Study Objectives The circadian system must perform daily adjustments to align sleep-wake and other physiologic rhythms with the environmental light-dark cycle: this is mediated primarily through melanopsin containing intrinsically photosensitive retinal ganglion cells. Individuals with delayed sleep-wake phase disorder (DSWPD) exhibit a delay in sleep-wake timing relative to the average population, while those with sighted non-24-hour sleep-wake rhythm disorder (N24SWD) exhibit progressive delays. An inability to maintain appropriate entrainment is characteristic of both disorders. In this study, we test the hypothesis that individuals with DSWPD exhibit alteration in melanopsin dependent retinal photo-transduction as measured with the post-illumination pupil response (PIPR). Methods Twenty-one control and 29 participants with DSWPD were recruited from the community and clinic. Of the 29 DSWPD participants, 17 reported a history of N24SWD. A pupillometer was used to measure the post-illumination pupil response (PIPR) in response to a bright 30s blue or red-light stimulus. The PIPR was calculated as the difference in average pupil diameter at baseline and 10-40s after light stimulus offset. Results The PIPR was significantly reduced in the DSWPD group when compared to the control group (1.26±1.11 mm vs. 2.05±1.04 mm, p&0.05, t-test). The PIPR was significantly reduced in the sighted N24SWD subgroup when compared to individuals with history of only DSWPD (0.88±0.58 mm vs 1.82±1.44 mm, p&0.05, ANOVA) or controls (0.88±0.58 mm vs 2.05±1.04 mm, p&0.01, ANOVA). Conclusions These results indicate that reduced melanopsin dependent retinal photo-transduction may be a novel mechanism involved in the development of DSWPD and sighted N24SWD.
Tissue tumor mutational burden (tTMB) is calculated to aid in cancer treatment selection. High tTMB predicts a favorable response to immunotherapy in patients with non-small cell lung cancer. Blood TMB (bTMB) from circulating tumor DNA is reported to have similar predictive power and has been proposed as an alternative to tTMB. Across many studies not only are tTMB and bTMB not concordant but also as reported previously by our group predict conflicting outcomes. This implies that bTMB is not a substitute for tTMB, but rather a composite index that may encompass tumor heterogeneity. Here, we provide a thorough overview of the predictive power of TMB, discuss the use of tumor heterogeneity alongside TMB to predict treatment response and review several methods of tumor heterogeneity assessment. Furthermore, we propose a hypothetical method of estimating tumor heterogeneity and touch on its clinical implications.
BackgroundTumors with high tumor mutational burden (TMB) or defects in mismatch repair (dMMR) respond well to immune checkpoint inhibitors (ICIs).1 2 TMB and DNA repair gene mutations including dMMR are closely related to the increase of neoantigens, which are recognized by immune cells to trigger an immune response.1 3 Although not a standard of care in thyroid cancer treatment, there are ongoing clinical trials for ICI use in differentiated thyroid carcinoma. However, not much has been explored concerning the neoantigen landscape and its association with immune traits in papillary thyroid cancer (PTC). We aim to analyze the immune landscape of PTC in association with neoantigen burden, TMB, and DNA repair gene mutations.MethodsWe used the PTC cohort data from The Cancer Genome Atlas (TCGA). The mutation counts and data for neoantigen prediction were acquired from TCGA mutation calling. CloudNeo pipeline was used for neoantigen prediction. TMB was calculated as the sum of missense and indel mutation counts per megabase pairs covered by whole-exome sequencing. Tumor-infiltrating immune cells were estimated using CIBERSORT.ResultsOut of the 496 PTC patients from cBioPortal, a subset of 400 patients with available mutation counts and predicted neoantigen burden was included in the study. Immune cell infiltration estimated by CIBERSORT showed macrophage M2 as the most abundant, followed by macrophage M0 and other T cells (figure 1). The TMB ranged from 0.03 to 2.05 with a median value of 0.2. Neoantigen burden ranged from 0 to 18 with a median value of 1, which is relatively low compared to the median value of 18 in non-small cell lung cancer (NSCLC)1 (figure 2). One or more DNA repair gene mutations were discovered in 32 patients (8%). The mutation status of repair genes was not related to TMB or neoantigen burden. TMB or neoantigen burden was not related to immune traits such as infiltration of CD8+ T cells or regulatory T cells, cytolytic activity score, and PD-L1 expression.Abstract 753 Figure 1Immune cell infiltration estimated by CIBERSORTAbstract 753 Figure 2Histogram of neoantigen burdenConclusionsThis is the first study to report the immune landscape of PTC in the context of neoantigen. The lack of association between TMB or neoantigen burden with immune traits may be due to the relatively low number of neoantigens in PTC compared to other immunogenic cancers such as NSCLC. Our results suggest that mutations in DNA repair genes or TMB are likely to have limited value in predicting response to ICI treatment in PTC.ReferencesChae YK, et al., Mutations in DNA repair genes are associated with increased neoantigen burden and a distinct immunophenotype in lung squamous cell carcinoma. Sci Rep 2019; 9:3235.Rizvi NA, et al., Cancer immunology. mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 2015; 348:124–128.Schumacher TN, Schreiber RD, Neoantigens in cancer immunotherapy. Science 2015; 348:69–74.
BackgroundTumor heterogeneity assessment may help predict response to immunotherapy. In melanoma mouse models, tumor heterogeneity impaired immune response.1 In addition, among lung cancer patients receiving immunotherapy, the high clonal neoantigen group had favorable survival and outcomes.2 Ideal methods of quantifying tumor heterogeneity are multiple biopsies or autopsy. However, these are not feasible in routine clinical practice. Circulating tumor DNA (ctDNA) is emerging as an alternative. Here, we reviewed the current state of tumor heterogeneity quantification from ctDNA. Furthermore, we propose a new tumor heterogeneity index(THI) based on our own scoring system, utilizing both ctDNA and tissue DNA.MethodsSystematic literature search on Pubmed was conducted up to August 18, 2020. A scoring system and THI were theoretically derived.ResultsTwo studies suggested their own methods of assessing tumor heterogeneity. One suggested clustering mutations with Pyclone,3 and the other suggested using the ratio of allele frequency (AF) to the maximum somatic allele frequency (MSAF).4 According to the former, the mutations in the highest cellular prevalence cluster can be defined as clonal mutations. According to the latter, the mutations with AF/MSAF<10% can be defined as subclonal mutations. To date, there have been no studies on utilizing both ctDNA and tissue DNA simultaneously to quantify tumor heterogeneity. We hypothesize that a mutation found in only one of either ctDNA or tissue DNA has a higher chance of being subclonal.We suggest a scoring system based on the previously mentioned methods to estimate the probability for a mutant allele to be subclonal. Adding up the points that correspond to the conditions results in a subclonality score (table 1). In a given ctDNA, the number of alleles with a subclonality score greater than or equal to 2 divided by the total number of alleles is defined as blood THI (bTHI) (figure 1). We can repeat the same calculation in a given tissue DNA for tissue THI (tTHI) (figure 2). Finally, we define composite THI (cTHI) as the mean of bTHI and tTHI.Abstract 18 Table 1Subclonality scoreAbstract 18 Figure 1Hypothetical distribution of all alleles found in ctDNA bTHI = the number of alleles with a subclonality score greater than or equal to 2/the total number of alleles found in ctDNA = 10/20 =50%Abstract 18 Figure 2Hypothetical distribution of all alleles found in tissue DNA tTHI= the number of alleles with a subclonality score greater than or equal to 2/the total number of alleles found in tissue DNA = 16/40 = 40% cTHI= (bTHI + tTHI)/2 = 45%ConclusionsTumor heterogeneity is becoming an important biomarker for predicting response to immunotherapy. Because autopsy and multiple biopsies are not feasible, utilizing both ctDNA and tissue DNA is the most comprehensive and practical approach. Therefore, we propose cTHI, for the first time, as a quantification measure of tumor heterogeneity.ReferencesWolf Y, Bartok O. UVB-Induced Tumor Heterogeneity Diminishes Immune Response in Melanoma. Cell 2019;179:219–235.McGranahan N, Swanton C. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 2016;351:1463–1469.Ma F, Guan Y. Assessing tumor heterogeneity using ctDNA to predict and monitor therapeutic response in metastatic breast cancer. Int J Cancer 2020;146:1359–1368.Liu Z, Xie Z. Presence of allele frequency heterogeneity defined by ctDNA profiling predicts unfavorable overall survival of NSCLC. Transl Lung Cancer Res 2019;8:1045–1050.
BackgroundThe use of immune checkpoint inhibitors (ICIs) in cancer treatment has been approved by the FDA, but its application is experimental in the treatment of papillary thyroid cancer (PTC). Induction of immune response via recognition of neoantigens is considered to be the basis for the treatment mechanism of ICIs.1 However, the neoantigen landscape has not been explored in PTC. Our aim is to investigate the immune landscape of PTC in relation to neoantigens, taking into account the BRAF mutation status and grade of differentiation as contributing factors.MethodsBRAF V600E mutation status and thyroid differentiation scores (TDSs) were gathered from the PTC cohort of The Cancer Genome Atlas (TCGA). TDS was derived from the mRNA expression levels of 16 thyroid function genes to quantify the grade of differentiation. Tumors with TDSs in the 1st quartile and 4th quartile were defined as poorly differentiated and well differentiated, respectively. The neoantigen burden for each sample was predicted using CloudNeo pipeline. The infiltration of immune cells was calculated through CIBERSORT.ResultsAmong 400 patients with predicted neoantigen data, 187 (47%) had BRAF mutations. The BRAF mutated tumors showed increased cytolytic activity score (CYT, p=0.001), increased infiltration of regulatory T cells (Treg, p<0.001), and higher PD-L1 expression (p<0.001) compared to BRAF wild-type tumors (figure 1). In regard to grade of differentiation, poorly differentiated tumors showed increased CYT (p=0.002), increased infiltration of Treg (p<0.001), and higher PD-L1 expression (p<0.001) compared to well differentiated tumors (figure 2). However, BRAF mutation status or grade of differentiation did not correlate with the neoantigen burden. Also, the neoantigen burden did not show any correlations with immune landscape features such as infiltration of CD8+ T cells or Treg, CYT, and PD-L1 expression.Abstract 752 Figure 1Immune traits according to BRAF mutation status. (a) Cytolytic activity score(CYT). (b) Infiltration of regulatory T cells(Tregs). (c) PD-L1 expression.Abstract 752 Figure 2Immune traits according to grade of differentiation. (a) Cytolytic activity score(CYT). (b) Infiltration of regulatory T cells(Tregs). (c) PD-L1 expression.ConclusionsIncreased CYT and higher expression of PD-L1 in the BRAF mutated or the poorly differentiated tumors imply the possible role of ICI use in these subgroups of patients. However, the immune response to these subgroups does not seem to be mediated through the increase in neoantigen formation. Further studies are warranted to explore markers for immunotherapy implication.ReferencesSchumacher TN, Schreiber RD, Neoantigens in cancer immunotherapy. Science 2015; 348:69–74.
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