed to generate more human-relevant data.

Liposomal Irinotecan Shows a Larger Therapeutic Index than Non-liposomal Irinotecan in Patient-Derived Xenograft Models of Pancreatic Cancer

INTRODUCTION

Liposomal irinotecan promotes controlled sustained release of irinotecan (CPT-11), therefore, we hypothesize that the therapeutic index (quantitative measurement of the relative efficacy/safety ratio of a drug) will be higher for liposomal than non-liposomal irinotecan.

METHODS

We compared the therapeutic indexes of liposomal and non-liposomal irinotecan in mice bearing subcutaneous patient-derived xenograft (PDX) pancreatic tumors under dosing regimens approximating the clinical setting. Following preliminary drug sensitivity/antitumor activity analyses on three PDX tumor models, one model was selected for analyses of efficacy, biomarker, toxicology, pharmacokinetics in mice receiving liposomal irinotecan (2.5, 10, 50 mg/kg/week) or non-liposomal irinotecan (10, 25, 50 mg/kg/week). The maximum tolerated dose (MTD) for each treatment was 50 mg/kg/week.

RESULTS

Using the selected IM-PAN-001 model at the MTD (both treatments, 50 mg/kg/week), antitumor activity, phospho-histone gamma-H2AX protein staining in cancer cell nuclei, histological tumor regression, and plasma levels of CPT-11 and its active metabolite SN-38 after 24 h were greater with liposomal than non-liposomal irinotecan, but tumor SN-38 levels were similar. At the lowest doses assessed, antitumor activity, histological tumor regression, and jejunum and bone marrow toxicity were similar. Based on these findings, liposomal and non-liposomal irinotecan had therapeutic indexes of 20 and 5, respectively.

CONCLUSION

This non-clinical study showed a fourfold broader therapeutic index with liposomal than non-liposomal irinotecan in mice bearing IM-PAN-001 PDX pancreatic tumors, even at optimal dosing for the two drugs. These findings support the clinical benefit observed with liposomal irinotecan in patients with pancreatic cancer.

Bioinformatics Tools and Knowledgebases to Assist Generating Targeted Assays for Plasma Proteomics

In targeted proteomics experiments, selecting the appropriate proteotypic peptides as surrogate for the target protein is a crucial pre-acquisition step. This step is largely a bioinformatics exercise that involves integrating information on the peptides and proteins and using various software tools and knowledgebases. We present here a few resources that automate and simplify the selection process to a great degree. These tools and knowledgebases were developed primarily to streamline targeted proteomics assay development and include PeptidePicker, PeptidePickerDB, MRMAssayDB, MouseQuaPro, and PeptideTracker. We have used these tools to develop and document thousands of targeted proteomics assays, many of them for plasma proteins with focus on human and mouse. An important aspect in all these resources is the integrative approach on which they are based. Using these tools in the first steps of designing a singleplexed or multiplexed targeted proteomic experiment can reduce the necessary experimental steps tremendously. All the tools and knowledgebases we describe here are Web-based and freely accessible so scientists can query the information conveniently from the browser. This chapter provides an overview of these software tools and knowledgebases, their content, and how to use them for targeted plasma proteomics. We further demonstrate how to use them with the results of the HUPO Human Plasma Proteome Project to produce a new database of 3.8 k targeted assays for known human plasma proteins. Upon experimental validation, these assays should help in the further quantitative characterizing of the plasma proteome.

Volumetric imaging of optically cleared and fluorescently labeled animal tissue (VIOLA) for quantifying the 3D biodistribution of nanoparticles at cellular resolution in tumor tissue

Nanoparticle (NP) technology holds significant promise to mediate targeted drug delivery to specific organs in the body. Understanding the 3D biodistribution of NPs in heterogeneous environments such as the tumor tissue can provide crucial information on efficacy, safety and potential clinical outcomes. Here we present a novel end-to-end workflow, VIOLA, which makes use of tissue clearing methodology in conjunction with high resolution imaging and advanced 3D image processing to quantify the spatiotemporal 3D biodistribution of fluorescently labeled ACCURIN® NPs. Specifically, we investigate the spatiotemporal biodistribution of NPs in three different murine tumor models (CT26, EMT6, and KPC-GEM) of increasing complexity and translational relevance. We have developed new endpoints to characterize NP biodistribution at multiple length scales. Our observations reveal that the macroscale NP biodistribution is spatially heterogeneous and exhibits a gradient with relatively high accumulation at the tumor periphery that progressively decreases towards the tumor core in all the tumor models. Microscale analysis revealed that NP extravasation from blood vessels increases in a time dependent manner and plateaus at 72 h post injection. Volumetric analysis and pharmacokinetic modeling of NP biodistribution in the vicinity of the blood vessels revealed that the local NP density exhibits a distance dependent spatiotemporal biodistribution which provide insights into the dynamics of NP extravasation in the tumor tissue. Our data represents a comprehensive analysis of NP biodistribution at multiple length scales in different tumor models providing unique insights into their spatiotemporal dynamics. Specifically, our results show that NPs exhibit a dynamic equilibrium with macroscale heterogeneity combined with microscale homogeneity.

BET Bromodomain Degradation Disrupts Function but Not 3D Formation of RNA Pol2 Clusters

Fusion-positive rhabdomyosarcoma (FP-RMS) is driven by a translocation that creates the chimeric transcription factor PAX3-FOXO1 (P3F), which assembles de novo super enhancers to drive high levels of transcription of other core regulatory transcription factors (CRTFs). P3F recruits co-regulatory factors to super enhancers such as BRD4, which recognizes acetylated lysines via BET bromodomains. In this study, we demonstrate that inhibition or degradation of BRD4 leads to global decreases in transcription, and selective downregulation of CRTFs. We also show that the BRD4 degrader ARV-771 halts transcription while preserving RNA Polymerase II (Pol2) loops between super enhancers and their target genes, and causes the removal of Pol2 only past the transcriptional end site of CRTF genes, suggesting a novel effect of BRD4 on Pol2 looping. We finally test the most potent molecule, inhibitor BMS-986158, in an orthotopic PDX mouse model of FP-RMS with additional high-risk mutations, and find that it is well tolerated in vivo and leads to an average decrease in tumor size. This effort represents a partnership with an FP-RMS patient and family advocates to make preclinical data rapidly accessible to the family, and to generate data to inform future patients who develop this disease.

Brain metastatic outgrowth and osimertinib resistance are potentiated by RhoA in EGFR-mutant lung cancer

The brain is a major sanctuary site for metastatic cancer cells that evade systemic therapies. Through pre-clinical pharmacological, biological, and molecular studies, we characterize the functional link between drug resistance and central nervous system (CNS) relapse in Epidermal Growth Factor Receptor- (EGFR-) mutant non-small cell lung cancer, which can progress in the brain when treated with the CNS-penetrant EGFR inhibitor osimertinib. Despite widespread osimertinib distribution in vivo, the brain microvascular tumor microenvironment (TME) is associated with the persistence of malignant cell sub-populations, which are poised to proliferate in the brain as osimertinib-resistant lesions over time. Cellular and molecular features of this poised state are regulated through a Ras homolog family member A (RhoA) and Serum Responsive Factor (SRF) gene expression program. RhoA potentiates the outgrowth of disseminated tumor cells on osimertinib treatment, preferentially in response to extracellular laminin and in the brain. Thus, we identify pre-existing and adaptive features of metastatic and drug-resistant cancer cells, which are enhanced by RhoA/SRF signaling and the brain TME during the evolution of osimertinib-resistant disease.

Optimizing Drug Response Study Design in Patient-Derived Tumor Xenografts

Patient-derived tumor xenograft (PDX) models were used to evaluate the effectiveness of preclinical anticancer agents. A design using 1 mouse per patient per drug (1 × 1 × 1) was considered practical for large-scale drug efficacy studies. We evaluated modifiable parameters that could increase the statistical power of this design based on our consolidated PDX experiments. Real studies were used as a reference to investigate the relationship between statistical power with treatment effect size, inter-mouse variation, and tumor measurement frequencies. Our results showed that large effect sizes could be detected at a significance level of .2 or .05 under a 1 × 1 × 1 design. We found that the minimum number of mice required to achieve 80% power at an alpha level of .05 under all situations explored was 21 mice per group for a small effect size and 5 mice per group for a medium effect size.

A hitchhiker's guide to cancer models

Cancer is a complex and uniquely personal disease. More than 1.7 million people in the United States are diagnosed with cancer every year. As the burden of cancer grows, so does the need for new, more effective therapeutics and for predictive tools to identify optimal, personalized treatment options for every patient. Cancer models that recapitulate various aspects of the disease are fundamental to making advances along the continuum of cancer treatment from benchside discoveries to bedside delivery. In this review, we use a thought experiment as a vehicle to arrive at four broad categories of cancer models and explore the strengths, weaknesses, opportunities, and threats for each category in advancing our understanding of the disease and improving treatment strategies.

Research Status of Mouse Models for Non-Small-Cell Lung Cancer (NSCLC) and Antitumor Therapy of Traditional Chinese Medicine (TCM) in Mouse Models

Non-small-cell lung cancer (NSCLC) is known as one of the most lethal cancers, causing more than 1 million deaths annually worldwide. Therefore, the development of novel therapeutic drugs for NSCLC has become an urgent need. Herein, various mouse models provide great convenience not only for researchers but also for the development of antitumor drug. Meanwhile, TCM, as a valuable and largely untapped resource pool for modern medicine, provides research resources for the treatment of various diseases. Until now, cell-derived xenograft (CDX) model, patient-derived xenograft (PDX) model, syngeneic model, orthotopic model, humanized mouse model (HIS), and genetically engineered mouse models (GEMMs) have been reported in TCM evaluation. This review shows the role and current status of kinds of mouse models in antitumor research and summarizes the application progress of TCM including extracts, formulas, and isolated single molecules for NSCLC therapy in various mouse models; more importantly, it provides a theoretical exploration of what kind of mouse models is ideal for TCM efficacy evaluation in future. However, there are still huge challenges and limitations in the development of mouse models specifically for the TCM research, and none of the available models are perfectly matching the characteristics of TCM, which suppress the tumor growth through various mechanisms, especially by regulating immune function. Nevertheless, with fully functional immune system existing in syngeneic model and humanized mouse model (HIS), it is still suggested that these two models are more suitable for development of TCM especially for TCM extracts or formulas. Moreover, continued efforts are needed to generate more reliable mouse models to test TCM formulas in future research.

Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics

Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.

Assessing therapy response in patient-derived xenografts

[Figure: see text].

Antitumor efficacy of XPO1 inhibitor Selinexor in KRAS-mutant lung adenocarcinoma patient-derived xenografts

Gain-of-function Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations occur in 25% of lung adenocarcinomas, and these tumors are challenging to treat. Some preclinical work, largely based on cell lines, suggested KRASmut lung cancers are especially dependent on the nuclear export protein exportin-1 (XPO1), while other work supports XPO1 being a broader cancer dependency. To investigate the sensitivity of KRASmut lung cancers to XPO1 inhibition in models that more closely match clinical tumors, we treated 10 independently established lung cancer patient-derived tumor xenografts (PDXs) with the clinical XPO1 inhibitor, Selinexor. Monotherapy with Selinexor reduced tumor growth in all KRASmut PDXs, which included 4 different codon mutations, and was more effective than the clinical MEK1/2 inhibitor, Trametinib. Selinexor was equally effective in KRASG12C and KRASG12D tumors, with TP53 mutations being a biomarker for a weaker drug response. By mining genome-wide dropout datasets, we identified XPO1 as a universal cancer cell dependency and confirmed this functionally in two KRASWT PDX models harboring kinase drivers. However, targeted kinase inhibitors were more effective than Selinexor in these models. Our findings support continued investigation of XPO1 inhibitors in KRASmut lung adenocarcinoma, regardless of the codon alteration.

Statistical analysis of comparative tumor growth repeated measures experiments in the ovarian cancer patient derived xenograft (PDX) setting

Repeated measures studies are frequently performed in patient-derived xenograft (PDX) models to evaluate drug activity or compare effectiveness of cancer treatment regimens. Linear mixed effects regression models were used to perform statistical modeling of tumor growth data. Biologically plausible structures for the covariation between repeated tumor burden measurements are explained. Graphical, tabular, and information criteria tools useful for choosing the mean model functional form and covariation structure are demonstrated in a Case Study of five PDX models comparing cancer treatments. Power calculations were performed via simulation. Linear mixed effects regression models applied to the natural log scale were shown to describe the observed data well. A straight growth function fit well for two PDX models. Three PDX models required quadratic or cubic polynomial (time squared or cubed) terms to describe delayed tumor regression or initial tumor growth followed by regression. Spatial(power), spatial(power) + RE, and RE covariance structures were found to be reasonable. Statistical power is shown as a function of sample size for different levels of variation. Linear mixed effects regression models provide a unified and flexible framework for analysis of PDX repeated measures data, use all available data, and allow estimation of tumor doubling time.

Patient Derived Xenografts for Genome-Driven Therapy of Osteosarcoma

Osteosarcoma (OS) is a rare malignant primary tumor of mesenchymal origin affecting bone. It is characterized by a complex genotype, mainly due to the high frequency of chromothripsis, which leads to multiple somatic copy number alterations and structural rearrangements. Any effort to design genome-driven therapies must therefore consider such high inter- and intra-tumor heterogeneity. Therefore, many laboratories and international networks are developing and sharing OS patient-derived xenografts (OS PDX) to broaden the availability of models that reproduce OS complex clinical heterogeneity. OS PDXs, and new cell lines derived from PDXs, faithfully preserve tumor heterogeneity, genetic, and epigenetic features and are thus valuable tools for predicting drug responses. Here, we review recent achievements concerning OS PDXs, summarizing the methods used to obtain ectopic and orthotopic xenografts and to fully characterize these models. The availability of OS PDXs across the many international PDX platforms and their possible use in PDX clinical trials are also described. We recommend the coupling of next-generation sequencing (NGS) data analysis with functional studies in OS PDXs, as well as the setup of OS PDX clinical trials and co-clinical trials, to enhance the predictive power of experimental evidence and to accelerate the clinical translation of effective genome-guided therapies for this aggressive disease.

Mouse Quantitative Proteomics Knowledgebase: reference protein concentration ranges in 20 mouse tissues using 5000 quantitative proteomics assays

Laboratory mouse is the most used animal model in biological research, largely due to its high conserved synteny with human. Researchers use mice to answer various questions ranging from determining a pathological effect of knocked out/in gene to understanding drug metabolism. Our group developed >5000 quantitative targeted proteomics assays for 20 mouse tissues and determined the concentration ranges of a total of more than 1600 proteins using heavy labelled internal standards. We describe here MouseQuaPro; a knowledgebase that hosts this collection of carefully curated experimental data. The Web-based application includes protein concentrations from >700 mouse tissue samples from three common research strains, corresponding to more than 200k experimentally determined concentrations. The knowledgebase integrates the assay and protein concentration information with their human orthologs, functional and molecular annotations, biological pathways, related human diseases, and known gene expressions. At its core are the protein concentration ranges, which provide insights into (dis)similarities between tissues, strains, and sexes. MouseQuaPro implements advanced search as well as filtering functionalities with a simple interface and interactive visualization. This information-rich resource provides an initial map of protein absolute concentration in mouse tissues and allows guided design of proteomics phenotyping experiments. The knowledgebase is available at mousequapro.proteincentre.com. (Reviewer access username and password: mousequapro_reviewer1234567).

Hydrogels as Drug Delivery Systems: A Review of Current Characterization and Evaluation Techniques

Owing to their tunable properties, controllable degradation, and ability to protect labile drugs, hydrogels are increasingly investigated as local drug delivery systems. However, a lack of standardized methodologies used to characterize and evaluate drug release poses significant difficulties when comparing findings from different investigations, preventing an accurate assessment of systems. Here, we review the commonly used analytical techniques for drug detection and quantification from hydrogel delivery systems. The experimental conditions of drug release in saline solutions and their impact are discussed, along with the main mathematical and statistical approaches to characterize drug release profiles. We also review methods to determine drug diffusion coefficients and in vitro and in vivo models used to assess drug release and efficacy with the goal to provide guidelines and harmonized practices when investigating novel hydrogel drug delivery systems.

Combination of copanlisib with cetuximab improves tumor response in cetuximab-resistant patient-derived xenografts of head and neck cancer

Despite recent advances, the treatment of head and neck squamous cell carcinoma (HNSCC) remains an area of high unmet medical need. HNSCC is frequently associated with either amplification or mutational changes in the PI3K pathway, making PI3K an attractive target particularly in cetuximab-resistant tumors. Here, we explored the antitumor activity of the selective, pan-class I PI3K inhibitor copanlisib with predominant activity towards PI3Kα and δ in monotherapy and in combination with cetuximab using a mouse clinical trial set-up with 33 patient-derived xenograft (PDX) models with known HPV and PI3K mutational status and available data on cetuximab sensitivity. Treatment with copanlisib alone resulted in moderate antitumor activity with 12/33 PDX models showing either tumor stabilization or regression. Combination treatment with copanlisib and cetuximab was superior to either of the monotherapies alone in the majority of the models (21/33), and the effect was particularly pronounced in cetuximab-resistant tumors (14/16). While no correlation was observed between PI3K mutation status and response to either cetuximab or copanlisib, increased PI3K signaling activity evaluated through gene expression profiling showed a positive correlation with response to copanlisib. Together, these data support further investigation of PI3K inhibition in HNSCC and suggests gene expression patterns associated with PI3K signaling as a potential biomarker for predicting treatment responses.

Recent advances in mass spectrometry based clinical proteomics: applications to cancer research

Cancer biomarkers have transformed current practices in the oncology clinic. Continued discovery and validation are crucial for improving early diagnosis, risk stratification, and monitoring patient response to treatment. Profiling of the tumour genome and transcriptome are now established tools for the discovery of novel biomarkers, but alterations in proteome expression are more likely to reflect changes in tumour pathophysiology. In the past, clinical diagnostics have strongly relied on antibody-based detection strategies, but these methods carry certain limitations. Mass spectrometry (MS) is a powerful method that enables increasingly comprehensive insights into changes of the proteome to advance personalized medicine. In this review, recent improvements in MS-based clinical proteomics are highlighted with a focus on oncology. We will provide a detailed overview of clinically relevant samples types, as well as, consideration for sample preparation methods, protein quantitation strategies, MS configurations, and data analysis pipelines currently available to researchers. Critical consideration of each step is necessary to address the pressing clinical questions that advance cancer patient diagnosis and prognosis. While the majority of studies focus on the discovery of clinically-relevant biomarkers, there is a growing demand for rigorous biomarker validation. These studies focus on high-throughput targeted MS assays and multi-centre studies with standardized protocols. Additionally, improvements in MS sensitivity are opening the door to new classes of tumour-specific proteoforms including post-translational modifications and variants originating from genomic aberrations. Overlaying proteomic data to complement genomic and transcriptomic datasets forges the growing field of proteogenomics, which shows great potential to improve our understanding of cancer biology. Overall, these advancements not only solidify MS-based clinical proteomics' integral position in cancer research, but also accelerate the shift towards becoming a regular component of routine analysis and clinical practice.

Chronic polypharmacy impairs explorative behavior and reduces synaptic functions in young adult mice

A major challenge in the health care system is the lack of knowledge about the possible harmful effects of multiple drug treatments in old age. The present study aims to characterize a mouse model of polypharmacy, in order to investigate whether long-term exposure to multiple drugs could lead to adverse outcomes. To this purpose we selected five drugs from the ten most commonly used by older adults in Sweden (metoprolol, paracetamol, aspirin, simvastatin and citalopram). Five-month-old wild type male mice were fed for eight weeks with control or polypharmacy diet. We report for the first time that young adult polypharmacy-treated mice showed a significant decrease in exploration and spatial working memory compared to the control group. This memory impairment was further supported by a significant reduction of synaptic proteins in the hippocampus of treated mice. These novel results suggest that already at young adult age, use of polypharmacy affects explorative behavior and synaptic functions. This study underlines the importance of investigating the potentially negative outcomes from concomitant administration of different drugs, which have been poorly explored until now. The mouse model proposed here has translatable findings and can be applied as a useful tool for future studies on polypharmacy.

Frailty-A promising concept to evaluate disease vulnerability

With the emergence of diseases, people become frailer and are expected to be less tolerant of adverse outcomes. Frailty was first described to explain the variability in life expectancy in individuals of the same age. Nowadays, it is described as a syndrome and as a state. It is used to explain the heterogeneity of people not only in their responses to biological ageing but also in their responses to illness. In this review, we explore the role of frailty both in age-related diseases, including dementia, cancer and cardiovascular disease, and in non-age-related diseases, such as Human Immunodeficiency Virus. We describe how high levels of frailty in such disorders predict worse outcomes and play a direct role in disease progression and in prognostic prediction. Overall, the potential for frailty to predict adverse health outcomes among young people as well as in non-age-related diseases is an evolving topic. Understanding how frailty contributes to poor health and how it can be modified to prevent or delay disease progression will ultimately enhance quality of life in affected individuals.

Patient-Derived Orthotopic Xenograft Models of Pediatric Brain Tumors: In a Mature Phase or Still in Its Infancy?

In recent years, molecular profiling has led to the discovery of an increasing number of brain tumor subtypes, and associated therapeutic targets. These molecular features have been incorporated in the 2016 new World Health Organization (WHO) Classification of Tumors of the Central Nervous System (CNS), which now distinguishes tumor subgroups not only histologically, but also based on molecular characteristics. Despite an improved diagnosis of (pediatric) tumors in the CNS however, the survival of children with malignant brain tumors still is far worse than for those suffering from other types of malignancies. Therefore, new treatments need to be developed, based on subgroup-specific genetic aberrations. Here, we provide an overview of the currently available orthotopic xenograft models for pediatric brain tumor subtypes as defined by the 2016 WHO classification, to facilitate the choice of appropriate animal models for the preclinical testing of novel treatment strategies, and to provide insight into the current gaps and challenges.

Mass spectrometry imaging to detect lipid biomarkers and disease signatures in cancer

BACKGROUND

Current methods to identify, classify, and predict tumor behavior mostly rely on histology, immunohistochemistry, and molecular determinants. However, better predictive markers are required for tumor diagnosis and evaluation. Due, in part, to recent technological advancements, metabolomics and lipid biomarkers have become a promising area in cancer research. Therefore, there is a necessity for novel and complementary techniques to identify and visualize these molecular markers within tumors and surrounding tissue.

RECENT FINDINGS

Since its introduction, mass spectrometry imaging (MSI) has proven to be a powerful tool for mapping analytes in biological tissues. By adding the label-free specificity of mass spectrometry to the detailed spatial information of traditional histology, hundreds of lipids can be imaged simultaneously within a tumor. MSI provides highly detailed lipid maps for comparing intra-tumor, tumor margin, and healthy regions to identify biomarkers, patterns of disease, and potential therapeutic targets. In this manuscript, recent advancement in sample preparation and MSI technologies are discussed with special emphasis on cancer lipid research to identify tumor biomarkers.

CONCLUSION

MSI offers a unique approach for biomolecular characterization of tumor tissues and provides valuable complementary information to histology for lipid biomarker discovery and tumor classification in clinical and research cancer applications.

The hubris and humility of cancer pharmacology in the post immuno-oncology era

Cancer is a dreaded word, which has stimulated monumental efforts to discover and deliver effective cancer treatments for more than half a century. During the past two decades, our understanding of the molecular pathogenesis of cancer has increased remarkably. This has fostered an explosion in the number of experimental agents and clinical trials coupled with a dramatic rise in the regulatory approval of therapies for human cancers. Unfortunately, our preclinical models perform poorly as predictive platforms for the ultimate success of clinical candidates, reflecting the complexity of cancer. Moreover the common combination of cancer drugs prescribes the need for a better understanding of the fundamental pharmacology of each agent. Here I briefly outline some of the fundamental changes that have and have not occurred in cancer pharmacology during the past two decades and prognosticate on possible future directions.