Moving Molecular Profiling to Routine Clinical Practice: A Way Forward?

Molecular profiling of a patient's tumor to guide targeted treatment selection offers the potential to advance patient care by improving outcomes and minimizing toxicity (by avoiding ineffective treatments). However, current development of molecular profile (MP) panels is often based on applying institution-specific or subjective algorithms to nonrandomized patient cohorts. Consequently, obtaining reliable evidence that molecular profiling is offering clinical benefit and is ready for routine clinical practice is challenging. In particular, we discuss here the problems with interpreting for clinical utility nonrandomized studies that compare outcomes in patients treated based on their MP vs those treated with standard of care, studies that compare the progression-free survival (PFS) seen on a MP-directed treatment to the PFS seen for the same patient on a previous standard treatment (PFS ratio), and multibasket trials that evaluate the response rates of targeted therapies in specific molecularly defined subpopulations (regardless of histology). We also consider some limitations of randomized trial designs. A two-step strategy is proposed in which multiple mutation-agent pairs are tested for activity in one or more multibasket trials in the first step. The results of the first step are then used to identify promising mutation-agent pairs that are combined in a molecular panel that is then tested in the step-two strategy-design randomized clinical trial (the molecular panel-guided treatment for the selected mutations vs standard of care). This two-step strategy should allow rigorous evidence-driven identification of mutation-agent pairs that can be moved into routine clinical practice.

Evidence to Support Inclusion of Pharmacogenetic Biomarkers in Randomised Controlled Trials

Pharmacogenetics and biomarkers are becoming normalised as important technologies to improve drug efficacy rates, reduce the incidence of adverse drug reactions, and make informed choices for targeted therapies. However, their wider clinical implementation has been limited by a lack of robust evidence. Suitable evidence is required before a biomarker’s clinical use, and also before its use in a clinical trial. We have undertaken a review of five pharmacogenetic biomarker-guided randomised controlled trials (RCTs) and evaluated the evidence used by these trials to justify biomarker inclusion. We assessed and quantified the evidence cited in published rationale papers, or where these were not available, obtained protocols from trial authors. Very different levels of evidence were provided by the trials. We used these observations to write recommendations for future justifications of biomarker use in RCTs and encourage regulatory authorities to write clear guidelines.

Immunohistochemical analysis of PDK1, PHD3 and HIF-1α expression defines the hypoxic status of neuroblastoma tumors

Neuroblastoma (NB) is the most common solid tumor during infancy and the first cause of death among the preschool age diseases. The availability of several NB genomic profiles improves the prognostic ability, but the outcome prediction for this pathology remains imperfect. We previously produced a novel prognostic gene signature based on the response of NB cells to hypoxia, a condition of tumor microenvironment strictly connected with cancer aggressiveness. Here we attempted to further define the expression of hypoxia-modulated specific genes, looking at their protein level in NB specimens, considering in particular the hypoxia inducible factor-1α (HIF-1α), the mitochondrial pyruvate dehydrogenase kinase 1 (PDK1), and the HIF-prolyl hydroxylase domain 3 (PHD3). The evaluation of expression was performed by Western blot and immunocytochemistry on NB cell lines and by immunohistochemistry on tumor specimens. Stimulation of both HIF-1α and PDK1 and inhibition of PHD3 expression were observed in NB cell lines cultured under prolonged hypoxic conditions as well as in most of the tumors with poor outcome. Our results indicate that the immunohistochemistry analysis of the protein expression of PDK1, PHD3, and HIF-1α defines the hypoxic status of NB tumors and can be used as a simple and relevant tool to stratify high-risk patients.

Precision Therapy for Lung Cancer: Tyrosine Kinase Inhibitors and Beyond

For patients with advanced cancers there has been a concerted effort to transition from a generic treatment paradigm to one based on tumor-specific biologic, and patient-specific clinical characteristics. This approach, known as precision therapy has been made possible owing to widespread availability and a reduction in the cost of cutting-edge technologies that are used to study the genomic, proteomic, and metabolic attributes of individual tumors. This review traces the evolution of precision therapy for lung cancer from the identification of molecular subsets of the disease to the development and approval of tyrosine kinase, as well as immune checkpoint inhibitors for lung cancer therapy. Challenges of the precision therapy era including the emergence of acquired resistance, identification of untargetable mutations, and the effect on clinical trial design are discussed. We conclude by highlighting newer applications for the concept of precision therapy.

GeneMed: An Informatics Hub for the Coordination of Next-Generation Sequencing Studies that Support Precision Oncology Clinical Trials

We have developed an informatics system, GeneMed, for the National Cancer Institute (NCI) molecular profiling-based assignment of cancer therapy (MPACT) clinical trial (NCT01827384) being conducted in the National Institutes of Health (NIH) Clinical Center. This trial is one of the first to use a randomized design to examine whether assigning treatment based on genomic tumor screening can improve the rate and duration of response in patients with advanced solid tumors. An analytically validated next-generation sequencing (NGS) assay is applied to DNA from patients’ tumors to identify mutations in a panel of genes that are thought likely to affect the utility of targeted therapies available for use in the clinical trial. The patients are randomized to a treatment selected to target a somatic mutation in the tumor or with a control treatment. The GeneMed system streamlines the workflow of the clinical trial and serves as a communications hub among the sequencing lab, the treatment selection team, and clinical personnel. It automates the annotation of the genomic variants identified by sequencing, predicts the functional impact of mutations, identifies the actionable mutations, and facilitates quality control by the molecular characterization lab in the review of variants. The GeneMed system collects baseline information about the patients from the clinic team to determine eligibility for the panel of drugs available. The system performs randomized treatment assignments under the oversight of a supervising treatment selection team and generates a patient report containing detected genomic alterations. NCI is planning to expand the MPACT trial to multiple cancer centers soon. In summary, the GeneMed system has been proven to be an efficient and successful informatics hub for coordinating the reliable application of NGS to precision medicine studies.

Application of molecular profiling in clinical trials for advanced metastatic cancers

There is growing interest in the application of molecular profiling, including sequencing, genotyping, and/or mRNA expression profiling, to the analysis of patient tumors with the objective of applying these data to inform therapeutic choices for patients with advanced cancers. Multiple clinical trials that are attempting to validate this personalized or precision medicine approach are in various stages of development and execution. Although preliminary data from some of these efforts have fueled excitement about the value and utility of these studies, their execution has also provoked many questions about the best way to approach complicating factors such as tumor heterogeneity and the choice of which genetic mutations to target. This commentary highlights some of the challenges confronting the clinical application of molecular tumor profiling and the various trial designs being utilized to address these challenges. Randomized trials that rigorously test patient response to molecularly targeted agents assigned based on the presence of a defined set of mutations in putative cancer-driving pathways are required to address some of the current challenges and to identify patients likely to benefit from this approach.

IBM's Health Analytics and Clinical Decision Support

OBJECTIVES

This survey explores the role of big data and health analytics developed by IBM in supporting the transformation of healthcare by augmenting evidence-based decision-making.

METHODS

Some problems in healthcare and strategies for change are described. It is argued that change requires better decisions, which, in turn, require better use of the many kinds of healthcare information. Analytic resources that address each of the information challenges are described. Examples of the role of each of the resources are given.

RESULTS

There are powerful analytic tools that utilize the various kinds of big data in healthcare to help clinicians make more personalized, evidenced-based decisions. Such resources can extract relevant information and provide insights that clinicians can use to make evidence-supported decisions. There are early suggestions that these resources have clinical value. As with all analytic tools, they are limited by the amount and quality of data.

CONCLUSION

Big data is an inevitable part of the future of healthcare. There is a compelling need to manage and use big data to make better decisions to support the transformation of healthcare to the personalized, evidence-supported model of the future. Cognitive computing resources are necessary to manage the challenges in employing big data in healthcare. Such tools have been and are being developed. The analytic resources, themselves, do not drive, but support healthcare transformation.

Challenges for the implementation of high-throughput testing and liquid biopsies in personalized medicine cancer trials

During recent decades, major advances in the comprehension of biology and in biotechnologies have paved the way for what is commonly named personalized medicine. For cancer therapy, personalized medicine represents a paradigm shift in which patient treatment is based on biology in addition to histology and tumor location. Here, we report the major personalized medicine trials in oncology that are either based on molecular alterations from tumor tissue or from circulating blood markers. We next review important challenges facing the implementation of personalized medicine in daily clinical practice, including tumor heterogeneity, reliability of high-throughput technologies, the key role of bioinformatics and the assessment of biomarkers and synthetic models, in order to use big data in actual cancer biology.

Use of Attribute Driven Incremental Discretization and Logic Learning Machine to build a prognostic classifier for neuroblastoma patients

BACKGROUND

Cancer patient's outcome is written, in part, in the gene expression profile of the tumor. We previously identified a 62-probe sets signature (NB-hypo) to identify tissue hypoxia in neuroblastoma tumors and showed that NB-hypo stratified neuroblastoma patients in good and poor outcome 1. It was important to develop a prognostic classifier to cluster patients into risk groups benefiting of defined therapeutic approaches. Novel classification and data discretization approaches can be instrumental for the generation of accurate predictors and robust tools for clinical decision support. We explored the application to gene expression data of Rulex, a novel software suite including the Attribute Driven Incremental Discretization technique for transforming continuous variables into simplified discrete ones and the Logic Learning Machine model for intelligible rule generation.

RESULTS

We applied Rulex components to the problem of predicting the outcome of neuroblastoma patients on the bases of 62 probe sets NB-hypo gene expression signature. The resulting classifier consisted in 9 rules utilizing mainly two conditions of the relative expression of 11 probe sets. These rules were very effective predictors, as shown in an independent validation set, demonstrating the validity of the LLM algorithm applied to microarray data and patients' classification. The LLM performed as efficiently as Prediction Analysis of Microarray and Support Vector Machine, and outperformed other learning algorithms such as C4.5. Rulex carried out a feature selection by selecting a new signature (NB-hypo-II) of 11 probe sets that turned out to be the most relevant in predicting outcome among the 62 of the NB-hypo signature. Rules are easily interpretable as they involve only few conditions.

CONCLUSIONS

Our findings provided evidence that the application of Rulex to the expression values of NB-hypo signature created a set of accurate, high quality, consistent and interpretable rules for the prediction of neuroblastoma patients' outcome. We identified the Rulex weighted classification as a flexible tool that can support clinical decisions. For these reasons, we consider Rulex to be a useful tool for cancer classification from microarray gene expression data.

Randomised proof-of-concept phase II trial comparing targeted therapy based on tumour molecular profiling vs conventional therapy in patients with refractory cancer: results of the feasibility part of the SHIVA trial

Background:

The SHIVA trial is a multicentric randomised proof-of-concept phase II trial comparing molecularly targeted therapy based on tumour molecular profiling vs conventional therapy in patients with any type of refractory cancer. Results of the feasibility study on the first 100 enrolled patients are presented.

Methods:

Adult patients with any type of metastatic cancer who failed standard therapy were eligible for the study. The molecular profile was performed on a mandatory biopsy, and included mutations and gene copy number alteration analyses using high-throughput technologies, as well as the determination of oestrogen, progesterone, and androgen receptors by immunohistochemistry (IHC).

Results:

Biopsy was safely performed in 95 of the first 100 included patients. Median time between the biopsy and the therapeutic decision taken during a weekly molecular biology board was 26 days. Mutations, gene copy number alterations, and IHC analyses were successful in 63 (66%), 65 (68%), and 87 (92%) patients, respectively. A druggable molecular abnormality was present in 38 patients (40%).

Conclusions:

The establishment of a comprehensive tumour molecular profile was safe, feasible, and compatible with clinical practice in refractory cancer patients.

Multiomics medicine in oncology: assessing effectiveness, cost–effectiveness and future research priorities for the molecularly unique individual

The development of genomic technologies has ushered in the era of pharmacogenomics. However, discoveries and clinical use of targeted therapies are still in their infancy. A focus on monogenic pharmacogenetic traits may contribute to this lack of progress. Variation in drug response is likely a complex paradigm involving not only genomic factors but proteomic, metabolomic and epigenomic influences. The incorporation of these omics elements into pharmaceutical development and clinical decision-making will ultimately require the use of methods to determine clinical and economic value. Current methodologies and guidelines for determining clinical effectiveness and cost–effectiveness may have limited applicability to the increasingly personalized nature of omics treatment strategies. Using examples from oncology, this article argues for the adaptation and tailoring of three existing methods for ensuring development and clinical use of multiomics-guided therapies that are effective, safe and offer value for money.

Logic Learning Machine creates explicit and stable rules stratifying neuroblastoma patients

BACKGROUND

Neuroblastoma is the most common pediatric solid tumor. About fifty percent of high risk patients die despite treatment making the exploration of new and more effective strategies for improving stratification mandatory. Hypoxia is a condition of low oxygen tension occurring in poorly vascularized areas of the tumor associated with poor prognosis. We had previously defined a robust gene expression signature measuring the hypoxic component of neuroblastoma tumors (NB-hypo) which is a molecular risk factor. We wanted to develop a prognostic classifier of neuroblastoma patients' outcome blending existing knowledge on clinical and molecular risk factors with the prognostic NB-hypo signature. Furthermore, we were interested in classifiers outputting explicit rules that could be easily translated into the clinical setting.

RESULTS

Shadow Clustering (SC) technique, which leads to final models called Logic Learning Machine (LLM), exhibits a good accuracy and promises to fulfill the aims of the work. We utilized this algorithm to classify NB-patients on the bases of the following risk factors: Age at diagnosis, INSS stage, MYCN amplification and NB-hypo. The algorithm generated explicit classification rules in good agreement with existing clinical knowledge. Through an iterative procedure we identified and removed from the dataset those examples which caused instability in the rules. This workflow generated a stable classifier very accurate in predicting good and poor outcome patients. The good performance of the classifier was validated in an independent dataset. NB-hypo was an important component of the rules with a strength similar to that of tumor staging.

CONCLUSIONS

The novelty of our work is to identify stability, explicit rules and blending of molecular and clinical risk factors as the key features to generate classification rules for NB patients to be conveyed to the clinic and to be used to design new therapies. We derived, through LLM, a set of four stable rules identifying a new class of poor outcome patients that could benefit from new therapies potentially targeting tumor hypoxia or its consequences.

How many diseases are colorectal cancer?

The development of personalised therapy and mechanism-targeted agents in oncology mandates the identification of the patient populations most likely to benefit from therapy. This paper discusses the increasing evidence as to the heterogeneity of the group of diseases called colorectal cancer. Differences in the aetiology and epidemiology of proximal and distal cancers are reflected in different clinical behaviour, histopathology, and molecular characteristics of these tumours. This may impact response both to standard cytotoxic therapies and mechanism-targeted agents. This disease heterogeneity leads to challenges in the design of clinical trials to assess novel therapies in the treatment of "colorectal cancer."

Overcoming implementation challenges of personalized cancer therapy

Personalized cancer therapy is based on the precept that detailed molecular characterization of the patient's tumour and its microenvironment will enable tailored therapies to improve outcomes and decrease toxicity. The goal of personalized therapy is to target aberrations that drive tumour growth and survival, by administering the right drug combination for the right person. This is becoming increasingly achievable with advances in high-throughput technologies to characterize tumours and the expanding repertoire of molecularly targeted therapies. However, there are numerous challenges that need to be surpassed before delivering on the promise of personalized cancer therapy. These include tumour heterogeneity and molecular evolution, costs and potential morbidity of biopsies, lack of effective drugs against most genomic aberrations, technical limitations of molecular tests, and reimbursement and regulatory hurdles. Critically, the 'hype' surrounding personalized cancer therapy must be tempered with realistic expectations, which, today, encompass increased survival times for only a portion of patients.

Design of a multi-signature ensemble classifier predicting neuroblastoma patients' outcome

Background

Neuroblastoma is the most common pediatric solid tumor of the sympathetic nervous system. Development of improved predictive tools for patients stratification is a crucial requirement for neuroblastoma therapy. Several studies utilized gene expression-based signatures to stratify neuroblastoma patients and demonstrated a clear advantage of adding genomic analysis to risk assessment. There is little overlapping among signatures and merging their prognostic potential would be advantageous. Here, we describe a new strategy to merge published neuroblastoma related gene signatures into a single, highly accurate, Multi-Signature Ensemble (MuSE)-classifier of neuroblastoma (NB) patients outcome.

Methods

Gene expression profiles of 182 neuroblastoma tumors, subdivided into three independent datasets, were used in the various phases of development and validation of neuroblastoma NB-MuSE-classifier. Thirty three signatures were evaluated for patients' outcome prediction using 22 classification algorithms each and generating 726 classifiers and prediction results. The best-performing algorithm for each signature was selected, validated on an independent dataset and the 20 signatures performing with an accuracy > = 80% were retained.

Results

We combined the 20 predictions associated to the corresponding signatures through the selection of the best performing algorithm into a single outcome predictor. The best performance was obtained by the Decision Table algorithm that produced the NB-MuSE-classifier characterized by an external validation accuracy of 94%. Kaplan-Meier curves and log-rank test demonstrated that patients with good and poor outcome prediction by the NB-MuSE-classifier have a significantly different survival (p < 0.0001). Survival curves constructed on subgroups of patients divided on the bases of known prognostic marker suggested an excellent stratification of localized and stage 4s tumors but more data are needed to prove this point.

Conclusions

The NB-MuSE-classifier is based on an ensemble approach that merges twenty heterogeneous, neuroblastoma-related gene signatures to blend their discriminating power, rather than numeric values, into a single, highly accurate patients' outcome predictor. The novelty of our approach derives from the way to integrate the gene expression signatures, by optimally associating them with a single paradigm ultimately integrated into a single classifier. This model can be exported to other types of cancer and to diseases for which dedicated databases exist.

Correlation of drug-induced apoptosis assay results with oncologist treatment decisions and patient response and survival

BACKGROUND

An observational prospective nonblinded clinical trial was performed to determine the effect of a drug-induced apoptosis assay results on treatments planned by oncologists.

METHODS

Purified cancer cells from patient biopsies were placed into the MiCK (Microculture Kinetic) assay, a short-term culture, which determined the effects of single drugs or combinations of drugs on tumor cell apoptosis. An oncologist received the assay results before finalizing the treatment plan. Use of the MiCK assay was evaluated and correlated with patient outcomes.

RESULTS

Forty-four patients with successful MiCK assays from breast cancer (n = 16), nonsmall cell lung cancer (n = 6), non-Hodgkin lymphoma (n = 4), and others were evaluated. Four patients received adjuvant chemotherapy after MiCK, and 40 received palliative chemotherapy with a median line of therapy of 2. Oncologists used the MiCK assay to determine chemotherapy (users) in 28 (64%) and did not (nonusers) in 16 patients (36%). In users receiving palliative chemotherapy, complete plus partial response rate was 44%, compared with 6.7% in nonusers (P < .02). The median overall survival was 10.1 months in users versus 4.1 months in nonusers (P = .02). Relapse-free interval was 8.6 months in users versus 4.0 months in nonusers (P < .01).

CONCLUSIONS

MiCK assay results are frequently used by oncologists. Outcomes appear to be statistically superior when oncologists use chemotherapy based on MiCK assay results compared with when they do not use the assay results. When available to oncologists, MiCK assay results help to determine patient treatment plans.

Cancer Genomics: Technology, Discovery, and Translation

In recent years, the increasing awareness that somatic mutations and other genetic aberrations drive human malignancies has led us within reach of personalized cancer medicine (PCM). The implementation of PCM is based on the following premises: genetic aberrations exist in human malignancies; a subset of these aberrations drive oncogenesis and tumor biology; these aberrations are actionable (defined as having the potential to affect management recommendations based on diagnostic, prognostic, and/or predictive implications); and there are highly specific anticancer agents available that effectively modulate these targets. This article highlights the technology underlying cancer genomics and examines the early results of genome sequencing and the challenges met in the discovery of new genetic aberrations. Finally, drawing from experiences gained in a feasibility study of somatic mutation genotyping and targeted exome sequencing led by Princess Margaret Hospital–University Health Network and the Ontario Institute for Cancer Research, the processes, challenges, and issues involved in the translation of cancer genomics to the clinic are discussed.

Integrating biomarkers in clinical trials

Biomarkers have a growing role in clinical trials. With the advent of the targeted therapy era, molecular biomarkers in particular are becoming increasingly important within both clinical research and clinical practice. This article focuses on biomarkers that anticipate the prognosis of individual patients ('prognostic' biomarkers) and on biomarkers that predict how individual patients will respond to specific treatments ('predictive' biomarkers, also called 'effect modifiers'). Specific Phase II and III clinical trial designs are discussed in detail for their ability to validate the biomarker and/or to establish the effect of an experimental therapy in patient populations defined by the presence or absence of the biomarker. Contemporary biomarker-based clinical trials in oncology are used as examples.