Rotavirus antigenemia and RNAemia (the presence of rotavirus RNA in serum) have been commonly identified among paediatric patients with acute gastroenteritis. In this study we examined the association between rotavirus antigenemia and clinical features, and sought to determine the genotypes of rotaviruses detected in paired stool and serum samples.Paired stool and serum samples were obtained from children hospitalised for acute gastroenteritis in Belém, Brazil, between June 2012 and June 2015. The 20-point Vesikari scoring system was used to assess the disease severity upon a retrospective medical record review. Stool and serum samples were primarily screened for the presence of rotavirus antigen using a commercial ELISA assay. The rotavirus isolates from stool and serum samples were genotyped by using the classical reverse-transcriptase polymerase chain reaction (RT-PCR) and/or through nucleotide sequencing of VP4 and VP7 genes. Viral load was estimated using real-time RT-PCR.p = 0.0035). Our findings also suggested that children not vaccinated against rotavirus are more likely to develop antigenemia, as compared to those given at least one vaccine dose (p = 0.0151). G12P [8] and G2P [4] genotypes were predominant throughout the study period, accounting for 52.3% (57/109) and 27.5% (30/109) of the typed isolates, respectively. Ten stool-serum pairs could be typed for VP4 and VP7 genes. Seven of these pairs showed concordant results with G2P [4] genotype being detected in stool and serum samples, whereas discrepancies between genotypes (G2P [4]/G2P[NT] and G12P [8]/G2P[NT]) were seen in three pairs.In total rotavirus antigen was detected in 109 (24.2%) stool samples from 451 children, whereas antigenemia occurred in 38.5% (42/109) of these patients. We demonstrated that patients positive for rotavirus RNA in paired stool and serum samples were more likely to have a higher frequency of vomiting episodes in a 24-h period  contains supplementary material, which is available to authorized users. Worldwide rotavirus still remains a major cause of deaths and hospitalisations due to gastroenteritis among children younger than five years, even though almost 100 countries have incorporated to date rotavirus vaccination into their national immunisation programs (NIPs) . AccordiTypically the clinical course of rotaviral enteritis can show a wide spectrum of symptoms ranging from mild, watery diarrhoea to severe gastroenteritis with vomiting, fever, abdominal pain and dehydration which may last 3 to 8 days , 5. AlthIt has been described that rotavirus RNA (which may imply viraemia) and antigen(s) (namely VP6) are detected in 64–93% and 33–90% in serum of children with rotavirus gastroenteritis, respectively . SystemiAs based on a binary classification system that focuses on the outer layer VP7 and VP4 proteins there are currently 28 G (of glycoprotein) and 39 P (protease-sensitive) genotypes, respectively. Of these, only 12 G and 15 P types are commonly associated with infections in humans, particularly those strains bearing G1P [8], G2P [4], G3P [8], G4P [8], G9P [8] and G12P [8] specificities, which altogether account for greater than 80% of circulating rotaviruses . The issIn the current post-rotavirus vaccination scenario, where two live, attenuated, oral rotavirus vaccines  are licensed for use in over 100 countries , studiesThis study aimed primarily at determining if children hospitalised for acute gastroenteritis with rotavirus RNA and/or rotavirus antigen in stools and serum samples were more prone to develop severe gastroenteritis symptoms, as compared to those with RNA and/or rotavirus antigen detected in stools only. We also sought to assess whether or not rotavirus genotypes recovered from stool samples are homologous to those detected in sera.Clínica Pediátrica do Pará (which has closed as from March 2013 onwards); and from April 2013 to June 2015 at Clínica Pio XII). The recruitment of these children took place in these large paediatric hospitals, which then accounted for over 50% of all hospital admissions for gastroenteritis in the Belém area, as based on our previous local surveillance studies .As demonstrated in Fig. In spite of the current scenario where two licensed vaccines are increasingly being made available for introduction in many countries around the world, there remains to be fully elucidated the mechanisms of pathogenesis in rotavirus infection, particularly that underlying virus spreading beyond the gastrointestinal tract into the blood stream . The curNoticeably in our cohort, rotavirus antigen was detected in 24% of the stool specimens tested by ELISA, an overall positivity rate found to be higher than that of 17% reported in a study also conducted during the early years post-rotavirus vaccine introduction in Brazil . The disOur results showed that antigenemia was present in around 40% of children hospitalised for acute RV-related gastroenteritis in Belém, a rate similar to those reported in previous studies conducted elsewhere which were in the range of 33–75% , 18, 36.As also reported elsewhere , we haveIn our study we performed a correlation analysis of paired stool and serum samples obtained from 96 paediatric patients which provided evidence that the stool rotavirus copy numbers correlate significantly with the ELISA OD values in sera Fig. . These fIn the present study we were able to assess whether or not VP7 and VP4 genotype specificities were concordant in comparing paired stool-serum samples from ten paediatric patients hospitalised for rotavirus-related gastroenteritis during the first year of surveillance. Rotavirus strains bearing G2 and P [4] genotype specificities – which were found to be dominant during June 2012–July 2013 – could be identified in seven of the ten pairs, suggesting that rotaviruses infecting the gut were the same strains which disseminated beyond the intestine. These concordant results appear to be consistent with findings from a few other studies conducted elsewhere, even though discrepancy of genotypes in stool and serum samples (G12P [8] and G2P[NT] pairs) of individual patients was also see in our two of our study patients. These latter findings raise the hypothesis that identification of distinct genotypes in two specimens from a single patient may reflect either consecutive infections at different time points or the occurrence of intrinsic difference in growth characteristics and/or tissue tropism, leading to speculate that during co-infections a preferential extra-intestinal spread of some strains may occur , 39, 40.In the present study we also sought to determine whether children with antigenemia presented with more severe rotavirus disease than children without antigenemia. It was observed that patients with rotavirus antigen in serum were more likely to develop frequent vomiting, as compared to patients without antigenemia. In contrast, we could not find any significant difference between the groups in regards to the severity of other clinical parameters including fever, duration of vomiting, diarrhoea and white blood cell count. Unlike other studies conducted elsewhere , 18, ourOf potential importance in the current post-rotavirus vaccine introduction scenario, our findings suggest that children not vaccinated against rotavirus appear to be more likely to develop antigenemia, as compared to those given at least one vaccine dose. However, further studies are needed to confirm this finding since such a difference may have occurred by chance owing to the small sample size in our study.Although a significant proportion of children in our study were found to develop antigenemia/RNAemia, we could not detect any clinical manifestations which might be suggestive of extra-intestinal involvement of rotavirus infection. This supports the notion that while viraemia constitutes a common event, it does not appear that systemic infection is frequently associated with clinically significant non-gastrointestinal disease. Furthermore, it has also been postulated that subclinical extra-intestinal infections often occur in children and may occasionally progress to an overt clinical condition , 44, 45.The findings of this study are subject to some limitations. First, our antigenemia results should be interpreted with caution, since all currently available ELISA commercial kits are designed to detect rotavirus antigen in stool samples and had not been validated for use with either human or plasma samples. Nonetheless, our data suggest that ELISA and RT-PCR display similar sensitivities for the detection of antigenemia and RNAemia, respectively. Of note in this regard, earlier studies have suggested that ELISA may potentially offer a new practical diagnostic tool for the detection of rotavirus from serum/plasma samples in situations where a stool sample is not readily available (e.g. severe dehydration) . Second,In summary, our results confirm and extend previous well documented findings that rotavirus antigenemia/ RNAemia occurs routinely among children with acute rotavirus gastroenteritis and may contribute to a greater disease severity by increasing the number of vomiting episodes. Of particular interest in the current context of growing rotavirus vaccine introduction, it is has been pointed out that detection of antigenemia/RNAemia may represent an additional, useful approach to improve the sensitivity of post-licensure epidemiological studies of vaccine effectiveness . In addiTo our knowledge this is the first report in Brazil showing that rotavirus antigenemia and RNAemia are detectable in a significant proportion of infants and young children admitted to hospital due to acute gastroenteritis. According to the Vesikari scoring system a trend for greater clinical severity was seen among children with rotavirus-positive serum, as compared to those with rotavirus-negative serum. A statistically significant difference between these groups was particularly seen with regards to the number of vomiting episodes. It has also been demonstrated a positive correlation between the levels of rotavirus antigen in sera, as determined by ELISA, with the stool viral load, as assessed by qRT-PCR. Both G2P [4] and G12P [8] rotavirus genotypes accounted for most of the isolates throughout the study period; in addition, we have documented a high concordance of genotypes detected in a subgroup of paired stool and serum samples. Further studies on rotavirus antigenemia/RNAemia are warranted to better assess the effectiveness of currently used rotavirus vaccines, as well as to determine whether licensed vaccines may eventually spread beyond the intestines.Additional file 1:Figure S1. Agarose gel electrophoresis of RT-PCR VP7 (A) and VP4 (B) gene products obtained from the sera of ten rotavirus-positive children with gastroenteritis in Belém, Brazil. (PPTX 121 kb)


Metallic antimicrobial materials are of growing interest due to their potential to control pathogenic and multidrug-resistant bacteria. Yet we do not know if utilizing these materials can lead to genetic adaptations that produce even more dangerous bacterial varieties.Escherichia coli K-12 MG1655 resistant to, the iron analog, gallium nitrate (Ga(NO3)3). Whole genome sequencing was utilized to determine genomic changes associated with gallium resistance. Computational modeling was utilized to propose potential molecular mechanisms of resistance.Here we utilize experimental evolution to produce strains of fecA (iron citrate outer membrane transporter), insl1 (IS30 tranposase) one intergenic mutations arsC →/→ yhiS; (arsenate reductase/pseudogene) and in one pseudogene yedN ←; (iapH/yopM family). Two additional significant intergenic polymorphisms were found at frequencies > 0.500 in fepD ←/→ entS  and yfgF ←/→ yfgG . The control populations displayed mutations in the rpoB gene, a gene associated with rifampicin resistance.By day 10 of evolution, increased gallium resistance was evident in populations cultured in medium containing a sublethal concentration of gallium. Furthermore, these populations showed increased resistance to ionic silver and iron (III), but not iron (II) and no increase in traditional antibiotic resistance compared with controls and the ancestral strain. In contrast, the control populations showed increased resistance to rifampicin relative to the gallium-resistant and ancestral population. Genomic analysis identified hard selective sweeps of mutations in several genes in the gallium (III)-resistant lines including: Pseudomonas strains that also showed that Gram-negative bacteria can rapidly evolve resistance to an atom that mimics an essential micronutrient and shows the pleiotropic consequences associated with this adaptation.This study corroborates recent results observed in experiments utilizing pathogenic Escherichia coli K-12 MG1655 resistant to, the iron analog, gallium nitrate (Ga(NO3)3). Whole genome sequencing was utilized to determine genomic changes associated with gallium resistance. Computational modeling was utilized to propose potential molecular mechanisms of resistance.We utilize experimental evolution to produce strains of  The spread of resistance to traditional antibiotics has spurred the search for new antimicrobial substances . Ionic a3+) to saturate bacterial siderophores that normally take up ferric iron (Fe3+). Studies on the impact of gallium suggest that it may impede growth without killing cells (bacteriostatic) or eventually kill cells via prolonged iron starvation  . Furthermore, an unexpected result of excess iron resistance in these strains was resistance to gallium which is an iron analog possessing the same nuclear radius as iron (Fe3+). For this reason, gallium has been shown to exchange for iron in siderophores such as in Pseudomonas aeruginosa where the siderophore pyochelin transports gallium into the cell . Finally, six low-level polymorphisms were observed. Ga3+_1 displayed a mutation in prlF UniProtKB-15373, antitoxin component of a type II toxin-antitoxin (TA) system, Ga3+_2 had a mutation in nhaB UniProtKB-P0AFA7, a Na+/H+ antiporter that extrudes sodium in exchange for external protons and Ga3+_5 had one in rpoS UniProtKB-P13445, RNA polymerase sigma factor S. The remaining low-level polymorphisms were intergenic with Ga3+_1 showing ypjC ←/← ileY; ypjC UniProtKB-76613 predicted protein and ileY tRNA isoleucine; agaI →/→ yraH, agaI UniProtKB-P42912, putative deaminase, galactosamine-6 phosphate isomerase; yraH UniProtKB-P42913, putative fimbrial-like adhesin protein; dcuB ←/← dcuR, dcuB, UniProtKB-P0ABN9, anaerobic C4-dicarboxylate transporter DcuB and DcuR are members of the two-component regulatory system DcuB/DcuR, involved in the C4-dicarboxylate-stimulated regulation of the genes encoding the anaerobic fumarate respiratory system. arsC →/→ yhiS in the C4 population; and a series of mutations in rpoB → in C1–C3, at position 4 183 378 ranging in frequency from 0.655 to 0.320; in C4 at position 4 182 820 in a frequency of 0.338 and in C4 at position 4 183 379 in frequency of 0.607. The arsC (UniProtKB-POAB96) gene is involved in reducing arsenate to arsenite allowing for efflux of this toxic material from the cell and yhiS is an uncharacterized protein. RNA polymerase subunit B (rpoB) is a DNA-dependent RNA polymerase that catalyzes the transcription of DNA into RNA using the four ribonucleoside triphosphates as substrates; and resistance to the antibiotics salinamide A, salinamide B, rifampicin, streptolydigin, CBR703, myxopyronin and lipiarmycin can result from mutations in this protein (UniProtKB-POA8V2).The most notable mutations observed in the control populations were in an intergenic mutation occurring between 3+-selected populations and the controls are found in 3+ resistance were found in the controls, or have ever been observed in the control populations of our previous studies [All polymorphisms classified by breseq in the Ga studies , 33, 34.E. coli K-12 MG1655 can rapidly evolve resistance to gallium. We predicted that this would be true based upon our previous work that demonstrated that this strain could evolve resistance to excess iron  and that in each case, increased resistance to gallium resulted [P. aeruginosa. On the face of it that result is counter intuitive, given that a 2013 study did find evidence of anti-gallium mutations in P. aeruginosa [These studies have clearly shown that resulted , 22. Ourresulted . That struginosa  and a reruginosa . Thus, i3+) and ionic silver (Ag+) was observed in the Ga3+-resistant populations. The latter result can be questioned, in that the genomic analysis showed no selection in genes that have previously been associated with silver resistance. Although selection for iron (II) and iron (III) resistance in our previous studies did confer a minor increase in silver resistance without evidence of selection in the genes cusS, ompR, that played a major role in conferring resistance [2+- and Fe3+-resistant populations showed cross resistance to iron (II) and iron (III). The Ga3+-resistant populations are resistant to iron (III), but not to iron (II).In addition to gallium resistance, correlated resistance to excess iron  and these are associated with increased resistance to rifampicin [The Gafampicin . It seemfampicin , 44.3+-resistant, control and ancestral populations in chloramphenicol and tetracycline. The Ga3+-resistant, control and ancestral populations showed equivalent growth at lower concentrations of sulfanilamide (6–75 mg/l). However, both the Ga3+-resistant and control populations performed better at higher concentrations (100–250 mg/l) of sulfanilamide compared with the ancestor. As neither of these populations were exposed to sulfanilamide, it is likely that this improvement in growth is due to some other aspect of the environment shared by both populations (such as minimum sugar content). ATCC cultures this strain using Luria broth which has a higher sugar content than DMB .There was no significant difference between the Ga3+-resistant populations is significant in that our iron-selected populations (Fe2+ and Fe3+) did show correlated resistance to antibiotics [3+-resistant populations showed a mutation in the fecA gene . This gene encodes the protein FecA is responsible for Fe3+ transport in aerobic conditions [3+ import in these strains. For example, we have shown the FecA mutations (A559T and G243C) observed in our iron (II)-resistant populations express 128-fold less fecA than the controls in absence of iron indicating a likely preemptive mechanism of defense [The lack of correlated antibiotic resistance in the Gaibiotics , 22. Thenditions . We propfecE; fecB; qmcA/fetA; fepD/entS). The remaining polymorphisms showing evidence of selection have no clear relationship to iron metabolism, thus some could be responses to excess NO3− in the medium, or they could have yet undiscovered relationships to gallium resistance. It is also important to note the difference in our genomics results  and those obtained via knock out mutations in E. coli [E. coli K-12 [E. coli K-12 . In addition, the knock out method cannot identify intergenic regions that contribute to gallium adaptation, as no intergenic regions are knocked out in these strains. Thus, at best, knock out methods can identify genes that could possibly play a role in adaptation, but this technique is not necessarily going to tell you which genes will play a role in adaptation.Several of the other genomic variants showing strong selection involved in conferring gallium resistance are in genes and intergenic regions associated with iron metabolism  populations showed positive pleiotropy for five antibiotics: ampicillin, chloramphenicol, rifampicin, sulfanilamide and tetracycline [2+-selected replicates showed at least one or more of these populations identified hard sweeps in genes such as murC, cueR, tolC, yeaG and ptsP. Several of these genes have been shown to be associated with antibiotic resistance, such as in the case of the murC which plays a role in cell wall biosynthesis as well as repairing oxidative damage [rpoB, that are consistently associated with antibiotic resistance [However, our Gaacycline , 34. Gene damage . Howeversistance . We belisistance . We usedE. coli K-12 MG1655 can rapidly evolve resistance to gallium in opposition to claims of the evolutionary stability of gallium quenching. This trait also conferred resistance to ferric iron (iron III) and ionic silver, but no correlated responses to antibiotics were observed. The genomic results indicated that gallium resistance results from selection on mutations associated with iron metabolism. This is not surprising as gallium is an iron analog. We propose that these mutations are likely to be associated with the down regulation of genes involved in iron acquisition; as we observed in our iron (II)- and iron (III)-resistant populations [In summary, we have found that ulations , 34.fecA). The parallelism of the mutations in fecA is a strong evidence that this gene played a major role in conferring gallium resistance. The Ga3+_4 replicate showed three additional hard sweeps, while the other populations had associated polymorphisms associated with iron metabolism not observed in the ancestor or control populations. We also suspect that these genomic changes must be associated with a profound difference in gene expression associated with iron and general metal metabolism. This was unmeasured in this study but is consistent with our previous results [As in the evolution of silver, copper and iron resistance, a small number of genomic changes were required to produce the gallium-resistant phenotypes. All replicates showed at least one hard selective sweep ( results , 34. Fin results , 24, baceoz025_Supplementary_DataClick here for additional data file.


Focal thyroid lesions are common ultrasound findings with the estimated prevalence up to 67% of the population. They form characteristically enveloped regions with individual encapsulated microenvironment that may involve the specific distribution of immune system compounds—especially antigen presenting cells (APC). We analyzed and compared the most potent APC—plasmacytoid and conventional dendritic cells (DCs) subpopulations and three monocyte subpopulations as well as other immune cells—in peripheral blood and local blood of thyroid gland obtained parallelly in patients with focal thyroid lesions using flow cytometry. The analysis revealed significant differences in the distribution of main subsets of assessed cells between peripheral blood and biopsy material. The results support the existence of local, organ-specific immune reaction control networks within thyroid nodules. Focal lesions of the thyroid gland are differentiated fragments of organ tissue characterized by disturbed structure of the parenchyma, visible in ultrasonography (US) as isolated areas of altered echogenicity. Palpable thyroid nodules occur in 5% of the population, however, on the basis of neck US it is suggested that thyroid nodules can be present in 20–70% of the total population ,2. Many Thyroid nodules often represent characteristically enveloped regions with individual encapsulated microenvironment. The involvement of the immune system elements, present both in the tissue and in the local organ blood circulation, is postulated in the pathogenesis of both benign and malignant thyroid lesions. This applies in particular to antigen presenting cells (APC).While most of focal thyroid lesions are of benign nature, thyroid carcinomas occur in approximately 4–5% of palpable nodules . ThyroidAPC are heterogeneous group of cells that exhibit common ability to capture and process antigens in order to present them in the context of appropriate major histocompatibility complex (MHC) molecules . Among dIt is believed that DCs—the most effective type of APC—play a critical role in the control of the function of the immune system, also as a component of local thyroid regulatory systems. Healthy thyroid tissue is characterized by a small amount of DCs located outside thyroid follicles. Increased number of DCs infiltrating the thyroid gland was found in the course of AITD and thyroid cancer ,7.The immunoregulatory function differs considerably among specific DC subpopulations. Two main lineage backgrounds, which provide cells characterized by distinct functional properties, surface molecule expression, efficacy of antigen uptake and presentation, as well as cytokine secretion profile were described . ConventMonocytes originate from bone marrow myeloid progenitor and represent 5–10% of human peripheral blood leukocyte population. After migration to target organs they differentiate into macrophages or DCs . It is ghighCD16− classical monocytes, CD14highCD16+ intermediate monocytes and CD14lowCD16+ non-classical monocytes [+ non-classical and intermediate monocyte subsets are generally termed proinflammatory monocytes because of their ability to secrete high amounts of TNF-α and IL-1β [+ monocyte subsets in promotion, propagation and maintaining of immune reaction remains to be elucidated [Based on the surface expression of CD14 (monocyte specific lipopolysaccharide (LPS) receptor) and CD16 (Fc type G immunoglobulin receptor (FcγRIII)), human peripheral blood monocytes were divided into three functionally different subpopulations: CD14onocytes . The CD1nd IL-1β ,22. Howeucidated . Presence of various populations of APC in the thyroid and their potential involvement in the pathomechanisms of different thyroid disorders have been indicated in earlier studies . HoweverThe aim of this study was to analyze and compare APC subpopulations in peripheral blood and local blood flow of thyroid gland in patients with focal thyroid lesions.Cytometric analysis of peripheral blood and fine needle aspiration biopsy (FNAB) samples showed differences in structure and phenotype of the studied populations of immune cells.The percentage of CD14+ monocytes in leukocyte mononuclear fraction did not differ between FNAB and peripheral blood. Similarly, no difference was observed in particular patient groups . In orde+ cells from FNAB were characterized by significantly higher mean fluorescence of CD16 staining (p < 0.00001) indicating higher level of surface expression of this molecule ) was described as a marker of new subset of DC/monocyte . In agrepatients .highCD16− monocytes was shown in FNAB material as compared to peripheral blood. Interestingly, the associated shift of monocyte structure in FNAB material was expressed differently in particular patient groups. Higher percentage of CD14highCD16+ intermediate monocytes was shown in FNABs of non-AITD patients while AITD patients had higher percentage of CD14lowCD16+ non-classical monocytes . The important role of CD14highCD16+ monocytes is suggested in many inflammatory conditions [highCD16+ subpopulation represented a large percentage of monocytes in inflammatory infiltrates of the intestinal mucosa in Crohn’s disease [highCD16+ monocyte subpopulation was found in non-AITD group, while FNAB material of AITD patients showed an increased percentage of CD14lowCD16+ but not CD14highCD16+ monocyte subset. Non-classical CD14lowCD16+ cells are considered to be a more mature monocyte population [lowCD16+ monocytes into the site of ongoing autoimmune reaction. It can, therefore, be assumed that depending on the type and severity of the immune response, monocytes of the intermediate and non-classical subsets may, in addition to increased presence in the systemic and local circulation, show an increased affinity to peripheral tissues in organ specific pattern.In our study we report, for the first time, significant differences in the distribution of main monocyte subsets between peripheral blood and local thyroid blood in patients with thyroid nodules. Leukocyte fraction of FNAB specimens was characterized by increased percentage of CD14nditions . An incrnditions  as well nditions . Most re disease , as well disease . In our pulation , charactpulation . Moreovepulation ,31. ThuslowCD16+ and CD14highCD16+ monocyte subsets obtained from thyroid focal lesions were characterized by an increased percentage of cells with surface expression of the slan molecule in comparison with peripheral blood. The exact role and linear background of slan+ cells remain unclear. Slan+ cells were suggested to represent a distinct subtype of DCs—so called slanDCs [lowCD16+ monocytes. However, it was shown that the slan molecule is present on the surface of about 50% CD14lowCD16+ monocytes only, therefore it is uncertain whether the slanDCs and CD14lowCD16+ monocytes should be classified as the same population [lowCD16+ monocytes expressed slan in both peripheral blood and FNAB material. Moreover, the difference in slan expression was dependent on the thyrometabolic state of patients. In euthyroid patients group only CD14lowCD16+slan+ monocytes in FNAB material of AITD patients were significantly increased as compared to peripheral blood. These findings once again point to a local organ specific to residing monocyte subpopulations. In the course of further phenotype analysis of monocytes, it was found that the CD14 slanDCs . SlanDCspulation . Neverthpulation . As prevpulation ,34, alsoA very interesting observation made in the present study is also the significantly higher level of CD16 surface expression on cells obtained from the thyroid gland. This observation was made in both patient groups and was independent of thyrometabolic state. The CD16 molecule binds IgG immune complexes and activates intracellular pathways involved in stimulation of the mechanisms of natural cytotoxicity, secretion of proinflammatory cytokines, as well as phagocytosis . The CD1The excessive engagement of DCs has been already suggested in the pathophysiology of different thyroid disorders as reviewed extensively in our previous publication . Until r+ lymphocytes, anergy of CD4+ lymphocytes, or differentiation of regulatory T cells [The increased percentage of pDCs subpopulations in the FNAB mononuclear leukocyte fraction may be related to the response of these cells to chemotactic stimuli generated during a local inflammatory process occurring inside the focal lesions of the thyroid or in surrounding tissue. Plasmacytoid DCs are considered to be the main source of type I IFN secreted in the early response to pathogenic antigens. On the other hand, these cells are characterized by a strong ability to induce tolerance in the adaptive arm of the immune response, in the mechanisms dependent on deletion of CD8 T cells . In norm T cells . Tolerog T cells . In addition to differences in pDCs subset, the current study showed an increase in percentage of cDCs in the FNAB material as compared to peripheral blood. Similarly, as in the case of pDCs, the increased presence of cDCs in the FNAB material can potentially be understood as a migration step from the systemic circulation to peripheral tissues. Whereas, the differences found between patients with different pathologies of the thyroid may be associated with different intensity of local inflammatory processes and intensity of chemotactic signals. The important thing is that DCs accumulating in target tissues, become themselves a very efficient source of a wide range of humoral factors, including IL 8—one of the key chemoattractants for granulocytes, monocytes, and lymphocytes . Thus, a+ NK cells in FNAB material. CD56lowCD16+ NK cells constitute ca. 90% of NK cells in the bloodstream and are considered a highly cytotoxic subpopulation. In contrast, CD56lowCD16− NK cells are observed in the parenchyma of most organs. It is suggested that a high percentage of CD56lowCD16+ NK cells in tissues may be associated with an intense cytotoxic reaction, e.g., against tumor cells or cells infected with intracellular pathogen. It may also indicate ongoing autoimmune response [+ NK cells over CD16− NK cells was also reported in the tissue of patients with non-toxic thyroid goiter [In our study, we also demonstrated higher percentage of CD16response . Importad goiter . Whereasd goiter ,51.In conclusion, in our work we demonstrated a broad set of data increasing the knowledge of local immune environment in focal thyroid lesions in humans. A group of 90 patients with thyroid focal lesions found on US examination were included into the study. Patients were diagnosed and recruited in the Department of Endocrinology and Metabolic Diseases—Polish Mother’s Memorial Hospital—Research Institute, Lodz, Poland. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of the Polish Mother’s Memorial Hospital—Research Institute, Lodz, Poland . Prior to the enrolment, all of the participants signed an informed consent, according to the study protocol. Analysis was performed on paired FNAB and peripheral blood samples obtained at the same time point. Complete clinical and demographic data were registered .n = 67) and patients with AITD (n = 23). All analyses were performed additionally on analogous groups included only euthyroid patients (n = 72): non-AITD euthyroid patients (n = 51) and AITD euthyroid patients (n = 21). On the basis of clinical diagnosis patients were divided into two groups: non-AITD patients , with high frequency 7–14 MHz linear transducer, with standard settings optimized for thyroid imaging. Fine needle aspiration biopsy was performed in all patients using a 23-gauge needle. All FNAB specimens were obtained from standard clinical indications by trained medical personnel under the control of US in the Department of Endocrinology and Metabolic Diseases—Polish Mother’s Memorial Hospital, Lodz, Poland. The FNAB needle after aspirate collection was immediately washed with phosphate buffered saline (PBS)  into EDTA containing Blood Collecting System . Directly after FNAB procedure a sample of peripheral blood (2.7 mL) was collected from each patient by venipuncture into EDTA containing Blood Collecting System (the same type as in the case of FNAB).Fluorescein isothiocyanate (FITC) conjugated mAb: anti-CD16  ,Phycoerythrin (PE) conjugated mAb: anti-SLAN  , anti-CD86 (2331 (FUN-1), Mouse IgG1) (BD Biosciences), anti-CD11c  (BD Biosciences),Peridinin-chlorophyll-protein complex (PerCP) conjugated mAb: anti-CD14  (BD Biosciences), anti-CD19  (BD Biosciences),Allophycocyanin conjugated mAb: anti-CD3  (BD Biosciences), anti-BDCA1-(CD1c)  (Miltenyi Biotec), anti-BDCA2-(CD303)  (Miltenyi Biotec), anti-BDCA3-(CD141)  (Miltenyi Biotec),Phycoerythrin-cyanin conjugate 7 (PE-Cy7) conjugated mAb: anti-CD56  (BD Biosciences).Cells obtained by FNAB and peripheral blood cells were subjected to extracellular labeling using a panel of fluorochrome-labeled antibodies specific for selected surface molecules. The concentration of used antibodies was standardized in accordance with the manufacturer’s instructions and adjusted to the number of cells in individual samples. The respective samples were subjected to multiple extracellular staining with following monoclonal antibodies (mAb):Post-staining red blood cell lysis was performed using BD FACS lysing solution . Before measurement samples were washed in PBS and centrifuged at 1400 RPM for 5 min at 20 °C. ® cytometer and BD FACSDiva® software . At least 10,000 cells were counted in mononuclear leukocyte gate in each sample.FACS measurements were performed with a BD FACSCanto IIThe population of mononuclear cells and its main fractions—lymphocytic and monocytic fractions were identified on the basis of size (FSC—forward side scatter) and granularity (SSC—side scatter) parameters .-+ (pDCs—BDCA2 (CD303)+ -+ CD19− (cDCs—BDCA1 (CD1c)+ CD19− DCs subsets were recognized on the basis of expression of a panel of surface molecules known as Blood Dendritic Cell Antigens (BDCA) and CD19 .-pDCs—BMonocyte subsets were recognized on the basis of the surface expression level of CD14 and CD16 molecules .+ . A graphical representation of the results was prepared using the SigmaPlot 11 software . The normality of the distribution was assessed using the Shapiro–Wilk test. The Student’s This study is one of the first attempts to gain insight into the structure of the regulatory cell population potentially involved in the immunopathological processes of thyroid focal lesions.The results of parallel analysis in the peripheral blood and FNAB material suggest the existence of local, organ-specific immune reaction control networks, associated not only with the most effective APC—DCs and monocytes, but also with lymphocytic populations with known immunoregulatory properties such as NK and NKT cells.In addition to the organ specificity, the differences found between individual groups of patients suggest a different involvement of the regulatory elements in the immunopathology of specific thyroid diseases. However, further research involving large clinically homogenous groups of patients is needed to elucidate potential differences between particular disorders of the thyroid, especially autoimmune conditions such as HT and GD.These observations may be of potential value in further research aimed at the development of diagnostic and therapeutic tools.


The nature and extent of immune cell infiltration into solid tumours are key determinants of therapeutic response. Here, using a DNA methylation-based approach to tumour cell fraction deconvolution, we report the integrated analysis of tumour composition and genomics across a wide spectrum of solid cancers. Initially studying head and neck squamous cell carcinoma, we identify two distinct tumour subgroups: ‘immune hot’ and ‘immune cold’, which display differing prognosis, mutation burden, cytokine signalling, cytolytic activity and oncogenic driver events. We demonstrate the existence of such tumour subgroups pan-cancer, link clonal-neoantigen burden to cytotoxic T-lymphocyte infiltration, and show that transcriptional signatures of hot tumours are selectively engaged in immunotherapy responders. We also find that treatment-naive hot tumours are markedly enriched for known immune-resistance genomic alterations, potentially explaining the heterogeneity of immunotherapy response and prognosis seen within this group. Finally, we define a catalogue of mediators of active antitumour immunity, deriving candidate biomarkers and potential targets for precision immunotherapy. Determining the extent of immune cell infiltration into solid tumours is essential for adequate therapeutic response. Here the authors develop a DNA methylation-based approach for tumour cell fraction deconvolution and analyse tumour composition and genomics across a wide spectrum of solid cancers. The recent clinical success of immunotherapy in subpopulations of patients with previously intractable malignancies has also highlighted the importance of understanding the tumour microenvironment in order to identify those patients who will derive the most benefit from targeted therapies2. Although responses to immune checkpoint blockade (ICB), e.g. antibodies against PD-1 (programmed cell death protein 1), PD-L1 (programmed death-ligand 1) and CTLA-4 (cytotoxic T-lymphocyte-associated protein 4), are seen across many solid tumours, the proportion of patients that benefit varies widely by cancer type and we currently lack biomarkers with which to reliably predict immunotherapy response3. Emerging evidence from clinical trials indicates higher response rates in those cancer types that typically display greater lymphocyte infiltration  and that the tumour neoantigen repertoire (a function of somatic mutation load) is a key determinant7. These observations point to a model in which, within any given cancer type, there are immune hot and immune cold tumours. Immune hot tumours display greater cytotoxic T-lymphocyte (CTL) infiltration, and reactivation of these tumour-resident CTLs by checkpoint inhibition can result in dramatic tumour regression. Conversely, immune cold tumours display minimal CTL infiltrates and typically fail to respond to checkpoint modulation. If one could accurately identify likely responders for patient stratification and devise strategies by which to convert cold tumours to hot tumours, these would be major steps forward in realising the full clinical potential of cancer immunotherapy.The tumour microenvironment plays key roles in shaping tumour evolution and in determining treatment responses; prominent intratumoural lymphocyte infiltration is a favourable prognostic marker in multiple tumour types, while a high stromal content of extracellular matrix-producing cancer-associated fibroblasts (CAF), is associated with poor outcomes15. For example, accurate deconvolution of complex cellular mixtures, including tumours, has recently been achieved by the application of support vector regression modelling (CIBERSORT) to gene expression microarray data16. Notably, DNA methylation data are also suitable for deconvolution of tissue mixtures, although studies so far have focussed primarily on simple tissues such as blood, where cell type differences are a major confounder in Epigenome Wide Association Studies10.Although flow cytometry of disaggregated tumour biopsies is commonly used for investigating cellular composition, this is often unfeasible for several reasons: difficulty in obtaining fresh tumour tissue; lack of defined markers for poorly characterised cell types (e.g. CAFs); and high cost of labour, reagents and equipment required for such analyses. Cellular disaggregation of collagen-rich tumours is also problematic, where cells are embedded in a dense extracellular matrix. To overcome these difficulties, multiple reference-free or reference-based methods have recently been developed to permit the in-silico deconvolution of complex cellular mixtures or to estimate tumour purityPTEN and amplifications in MYC and EGFR. We show that responses to PD1-blockade are associated with a transcriptional signature for hot tumours post-treatment, while the cold signature, and specifically a gene expression module we previously linked to increased aerobic glycolysis downstream of EGFR in head and neck squamous cell carcinoma (HNSCC)17, is enriched in non-responders. Importantly however, defining whether a tumour is hot or cold is not sufficient to accurately predict response to ICB, and by interrogating matched genomic data, we show that treatment-naive hot tumours frequently display genomic alterations known to confer immunotherapy resistance.Here, we present CIBERSORT-based deconvolution to genome-wide DNA methylation data from whole tumour tissue (hereafter referred to as ‘MethylCIBERSORT’). We provide accurate estimates of tumour purity and cellular composition, and identify immune hot and cold tumours across a broad spectrum of cancer types profiled by The Cancer Genome Atlas Project (TCGA). Using matched genomic and transcriptomic data, we identify multiple copy number alterations enriched in cold tumours, including deletions in R = 0.986, p < 2.2e−16, Fig. 14. This revealed that MethylCIBERSORT estimates demonstrate higher correlations, both at the cell-type and the sample level . We then evaluated the ability of our feature selection heuristic to accurately deconvolute mixtures of leukocytes using publicly available methylation data from mixtures of peripheral blood mononuclear cells (PBMCs) with composition verified by flow-cytometry (gold standard). This showed an extremely high correlation between the estimated and gold-standard fractions  estimates derived using MethylCIBERSORT with estimates derived from ABSOLUTE21 (which jointly estimates purity and ploidy using mutation and copy number data) relative to other previously published methods of estimating purity (LUMP22 and ESTIMATE23) with data aggregated in ref. 22, MethylCIBERSORT displayed the highest correlation (R = 0.82) and better concordance with ABSOLUTE than other methods , particularly in those cancers driven by human papillomavirus (HPV)ods Fig. . Analysiods Fig. , resultiWe also compared the mRNA expression of a panel of cellular lineage markers with MethylCIBERSORT estimates and found significant associations for multiple cell types  but more specifically attributed this to increased numbers of B (CD19+) and cytotoxic T (CD8+) lymphocytes  HNSCCR = −0.41) and Tregs (R = −0.38). CD56+ natural killer (NK) cell abundance is also inversely correlated with CTLs (R = −0.45). Of note, CTLs are inversely correlated with fibroblast abundance (R = −0.12) and to validate this latter finding, we analysed data from two large studies in which these parameters had been quantified in HNSCC28. In a pooled analysis of these data, TIL content and SMA expression (a CAF marker) are inversely correlated  cohort .Next, we extended our analysis to HPV-negative (HPV−) HNSCC, a heterogeneous, anatomically-diverse group of tumours in which prognosis is typically much poorer than in HPV+ disease. Again, using TCGA data (available for 398 HPV− HNSCCs) we observed interesting relationships between multiple cell types, with 24/36 pairs of cell types showing significant correlations 29. Most infiltrating cell fractions display significant correlations with cytolytic activity, with CD8+ cells showing the maximum positive correlation , and increased ratios of CTLs to Tregs ; a metric that is prognostic in multiple settings32.Given the complex nature of associations between different cell types, we performed consensus PAM clustering on the estimated cellular fractions to define subgroups by infiltration patterns. We derived two clusters  that show markedly different distributions of multiple cell types, most notably CTLs, Tregs, CD4+ effector T-lymphocytes, CD19+ B-lymphocytes and NK cells, all of which are implicated in antitumour immunity Fig. . Consistp < 2e−16), CD14+ , and CD56+  cell abundance as positive predictors and fibroblast abundance as a negative predictor .Together, our analyses suggest a tilting of the balance towards CTL activity in the microenvironment of tumours from the hot cluster. Integrated analysis of the impact of different cell populations on cytolytic activity using linear modelling identified CD8+ , CD79A and CD19 (B-lymphocyte markers), are all upregulated in the hot tumours, as are multiple chemokines and their receptors , immune checkpoint gene transcripts , and as expected, the cytolytic markers PRF1 and GZMA. In extended analyses of all genes at FDR < 0.01 (Supplementary Data B2M (FC = 1.39), PD-1 ligand CD274 (FC = 1.62) and ACTA2, which encodes SMA (FC = 0.68), are also differentially expressed between the two clusters, the latter validating fibroblast estimates from MethylCIBERSORT  between the hot and cold clusters at a fold-change of greater than 2  associated with immune cluster using negative binomial regression. This identified enrichment of CASP8, PIK3CA, CREBBP, EP300 and HLA-A mutations in hot HNSCCs, while TP53 and KDM6A mutations are enriched in cold HNSCCs , an upstream activator of pro-apoptotic signalling through Caspase 8 is also upregulated in the immune hot tumours, further highlighting the importance of this pathway  and a smaller increase in overall mutational burden . Moreover, in 15 tumours from the hot cluster versus five in the cold cluster, CASP8 mutations themselves encoded at least one neoantigenic peptide 39 and recorded very high correlations, indicating our method is applicable also to the archival material , and predicted immune cluster membership for the validation set of 7596 samples representing 21 further tumour types from TCGA Fig. . As expe29) of observed neoantigens to total nonsilent mutations per tumour and adapted this approach to derive the estimated number of neoepitopes lost through immune editing while controlling for tumour type. Accordingly, we found significant enrichment for editing in hot tumours compared to cold tumours . Additionally, upon integration with T-cell receptor (TCR) repertoire data from Li et al.40, we found more diversity  in the immune hot tumours , suggesting that broader immune responses may underlie the greater depletion of neoantigens in this group.To further determine if the immune infiltrate was active in these tumours, we assayed immunoediting by testing for reductions from the expected ratio , along with costimulatory and coinhibitory receptors, suggesting these tumours were marked by a state of lymphocyte activation and counter-responses thereto. We next scored proinflammatory  and suppressive (Th2) CD4+ cell populations using RNA-seq reference profiles from purified cells to derive relative estimates using CIBERSORT14. Consistent with our inferences from pathway analysis, we found enrichment for Th1 cells in hot, and Th2 and Th17 cells in cold tumours  versus T-helper 2 (Th2) lymphocyte responses , and identified substantially higher fractions of M1 relative to M2 macrophages in hot tumours  treatment44. Analysis of these transcriptional patterns indicated differential expression between responders and non-responders  and more predicted neoantigens than immune cold tumours . We found that the abundance of both CTLs and Tregs is correlated with clonal neoantigen load pan-cancer, while the relationship is much weaker when subclonal neoantigens are considered. CD4+/FOXP3− effector lymphocytes display a striking inverse correlation with clonal neoantigens  as well as a skew in the neoantigen burden towards clonal neoantigens after adjusting for tumour type . These findings provide evidence for a direct link between Class I MHC clonal neoantigen burden and patterns of TIL abundance and may help to explain the observations of McGranahan and colleagues, that high clonal neoantigen burden predicts favourable response to immune checkpoint modulation using Pembrolizumab5.Consistent with our observations in HNSCC, and with estimates from gene expression-based deconvolutionurs Fig. . Given tens Fig. . Consist46 and recorded 56 hits at FDR < 0.05  in a list of genes demonstrated to confer resistance to CD8 T-cell-mediated killing in a recent CRISPR-Cas9 screen47. The functionally-verified immune-resistance genes that were disproportionally mutated in hot tumours included those involved in antigen presentation (B2M and HLA-A), apoptosis (CASP8) and interferon signalling (JAK1). ARID2, which encodes a component of the SWI/SNF chromatin remodelling complex also implicated in modulating sensitivity to T-cell-mediated killing downstream of JAK-STAT signalling, was also disproportionately mutated in hot tumours49. Mutant KRAS, recently implicated in generating a fibrotic tumour microenvironment by activating paracrine signalling with CAFs in pancreatic cancer50, is enriched in cold tumours. Taken together, these findings support a model wherein mutations in certain genes render tumours hot as a consequence (and therefore susceptible to checkpoint blockade), or may enable tumours to survive in a hot tumour microenvironment, potentially also bestowing resistance to checkpoint blockade. We sought to test this model by linking our candidate immune-resistance mutations to lack of ICB response in pre-treatment hot tumours and although we observed a trend, , the number of treated tumours with sequence data available is currently too low (54 hot tumours across four studies) to gain a definitive answer.Next, we examined if the genomic features associated with immune cluster were also reproducible across cancer types, performing adjusted binomial regressions to estimate cluster association after controlling for tumour type for genes previously implicated as pan-cancer drivers based on signatures of positive selectionEGFR) (7p11.2) and MYC (8q24.3) and deletions at 10q23.31, encompassing the PTEN tumour suppressor gene in cold tumours and JAK2 (9p24.1) amplifications in hot tumours. Some of these candidates already have known associations with immune evasion; MYC has been linked to an immune evasion phenotype that is amenable to targeting through gene–body demethylation51 and PTEN deletion has recently been linked to the failure of immunotherapy and decreased cytotoxic T-lymphocyte infiltration in patients and in a mouse model of melanoma53. Among the genomic alterations we identify 28 we found that samples classified as EGFR high and moderate were significantly more likely to be TIL low than EGFR low cancers after accounting for anatomic site and HPV status . The positive correlation between EGFR levels (which are themselves correlated with EGFR phosphorylation (activation), Supplementary Figure p = 0.06, t-test, p = 0.02 when excluding stable disease) and is inversely associated with expression of the hot transcriptional signature (Rho = −0.44) that is associated with ICB response  or perform low resolution deconvolution (ESTIMATE)7 and in identifying the role of mutations in genes like CASP8 in immune evasion29, the deeper level of deconvolution made feasible using DNA methylation data allows the roles of distinct cellular subsets and their interdependencies to be dissected. Here, by applying the method to HNSCC, which is marked by a great degree of clinical heterogeneity, we identified lymphocyte-rich and stromal-rich prognostic subgroups consistent with those discovered previously using a variety of independent methods57 and derived insights into the microenvironmental alterations that might be relevant for prognosis. In the process, we showed that our scheme for classifying cancers correlates with well-established immune metrics such as cytolytic activity, neoantigen/mutational load and CTL:Treg ratios. We demonstrated that tumours similar to the HNSCC immune-hot subgroup exist in varying fractions across the vast majority of cancer types. The congruence of our classification with the aforementioned metrics is maintained throughout and translates to broader TCR responses which presumably drive the greater depletion of neoantigens seen in the hot tumours. Combining the accuracy and ability to determine absolute cell fractions of MethylCIBERSORT with the high resolution afforded by CIBERSORT enabled detection of a skew towards antitumour T-helper and macrophage phenotypes in hot tumours, reinforcing our hypothesis that this cluster is enriched for active antitumour immune responses. Consistent with this, we also showed in the limited post-treatment cohorts with gene expression data, that melanomas responding to ICB (particularly anti-CTLA4) display a gene expression signature derived from our hot cluster. Our ICB response analysis also indicates that hot/cold classification based on cellular composition is a superior predictor of response than total mutation or predicted Class I neoantigen loads (both previously proposed ICB response biomarkers), however none of these metrics displayed sufficient accuracy to be of clinical use. Given the complex interplay between tumour genomics, epigenomics and anti-tumour immune responses, it is likely that prediction algorithms incorporating multiple biomarkers such as these, in addition to checkpoint protein expression and spatial information on immune infiltrates (e.g. Immunoscore58) will be required for patient stratification.While approaches using RNA-sequencing or other transcriptional profiling, such as the construction of an index of cytolytic activity, have been useful in predicting immunotherapy responsePTEN deletion, MYC amplification and EGFR amplification are associated with immune depletion. All three events have been associated with increased glycolysis, which we have previously linked to immune evasion17. Our finding that PTEN deletion is associated with poor CTL infiltration in this pan-cancer cohort adds substantial support and mechanistic rationale for its proposed role as a determinant of response to ICB. Taken together with the identification of EGFR and MYC amplification in cold tumours, our analysis suggests that pharmacological inhibition of EGFR/mTORC1/MYC-driven glycolysis could be an effective means by which to ‘warm-up’ these tumours and potentially enhance responses to ICB. The finding that hot tumours frequently harbour functionally-validated immune-resistance mutations offers a potential explanation for the heterogeneity in ICB response even amongst hot tumours . Secondly, the relative paucity of these mutations in cold tumours (presumably due to the absence of a selection pressure for them) suggests that if we could induce lymphocyte infiltration (e.g. by targeting glycolysis or CAFs59), we may improve the effectiveness of checkpoint blockade across a broader range of patients.Genomic analysis identified significant enrichment for events that confer resistance to T-cell-mediated destruction in hot tumours as well as potential sensitisers. Our copy number analysis revealed that 5. In particular, we show that clonal neoantigens are associated with infiltration of CTLs and Tregs, while Th2 cells and CAFs are enriched in tumours with lower clonal neoantigen loads. Why these relationships between neoantigen loads and T-lymphocytes are apparent only when one considers clonal neoantigens is an intriguing question. It could be that since many subclonal neoantigens are expressed by a small minority of cells within the tumour, these evade effective presentation to the immune system. Indeed, in a previous study by several of the authors, it was possible to isolate T-lymphocytes reactive against clonal but not subclonal neoantigens from lung cancer patients5. Our data suggest that this is due to a relative paucity of CTLs in tumours with low clonal neoantigen loads and that this is true across a wide range of cancer types.Finally, our analysis of neoantigen clonality and immune infiltration patterns adds mechanistic insight to the value of clonal neoantigen burden in predicting response to ICBIn summary, the development of a stand-alone method to estimate both tumour purity and stromal composition from DNA methylation data has provided a number of insights that shed light on potential biomarkers for immunotherapy response and the way in which tumour genomes influence, and are shaped by, the immune microenvironment. Beyond analyses of publicly available data such as those we present here, the applicability of the method to both fresh and archival samples should readily allow researchers to explore questions related to the tumour microenvironment and potential therapeutic response across a diverse range of experimental settings.FlowSorted.Blood.450k Bioconductor package60. CD4+ cells were removed from the Blood.450k dataset and CD4+ T-cells from the Zhang dataset61 (data kindly provided by Dr. Alicia Oshlack) were further divided into FOXP3+ (Tregs) (4) or FOXP3− (6) groups. Fibroblast profiles (4) were from the Gene Expression Omnibus (GSE74877). Neutrophils are the most abundant subset of granulocytes and these samples were therefore aggregated into a single category for further analysis. To generate a DNA methylation signature for cancer cells, we used 450k methylation profiles we previously obtained from a series of six HNSCC cell lines: UM-SCC47; 93VU147T; UPCI:SCC090; PCI-30; UPCI:SCC036 and UPCI:SCC003  and additionally those from Iorio et al. (GSE68379)37. The files were parsed into R using the minfi63 Bioconductor package and were normalised using single sample Noob as implemented in minfi. To derive signature features, a custom limma-based wrapper function was used to fit a series of linear models for all pairwise comparisons between candidate cell types. Features from this set of analyses were then restricted to MVPs that showed a median beta-value difference of 0.25 at an FDR of 0.01 for that fit or less, with a maximum of 100 MVPs per pairwise comparison. Finally, for use with CIBERSORT, data were transformed from beta values (bound between 0 and 1) to percentages (0–100). Type-wise means were estimated for each probe and cell type and the matrix exported for upload to CIBERSORT.Raw data were obtained in the form of IDAT files from the following sources (the number of samples from which each profile was derived is shown in parentheses): granulocytes (12), CD8+ (cytotoxic T-lymphocytes) (6), CD19+ (B-lymphocytes) (6), CD56+ (NK cells) (6), CD14+ (monocyte lineage) (6), eosinophils (6) were from the LM22 (expression-based CIBERSORT) to datasets consisting of PBMC samples and Follicular Lymphoma biopsies originally evaluated in CIBERSORT14. Wilcoxon’s Rank Sum Tests were used to test for differences in correlations with flow-cytometry for cell types and samples, and absolute errors between flow-cytometry and deconvolution estimate. For the expression-based CIBERSORT estimates, we performed comparisons against both calibrated (i.e. enforcing a sum-to-one constraint as reflected in the flow cytometry) and uncalibrated (straight estimates of cell fractions from CIBERSORT) estimates.We applied the feature selection pipeline to the matrix of stromal cells that we assembled and then tested performance against 450k profiles of PBMC mixtures with flow-cytometry gold standards. We also applied 64 in the minfi63 package and BMIQ65, with 10,000 reference probes for Expectation Maximisation fitting. HPV status was determined using VirusSeq66 based on detection of viral gene transcripts.Data for 464 methylation profiled TCGA HNSCC samples were downloaded in the form of raw IDAT files for the 450k array from the TCGA data. Data were normalised using functional normalisationhttps://cibersort.stanford.edu. The data were not quantile normalised due to the potential for global methylation shifts in cancers, and CIBERSORT was run using 1000 permutations. Output files were downloaded as tab-delimited text files and custom parsers were used to import results into R for downstream analysis. FFPE methylation profiles for 42 HNSCC were obtained from Gene Expression Omnibus (Accession GSE38266) using the GEOquery R package, and beta values were BMIQ normalised and analysed using CIBERSORT as described for the TCGA cohort. Wilcoxon’s Rank Sum Tests were used to test for differences in total TIL abundance and TIL subsets.Beta values for deconvolution associated features, and the signature matrix derived in the previous step, were uploaded to CIBERSORT at 22 using Spearman’s Rank Correlation. Correlations were between ABSOLUTE and other methods of estimating purity/immune cell fraction in this subset of tumours; LUMP, ESTIMATE23 and H&E staining assessment of tumour purity (data available in ref. 22). Residuals were computed by subtracting the method estimate from the ABSOLUTE value. Distributions were compared using Wilcoxon’s Rank Sum Test. Spearman’s Rank Correlation was used to estimate correlations between expression of marker transcripts and MethylCIBERSORT estimates for multiple cellular populations. Where applicable, multiple testing correction was performed using the Benjamini–Hochberg approach.In the absence of flow-cytometry-based estimates for the different cell types in the analysed tumours, the estimated fraction of cancer cells from MethylCIBERSORT was compared to sequencing-data based estimates from ABSOLUTE available for 466 HNSCCs from previously published workEstimates of immune cell fractions in HPV− HNSCC (HPV-transcript negative) were examined for correlations with other infiltrating cell types using Spearman’s Rank Correlation with BH correction for multiple testing. Clustering was carried out using the clusterCons package with 100 iterations using a Manhattan distance metric. The most robust number of clusters was then selected.Differences in the distribution of infiltrating cell types by immune cluster were summarised using mean fold changes and tested using Wilcoxon’s Rank Sum Test with BH-correction for multiple testing.GZMA and PRF1 expression as defined previously29. To estimate the contributions of cell population abundances to this, a linear model was fit against log2(CYT) with the different populations as predictors. Wilcoxon’s Rank Sum Tests were used to test differences in CYT and CD8:Treg ratios between the immune clusters.DEGs were identified using limma-trend and were defined at a threshold of a 2-fold change and BHFDR < 0.01. Pathway analysis was carried out using Ingenuity Pathway Analysis, with findings restricted to experimentally confirmed direct interactions in human cells/tissues. Cytolytic activity (CYT) was calculated as the geometric mean of Multivariate Cox Regression was used to estimate the prognostic utility of clusters derived using infiltration patterns with age and stage as covariates. The survival effect of estimated purity was regressed with the same covariates using a Cox regression with coefficients defined per percent increase in purity.33 in TCGA HNSCC cohort from the Broad Institute’s GDAC. GISTIC Copy number estimates thresholded by genes were also obtained from this source. MAF files were obtained from the TCGA data portal. MutSigCV drivers were filtered at a q value threshold of 0.01 and mutations in this set were tested for differences in frequencies of occurrence using a Chi-squared test for differences in proportion. Multiple testing correction was carried out using the Benjamini–Hochberg method. Tables of predicted neoantigens were downloaded from The Cancer Immunome Atlas (http://tcia.at).We obtained a list of driver genes inferred by MutSigCVGSE6837937 (Table 22.Signature features were derived from 450k profiles using the aforementioned heuristic (delta-Beta and FDR cutoffs) with a maximum of 100 features per cell type for a wide range of tumour types, using cell lines allocated to the corresponding tissue in 37 Table  and the syn2812961) and a custom function was used to extract signature probes and generate methylation percentage matrices for deconvolution with CIBERSORT CIBERSORT was run using the graphical user interface [https://cibersort.stanford.edu]. Correlation and residuals analysis were carried out as described above with MethylCIBERSORT purity estimates versus ABSOLUTE, and between previously published methods and ABSOLUTE. Wilcoxon’s Rank Sum Test with Benjamini–Hochberg correction for multiple testing were used to compare distributions, with these estimates sourced from ref. 22.The cell line data were functionally normalised with the infiltrating cell types described earlier before signature extraction was carried out. 450k data for the aforementioned tumour types were loaded from a pan-cancer freeze derived from SAGE synapse for TCGA pan-cancer . Driver mutations were defined based on pan-cancer MutSig analyses previously published33 and logistic regression GLMs were used to estimate coefficients for mutation frequencies for immune cluster with tumour type as a covariate. Significant genes were defined at BHFDR < 0.05. Survival analyses were performed using data downloaded from Synapse (syn7343873) using Cox proportional hazards regression with stage and cancer type as covariates. Substages were aggregated into stages and only Stages I–IV were considered. Neoantigen abundance and clonality data were downloaded from The Cancer Immunome Atlas45.MAF files for mutations were again downloaded from SAGE synapse for all tumours from the MC3 calling effort  and modelled similarly to mutation data.Negative binomial modelling was used to model all count data, cytolytic activity was modelled using linear models and binomial GLMs were used to model proportions. Details of covariates, hypotheses and response variables are presented inline. For copy number analyses, SNP6 data were downloaded from the GDC data portal and processed using GISTIC 2.068; PRJNA25821669; PRJEB546870. Kallisto71 was used to quantify gene expression with a reference transcriptome consisting of Gencode Grch37 assembly of protein coding and lincRNA transcripts. Data were then modelled using limma trend and the top 50 markers by t-statistics were selected for each cell subset from one versus all comparisons after thresholding with a 2-fold change and FDR < 0.05. These cell types were used to generate a reference profile and CIBERSORT was run to deconvolute samples. For M1/M2 macrophage analyses we used LM22 from the CIBERSORT server as the reference. In both cases, Wilcoxon’s Rank Sum Test was used to estimate differences in distributions.RNA-seq data were downloaded from the European Nucleotide Archive for the following datasets: PRJEB1184442 and Roh et al.43, respectively for patients treated using sequential anti-CTLA4 and anti-PD1 checkpoint blockade.Nanostring data for a panel of immune genes and exome sequencing data were obtained from Chen et al.Clustering and machine learning were carried out using the subset of genes intersecting with the Hot-vs-Cold pancancer signature. .632 bootstrapping was used for hyperparameter tuning and ROC estimation. Negative binomial regression was used to model neoantigen, mutation and subclone numbers, and logistic regression to estimate predictive performance of count data on response.72. RNAseq data for aCTLA4 pretreatment biopsies were kindly provided by Eliezer Van Allen and genomic data were from the associated publication7. Data for post-treatment Nivolumab treated melanomas were obtained from ref. 44.The number of subclones present in each tumour from the Roh cohort, derived from the EXPANDS algorithm, were obtained from the associated publication28 for comparison of EGFR protein levels versus TIL levels, previously defined in refs. 28. ssGSEA scores were used to summarise the activity of the glycolytic gene signature (described in ref. 17) and standard statistical procedures were used to assess interrelationships.RPPA data were downloaded for TCGA cancers from the TCGA portal. IHC data were derived from refs. https://groups.google.com/d/forum/methyldeconvolution.Knit R-markdowns of the code used for analysis have been deposited on Zenodo at https://doi.org/10.5281/zenodo.1304766. The MethylCIBERSORT R-package and the signatures we generated are on Zenodo at https://doi.org/10.5281/zenodo.1298968. A Google Group for users of the package can be found here: GSE35069; GSE74877; GSE38270; GSE68379), the European Nucleotide Archive , SAGE Synapse , The Cancer Genome Atlas Project or from the authors upon reasonable request.The data analysed in this study are available either from the Gene Expression Omnibus (accession numbers: Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8


Approaches are needed to better delineate the continuum of opioid misuse that occurs in hospitalized patients. A prognostic enrichment strategy with latent class analysis (LCA) may facilitate treatment strategies in subtypes of opioid misuse. We aim to identify subtypes of patients with opioid misuse and examine the distinctions between the subtypes by examining patient characteristics, topic models from clinical notes, and clinical outcomes.This was an observational study of inpatient hospitalizations at a tertiary care center between 2007 and 2017. Patients with opioid misuse were identified using an operational definition applied to all inpatient encounters. LCA with eight class-defining variables from the electronic health record (EHR) was applied to identify subtypes in the cohort of patients with opioid misuse. Comparisons between subtypes were made using the following approaches: (1) descriptive statistics on patient characteristics and healthcare utilization using EHR data and census-level data; (2) topic models with natural language processing (NLP) from clinical notes; (3) association with hospital outcomes.The analysis cohort was 6,224  patient encounters with opioid misuse with a data corpus of 422,147 clinical notes. LCA identified four subtypes with differing patient characteristics, topics from the clinical notes, and hospital outcomes. Class 1 was categorized by high hospital utilization with known opioid-related conditions (36.5%); Class 2 included patients with illicit use, low socioeconomic status, and psychoses (12.8%); Class 3 contained patients with alcohol use disorders with complications (39.2%); and class 4 consisted of those with low hospital utilization and incidental opioid misuse (11.5%). The following hospital outcomes were the highest for each subtype when compared against the other subtypes: readmission for class 1 ; discharge against medical advice for class 2 ; and in-hospital death for classes 3 and 4 .A 4-class latent model was the most parsimonious model that defined clinically interpretable and relevant subtypes for opioid misuse. Distinct subtypes were delineated after examining multiple domains of EHR data and applying methods in artificial intelligence. The approach with LCA and readily available class-defining substance use variables from the EHR may be applied as a prognostic enrichment strategy for targeted interventions. The principles of personalized medicine to find the appropriate treatment based on a patient’s individualized determinants of health and clinical needs are a priority for improving clinical outcomes . The abiA data-driven approach to developing subtypes of opioid misuse using electronic health record (EHR) data has not been published in previous work. A major target group in clinical studies is patients with chronic pain and/or long-term prescription opioid use, but these targeted cohorts fail to address other types of opioid misuse behaviors that may be common in hospitalized patients . CommuniThe aim of this study is to identify subtypes of opioid misuse using readily available structured EHR data . Additionally, the clinical notes are the largest domain of the EHR that frequently contain unstructured data (free text) about social and behavioral determinants of health that cannot be comprehensively examined manually; therefore, topic modelling was applied to summarize the corpus of text. The aim is to identify distinct subtypes of patients with opioid misuse, and provide validity with topic modelling and associations with health outcomes. We hypothesize LCA will identify distinct subtypes in our patient cohort with clinically meaningful traits and demonstrate different risks for negative health outcomes.This study utilized data from the EHR of an urban tertiary academic center between January 1, 2007 and September 30, 2017. An operational definition for opioid misuse was developed following the National Survey on Drug Use and Health criteria for opioid misuse with criterion input from a board-certified addiction specialist (ESA) and psychiatrist (NSK). The analysis cohort included consecutive adult (≥18 years of age) emergency department and inpatient encounters meeting criteria for opioid misuse during the study period. The criteria for opioid misuse were any of the following: (1) positive urine drug screen for an opiate with polysubstance use with any of the following: an illicit drug (phencyclidine or cocaine), a benzodiazepine that is not on the patient’s medication administration record, or an amphetamine that is not on the patient’s medication administration record; (2) positive urine drug screen for an opiate but without a prescription for an opioid on the patient’s admission administration record. Urine drug screens were eligible only if no opioid or benzodiazepine drug was dispensed by the hospital pharmacy before the urine drug screen was ordered.; (3) International Classification of Diseases (ICD)-9 and –10 codes for opioid-related hospitalizations were adopted from the Healthcare Cost and Utilization Project (HCUP) . ICD codTo validate the operational definition, a random sample of hospital encounters was extracted from the EHR during the study period for chart review. A sample of 1,000 patient encounters including age-sex matched controls were reviewed. The annotations included an oversampling of patients who met case criteria and non-cases who had ICD codes for chronic pain, naloxone administration, or a physician order for a urine drug screen. An annotator (KS) who is an MD, MPH candidate received substance use training through Loyola’s Institute for Transformative Interprofessional Education and completed Screening, Brief Intervention, and Referral to Treatment (SBIRT) through online training. Additional training was provided to screen for likelihood of opioid misuse on a Likert Scale (1–5), and the annotator met an inter-rater reliability of a Cohen’s kappa coefficient greater than 0.80 with a critical care physician and addiction specialist  before independent review was performed.The operational definition had a sensitivity of 88.6% (95% CI 85.2%-91.9%) and specificity of 78.5% (95% CI 75.4% - 81.7%). Chart review identified many false positives that occurred in outside hospital transfers that administered an opioid during care; therefore, hospital transfers were excluded from this analysis. Cases of overdose could not be reliably discriminated using billing codes or naloxone administration with many false positives occurring as well.Multiple encounters by the same patient were included as independent observations during analysis. As patients’ severity and subtype of misuse may change over time, our primary unit of analysis is the patient encounter in order to provide actionable insight into the subtype of misuse at hospitalization which could inform timely custom interventions. To address the potentially high correlation of intra-patient encounters, sensitivity analysis was performed to remove multiple encounters by analyzing the most recent inpatient encounter by each patient in our cohort.a priori based on existing evidence for identifying cases of misuse and risk factors for misuse [Latent class analysis (LCA) is a statistical technique that uses mixture modelling to identify mutually exclusive and qualitatively different subgroups from multivariate categorical data ,16. LCA r misuse –21. LCA r misuse . Each paThe face validity and clinical utility are examined by comparisons between latent classes using the following approaches: (1) descriptive statistics on patient characteristics and health utilization (structured EHR and census-level data) for each subtype; (2) topic models from natural language processing  and their probability assignment to each subtype; (3) association of subtypes with clinical outcomes (described below).Individual patient measures from the EHR included the following: (1) demographics and insurance status; (2) Elixhauser mortality score; (3) ICD codes for chronic pain and other disease categories developed by the Agency for Healthcare Research and Quality ,24; (4) For identifying conditions of chronic pain , ICD codPre-processing of all clinical notes was performed in the Apache clinical Text Analysis Knowledge Extraction System (cTAKES) . cTAKES Latent Dirichlet Allocation (LDA) is an unsupervised text mining approach used for topic modeling . The LDAAll-cause unplanned readmission was identified using the Center for Medicare and Medicaid Services (CMS) rules for index (eligible) admission and unplanned 30-day readmission . Pre-spehttp://dlinzer.github.com/poLCA) and followed the analysis plan by Zhang et al. [https://bitbucket.org/afsharjoycelab/opioid-misuse-lca/. The GenSim package was used in Python to infer topic model structure [Analysis was performed using Python Version 3.6.5 (Python Software Foundation) and RStudio Version 1.1.463 . Latent class analysis was performed with the poLCA package in R  met inclusion criteria for opioid misuse. In topic modelling, the final data corpus in the 6,224 patient encounters was comprised of 25,801 unique CUIs across 422,147 clinical notes. Twenty topics were identified to have the best model fit from the cohort . The topFit statistics and model interpretability suggest a 4-class model was optimal. Improvements to model fit begin to diminish around 5 classes .Although lower model fit statistics could be achieved with more classes, the 4-class model represents meaningful and distinct clinical representation with better distribution of class prevalence and less complexity . DetailsIn sensitivity analysis examining patient-level data using the most recent hospital encounter, model fit statistics and interpretability continued to show a 4-class model to be optimal . Good clThe 6,224 encounters were categorized within one of the four latent classes. Class 1 represents encounters that carried a higher probability for topics on pain procedures and medical conditions associated with chronic pain (pancreatitis and metastatic cancer) . In TablClass 2 are patient encounters with topics that had the highest probability for polysubstance use and mental health conditions and the greatest proportions with Elixhauser ICD codes for psychoses and drug use . In TablClass 3 are patient encounters with topics that had the highest probability for alcohol use disorders and liver disease . In TablClass 4 patient encounters contained topics that had the highest probability for trauma and neurological diseases (seizures) . In TablThe class labelled as “High hospital utilization with known opioid-related conditions” (Class 1) had the greatest proportion with 30-day unplanned hospital readmission at 13.9% . This waWe identified a four-class model of clinically interpretable and relevant subtypes for opioid misuse, with good class separation and face validity based on documentation in the notes, structured data, and clinical outcomes. The following distinctions were made for each class: (1) high hospital utilization with opioid-related hospitalizations; (2) illicit use, low SES, and psychoses; (3) alcohol use disorders (AUD) with complications; (4) low hospital utilization and incidental opioid misuse. We demonstrate major differences in comorbidities, utilization patterns, polysubstance use, and SES across subtypes that may help health systems to better understand the needs of their patient population and to identify appropriate treatment options and pathways. Using an LCA approach with EHR data may better inform health systems like ours that serve diverse communities and give a level of detail that is greater than what is reported in regional or state epidemiology and surveillance data.The subtype with the greatest proportion of patient encounters was “Alcohol use disorder with complications”. Prevalence of drug misuse is high among hospitalized patients with an AUD, but this group typically receives lower rates of treatment for addiction following hospitalization . Very spThe “high utilization with known opioid-related conditions” subtype was found to have a diverse group of patients with chronic comorbidities and pain conditions and all had an opioid-related hospitalization code. Another study identified a similar subgroup using LCA among individuals filling opioid prescriptions . Many ofIn contrast, the “Low hospital utilization and incidental opioid misuse” subtype was found to visit the trauma center more frequently and have a greater proportion of patients with positive urine drug screens for nonmedical opioid and benzodiazepine use. Naloxone was administered more frequently and this subtype had a higher proportion of in-hospital deaths. This subtype has also been previously identified using LCA for polysubstance use among trauma patients . DespiteLastly, the “Illicit use, low SES, and psychoses” subtype has the largest proportion of patient encounters with Medicaid or no insurance, suggesting patients in this subtype likely have less access to healthcare. One quarter of patients in this subtype had been diagnosed with a mental health condition in our health system, and this subtype had the highest proportion of patients leaving against medical advice. Buprenorphine initiation during the hospital encounter has been shown to reduce hospitalizations in a similar cohort of patients with heroin use . The higPrior work examining LCA in opioid misuse were focused on self-report data in specific cohorts of individuals. One study examining approximately 200 military veterans  used theThe heterogeneity in characteristics and outcomes across subtypes present opportunities to better align intervention types and level of care/intensity of services for each subtype. Other approaches that address heterogeneity in treatment effect include adaptive treatment designs  as well Methods in machine learning and NLP are important tools to handle the large volume and variability of EHR data. Topic modelling has been used successfully in the EHR to detect themes and relevant concepts in patient care to inform clinical decision support ,55. In pSeveral limitations are present in this single-center study. Although we attempt to account for biases introduced in our health system with methods in NLP, topic modeling across sites may not be consistent. In addition, our operational definition for opioid misuse may introduce misclassification bias with a urine drug screen that did not capture synthetics and semi-synthetic opioids. The subtypes identified here require external validation to demonstrate if the class-defining characteristics remain consistent across multiple health systems. Bias may have been introduced by patients with multiple encounters to our health system as well as not capturing encounters at other health systems. We attempted to reduce this bias by performing sensitivity analysis of patient-level data that continued to show a 4-class model was optimal and represented the same clinical subtypes.Unsupervised statistical approaches using all domains of the EHR may be leveraged to better identify subtypes of opioid misuse in the patients served by a health system. This is a comprehensive approach to better delineate clinically meaningful subtypes so targeted treatment strategies may be employed for the patients served by the individual health system.S1 Appendix Table(DOCX)Click here for additional data file.S2 Appendix Table(DOCX)Click here for additional data file.S3 Appendix Table(DOCX)Click here for additional data file.S4 Appendix Figure(PNG)Click here for additional data file.S5 Appendix Table(DOCX)Click here for additional data file.S6 Appendix Figure/Table(DOCX)Click here for additional data file.