New Potential for Presonalized Medicine
Larry H. Bernstein, MD, FCAP, Curator
LPBI
Updated 11/22/2015
New Tool to Identify Tumor Heterogeneity Could Help Pave Way for Personalized Cancer Therapies and Help Pathologists Add Value for Oncologists
November 20, 2015
Ohio State University study shows correlation between genetic variability among cancer cells within tumors and the survival of patients with head-and-neck cancers
Anatomic pathologists and clinical laboratories may gain a tool to identify tumorheterogeneity. This would enable them to ultimately guide personalized cancer therapies if a new method for measuring genetic variability within a tumor and predicting outcomes is confirmed in future studies.
Scientists Seek Cause of Resistance to Cancer Treatment
The new tool was dubbed “MATH” by researchers at The Ohio State University Comprehensive Cancer Center–Arthur G. James Cancer Hospital and Richard J. Solove Research Institute (OSUCCC–James). MATH is the scoring method they developed and stands for mutant-allele tumor heterogeneity. MATH was used to measure the genetic variability among cancer cells within tumors from 305 patients with head and neck squamous cell carcinoma (HNSCC), treated at multiple institutions, from The Cancer Genome Atlas.
In announcing the study results, OSUCCC-James stated that cancers that showed high genetic variability—called “intra-tumor heterogeneity”—correlated with lower patient survival.
James Rocco, MD, PhD, Professor in the Department of Otolaryngology-Head and Neck Surgery at The Ohio State University Wexner Medical Center, led the research team that developed a new method for measuring genetic variability within a tumor. The team showed that high MATH (mutant-allele tumor heterogeneity) scores correlated to lower patient survival. the team used MATH values “to document a relation between intra-tumor heterogeneity and overall survival in any type of cancer.”
Genetic Variability Linked with Treatment Failure
Their findings were published in the February 2015 issue of the journal PLOS Medicine.
“Genetic variability within tumors is likely why people fail treatment,” Rocco said in the statement. “In patients who have high heterogeneity tumors it is likely that there are several clusters of underlying mutations—in the same tumor—driving the cancer. So their tumors are likely to have some cells that are already resistant to any particular therapy.”
Medical News Today reported that each 10% increase in MATH score corresponded to an 8.8% increased likelihood of death.
“Our retrospective analysis showed that patients with high heterogeneity tumors were more than twice as likely to die compared to patients with low heterogeneity tumors,” Rocco told Medical News Today. “This type of information could refine the dialog about how we tackle cancer by helping us predict a patient’s treatment success and justify clinical decisions based on the unique makeup of a patient’s tumor.”
MATH Scores of Tumor Heterogeneity in Clinical Settings to Guide Diagnostics
Until now, oncologists have been reluctant to use “tumor heterogeneity to guide clinical care decisions or assess disease prognosis because there is no single, easy-to-implement method of doing so in clinical practice,” reported the OSUCCC-James statement. The MATH score, however, overcomes that issue since it can be computed from whole-exome sequencing data obtained from a single formalin-fixed, paraffin-embedded tumor sample.
It is pathologists who take tumor tissues and produce formalin-fixed, paraffin-embedded samples in their histology laboratories. Thus, as further clinical studies confirm that the use of the MATH tool can produce useful diagnostic and prognostic information for oncologists, pathologist will be perfectly positioned to add MATH to their menu of pathology services.
In a guest editorial in PLOS Medicine , Andrew H. Beck, MD, PhD, of Beth Israel Deaconess Medical Center and Harvard Medical School in Boston, pointed out that Rocco’s MATH score “approach may be more easily translated into clinical use, as compared with approaches requiring multiregion sampling and more complex computational algorithms for the assessment of intratumoral heterogeneity.”
Beck also discussed the important role large sets of cancer samples have in cancer research and in the development of improved personalized therapies for the disease. He observed that open access to large-scale datasets from large populations of cancer patients is “critically important” for devising computational methods for using cancer heterogeneity in clinical settings during the diagnostic process.
“The continuing generation of high-quality, open-access Omics datasets from large populations of cancer patients will be critically important to enable the development of computational methods to translate knowledge of cancer heterogeneity into new diagnostics and improved clinical outcomes for cancer patients,” Beck wrote.
Researchers Suggest MATH Should Be Biomarker for Treatment Decision-making
While their results must be confirmed in further studies and with other cancers, Rocco’s team believes their scoring method holds great promise as prognostic tool.
“These findings suggest that MATH should be considered a biomarker for survival in HNSCC and other tumor types, and raise the possibility that clinicians could use MATH values to decide on the best treatment for individual patients and to choose patients for inclusion in clinical trials,” they wrote in PLOS Medicine. Pathologists, particularly in academic pathology departments, might want to track the ongoing development of MATH and how it could be used in patient care.
—Andrea Downing Peck
Related Information:
Intra-tumor Genetic Heterogeneity and Mortality in Head and Neck Cancer: Analysis of Data from The Cancer Genome Atlas
New Genomics Tool Could Help Predict Tumor Aggressiveness, Treatment Outcomes
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Open Access to Large Scale Datasets Is Needed to Translate Knowledge of Cancer Heterogeneity into Better Patient Outcomes
Cancer is a heterogeneous disease, which is comprised of a collection of diseases traditionally categorized by tissue type of origin. A distinct set of etiologic causes, treatments, and prognoses are associated with different cancers, and even within a given tissue type, cancer shows significant variability in molecular and clinical features across patients. This interpatient heterogeneity is a major rationale for large-scale research efforts (such as The Cancer Genome Atlas [TCGA] and the International Cancer Genome Consortium [ICGC]) to comprehensively profile the molecular landscape of patient cancer samples across all major cancers [1,2]. These efforts have been bolstered by the recent development of new genomic [3] and computational [4] technologies to enable increasingly detailed and comprehensive analyses of the molecular landscape of solid cancers. It is hoped that the comprehensive molecular characterization of large sets of cancer samples will lead to the identification of new therapeutic targets and the development of improved personalized therapies for cancer patients.
A major challenge in cancer therapy is the development of resistance to molecularly targeted therapies. Although targeted therapies may show initial benefit in the subset of patients carrying a targeted molecular alteration, most patients will nevertheless go on to develop resistance for most advanced solid cancers. Identifying and overcoming drug resistance represents one of the most significant challenges facing cancer researchers today [5]. It is increasingly recognized that cancer is not only a heterogeneous disease across patients but also a heterogeneous disease within individual patients, with different regions of a tumor showing different molecular features at the DNA, RNA, and protein levels [6–9]. This intratumoral molecular heterogeneity is hypothesized to be a major cause of drug resistance and treatment failure in cancer [10]. However, the clinical significance of intratumoral molecular heterogeneity is not yet well-defined, and assessment of intratumoral molecular heterogeneity is not currently used in clinical cancer medicine for assessing disease prognosis or guiding therapy. Two recent research articles published in PLOS Medicine show the potential clinical utility of measuring intratumoral genetic heterogeneity in clinical cancer samples.
In one, James Brenton, Florian Markowetz, and colleagues applied the Minimum Event Distance for Intra-tumour Copy-number Comparisons (MEDICC) algorithm they recently developed for phylogenetic quantification of intratumoral genetic heterogeneity from multiregion DNA copy number profiling data [11] to predict treatment resistance in high-grade serous ovarian cancer [12]. Their analysis suggests that multiregion tumor sampling, DNA copy number profiling, and quantification of intratumoral genetic heterogeneity with the MEDICC algorithm could be a useful approach for predicting patient survival in ovarian cancer, in which higher levels of heterogeneity associated with decreased survival. This study provides data to support the long-standing hypothesis regarding treatment resistance and intratumoral genetic heterogeneity [10]. Although these results are promising, the developed approach requires sampling multiple distinct regions of tumor, which would be more expensive and complex than molecular profiling from a single tissue sample. It is not yet known how much tumor sampling will be required to adequately quantify intratumoral heterogeneity in the clinic or if measuring intratumoral heterogeneity from multiple tumor samples will outperform other molecular approaches (e.g., prognostic expression signatures [13,14]) for predicting response to therapy in ovarian cancer. These are important research questions that will need to be answered prior to clinical translation.
The second study comes from James Rocco and colleagues [15]. Previously, these investigators used a publicly available data set of whole exome sequencing data in head and neck squamous cell carcinoma (HNSCC) from Stransky et al. [16] to develop a simple quantitative measure of intratumoral heterogeneity (mutant-allele tumor heterogeneity [MATH]) and showed that MATH scores were higher in poor outcome classes of HNSCC [17]. In the current study, the authors used publicly available whole exome sequencing data provided by TCGA and showed that the MATH score is associated with prognosis in HNSCC and contributes additional prognostic information beyond that provided by traditional clinical and molecular features. Since the MATH score can be computed from whole exome sequencing data obtained from a single tumor sample (which is a data type that can be obtained from formalin-fixed, paraffin-embedded tumor tissue, as is routinely collected in pathology laboratories [18]), this approach may be more easily translated into clinical use, as compared with approaches requiring multiregion sampling and more complex computational algorithms for the assessment of intratumoral heterogeneity. Nonetheless, establishing the utility of the MATH score as an effective prognostic and/or predictive biomarker in HNSCC will require additional studies of the MATH score on well-controlled clinical cohorts comprised of homogeneously treated patients with tumors at specific head and neck anatomic locations. It is important to note that the development and application of MATH for assessing prognosis in HNSCC was based entirely on the analysis of publically available clinically annotated whole exome sequencing data, which demonstrates the value in making these data open to the community.
The continuing generation of high-quality, open-access Omics data sets from large populations of cancer patients will be critically important to enable the development of computational methods to translate knowledge of cancer heterogeneity into new diagnostics and improved clinical outcomes for cancer patients. As one step towards this goal, the DREAM (Dialogue for Reverse Engineering Assessments and Methods) consortium will use open innovation crowd sourcing to identify top-performing computational methods for inferring genetic heterogeneity from next-generation sequencing data provided by a large multi-institutional community of cancer genomics projects, including the ICGC and TCGA [19]. If successful, this open innovation competition may identify a set of best-in-class methods for measuring intratumoral genetic heterogeneity in cancer.
In parallel with these advances in computational methods for inferring intratumoral heterogeneity from genomics data, genomics technologies for measuring intratumoral heterogeneity at increasingly fine levels of granularity continue to improve. For example, recent advances in single-cell sequencing of DNA have provided detailed portraits of intratumoral genetic heterogeneity and clonal evolution in cancer [20,21], and recent advances in single-cell RNA sequencing [22], in situ RNA sequencing [23,24], and highly multiplexed next-generation immunohistochemistry [25–28] enable characterization of intratumoral heterogeneity in gene expression at a single cell level with subcellular resolution. Thus, there are now many options—both molecular and computational—for measuring and analyzing intratumoral molecular heterogeneity from clinical cancer samples.
Establishing the clinical utility of these new approaches for measuring intratumoral molecular heterogeneity will require applying these methods to large sets of archival tumor samples from randomized trials of cancer therapeutics [29] and high-quality prospective observational studies [30]. To maximize the value of the data that would be produced from such an undertaking, it is critical that infrastructure be created and supported to enable sharing of the Omics and clinical data with a large community of cancer researchers and data scientists. Ensuring open access to high-quality datasets will ensure that the largest possible community of researchers is able to address the most important problems in cancer medicine today. And in generating and sharing these data widely, we will massively increase our chances of effectively translating knowledge of intratumoral heterogeneity into meaningful advances for cancer patients.
References
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- 2.Garraway LA, Lander ES (2013) Lessons from the cancer genome. Cell 153: 17–37. doi: 10.1016/j.cell.2013.03.002. pmid:23540688
- 3.Meyerson M, Gabriel S, Getz G (2010) Advances in understanding cancer genomes through second-generation sequencing. Nat Rev Genet 11: 685–696. doi: 10.1038/nrg2841. pmid:20847746
- 4.Ding L, Wendl MC, McMichael JF, Raphael BJ (2014) Expanding the computational toolbox for mining cancer genomes. Nat Rev Genet 15: 556–570. doi: 10.1038/nrg3767. pmid:25001846
- 5.Garraway LA, Jänne PA (2012) Circumventing cancer drug resistance in the era of personalized medicine. Cancer Discov 2: 214–226. doi: 10.1158/2159-8290.CD-12-0012. pmid:22585993
- 6.Burrell RA, McGranahan N, Bartek J, Swanton C (2013) The causes and consequences of genetic heterogeneity in cancer evolution. Nature 501: 338–345. doi: 10.1038/nature12625. pmid:24048066
- 7.Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, et al. (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 366: 883–892. doi: 10.1056/NEJMoa1113205. pmid:22397650
- 8.Bashashati A, Ha G, Tone A, Ding J, Prentice LM, et al. (2013) Distinct evolutionary trajectories of primary high-grade serous ovarian cancers revealed through spatial mutational profiling. J Pathol 231: 21–34. doi: 10.1002/path.4230. pmid:23780408
- 9.De Bruin EC, McGranahan N, Mitter R, Salm M, Wedge DC, et al. (2014) Spatial and temporal diversity in genomic instability processes defines lung cancer evolution. Science (80-) 346: 251–256. doi: 10.1126/science.1253462
- 10.Burrell RA, Swanton C (2014) Tumour heterogeneity and the evolution of polyclonal drug resistance. Mol Oncol 8: 1095–1111. doi: 10.1016/j.molonc.2014.06.005. pmid:25087573
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New Genomics Tool Could Help Predict Tumor Aggressiveness, Treatment Outcomes
APRIL 16, 2015
OSUCCC – James researchers Edmund Mroz, PhD, and James Rocco, MD, PhD, developed the MATH method.
http://cancer.osu.edu/news-and-media/news/new-genomics-tool-could-help-predict-tumor-aggressiveness-treatment-outcomes
COLUMBUS, Ohio — A new method for measuring genetic variability within a tumor might one day help doctors identify patients with aggressive cancers that are more likely to resist therapy, according to a study led by researchers now at The Ohio State University Comprehensive Cancer Center – Arthur G. James Cancer Hospital and Richard J. Solove Research Institute (OSUCCC – James).
Researchers used a new scoring method they developed called MATH (mutant-allele tumor heterogeneity) to measure the genetic variability among cancer cells within tumors from 305 patients with head and neck cancer. High MATH scores corresponded to tumors with many differences among the gene mutations present in different cancer cells.
Cancers that showed high genetic variability – called “intra-tumor heterogeneity” – correlated with lower patient survival. If prospective studies verify the findings, MATH scores could help identify the most effective treatment for patients and predict a patient’s prognosis.
Researchers have long hypothesized that multiple sub-populations of mutated cells within a single cancer lead to worse clinical outcomes; however, oncologists do not use tumor heterogeneity to guide clinical care decisions or assess disease prognosis because there is no single, easy-to-implement method of doing so in clinical practice.
Intra-tumor Genetic Heterogeneity and Mortality in Head and Neck Cancer: Analysis of Data from The Cancer Genome Atlas
Although the involvement of intra-tumor genetic heterogeneity in tumor progression, treatment resistance, and metastasis is established, genetic heterogeneity is seldom examined in clinical trials or practice. Many studies of heterogeneity have had prespecified markers for tumor subpopulations, limiting their generalizability, or have involved massive efforts such as separate analysis of hundreds of individual cells, limiting their clinical use. We recently developed a general measure of intra-tumor genetic heterogeneity based on whole-exome sequencing (WES) of bulk tumor DNA, called mutant-allele tumor heterogeneity (MATH). Here, we examine data collected as part of a large, multi-institutional study to validate this measure and determine whether intra-tumor heterogeneity is itself related to mortality.
Methods and Findings
Clinical and WES data were obtained from The Cancer Genome Atlas in October 2013 for 305 patients with head and neck squamous cell carcinoma (HNSCC), from 14 institutions. Initial pathologic diagnoses were between 1992 and 2011 (median, 2008). Median time to death for 131 deceased patients was 14 mo; median follow-up of living patients was 22 mo. Tumor MATH values were calculated from WES results. Despite the multiple head and neck tumor subsites and the variety of treatments, we found in this retrospective analysis a substantial relation of high MATH values to decreased overall survival (Cox proportional hazards analysis: hazard ratio for high/low heterogeneity, 2.2; 95% CI 1.4 to 3.3). This relation of intra-tumor heterogeneity to survival was not due to intra-tumor heterogeneity’s associations with other clinical or molecular characteristics, including age, human papillomavirus status, tumor grade and TP53 mutation, and N classification. MATH improved prognostication over that provided by traditional clinical and molecular characteristics, maintained a significant relation to survival in multivariate analyses, and distinguished outcomes among patients having oral-cavity or laryngeal cancers even when standard disease staging was taken into account. Prospective studies, however, will be required before MATH can be used prognostically in clinical trials or practice. Such studies will need to examine homogeneously treated HNSCC at specific head and neck subsites, and determine the influence of cancer therapy on MATH values. Analysis of MATH and outcome in human-papillomavirus-positive oropharyngeal squamous cell carcinoma is particularly needed.
Conclusions
To our knowledge this study is the first to combine data from hundreds of patients, treated at multiple institutions, to document a relation between intra-tumor heterogeneity and overall survival in any type of cancer. We suggest applying the simply calculated MATH metric of heterogeneity to prospective studies of HNSCC and other tumor types.
Editors’ Summary
Background
Normally, the cells in human tissues and organs only reproduce (a process called cell division) when new cells are needed for growth or to repair damaged tissues. But sometimes a cell somewhere in the body acquires a genetic change (mutation) that disrupts the control of cell division and allows the cell to grow continuously. As the mutated cell grows and divides, it accumulates additional mutations that allow it to grow even faster and eventually from a lump, or tumor (cancer). Other mutations subsequently allow the tumor to spread around the body (metastasize) and destroy healthy tissues. Tumors can arise anywhere in the body—there are more than 200 different types of cancer—and about one in three people will develop some form of cancer during their lifetime. Many cancers can now be successfully treated, however, and people often survive for years after a diagnosis of cancer before, eventually, dying from another disease.
Why Was This Study Done?
The gradual acquisition of mutations by tumor cells leads to the formation of subpopulations of cells, each carrying a different set of mutations. This “intra-tumor heterogeneity” can produce tumor subclones that grow particularly quickly, that metastasize aggressively, or that are resistant to cancer treatments. Consequently, researchers have hypothesized that high intra-tumor heterogeneity leads to worse clinical outcomes and have suggested that a simple measure of this heterogeneity would be a useful addition to the cancer staging system currently used by clinicians for predicting the likely outcome (prognosis) of patients with cancer. Here, the researchers investigate whether a measure of intra-tumor heterogeneity called “mutant-allele tumor heterogeneity” (MATH) is related to mortality (death) among patients with head and neck squamous cell carcinoma (HNSCC)—cancers that begin in the cells that line the moist surfaces inside the head and neck, such as cancers of the mouth and the larynx (voice box). MATH is based on whole-exome sequencing (WES) of tumor and matched normal DNA. WES uses powerful DNA-sequencing systems to determine the variations of all the coding regions (exons) of the known genes in the human genome (genetic blueprint).
What Did the Researchers Do and Find?
The researchers obtained clinical and WES data for 305 patients who were treated in 14 institutions, primarily in the US, after diagnosis of HNSCC from The Cancer Genome Atlas, a catalog established by the US National Institutes of Health to map the key genomic changes in major types and subtypes of cancer. They calculated tumor MATH values for the patients from their WES results and retrospectively analyzed whether there was an association between the MATH values and patient survival. Despite the patients having tumors at various subsites and being given different treatments, every 10% increase in MATH value corresponded to an 8.8% increased risk (hazard) of death. Using a previously defined MATH-value cutoff to distinguish high- from low-heterogeneity tumors, compared to patients with low-heterogeneity tumors, patients with high-heterogeneity tumors were more than twice as likely to die (a hazard ratio of 2.2). Other statistical analyses indicated that MATH provided improved prognostic information compared to that provided by established clinical and molecular characteristics and human papillomavirus (HPV) status (HPV-positive HNSCC at some subsites has a better prognosis than HPV-negative HNSCC). In particular, MATH provided prognostic information beyond that provided by standard disease staging among patients with mouth or laryngeal cancers.
What Do These Findings Mean?
By using data from more than 300 patients treated at multiple institutions, these findings validate the use of MATH as a measure of intra-tumor heterogeneity in HNSCC. Moreover, they provide one of the first large-scale demonstrations that intra-tumor heterogeneity is clinically important in the prognosis of any type of cancer. Before the MATH metric can be used in clinical trials or in clinical practice as a prognostic tool, its ability to predict outcomes needs to be tested in prospective studies that examine the relation between MATH and the outcomes of patients with identically treated HNSCC at specific head and neck subsites, that evaluate the use of MATH for prognostication in other tumor types, and that determine the influence of cancer treatments on MATH values. Nevertheless, these findings suggest that MATH should be considered as a biomarker for survival in HNSCC and other tumor types, and raise the possibility that clinicians could use MATH values to decide on the best treatment for individual patients and to choose patients for inclusion in clinical trials.
Additional Information
Please access these websites via the online version of this summary athttp://dx.doi.org/10.1371/journal.pmed.1001786.
SJ Williams, PhD
There are two very important criteria which is located in the papers: First these are data from WES sequencing form the TCGA database therefore the method makes an assumption on INTRA tumoral heterogeneity as there the algorithm test cases were based on whole tumor and not compared to temporal or spatial distribution (a simple solution would be to compare the sequencing results from Dr. Sawyers studies with this algorithm). The model also assumes that the distribution of loci mutants predicts the temporal accumulation of mutants during a clonal evolution. Secondly the authors segregate out HPV positive and negative Head and neck cancers and curious why this was the observed case: is this algorithm good at analyzing the clonal evolution of cancers containing indels or just point mutants. Interesting if they do a larger prospective study where they compare their algorithm versus the multi-core biopsy method. The editor is correct is justifying the need for further larger studies, especially for tumors like lung which is hard or dangerous to biopsy.
Mood Regulation Subject to Mixed Serotonin Signals
http://www.genengnews.com/gen-news-highlights/mood-regulation-subject-to-mixed-serotonin-signals/81252009/
Gene drive, an emerging technology for ecosystem management, is being considered for a range of applications. For example, it could be used to render mosquito populations unable to transmit malaria. Prominent gene-drive researchers are calling for open, well-informed discussion of the technology, which has far-reaching implications for the shared environment, well in advance of any field tests.[Columbia University Department of Psychiatry]
A new study indicates different serotonin-producing brain regions can have opposing effects on emotional behaviors. According to this study, two brain regions in particular, the dorsal raphe nucleus (DRN) and the median raphe nucleus (MRN), appear to have a yin-and-yang relationship when it comes to mood regulation.
Specifically, one region’s serotonergic activity can offset the other region’s serotonergic activity. This finding, which emerged from pharmacogenetic research conducted at Columbia University, provides new insights into the development of mood disorders and may aid in designing improved therapies.
The Columbia University research effort was led by Mark S. Ansorge, Ph.D. “Our study breaks with the simplistic view that ‘more is good and less is bad,’ when it comes to serotonin for mood regulation,” he said. “Rather, it tells us that a more nuanced view is necessary.”
The study’s details appeared November 19 in Cell Reports, in an article entitled, “Activity of Raphé Serotonergic Neurons Controls Emotional Behaviors.” The article noted that even though serotonin signaling has a well-established role in mood regulation, the causal relationships between serotonergic neuronal activity and behavior remain unclear.
To explore these relationships, Dr. Ansorge’s team used a technique called pharmacogenetics to control the activity of serotonergic neurons in the DRN and MRN in both normal mice and in a mouse model of depression- and anxiety-like behavior. (The model was created by giving mice the drug fluoxetine shortly after birth, which produces long-lasting behavioral changes.)
“[Selectively] increasing serotonergic neuronal activity in wild-type mice is anxiogenic and reduces floating in the forced-swim test, whereas inhibition has no effect on the same measures,” wrote the authors of the Cell Reports article. “In a developmental mouse model of altered emotional behavior, increased anxiety and depression-like behaviors correlate with reduced dorsal raphé and increased median raphé serotonergic activity. These mice display blunted responses to serotonergic stimulation and behavioral rescues through serotonergic inhibition.”
In addition, the researchers identified opposing consequences of dorsal versus median raphé serotonergic neuron inhibition on floating behavior. This observation, the researchers surmised, could mean that median raphé hyperactivity increases anxiety, whereas a low dorsal/median raphé serotonergic activity ratio increases depression-like behavior.
http://www.cell.com/cell-reports/abstract/S2211-1247(15)01250-4
Anne Teissier, Alexei Chemiakine, Benjamin Inbar, Sneha Bagchi, Russell S. Ray, et al. http://dx.doi.org/10.1016/j.celrep.2015.10.061
http://www.cell.com/cms/attachment/2040587978/2054165588/fx1.jpg
http://www.cell.com/cms/attachment/2040587978/2054165588/fx1.jpg
- •Increasing 5-HT neuronal activity increases anxiety-like behavior
- •Low DR/MR 5-HTergic activity correlates with altered emotional behavior in PNFLX mice
- •Reducing 5-HT neuronal activity normalizes emotional behavior in PNFLX mice
- •MR and DR 5-HT neuronal activity exert opposing consequences on floating behavior
Despite the well-established role of serotonin signaling in mood regulation, causal relationships between serotonergic neuronal activity and behavior remain poorly understood. Using a pharmacogenetic approach, we find that selectively increasing serotonergic neuronal activity in wild-type mice is anxiogenic and reduces floating in the forced-swim test, whereas inhibition has no effect on the same measures. In a developmental mouse model of altered emotional behavior, increased anxiety and depression-like behaviors correlate with reduced dorsal raphé and increased median raphé serotonergic activity. These mice display blunted responses to serotonergic stimulation and behavioral rescues through serotonergic inhibition. Furthermore, we identify opposing consequences of dorsal versus median raphé serotonergic neuron inhibition on floating behavior, together suggesting that median raphé hyperactivity increases anxiety, whereas a low dorsal/median raphé serotonergic activity ratio increases depression-like behavior. Thus, we find a critical role of serotonergic neuronal activity in emotional regulation and uncover opposing roles of median and dorsal raphé function.
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Feb 16, 2016 
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