Cold Spring Harbor Perspectives in Medicine
ISSN / EISSN : 2157-1422 / 2157-1422
Published by: Cold Spring Harbor Laboratory (10.1101)
Total articles ≅ 1,073
Latest articles in this journal
Published: 13 September 2021
Cold Spring Harbor Perspectives in Medicine; https://doi.org/10.1101/cshperspect.a037820
Experimental preclinical models have been a cornerstone of lung cancer translational research. Work in these model systems has provided insights into the biology of lung cancer subtypes and their origins, contributed to our understanding of the mechanisms that underlie tumor progression, and revealed new therapeutic vulnerabilities. Initially patient-derived lung cancer cell lines were the main preclinical models available. The landscape is very different now with numerous preclinical models for research each with unique characteristics. These include genetically engineered mouse models (GEMMs), patient-derived xenografts (PDXs) and three-dimensional culture systems (“organoid” cultures). Here we review the development and applications of these models and describe their contributions to lung cancer research.
Published: 23 August 2021
Cold Spring Harbor Perspectives in Medicine; https://doi.org/10.1101/cshperspect.a040501
Mendelian randomization (MR) is a method of studying the causal effects of modifiable exposures (i.e., potential risk factors) on health, social, and economic outcomes using genetic variants associated with the specific exposures of interest. MR provides a more robust understanding of the influence of these exposures on outcomes because germline genetic variants are randomly inherited from parents to offspring and, as a result, should not be related to potential confounding factors that influence exposure–outcome associations. The genetic variant can therefore be used as a tool to link the proposed risk factor and outcome, and to estimate this effect with less confounding and bias than conventional epidemiological approaches. We describe the scope of MR, highlighting the range of applications being made possible as genetic data sets and resources become larger and more freely available. We outline the MR approach in detail, covering concepts, assumptions, and estimation methods. We cover some common misconceptions, provide strategies for overcoming violation of assumptions, and discuss future prospects for extending the clinical applicability, methodological innovations, robustness, and generalizability of MR findings.
Published: 14 June 2021
Cold Spring Harbor Perspectives in Medicine; https://doi.org/10.1101/cshperspect.a037838
Lung cancer is a heterogeneous disease that is subdivided into histopathological subtypes with distinct behaviors. Each subtype is characterized by distinct features and molecular alterations that influence tumor metabolism. Alterations in tumor metabolism can be exploited by imaging modalities that use metabolite tracers for the detection and characterization of tumors. Microenvironmental factors, including nutrient and oxygen availability and the presence of stromal cells, are a critical influence on tumor metabolism. Recent technological advances facilitate the direct evaluation of metabolic alterations in patient tumors in this complex microenvironment. In addition, molecular alterations directly influence tumor cell metabolism and metabolic dependencies that influence response to therapy. Current therapeutic approaches to target tumor metabolism are currently being developed and translated into the clinic for patient therapy.
Published: 14 June 2021
Cold Spring Harbor Perspectives in Medicine; https://doi.org/10.1101/cshperspect.a037713
The management of non-small-cell lung cancer (NSCLC) varies according to stage. Surgical resection is reserved for operable patients with early-stage NSCLC, while high-dose target radiation—stereotactic body radiation therapy (SBRT)—is reserved for patients whose comorbidities prohibit them from a major surgical procedure. The treatment of locally advanced NSCLC (LA-NSCLC) is stratified according to resectability. Those with resectable disease may require additional treatments such as chemotherapy and radiation, while patients with unresectable disease will require definitive chemoradiation therapy with adjuvant durvalumab. Patients with limited metastatic disease benefit from the combination of SBRT and systemic therapy.
Published: 17 May 2021
Cold Spring Harbor Perspectives in Medicine, Volume 11; https://doi.org/10.1101/cshperspect.a037994
Cancer interception refers to actively blocking the cancer development process by preventing progression of premalignancy to invasive disease. The rate-limiting steps for effective lung cancer interception are the incomplete understanding of the earliest molecular events associated with lung carcinogenesis, the lack of preclinical models of pulmonary premalignancy, and the challenge of developing highly sensitive and specific methods for early detection. Recent advances in cancer interception are facilitated by developments in next-generation sequencing, computational methodologies, as well as the renewed emphasis in precision medicine and immuno-oncology. This review summarizes the current state of knowledge in the areas of molecular abnormalities in lung cancer continuum, preclinical human models of lung cancer pathogenesis, and the advances in early lung cancer diagnostics.
Published: 22 March 2021
Cold Spring Harbor Perspectives in Medicine, Volume 11; https://doi.org/10.1101/cshperspect.a040519
Causation has multiple distinct meanings in genetics. One reason for this is meaning slippage between two concepts of the gene: Mendelian and molecular. Another reason is that a variety of genetic methods address different kinds of causal relationships. Some genetic studies address causes of traits in individuals, which can only be assessed when single genes follow predictable inheritance patterns that reliably cause a trait. A second sense concerns the causes of trait differences within a population. Whereas some single genes can be said to cause population-level differences, most often these claims concern the effects of many genes. Polygenic traits can be understood using heritability estimates, which estimate the relative influences of genetic and environmental differences to trait differences within a population. Attempts to understand the molecular mechanisms underlying polygenic traits have been developed, although causal inference based on these results remains controversial. Genetic variation has also recently been leveraged as a randomizing factor to identify environmental causes of trait differences. This technique—Mendelian randomization—offers some solutions to traditional epidemiological challenges, although it is limited to the study of environments with known genetic influences.
Published: 1 March 2021
Cold Spring Harbor Perspectives in Medicine, Volume 11; https://doi.org/10.1101/cshperspect.a035857
Observations of the incidence of tumors among chimney sweeps in the eighteenth century and later experiments with coal tars provided early evidence that carcinogens in the environment can promote cancer. Subsequent studies of individuals exposed to radiation, work on fly genetics, and the discovery that DNA was the genetic material led to the idea that these carcinogens act by inducing mutations in DNA that change the behavior of cells and ultimately cause cancer. In this excerpt from his forthcoming book, Joe Lipsick looks back at how the concepts of mutagenesis and carcinogenesis emerged, how these converged with development of the Ames test, and how biochemistry and crystallography ultimately revealed the underlying molecular basis.
Published: 11 January 2021
Cold Spring Harbor Perspectives in Medicine; https://doi.org/10.1101/cshperspect.a039537
Medical imaging is the standard-of-care for early detection, diagnosis, treatment planning, monitoring, and image-guided interventions of lung cancer patients. Most medical images are stored digitally in a standardized Digital Imaging and Communications in Medicine format that can be readily accessed and used for qualitative and quantitative analysis. Over the several last decades, medical images have been shown to contain complementary and interchangeable data orthogonal to other sources such as pathology, hematology, genomics, and/or proteomics. As such, “radiomics” has emerged as a field of research that involves the process of converting standard-of-care images into quantitative image-based data that can be merged with other data sources and subsequently analyzed using conventional biostatistics or artificial intelligence (AI) methods. As radiomic features capture biological and pathophysiological information, these quantitative radiomic features have shown to provide rapid and accurate noninvasive biomarkers for lung cancer risk prediction, diagnostics, prognosis, treatment response monitoring, and tumor biology. In this review, radiomics and emerging AI methods in lung cancer research are highlighted and discussed including advantages, challenges, and pitfalls.
Published: 11 January 2021
Cold Spring Harbor Perspectives in Medicine; https://doi.org/10.1101/cshperspect.a040980
Randomized controlled trials and Mendelian randomization studies are two study designs that provide randomized evidence in human biological and medical research. Both exploit the power of randomization to provide unconfounded estimates of causal effect. However, randomized trials and Mendelian randomization studies have very different study designs and scientific objectives. As a result, despite sometimes being referred to as “nature's randomized trial,” a Mendelian randomization study cannot be used to replace a randomized trial but instead provides complementary information. In this review, we explain the similarities and differences between randomized trials and Mendelian randomization studies, and suggest several ways that Mendelian randomization can be used to directly inform and improve the design of randomized trials illustrated with practical examples. We conclude by describing how Mendelian randomization studies can employ the principles of trial design to be framed as “naturally randomized trials” that can provide a template for the design of future randomized trials evaluating therapies directed against genetically validated targets.
Published: 21 December 2020
Cold Spring Harbor Perspectives in Medicine, Volume 11; https://doi.org/10.1101/cshperspect.a040659
Much research effort is invested in attempting to determine causal influences on disease onset and progression to inform prevention and treatment efforts. However, this is often dependent on observational data that are prone to well-known limitations, particularly residual confounding and reverse causality. Several statistical methods have been developed to support stronger causal inference. However, a complementary approach is to use design-based methods for causal inference, which acknowledge sources of bias and attempt to mitigate these through the design of the study rather than solely through statistical adjustment. Genetically informed methods provide a novel and potentially powerful extension to this approach, accounting by design for unobserved genetic and environmental confounding. No single approach will be absent from bias. Instead, we should seek and combine evidence from multiple methodologies that each bring different (and ideally uncorrelated) sources of bias. If the results of these different methodologies align—or triangulate—then we can be more confident in our causal inference. To be truly effective, this should ideally be done prospectively, with the sources of evidence specified in advance, to protect against one final source of bias—our own cognitions, expectations, and fondly held beliefs.