Translational research in oncology: key bottlenecks and new paradigms

Integrating frontiers: a holistic, quantum and evolutionary approach to conquering cancer through systems biology and multidisciplinary synergy

Cancer therapy is facing increasingly significant challenges, marked by a wide range of techniques and research efforts centered around somatic mutations, precision oncology, and the vast amount of big data. Despite this abundance of information, the quest to cure cancer often seems more elusive, with the "war on cancer" yet to deliver a definitive victory. A particularly pressing issue is the development of tumor treatment resistance, highlighting the urgent need for innovative approaches. Evolutionary, Quantum Biology and System Biology offer a promising framework for advancing experimental cancer research. By integrating theoretical studies, translational methods, and flexible multidisciplinary clinical research, there's potential to enhance current treatment strategies and improve outcomes for cancer patients. Establishing stronger links between evolutionary, quantum, entropy and chaos principles and oncology could lead to more effective treatments that leverage an understanding of the tumor's evolutionary dynamics, paving the way for novel methods to control and mitigate cancer. Achieving these objectives necessitates a commitment to multidisciplinary and interprofessional collaboration at the heart of both research and clinical endeavors in oncology. This entails dismantling silos between disciplines, encouraging open communication and data sharing, and integrating diverse viewpoints and expertise from the outset of research projects. Being receptive to new scientific discoveries and responsive to how patients react to treatments is also crucial. Such strategies are key to keeping the field of oncology at the forefront of effective cancer management, ensuring patients receive the most personalized and effective care. Ultimately, this approach aims to push the boundaries of cancer understanding, treating it as a manageable chronic condition, aiming to extend life expectancy and enhance patient quality of life.

Frameworks for Proof-of-Concept Clinical Trials of Interventions That Target Fundamental Aging Processes

Therapies targeted at fundamental processes of aging may hold great promise for enhancing the health of a wide population by delaying or preventing a range of age-related diseases and conditions—a concept dubbed the “geroscience hypothesis.” Early, proof-of-concept clinical trials will be a key step in the translation of therapies emerging from model organism and preclinical studies into clinical practice. This article summarizes the outcomes of an international meeting partly funded through the NIH R24 Geroscience Network, whose purpose was to generate concepts and frameworks for early, proof-of-concept clinical trials for therapeutic interventions that target fundamental processes of aging. The goals of proof-of-concept trials include generating preliminary signals of efficacy in an aging-related disease or outcome that will reduce the risk of conducting larger trials, contributing data and biological samples to support larger-scale research by strategic networks, and furthering a dialogue with regulatory agencies on appropriate registration indications. We describe three frameworks for proof-of-concept trials that target age-related chronic diseases, geriatric syndromes, or resilience to stressors. We propose strategic infrastructure and shared resources that could accelerate development of therapies that target fundamental aging processes.

Biomarkers and receptor targeted therapies reduce clinical trial risk in non-small-cell lung cancer

INTRODUCTION

This study analyzed the risk of clinical trial failure during non-small-cell lung cancer (NSCLC) drug development between 1998 and January 2012. We also looked for factors that impacted clinical trial risk in NSCLC.

METHODS

NSCLC drug development was investigated using trial disclosures from http://www.clinicaltrials.gov and other publically available resources. Compounds were excluded from the analysis if they had begun phase I clinical testing before 1998, did not use treatment-relevant endpoints, or if they did not have a completed phase I trial in NSCLC. Analysis was conducted in regard to treatment indication, compound classification, and mechanism of action.

RESULTS

Six hundred seventy-six clinical trials that included 199 unique compounds met our inclusion criteria. The likelihood, or cumulative clinical trial success rate, that a new drug would pass all phases of clinical testing and be approved was found to be 11%, which is less than industry aggregate rates. Over half of the biomarkers used in NSCLC have not yet been approved by the Food and Drug Administration in any indication. Biomarker targeted therapies (62%) and receptor targeted therapies (31%) were found to have the highest success rates. The risk-adjusted cost for NSCLC clinical drug development was calculated to be U.S. $1.89 billion.

CONCLUSION

Biomarker use alone in this indication resulted in a sixfold increase in clinical trial success whereas receptor targeted therapies did so by almost threefold. Physicians who enroll patients in NSCLC trials should prioritize their participation in clinical trial programs that use biomarkers and receptor targeted therapies.

The unpluggable in pursuit of the undruggable: tackling the dark matter of the cancer therapeutics universe

The notion that targeted drugs can unplug gain-of-function tumor pathways has revitalized pharmaceutical research, but the survival benefits of this strategy have so far proven modest. A weakness of oncogene-blocking approaches is that they do not address the problem of cancer progression as selected by the recessive phenotypes of genetic instability and apoptotic resistance which in turn arise from loss-of-function – i.e., undruggable – defects of caretaker (e.g., BRCA, MLH1) or gatekeeper (e.g., TP53, PTEN) suppressor genes. Genetic instability ensures that rapid cell kill is balanced by rapid selection for apoptotic resistance and hence for metastasis, casting doubt on the assumption that cytotoxicity (“response”) remains the best way to identify survival-enhancing drugs. In the absence of gene therapy, it is proposed here that caretaker-defective (high-instability) tumors may be best treated with low-lethality drugs inducing replicative (RAS-RAF-ERK) arrest or dormancy, causing “stable disease” rather than tumorilytic remission. Gatekeeper-defective (death-resistant) tumors, on the other hand, may be best managed by combining survival (PI3K-AKT-mTOR) pathway blockade with metronomic or sequential pro-apoptotic drugs.

SPARSE INTEGRATIVE CLUSTERING OF MULTIPLE OMICS DATA SETS

High resolution microarrays and second-generation sequencing platforms are powerful tools to investigate genome-wide alterations in DNA copy number, methylation, and gene expression associated with a disease. An integrated genomic profiling approach measuring multiple omics data types simultaneously in the same set of biological samples would render an integrated data resolution that would not be available with any single data type. In this study, we use penalized latent variable regression methods for joint modeling of multiple omics data types to identify common latent variables that can be used to cluster patient samples into biologically and clinically relevant disease subtypes. We consider lasso (Tibshirani, 1996), elastic net (Zou and Hastie, 2005), and fused lasso (Tibshirani et al., 2005) methods to induce sparsity in the coefficient vectors, revealing important genomic features that have significant contributions to the latent variables. An iterative ridge regression is used to compute the sparse coefficient vectors. In model selection, a uniform design (Fang and Wang, 1994) is used to seek "experimental" points that scattered uniformly across the search domain for efficient sampling of tuning parameter combinations. We compared our method to sparse singular value decomposition (SVD) and penalized Gaussian mixture model (GMM) using both real and simulated data sets. The proposed method is applied to integrate genomic, epigenomic, and transcriptomic data for subtype analysis in breast and lung cancer data sets.

Model of translational cancer research in multiple myeloma

Recently, intensive laboratory and preclinical studies have identified and validated therapeutic molecular targets in multiple myeloma (MM). The introduction of novel agents such as the proteasome inhibitor bortezomib and the immunomodulatory drugs thalidomide and lenalidomide, which were rapidly translated from preclinical studies at the Dana-Farber Cancer Institute into clinical trials, has changed the treatment paradigm and markedly extended overall survival; MM has therefore become a remarkable example of translational cancer research in new drug development. In this article, with the aim of determining the key factors underlying success in translational research, we focus on our studies of MM at Dana-Farber Cancer Institute as well as at our institutes. The identification of these key factors will help to promote translational cancer research not only in MM but also in other hematologic malignancies and solid tumors, to develop novel therapies, to overcome drug resistance, and to thereby improve the prognosis of cancer patients. (Cancer Sci, doi: 10.1111/j.1349-7006.2012.02384.x, 2012)

Estimating the order of mutations during tumorigenesis from tumor genome sequencing data

MOTIVATION

Tumors are thought to develop and evolve through a sequence of genetic and epigenetic somatic alterations to progenitor cells. Early stages of human tumorigenesis are hidden from view. Here, we develop a method for inferring some aspects of the order of mutational events during tumorigenesis based on genome sequencing data for a set of tumors. This method does not assume that the sequence of driver alterations is the same for each tumor, but enables the degree of similarity or difference in the sequence to be evaluated.

RESULTS

To evaluate the new method, we applied it to colon cancer tumor sequencing data and the results are consistent with the multi-step tumorigenesis model previously developed based on comparing stages of cancer. We then applied the new method to DNA sequencing data for a set of lung cancers. The model may be a useful tool for better understanding the process of tumorigenesis.

AVAILABILITY

The software is available at: http://linus.nci.nih.gov/Data/YounA/OrderMutation.zip.

Anticancer drug development from traditional cytotoxic to targeted therapies: evidence of shorter drug research and development time, and shorter drug lag in Japan

WHAT IS KNOWN AND OBJECTIVE

Concern about the drug lag, the delay in marketing approval between one country and another, for anticancer drugs has increased in Japan. Although a number of studies have investigated the drug lag, none has investigated it in relation to the transition of anticancer therapy from traditional cytotoxic drugs to molecularly targeted agents. Our aim was to investigate current trend in oncology drug lag between the US and Japan and identify oncology drugs approved in only one of the two countries.

METHODS

Publicly and commercially available data sources were used to identify drugs approved in the US and Japan as of 31 December 2010 and the data used to calculate the drug lag for individual drugs.

RESULTS AND DISCUSSION

Fifty-one drugs were approved in both the US and Japan, whereas 34 and 19 drugs were approved only in the US or Japan, respectively. Of the 19 drugs approved only in Japan, 12 had not been subject to development for a cancer indication in the US, and all were approved before 1996 in Japan. Of the 34 drugs approved only in the US, 20 had not been subject to development in Japan, and none was in the top 25 by annual US anticancer drug-class sales. For drugs approved in both countries, the mean approval lag of the molecularly targeted drugs (MTDs) was significantly shorter than that of the non-molecularly targeted drugs (non-MTDs) (3·3 vs. 5·4 years). Further, mean R&D time of the MTDs was significantly shorter than that of non-MTDs (10·0 vs. 13·7 years). The price of MTDs had increased on average by 6·6% annually in the US, whereas it had decreased on average by 4·3% biyearly in Japan.

WHAT IS NEW AND CONCLUSION

The emergence of new molecularly targeted agents has contributed to reducing the approval lag, most likely due to improvements in R&D strategy.