Companion biomarkers: paving the pathway to personalized treatment for cancer

CCPA: cloud-based, self-learning modules for consensus pathway analysis using GO, KEGG and Reactome

This manuscript describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' (https://github.com/NIGMS/NIGMS-Sandbox). The module delivers learning materials on Cloud-based Consensus Pathway Analysis in an interactive format that uses appropriate cloud resources for data access and analyses. Pathway analysis is important because it allows us to gain insights into biological mechanisms underlying conditions. But the availability of many pathway analysis methods, the requirement of coding skills, and the focus of current tools on only a few species all make it very difficult for biomedical researchers to self-learn and perform pathway analysis efficiently. Furthermore, there is a lack of tools that allow researchers to compare analysis results obtained from different experiments and different analysis methods to find consensus results. To address these challenges, we have designed a cloud-based, self-learning module that provides consensus results among established, state-of-the-art pathway analysis techniques to provide students and researchers with necessary training and example materials. The training module consists of five Jupyter Notebooks that provide complete tutorials for the following tasks: (i) process expression data, (ii) perform differential analysis, visualize and compare the results obtained from four differential analysis methods (limma, t-test, edgeR, DESeq2), (iii) process three pathway databases (GO, KEGG and Reactome), (iv) perform pathway analysis using eight methods (ORA, CAMERA, KS test, Wilcoxon test, FGSEA, GSA, SAFE and PADOG) and (v) combine results of multiple analyses. We also provide examples, source code, explanations and instructional videos for trainees to complete each Jupyter Notebook. The module supports the analysis for many model (e.g. human, mouse, fruit fly, zebra fish) and non-model species. The module is publicly available at https://github.com/NIGMS/Consensus-Pathway-Analysis-in-the-Cloud. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Site-Specific Glycosylation Analysis of Murine and Human Fcγ Receptors Reveals High Heterogeneity at Conserved N-Glycosylation Site

Fc γ-receptors (FcγRs) on leukocytes bind immunoglobulin G (IgG) immune complexes to mediate effector functions. Dysregulation of FcγR-mediated processes contributes to multiple inflammatory diseases, including rheumatoid arthritis, lupus, and immune thrombocytopenia. Critically, immunoregulatory N-glycan modifications on both FcγRs and IgGs alter FcγR-IgG binding affinity. Rapid methods for the characterization of N-glycans across multiple Fcγ receptors are needed to propel investigations into disease-specific contributions of FcγR N-glycans. Here, we utilize nanoliquid chromatography tandem mass spectrometry (nLC-MS/MS) to characterize FcγR glycosylation and report quantitative and site-specific N-glycan characterization of recombinant human FcγRI, FcγRIIIA V158, and FcγRIIIA F158 from CHO cells and murine FcγRI, FcγRIII, and FcγRIV from NS0 cells. Data are available via ProteomeXchange with identifier PXD043966. Broad glycoform distribution (≥30) was observed at mouse FcγRIV site N159 and human FcγRIIIA site N162, an evolutionarily conserved site. Further, mouse FcγRIII N-glycopeptides spanning all four predicted N-glycosylation sequons were detected. Glycoform relative abundances for hFcγRIIIA V/F158 polymorphic variants are reported, demonstrating the clinical potential of this workflow to measure differences in glycosylation between common human FcγRIIIA allelic variants with disease-associated outcomes. The multi-Fcγ receptor glycoproteomic workflow reported here will empower studies focused on the role of FcγR N-glycosylation in autoimmune diseases.

Bimodal gene expression in cancer patients provides interpretable biomarkers for drug sensitivity

Identifying biomarkers predictive of cancer cell response to drug treatment constitutes one of the main challenges in precision oncology. Recent large-scale cancer pharmacogenomic studies have opened new avenues of research to develop predictive biomarkers by profiling thousands of human cancer cell lines at the molecular level and screening them with hundreds of approved drugs and experimental chemical compounds. Many studies have leveraged these data to build predictive models of response using various statistical and machine learning methods. However, a common pitfall to these methods is the lack of interpretability as to how they make predictions, hindering the clinical translation of these models. To alleviate this issue, we used the recent logic modeling approach to develop a new machine learning pipeline that explores the space of bimodally expressed genes in multiple large in vitro pharmacogenomic studies and builds multivariate, nonlinear, yet interpretable logic-based models predictive of drug response. The performance of this approach was showcased in a compendium of the three largest in vitro pharmacogenomic data sets to build robust and interpretable models for 101 drugs that span 17 drug classes with high validation rates in independent datasets. These results along with in vivo and clinical validation, support a better translation of gene expression biomarkers between model systems using bimodal gene expression.

Therapeutic Application of Monoclonal Antibodies in Pancreatic Cancer: Advances, Challenges and Future Opportunities

Pancreatic cancer remains as one of the most aggressive cancer types. In the absence of reliable biomarkers for its early detection and more effective therapeutic interventions, pancreatic cancer is projected to become the second leading cause of cancer death in the Western world in the next decade. Therefore, it is essential to discover novel therapeutic targets and to develop more effective and pancreatic cancer-specific therapeutic agents. To date, 45 monoclonal antibodies (mAbs) have been approved for the treatment of patients with a wide range of cancers; however, none has yet been approved for pancreatic cancer. In this comprehensive review, we discuss the FDA approved anticancer mAb-based drugs, the results of preclinical studies and clinical trials with mAbs in pancreatic cancer and the factors contributing to the poor response to antibody therapy (e.g. tumour heterogeneity, desmoplastic stroma). MAb technology is an excellent tool for studying the complex biology of pancreatic cancer, to discover novel therapeutic targets and to develop various forms of antibody-based therapeutic agents and companion diagnostic tests for the selection of patients who are more likely to benefit from such therapy. These should result in the approval and routine use of antibody-based agents for the treatment of pancreatic cancer patients in the future.

Can conventional clinical chemistry tests help doctors in the monitoring of oncology patients?

The use of laboratory assays in the diagnostic care of oncology patients can markedly increase the efficacy of cancer treatments. Many cancer-specific biomarker assays have been developed. However, the use of these has some limitations due to their cost. Moreover, not every diagnostic laboratory can perform a complete set of these assays. On the other hand, the smart use of conventional clinical chemistry tests could improve the management of cancer. They could be especially valuable tools in the long-term care of patients with a verified diagnosis. In this review, we discuss the utilization of the conventional clinical chemistry assays for the diagnosis, monitoring and prognosis of various oncological diseases. The use of conventional blood tests to assess the levels of chemical elements, metabolites and proteins (including enzymatic activity measurements) in the care of oncology patients is discussed. We have shown that some clinical chemistry assays could be used in the management of distinct kinds of cancer.

Nanobody: A Small Antibody with Big Implications for Tumor Therapeutic Strategy

The development of monoclonal antibody treatments for successful tumor-targeted therapies took several decades. However, the efficacy of antibody-based therapy is still confined and desperately needs further improvement. Nanobodies are the recombinant variable domains of heavy-chain-only antibodies, with many unique properties such as small size (~15kDa), excellent solubility, superior stability, ease of manufacture, quick clearance from blood, and deep tissue penetration, which gain increasing acceptance as therapeutical tools and are considered also as building blocks for chimeric antigen receptors as well as for targeted drug delivery. Thus, one of the promising novel developments that may address the deficiency of monoclonal antibody-based therapies is the utilization of nanobodies. This article provides readers the significant factors that the structural and biochemical properties of nanobodies and the research progress on nanobodies in the fields of tumor treatment, as well as their application prospect.

Clinical Applications of Extracellular Vesicles in the Diagnosis and Treatment of Traumatic Brain Injury

Extracellular vesicles (EVs) have emerged as key mediators of cell-cell communication during homeostasis and in pathology. Central nervous system (CNS)-derived EVs contain cell type-specific surface markers and intralumenal protein, RNA, DNA, and metabolite cargo that can be used to assess the biochemical and molecular state of neurons and glia during neurological injury and disease. The development of EV isolation strategies coupled with analysis of multi-plexed biomarker and clinical data have the potential to improve our ability to classify and treat traumatic brain injury (TBI) and resulting sequelae. Additionally, their ability to cross the blood-brain barrier (BBB) has implications for both EV-based diagnostic strategies and for potential EV-based therapeutics. In the present review, we discuss encouraging data for EV-based diagnostic, prognostic, and therapeutic strategies in the context of TBI monitoring and management.

Differential Dependency of Human Pancreatic Cancer Cells on Targeting PTEN via PLK 1 Expression

Even though the tumour suppressive role of PTEN is well-known, its prognostic implications are ambiguous. The objective of this study was to further explore the function of PTEN expression in human pancreatic cancer. The expression of PTEN has been dominant in various human cancers including pancreatic cancer when compared with their matched normal tissues. The pancreatic cancer cells have been divided into PTEN blockade-susceptible and PTEN blockade-impassible groups dependent on targeting PTEN by altering intracellular signaling. The expression of PTEN has led to varying clinical outcomes of pancreatic cancer based on GEO Series (GSE) data analysis and Liptak’s z analysis. Differential dependency to PTEN blockade has been ascertained based on the expression of polo-like kinase1 PLK1 in pancreatic cancer cells. The prognostic value of PTEN also depends on PLK1 expression in pancreatic cancer. Collectively, the present study provides a rationale for targeting PTEN as a promising therapeutic strategy dependent on PLK1 expressions using a companion biomarker discovery platform.

The emerging role of cell-free DNA as a molecular marker for cancer management

An increasing number of studies demonstrate the potential use of cell-free DNA (cfDNA) as a surrogate marker for multiple indications in cancer, including diagnosis, prognosis, and monitoring. However, harnessing the full potential of cfDNA requires (i) the optimization and standardization of preanalytical steps, (ii) refinement of current analysis strategies, and, perhaps most importantly, (iii) significant improvements in our understanding of its origin, physical properties, and dynamics in circulation. The latter knowledge is crucial for interpreting the associations between changes in the baseline characteristics of cfDNA and the clinical manifestations of cancer. In this review we explore recent advancements and highlight the current gaps in our knowledge concerning each point of contact between cfDNA analysis and the different stages of cancer management.

Assessment of Digital PCR as a Primary Reference Measurement Procedure to Support Advances in Precision Medicine

BACKGROUND

Genetic testing of tumor tissue and circulating cell-free DNA for somatic variants guides patient treatment of many cancers. Such measurements will be fundamental in the future support of precision medicine. However, there are currently no primary reference measurement procedures available for nucleic acid quantification that would support translation of tests for circulating tumor DNA into routine use.

METHODS

We assessed the accuracy of digital PCR (dPCR) for copy number quantification of a frequently occurring single-nucleotide variant in colorectal cancer (KRAS c.35G>A, p.Gly12Asp, from hereon termed G12D) by evaluating potential sources of uncertainty that influence dPCR measurement.

RESULTS

Concentration values for samples of KRAS G12D and wild-type plasmid templates varied by <1.2-fold when measured using 5 different assays with varying detection chemistry (hydrolysis, scorpion probes, and intercalating dyes) and <1.3-fold with 4 commercial dPCR platforms. Measurement trueness of a selected dPCR assay and platform was validated by comparison with an orthogonal method (inductively coupled plasma mass spectrometry). The candidate dPCR reference measurement procedure showed linear quantification over a wide range of copies per reaction and high repeatability and interlaboratory reproducibility (CV, 2%-8% and 5%-10%, respectively).

CONCLUSIONS

This work validates dPCR as an SI-traceable reference measurement procedure based on enumeration and demonstrates how it can be applied for assignment of copy number concentration and fractional abundance values to DNA reference materials in an aqueous solution. High-accuracy measurements using dPCR will support the implementation and traceable standardization of molecular diagnostic procedures needed for advancements in precision medicine.

Determining personalized treatment by gene expression profiling in metastatic breast carcinoma patients: a pilot study

PURPOSE

The present study evaluates the massive study of gene expression in metastatic breast carcinoma (MBC) patients using microarray gene expression profiling (MAGE) complemented with conventional sequencing, immunohistochemistry (IHC) and fluorescent "in situ" hybridization (FISH), seeking to optimize the treatment in a subset of heavily pretreated patients and with limited life expectancy.

PATIENTS, MATERIAL AND METHODS

MBC patients in hormone therapy progression with survival expectancy of at least 3 months (m) have been included. The MAGE contains gene probes representing genes known to potentially interact with available drugs as cited in the literature.

RESULTS

Thirty-nine procedures were performed from October 2010 to April 2016. Within the 30 evaluable procedures, considering all hormonal manipulations as a single line, the patients had received a median of 4 treatment lines prior to MAGE (range 1-7). Progression was observed in 6 cases, stable disease (SD) in 7 cases and partial response (PR) in 16 cases, which implies a clinical benefit rate (SD + PR) of 76%. Actuarial median progression-free survival (PFS) was 6 m (95% CI 2.5-9.5) in patients with clinical benefit. The median overall survival (OS) for the entire series was 11 m (95% CI 2.2-19.8).

CONCLUSION

Data presented here indicate that the use of MAGE provides relevant information to establish personalized treatment in frail patients with limited life expectancy in which therapeutic futility is a particularly difficult burden to assume.

Serum SP70 is a sensitive predictor of chemotherapy response in patients with advanced nonsmall cell lung cancer

SP70 is a novel tumor biomarker in patients with nonsmall cell lung cancer (NSCLC). However, its role as a marker for predicting the response to chemotherapy for patients with advanced NSCLC has not been investigated. A total of 152 patients were enrolled. Serum SP70, carcinoembryonic antigen (CEA), cytokeratin 19 fragment (CYFRA21-1), and neuron-specific enolase (NSE) were detected before and after 2 cycles of chemotherapy. The correlation between serum tumor biomarker levels and chemotherapy responses and their association with epidermal growth factor receptor (EGFR) mutation status and progression-free survival (PFS) were analyzed. Serum SP70 levels were significantly decreased after chemotherapy in the partial remission (PR) group (P < .001) and increased in the progressive disease (PD) group (P < .001), but not significantly changed in the stable disease (SD) group (P = .114). Although similar changes were observed on CEA and CYFRA21-1 levels but not NSE, ROC analysis demonstrated that SP70 is superior to the others. Additionally, patients with EGFR mutation had higher serum SP70 levels and tissue SP70 expression than patients without EGFR mutation (P = .014 and P = .002, respectively). The median PFS of patients with decreased SP70 levels after chemotherapy was longer than that of patients with stable or increased serum SP70 level (24 months vs 12 months vs 2 months, P < .001), and the differences of all other 3 tumor markers were not obvious. Serum SP70 is a sensitive and real-time indicator of chemotherapeutic efficacy in patients with advanced NSCLC and related to PFS.

Poly-ligand profiling differentiates trastuzumab-treated breast cancer patients according to their outcomes

Assessing the phenotypic diversity underlying tumour progression requires the identification of variations in the respective molecular interaction networks. Here we report proof-of-concept for a platform called poly-ligand profiling (PLP) that surveys these system states and distinguishes breast cancer patients who did or did not derive benefit from trastuzumab. We perform tissue-SELEX on breast cancer specimens to enrich single-stranded DNA (ssDNA) libraries that preferentially interact with molecular components associated with the two clinical phenotypes. Testing of independent sample sets verifies the ability of PLP to classify trastuzumab-treated patients according to their clinical outcomes with ROC-AUC of 0.78. Standard HER2 testing of the same patients gives a ROC-AUC of 0.47. Kaplan–Meier analysis reveals a median increase in benefit from trastuzumab-containing treatments of 300 days for PLP-positive compared to PLP-negative patients. If prospectively validated, PLP may increase success rates in precision oncology and clinical trials, thus improving both patient care and drug development.

Patients’ selection is particularly important in cancer treatment. Here the authors present a proof-of-principle methodology that could be potentially important in assisting therapeutic decisions in the treatment of breast cancer patients.

Improved decision making for prioritizing tumor targeting antibodies in human xenografts: Utility of fluorescence imaging to verify tumor target expression, antibody binding and optimization of dosage and application schedule

Preclinical efficacy studies of antibodies targeting a tumor-associated antigen are only justified when the expression of the relevant antigen has been demonstrated. Conventionally, antigen expression level is examined by immunohistochemistry of formalin-fixed paraffin-embedded tumor tissue section. This method represents the diagnostic "gold standard" for tumor target evaluation, but is affected by a number of factors, such as epitope masking and insufficient antigen retrieval. As a consequence, variances and discrepancies in histological staining results can occur, which may influence decision-making and therapeutic outcome. To overcome these problems, we have used different fluorescence-labeled therapeutic antibodies targeting human epidermal growth factor receptor (HER) family members and insulin-like growth factor-1 receptor (IGF1R) in combination with fluorescence imaging modalities to determine tumor antigen expression, drug-target interaction, and biodistribution and tumor saturation kinetics in non-small cell lung cancer xenografts. For this, whole-body fluorescence intensities of labeled antibodies, applied as a single compound or antibody mixture, were measured in Calu-1 and Calu-3 tumor-bearing mice, then ex vivo multispectral tumor tissue analysis at microscopic resolution was performed. With the aid of this simple and fast imaging method, we were able to analyze the tumor cell receptor status of HER1-3 and IGF1R, monitor the antibody-target interaction and evaluate the receptor binding sites of anti-HER2-targeting antibodies. Based on this, the most suitable tumor model, best therapeutic antibody, and optimal treatment dosage and application schedule was selected. Predictions drawn from obtained imaging data were in excellent concordance with outcome of conducted preclinical efficacy studies. Our results clearly demonstrate the great potential of combined in vivo and ex vivo fluorescence imaging for the preclinical development and characterization of monoclonal antibodies.

Nanopore-Based Target Sequence Detection

The promise of portable diagnostic devices relies on three basic requirements: comparable sensitivity to established platforms, inexpensive manufacturing and cost of operations, and the ability to survive rugged field conditions. Solid state nanopores can meet all these requirements, but to achieve high manufacturing yields at low costs, assays must be tolerant to fabrication imperfections and to nanopore enlargement during operation. This paper presents a model for molecular engineering techniques that meets these goals with the aim of detecting target sequences within DNA. In contrast to methods that require precise geometries, we demonstrate detection using a range of pore geometries. As a result, our assay model tolerates any pore-forming method and in-situ pore enlargement. Using peptide nucleic acid (PNA) probes modified for conjugation with synthetic bulk-adding molecules, pores ranging 15-50 nm in diameter are shown to detect individual PNA-bound DNA. Detection of the CFTRΔF508 gene mutation, a codon deletion responsible for ∼66% of all cystic fibrosis chromosomes, is demonstrated with a 26-36 nm pore size range by using a size-enhanced PNA probe. A mathematical framework for assessing the statistical significance of detection is also presented.

Epigenetic mechanisms involved in melanoma pathogenesis and chemoresistance

The discovery of highly recurrent mutations in melanoma, such as BRAF(V600E), completely changed the clinical management including therapy of melanoma patients. In the era of Personalized Medicine targeted melanoma therapies showed high response rates, currently evidenced by BRAF inhibitors or immune-stimulating therapies. In addition to genetic biomarkers, epigenetic knowledge in melanoma has undergone a major step forward in recent years. In particular, epigenetics is unveiling new perspectives to fight this disease, providing an encouraging number of DNA methylation based biomarkers that will likely improve patient stratification for prognosis and treatment. In this regard, putative targetable biomarkers or those with predictive value for the outcome of currently applied therapies are promising tools for future precision oncology strategies. In addition, the progress made in genetic and epigenetic profiling technologies and their reconfiguration to real-time clinical screening approaches makes personalized medicine in melanoma an achievable objective in upcoming years.

Routine Clinical Mutation Profiling of Non–Small Cell Lung Cancer Using Next-Generation Sequencing

Context

The availability of massive, parallel-sequencing technologies makes possible efficient, simultaneous detection of driver and druggable mutations in cancer.

Objective

To develop an amplicon-based, next-generation sequencing, mutation-detection assay for lung cancer using the 454 GS Junior (Roche Applied Science, Indianapolis, Indiana) platform.

Design

Fusion primers incorporating target sequence, 454 adaptors, and multiplex identifiers were designed to generate 35 amplicons (median length 246 base pairs) covering 8.9 kilobases of mutational hotspots in AKT1, BRAF, EGFR, ERBB2, HRAS, KRAS, NRAS, PIK3CA, and MAP2K1 genes and all exons of the PTEN gene.

Results

The assay was validated on 23 formalin-fixed, paraffin-embedded lung cancer specimens. A minimum number of reads was consistently achieved with overall median read depth of 529× per amplicon. In total, 25 point mutations and 4 insertions/deletions (indels) with a frequency of 5.5% to 93.1% mutant alleles were detected. All EGFR, ERBB2, KRAS, PIK3CA, KRAS, and PTEN mutations, as detected by next-generation sequencing, were confirmed by pyrosequencing, with the exception of 3 point mutations in a tumor sample with low mutant-allele burden (below the pyrosequencing limit of detection).

Conclusions

The GS Junior–based, targeted, resequencing assay for a focused set of non–small cell lung cancer driver genes allows for quick and sensitive detection of point mutations and indels for the most relevant therapeutic genes in this type of cancer.

Quantitative cell-free circulating BRAFV600E mutation analysis by use of droplet digital PCR in the follow-up of patients with melanoma being treated with BRAF inhibitors

BACKGROUND

Around 50% of cutaneous melanomas harbor the BRAF(V600E) mutation and can be treated with BRAF inhibitors. DNA carrying this mutation can be released into circulation as cell-free BRAF(V600E) (cfBRAF(V600E)). Droplet digital PCR (ddPCR) is an analytically sensitive technique for quantifying small concentrations of DNA. We studied the plasma concentrations of cfBRAF(V600E) by ddPCR in patients with melanoma during therapy with BRAF inhibitors.

METHODS

Plasma concentrations of cfBRAF(V600E) were measured in 8 controls and 20 patients with advanced melanoma having the BRAF(V600E) mutation during treatment with BRAF inhibitors at baseline, first month, best response, and progression.

RESULTS

The BRAF(V600E) mutation was detected by ddPCR even at a fractional abundance of 0.005% in the wild-type gene. Agreement between tumor tissue BRAF(V600E) and plasma cfBRAF(V600E) was 84.3%. Baseline cfBRAF(V600E) correlated with tumor burden (r = 0.742, P < 0.001). cfBRAF(V600E) concentrations decreased significantly at the first month of therapy (basal median, 216 copies/mL; Q1-Q3, 27-647 copies/mL; first response median, 0 copies/mL; Q1-Q3, 0-49 copies/mL; P < 0.01) and at the moment of best response (median, 0 copies/mL; Q1-Q3, 0-33 copies/mL; P < 0.01). At progression, there was a significant increase in the concentration of cfBRAF(V600E) compared with best response (median, 115 copies/mL; Q1-Q3, 3-707 copies/mL; P = 0.013). Lower concentrations of basal cfBRAF(V600E) were significantly associated with longer overall survival and progression-free survival (27.7 months and 9 months, respectively) than higher basal concentrations (8.6 months and 3 months, P < 0.001 and P = 0.024, respectively).

CONCLUSIONS

cfBRAF(V600E) quantification in plasma by ddPCR is useful as a follow-up to treatment response in patients with advanced melanoma.

Using a rhabdomyosarcoma patient-derived xenograft to examine precision medicine approaches and model acquired resistance

BACKGROUND

Precision (Personalized) medicine has the potential to revolutionize patient health care especially for many cancers where the fundamental disease etiology remains either elusive or has no available therapy. Here we outline a study in alveolar rhabdomyosarcoma, in which we use gene expression profiling and a series of drug prediction algorithms combined with a matched patient-derived xenograft (PDX) model to test bioinformatically predicted therapies.

PROCEDURE

A PDX model was developed from a patient biopsy and a number of drugs identified using gene expression analysis in combination with drug prediction algorithms. Drugs chosen from each of the predictive methodologies, along with the patient's standard-of-care therapy (ICE-T), were tested in vivo in the PDX tumor. A second study was initiated using the tumors that re-grew following the ICE-T treatment. Further expression analysis identified additional therapies with potential anti-tumor efficacy.

RESULTS

A number of the predicted therapies were found to be active against the tumors in particular BGJ398 (FGFR2) and ICE-T. Re-transplanted ICE-T treated tumorgrafts demonstrated a decreased response to ICE-T recapitulating the patient's refractory disease. Gene expression profiling of the ICE-T treated tumorgrafts identified cytarabine (SLC29A1) as a potential therapy, which was shown, along with BGJ398, to be highly active in vivo.

CONCLUSIONS

This study illustrates that PDX models are suitable surrogates for testing potential therapeutic strategies based on gene expression analysis, modeling clinical drug resistance and hold the potential to assist in guiding prospective patient care.

Gene Expression Profiling of Tumors From Heavily Pretreated Patients With Metastatic Cancer for the Selection of Therapy: A Pilot Study

BACKGROUND

Recently, it has been shown that it is possible to identify tumor profiles of sensitivity for potentially useful drugs, both conventional and experimental, based on whole oligonucleotide microarray gene expression studies in heavily pretreated patients with metastatic solid tumors.

METHODS

Fresh-frozen tumor biopsies for molecular profiling (MP) were obtained from patients with advanced and refractory cancer. Total tumor and control tissue RNA was hybridized to a whole human genome oligonucleotide microarray. Differentially expressed genes interacting with potential therapeutic targets were identified. Results were complemented with DNA sequencing of selected driver genes and with immunohistochemistry and fluorescent "in situ" hybridization. The results were used to guide experimental treatment.

RESULTS

MP assays led to a potentially active available drug in 91.2% of the patients. The median number of available active drugs per tumor was 5 (range, 1 to 9). Nine treated patients were not evaluable for response. Partial response was observed in 18 patients (33%), stable disease in 22 patients (40%) (clinical benefit rate of 73%), and progression in 15 (27%). Overall median progression-free survival and overall survival were 8 and 13 months, respectively.

CONCLUSION

MP-guided therapy is feasible and seems to improve the clinical outcome of extensively pretreated patients but prospective and confirmatory trials are needed.

cMET in triple-negative breast cancer: is it a therapeutic target for this subset of breast cancer patients?

INTRODUCTION

The identification and validation of a targeted therapy for triple-negative breast cancer (TNBC) is currently one of the most urgent needs in breast cancer therapeutics. The cMET oncogene encodes a membrane-bound tyrosine kinase implicated in the formation and/or progression of several cancer types, including TNBC. Currently, inhibitors targeting cMET are undergoing clinical trials for a variety of cancers, including TNBC. These include anti-cMET and anti-hepatocyte growth factor (HGF) monoclonal antibodies and tyrosine kinase inhibitors.

AREAS COVERED

This article reviews the structure and mode of action of cMET, the role of cMET in cancer formation/development, with particular emphasis on its role in basal/TNBC and its potential as a therapeutic target for this subtype of breast cancer.

EXPERT OPINION

Due to cancer heterogeneity, it is unlikely that all TNBC patients will be responsive to anti-cMET drugs. Therefore, if cMET is to be used as a target for treatment, it will be important to identify predictive biomarkers to select, upfront, those patients likely to benefit. Potential predictive biomarkers for anti-cMET treatments in basal/TNBC include cMET, phospho-cMET, downstream signaling proteins or HGF. These putative predictive biomarkers should be evaluated in a large panel of basal/TNBC cell lines before incorporation into clinical trials involving anti-cMET drugs.

The hallmarks of osteoarthritis and the potential to develop personalised disease-modifying pharmacological therapeutics

Osteoarthritis (OA) is an age-related condition and the leading cause of pain, disability and shortening of adult working life in the UK. The incidence of OA increases with age, with 25% of the over 50s population having OA of the knee. Despite promising preclinical data covering various molecule classes, there is regrettably at present no approved disease-modifying OA drugs (DMOADs). With the advent of next generation sequencing technologies, other therapeutic areas, in particular oncology, have experienced a paradigm shift towards defining disease by its molecular composition. This paradigm shift has enabled high resolution patient stratification and supported the emergence of personalised or precision medicines. In this review we evaluate the potential for the development of OA therapeutics to undergo a similar paradigm shift given that OA is increasingly being recognised as a heterogeneous disease affecting multiple joint tissues. We highlight the evidence for the role of these tissues in OA pathology as different "hallmarks" of OA biology and review the opportunities to identify and develop targeted disease-modifying pharmacological therapeutics. Finally, we consider whether it is feasible to expect the emergence of personalised disease-modifying medicines for patients with OA and how this might be achieved.

Regional bias of intratumoral genetic heterogeneity of nucleotide repeats in colon cancers with microsatellite instability

Intratumoral heterogeneity (ITH) may produce regional biases in genotype and phenotype evaluation in a single tumor and may impede proper cancer diagnosis. To evaluate the extent of ITH in colorectal cancer (CRC) with microsatellite instability (MSI), we obtained 4-7 biopsies from 39 CRCs followed by MSI analysis either using the Bethesda MSI evaluation system or Promega system with 5 mononucleotide markers. We found decreased prevalence of MSI (+) by the Promega system compared to the Bethesda system. The overall discordance between the two systems was 54 %. In contrast to the previous studies that had shown discordance only in low MSI (MSI-L), our results showed the discordance not only in MSI-L, but also in high MSI (MSI-H) cases. Among the MSI (+) CRCs, ITH of MSI status was identified in 41.7 % of CRC by the Bethesda system and 22.2 % by the Promega system. In terms of MSI markers, the ITH originated from dinucleotide markers in most cases (69 %), but it originated from mononucleotide markers (31 %) as well. Pooling of DNA from a regional biopsy with MSI (+) with additional biopsies from stable MSI (MSS) showed that this approach was beneficial to increase the sensitivity of MSI detection. Our results indicate that ITH of MSI phenotype by the Bethesda system is more overestimated than previously identified. However, because there was considerable ITH of MSI subtypes and markers even by the Promega system, our data suggest that analysis of MSI status in multiple regional biopsies is needed for a better evaluation of MSI status in CRC.

Analytical validation of the PAM50-based Prosigna Breast Cancer Prognostic Gene Signature Assay and nCounter Analysis System using formalin-fixed paraffin-embedded breast tumor specimens

Background

NanoString’s Prosigna™ Breast Cancer Prognostic Gene Signature Assay is based on the PAM50 gene expression signature. The test outputs a risk of recurrence (ROR) score, risk category, and intrinsic subtype (Luminal A/B, HER2-enriched, Basal-like). The studies described here were designed to validate the analytical performance of the test on the nCounter Analysis System across multiple laboratories.

Methods

Analytical precision was measured by testing five breast tumor RNA samples across 3 sites. Reproducibility was measured by testing replicate tissue sections from 43 FFPE breast tumor blocks across 3 sites following independent pathology review at each site. The RNA input range was validated by comparing assay results at the extremes of the specified range to the nominal RNA input level. Interference was evaluated by including non-tumor tissue into the test.

Results

The measured standard deviation (SD) was less than 1 ROR unit within the analytical precision study and the measured total SD was 2.9 ROR units within the reproducibility study. The ROR scores for RNA inputs at the extremes of the range were the same as those at the nominal input level. Assay results were stable in the presence of moderate amounts of surrounding non-tumor tissue (<70% by area).

Conclusions

The analytical performance of NanoString’s Prosigna assay has been validated using FFPE breast tumor specimens across multiple clinical testing laboratories.

A matrix approach to guide IHC-based tissue biomarker development in oncology drug discovery

Immunohistochemistry (IHC) is a core platform for the analysis of tissue samples, and there is an increasing demand for reliable and quantitative IHC-based tissue biomarkers in oncology clinical research and development (R&D) environments. Biomarker assay and drug development proceed in parallel. Furthermore, biomarker assay requirements change with each phase of drug development. We have therefore developed a matrix tool to enable researchers to evaluate whether a particular IHC biomarker assay is fit for purpose. Experience gained from the development of 130 IHC biomarkers, supporting a large number of oncology drug projects, was used to formulate a practical approach to IHC assay development. The resultant matrix grid and accompanying work flow incorporates 16 core decision points that link antibody and assay specificity and sensitivity, and assay performance in preclinical and clinical samples, with stages of drug development. The matrix provides a means to ensure that relevant information on an IHC assay in development is recorded and communicated consistently and that minimum assay validation requirements are met.