From bench to bedside: lessons learned in translating preclinical studies in cancer drug development

Unveiling the Promise: A Comprehensive Review of Salpingectomy as a Vanguard for Ovarian Cancer Prevention

This comprehensive review explores the potential of salpingectomy as a groundbreaking strategy for the prevention of ovarian cancer. The discussion encompasses the biological rationale behind salpingectomy, emphasizing its foundation in the tubal hypothesis, which posits the fallopian tubes as a possible origin site for certain ovarian cancers. Ongoing clinical trials and observational studies provide evolving evidence supporting the safety and efficacy of salpingectomy, particularly in high-risk populations. The procedure's ethical considerations, including its impact on fertility and equitable access, are thoroughly examined. Implications for clinical practice underscore the importance of informed decision-making, risk-benefit assessments, and the integration of emerging evidence into reproductive health discussions. Looking ahead, the future landscape of ovarian cancer prevention involves continued research, technological innovations, and collaborative efforts to ensure a holistic and evidence-based approach. The goal is to forge a future where ovarian cancer is not only treatable but also preventable, with salpingectomy potentially playing a pivotal role in this transformative journey.

Sources of inter-individual variability leading to significant changes in anti-PD-1 and anti-PD-L1 efficacy identified in mouse tumor models using a QSP framework

While anti-PD-1 and anti-PD-L1 [anti-PD-(L)1] monotherapies are effective treatments for many types of cancer, high variability in patient responses is observed in clinical trials. Understanding the sources of response variability can help prospectively identify potential responsive patient populations. Preclinical data may offer insights to this point and, in combination with modeling, may be predictive of sources of variability and their impact on efficacy. Herein, a quantitative systems pharmacology (QSP) model of anti-PD-(L)1 was developed to account for the known pharmacokinetic properties of anti-PD-(L)1 antibodies, their impact on CD8⁺ T cell activation and influx into the tumor microenvironment, and subsequent anti-tumor effects in CT26 tumor syngeneic mouse model. The QSP model was sufficient to describe the variability inherent in the anti-tumor responses post anti-PD-(L)1 treatments. Local sensitivity analysis identified tumor cell proliferation rate, PD-1 expression on CD8⁺ T cells, PD-L1 expression on tumor cells, and the binding affinity of PD-1:PD-L1 as strong influencers of tumor growth. It also suggested that treatment-mediated tumor growth inhibition is sensitive to T cell properties including the CD8⁺ T cell proliferation half-life, CD8⁺ T cell half-life, cytotoxic T-lymphocyte (CTL)-mediated tumor cell killing rate, and maximum rate of CD8⁺ T cell influx into the tumor microenvironment. Each of these parameters alone could not predict anti-PD-(L)1 treatment response but they could shift an individual mouse's treatment response when perturbed. The presented preclinical QSP modeling framework provides a path to incorporate potential sources of response variability in human translation modeling of anti-PD-(L)1.

Neuroprotection in neurodegenerations of the brain and eye: Lessons from the past and directions for the future

Background

Neurological and ophthalmological neurodegenerative diseases in large part share underlying biology and pathophysiology. Despite extensive preclinical research on neuroprotection that in many cases bridges and unifies both fields, only a handful of neuroprotective therapies have succeeded clinically in either.

Main body

Understanding the commonalities among brain and neuroretinal neurodegenerations can help develop innovative ways to improve translational success in neuroprotection research and emerging therapies. To do this, analysis of why translational research in neuroprotection fails necessitates addressing roadblocks at basic research and clinical trial levels. These include optimizing translational approaches with respect to biomarkers, therapeutic targets, treatments, animal models, and regulatory pathways.

Conclusion

The common features of neurological and ophthalmological neurodegenerations are useful for outlining a path forward that should increase the likelihood of translational success in neuroprotective therapies.

Optimized scaling of translational factors in oncology: from xenografts to RECIST

Purpose

Tumor growth inhibition (TGI) models are regularly used to quantify the PK–PD relationship between drug concentration and in vivo efficacy in oncology. These models are typically calibrated with data from xenograft mice and before being used for clinical predictions, translational methods have to be applied. Currently, such methods are commonly based on replacing model components or scaling of model parameters. However, difficulties remain in how to accurately account for inter-species differences. Therefore, more research must be done before xenograft data can fully be utilized to predict clinical response.

Method

To contribute to this research, we have calibrated TGI models to xenograft data for three drug combinations using the nonlinear mixed effects framework. The models were translated by replacing mice exposure with human exposure and used to make predictions of clinical response. Furthermore, in search of a better way of translating these models, we estimated an optimal way of scaling model parameters given the available clinical data.

Results

The predictions were compared with clinical data and we found that clinical efficacy was overestimated. The estimated optimal scaling factors were similar to a standard allometric scaling exponent of − 0.25.

Conclusions

We believe that given more data, our methodology could contribute to increasing the translational capabilities of TGI models. More specifically, an appropriate translational method could be developed for drugs with the same mechanism of action, which would allow for all preclinical data to be leveraged for new drugs of the same class. This would ensure that fewer clinically inefficacious drugs are tested in clinical trials.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00280-022-04458-8.

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.

EUS-FNA Biopsies to Guide Precision Medicine in Pancreatic Cancer: Results of a Pilot Study to Identify KRAS Wild-Type Tumours for Targeted Therapy

Background

Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer death and lacks effective treatment options. Diagnostic endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) biopsies represent an appealing source of material for molecular analysis to inform targeted therapy, as they are often the only available tissue for patients presenting with PDAC irrespective of disease stage. However, EUS-FNA biopsies are typically not used to screen for precision medicine studies due to concerns about low tissue yield and quality. Epidermal growth factor receptor (EGFR) inhibition has shown promise in clinical trials of unselected patients with advanced pancreatic cancer, but has not been prospectively tested in KRAS wild-type patients. Here, we examine the clinical utility of EUS-FNA biopsies for molecular screening of KRAS wild-type PDAC patients for targeted anti-EGFR therapy to assess the feasibility of this approach.

Patients and Methods

Fresh frozen EUS-FNA or surgical biopsies from PDAC patient tumours were used to screen for KRAS mutations. Eligible patients with recurrent, locally advanced, or metastatic KRAS wild-type status who had received at least one prior line of chemotherapy were enrolled in a pilot study (ACTRN12617000540314) and treated with panitumumab at 6mg/kg intravenously every 2 weeks until progression or unacceptable toxicity. The primary endpoint was 4-month progression-free survival (PFS).

Results

275 patient biopsies were screened for KRAS mutations, which were detected in 88.3% of patient samples. 8 eligible KRAS wild-type patients were enrolled onto the interventional study between November 2017 and December 2020 and treated with panitumumab. 4-month PFS was 14.3% with no objective tumour responses observed. The only grade 3/4 treatment related toxicity observed was hypomagnesaemia.

Conclusions

This study demonstrates proof-of-principle feasibility to molecularly screen patients with pancreatic cancer for targeted therapies, and confirms diagnostic EUS-FNA biopsies as a reliable source of tumour material for molecular analysis. Single agent panitumumab was safe and tolerable but led to no objective tumour responses in this population.

KRAS mutation-independent downregulation of MAPK/PI3K signaling in colorectal cancer

KRAS is a gatekeeper gene in human colorectal tumorigenesis. KRAS is ‘undruggable’; hence, efforts have been diverted to inhibit downstream RAF/MEK/ERK and PI3K/Akt signaling. Nevertheless, none of these inhibitors has progressed to clinical use despite extensive trials. We examined levels of phospho‐ERK1/2(T202/Y204) and phospho‐Akt1/2/3(S473) in human colorectal tumor compared to matched mucosa with semi‐quantitative near‐infrared western blot and confocal fluorescence immunohistochemistry imaging. Surprisingly, 75.5% (25/33) of tumors had lower or equivalent phospho‐ERK1/2 and 96.9% (31/32) of tumors had lower phospho‐Akt1/2/3 compared to matched mucosa, irrespective of KRAS mutation status. In contrast, we discovered KRAS‐dependent SOX9 upregulation in 28 of the 31 (90.3%) tumors. These observations were substantiated by analysis of the public domain transcriptomics The Cancer Genome Atlas (TCGA) and NCBI Gene Expression Omnibus (GEO) datasets and proteomics Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset. These data suggest that RAF/MEK/ERK and PI3K/Akt signaling are unlikely to be activated in most human colorectal cancer.

Prospective pharmacological methodology for establishing and evaluating anti-cancer drug resistant cell lines

Background

Cell lines are often used to assess the resistance of anticancer drugs when in vivo analysis is not possible. However, the process for establishing anti-cancer drug resistance in cell cultures in vitro and the subsequent method of then evaluating resistance are not clearly established. Traditionally, the IC₅₀ is the most commonly used indicator of resistance evaluation but it cannot represent the effectiveness of anti-cancer drugs in a clinical setting and lacks reliability because it is heavily affected by the cell doubling time. Hence, new indicators that can evaluate anti-cancer drug resistance are needed.

Methods

A novel resistance evaluation methodology was validated in this present study by establishing sunitinib resistance in renal cell carcinoma cells and assessing the cross-resistance of five different anti-cancer drugs.

Results

It was confirmed in this present study that the IC₅₀ does not reflect the cell proliferation rates in a way that represents anti-cancer drug resistance. An alternative indicator that can also be clinically meaningful when using in vitro cell line systems is GI₁₀₀. Additionally, the GR₁₀₀ allows different cell populations to be calibrated on the same basis when multiple experimental results are compared.

Conclusion

Since the GR₁₀₀ has properties that indicate the efficiency of anti-cancer drugs, both the efficacy and GR₁₀₀ of a particular anti-cancer drug can be used to effectively assess the resistance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08784-7.

A gentle introduction to understanding preclinical data for cancer pharmaco-omic modeling

A central goal of precision oncology is to administer an optimal drug treatment to each cancer patient. A common preclinical approach to tackle this problem has been to characterize the tumors of patients at the molecular and drug response levels, and employ the resulting datasets for predictive in silico modeling (mostly using machine learning). Understanding how and why the different variants of these datasets are generated is an important component of this process. This review focuses on providing such introduction aimed at scientists with little previous exposure to this research area.

Establishment of patient-derived organotypic tumor spheroid models for tumor microenvironment modeling

Patient‐derived cancer models that reconstitute the characteristics of the tumor microenvironment may facilitate efforts in precision immune‐oncology and the discovery of effective anticancer therapies. Organoids that have recently emerged as robust preclinical models typically contain tumor epithelial cells and lack the native tumor immune microenvironment. A patient‐derived organotypic tumor spheroid (PDOTS) is a novel and innovative ex vivo system that retains key features of the native tumor immune microenvironment. Here, we established and characterized a series of colorectal cancer PDOTS models for use as a preclinical platform for testing effective immunotherapy and its combinations with other drugs. Partially dissociated (> 100 μm in diameter) tumor tissues were embedded in Matrigel‐containing organoid media and subsequently formed into organoid structures within 3 to 7 days of culture. The success rate of growing PDOTS from fresh tissues was ~86%. Morphological analysis showed that the PDOTSs varied in size and structure. Immunofluorescence and flow cytometry analysis revealed that the PDOTSs retained autologous tumor‐infiltrating lymphoid cells and tumor‐infiltrating lymphoid cells were continually decreased through serial passages. Notably, PDOTSs from tumors from a high‐level microsatellite instability‐harboring patient were sensitive to anti‐PD‐1 or anti‐PD‐L1 antibodies. Our results demonstrate that the PDOTS model in which the tumor immune microenvironment is preserved may represent an advantageous ex vivo system to develop effective immune therapeutics.

Visualizing cancer extravasation: from mechanistic studies to drug development

Metastasis is a multistep process that accounts for the majority of cancer-related death. By the end of metastasize dissemination, circulating tumor cells (CTC) need to extravasate the blood vessels at metastatic sites to form new colonization. Although cancer cell extravasation is a crucial step in cancer metastasis, it has not been successfully targeted by current anti-metastasis strategies due to the lack of a thorough understanding of the molecular mechanisms that regulate this process. This review focuses on recent progress in cancer extravasation visualization techniques, including the development of both in vitro and in vivo cancer extravasation models, that shed light on the underlying mechanisms. Specifically, multiple cancer extravasation stages, such as the adhesion to the endothelium and transendothelial migration, are successfully probed using these technologies. Moreover, the roles of different cell adhesive molecules, chemokines, and growth factors, as well as the mechanical factors in these stages are well illustrated. Deeper understandings of cancer extravasation mechanisms offer us new opportunities to escalate the discovery of anti-extravasation drugs and therapies and improve the prognosis of cancer patients.

Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients

Cell-line screens create expansive datasets for learning predictive markers of drug response, but these models do not readily translate to the clinic with its diverse contexts and limited data. In the present study, we apply a recently developed technique, few-shot machine learning, to train a versatile neural network model in cell lines that can be tuned to new contexts using few additional samples. The model quickly adapts when switching among different tissue types and in moving from cell-line models to clinical contexts, including patient-derived tumor cells and patient-derived xenografts. It can also be interpreted to identify the molecular features most important to a drug response, highlighting critical roles for RB1 and SMAD4 in the response to CDK inhibition and RNF8 and CHD4 in the response to ATM inhibition. The few-shot learning framework provides a bridge from the many samples surveyed in high-throughput screens (n-of-many) to the distinctive contexts of individual patients (n-of-one).

Preclinical Metrics Correlate With Drug Activity in Phase II Trials of Targeted Therapies for Non-Small Cell Lung Cancer

Novel oncology drugs often fail to progress from preclinical experiments to FDA approval. Therefore, determining which preclinical or clinical factors associate with drug activity could accelerate development of effective therapies. We investigated whether preclinical metrics and patient characteristics are associated with objective response rate (ORR) in phase II clinical trials of targeted therapies for non-small cell lung cancer (NSCLC). We developed a reproducible process to select a single phase II trial and supporting preclinical publication for a given drug-indication pair, which we defined as the pairing of a small molecule inhibitor (e.g., crizotinib) with the specific patient population for which it was designed to work (e.g., patients with an ALK aberration). We demonstrated that robust drug activity in mice, as measured by change in tumor size, is independently associated with improved ORR in phase II clinical trials. The number of mice utilized in experiments, the number of publications referencing the drug for NSCLC before the phase II clinical trial, and whether the drug was approved for a cancer other than NSCLC also significantly correlated with ORR. Among clinical characteristics, sex, race, histology, and smoking history were significantly associated with ORR. Further research into metrics that correlate with drug activity has the potential to optimize selection of novel therapies for clinical trials and enrich the drug development pipeline, particularly for patients with targetable genetic aberrations and rare cancers.

Patient-derived cancer modeling for precision medicine in colorectal cancer: beyond the cancer cell line

Since effective immunotherapeutic agents such as immune checkpoint blockade to treat cancer have emerged, the need for reliable preclinical cancer models that can evaluate and discover such drugs became stronger than ever before. The traditional preclinical cancer model using a cancer cell line has several limitations to recapitulate intra-tumor heterogeneity and in-vivo tumor activity including interactions between tumor-microenvironment. In this review, we will go over various preclinical cancer models recently discovered including patient-derived xenografts, humanized mice, organoids, organotypic-tumor spheroids, and organ-on-a-chip models. Moreover, we will discuss the future directions of preclinical cancer research.

A Microfluidic Perfusion Platform for In Vitro Analysis of Drug Pharmacokinetic-Pharmacodynamic (PK-PD) Relationships

Static in vitro cell culture studies cannot capture the dynamic concentration profiles of drugs, nutrients, and other factors that cells experience in physiological systems. This limits the confidence in the translational relevance of in vitro experiments and increases the reliance on empirical testing of exposure-response relationships and dose optimization in animal models during preclinical drug development, introducing additional challenges owing to species-specific differences in drug pharmacokinetics (PK) and pharmacodynamics (PD). Here, we describe the development of a microfluidic cell culture device that enables perfusion of cells under 2D or 3D culture conditions with temporally programmable concentration profiles. Proof-of-concept studies using doxorubicin and gemcitabine demonstrated the ability of the microfluidic PK-PD device to examine dose- and time-dependent effects of doxorubicin as well as schedule-dependent effects of doxorubicin and gemcitabine combination therapy on cell viability using both step-wise drug concentration profiles and species-specific (i.e., mouse, human) drug PK profiles. The results demonstrate the importance of including physiologically relevant dynamic drug exposure profiles during in vitro drug testing to more accurately mimic in vivo drug effects, thereby improving translatability across nonclinical studies and reducing the reliance on animal models during drug development.

Virtual clinical trials identify effective combination therapies in ovarian cancer

A major issue in oncology is the high failure rate of translating preclinical results in successful clinical trials. Using a virtual clinical trial simulations approach, we present a mathematical framework to estimate the added value of combinatorial treatments in ovarian cancer. This approach was applied to identify effective targeted therapies that can be combined with the platinum-taxane regimen and overcome platinum resistance in high-grade serous ovarian cancer. We modeled and evaluated the effectiveness of three drugs that target the main platinum resistance mechanisms, which have shown promising efficacy in vitro, in vivo, and early clinical trials. Our results show that drugs resensitizing chemoresistant cells are superior to those aimed at triggering apoptosis or increasing the bioavailability of platinum. Our results further show that the benefit of using biomarker stratification in clinical trials is dependent on the efficacy of the drug and tumor composition. The mathematical framework presented herein is suitable for systematically testing various drug combinations and clinical trial designs in solid cancers.

Development of transplantable B-cell lymphomas in the MHC-defined miniature swine model

Background

Establishment of transplantable tumors in clinically relevant large animals allows translational studies of novel cancer therapeutics.

Methods

Here we describe the establishment, characterization, and serial transplantation of a naturally occurring B-cell lymphoma derived from a unique, highly inbred sub-line of Massachusetts General Hospital (MGH) major histocompatibility complex (MHC)-defined miniature swine.

Results

The lymphoblastic cell line (LCL) originated from peripheral blood of a 2.5 year old female swine leukocyte antigen (SLA)^dd-inbred miniature swine breeder demonstrating clinical signs of malignancy. Flow cytometric phenotypic analysis of subclones derived from the original cell line revealed surface markers commonly expressed in a B-cell lineage neoplasm. A subclone of the original LCL was transplanted into mildly-conditioned histocompatible miniature swine and immunocompromised NOD.Cg-Prkdc^scidIl2rg^tm1Wjl/SzJ (NSG) mice. Tissue and blood samples harvested 2 weeks following subcutaneous and intravenous injection in a highly inbred SLA^dd pig were cultured for tumor growth and phenotypic analysis before serial transfer into NSG mice. Evidence of tumor growth in vivo was found in all tumor cell recipients. In vitro growth characteristics and surface phenotype were comparable between the original and serially transplanted tumor cell lines.

Conclusions

These results indicate the feasibility of developing a large-animal transplantable tumor model using cells derived from spontaneously occurring hematologic malignancies within the highly inbred miniature swine herd.

Predicting Synergism of Cancer Drug Combinations Using NCI-ALMANAC Data

Drug combinations are of great interest for cancer treatment. Unfortunately, the discovery of synergistic combinations by purely experimental means is only feasible on small sets of drugs. In silico modeling methods can substantially widen this search by providing tools able to predict which of all possible combinations in a large compound library are synergistic. Here we investigate to which extent drug combination synergy can be predicted by exploiting the largest available dataset to date (NCI-ALMANAC, with over 290,000 synergy determinations). Each cell line is modeled using primarily two machine learning techniques, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), on the datasets provided by NCI-ALMANAC. This large-scale predictive modeling study comprises more than 5,000 pair-wise drug combinations, 60 cell lines, 4 types of models, and 5 types of chemical features. The application of a powerful, yet uncommonly used, RF-specific technique for reliability prediction is also investigated. The evaluation of these models shows that it is possible to predict the synergy of unseen drug combinations with high accuracy (Pearson correlations between 0.43 and 0.86 depending on the considered cell line, with XGBoost providing slightly better predictions than RF). We have also found that restricting to the most reliable synergy predictions results in at least 2-fold error decrease with respect to employing the best learning algorithm without any reliability estimation. Alkylating agents, tyrosine kinase inhibitors and topoisomerase inhibitors are the drugs whose synergy with other partner drugs are better predicted by the models. Despite its leading size, NCI-ALMANAC comprises an extremely small part of all conceivable combinations. Given their accuracy and reliability estimation, the developed models should drastically reduce the number of required in vitro tests by predicting in silico which of the considered combinations are likely to be synergistic.

Using Cell Cultures for the Investigation of Treatments for Attention Deficit Hyperactivity Disorder: A Systematic Review

BACKGROUND

Advances in basic and molecular biology have promoted the use of cell cultures in a wide range of areas, including the evaluation of drug efficacy, safety and toxicity.

OBJECTIVE

This article aims to provide a general overview of the methodological parameters of cell cultures used to investigate therapeutic options for Attention Deficit Hyperactivity Disorder.

METHOD

A systematic search was performed in the electronic databases PubMed, Scopus, and DOAJ. In vitro experimental studies using cell cultures were included.

RESULTS

A total of 328 studies were initially identified, with 16 included for qualitative synthesis. Seven studies used neuronal cells (SH-SY5Y neuroblastoma and PC12 cell line) and nine used nonneuronal cells. All the studies described the culture conditions, but most studies were inconsistent with regard to reporting results and raw data. Only one-third of the studies performed cell viability assays, while a further 30% conducted gene expression analysis. Other additional tests included electrophysiological evaluation and transporter activity. More than 50% of the studies evaluated the effects of drugs such as methylphenidate and atomoxetine, while plant extracts were assessed in four studies and polyunsaturated fatty acids in one.

CONCLUSION

We suggested a flowchart to guide the planning and execution of studies, and a checklist to be completed by authors to allow the standardized reporting of results. This may guide the elaboration of laboratory protocols and further in vitro studies.

Recent advances in combinatorial drug screening and synergy scoring

Treatment of complex diseases such as cancer, cardiovascular disease, diabetes or neurological disorders frequently warrants the utilization of drug combinations for therapeutic intervention. In fact, the most successful example is the current standard of care for HIV patients. However, identification of successful drug cocktails is not a simple task and is hampered by lack of standardization in terminology, experimental protocols and models as well as data analysis. Here we discuss the most recent developments in combinatorial drug screening by covering technological advancements in screening strategies, cellular model systems as well as novel drug classes. We believe the research progress being made provides promising basis to build on and identify, develop and optimize efficacious clinically relevant combinatorial drug treatments.

Mechanistic Distinctions between CHK1 and WEE1 Inhibition Guide the Scheduling of Triple Therapy with Gemcitabine

Combination of cytotoxic therapy with emerging DNA damage response inhibitors (DDRi) has been limited by tolerability issues. However, the goal of most combination trials has been to administer DDRi with standard-of-care doses of chemotherapy. We hypothesized that mechanism-guided treatment scheduling could reduce the incidence of dose-limiting toxicities and enable tolerable multitherapeutic regimens. Integrative analyses of mathematical modeling and single-cell assays distinguished the synergy kinetics of WEE1 inhibitor (WEE1i) from CHEK1 inhibitor (CHK1i) by potency, spatiotemporal perturbation, and mitotic effects when combined with gemcitabine. These divergent properties collectively supported a triple-agent strategy, whereby a pulse of gemcitabine and CHK1i followed by WEE1i durably suppressed tumor cell growth. In xenografts, CHK1i exaggerated replication stress without mitotic CDK hyperactivation, enriching a geminin-positive subpopulation and intratumoral gemcitabine metabolite. Without overt toxicity, addition of WEE1i to low-dose gemcitabine and CHK1i was most effective in tumor control compared with single and double agents. Overall, our work provides quantitative insights into the mechanisms of DDRi chemosensitization, leading to the rational development of a tolerable multitherapeutic regimen.Significance: Multiple lines of mechanistic insight regarding DNA damage response inhibitors rationally guide the preclinical development of a tolerable multitherapeutic regimen.Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/78/11/3054/F1.large.jpg Cancer Res; 78(11); 3054-66. ©2018 AACR.

Bridging the divide: preclinical research discrepancies between triple-negative breast cancer cell lines and patient tumors

Triple-negative breast cancer (TNBC) is the most refractory subtype of breast cancer and disproportionately accounts for the majority of breast cancer related deaths. Effective treatment of this disease remains an unmet medical need. Over the past several decades, TNBC cell lines have been used as the foundation for drug development and disease modeling. However, ever-mounting research demonstrates striking differences between cell lines and clinical TNBC tumors, disconnecting bench research and actual clinical responses. In this review, we discuss the limitations of cell lines and the importance of using patients' tumors for translational research, and highlight the usage of patient-derived xenograft (PDXs) models that have emerged as a clinically relevant platform for preclinical studies. PDX tumors possess tumor heterogeneity with similar cellular, molecular, genetic and epigenetic properties akin to those found within patients' tumors. Moreover, PDX and clinical tumors possess abnormal vasculature with higher blood vessel permeability, a feature that is not always demonstrated in in vivo cell line xenografts. Development of clinically relevant, novel drug-nanoparticles capable of accumulating in PDX tumors through the enhanced permeability and retention effect in tumor vasculature may lead to new and effective TNBC treatments.

Translatability score revisited: differentiation for distinct disease areas

Background

Translational science supports successful transition of early biomedical research into human applications. In 2009 a translatability score to assess risk and identify strengths and weaknesses of a given project has been designed and successfully tested in case studies. The score elements, in particular the contributing weight factors, are heterogeneous for different disease areas; therefore, the score was individualized for six areas (cardiovascular, oncology, psychiatric, anti-viral, anti-bacterial/fungal and monogenetic diseases).

Results

FDA reviews and related literature were used for modifications of the score with emphasis on biomarkers, personalized medicine and animal models. 113 new medical entities approved by FDA from 2012 through 2016 were evaluated and metrics obtained for companion diagnostics and animal models as starting points for author-based individualization of the score. Most drugs approved in this period were related to oncology (46%), while the approvals were lowest for psychiatrics (4%). The evaluation of the FDA package inserts revealed that companion diagnostics play an important role in every field except psychiatrics. Further the analysis of the FDA reviews showed the weakness of animal models in psychiatrics and anti-virals, while useful animal models were present for all other fields. Consequently the scoring system was adapted to the different fields, resulting in increased weights for animal models, biomarker and personalized medicine in oncology. For psychiatrics the weights for animal models, biomarker and personalized medicine were decreased, while the weight for model compounds, clinical trials and surrogate or endpoint strategy were increased. For anti-viral drugs weights for in vitro data and personalized medicine were increased, while the weight for animal models was decreased. Further, for anti-bacterial/fungal drugs weights for animal models and personalized medicine were increased. Weights were increased for genetics and personalized medicine and decreased for model compounds for monogenetic orphans.

Conclusions

Adaptation of the score to different disease areas should help to support a structured and diverse approach to translation and encourage researchers in the private or public sectors to further customize the score.

Electronic supplementary material

The online version of this article (10.1186/s12967-017-1329-y) contains supplementary material, which is available to authorized users.

Sex-Divergent Clinical Outcomes and Precision Medicine: An Important New Role for Institutional Review Boards and Research Ethics Committees

The efforts toward individualized medicine have constantly increased in an attempt to improve treatment options. These efforts have led to the development of small molecules which target specific molecular pathways involved in cancer progression. We have reviewed preclinical studies of sunitinib that incorporate sex as a covariate to explore possible sex-based differences in pharmacokinetics and drug–drug interactions (DDI) to attempt a relationship with published clinical outputs. We observed that covariate sex is lacking in most clinical outcome reports and suggest a series of ethic-based proposals to improve research activities and identify relevant different sex outcomes. We propose a deeper integration of preclinical, clinical, and translational research addressing statistical and clinical significance jointly; to embed specific sex-divergent endpoints to evaluate possible gender differences objectively during all stages of research; to pay greater attention to sex-divergent outcomes in polypharmacy scenarios, DDI and bioequivalence studies; the clear reporting of preclinical and clinical findings regarding sex-divergent outcomes; as well as to encourage the active role of scientists and the pharmaceutical industry to foster a new scientific culture through their research programs, practice, and participation in editorial boards and Institutional Ethics Review Boards (IRBs) and Research Ethics Committees (RECs). We establish the IRB/REC as the centerpiece for the implementation of these proposals. We suggest the expansion of its competence to follow up clinical trials to ensure that sex differences are addressed and recognized; to engage in data monitoring committees to improve clinical research cooperation and ethically address those potential clinical outcome differences between male and female patients to analyze their social and clinical implications in research and healthcare policies.

Ex-vivo assessment of drug response on breast cancer primary tissue with preserved microenvironments

Interaction between cancerous, non-transformed cells, and non-cellular components within the tumor microenvironment plays a key role in response to treatment. However, short-term culture or xenotransplantation of cancer specimens in immunodeficient animals results in dramatic modifications of the tumor microenvironment, thus preventing reliable assessment of compounds or biologicals of potential therapeutic relevance. We used a perfusion-based bioreactor developed for tissue engineering purposes to successfully maintain the tumor microenvironment of freshly excised breast cancer tissue obtained from 27 breast cancer patients and used this platform to test the therapeutic effect of antiestrogens as well as checkpoint-inhibitors on the cancer cells. Viability and functions of tumor and immune cells could be maintained for over 2 weeks in perfused bioreactors. Next generation sequencing authenticated cultured tissue specimens as closely matching the original clinical samples. Anti-estrogen treatment of cultured estrogen receptor positive breast cancer tissue as well as administration of pertuzumab to a Her2 positive breast cancer both had an anti-proliferative effect. Treatment with anti-programmed-death-Ligand (PD-L)-1 and anti-cytotoxic T lymphocyte-associated protein (CTLA)-4 antibodies lead to immune activation, evidenced by increased lymphocyte proliferation, increased expression of IFNγ, and decreased expression of IL10, accompanied by a massive cancer cell death in ex vivo triple negative breast cancer specimens. In the era of personalized medicine, the ex vivo culture of breast cancer tissue represents a promising approach for the pre-clinical evaluation of conventional and immune-mediated treatments and provides a platform for testing of innovative treatments.

Found in Translation: Maximizing the Clinical Relevance of Nonclinical Oncology Studies

Purpose: The translation of nonclinical oncology studies is a subject of continuous debate. We propose that translational oncology studies need to optimize both pharmacokinetic (drug exposure) and pharmacodynamic (xenograft model) aspects. While improvements in pharmacodynamic translatability can be obtained by choosing cell lines or patient-derived xenograft models closer to the clinical indication, significant ambiguity and variability exists when optimizing the pharmacokinetic translation of small molecule and biotherapeutic agents.Experimental Design and Results: In this work, we propose a pharmacokinetic-based strategy to select nonclinical doses for approved drug molecules. We define a clinically relevant dose (CRD) as the dosing regimen in mice that most closely approximates the relevant pharmacokinetic metric in humans. Such metrics include area under the time-concentration curve and maximal or minimal concentrations within the dosing interval. The methodology is applied to six drugs, including targeted agents and chemotherapeutics, small and large molecules (erlotinib, dasatinib, vismodegib, trastuzumab, irinotecan, and capecitabine). The resulting efficacy response at the CRD is compared with clinical responses.Conclusions: We conclude that nonclinical studies designed with the appropriate CRDs of approved drug molecules will maximize the translatability of efficacy results, which is critical when testing approved and investigational agents in combination. Clin Cancer Res; 23(4); 1080-90. ©2016 AACR.

An Integrated Analysis of Heterogeneous Drug Responses in Acute Myeloid Leukemia That Enables the Discovery of Predictive Biomarkers

Many promising new cancer drugs proceed through preclinical testing and early-phase trials only to fail in late-stage clinical testing. Thus, improved models that better predict survival outcomes and enable the development of biomarkers are needed to identify patients most likely to respond to and benefit from therapy. Here, we describe a comprehensive approach in which we incorporated biobanking, xenografting, and multiplexed phospho-flow (PF) cytometric profiling to study drug response and identify predictive biomarkers in acute myeloid leukemia (AML) patients. To test the efficacy of our approach, we evaluated the investigational JAK2 inhibitor fedratinib (FED) in 64 patient samples. FED robustly reduced leukemia in mouse xenograft models in 59% of cases and was also effective in limiting the protumorigenic activity of leukemia stem cells as shown by serial transplantation assays. In parallel, PF profiling identified FED-mediated reduction in phospho-STAT5 (pSTAT5) levels as a predictive biomarker of in vivo drug response with high specificity (92%) and strong positive predictive value (93%). Unexpectedly, another JAK inhibitor, ruxolitinib (RUX), was ineffective in 8 of 10 FED-responsive samples. Notably, this outcome could be predicted by the status of pSTAT5 signaling, which was unaffected by RUX treatment. Consistent with this observed discrepancy, PF analysis revealed that FED exerted its effects through multiple JAK2-independent mechanisms. Collectively, this work establishes an integrated approach for testing novel anticancer agents that captures the inherent variability of response caused by disease heterogeneity and in parallel, facilitates the identification of predictive biomarkers that can help stratify patients into appropriate clinical trials.

Bioreactor-engineered cancer tissue-like structures mimic phenotypes, gene expression profiles and drug resistance patterns observed "in vivo"

Anticancer compound screening on 2D cell cultures poorly predicts "in vivo" performance, while conventional 3D culture systems are usually characterized by limited cell proliferation, failing to produce tissue-like-structures (TLS) suitable for drug testing. We addressed engineering of TLS by culturing cancer cells in porous scaffolds under perfusion flow. Colorectal cancer (CRC) HT-29 cells were cultured in 2D, on collagen sponges in static conditions or in perfused bioreactors, or injected subcutaneously in immunodeficient mice. Perfused 3D (p3D) cultures resulted in significantly higher (p < 0.0001) cell proliferation than static 3D (s3D) cultures and yielded more homogeneous TLS, with morphology and phenotypes similar to xenografts. Transcriptome analysis revealed a high correlation between xenografts and p3D cultures, particularly for gene clusters regulating apoptotic processes and response to hypoxia. Treatment with 5-Fluorouracil (5-FU), a frequently used but often clinically ineffective chemotherapy drug, induced apoptosis, down-regulation of anti-apoptotic genes (BCL-2, TRAF1, and c-FLIP) and decreased cell numbers in 2D, but only "nucleolar stress" in p3D and xenografts. Conversely, BCL-2 inhibitor ABT-199 induced cytotoxic effects in p3D but not in 2D cultures. Our findings advocate the importance of perfusion flow in 3D cultures of tumor cells to efficiently mimic functional features observed "in vivo" and to test anticancer compounds.

Systems biology approaches for advancing the discovery of effective drug combinations

Complex diseases like cancer are regulated by large, interconnected networks with many pathways affecting cell proliferation, invasion, and drug resistance. However, current cancer therapy predominantly relies on the reductionist approach of one gene-one disease. Combinations of drugs may overcome drug resistance by limiting mutations and induction of escape pathways, but given the enormous number of possible drug combinations, strategies to reduce the search space and prioritize experiments are needed. In this review, we focus on the use of computational modeling, bioinformatics and high-throughput experimental methods for discovery of drug combinations. We highlight cutting-edge systems approaches, including large-scale modeling of cell signaling networks, network motif analysis, statistical association-based models, identifying correlations in gene signatures, functional genomics, and high-throughput combination screens. We also present a list of publicly available data and resources to aid in discovery of drug combinations. Integration of these systems approaches will enable faster discovery and translation of clinically relevant drug combinations.

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Zebrafish: a new companion for translational research in oncology

In an era of high-throughput "omic" technologies, the unprecedented amount of data that can be generated presents a significant opportunity but simultaneously an even greater challenge for oncologists trying to provide personalized treatment. Classically, preclinical testing of new targets and identification of active compounds against those targets have entailed the extensive use of established human cell lines, as well as genetically modified mouse tumor models. Patient-derived xenografts in zebrafish may in the near future provide a platform for selecting an appropriate personalized therapy and together with zebrafish transgenic tumor models represent an alternative vehicle for drug development. The zebrafish is readily genetically modified. The transparency of zebrafish embryos and the recent development of pigment-deficient zebrafish afford researchers the valuable capacity to observe directly cancer formation and progression in a live vertebrate host. The zebrafish is amenable to transplantation assays that test the serial passage of fluorescently labeled tumor cells as well as their capacity to disseminate and/or metastasize. Progress achieved to date in genetic engineering and xenotransplantation will establish the zebrafish as one of the most versatile animal models for cancer research. A model organism that can be used in transgenesis, transplantation assays, single-cell functional assays, and in vivo imaging studies make zebrafish a natural companion for mice in translational oncology research.

Hydrogels to model 3D in vitro microenvironment of tumor vascularization

A growing number of failing clinical trials for cancer therapy are substantiating the need to upgrade the current practice in culturing tumor cells and modeling tumor angiogenesis in vitro. Many attempts have been made to engineer vasculature in vitro by utilizing hydrogels, but the application of these tools in simulating in vivo tumor angiogenesis is still very new. In this review, we explore current use of hydrogels and their design parameters to engineer vasculogenesis and angiogenesis and to evaluate the angiogenic capability of cancerous cells and tissues. By coupling these hydrogels with other technologies such as lithography and three-dimensional printing, one can create an advanced microvessel model as microfluidic channels to more accurately capture the native angiogenesis process.

A low-cost, high-quality new drug discovery process using patient-derived induced pluripotent stem cells

Knockout, knock-in and conditional mutant gene-targeted mice are routinely used for disease modeling in the drug discovery process, but the human response is often difficult to predict from these models. It is believed that patient-derived induced pluripotent stem cells (iPSCs) could replace millions of animals currently sacrificed in preclinical testing and provide a route to new safer pharmaceutical products. In this review, we discuss the use of IPSCs in the drug discovery process. We highlight how they can be used to assess the toxicity and clinical efficacy of drug candidates before the latter are moved into costly and lengthy preclinical and clinical trials.

Translational research in oncology--10 years of progress and future prospects

International efforts to sequence the genomes of various human cancers have been broadly deployed in drug discovery programmes. Diagnostic tests that predict the value of the molecularly targeted anticancer agents used in such programmes are conceived and validated in parallel with new small-molecule treatments and immunotherapies. This approach has been aided by better preclinical cancer models; an enhanced appreciation of the complex interactions that exist between tumour cells and their microenvironment; the elucidation of interactions between many of the genetic drivers of cancer, including oncogenes and tumour suppressors; and recent insights into the genetic heterogeneity of human tumours made possible by extraordinary improvements in DNA-sequencing techniques. These advances are being employed in the first generation of genomic clinical trials that will examine the feasibility of matching a broad range of systemic therapies to specific molecular tumour characteristics. More-extensive molecular characterization of tumours and their supporting matrices are anticipated to become standard aspects of oncological practice, permitting continuous molecular re-evaluations of human malignancies on a patient-by-patient and treatment-by-treatment basis. We review selected developments in translational cancer biology, diagnostics, and therapeutics that have occurred over the past decade and offer our thoughts on future prospects for the next few years.

Lentiviral vector-based insertional mutagenesis identifies genes involved in the resistance to targeted anticancer therapies

The high transduction efficiency of lentiviral vectors in a wide variety of cells makes them an ideal tool for forward genetics screenings addressing issues of cancer research. Although molecular targeted therapies have provided significant advances in tumor treatment, relapses often occur by the expansion of tumor cell clones carrying mutations that confer resistance. Identification of the culprits of anticancer drug resistance is fundamental for the achievement of long-term response. Here, we developed a new lentiviral vector-based insertional mutagenesis screening to identify genes that confer resistance to clinically relevant targeted anticancer therapies. By applying this genome-wide approach to cell lines representing two subtypes of HER2(+) breast cancer, we identified 62 candidate lapatinib resistance genes. We validated the top ranking genes, i.e., PIK3CA and PIK3CB, by showing that their forced expression confers resistance to lapatinib in vitro and found that their mutation/overexpression is associated to poor prognosis in human breast tumors. Then, we successfully applied this approach to the identification of erlotinib resistance genes in pancreatic cancer, thus showing the intrinsic versatility of the approach. The acquired knowledge can help identifying combinations of targeted drugs to overcome the occurrence of resistance, thus opening new horizons for more effective treatment of tumors.

Epidermal growth factor receptor signalling in keratinocyte biology: implications for skin toxicity of tyrosine kinase inhibitors

The epidermal growth factor receptor (EGFR) and its ligands have been long recognized as centrally involved in the growth and repair process of epithelia, as well as in carcinogenesis. In addition, the EGFR has been demonstrated to be importantly involved in the control of inflammatory responses. During this last decade, a number of highly specific agents targeting this system have become an integral component of pharmacologic strategies against many solid malignancies. These drugs have led to increased patient survival and made therapy more tolerant when compared to conventional cytotoxic drugs. Nonetheless, their use is associated with a constellation of toxic effects on the skin, including follicular pustules, persistent inflammation, xerosis and pruritus, and enhanced susceptibility to infections. This dramatic impairment of skin homoeostasis underscores the centrality of the EGFR-ligand system in the whole skin immune system. So far, no mechanism-based approaches are available to specifically counteract the adverse effects of anti-EGFR drugs or any other class of tyrosine kinase inhibitors. Only the knowledge of the cellular and molecular events underlying these adverse effects in humans, combined with in vitro/in vivo models able to mimic these toxic responses, may guide the development of mechanism-based treatment or prevention strategies.