Making personalized cancer medicine a reality: challenges and opportunities in the development of biomarkers and companion diagnostics

Analysis of recurrent research pathways for assessing and improving effectiveness in life sciences laboratories

Background

Life sciences research often turns out to be ineffective. Our aim was to develop a method for mapping repetitive research processes, detecting practice variations, and exploring inefficiencies.

Methods

Three samples of R&I projects were used: companion diagnostics of cancer treatments, identification of COVID-19 variants, and COVID-19 vaccine development. Major steps involved: defined starting points, desired end points; measurement of transition times and success rates; exploration of variations, and recommendations for improved efficiency.

Results

Over 50% of CDX developments failed to reach market simultaneously with new drugs. There were significant variations among phases of co-development (Bartlett test P<0.001). Length of time in vaccine development also shows variations (P<0.0001). Similarly, subject participation indicates unexplained variations in trials (Phase I: 489.7 (±461.8); Phase II: 857.3 (±450.1); Phase III: 35402 (±18079).

Conclusion

Analysis of repetitive research processes can highlight inefficiencies and show ways to improve quality and productivity in life sciences.

The Role of Imaging Biomarkers to Guide Pharmacological Interventions Targeting Tumor Hypoxia

Hypoxia is a common feature of solid tumors that contributes to angiogenesis, invasiveness, metastasis, altered metabolism and genomic instability. As hypoxia is a major actor in tumor progression and resistance to radiotherapy, chemotherapy and immunotherapy, multiple approaches have emerged to target tumor hypoxia. It includes among others pharmacological interventions designed to alleviate tumor hypoxia at the time of radiation therapy, prodrugs that are selectively activated in hypoxic cells or inhibitors of molecular targets involved in hypoxic cell survival (i.e., hypoxia inducible factors HIFs, PI3K/AKT/mTOR pathway, unfolded protein response). While numerous strategies were successful in pre-clinical models, their translation in the clinical practice has been disappointing so far. This therapeutic failure often results from the absence of appropriate stratification of patients that could benefit from targeted interventions. Companion diagnostics may help at different levels of the research and development, and in matching a patient to a specific intervention targeting hypoxia. In this review, we discuss the relative merits of the existing hypoxia biomarkers, their current status and the challenges for their future validation as companion diagnostics adapted to the nature of the intervention.

A Systematic Review of Companion Diagnostic Tests by Immunohistochemistry for the Screening of Alectinib-Treated Patients in ALK-Positive Non-Small Cell Lung Cancer

Companion diagnostic tests and targeted therapy changed the management of non-small cell lung cancer by diagnosing genetic modifications and enabling individualized treatment. The purpose of this systematic review is to assess the clinical applicability of companion diagnostic tests (IHC method) by comparing the effects of alectinib and crizotinib in patients with ALK-positive NSCLC. We searched for literature up to March 2022 in PubMed, Web of Science, Cochrane, and Google Scholar. The inclusion criteria were randomized controlled trials comparing the effectiveness of alectinib and crizotinib using an IHC-based companion diagnostic test. The primary outcome was progression-free survival (PFS). The secondary outcomes were objective response rate (ORR), duration of response (DOR), and overall survival (OS). PFS was longer in alectinib (68.4 [61.0, 75.9]) than crizotinib (48.7 [40.4, 56.9]). This indicated that alectinib had a superior efficacy to that of crizotinib (HR range 0.15–0.47). In all secondary outcomes, alectinib was better than crizotinib. Particularly for the ORR, the odds ratio (OR) confirmed that alectinib had a lower risk rate (OR: 2.21, [1.46–3.36], p = 0.0002, I² = 39%). Therefore, the companion diagnostic test (immunohistochemistry) is an effective test to determine whether to administer alectinib to ALK-positive NSCLC patients.

Identifying the Steps Required to Effectively Implement Next-Generation Sequencing in Oncology at a National Level in Europe

Next-generation sequencing (NGS) may enable more focused and highly personalized cancer treatment, with the National Comprehensive Cancer Network and European Society for Medical Oncology guidelines now recommending NGS for daily clinical practice for several tumor types. However, NGS implementation, and therefore patient access, varies across Europe; a multi-stakeholder collaboration is needed to establish the conditions required to improve this discrepancy. In that regard, we set up European Alliance for Personalised Medicine (EAPM)-led expert panels during the first half of 2021, including key stakeholders from across 10 European countries covering medical, economic, patient, industry, and governmental expertise. We describe the outcomes of these panels in order to define and explore the necessary conditions for NGS implementation into routine clinical care to enable patient access, identify specific challenges in achieving them, and make short- and long-term recommendations. The main challenges identified relate to the demand for NGS tests (governance, clinical standardization, and awareness and education) and supply of tests (equitable reimbursement, infrastructure for conducting and validating tests, and testing access driven by evidence generation). Recommendations made to resolve each of these challenges should aid multi-stakeholder collaboration between national and European initiatives, to complement, support, and mutually reinforce efforts to improve patient care.

A scalable high-throughput targeted next-generation sequencing assay for comprehensive genomic profiling of solid tumors

Timely and accurate identification of molecular alterations in solid tumors is essential for proper management of patients with advanced cancers. This has created a need for rapid, scalable comprehensive genomic profiling (CGP) systems that detect an increasing number of therapeutically-relevant variant types and molecular signatures. In this study, we assessed the analytical performance of the TruSight Oncology 500 High-Throughput assay for detection of somatic alterations from formalin-fixed paraffin-embedded tissue specimens. In parallel, we developed supporting software and automated sample preparation systems designed to process up to 70 clinical samples in a single NovaSeq 6000TM sequencing run with a turnaround time of <7 days from specimen receipt to report. The results demonstrate that the scalable assay accurately and reproducibly detects small variants, copy number alterations, microsatellite instability (MSI) and tumor mutational burden (TMB) from 40ng DNA, and multiple gene fusions, including known and unknown partners and splice variants from 20ng RNA. 717 tumor samples and reference materials with previously known alterations in 96 cancer-related genes were sequenced to evaluate assay performance. All variant classes were reliably detected at consistent and reportable variant allele percentages with >99% overall accuracy and precision. Our results demonstrate that the high-throughput CGP assay is a reliable method for accurate detection of molecular alterations in support of precision therapeutics in oncology. The supporting systems and scalable workflow allow for efficient interpretation and prompt reporting of hundreds of patient cancer genomes per week with excellent analytical performance.

Companion Diagnostics: State of the Art and New Regulations

Companion diagnostics (CDx) hail promise of improving the drug development process and precision medicine. However, there are various challenges involved in the clinical development and regulation of CDx, which are considered high-risk in vitro diagnostic medical devices given the role they play in therapeutic decision-making and the complications they may introduce with respect to their sensitivity and specificity. The European Union (E.U.) is currently in the process of bringing into effect in vitro Diagnostic Medical Devices Regulation (IVDR). The new Regulation is introducing a wide range of stringent requirements for scientific validity, analytical and clinical performance, as well as on post-market surveillance activities throughout the lifetime of in vitro diagnostics (IVD). Compliance with General Safety and Performance Requirements (GSPRs) adopts a risk-based approach, which is also the case for the new classification system. This changing regulatory framework has an impact on all stakeholders involved in the IVD Industry, including Authorized Representatives, Distributors, Importers, Notified Bodies, and Reference Laboratories and is expected to have a significant effect on the development of new CDx.

Multi-Omics Model Applied to Cancer Genetics

In this review, we focus on bioinformatic oncology as an integrative discipline that incorporates knowledge from the mathematical, physical, and computational fields to further the biomedical understanding of cancer. Before providing a deeper insight into the bioinformatics approach and utilities involved in oncology, we must understand what is a system biology framework and the genetic connection, because of the high heterogenicity of the backgrounds of people approaching precision medicine. In fact, it is essential to providing general theoretical information on genomics, epigenomics, and transcriptomics to understand the phases of multi-omics approach. We consider how to create a multi-omics model. In the last section, we describe the new frontiers and future perspectives of this field.

Bringing Greater Accuracy to Europe's Healthcare Systems: The Unexploited Potential of Biomarker Testing in Oncology

Rapid and continuing advances in biomarker testing are not being matched by take-up in health systems, and this is hampering both patient care and innovation. It also risks costing health systems the opportunity to make their services more efficient and, over time, more economical. This paper sets out the potential of biomarker testing, the unfolding precision and range of possible diagnosis and prediction, and the many obstacles to adoption. It offers case studies of biomarker testing in breast, ovarian, prostate, lung, thyroid and colon cancers, and derives specific lessons as to the potential and actual use of each of them. It also draws lessons about how to improve access and alignment, and to remedy the data deficiencies that impede development. And it suggests solutions to outstanding issues - notably including funding and the tangled web of obtaining reimbursement or equivalent coverage that Europe's fragmented health system implies. It urges a European evolution towards an initial minimum testing scenario, which would guarantee universal access to a suite of biomarker tests for the currently most common conditions, and, further into the future, to an optimum testing scenario in which a much wider range of biomarker tests would be introduced and become part of a more sophisticated health system articulated around personalised medicine. For exploiting genomics to the full, it argues the need for a new policy framework for Europe. Biomarker testing is not an issue that can be treated in isolation, since the purpose of testing is to improve health. Its use is therefore always closely linked to specific health challenges and needs to be viewed in the broader policy context in the EU and more widely. The paper is the result of extensive engagement with experts and decision makers to develop the framework, and consequently represents a wide consensus of views on how healthcare systems should respond from push and pull factors at local, national and cross-border and EU level. It contains strong views and clear recommendations springing from the convictions of patients, clinicians, academics, medicines authorities, HTA bodies, payers, the diagnostic, pharmaceutical and ICT industries, and national policy makers.

Computational Oncology in the Multi-Omics Era: State of the Art

Cancer is the quintessential complex disease. As technologies evolve faster each day, we are able to quantify the different layers of biological elements that contribute to the emergence and development of malignancies. In this multi-omics context, the use of integrative approaches is mandatory in order to gain further insights on oncological phenomena, and to move forward toward the precision medicine paradigm. In this review, we will focus on computational oncology as an integrative discipline that incorporates knowledge from the mathematical, physical, and computational fields to further the biomedical understanding of cancer. We will discuss the current roles of computation in oncology in the context of multi-omic technologies, which include: data acquisition and processing; data management in the clinical and research settings; classification, diagnosis, and prognosis; and the development of models in the research setting, including their use for therapeutic target identification. We will discuss the machine learning and network approaches as two of the most promising emerging paradigms, in computational oncology. These approaches provide a foundation on how to integrate different layers of biological description into coherent frameworks that allow advances both in the basic and clinical settings.

Imaging tumour hypoxia with oxygen-enhanced MRI and BOLD MRI

Hypoxia is known to be a poor prognostic indicator for nearly all solid tumours and also is predictive of treatment failure for radiotherapy, chemotherapy, surgery and targeted therapies. Imaging has potential to identify, spatially map and quantify tumour hypoxia prior to therapy, as well as track changes in hypoxia on treatment. At present no hypoxia imaging methods are available for routine clinical use. Research has largely focused on positron emission tomography (PET)-based techniques, but there is gathering evidence that MRI techniques may provide a practical and more readily translational alternative. In this review we focus on the potential for imaging hypoxia by measuring changes in longitudinal relaxation [R1; termed oxygen-enhanced MRI or tumour oxygenation level dependent (TOLD) MRI] and effective transverse relaxation [R2*; termed blood oxygenation level dependent (BOLD) MRI], induced by inhalation of either 100% oxygen or the radiosensitising hyperoxic gas carbogen. We explain the scientific principles behind oxygen-enhanced MRI and BOLD and discuss significant studies and their limitations. All imaging biomarkers require rigorous validation in order to translate into clinical use and the steps required to further develop oxygen-enhanced MRI and BOLD MRI into decision-making tools are discussed.

Delivering Functional Imaging on the MRI-Linac: Current Challenges and Potential Solutions

Magnetic resonance imaging (MRI) is a highly versatile imaging modality that can be used to measure features of the tumour microenvironment including cell death, proliferation, metabolism, angiogenesis, and hypoxia. Mapping and quantifying these pathophysiological features has the potential to alter the use of adaptive radiotherapy planning. Although these methods are available for use on diagnostic machines, several challenges exist for implementing these functional MRI methods on the MRI-linear accelerators (linacs). This review considers these challenges and potential solutions.

Preclinical models for precision oncology

Precision medicine approaches have revolutionized oncology. Personalized treatments require not only identification of the driving molecular alterations, but also development of targeted therapies and diagnostic tests to identify the appropriate patient populations for clinical trials and subsequent therapeutic implementation. Preclinical in vitro and in vivo models are widely used to predict efficacy of newly developed treatments. Here we discuss whether, and to what extent, preclinical models including cell lines, organoids and tumorgrafts recapitulate key features of human tumors. The potential of preclinical models to anticipate treatment efficacy and clinical benefit is also presented, using examples in different tumor types.

Managing unsolicited findings in genomics: A qualitative interview study with cancer patients

OBJECTIVE

Next-generation sequencing (NGS) is increasingly being employed in the context of personalized cancer treatment. Anticipating unsolicited findings that may arise during a NGS procedure is a key consideration; however, little is known about cancer patients' intentions, needs, and preferences concerning the return of unsolicited findings.

METHODS

A qualitative design using individual semi-structured interviews with 24 cancer patients was utilized to explore patients' decisions on whether to receive unsolicited findings from NGS. These interviews were subsequently analyzed using the constant comparative method to develop codes and themes.

RESULTS

We identified 4 interrelated themes that emerged in the context of the return of unsolicited findings. First, we describe how cancer patients expressed a strong need to control their lives. Second, we show the importance of family dynamics. Third, the NGS procedure regarding unsolicited findings is perceived as cognitively complex, and fourth, the procedure is also considered emotionally complex.

CONCLUSIONS

The results of our study contribute to a better understanding of what cancer patients consider important and what may motivate and influence them when making decisions on the disclosure of unsolicited findings following NGS. We show how Joel Feinberg's classification of autonomy may help clinicians to better understand cancer patients' desire for autonomous decision making while also acknowledging the emotional and cognitive difficulties regarding the disclosure of unsolicited findings. These insights could be helpful for clinicians to guide patients through this complex process.

Precision Medicine from a Public Health Perspective

Over the past decade, precision medicine (PM) approaches have received significant investment to create new therapies, learn more about disease processes, and potentially prevent diseases before they arise. However, in many ways, PM investments may come at the expense of existing public health measures that could have a greater impact on population health. As we tackle burgeoning public health concerns, such as obesity, and chronic diseases, such as cancer, it is not clear whether PM is aligned with public health or in conflict with its goals. We summarize the areas of promise demonstrated by PM, discuss the limitations of each of these areas from a population health perspective, and discuss how we can approach PM in a manner that is congruent with the core aims of public health.

Applying nanomedicine in maladaptive inflammation and angiogenesis

Inflammation and angiogenesis drive the development and progression of multiple devastating diseases such as atherosclerosis, cancer, rheumatoid arthritis, and inflammatory bowel disease. Though these diseases have very different phenotypic consequences, they possess several common pathophysiological features in which monocyte recruitment, macrophage polarization, and enhanced vascular permeability play critical roles. Thus, developing rational targeting strategies tailored to the different stages of the journey of monocytes, from bone marrow to local lesions, and their extravasation from the vasculature in diseased tissues will advance nanomedicine. The integration of in vivo imaging uniquely allows studying nanoparticle kinetics, accumulation, clearance, and biological activity, at levels ranging from subcellular to an entire organism, and will shed light on the fate of intravenously administered nanomedicines. We anticipate that convergence of nanomedicines, biomedical engineering, and life sciences will help to advance clinically relevant therapeutics and diagnostic agents for patients with chronic inflammatory diseases.

Imaging biomarker roadmap for cancer studies

Imaging biomarkers (IBs) are integral to the routine management of patients with cancer. IBs used daily in oncology include clinical TNM stage, objective response and left ventricular ejection fraction. Other CT, MRI, PET and ultrasonography biomarkers are used extensively in cancer research and drug development. New IBs need to be established either as useful tools for testing research hypotheses in clinical trials and research studies, or as clinical decision-making tools for use in healthcare, by crossing 'translational gaps' through validation and qualification. Important differences exist between IBs and biospecimen-derived biomarkers and, therefore, the development of IBs requires a tailored 'roadmap'. Recognizing this need, Cancer Research UK (CRUK) and the European Organisation for Research and Treatment of Cancer (EORTC) assembled experts to review, debate and summarize the challenges of IB validation and qualification. This consensus group has produced 14 key recommendations for accelerating the clinical translation of IBs, which highlight the role of parallel (rather than sequential) tracks of technical (assay) validation, biological/clinical validation and assessment of cost-effectiveness; the need for IB standardization and accreditation systems; the need to continually revisit IB precision; an alternative framework for biological/clinical validation of IBs; and the essential requirements for multicentre studies to qualify IBs for clinical use.

Genome-wide analysis of gynecologic cancer: The Cancer Genome Atlas in ovarian and endometrial cancer

Cancer typically develops due to genetic abnormalities, but a single gene abnormality cannot completely account for the onset of cancer. The Cancer Genome Atlas (CGA) project was conducted for the cross-sectional genome-wide analysis of numerous genetic abnormalities in various types of cancer. This approach has facilitated the identification of novel AT-rich interaction domain 1A gene mutations in ovarian clear cell carcinoma, frequent tumor protein 53 (TP53) gene mutations in high-grade ovarian serous carcinoma, and Kirsten rat sarcoma and B-rapidly accelerated fibrosarcoma proto-oncogene, serine/threonine kinase gene mutations in low-grade ovarian serous carcinoma. Genome-wide analysis of endometrial cancers has led to the establishment of four subgroups: Polymerase ultramutated, microsatellite instability hypermutated, genome copy-number low and genome copy-number high. These results may facilitate the improvement of the prediction of patient prognosis and therapeutic sensitivity in various types of gynecologic cancer. The enhanced use of currently available therapeutic agents and the development of novel drugs may be facilitated by the novel classification of ovarian cancer based on TP53 mutations, the efficacy of poly (ADP-ribose) polymerase inhibitors for tumors with breast cancer 1/2 mutations and the effect of phosphoinositide-3-kinase (PI3K)/mammalian target of rapamycin inhibitors for tumors with mutations in the PI3K/protein kinase B signaling pathway. Important results have been revealed by genome-wide analyses; however, the pathogenic underlying mechanisms of gynecologic cancer will require further studies and multilateral evaluation using epigenetic, transcriptomic and proteomic analyses, in addition to genomic analysis.

Patient and Other Stakeholder Engagement in Patient-Centered Outcomes Research Institute Funded Studies of Patients with Kidney Diseases

Including target populations in the design and implementation of research trials has been one response to the growing health disparities endemic to our health care system, as well as an aid to study generalizability. One type of community-based participatory research is "Patient Centered-Research", in which patient perspectives on the germane research questions and methodologies are incorporated into the study. The Patient-Centered Outcomes Research Institute (PCORI) has mandated that meaningful patient and stakeholder engagement be incorporated into all applications. As of March 2015, PCORI funded seven clinically-focused studies of patients with kidney disease. The goal of this paper is to synthesize the experiences of these studies to gain an understanding of how meaningful patient and stakeholder engagement can occur in clinical research of kidney diseases, and what the key barriers are to its implementation. Our collective experience suggests that successful implementation of a patient- and stakeholder-engaged research paradigm involves: (1) defining the roles and process for the incorporation of input; (2) identifying the particular patients and other stakeholders; (3) engaging patients and other stakeholders so they appreciate the value of their own participation and have personal investment in the research process; and (4) overcoming barriers and challenges that arise and threaten the productivity of the collaboration. It is our hope that the experiences of these studies will further interest and capacity for incorporating patient and stakeholder perspectives in research of kidney diseases.

Pilot Study of a Next-Generation Sequencing-Based Targeted Anticancer Therapy in Refractory Solid Tumors at a Korean Institution

We evaluated the preliminary efficacy and feasibility of a next-generation sequencing (NGS)-based targeted anticancer therapy in refractory solid tumors at a Korean institution. Thirty-six patients with advanced cancer underwent molecular profiling with NGS with the intent of clinical application of available matched targeted agents. Formalin-fixed paraffin-embedded (FFPE) tumors were sequenced using the Comprehensive Cancer Panel (CCP) or FoundationOne in the Clinical Laboratory Improvement Amendments-certified laboratory in the USA. Response evaluations were performed according to RECIST v1.1. Four specimens did not pass the DNA quality test and 32 specimens were successfully sequenced with CCP (n = 31) and FoundationOne (n = 1). Of the 32 sequenced patients, 10 (31.3%) were ≤40 years. Twelve patients (37.5%) had received ≥3 types of prior systemic therapies. Of 24 patients with actionable mutations, five were given genotype-matched drugs corresponding to actionable mutations: everolimus to PIK3CA mutation in parotid carcinosarcoma (partial response) and tracheal squamous cell carcinoma (stable disease; 21% reduction), sorafenib to PDGFRA mutation in auditory canal adenocarcinoma (partial response), sorafenib to BRAF mutation in microcytic adnexal carcinoma (progressive disease), and afatinib to ERBB2 mutation in esophageal adenocarcinoma (progressive disease). Nineteen of 24 patients with actionable mutations could not undergo targeted therapy based on genomic testing because of declining performance status (10/24, 41.7%), stable disease with previous treatment (5/24, 20.8%), and lack of access to targeted medication (4/24, 16.7%). NGS-based targeted therapy may be a good option in selected patients with refractory solid tumors. To pursue this strategy in Korea, lack of access to clinical-grade NGS assays and a limited number of genotype-matched targeted medications needs to be addressed and resolved.

Identification of a long non-coding RNA NR_026689 associated with lung carcinogenesis induced by NNK

Long non-coding RNAs (lncRNA) are thought to be important epigenetic regulators involved in the development of a variety of cancers. Alterations in lncRNA expression are associated with exposure to chemical carcinogens. However, it is still unclear whether lncRNA expression during lung carcinogenesis is induced by chemical carcinogens. In this study, using NNK-induced rat lung cancer model established by our previous study, we determined the lncRNA expression profiles, and an alteration in lncRNA expression was observed in lung cancer tissues and blood in the NNK treatment group. Using quantitative reverse-transcription PCR (qRT-PCR), five differentially expressed lncRNAs were further detected and validated. We identified a novel lncRNA, NR_026689, which showed increased expression in lung cancer tissues induced by NNK and the alteration of lncRNA NR_026689 was specifically observed in lung tissue. The level of NR_026689 was determined and significantly increased in rat whole blood at the 10th and 20th week after NNK treatment to evaluate it as a potential early marker for lung cancer. Together, these findings suggest that lncRNA NR_026689 may be a potential early biomarker for lung cancer and is associated with lung carcinogenesis induced by NNK.

Oxygen-Enhanced MRI Accurately Identifies, Quantifies, and Maps Tumor Hypoxia in Preclinical Cancer Models

There is a clinical need for noninvasive biomarkers of tumor hypoxia for prognostic and predictive studies, radiotherapy planning, and therapy monitoring. Oxygen-enhanced MRI (OE-MRI) is an emerging imaging technique for quantifying the spatial distribution and extent of tumor oxygen delivery in vivo. In OE-MRI, the longitudinal relaxation rate of protons (ΔR1) changes in proportion to the concentration of molecular oxygen dissolved in plasma or interstitial tissue fluid. Therefore, well-oxygenated tissues show positive ΔR1. We hypothesized that the fraction of tumor tissue refractory to oxygen challenge (lack of positive ΔR1, termed "Oxy-R fraction") would be a robust biomarker of hypoxia in models with varying vascular and hypoxic features. Here, we demonstrate that OE-MRI signals are accurate, precise, and sensitive to changes in tumor pO2 in highly vascular 786-0 renal cancer xenografts. Furthermore, we show that Oxy-R fraction can quantify the hypoxic fraction in multiple models with differing hypoxic and vascular phenotypes, when used in combination with measurements of tumor perfusion. Finally, Oxy-R fraction can detect dynamic changes in hypoxia induced by the vasomodulator agent hydralazine. In contrast, more conventional biomarkers of hypoxia (derived from blood oxygenation-level dependent MRI and dynamic contrast-enhanced MRI) did not relate to tumor hypoxia consistently. Our results show that the Oxy-R fraction accurately quantifies tumor hypoxia noninvasively and is immediately translatable to the clinic.

Prognostic Value of KIF2A and HER2-Neu Overexpression in Patients With Epithelial Ovarian Cancer

Kinesin family member 2A (KIF2A) is a member of Kinesin-13 family and involved in cell migration and cell signaling. Human epidermal growth factor receptor 2 (HER2-neu) is implicated in the development of many cancers. Both of these 2 proteins are upstream inducer of PI3K/AKT signaling pathway that plays an important role in the regulation of many cellular events including proliferation, survival, and invasion. We hypothesized that aberrant KIF2A and HER2-neu expression might be associated with aggressive behavior of epithelial ovarian cancer (EOC).To address the prognostic implications of KIF2A and HER2-neu in EOC, we assessed protein levels of KIF2A and HER2-neu in 159 ovarian and fallopian tube tissues (111 carcinomas and 48 normal ovary or fallopian tube tissues) by immunohistochemistry (IHC) analysis on tissue microarray and KIF2A mRNA levels in 35 ovarian and fallopian tube tissues (15 carcinomas and 20 normal ovary or fallopian tube tissues) by real-time PCR.We found that significantly higher KIF2A mRNA expression in EOC tumors than that in normal ovary or fallopian tube tissues. The IHC results showed that protein of KIF2A and HER2-neu was overexpressed in EOC tissues compared with normal ovary or fallopian tube tissues, and KIF2A expression level was significantly associated with lymph nodes, metastasis, ascites cells, and FIGO stage. No correlation between KIF2A and HER2-neu expression was observed. Survival analysis showed that patients with KIF2A and HER2-neu overexpression had a worse overall survival (OS) as compared to patients with low or none expression of the 2 proteins. Multivariate analysis of variance revealed that overexpression of KIF2A was an independent prognostic factor for OS.These findings indicate the important role of KIF2A in predicting EOC prognosis.

Development and validation of a scalable next-generation sequencing system for assessing relevant somatic variants in solid tumors

Next-generation sequencing (NGS) has enabled genome-wide personalized oncology efforts at centers and companies with the specialty expertise and infrastructure required to identify and prioritize actionable variants. Such approaches are not scalable, preventing widespread adoption. Likewise, most targeted NGS approaches fail to assess key relevant genomic alteration classes. To address these challenges, we predefined the catalog of relevant solid tumor somatic genome variants (gain-of-function or loss-of-function mutations, high-level copy number alterations, and gene fusions) through comprehensive bioinformatics analysis of >700,000 samples. To detect these variants, we developed the Oncomine Comprehensive Panel (OCP), an integrative NGS-based assay [compatible with < 20 ng of DNA/RNA from formalin-fixed paraffin-embedded (FFPE) tissues], coupled with an informatics pipeline to specifically identify relevant predefined variants and created a knowledge base of related potential treatments, current practice guidelines, and open clinical trials. We validated OCP using molecular standards and more than 300 FFPE tumor samples, achieving >95% accuracy for KRAS, epidermal growth factor receptor, and BRAF mutation detection as well as for ALK and TMPRSS2:ERG gene fusions. Associating positive variants with potential targeted treatments demonstrated that 6% to 42% of profiled samples (depending on cancer type) harbored alterations beyond routine molecular testing that were associated with approved or guideline-referenced therapies. As a translational research tool, OCP identified adaptive CTNNB1 amplifications/mutations in treated prostate cancers. Through predefining somatic variants in solid tumors and compiling associated potential treatment strategies, OCP represents a simplified, broadly applicable targeted NGS system with the potential to advance precision oncology efforts.

Precision medicine in the age of big data: The present and future role of large-scale unbiased sequencing in drug discovery and development

High throughput molecular and functional profiling of patients is a key driver of precision medicine. DNA and RNA characterization has been enabled at unprecedented cost and scale through rapid, disruptive progress in sequencing technology, but challenges persist in data management and interpretation. We analyze the state-of-the-art of large-scale unbiased sequencing in drug discovery and development, including technology, application, ethical, regulatory, policy and commercial considerations, and discuss issues of LUS implementation in clinical and regulatory practice.

Pharmacogenomic biomarkers as inclusion criteria in clinical trials of oncology-targeted drugs: a mapping of ClinicalTrials.gov

PURPOSE

The aim of this study was to describe pharmacogenomics-based inclusion criteria (enrichment) and the main characteristics of clinical trials involving oncology-targeted therapies.

METHODS

Clinical trials of oncology-targeted therapies approved after 2005 with pharmacogenomic testing required or recommended in their label were retrieved from a mapping of the ClinicalTrials.gov database.

RESULTS

We examined information for 12 drugs and 858 trials. Overall, 434 trials (51%) were enriched on the biomarker first mentioned in the label and 145 (17%) were enriched on another biomarker, whereas 270 trials (31%) included all patients. The median proportion of trials corresponding to both the drug's indication and drug's target was 35%. Of the 361 trials that tested drugs in another disease than the first one in the label, 219 (61%) were without enrichment and 87 (24%) were actually enriched but on another biomarker than the first one in the label.

CONCLUSION

Several drugs have been tested in trials enriched on many different biomarkers. Nonetheless, most targeted therapies have been developed only using biomarker-positive patients; therefore, exclusion of biomarker-negative patients from treatment relies on only preclinical data and on biological understanding of the disease and target.Genet Med 18 8, 796-805.

Identifying potential indicators to measure the outcome of translational cancer research: a mixed methods approach

Background

In a context where there is an increasing demand to evaluate the outcome of bio-medical research, our work aims to develop a set of indicators to measure the impact of translational cancer research. The objective of our study was to explore the scope and issues of translational research relevant to evaluation, explore the views of researchers on the evaluation of oncological translational research, and select indicators measuring the outcomes and outputs of translational research in oncology by consensus.

Methods

Semi-structured interviews amongst 23 researchers involved in translational cancer research were conducted and analysed using thematic analysis. A two-round modified Delphi survey of 35 participants with similar characteristics was then performed followed by a physical meeting. Participants rated the feasibility and validity of 60 indicators. The physical meeting was held to discuss the methodology of the new indicators.

Results

The main themes emerging from the interviews included a common definition for translational research but disagreements about the exact scope and limits of this research, the importance of multidisciplinarity and collaboration for the success of translational research, the disadvantages that translational research faces in current evaluation systems, the relative lack of pertinence of existing indicators, and propositions to measure translational cancer research in terms of clinical applications and patient outcomes. A total of 35 participants took part in the first round survey and 12 in the second round. The two-round survey helped us select a set of 18 indicators, including four that seemed to be particularly adapted to measure translational cancer research impact on health service research (number of biomarkers identified, generation of clinical guidelines, citation of research in clinical guidelines, and citation of research in public health guidelines). The feedback from participants helped refine the methodology and definition of indicators not commonly used.

Conclusion

Indicators need to be accepted by stakeholders under evaluation. This study helped the selection and refinement of indicators considered as the most relevant by researchers in translational cancer research. The feasibility and validity of those indicators will be tested in a scientometric study.

Electronic supplementary material

The online version of this article (doi:10.1186/s12961-015-0060-5) contains supplementary material, which is available to authorized users.

Overexpression of MAGE-A9 Is Predictive of Poor Prognosis in Epithelial Ovarian Cancer

The cancer testis antigen, melanoma-associated antigen A9 (MAGE-A9), is expressed in many kinds of different human cancers, and is an important target for immunotherapy. However, the clinicopathologic significance of MAGE-A9 in epithelial ovarian cancer (EOC) is unknown. In this study, real-time PCR (12 carcinomas of high FIGO stage, 12 carcinomas of low FIGO stage, and 20 normal ovary or fallopian tube tissues) and immunohistochemistry by tissue microarrays (128 carcinomas and 112 normal ovary or fallopian tube tissues, benign or borderline ovarian tumor tissues) were performed to characterize expression of MAGE-A9 in EOC. We found that significantly higher MAGE-A9 mRNA expression in EOC tumors than that in normal ovary or fallopian tube tissues (all P < 0.05). Protein expression of MAGE-A9 was significantly associated with FIGO stage, high histological grade, level of CA-125 and metastasis. Consistent with the associated poor clinicopathologic features, patients with MAGE-A9^{H (high-expressing)} tumors had a worse overall survival as compared to patients with MAGE-A9^{L (low or none-expressing)} tumors. Further studies revealed that MAGE-A9 overexpression was an independent prognostic factor for overall survival (OS). Multivariate analysis showed that patients with MAGE-A9 overexpressing tumors had extremely poor OS. These findings indicate that MAGE-A9 expression may be helpful in predicting EOC prognosis.

Cancer proteomics: developments in technology, clinical use and commercialization

In the last two decades, advances in genomic, transcriptomic and proteomic methods have enabled us to identify and classify cancers by their molecular profiles. Many anticipate that a molecular taxonomy of cancer will not only lead to more effective subtyping of cancers but also earlier diagnoses, more informative prognoses and more targeted treatments. This article reviews recent technological developments in the field of proteomics, recent discoveries in proteomic cancer biomarker research and trends in clinical use. Readers are also informed of examples of successful commercialization, and the future of proteomics in cancer diagnostics.

Next-generation clinical trials: Novel strategies to address the challenge of tumor molecular heterogeneity

The promise of 'personalized cancer care' with therapies toward specific molecular aberrations has potential to improve outcomes. However, there is recognized heterogeneity within any given tumor-type from patient to patient (inter-patient heterogeneity), and within an individual (intra-patient heterogeneity) as demonstrated by molecular evolution through space (primary tumor to metastasis) and time (after therapy). These issues have become hurdles to advancing cancer treatment outcomes with novel molecularly targeted agents. Classic trial design paradigms are challenged by heterogeneity, as they are unable to test targeted therapeutics against low frequency genomic 'oncogenic driver' aberrations with adequate power. Usual accrual difficulties to clinical trials are exacerbated by low frequencies of any given molecular driver. To address these challenges, there is need for innovative clinical trial designs and strategies implementing novel diagnostic biomarker technologies to account for inter-patient molecular diversity and scarce tissue for analysis. Importantly, there is also need for pre-defined treatment priority algorithms given numerous aberrations commonly observed within any one individual sample. Access to multiple available therapeutic agents simultaneously is crucial. Finally intra-patient heterogeneity through time may be addressed by serial biomarker assessment at the time of tumor progression. This report discusses various 'next-generation' biomarker-driven trial designs and their potentials and limitations to tackle these recognized molecular heterogeneity challenges. Regulatory hurdles, with respect to drug and companion diagnostic development and approval, are considered. Focus is on the 'Expansion Platform Design Types I and II', the latter demonstrated with a first example, 'PANGEA: Personalized Anti-Neoplastics for Gastro-Esophageal Adenocarcinoma'. Applying integral medium-throughput genomic and proteomic assays along with a practical biomarker assessment and treatment algorithm, 'PANGEA' attempts to address the problem of heterogeneity towards successful implementation of molecularly targeted therapies.

On the Road to Precision Cancer Medicine: Analysis of Genomic Biomarker Actionability in 439 Patients

Despite the increased use of molecular diagnostics, the extent to which patients who have these tests harbor potentially actionable aberrations is unclear. We retrospectively reviewed 439 patients with diverse cancers, for whom next-generation sequencing (mostly 236-gene panel) had been performed. Data pertaining to the molecular alterations identified, as well as associated treatment suggestions (on- or off-label, or experimental), were extracted from molecular diagnostic reports. Most patients (420/439; 96%) had at least one molecular alteration: 1,813 alterations (in 207 distinct genes) were identified [the majority being mutations (62%) or amplifications (29%)]. The three most common gene abnormalities were TP53 (44%), KRAS (16%), and PIK3CA (12%). The median number of alterations per patient was 3 (range, 0-16). Nineteen patients (4%) had no alterations; 48 patients (11%) had only one alteration; and 372 patients had two or more abnormalities (85%). The median number of potentially actionable anomalies per patient was 2 (range, 0-8). Most patients (393/439; 90%) had at least one potentially actionable alteration, and in all these cases the aberration could at least be targeted by an experimental drug in a clinical trial. A total of 307 patients (70%) had an alteration that was actionable with an approved drug, but in only 89 patients (20%) was the drug approved for their disease (on-label). Next-generation sequencing identified theoretically actionable aberrations in 90% of our patients. Many of the drugs are, however, experimental or would require off-label use. Strategies to address drug access for patients harboring potentially actionable mutations are needed.

Next-generation sequencing in clinical oncology: next steps towards clinical validation

Compelling evidence supports the transition of next generation sequencing (NGS) technology from a research environment into clinical practice. Before NGS technologies are fully adopted in the clinic, they should be thoroughly scrutinised for their potential as powerful diagnostic and prognostic tools. The importance placed on generating accurate NGS data, and consequently appropriate clinical interpretation, has stimulated much international discussion regarding the creation and implementation of strict guidelines and regulations for NGS clinical use. In the context of clinical oncology, NGS technologies are currently transitioning from a clinical research background into a setting where they will contribute significantly to individual patient cancer management. This paper explores the steps that have been taken, and those still required, for the transition of NGS into the clinical area, with particular emphasis placed on validation in the setting of clinical oncology.

Molecular profiling and companion diagnostics: where is personalized medicine in cancer heading?

The goal of personalized medicine is to use the right drug at the right dose - with minimal or no toxicity - for the right patient at the right time. Recent advances in understanding cell biology and pathways, and in using molecular 'omics' technologies to diagnose cancer, offer a strategic bridge to personalized medicine in cancer. Modern personalized medicine takes into account an individual's genetic makeup and disease history before developing a treatment regimen. The future of clinical oncology will be based on the use of predictive and prognostic biomarkers in patient management. Once implemented widely, personalized medicine will benefit patients and the healthcare system greatly.

'Toxgnostics': an unmet need in cancer medicine

If we were to summarize the rationale that underpins medical oncology in a Latin aphorism, it might be 'veneno ergo sum'; that is, I poison, therefore I am. The burden of chemotherapy-associated toxicity is well recognized, but we have relatively few tools that increase the precision of anticancer drug prescribing. We propose a shift in emphasis from the focussed study of polymorphisms in drug metabolic pathways in small sets of patients to broader agnostic analyses to systematically correlate germline genetic variants with adverse events in large, well-defined cancer populations. Thus, we propose the new science of 'toxgnostics' (that is, the systematic, agnostic study of genetic predictors of toxicity from anticancer therapy).

Clinical application of genetics to guide prevention and treatment of oral diseases

Dental care costs in the United States exceed $100 billion annually. Personalized medicine efforts in dentistry are driven by potentially compelling clinical utility and cost-effectiveness prospects in the major diseases of periodontitis, caries, and oral cancers. This review discusses progress and challenges identifying genetic markers and showing clinical utility in dentistry. Genome-wide association studies (GWAS) of chronic periodontitis (CP) identified no significant variants, but CDKN2BAS variants on chromosome 9 were significantly associated with aggressive periodontitis. Stratifying patients by interleukin (IL)-1 gene variants, smoking and diabetes differentiated CP prevention outcomes. Dental caries' GWAS identified significant signals in LYZL2, AJAp1, and KPNA4; and efforts are ongoing to identify genetic factors for multiple caries phenotypes. Trials of molecularly targeted therapies are in progress for oral, head, and neck squamous cell carcinomas (OHNSCC) and results have been promising but limited in their effectiveness. Current opportunities and challenges for molecular targeting for OHNSCC are discussed.

Integrated next-generation sequencing and avatar mouse models for personalized cancer treatment

BACKGROUND

Current technology permits an unbiased massive analysis of somatic genetic alterations from tumor DNA as well as the generation of individualized mouse xenografts (Avatar models). This work aimed to evaluate our experience integrating these two strategies to personalize the treatment of patients with cancer.

METHODS

We performed whole-exome sequencing analysis of 25 patients with advanced solid tumors to identify putatively actionable tumor-specific genomic alterations. Avatar models were used as an in vivo platform to test proposed treatment strategies.

RESULTS

Successful exome sequencing analyses have been obtained for 23 patients. Tumor-specific mutations and copy-number variations were identified. All samples profiled contained relevant genomic alterations. Tumor was implanted to create an Avatar model from 14 patients and 10 succeeded. Occasionally, actionable alterations such as mutations in NF1, PI3KA, and DDR2 failed to provide any benefit when a targeted drug was tested in the Avatar and, accordingly, treatment of the patients with these drugs was not effective. To date, 13 patients have received a personalized treatment and 6 achieved durable partial remissions. Prior testing of candidate treatments in Avatar models correlated with clinical response and helped to select empirical treatments in some patients with no actionable mutations.

CONCLUSION

The use of full genomic analysis for cancer care is encouraging but presents important challenges that will need to be solved for broad clinical application. Avatar models are a promising investigational platform for therapeutic decision making. While limitations still exist, this strategy should be further tested.

Protein Quantification by MRM for Biomarker Validation

In this chapter we describe how mass spectrometry-based quantitative protein measurements by multiple reaction monitoring (MRM) have opened up the opportunity for the assembly of large panels of candidate protein biomarkers that can be simultaneously validated in large clinical cohorts to identify diagnostic protein biomarker signatures. We outline a workflow in which candidate protein biomarker panels are initially assembled from multiple diverse sources of discovery data, including proteomics and transcriptomics experiments, as well as from candidates found in the literature. Subsequently, the individual candidates in these large panels may be prioritised by application of a range of bioinformatics tools to generate a refined panel for which MRM assays may be developed. We describe a process for MRM assay design and implementation, and illustrate how the data generated from these multiplexed MRM measurements of prioritised candidates may be subjected to a range of statistical tools to create robust biomarker signatures for further clinical validation in large patient sample cohorts. Through this overall approach MRM has the potential to not only support individual biomarker validation but also facilitate the development of clinically useful protein biomarker signatures.

POSTN/TGFBI-associated stromal signature predicts poor prognosis in serous epithelial ovarian cancer

OBJECTIVE

To identify molecular prognosticators and therapeutic targets for high-grade serous epithelial ovarian cancers (EOCs) using genetic analyses driven by biologic features of EOC pathogenesis.

METHODS

Ovarian tissue samples (n = 172; 122 serous EOCs, 30 other EOCs, 20 normal/benign) collected prospectively from sequential patients undergoing gynecologic surgery were analyzed using RNA expression microarrays. Samples were classified based on expression of genes with potential relevance in ovarian cancer. Gene sets were defined using Rosetta Similarity Search Tool (ROAST) and analysis of variance (ANOVA). Gene copy number variations were identified by array comparative genomic hybridization.

RESULTS

No distinct subgroups of EOC could be identified by unsupervised clustering, however, analyses based on genes correlated with periostin (POSTN) and estrogen receptor-alpha (ESR1) yielded distinct subgroups. When 95 high-grade serous EOCs were grouped by genes based on ANOVA comparing ESR1/WT1 and POSTN/TGFBI samples, overall survival (OS) was significantly shorter for 43 patients with tumors expressing genes associated with POSTN/TGFBI compared to 52 patients with tumors expressing genes associated with ESR1/WT1 (median 30 versus 49 months, respectively; P = 0.022). Several targets with therapeutic potential were identified within each subgroup. BRCA germline mutations were more frequent in the ESR1/WT1 subgroup. Proliferation-associated genes and TP53 status (mutated or wild-type) did not correlate with survival. Findings were validated using independent ovarian cancer datasets.

CONCLUSIONS

Two distinct molecular subgroups of high-grade serous EOCs based on POSTN/TGFBI and ESR1/WT1 expressions were identified with significantly different OS. Specific differentially expressed genes between these subgroups provide potential prognostic and therapeutic targets.

Implementing personalized cancer genomics in clinical trials

The recent surge in high-throughput sequencing of cancer genomes has supported an expanding molecular classification of cancer. These studies have identified putative predictive biomarkers signifying aberrant oncogene pathway activation and may provide a rationale for matching patients with molecularly targeted therapies in clinical trials. Here, we discuss some of the challenges of adapting these data for rare cancers or molecular subsets of certain cancers, which will require aligning the availability of investigational agents, rapid turnaround of clinical grade sequencing, molecular eligibility and reconsidering clinical trial design and end points.

Design and conduct of early clinical studies of two or more targeted anticancer therapies: recommendations from the task force on Methodology for the Development of Innovative Cancer Therapies

The Methodology for the Development of Innovative Cancer Therapies (MDICT) task force considered aspects of the design and conduct of early (phase I and II) studies of combinations of molecular targeted agents during their 2012 meeting. The task force defined necessary non-clinical data, such as evidence of additive or synergistic effects in multiple molecularly credentialed and validated models, and appropriate pharmacodynamic marker development. A robust hypothesis was considered critical while non-clinical pharmacokinetic studies were also considered valuable. Clinical trials should include clear objectives that will prove or disprove the hypothesis. Predictive biomarkers/classifiers should be explored in phase I studies, rather than used to select patients. Trial design should be efficient and flexible rather than based on a strict progression from phase I to II to III; researchers could consider phase I studies with an expansion cohort, Phase I/II designs or phase II studies with a safety run in. Pharmacokinetics are recommended when interactions or overlapping toxicity is expected. Pharmacodynamic evaluations should be considered especially in a subset of patients closest to the recommended dose; an attempt should be made to validate surrogate tissues to enable inclusion for all patients. Schedule and or dose should be formally explored for e.g. with a randomised or an adaptive design. Data and knowledge sharing was strongly recommended, including the creation of formal or informal consortia of laboratories with individual expertise in pathway or target based models, collaboration between companies to ensure that agents which are 'best in class' are combined, and the development of databases which will be able to inform the development of future recommendations/guidelines.

A rational free energy-based approach to understanding and targeting disease-causing missense mutations

BACKGROUND AND SIGNIFICANCE

Intellectual disability is a condition characterized by significant limitations in cognitive abilities and social/behavioral adaptive skills and is an important reason for pediatric, neurologic, and genetic referrals. Approximately 10% of protein-encoding genes on the X chromosome are implicated in intellectual disability, and the corresponding intellectual disability is termed X-linked ID (XLID). Although few mutations and a small number of families have been identified and XLID is rare, collectively the impact of XLID is significant because patients usually are unable to fully participate in society.

OBJECTIVE

To reveal the molecular mechanisms of various intellectual disabilities and to suggest small molecules which by binding to the malfunctioning protein can reduce unwanted effects.

METHODS

Using various in silico methods we reveal the molecular mechanism of XLID in cases involving proteins with known 3D structure. The 3D structures were used to predict the effect of disease-causing missense mutations on the folding free energy, conformational dynamics, hydrogen bond network and, if appropriate, protein-protein binding free energy.

RESULTS

It is shown that the vast majority of XLID mutation sites are outside the active pocket and are accessible from the water phase, thus providing the opportunity to alter their effect by binding appropriate small molecules in the vicinity of the mutation site.

CONCLUSIONS

This observation is used to demonstrate, computationally and experimentally, that a particular condition, Snyder-Robinson syndrome caused by the G56S spermine synthase mutation, might be ameliorated by small molecule binding.

Personal genomes, quantitative dynamic omics and personalized medicine

The rapid technological developments following the Human Genome Project have made possible the availability of personalized genomes. As the focus now shifts from characterizing genomes to making personalized disease associations, in combination with the availability of other omics technologies, the next big push will be not only to obtain a personalized genome, but to quantitatively follow other omics. This will include transcriptomes, proteomes, metabolomes, antibodyomes, and new emerging technologies, enabling the profiling of thousands of molecular components in individuals. Furthermore, omics profiling performed longitudinally can probe the temporal patterns associated with both molecular changes and associated physiological health and disease states. Such data necessitates the development of computational methodology to not only handle and descriptively assess such data, but also construct quantitative biological models. Here we describe the availability of personal genomes and developing omics technologies that can be brought together for personalized implementations and how these novel integrated approaches may effectively provide a precise personalized medicine that focuses on not only characterization and treatment but ultimately the prevention of disease.

Evidence-based laboratory medicine in oncology drug development: from biomarkers to diagnostics

BACKGROUND

The promise of targeted therapies in molecularly defined subsets of cancer has led to a transformation of the process of drug development in oncology. To target cancer successfully and precisely requires high-quality translational data. Such data can be generated by the use of biomarkers that answer key questions in drug development.

CONTENT

Translational data for aiding in decision-making and driving cancer drug development can be generated by systematic assessments with biomarkers. Types of biomarkers that support decisions include: pharmacodynamic assessments for selecting the best compound or dosage; assessment of early tumor response with tissue biomarkers and imaging, mutation, and other assessment strategies for patient selection; and the use of markers of organ injury to detect toxicity and improve safety. Tactics used to generate biomarker data include fit-for-purpose assay validation and real-time biomarker assessments. Successfully translated and clinically informative biomarkers can mature into novel companion diagnostic tests that expand the practice of laboratory medicine.

SUMMARY

Systematic biomarker assessments are a key component of the clinical development of targeted therapies for cancer. The success of these biomarker assessments requires applying basic principles of laboratory medicine to generate the data required to make informed decisions. Successful biomarkers can transition into diagnostic tests that expand the laboratory medicine armamentarium.

From drug discovery to biomarker-driven clinical trials in lymphoma

Over the past three decades, the pathological classification of lymphoma has substantially improved. The early Rappaport classification included a handful of subtypes that did not reflect the cell of origin and, not surprisingly, resulted in diagnostic inaccuracies. The WHO currently classifies lymphoma into 30 major distinctive types. While this classification improved the accuracy and consistency of the histological diagnosis of lymphoma, it had little impact on advancing drug development or improving the cure rate of this disease. One reason for this lack of improvement is that recent developments in cancer genomics show these histopathological subtypes to be heterogeneous. Basing treatment decisions on histopathological subtypes is inefficient as it groups different underlying molecular characteristics into one category. Such a strategy exposes many patients to potentially toxic drugs without providing benefits. The recent approval of two new cancer drugs with companion diagnostics to allow selection and treatment of patients with melanoma and non-small-cell lung cancer has raised hope that a similar approach may also expedite successful drug development in lymphoma. We review the current status of biomarker development in lymphoma, and discuss novel biomarker-directed clinical trial designs for lymphoma.

Getting personalized cancer genome analysis into the clinic: the challenges in bioinformatics

Progress in genomics has raised expectations in many fields, and particularly in personalized cancer research. The new technologies available make it possible to combine information about potential disease markers, altered function and accessible drug targets, which, coupled with pathological and medical information, will help produce more appropriate clinical decisions. The accessibility of such experimental techniques makes it all the more necessary to improve and adapt computational strategies to the new challenges. This review focuses on the critical issues associated with the standard pipeline, which includes: DNA sequencing analysis; analysis of mutations in coding regions; the study of genome rearrangements; extrapolating information on mutations to the functional and signaling level; and predicting the effects of therapies using mouse tumor models. We describe the possibilities, limitations and future challenges of current bioinformatics strategies for each of these issues. Furthermore, we emphasize the need for the collaboration between the bioinformaticians who implement the software and use the data resources, the computational biologists who develop the analytical methods, and the clinicians, the systems' end users and those ultimately responsible for taking medical decisions. Finally, the different steps in cancer genome analysis are illustrated through examples of applications in cancer genome analysis.

Challenges in the codevelopment of companion diagnostics

Therapeutics harnessing the power of personalized medicine have the potential to revolutionize healthcare. Companion diagnostics are critical to this goal and are increasingly relied upon to ensure the effective, safe development and use of a personalized therapeutic. Companion diagnostics are the focus of several recent regulatory guidance documents; the drug–diagnostic codevelopment process has become increasingly relevant and necessary. Despite this, the promise of companion diagnostics has not been fully realized and there are multiple difficulties that still need resolution. The path to codevelop a successful companion diagnostic with its complementary drug is complex, fragmented and fraught with several challenges. In this article, we discuss the logistic, strategic business, regulatory and financial challenges involved in drug-companion diagnostic codevelopment.

Cancer/testis antigens and urological malignancies

Cancer/testis antigens (CTAs) are a group of tumour-associated antigens (TAAs) that display normal expression in the adult testis--an immune-privileged organ--but aberrant expression in several types of cancers, particularly in advanced cancers with stem cell-like characteristics. There has been an explosion in CTA-based research since CTAs were first identified in 1991 and MAGE-1 was shown to elicit an autologous cytotoxic T-lymphocyte (CTL) response in a patient with melanoma. The resulting data have not only highlighted a role for CTAs in tumorigenesis, but have also underscored the translational potential of these antigens for detecting and treating many types of cancers. Studies that have investigated the use of CTAs for the clinical management of urological malignancies indicate that these TAAs have potential roles as novel biomarkers, with increased specificity and sensitivity compared to those currently used in the clinic, and therapeutic targets for cancer immunotherapy. Increasing evidence supports the utilization of these promising tools for urological indications.

Imaging: strategies, controversies, and opportunities

At a Clinical and Translational Cancer Research Think Tank meeting sponsored by the American Association for Cancer Research in 2010, one of the breakout groups focused on new technologies and imaging. The discussions emphasized new opportunities in translational imaging and its role in the future, rather than established techniques that are currently in clinical practice. New imaging methods under development are changing the approach of imaging science from a focus on the anatomic description of disease to a focus on the molecular basis of disease. Broadly referred to as molecular imaging, these new strategies directly embrace the incorporation of cell and molecular biology concepts and techniques into image generation and can involve the introduction of genes into cells with the explicit intent to image the end products of gene expression with external imaging devices. These new methods hold the promise of providing clinicians with (i) robust linkages between cell and animal models and clinical trials, (ii) in vivo biomarkers that can be measured repeatedly and sequentially over time to observe dynamic disease processes and responses to treatment, and (iii) tools for preselection and patient population enrichment in phase II and III trials to improve outcomes and better direct treatment. These strategies provide real-time pharmacodynamic parameters and can be powerful tools to monitor therapeutic effects in a spatially and tissue-specific manner, which may reduce cost during drug development, because pharmacodynamic studies in animals can inform clinical trials and accelerate the translation process. The Imaging Response Assessment Team (IRAT) program serves as an example of how imaging techniques can be incorporated into clinical trials. IRATs work to advance the role of imaging in assessment of response to therapy and to increase the application of quantitative anatomic, functional, and molecular imaging endpoints in clinical trials, and imaging strategies that will lead to individualized patient care.