Does biomarker use in oncology improve clinical trial failure risk? A large-scale analysis.

PURPOSE: To date there has not been an extensive analysis of the outcomes of biomarker use in oncology. METHODS: Data were pooled across four indications in oncology drawing upon trial outcomes from www.clinicaltrials.gov: breast cancer, non-small cell lung cancer (NSCLC), melanoma and colorectal cancer from 1998 to 2017. We compared the likelihood drugs would progress through the stages of clinical trial testing to approval based on biomarker status. This was done with multi-state Markov models, tools that describe the stochastic process in which subjects move among a finite number of states. RESULTS: Over 10000 trials were screened, which yielded 745 drugs. The inclusion of biomarker status as a covariate significantly improved the fit of the Markov model in describing the drug trajectories through clinical trial testing stages. Hazard ratios based on the Markov models revealed the likelihood of drug approval with biomarkers having nearly a fivefold increase for all indications combined. A 12, 8 and 7-fold hazard ratio was observed for breast cancer, melanoma and NSCLC, respectively. Markov models with exploratory biomarkers outperformed Markov models with no biomarkers. CONCLUSION: This is the first systematic statistical evidence that biomarkers clearly increase clinical trial success rates in three different indications in oncology. Also, exploratory biomarkers, long before they are properly validated, appear to improve success rates in oncology. This supports early and aggressive adoption of biomarkers in oncology clinical trials.

Biomarker testing in oncology - Requirements for organizing external quality assessment programs to improve the performance of laboratory testing: revision of an expert opinion paper on behalf of IQNPath ABSL.

In personalized medicine, predictive biomarker testing is the basis for an appropriate choice of therapy for patients with cancer. An important tool for laboratories to ensure accurate results is participation in external quality assurance (EQA) programs. Several providers offer predictive EQA programs for different cancer types, test methods, and sample types. In 2013, a guideline was published on the requirements for organizing high-quality EQA programs in molecular pathology. Now, after six years, steps were taken to further harmonize these EQA programs as an initiative by IQNPath ABSL, an umbrella organization founded by various EQA providers. This revision is based on current knowledge, adds recommendations for programs developed for predictive biomarkers by in situ methodologies (immunohistochemistry and in situ hybridization), and emphasized transparency and an evidence-based approach. In addition, this updated version also has the aim to give an overview of current practices from various EQA providers.

Study protocol: Whole genome sequencing Implementation in standard Diagnostics for Every cancer patient (WIDE).

BACKGROUND: 'Precision oncology' can ensure the best suitable treatment at the right time by tailoring treatment towards individual patient and comprehensive tumour characteristics. In current molecular pathology, diagnostic tests which are part of the standard of care (SOC) only cover a limited part of the spectrum of genomic changes, and often are performed in an iterative way. This occurs at the expense of valuable patient time, available tissue sample, and interferes with 'first time right' treatment decisions. Whole Genome Sequencing (WGS) captures a near complete view of genomic characteristics of a tumour in a single test. Moreover, WGS facilitates faster implementation of new treatment relevant biomarkers. At present, WGS mainly has been applied in study settings, but its performance in a routine diagnostic setting remains to be evaluated. The WIDE study aims to investigate the feasibility and validity of WGS-based diagnostics in clinical practice. METHODS: 1200 consecutive patients in a single comprehensive cancer centre with (suspicion of) a metastasized solid tumour will be enrolled with the intention to analyse tumour tissue with WGS, in parallel to SOC diagnostics. Primary endpoints are (1) feasibility of implementation of WGS-based diagnostics into routine clinical care and (2) clinical validation of WGS by comparing identification of treatment-relevant variants between WGS and SOC molecular diagnostics. Secondary endpoints entail (1) added clinical value in terms of additional treatment options and (2) cost-effectiveness of WGS compared to SOC diagnostics through a Health Technology Assessment (HTA) analysis. Furthermore, the (3) perceived impact of WGS-based diagnostics on clinical decision making will be evaluated through questionnaires. The number of patients included in (experimental) therapies initiated based on SOC or WGS diagnostics will be reported with at least 3 months follow-up. The clinical efficacy is beyond the scope of WIDE. Key performance indicators will be evaluated after every 200 patients enrolled, and procedures optimized accordingly, to continuously improve the diagnostic performance of WGS in a routine clinical setting. DISCUSSION: WIDE will yield the optimal conditions under which WGS can be implemented in a routine molecular diagnostics setting and establish the position of WGS compared to SOC diagnostics in routine clinical care.

Assessment of lipid load in tumor-infiltrating Tregs by flow cytometry.

Regulatory T cells (Tregs), expressing the transcription factor Foxp3, are defined as immunosuppressive cells able to modulate a variety of immune cells in order to avoid unwanted and excessive immune responses; however, in the tumor context, Treg function contribute to inhibit immune surveillance, thus promoting cancer progression. In tumor microenvironment, where the availability of metabolic resources is strongly limited, Tregs are expanded and may exploit different metabolic routes to achieve a metabolic advantage, prevailing over effector cells. In this context an important role of lipid metabolism has been described thanks to the possibility to evaluate the intracellular lipid content selectively in tumor-infiltrating Tregs (TUM-Tregs). Taking into account the heterogeneous and complex build of tumor mass, we set-up a combined protocol that optimizes tumor-infiltrating lymphocytes (TIL) extraction from the tissue through a Percoll density gradient, and assesses ex vivo the lipid load in whole TUM-Treg population, evaluating by flow cytometry the incorporation of an intensely fluorescent lipophilic fluorophore able to specifically stain neutral lipids. This method provides an important advantage compared to the traditional technique based on microscopy, whose lipid level evaluation is limited to a tissue section, and hence may not be representative of the entire population.

Impact of the biomarker enrichment strategy in drug development.

INTRODUCTION: Recently, new oncology therapies were developed using a biomarker for patient selection. In the era of cancer genomics, this paradigm is expected to increase. Most cytotoxic chemotherapies and other oncological treatments were historically approved without a biomarker. However, this strategy seems to be less efficient. We reviewed the biomarker-based strategy and its impact in cancer drug development. AREAS COVERED: Oncology drugs approval rates are low and most of the drugs that failed to be approved were in late stages of development. In addition to that, attrition rates are high. The use of biomarkers in drug development has shown higher response rates, longer progression-free survival rates and even higher overall survival rates. Hence, the biomarker-based strategy seems to be associated with more successful drug programs, including a shorter timeline and higher likelihood of success. EXPERT OPINION: Even though the development of biomarker-driven strategies is promising, there are some challenges surrounding this field of study, such as reducing the cost of drug development, enhancing the technique of biomarkers identification (aiming more specific biomarkers and considering tumor heterogeneity) and exploring the role of next-generation sequencing tests in drug development. Also, collaboration between clinicians, scientists and regulatory agencies is fundamental.

Residual Structures and Transient Long-Range Interactions of p53 Transactivation Domain: Assessment of Explicit Solvent Protein Force Fields.

Molecular dynamics simulations using physics-based atomistic force fields have been increasingly used to characterize the heterogeneous structural ensembles of intrinsically disordered proteins (IDPs). To evaluate the accuracy of the latest atomistic explicit-solvent force fields in modeling larger IDPs with nontrivial structural features, we focus on the 61-residue N-terminal transactivation domain (TAD) of tumor suppressor p53, an important protein in cancer biology that has been extensively studied, and abundant experimental data is available for evaluation of simulated ensembles. We performed extensive replica exchange with solute tempering simulations, in excess of 1.0 µs/replica, to generate disordered structural ensembles of p53-TAD using six latest explicit solvent protein force fields. Multiple local and long-range structural properties, including chain dimension, residual secondary structures, and transient long-range contacts, were analyzed and compared against available experimental data. The results show that IDPs such as p53-TAD remain highly challenging for atomistic simulations due to conformational complexity and difficulty in achieving adequate convergence. Structural ensembles of p53-TAD generated using various force fields differ significantly from each other. The a99SB-disp force field demonstrates the best agreement with experimental data at all levels and proves to be suitable for simulating unbound p53-TAD and how its conformational properties may be modulated by phosphorylation and other cellular signals or cancer-associated mutations. Feasibility of such detailed structural characterization is a key step toward establishing the sequence-disordered ensemble-function-disease relationship of p53 and other biologically important IDPs.

Assessment of programmed death-ligand 1 expression and tumor-associated immune cells in pediatric cancer tissues.

BACKGROUND: Programmed death 1 (PD-1) signaling in the tumor microenvironment dampens immune responses to cancer, and blocking this axis induces antitumor effects in several malignancies. Clinical studies of PD-1 blockade are only now being initiated in pediatric patients, and little is known regarding programmed death-ligand 1 (PD-L1) expression in common childhood cancers. The authors characterized PD-L1 expression and tumor-associated immune cells (TAICs) (lymphocytes and macrophages) in common pediatric cancers. METHODS: Whole slide sections and tissue microarrays were evaluated by immunohistochemistry for PD-L1 expression and for the presence of TAICs. TAICs were also screened for PD-L1 expression. RESULTS: Thirty-nine of 451 evaluable tumors (9%) expressed PD-L1 in at least 1% of tumor cells. The highest frequency histotypes comprised Burkitt lymphoma (80%; 8 of 10 tumors), glioblastoma multiforme (36%; 5 of 14 tumors), and neuroblastoma (14%; 17 of 118 tumors). PD-L1 staining was associated with inferior survival among patients with neuroblastoma (P = .004). Seventy-four percent of tumors contained lymphocytes and/or macrophages. Macrophages were significantly more likely to be identified in PD-L1-positive versus PD-L1-negative tumors (P < .001). CONCLUSIONS: A subset of diagnostic pediatric cancers exhibit PD-L1 expression, whereas a much larger fraction demonstrates infiltration with tumor-associated lymphocytes. PD-L1 expression may be a biomarker for poor outcome in neuroblastoma. Further preclinical and clinical investigation will define the predictive nature of PD-L1 expression in childhood cancers both at diagnosis and after exposure to chemoradiotherapy. Cancer 2017;123:3807-3815. © 2017 American Cancer Society.

Can we use high precision metal isotope analysis to improve our understanding of cancer?

High precision natural isotope analyses are widely used in geosciences to trace elemental transport pathways. The use of this analytical tool is increasing in nutritional and disease-related research. In recent months, a number of groups have shown the potential this technique has in providing new observations for various cancers when applied to trace metal metabolism. The deconvolution of isotopic signatures, however, relies on mathematical models and geochemical data, which are not representative of the system under investigation. In addition to relevant biochemical studies of protein-metal isotopic interactions, technological development both in terms of sample throughput and detection sensitivity of these elements is now needed to translate this novel approach into a mainstream analytical tool. Following this, essential background healthy population studies must be performed, alongside observational, cross-sectional disease-based studies. Only then can the sensitivity and specificity of isotopic analyses be tested alongside currently employed methods, and important questions such as the influence of cancer heterogeneity and disease stage on isotopic signatures be addressed.

The first protocol of stable isotope ratio assessment in tumor tissues based on original research.

Thanks to proteomics and metabolomics, for the past several years there has been a real explosion of information on the biology of cancer, which has been achieved by spectroscopic methods, including mass spectrometry. These modern techniques can provide answers to key questions about tissue structure and mechanisms of its pathological changes. However, despite the thousands of spectroscopic studies in medicine, there is no consensus on issues ranging from the choice of research tools, acquisition and preparation of test material to the interpretation and validation of the results, which greatly reduces the possibility of transforming the achieved knowledge to progress in the treatment of individual patients. The aim of this study was to verify the utility of isotope ratio mass spectrometry in the evaluation of tumor tissues. Based on experimentation on animal tissues and human neoplasms, the first protocol of stable isotope ratio assessment of carbon and nitrogen isotopes in tumor tissues was established.

Identification of Genomic Aberrations in Cancer Subclones from Heterogeneous Tumor Samples.

Tumor samples are usually heterogeneous, containing admixture of more than one kind of tumor subclones. Studies of genomic aberrations from heterogeneous tumor data are hindered by the mixed signal of tumor subclone cells. Most of the existing algorithms cannot distinguish contributions of different subclones from the measured single nucleotide polymorphism (SNP) array signals, which may cause erroneous estimation of genomic aberrations. Here, we have introduced a computational method, Cancer Heterogeneity Analysis from SNP-array Experiments (CHASE), to automatically detect subclone proportions and genomic aberrations from heterogeneous tumor samples. Our method is based on HMM, and incorporates EM algorithm to build a statistical model for modeling mixed signal of multiple tumor subclones. We tested the proposed approach on simulated datasets and two real datasets, and the results show that the proposed method can efficiently estimate tumor subclone proportions and recovery the genomic aberrations.

An Optimized Informatics Pipeline for Mass Spectrometry-Based Peptidomics.

The comprehensive MS analysis of the peptidome, the intracellular and intercellular products of protein degradation, has the potential to provide novel insights on endogenous proteolytic processing and its utility in disease diagnosis and prognosis. Along with the advances in MS instrumentation and related platforms, a plethora of proteomics data analysis tools have been applied for direct use in peptidomics; however, an evaluation of the currently available informatics pipelines for peptidomics data analysis has yet to be reported. In this study, we began by evaluating the results of several popular MS/MS database search engines, including MS-GF+, SEQUEST, and MS-Align+, for peptidomics data analysis, followed by identification and label-free quantification using the well-established accurate mass and time (AMT) tag and newly developed informed quantification (IQ) approaches, both based on direct LC-MS analysis. Our results demonstrated that MS-GF+ outperformed both SEQUEST and MS-Align+ in identifying peptidome peptides. Using a database established from MS-GF+ peptide identifications, both the AMT tag and IQ approaches provided significantly deeper peptidome coverage and less missing data for each individual data set than the MS/MS methods, while achieving robust label-free quantification. Besides having an excellent correlation with the AMT tag quantification results, IQ also provided slightly higher peptidome coverage. Taken together, we propose an optimized informatics pipeline combining MS-GF+ for initial database searching with IQ (or AMT tag) approaches for identification and label-free quantification for high-throughput, comprehensive, and quantitative peptidomics analysis. Graphical Abstract ᅟ.

New non-invasive safe, quick, economical method of detecting various cancers was found using QRS complex or rising part of T-wave of recorded ECGs. Cancers can be screened along with their biochemical parameters & therapeutic effects of any cancer treatments can be evaluated using recorded ECGs of the same individual.

There are many methods of detecting cancers including detection of cancer markers by blood test, (which is invasive, time consuming and relatively expensive), detection of cancers by non-invasive methods such as X-Ray, CT scan, and MRI & PET Scan (which are non-invasive and quick but very expensive). Our research was performed to develop new non-invasive, safe, quick economical method of detecting cancers and the 1st author already developed for clinically important non-invasive new methods including early stage of present method using his method of localizing accurate organ representation areas of face, eyebrows, upper lip, lower lip, surface and dorsal part of the tongue, surface backs, and palm side of the hands. This accurate localization of the organ representation area of the different parts of the body was performed using electromagnetic field resonance phenomenon between 2 identical molecules or tissues based on our US patented non-invasive method in 1993. Since year 2000, we developed the following non-invasive diagnostic methods that can be quickly identified by the patented simple non-invasive method without using expensive or bulky instrument at any office or field where there is no electricity or instrument available. The following are examples of non-invasive quick method of diagnosis and treatment of cancers using different approaches: 1) Soft red laser beam scanning of different parts of body; 2) By speaking voice; 3) Visible and invisible characteristic abnormalities on different organ representation areas of the different parts of the body, and 4) Mouth, Hand, and Foot Writings of both right and left side of the body. As a consequence of our latest research, we were able to develop a simple method of detecting cancer from existing recorded electrocardiograms. In this article, we are going to describe the method and result of clinical applications on many different cancers of different organs including lung, esophagus, breast, stomach, colon, uterus, ovary, prostate gland, as well as common bone marrow related malignancies such as Hodgkin's Lymphoma, Non-Hodgkin's Lymphoma, Multiple Myeloma as well as Leukemia.

A nano-patterned self assembled monolayer (SAM) rutile titania cancer chip for rapid, low cost, highly sensitive, direct cancer analysis in MALDI-MS.

We developed a cancer chip by nano-patterning a highly sensitive SAM titanium surface capable of capturing and sensing concentrations as low as 10 cancer cells/mL from the environment by Matrix Assisted Laser Desorption and Ionization Time of Flight Mass Spectrometry (MALDI-TOF MS). The current approach evades any form of pretreatment and sample preparation processes; it is time saving and does not require the (expensive) conventional MALDI target plate. The home made aluminium (Al) target holder cost, on which we loaded the cancer chips for MALDI-TOF MS analysis, is about 60 USD. While the conventional stainless steel MALDI target plate is more than 700 USD. The SAM surface was an effective platform leading to on-chip direct MALDI-MS detection of cancer cells. We compared the functionality of this chip with the unmodified titanium surfaces and thermally oxidized (TO) titanium surfaces. The lowest detectable concentration of the TO chip was 10(3) cells/mL, while the lowest detectable concentration of the control or unmodified titanium chips was 10(6) cells/mL. Compared to the control surface, the SAM cancer chip showed 100,000 times of enhanced sensitivity and compared with the TO chip, 1000 times of increased sensitivity. The high sensitivity of the SAM surfaces is attributed to the presence of the rutile SAM, surface roughness and surface wettability as confirmed by AFM, XRD, contact angle microscope and FE-SEM. This study opens a new avenue for the potent application of the SAM cancer chip for direct cancer diagnosis by MALDI-TOF MS in the near future.

Vision 20/20: the role of Raman spectroscopy in early stage cancer detection and feasibility for application in radiation therapy response assessment.

Raman spectroscopy is an optical technique capable of identifying chemical constituents of a sample by their unique set of molecular vibrations. Research on the applicability of Raman spectroscopy in the differentiation of cancerous versus normal tissues has been ongoing for many years, and has yielded successful results in the context of prostate, breast, brain, skin, and head and neck cancers as well as pediatric tumors. Recently, much effort has been invested on developing noninvasive "Raman" probes to provide real-time diagnosis of potentially cancerous tumors. In this regard, it is feasible that the Raman technique might one day be used to provide rapid, minimally invasive real-time diagnosis of tumors in patients. Raman spectroscopy is relatively new to the field of radiation therapy. Recent work involving cell lines has shown that the Raman technique is able to identify proteins and other markers affected by radiation therapy. Although this work is preliminary, one could ask whether or not the Raman technique might be used to identify molecular markers that predict radiation response. This paper provides a brief review of Raman spectroscopic investigations in cancer detection, benefits and limitations of this method, advances in instrument development, and also preliminary studies related to the application of this technology in radiation therapy response assessment.

Opportunities and challenges in the era of molecularly targeted agents and radiation therapy.

The first annual workshop for preclinical and clinical development of radiosensitizers took place at the National Cancer Institute on August 8-9, 2012. Radiotherapy is one of the most commonly applied and effective oncologic treatments for solid tumors. It is well recognized that improved clinical efficacy of radiotherapy would make a substantive impact in clinical practice and patient outcomes. Advances in genomic technologies and high-throughput drug discovery platforms have brought a revolution in cancer treatment by providing molecularly targeted agents for various cancers. Development of predictive biomarkers directed toward specific subsets of cancers has ushered in a new era of personalized therapeutics. The field of radiation oncology stands to gain substantial benefit from these advances given the concerted effort to integrate this progress into radiation therapy. This workshop brought together expert clinicians and scientists working in various disease sites to identify the exciting opportunities and expected challenges in the development of molecularly targeted agents in combination with radiation therapy.

Estimation of neoplasia-related biological parameters through modeling and sensitivity analysis of optical molecular imaging data.

We combine system's biology approaches with in vivo optical molecular imaging of epithelial neoplasia for estimating disease-specific biological parameters. Molecular imaging measures and maps the dynamic optical effects, generated by the topical application of acetic acid diluted solution. The dynamic characteristics of the in vivo measured optical signal are governed by the epithelial transport effects of the biomarker. Nine biological parameters, both structural and functional, have been identified to be potentially correlated with the neoplasia growth and to be manifested to the measured data in a convoluted manner. A compartmental model of the cervical neoplastic epithelium has been developed, which predicts the dynamic optical effects in all possible parameter value combinations. We have performed global sensitivity analysis for the purpose of identifying the subset of the input parameters that are the key determinants of the model's output. Finally, we have for the first time shown that it is possible to estimate, from in vivo measured dynamic optical data, the following neoplasia related parameters: number of neoplastic layers, intracellular and extracellular space dimensions, functionality of tight junctions, and extracellular pH. These findings have been (in part) validated with optical data and biopsies obtained from 30 women with cervical neoplasia.

Cancer imaging research looks ahead.

Innovative approaches in cancer imaging supported by the National Cancer Institute's $160-million Cancer Imaging Program will give researchers new tools and clinicians better diagnostic and treatment options.

Decision tree and ensemble learning algorithms with their applications in bioinformatics.

Machine learning approaches have wide applications in bioinformatics, and decision tree is one of the successful approaches applied in this field. In this chapter, we briefly review decision tree and related ensemble algorithms and show the successful applications of such approaches on solving biological problems. We hope that by learning the algorithms of decision trees and ensemble classifiers, biologists can get the basic ideas of how machine learning algorithms work. On the other hand, by being exposed to the applications of decision trees and ensemble algorithms in bioinformatics, computer scientists can get better ideas of which bioinformatics topics they may work on in their future research directions. We aim to provide a platform to bridge the gap between biologists and computer scientists.