Response biomarkers: re-envisioning the approach to tailoring drug therapy for cancer

Therapeutic Drug Monitoring and Biomarkers; towards Better Dosing of Antimicrobial Therapy

Due to variability in pharmacokinetics and pharmacodynamics, clinical outcomes of antimicrobial drug therapy vary between patients. As such, personalised medication management, considering both pharmacokinetics and pharmacodynamics, is a growing concept of interest in the field of infectious diseases. Therapeutic drug monitoring is used to adjust and individualise drug regimens until predefined pharmacokinetic exposure targets are achieved. Minimum inhibitory concentration (drug susceptibility) is the best available pharmacodynamic parameter but is associated with many limitations. Identification of other pharmacodynamic parameters is necessary. Repurposing diagnostic biomarkers as pharmacodynamic parameters to evaluate treatment response is attractive. When combined with therapeutic drug monitoring, it could facilitate making more informed dosing decisions. We believe the approach has potential and justifies further research.

Integrating Imaging and Circulating Tumor DNA Features for Predicting Patient Outcomes

Biomarkers for evaluating tumor response to therapy and estimating the risk of disease relapse represent tremendous areas of clinical need. To evaluate treatment efficacy, tumor response is routinely assessed using different imaging modalities like positron emission tomography/computed tomography or magnetic resonance imaging. More recently, the development of circulating tumor DNA detection assays has provided a minimally invasive approach to evaluate tumor response and prognosis through a blood test (liquid biopsy). Integrating imaging- and circulating tumor DNA-based biomarkers may lead to improvements in the prediction of patient outcomes. For this mini-review, we searched the scientific literature to find original articles that combined quantitative imaging and circulating tumor DNA biomarkers to build prediction models. Seven studies reported building prognostic models to predict distant recurrence-free, progression-free, or overall survival. Three discussed building models to predict treatment response using tumor volume, pathologic complete response, or objective response as endpoints. The limited number of articles and the modest cohort sizes reported in these studies attest to the infancy of this field of study. Nonetheless, these studies demonstrate the feasibility of developing multivariable response-predictive and prognostic models using regression and machine learning approaches. Larger studies are warranted to facilitate the building of highly accurate response-predictive and prognostic models that are generalizable to other datasets and clinical settings.

MiR-320a upregulation contributes to the effectiveness of pemetrexed by inhibiting the growth and invasion of human lung cancer cell line (Calu-6)

BACKGROUND

Lung cancer is a common and deadly disease. Chemotherapy is the most common treatment, which inhibits cancer cell growth. Pemetrexed (PMX) is often used with other drugs. Environmental stress can affect regulatory non-coding RNAs such as MicroRNAs that modify gene expression. This study investigates the effect of PMX on the hsa-miR-320a-3p expression in the Calu-6 lung cancer cell line.

METHODS AND RESULT

Calu-6 cells were cultured in an incubator with 37 °C, 5% CO2, and 98% humidity. The MTT test was performed to determine the concentration of PMX required to inhibit 50% of cell growth. To examine growth inhibition and apoptosis, release of lactate dehydrogenase (LDH), cell assays and caspase 3 and 7 enzyme activity were used. Finally, molecular studies were conducted to compare the expression of hsa-miR-320a-3p and genes including VDAC1, DHFR, STAT3, BAX and BCL2 before and after therapy.

RESULTS

According to a study, it has been observed that PMX therapy significantly increases LDH release after 24 h. The study found that PMX's IC50 on Calu-6 is 8.870 µM. In addition, the treated sample showed higher expression of hsa-miR-320a-3p and BAX, while the expression of VDAC1, STAT3, DHFR and BCL2 decreased compared to the control sample.

CONCLUSION

According to the findings of the current research, hsa-miR-320a-3p seems to have the potential to play an important role in the development of novel approaches to the treatment of lung cancer.

New role of bacteriophages in medical oncology

Targeted treatment of cancer is one of the most paramount approaches in cancer treatment. Despite significant advances in cancer diagnosis and treatment methods, there are still significant limitations and disadvantages in the field, including high costs, toxicity, and unwanted damage to healthy cells. The phage display technique is an innovative method for designing carriers containing exogenic peptides with cancer diagnostic and therapeutic properties. Bacteriophages possess unique properties making them effective in cancer treatment. These characteristics include the small size enabling them to penetrate vessels; having no pathogenicity to mammals; easy manipulation of their genetic information and surface proteins to introduce vaccines and drugs to cancer tissues; lower cost of large-scale production; and greater stimulation of the immune system. Bacteriophages will certainly play a more effective role in the future of medical oncology; however, studies are in the early stages of conception and require more extensive research. We aimed in this review to provide some related examples and bring insights into the potential of phages as targeted vectors for use in cancer diagnosis and treatment, especially regarding their capability in gene and drug delivery to cancer target cells, determination of tumor markers, and vaccine design to stimulate anticancer immunity.

DNA Methylation Biomarkers for Prediction of Response to Platinum-Based Chemotherapy: Where Do We Stand?

Simple Summary

Platinum-based agents are one of the most widely used chemotherapy drugs for various types of cancer. However, one of the main challenges in the application of platinum drugs is resistance, which is currently being widely investigated. Epigenetic DNA methylation-based biomarkers are promising to aid in the selection of patients, helping to foresee their platinum therapy response in advance. These biomarkers enable minimally invasive patient sample collection, short analysis, and good sensitivity. Hence, improved methodologies for the detection and quantification of DNA methylation biomarkers will facilitate their use in the choice of an optimal treatment strategy.

Abstract

Platinum-based chemotherapy is routinely used for the treatment of several cancers. Despite all the advances made in cancer research regarding this therapy and its mechanisms of action, tumor resistance remains a major concern, limiting its effectiveness. DNA methylation-based biomarkers may assist in the selection of patients that may benefit (or not) from this type of treatment and provide new targets to circumvent platinum chemoresistance, namely, through demethylating agents. We performed a systematic search of studies on biomarkers that might be predictive of platinum-based chemotherapy resistance, including in vitro and in vivo pre-clinical models and clinical studies using patient samples. DNA methylation biomarkers predictive of response to platinum remain mostly unexplored but seem promising in assisting clinicians in the generation of more personalized follow-up and treatment strategies. Improved methodologies for their detection and quantification, including non-invasively in liquid biopsies, are additional attractive features that can bring these biomarkers into clinical practice, fostering precision medicine.

Medical Imaging Biomarker Discovery and Integration Towards AI-Based Personalized Radiotherapy

Intensity-modulated radiation therapy (IMRT) has been used for high-accurate physical dose distribution sculpture and employed to modulate different dose levels into Gross Tumor Volume (GTV), Clinical Target Volume (CTV) and Planning Target Volume (PTV). GTV, CTV and PTV can be prescribed at different dose levels, however, there is an emphasis that their dose distributions need to be uniform, despite the fact that most types of tumour are heterogeneous. With traditional radiomics and artificial intelligence (AI) techniques, we can identify biological target volume from functional images against conventional GTV derived from anatomical imaging. Functional imaging, such as multi parameter MRI and PET can be used to implement dose painting, which allows us to achieve dose escalation by increasing doses in certain areas that are therapy-resistant in the GTV and reducing doses in less aggressive areas. In this review, we firstly discuss several quantitative functional imaging techniques including PET-CT and multi-parameter MRI. Furthermore, theoretical and experimental comparisons for dose painting by contours (DPBC) and dose painting by numbers (DPBN), along with outcome analysis after dose painting are provided. The state-of-the-art AI-based biomarker diagnosis techniques is reviewed. Finally, we conclude major challenges and future directions in AI-based biomarkers to improve cancer diagnosis and radiotherapy treatment.

Probabilistic Boolean Modeling of Pre-clinical Tumor Models for Biomarker Identification in Cancer Drug Development

As high-throughput sequencing experiments become more widely used in pre-clinical and clinical settings, pharmacogenetic and pharmacogenomic biomarker development plays an increasingly important role in oncology drug development pipelines and programs. Consequently, computer-based learning approaches have entered into use at multiple stages in pre-clinical and clinical pipelines. However, few approaches are available to identify interpretable and implementable biomarkers of response early in the drug development process when only small pre-clinical data packages are available. To address the need for early-stage biomarker development using pre-clinical tumor models, we have adapted the previously published Probabilistic Target Inhibitor Map (PTIM) platform to the challenge of biomarker hypothesis development, and denoted this approach the Probabilistic Target Map-Biomarker (PTM-Biomarker). In this article, we detail the history and design philosophy of PTM-Biomarker, and present two case studies using the biomarker discovery tool to illustrate its utility in guiding cancer drug development. © 2021 Wiley Periodicals LLC.

Joint Imaging Platform for Federated Clinical Data Analytics

PURPOSE

Image analysis is one of the most promising applications of artificial intelligence (AI) in health care, potentially improving prediction, diagnosis, and treatment of diseases. Although scientific advances in this area critically depend on the accessibility of large-volume and high-quality data, sharing data between institutions faces various ethical and legal constraints as well as organizational and technical obstacles.

METHODS

The Joint Imaging Platform (JIP) of the German Cancer Consortium (DKTK) addresses these issues by providing federated data analysis technology in a secure and compliant way. Using the JIP, medical image data remain in the originator institutions, but analysis and AI algorithms are shared and jointly used. Common standards and interfaces to local systems ensure permanent data sovereignty of participating institutions.

RESULTS

The JIP is established in the radiology and nuclear medicine departments of 10 university hospitals in Germany (DKTK partner sites). In multiple complementary use cases, we show that the platform fulfills all relevant requirements to serve as a foundation for multicenter medical imaging trials and research on large cohorts, including the harmonization and integration of data, interactive analysis, automatic analysis, federated machine learning, and extensibility and maintenance processes, which are elementary for the sustainability of such a platform.

CONCLUSION

The results demonstrate the feasibility of using the JIP as a federated data analytics platform in heterogeneous clinical information technology and software landscapes, solving an important bottleneck for the application of AI to large-scale clinical imaging data.

The importance of CDC27 in cancer: molecular pathology and clinical aspects

Background

CDC27 is one of the core components of Anaphase Promoting complex/cyclosome. The main role of this protein is defined at cellular division to control cell cycle transitions. Here we review the molecular aspects that may affect CDC27 regulation from cell cycle and mitosis to cancer pathogenesis and prognosis.

Main text

It has been suggested that CDC27 may play either like a tumor suppressor gene or oncogene in different neoplasms. Divergent variations in CDC27 DNA sequence and alterations in transcription of CDC27 have been detected in different solid tumors and hematological malignancies. Elevated CDC27 expression level may increase cell proliferation, invasiveness and metastasis in some malignancies. It has been proposed that CDC27 upregulation may increase stemness in cancer stem cells. On the other hand, downregulation of CDC27 may increase the cancer cell survival, decrease radiosensitivity and increase chemoresistancy. In addition, CDC27 downregulation may stimulate efferocytosis and improve tumor microenvironment.

Conclusion

CDC27 dysregulation, either increased or decreased activity, may aggravate neoplasms. CDC27 may be suggested as a prognostic biomarker in different malignancies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01860-9.

A machine learning-based gene signature of response to the novel alkylating agent LP-184 distinguishes its potential tumor indications

BACKGROUND

Non-targeted cytotoxics with anticancer activity are often developed through preclinical stages using response criteria observed in cell lines and xenografts. A panel of the NCI-60 cell lines is frequently the first line to define tumor types that are optimally responsive. Open data on the gene expression of the NCI-60 cell lines, provides a unique opportunity to add another dimension to the preclinical development of such drugs by interrogating correlations with gene expression patterns. Machine learning can be used to reduce the complexity of whole genome gene expression patterns to derive manageable signatures of response. Application of machine learning in early phases of preclinical development is likely to allow a better positioning and ultimate clinical success of molecules. LP-184 is a highly potent novel alkylating agent where the preclinical development is being guided by a dedicated machine learning-derived response signature. We show the feasibility and the accuracy of such a signature of response by accurately predicting the response to LP-184 validated using wet lab derived IC50s on a panel of cell lines.

RESULTS

We applied our proprietary RADR® platform to an NCI-60 discovery dataset encompassing LP-184 IC50s and publicly available gene expression data. We used multiple feature selection layers followed by the XGBoost regression model and reduced the complexity of 20,000 gene expression values to generate a 16-gene signature leading to the identification of a set of predictive candidate biomarkers which form an LP-184 response gene signature. We further validated this signature and predicted response to an additional panel of cell lines. Considering fold change differences and correlation between actual and predicted LP-184 IC50 values as validation performance measures, we obtained 86% accuracy at four-fold cut-off, and a strong (r = 0.70) and significant (p value 1.36e-06) correlation between actual and predicted LP-184 sensitivity. In agreement with the perceived mechanism of action of LP-184, PTGR1 emerged as the top weighted gene.

CONCLUSION

Integration of a machine learning-derived signature of response with in vitro assessment of LP-184 efficacy facilitated the derivation of manageable yet robust biomarkers which can be used to predict drug sensitivity with high accuracy and clinical value.

Bacteriophages as Therapeutic and Diagnostic Vehicles in Cancer

Evolution of nanomedicine is the re-design of synthetic and biological carriers to implement novel theranostic platforms. In recent years, bacteriophage research favors this process, which has opened up new roads in drug and gene delivery studies. By displaying antibodies, peptides, or proteins on the surface of different bacteriophages through the phage display technique, it is now possible to unravel specific molecular determinants of both cancer cells and tumor-associated microenvironmental molecules. Downstream applications are manifold, with peptides being employed most of the times to functionalize drug carriers and improve their therapeutic index. Bacteriophages themselves were proven, in this scenario, to be good carriers for imaging molecules and therapeutics as well. Moreover, manipulation of their genetic material to stably vehiculate suicide genes within cancer cells substantially changed perspectives in gene therapy. In this review, we provide examples of how amenable phages can be used as anticancer agents, especially because their systemic administration is possible. We also provide some insights into how their immunogenic profile can be modulated and exploited in immuno-oncology for vaccine production.

Dynamic Transitions of Pediatric Sepsis: A Markov Chain Analysis

Pediatric sepsis is a heterogeneous disease with varying physiological dynamics associated with recovery, disability, and mortality. Using risk scores generated from a sepsis prediction model to define illness states, we used Markov chain modeling to describe disease dynamics over time by describing how children transition among illness states. We analyzed 18,666 illness state transitions over 157 pediatric intensive care unit admissions in the 3 days following blood cultures for suspected sepsis. We used Shannon entropy to quantify the differences in transition matrices stratified by clinical characteristics. The population-based transition matrix based on the sepsis illness severity scores in the days following a sepsis diagnosis can describe a sepsis illness trajectory. Using the entropy based on Markov chain transition matrices, we found a different structure of dynamic transitions based on ventilator use but not age group. Stochastic modeling of transitions in sepsis illness severity scores can be useful in describing the variation in transitions made by patient and clinical characteristics.

Divining responder populations from survival data

BACKGROUND

Biomarkers that predict treatment response are the foundation of precision medicine in clinical decision-making and have the potential to significantly improve the efficiency of clinical trials. Such biomarkers may be identified before clinical testing but many trials enroll unselected populations. We hypothesized that time-varying treatment effects in unselected trials may result from identifiable responder subpopulations that may have associated biomarkers.

MATERIALS AND METHODS

We first simulated scenarios of clinical trials with biomarker populations of varying prevalence and prognostic and predictive associations to illustrate the impact of subgroup-specific effects on overall population estimates. To show a real-world example of time-dependent treatment effects resulting from a prognostic and predictive biomarker, we re-analyzed data from a published clinical trial (RTOG, Radiation Therapy Oncology Group, 9402). We then demonstrated a quantitative framework to fit survival data from clinical trials using statistical models incorporating known estimates of biomarker prevalence and prognostic value to prioritize predictive biomarker hypotheses.

RESULTS

Our simulation studies demonstrate how biomarker subgroups that are both predictive and prognostic can manifest as time-dependent treatment effects in overall populations. RTOG 9402 provides a representative example where 1p/19q co-deletion and IDH mutation biomarker-specific effects led to time-varying treatment effects and a considerable deviation from proportional hazards in the overall trial population. Finally, using biomarker data from The Cancer Genome Atlas, we were able to generate statistical models that correctly identified and prioritized a commonly used biomarker through retrospective analysis of published clinical trial data.

CONCLUSIONS

Biomarkers that are both predictive and prognostic can result in characteristic changes in survival results. Retrospectively analyzing survival data from clinical trials may highlight potential indications for which an underlying predictive biomarker may be found.

PRRT neuroendocrine tumor response monitored using circulating transcript analysis: the NETest

PURPOSE

Peptide receptor radionuclide therapy (PRRT) is effective for metastatic/inoperable neuroendocrine tumors (NETs). Imaging response assessment is usually efficient subsequent to treatment completion. Blood biomarkers such as PRRT Predictive Quotient (PPQ) and NETest are effective in real-time. PPQ predicts PRRT efficacy; NETest monitors disease. We prospectively evaluated: (1) NETest as a surrogate biomarker for RECIST; (2) the correlation of NETest levels with PPQ prediction.

METHODS

Three independent ¹⁷⁷Lu-PRRT-treated GEP-NET and lung cohorts (Meldola, Italy: n = 72; Bad-Berka, Germany: n = 44; Rotterdam, Netherlands: n = 41). Treatment response: RECIST1.1 (responder (stable, partial, and complete response) vs non-responder). Blood sampling: pre-PRRT, before each cycle and follow-up (2-12 months). PPQ (positive/negative) and NETest (0-100 score) by PCR. Stable < 40; progressive > 40). CgA (ELISA) as comparator. Samples de-identified, measurement and analyses blinded. Kaplan-Meier survival and standard statistics.

RESULTS

One hundred twenty-two of the 157 were evaluable. RECIST stabilization or response in 67%; 33% progressed. NETest significantly (p < 0.0001) decreased in RECIST "responders" (- 47 ± 3%); in "non-responders," it remained increased (+ 79 ± 19%) (p < 0.0005). NETest monitoring accuracy was 98% (119/122). Follow-up levels > 40 (progressive) vs stable (< 40) significantly correlated with mPFS (not reached vs. 10 months; HR 0.04 (95%CI, 0.02-0.07). PPQ response prediction was accurate in 118 (97%) with a 99% accurate positive and 93% accurate negative prediction. NETest significantly (p < 0.0001) decreased in PPQ-predicted responders (- 46 ± 3%) and remained elevated or increased in PPQ-predicted non-responders (+ 75 ± 19%). Follow-up NETest categories stable vs progressive significantly correlated with PPQ prediction and mPFS (not reached vs. 10 months; HR 0.06 (95%CI, 0.03-0.12). CgA did not reflect PRRT treatment: in RECIST responders decrease in 38% and in non-responders 56% (p = NS).

CONCLUSIONS

PPQ predicts PRRT response in 97%. NETest accurately monitors PRRT response and is an effective surrogate marker of PRRT radiological response. NETest decrease identified responders and correlated (> 97%) with the pretreatment PPQ response predictor. CgA was non-informative.

Image-based biomarkers for solid tumor quantification

The last few decades have witnessed tremendous technological developments in image-based biomarkers for tumor quantification and characterization. Initially limited to manual one- and two-dimensional size measurements, image biomarkers have evolved to harness developments not only in image acquisition technology but also in image processing and analysis algorithms. At the same time, clinical validation remains a major challenge for the vast majority of these novel techniques, and there is still a major gap between the latest technological developments and image biomarkers used in everyday clinical practice. Currently, the imaging biomarker field is attracting increasing attention not only because of the tremendous interest in cutting-edge therapeutic developments and personalized medicine but also because of the recent progress in the application of artificial intelligence (AI) algorithms to large-scale datasets. Thus, the goal of the present article is to review the current state of the art for image biomarkers and their use for characterization and predictive quantification of solid tumors. Beginning with an overview of validated imaging biomarkers in current clinical practice, we proceed to a review of AI-based methods for tumor characterization, such as radiomics-based approaches and deep learning.Key Points• Recent years have seen tremendous technological developments in image-based biomarkers for tumor quantification and characterization.• Image-based biomarkers can be used on an ongoing basis, in a non-invasive (or mildly invasive) way, to monitor the development and progression of the disease or its response to therapy.• We review the current state of the art for image biomarkers, as well as the recent developments in artificial intelligence (AI) algorithms for image processing and analysis.

Response-to-repeatability of quantitative imaging features for longitudinal response assessment

Quantitative imaging biomarkers (QIBs) are often selected and ranked based on their repeatability performance. In the context of treatment response assessment, however, one must also consider how sensitive a QIB is to measuring changes in the tumour. This work introduces response-to-repeatability ratio (R/R), which weighs the ability of a QIB to detect significant changes with respect to its measurement repeatability and applies it to the case of PET texture features. R/R is evaluated as the proportion of measurable changes from baseline to follow-up for each candidate QIB. We analyse 47 texture features extracted from lesions in bone-metastatic prostate cancer patients who received double baseline and/or baseline to treatment follow-up ¹⁸F-NaF PET/CT scans. R/R evaluates the proportion of follow-up changes outside of the 95% limits of agreement (LOA) defined by test-retest values. Intraclass correlation coefficient (ICC) and coefficient of variation (CV) are calculated for each feature. Relationship between ICC and R/R are evaluated with the Spearman's correlation coefficient. R/R varied significantly across texture features: 41/47 (87%) features demonstrated R/R  >  5%; 21/47 (45%) features demonstrated R/R  >  10%, and 11/47 (23%) features demonstrated R/R  >  20%. LOA of features ranged from [0.998, 1.001] to [0.22, 4.86]. Repeatability alone did not qualify a feature for its efficacy at detecting measurable change at follow-up, as shown by weak correlations between R/R and both CV and ICC (ρ  =  0.23 and ρ  =  0.40, respectively). Three features demonstrated excellent ICC (ICC  >  0.75) and R/R greater than that of SUV_max (R/R  =  41.8%): skewness (ICC  =  0.92, R/R  =  75.4%), kurtosis (ICC  =  0.88, R/R  =  47.0%) and diagonal moment (ICC  =  0.88, R/R  =  45.5%). R/R characterizes the sensitivity of candidate QIBs to detect measurable changes at follow-up. R/R supplements existing precision performance metrics (e.g. CV, ICC, and LOA) as an index to assess the utility of QIBs for response assessment.

Live Cell Mass Accumulation Measurement Non-Invasively Predicts Carboplatin Sensitivity in Triple-Negative Breast Cancer Patient-Derived Xenografts

Prompt and repeated assessments of tumor sensitivity to available therapeutics could reduce patient morbidity and mortality by quickly identifying therapeutic resistance and optimizing treatment regimens. Analysis of changes in cancer cell biomass has shown promise in assessing drug sensitivity and fulfilling these requirements. However, a major limitation of previous studies in solid tumors, which comprise 90% of cancers, is the use of cancer cell lines rather than freshly isolated tumor material. As a result, existing biomass protocols are not obviously extensible to real patient tumors owing to potential artifacts that would be generated by the removal of cells from their microenvironment and the deleterious effects of excision and purification. In this present work, we show that simple excision of human triple-negative breast cancer (TNBC) tumors growing in immunodeficient mouse, patient-derived xenograft (PDX) models, followed by enzymatic disaggregation into single cell suspension, is enabling for rapid and accurate biomass accumulation-based predictions of in vivo sensitivity to the chemotherapeutic drug carboplatin. We successfully correlate in vitro biomass results with in vivo treatment results in three TNBC PDX models that have differential sensitivity to this drug. With a maximum turnaround time of 40 h from tumor excision to useable results and a fully-automated analysis pipeline, the assay described here has significant potential for translation to clinical practice.

Assessment of metabolic patterns and new antitumoral treatment in osteosarcoma xenograft models by [18F]FDG and sodium [18F]fluoride PET

BACKGROUND

Osteosarcoma is the most common malignant bone tumor in children and young adults that produces aberrant osteoid. The aim of this study was to assess the utility of 2-deoxy-2-[18F-] fluoro-D-glucose ([18F] FDG) and sodium [18F] Fluoride (Na [18F] F) PET scans in orthotopic murine models of osteosarcoma to describe the metabolic pattern of the tumors, to detect and diagnose tumors and to evaluate the efficacy of a new treatment based in oncolytic adenoviruses.

METHODS

Orthotopic osteosarcoma murine models were created by the injection of 143B and 531MII cell lines. [18F]FDG and Na [18F] F PET scans were performed 30 days (143B) and 90 days (531MII) post-injection. The antitumor effect of two doses (107 and 108 pfu) of the oncolytic adenovirus VCN-01 was evaluated in 531 MII model by [18F] FDG PET studies. [18F] FDG uptake was quantified by SUVmax and Total Lesion Glycolysis (TLG) indexes. For Na [18F] F, the ratio tumor SUVmax/hip SUVmax was calculated. PET findings were confirmed by histopathological techniques.

RESULTS

The metabolic pattern of tumors was different between both orthotopic models. All tumors showed [18F] FDG uptake, with a sensitivity and specificity of 100%. The [18F] FDG uptake was significantly higher for the 143B model (p < 0.001). Sensitivity for Na [18F] F was around 70% in both models, with a specificity of 100%. 531MII tumors showed a heterogeneous Na [18F] F uptake, significantly higher than 143B tumors (p < 0.01). Importantly, [18F] FDG and Na [18F] F uptake corresponded to highly cellular or osteoid-rich tumors in the histopathological analysis, respectively. [18F] FDG data confirmed that the oncolytic treatment of 531MII tumors produced a significant reduction in growth even with the 107 pfu dose.

CONCLUSIONS

PET studies demonstrated that the different osteosarcoma xenograft models developed tumors with diverse metabolic patterns that can be described by multitracer PET studies. Since not all tumors produced abundant osteoid, [18F] FDG demonstrated a better sensitivity for tumor detection and was able to quantitatively monitor in vivo response to the oncolytic adenovirus VCN-01.

Evaluating Imaging Biomarkers of Acquired Resistance to Targeted EGFR Therapy in Xenograft Models of Human Head and Neck Squamous Cell Carcinoma

Background: Overexpression of EGFR is a negative prognostic factor in head and neck squamous cell carcinoma (HNSCC). Patients with HNSCC who respond to EGFR-targeted tyrosine kinase inhibitors (TKIs) eventually develop acquired resistance. Strategies to identify HNSCC patients likely to benefit from EGFR-targeted therapies, together with biomarkers of treatment response, would have clinical value. Methods: Functional MRI and 18F-FDG PET were used to visualize and quantify imaging biomarkers associated with drug response within size-matched EGFR TKI-resistant CAL 27 (CALR) and sensitive (CALS) HNSCC xenografts in vivo, and pathological correlates sought. Results: Intrinsic susceptibility, oxygen-enhanced and dynamic contrast-enhanced MRI revealed significantly slower baseline R 2 ∗ , lower hyperoxia-induced Δ R 2 ∗ and volume transfer constant Ktrans in the CALR tumors which were associated with significantly lower Hoechst 33342 uptake and greater pimonidazole-adduct formation. There was no difference in oxygen-induced ΔR1 or water diffusivity between the CALR and CALS xenografts. PET revealed significantly higher relative uptake of 18F-FDG in the CALR cohort, which was associated with significantly greater Glut-1 expression. Conclusions: CALR xenografts established from HNSCC cells resistant to EGFR TKIs are more hypoxic, poorly perfused and glycolytic than sensitive CALS tumors. MRI combined with PET can be used to non-invasively assess HNSCC response/resistance to EGFR inhibition.

Metabolic Abnormalities Detected in Phase II Evaluation of Doxycycline in Dogs with Multicentric B-Cell Lymphoma

Doxycycline has antiproliferative effects in human lymphoma cells and in murine xenografts. We hypothesized that doxycycline would decrease canine lymphoma cell viability and prospectively evaluated its clinical tolerability in client-owned dogs with spontaneous, nodal, multicentric, substage a, B-cell lymphoma, not previously treated with chemotherapy. Treatment duration ranged from 1 to 8 weeks (median and mean, 3 weeks). Dogs were treated with either 10 (n = 6) or 7.5 (n = 7) mg/kg by mouth twice daily. One dog had a stable disease for 6 weeks. No complete or partial tumor responses were observed. Five dogs developed grade 3 and/or 4 metabolic abnormalities suggestive of hepatopathy with elevations in bilirubin, ALT, ALP, and/or AST. To evaluate the absorption of oral doxycycline in our study population, serum concentrations in 10 treated dogs were determined using liquid chromatography tandem mass spectrometry. Serum levels were variable and ranged from 3.6 to 16.6 µg/ml (median, 7.6 µg/ml; mean, 8.8 µg/ml). To evaluate the effect of doxycycline on canine lymphoma cell viability in vitro, trypan blue exclusion assay was performed on canine B-cell lymphoma cell lines (17-71 and CLBL) and primary B-cell lymphoma cells from the nodal tissue of four dogs. A doxycycline concentration of 6 µg/ml decreased canine lymphoma cell viability by 80%, compared to matched, untreated, control cells (mixed model analysis, p < 0.0001; Wilcoxon signed rank test, p = 0.0313). Although the short-term administration of oral doxycycline is not associated with the remission of canine lymphoma, combination therapy may be worthwhile if future research determines that doxycycline can alter cell survival pathways in canine lymphoma cells. Due to the potential for metabolic abnormalities, close monitoring is recommended with the use of this drug in tumor-bearing dogs. Additional research is needed to assess the tolerability of chronic doxycycline therapy.

Making Meaningful Clinical Use of Biomarkers

This review discusses the current state of biomarker discovery for the purposes of diagnostics and therapeutic monitoring. We underscore relevant challenges that have defined the gap between biomarker discovery and meaningful clinical use. We highlight recent advancements in and propose a way to think about future biomarker development.