Soil-release polymers (SRPs) are essential additivesof laundrydetergents whose function is to enable soil release from fabric andto prevent soil redeposition during the washing cycle. The currentlyused SRPs are petrochemical-based; however, SRPs based on biorenewablepolymers would be preferred from an environmental and regulatory perspective.To explore this possibility, we have synthesized SRPs based on hydroxyethylcellulose (amphiphilic HEC) appended with controlled compositionsof hydrophobic and cationic appendages and assessed their cleaningabilities. The results demonstrate that the introduction of hydrophobiclauryl appendages onto the HEC backbone is essential to deliver anti-redepositionand soil-release performance. Conversely, further introduction ofcationic groups onto hydrophobic modified HECs had no clear impacton soil-release performance but caused significant disadvantages onanti-redeposition performance. We speculate that this poor performancearises on account of coacervation formation between the cationic HECpolymer and the anionic surfactant in the detergent, negatively impactingsoil suspension and suggests that the inclusion of cationic appendageson HECs can ultimately lead to detrimental effects on performance.Interestingly, in contrast to conventional SPRs that exhibit goodsoil-release performance exclusively on synthetic fabrics, amphiphilicHEC displayed encouraging results on both synthetic and cotton-basedtextiles, possibly as a result of a good chemical affinity with naturalfabrics. This work highlights that the nature and hydrophobic contentof HEC ethers are key variables that govern HEC applicability as SRPs,thus paving the way for the design and synthesis of new SRPs. Initially, polycarboxylates,in the form of homo- and copolymers of acrylic acid and/or maleicacid, were introduced to replace sodium tripolyphosphate  to address government regulations andrestrictions.2 Subsequently, a wide spectrumof other polymers was introduced into detergent formulations for differentpurposes .The formulation of modern laundry productshas dramatically changedthrough the last 20 years, mainly driven by changes in consumer washinghabits, needs, and as a consequence of the evolution of regulations.3 Furthermore, since synthetic garmentsare highly hydrophobic, they are not wetted by the washing liquor,thereby making the removal of oily stains even tougher. Notwithstanding,soil adhesion and penetration can be controlled by modifying the surfaceenergy or porosity of fabrics using either chemical or physical methods.4 Among chemical methods, plasma treatment is themost common, being a well-known procedure used to modify the surfacestructure of fabrics at the microscopic level (from angstrom to nanometersin thickness) without compromising the bulk properties.5 Plasma interacts with the textile surface byremoving hydrogen atoms, thereby producing free radicals; these furtherreact with oxygen resulting in the introduction of oxygen-containingfunctional groups onto the surface. As a result, fabric cleanabilityimproves as a consequence of enhanced wettability and increased surfacefree energy, improving oil repellence.5 Among physical methods, fabric surface modification via SRPs hasrecently received great attention. Their mechanism of action is stillto be understood, but it is presumed that it involves polymer adsorptionon the fibers of fabrics.1 SRPs normallycontain hydrophobic structural domains and hydrophilic structuraldomains. When synthetic fabrics are treated with a formulation comprisingSRPs, the hydrophobic domains of SRPs are adsorbed onto the hydrophobicfibers, while the hydrophilic moieties stretch out toward the washingliquor. As a result of such absorption, the synthetic textile’ssurface assumes a more hydrophilic finish. This offers two benefits:anti-redeposition and soil release. The former is important duringa laundry washing process, where stains washed off from dirty garmentstend to deposit back on fabrics. The formation of a hydrophilic filmon synthetic textiles as a result of SRP deposition prevents stainsfrom resettling on fabrics.The use of soil-release polymers (SRPs) isa relatively recentadvance that has followed the introduction of synthetic fibers intofabrics. Although the adoption of synthetic fibers (mostly polyesterfibers) has led to more robust and resistant fabrics, it has alsoinevitably led to problems with oily stains. It is well-known thatthe wetting of oily stains (characterized by low surface energy) onlow surface energy substrates  is thermodynamicallyfavored. Once adsorbed on the surface, hydrophobic soil penetratesthe fabric and is strongly retained by mechanical and electrostaticforces.6 and in-wear comfort.In the latter, deposition of SRPsinhibits soil from tightly adheringto fabrics. Indeed, soil sticks on top of the SRP film so that, duringthe next wash cycle, the adsorbed SRP layer desorbs from the fabricsurface, thus removing also the stain layer. In addition to anti-redepositionand soil release, SRPs can also provide other benefits such as malodourprevention7 These macromolecules possess a chemical architecturethat comprises hydrophobic domains that mimic the chemical structure of polyester fibers (PET fabrics)and hydrophilic domains made up of polyethylene oxide groups. Theformer are responsible for the adsorption of the polymer onto polyesterfibers, while the latter promote the desorption of the adsorbed stainand polymer layer since they exhibit a higher affinity with the washingliquor.8 Like many other polymers usedin laundry detergents, such as polycarboxylates, PET-POET copolymersare predominately petrochemical-based. Concerns regarding the irreversibledepletion of fossil resources and evolving government policies aswell as environmental concerns are driving the search for more sustainablealternatives. Polysaccharides are biorenewable or largely biodegradable,and consequentially, they have been a subject of a recent interestin the detergent industry. However, pure natural cellulose polymersdo not possess the necessary physicochemical properties required forlaundry applications and hence opportune chemical modifications arerequired to provide them with useful characteristics.The most widely used SRPs in consumer detergent systems are polyestersbased on terephthalate, namely, copolymer of polyand poly (PET-POET copolymer).d-glucoseunits, and due to its extended network of hydrogen bonds between andalong each polymer chain, cellulose exhibits a crystalline structurethat makes it practically insoluble in almost every organic solvent.Therefore, cellulose functionalization has been a key tool to enhanceits solubility and to endow specific properties. As an example, cellulosehydroxyalkyl ethers such as hydroxyethyl (HEC) or hydroxypropyl (HPC)cellulose are highly soluble in water and are currently used as emulsifiersand thickeners.9 Cellulose derivatives,such as carboxymethyl cellulose (CMC), have been used in laundry formulationsfor over a decade.10Amongall polysaccharides, the chemical modification of cellulose(the most abundant polysaccharide on Earth) is by far the most extensivelyexplored, more than any other natural polymer. Cellulose consistsof linear chains of β (1 → 4)-linked 11 in the patent literature to be an effective SRP for both syntheticand natural fibers. Although its performance as a soil removal agentis described, there is still a lack of understanding about the wayother aspects of the laundry washing process  are affectedin the presence of modified HECs with both hydrophobic and cationicappendages. This work aims to provide a better understanding of thecapacity of modified polysaccharides to act as a laundry aid. Librariesof HEC derivatives featuring controlled compositions of hydrophobicand cationic appendages were synthesized and characterized. Theircleaning performance in terms of soil release and soil anti-redepositionwas explored and the relationship between appendage compositions andwashing performance was investigated. It was found that hydrophobicmodified HEC possessing lauryl appendages exhibited good soil-releaseand anti-redeposition performance. By contrast, the addition of cationic groups ontohydrophobic HECs had no clear impact on soil-release performance butcaused significant disadvantages on anti-redeposition, probably asa consequence of coacervation formation between cationic modifiedhydrophobic HECs and surfactants. Interestingly, unlike conventionalSRPs that show soil-release benefits solely on synthetic fabrics,hydrophobic and cationic modified HECs herein exhibited clear soil-releaseperformance even on cotton-based textiles.More recently,modified HECs appended with multiple hydrophobicand cationic appendages have been reportedHydroxyethyl cellulose, (HEC)commercially available as Natrosol 250 GR, was purchased from Ashland.According to the supplier information, this HEC is characterized byan average molar substitution of 2.5 (moles of ethylene oxide persingle anhydroglucose unit) and an average molecular weight of approximately300,000 Da. 2-Propanol (>99%), acetone (>99%), glacial aceticacid,sodium hydroxide, glycidyl trimethyl ammonium chloride ,hexadecyl glycidyl ether , and dialysis membrane tubing(3500 Da MWCO) were purchased from Sigma-Aldrich. Lauryl glycidylether  and 2-ethylhexyl glycidyl ether werepurchased from TCI chemicals. Dirty motor oil was acquired from WarwickEquest. A typical laundry formulation without soil-release polymerswas provided by P&G (Newcastle Innovation Centre). All chemicalswere used without further purification.1L–6L, 1E–2E, and 1H were obtained similarly by changing the typeand amount of alkyl epoxide used , 50 mL of an isopropylalcohol/water solution (85:15), and an aqueous solution  of sodium hydroxide. The slurry was stirred at room temperaturefor about 30 min under a nitrogen atmosphere. Then, lauryl glycidylether  was added dropwise via a syringe and the reactionwas heated at 80 °C for 16 h. After cooling to room temperature,the reaction mixture was neutralized with glacial acetic acid andthe crude product was collected by vacuum filtration. The productwas washed with an acetone/water solution  and acetone(150 g) and then dried at 70 °C for 24 h under reduced pressure.Modified HEC was dissolved in 70 mL of water and mixed thoroughlyat 65 °C overnight. The obtained gel-like solution was dialyzedagainst water for 2 days, and then freeze-dried to afford a pale-yellowsolid . Samples used see 3.7LC–9LC, 3EC–4EC,and 2HC were obtained similarly by changing the typeand amount of alkyl epoxide used , 50 mL of anisopropyl alcohol/water solution (85:15), and an aqueous solution of sodium hydroxide. The slurry was stirred atroom temperature for 30 min under a nitrogen atmosphere. Then, laurylglycidyl ether  was added dropwise via a syringe andthe reaction was heated at 80 °C for 16 h. The temperature wasreduced to 50 °C and glycidyl trimethyl ammonium chloride  was added dropwise with a syringe and the mixture wasallowed to react for 2 h to afford a yellow slurry. Modified HEC wasdissolved in 70 mL of water and mixed thoroughly at 65 °C overnight.The obtained gel-like solution was dialyzed against water for 2 days,and then freeze-dried to afford a pale-yellow solid .Samples used see 3.1H NMR spectra were recordedon an Avance Bruker 700 MHz spectrometer operating at 25 °C.Samples were prepared by dissolving 5 mg of the product in 600 μLof D2O. Dissolution occurred immediately and clear solutionswere obtained. The degree of substitution of the hydrophobic moieties(DSH) was measured by comparing the integration of thesignal for the protons of the lauryl, hexadecyl or 2-ethylhexyl groupof the alkyl chain to the H-1 anomeric signal of the glucose unit,which was integrated to 1 proton.–1 wavenumbers) were recorded using an IRAffinity-1S Fourier transforminfrared spectrophotometer equipped with an attenuated total reflectance(ATR) sampler at 4 cm–1 spectral resolution. Foreach experiment, 50 scans were collected and averaged. Samples weredried overnight in a vacuum oven at 70 °C before each measurement.Fourier transform infrared(FTIR) spectra (in the range of 400–4000 cmC) was calculated according to MRU is the molar weight of the repeating unit of HEC and MGTAC is the molar weight of the cationic substituent12Elemental composition was evaluated using a Thermo Fisher ScientificCHN elemental analyzer. From the measured N%, the cationic degreeof substitution (DS2 pieces and conditioned withmodified HEC solutions in an automatic tergotometer as follows. Stocksolutions of modified HEC were prepared in Milli-Q water (5% w/w).The latter were further diluted with hard water (21 gpg) into thetergotometer chambers to 50 ppm and mixed at 200 rpm for 10 min. Subsequently,fabrics were added and mixed thoroughly for 40 min at 35 °C followedby two 5 min rinse cycles using hard water (21 gpg). Fabrics werethen dried overnight under humidity and temperature control . A list of the fabrics tested and the samplesused is reported in Polyester (PE) and knitcotton (KC) fabrics were purchased from WFK Testgewebe GmbH. Thesewere cut into 5 × 5 cmLn*, an*, and bn* defined in the CIELAB color system. From the measured coordinates,the differences in lightness (ΔLn*), redness (Δan*), and blueness (Δbn*) in contrast to the background were calculated.13 The relative color changes, ΔE*, were calculatedcomparing the variation of the coordinates before (n = 1) and after the washing cycle (n = 2) by applyingthe following equationSoil-release performance tests were performed to assessthe effectof textile surface modification by HEC ethers on the stain removalperformance. Fabrics conditioned with modified HEC solutions weretreated with dirty motor oil. Dirty motor oil (200 μL) was appliedonto each square of fabric that were then dried overnight. Stain removaltests were executed in an automatic tergotometer. Stained garmentswere washed using a laundry detergent formulation that did not containsoil-release polymers for 40 min at 35 °C followed by two 5 minrinse cycles using hard water (21 gpg). Four replicates were run foreach experiment. Stain removal performance was evaluated through imageanalysis. Stain images were collected before and after washing againsta white background with a reflection spectrophotometer (DigiEye).Images were analyzed using DigiEye software. For each fabric, thecolor of the motor oil stains was evaluated by measuring the coordinates 2 pieces. BS2004 is a syntheticsoil mixture mainly composed of vegetable oil, synthetic sebum, andsolid particles such as kaolin and carbon black. Polyester (PE), knitcotton (KC), polycotton (PC), and polyspandex (PS) fabrics were purchasedfrom WFK Testgewebe GmbH and cut into 5 × 5 cm2 pieces.Whiteness tests were performed in an automatic tergotometer. Washingloads were composed of four specimens (tracers) for each type of fabric: an adequate number of SBL swatches to simulatethe soil levels of typical consumers and an appropriate amount ofKC and PC garments in which the tracers were diluted to reproducethe washing conditions of the consumers. Stock solutions of modifiedHEC were prepared in Milli-Q water (5% w/w). The latter were furtherdiluted in hard water (21 gpg) into the tergotometer chambers to 50ppm together with a proper amount of a typical laundry detergent formulationand mixed at 300 rpm for 1 min. Subsequently, fabrics were added andmixed thoroughly for 40 min at 35 °C followed by two 5 min rinsecycles. Fabrics were collected from each chamber. Exhausted SBL swatcheswere replaced with fresh ones. A new washing cycle was performed underthe same conditions reported above. The process was repeated fourtimes in total. Lastly, tracers were separated from all other garmentsand dried overnight under humidity and temperature control .Polyester sheetsloaded with BS2004 soil (SBL) were acquired from WFK Testgewebe GmbHand cut into 5 × 5 cmLn*, an*, and bn* defined in the CIELAB color system.From the measured coordinates, the International Commission on Illumination(CIE) whiteness index (WI) was calculated by applying the followingequationY is the luminancefactor while x and y are the colorcoordinates of the observed garment defined in the Yxy color space. These can be easily obtained by opportunely convertingthe measured values of Ln*, an*, and bn*, which correspond to Y, x, and y, respectively. xn and ny are the color coordinates of the lighting source used.The WI values of tracers (fabric garments) washed solely with a laundrydetergent formulation were used as negative control. More specifically,whiteness results were expressed as the difference (ΔWI) betweenthe WI of tracers washed with modified HECs together with the detergentformulation and the WI of tracers washed with the detergent formulationonly. High ΔWI values correspond to high soil anti-redepositionperformance. A summary of the test conditions is displayed in The whiteness degree of a textile measuresthe capacity of a detergent to inhibit soil adsorption onto fabricsand to prevent soil redeposition during the washing cycle. The whitenessdegree of fabrics was evaluated through image analysis. Tracers imageswere collected before and after washing with a reflection spectrophotometer(Konica Minolta: CM-3630A). Images were analyzed using SpectraMagicNXsoftware. The color of each fabric was evaluated by measuring thecoordinates 3L and 7LC were chosen as representativeof hydrophobic and amphiphilic HECs, respectively. Stock solutionsof modified HEC and of a typical laundry detergent formulation wereprepared in Milli-Q water . A volumeof 0.5 mL of these stock solutions was transferred to a 30 mL glassvial containing 0.06 g of clay. Hard water (9 gpg) was added to bringthe total volume of the solution to 20 mL. The final concentrationsof the modified HEC and clay were 150 and 3000 ppm, respectively,and the concentration of the laundry detergent was either 200 or 2000ppm . Subsequently,the vial was sonicated in an ultrasonic bath for 15 min at 35 °C.Immediately after the sonication, the clay suspension was monitoredby collecting T and B profiles every min for 1 h. The stability ofeach suspension was quantitatively evaluated by the Turbiscan StabilityIndex (TSI), a parameter used to study the destabilization phenomenonoccurring in a colloidal system.14 TheTSI is based on an integrated algorithm that takes into account theevolution of T or B signals over time and along the height of thevial. It is calculated according to the following equationzmax and zmin are the higher and lower limits delimitingthe area of the vial’s height where the TSI is calculated; Nh is the number of points along the vial height; tmax is the time at which the TSI is quantified.High TSI values are typical of unstable systems experiencing destabilizationprocesses such as creaming, coalescence, or sedimentation. Conversely,low TSI values arise from highly stable systems.The ability of HEC ethersto maintain clay particles in suspension was investigated using aTurbiscan optical analyzer .This instrument was equipped with a near infrared light source (880nm) and two detectors working simultaneously. One detector measuresthe light flux transmitted (T) through the vial containing the sample,while the other monitors the backscattered light (Bs) at 135°.Samples 3L and 7LC were chosen as representativeof hydrophobic and amphiphilic HECs, respectively. Stock solutionsof modified HEC were prepared in Milli-Q water (5% w/w). The latterwere further diluted in 500 mL of hard water (9 gpg) into a 1 L beakerto 10 ppm. A stock solution of a typical laundry formulation was preparedin Milli-Q water (10000 ppm). A volume of 0.1 mL of this stock solutionwas transferred into a 1 L beaker containing the HEC solution. Themixture was allowed to stabilize for a few seconds and the correspondingoptical transmission value, recorded by the colorimetric probe, wascollected. Further stepwise additions of the laundry detergent stocksolution were performed until its final concentration in the beakerwas 4000 ppm. Finally, the obtained transmission values were reportedas a function of the laundry detergent level (ppm).The behavior of solutions containing a modified HEC and a typicallaundry detergent were monitored using a Brinkmann PC-950 colorimeterequipped with a 76.2 cm optic fiber and a 2 cm stainless steel probe.Samples 2 pieces and boiled inMilli-Q water for 30 min. Later, they were conditioned with modifiedHEC solutions and a laundry detergent solution in an automatic tergotometeras follows. Stock solutions of modified HEC and of a typical laundrydetergent were prepared in Milli-Q water (5% w/w). The latter werefurther diluted with hard (9 gpg) water into the tergotometer chambersto 50 ppm (modified HECs) and 2000 ppm (laundry detergent) and mixedat 200 rpm for 10 min. Subsequently, fabrics were added and mixedthoroughly for 40 min at 35 °C followed by two 5 min rinse cycles.Fabrics were then dried overnight under humidity and temperature control. The ζ-potential of fabrics wasevaluated with a SurPASS 3.0 (Anton Paar GmbH) equipped with a cylindricalcell. A concentration of 1 mM KCl solution was used as the streamingmedium. A pressure variation between 600 and 200 mbar was producedfor each zeta cycle. Each measurement was performed at the naturalpH of the streaming medium that was found to be around 6.5. Threeindependent samples were analyzed and two zeta cycles were executedon each garment. The ζ-potential values were collected and averagedto obtain the main value.Polyester (PE) fabrics were purchased from WFK Testgewebe GmbH.These were cut into 5 × 5 cm15 According to thistheory, the surface charge is neutralized by counterions located withintwo regions at increasing distance from the solid surface known asthe Helmholtz (or stationary) layer and diffuse layer. The Helmholtzlayer, contains immobile ions that are not considered in thermal motion.Conversely, the diffusion layer, is characterized by a dynamic atmosphereof mobile ions. Hydrophobic surfaces, such as polyester fabrics, despitenot having ionizable species, tend to exhibit negative surface chargeas a result of the absorption of hydroxyl ions. The presence of asurface charge gives rise to an electric potential that decays asthe distance from the charged surface increases. The ζ-potentialis the electric potential at the shear plane or slipping plane thatindicates the layer that separates mobile ions with those that arestrongly bonded to the charged surface.3The streaming potential is an electrokineticeffect that occurs at a solid–liquid interface as a consequenceof the relative movement of one phase over the other. The measurementof this electrokinetic effect is used for the determination of theζ-potential of porous materials such as fabrics or powders.The ζ-potential is based on the electrochemical double layer(EDL) theory that describes the ion distribution induced by a chargedsurface in a solution.Istr and an electrical potential Ustr or streaming potential that can be easily measured.For hard materials possessing a planar surface, the ζ-potentialcan be quantified by applying the Helmholtz–Smoluchowsky equationP is the appliedpressure, η is the viscosity of the streaming solution, εis the dielectric coefficient of the electrolyte solution, ε0 is the vacuum permittivity, and L/A is the cell constant. The application of the Helmholtz–Smoluchowskyequation requires precise information concerning the geometry of thestreaming channel (the space between two solid specimens), e.g., thecell constant.16Streaming potential is physically measured by forcing an electrolytesolution to flow tangentially to a target surface inside a propercell. Consequently, counterions are moved in the direction of theliquid flow. The presence of an electronic circuit with a high internalresistance results in a charge separation that causes a back currentthat partially compensates the current associated with the movementof the ions. The net charge variation gives rise to an electric current In the case of sampleswith irregular shapes and thus unknown cellconstants, the streaming potential is usually quantified through theHelmholtz–Smoluchowsky approximation that does not requireinformation about the geometry of the substrate according to the followingequation18 Lauryl glycidyl ether (LGE), 2-ethylhexyl glycidyl ether (GHE),and hexadecyl glycidyl ether (GXE) were used as O-alkylating agents, while glycidyl trimethyl ammonium chloride (GTAC)was used as the cationizating agent. Hydrophobic appended HECs , and hydrophobic and cationic HEC(7LC) are shown in 21 The broad peak centered at ca. 3400 cm–1 correspondsto the stretching vibration of −OH; the band ranging 3000–2750cm–1 was assigned to saturated C–H symmetricand asymmetric vibrations; the stretching vibration of C–O–Cwas observed at around 1050 cm–1 and the vibrationof C–O glycosidic bond was observed at 887 cm–1. Successful etherification of the HEC backbone was confirmed . Likewise, no remarkable differencesin FTIR spectra were observed by changing the alkylating glycidylether used. This observation might be explained by the fact that allof the modifying agents possess similar chemical structures, and generallylow level of modification (low DSH and DSC).The chemical structures of the modified HECs werestudied by FTIRand onfirmed 4b–c by t1H NMR spectroscopy. The 1H NMR spectrumof unmodified HEC is shown in 1H NMR spectrum of hydrophobic HEC modified with LGE (3L). A new series of signals were observed (1.5–0.8ppm) corresponding to the alkyl protons of the lauryl chains alkylatedonto the HEC backbone. The signal at 1.5 ppm was assigned to the methyleneprotons (labeled in purple) one carbon away from the oxygen atom ofthe glycidyl ether; the broad unresolved signal at 1.2 ppm (labeledin light blue) arises from the resonances of the (CH2)9 chain; the signal at 0.8 ppm represents the methyl protonsat the terminus of the alkyl chain. Chemical modification of hydrophobicHEC with GTAC resulted in a sequence of new signals as observed inthe spectrum of 7LC  proton as reference,whereas the DSC of the cationic groups was evaluated fromthe percentage N measured by elemental analysis  hasa significant impact on soil-release performance as suggested by thehigh SRI values recorded (blue). Amphiphilic HEC carrying both hydrophobicand cationic modifications appears to deliver similar levels of soil-release benefits as hydrophobicHEC does (light blue). This observation suggests that hydrophobicmodifications play a substantial role in stain removal, while cationicmoieties have little influence. Some images of tested polyester swatchesafter washing are depicted in 7LC (amphiphilic HEC)or 3L (hydrophobic HEC) as they look chromatically identical.Conversely, the appearance of the fabric washed with unmodified HECis very different. Indeed, as indicated by the intense dark colorof the textile, very little stain was removed from the textile surface,perhaps on account of weak adsorption of the corresponding SRP ontothe textile surface.Soil-release performance testswere performed on polyester fabrics with and without pretreatmentwith the HEC ethers that in these tests act as soil-release polymers(SRPs). Stain removal indexes (SRIs) were calculated through imageanalysis and are shown in 3L and 7LC weretested as representative hydrophobic and amphiphilic HECs, respectively.The stain removal test was performed under the same conditions asfor polyester fabrics. Calculated SRIs are shown in 3L) and amphiphilic HEC (7LC) are, respectively, two andthree times higher than those recorded for unmodified HEC. Imagesof stained and pretreated cotton swatches after washing are shownin 3L) possess a less intense dark color as a result of theirstain removal activity.The soil-release performance of modified HEC etherson cotton fabricswas also evaluated. Samples 3L–6L) only were tested.As shown in 3L–6L) resulted in high ΔWI values especially for PC and KC. Together,these observations suggest that the addition of hydrophobic moietiesseems not only to strengthen the benefit afforded by the HECs to cotton-basedtextiles but also provides a small benefit on synthetic fabrics suchas PE and PS . Overall, sample 3L, with the highest DSH (0.017) is able to providethe best whiteness performance on all tested garments. Conversely,the addition of cationic modifications (7LC–9LC) has a significant negative impact on the anti-redeposition performance.Indeed, for all samples carrying both hydrophobic and cationic modifications,negative ΔWI values were registered. Interestingly, a consistentdiminution of the whiteness index was observed for all types of fabricswhen the content of cationic groups increased (from 9LC to 7LC). Negative ΔWI values result from a highadsorption of soil material onto the garment surface. We can speculatethat this observation is a consequence of the weak capacity of amphiphilicHEC ethers to keep soil in suspension, thus leading to its precipitationin the washing liquor. On a molecular level, we can speculate thatthe addition of positively charged groups (via reaction with GTAC)onto the HEC backbone, exposes it to interact with other oppositelycharged species within the washing liquor, i.e., anionic surfactants.Driven by electrostatic interactions, amphiphilic HEC and anionicsurfactants form an insoluble colloidal inclusion or coacervate thatcompromises the capacity of the washing formulation to maintain thesuspended soil. As a result, the latter precipitates and eventuallydeposits onto the fabric surface, causing the observed whiteness loss.In a typical anti-redepositiontest, clean fabric tracers are washed together with stained fabricsin the presence of anti-redeposition additives. During the washingprocess, clean fabric tracers partially adsorb materials washed offfrom the stained fabrics. At the end of the washing cycle, the colorvariation of clean fabric tracers is quantified (ΔWI variation)to monitor the anti-redeposition performance of the additive. In thiswork, whiteness tests were performed on polyester (PE), knit cotton(KC), polycotton (PC), and polyspandex (PS) fabrics. The whitenessindexes (WIs) were calculated through image analysis. The resultsof test A are reported in 1E–4EC and 1H–2HC carrying2-ethylhexyl and hexadecyl modifications, respectively. Changing thehydrophobic moieties from lauryl to 2-ethylhexyl did not provide anynoticeable advantage. Indeed, similar ΔWI values were recordedfor samples 1E and 2E. No significant benefitwas observed for sample 1H that displays hexadecyl groups.Similar to that observed in test A, the introduction of cationic modificationscauses significant whiteness loss.Test B was performed to further understand theeffect of the natureof the hydrophobic moieties of HEC ethers on soil anti-redeposition.The effect of alternative hydrophobic groups was investigated by testingsamples H on soil anti-redepositionwas explored. To assess the impact of increasing the lauryl DSH samples, 1L (DSH = 0.071), 2L (DSH = 0.026), and 3L (DSH = 0.017) were directly compared. As shown in 1L and 2L were found to possess very low ΔWIs and therefore are lesseffective in soil anti-redeposition than sample 3L. Thisobservation, along with the results obtained in test A suggests thatsoil anti-redeposition benefits are only observed at an optimum DSH, namely, the lauryl content of modified HECs must be keptwithin a very specific and narrow range to deliver significant whitenessbenefits.Lastly, in test C, the effectof the DS23 The adsorption of hydrophilicsolid particles, such as clay, is typically driven by multiple factorssuch as van der Waals forces, hydrogen bonds, or bridging by positivelycharged polyvalent ions. Although the interactions of clay particleswith textile surfaces are decisive in conveying their adsorption,the stability of clay suspensions in the washing liquor is equallyimportant. In an attempt to shed light on the observed anti-redepositionresults, the ability of HEC ethers to maintain clay particles in suspensionwas explored. In this work, clay (in the form of kaolinite) is themain solid component of BS2004 soil used in whiteness tests. It istherefore crucial to understand clay suspension behavior in the presenceof modified HEC solutions. Samples 3L and 7LC were chosen as representative of hydrophobic and amphiphilic HECs,respectively. Clay suspension stability was monitored by followingthe evolution of the Turbiscan Stability index (TSI) over 1 h. Theresults obtained are displayed in The adsorption ofparticulate soil onto fabrics has been extensively studied over thepast 50 years.3L , 7LC , and a typical laundry detergent  are shown. Samples 7LC and 3L revealed comparable trends with low TSI values observed with time.Conversely, the TSI of the laundry detergent greatly increased overthe measurement time, thus indicating a highly unstable system. TheTSI of clay suspensions of HEC ethers (150 ppm) in the presence ofa laundry detergent (200 ppm) is depicted in 3L and 7LC weresignificantly different. Although the measured TSI values increasedin both cases, that of the solution containing sample 7LC witnessed a higher increment. This observation suggests that theaddition of the laundry detergent resulted in less stable clay suspensionsto a degree that depends on the type of HEC modification. Interestingly,as seen in 3L, and a complete phase separation was observed for sample 7LC within the same time. Indeed, the TSI of amphiphilic HEC(red line) soared over the first 4 min and then steadily increasedat a lower rate. By contrast, sample 3L (black line)exhibited much lower TSI values, reflecting a more stable suspension.This observation confirms that the interactions of the detergent withhydrophobic or amphiphilic HECs play an important role in drivingtheir ability to stabilize clay particle suspensions, especially foramphiphilic HEC that possess both cationic and hydrophobic appendages.Lastly, the laundry detergent alone at high concentration  displayed the lowest TSI values, thus suggesting that itscapacity to maintain clay particles suspended is strongly influencedby the concentration.In 24 It is well-known that at a specific concentration(known as cac), surfactant aggregates start to bind to the polymerbackbone, thus leading to a macroscopic associative phase separation(coacervate formation). Eventually, further increases in surfactantconcentration result in the dissolution of the coacervate. The dissolutionand precipitation process has been used as a tool to enhance depositionof insoluble cationic polymers and anionic surfactant complexes ontohard surfaces .25 In thiswork, the behavior of solutions of modified HECs was explored by monitoringthe changes in the transmitted light as a stock laundry detergentsolution was titrated. The results are shown in Laundry detergents typically contain mixtures ofanionic and nonionic surfactants whose interactions with polyelectrolytes govern the stability and performance ofthe cleaning system under washing conditions. The behavior of aqueoussolutions of oppositely charged species has been comprehensively studied.26 Sample 3L (red line), which possesses hydrophobic groups only, exhibitsthe same behavior observed in the absence of polymers. Indeed, despitesome small oscillations, the intensity of the transmitted light steadilyfalls. Conversely, the transmission observed for sample 7LC  in the presence of surfactants formedunstable clay suspensions under specific circumstances. More specifically,when the ratio between surfactants and amphiphilic HEC reached a certainvalue, insoluble complexes were formed between these two oppositelycharged species, driving a macroscopic phase separation. Therefore,no active species were available to stabilize the suspension of clayparticles suspended. As a consequence, clay particles rapidly aggregateand precipitate in the washing liquor.As shown in 3L or 7LC , and a typical laundry detergent. The results are shownin A streaming potentialstudy of polyester fabrics conditioned with modified HEC was performed.The streaming potential was measured with a Surpass 3.0 equipped witha cylindrical cell. In brief, a streaming medium is forced to flowthrough the cell containing a polyester tracer by applying a pressuregradient, causing the excess charges to move in the flow direction.The streaming potential is associated to the net charge separationoccurring (in the measuring capillary) at equilibrium between thestreaming current (excess charge circulation) and back current (causedby the presence of an electric circuit). Clean polyester fabrics wereconditioned with unmodified HEC, samples 4 Although polyester textiles donot possess ionizable species, these tend to exhibit a negative potentialas a result of the preferential adsorption of hydroxyl ions.4 Polyester fabrics conditioned with unmodified HEC displayed a ζ-potentialof approximately −40 mV (red). The increase in the potentialvalue arises on account of the adsorption of HEC chains onto the synthetictextiles causing a partial displacement of the adsorbed hydroxyl ions.A further increase of ζ-potential with respect to untreatedfabrics was registered for garments conditioned with samples 3L and 7LC. This observation can be explainedas a consequence of the higher adsorption of modified HEC onto thepolyester surface, which results in a more efficient displacementof adsorbed ions. These findings are in agreement with that previouslyobserved for stain removals where polyester garments conditioned withhydrophobic and amphiphilic HECs displayed a much higher efficiencyin the removal of motor oil than unmodified HEC, perhaps as a resultof more significant polymer deposition. Despite possessing positivelycharged groups, ζ-potential values of 7LC werecomparable with that observed for sample 3L. We speculatethat cationic groups do actively participate in the adsorption ofamphiphilic HEC, being electrostatically attracted by the weak anionicsurface of the polyester fabrics.27 Therefore,potentially no cationic appendage is directly exposed to the streamingmedium since this would have caused a much higher increase in theobserved ζ-potential. Lastly, the ζ-potential of polyesterfabrics conditioned with a typical laundry detergent was found tobe approximately −52 mV. The slight increase observed was probablydue to moderate deposition of the nonionic surfactant onto the syntheticgarments.The streaming potential for untreated polyesterfabrics (white)was found to be ∼−60 mV, which is in agreement withprevious findings.This work confirmed that hydroxyethyl cellulose(HEC) ethers couldbe used as soil-release and anti-redeposition additives in a laundryformulation. The introduction of lauryl hydrophobic appendages ontoHEC backbone was shown to be crucial in delivering anti-redepositionbenefits (measured as textile whiteness degree) on both syntheticand cotton-based fabrics. Interestingly, fabric cleanability was stronglyinfluenced by the degree of substitution of lauryl groups, and thehighest whiteness degree was observed only within a narrow contentof lauryl moieties. By contrast, the addition of cationic modifications(in the form of trimethyl ammonium chloride) resulted in poor fabriccleanability. A combined use of colorimetric and turbidity analysisallowed some light to be shed on this aspect. Turbidity studies suggestedthat HECs displaying cationic groups (amphiphilic HEC) are unableto maintain clay particles in suspension in the presence of surfactants.This observation, further confirmed by colorimetric analysis, canbe explained as a consequence of coacervate formation between amphiphilicHEC and anionic surfactants, which leads to macroscopic phase separation.The aggregation of HECs and anionic surfactants through electrostaticinteractions resulted in a lack of active specimens to maintain clayparticles suspended and hence clay particles aggregated and precipitatedin the washing liquor.Interestingly, it was found that thesoil-release ability of HEChydrophobic ethers on synthetic garments was not affected by the presenceof cationic moieties. Indeed, regardless of the composition of theirhydrophobic\cationic appendages, hydrophobic and amphiphilic HECsdisplayed similar soil-release indexes, suggesting that positivelycharged groups do not alter the ability of hydrophobic HEC to removethe adsorbed soil. The streaming potential analysis of the surfacecharge of polyester fabrics conditioned with modified HECs revealedthat hydrophobic and amphiphilic HECs provided polyester fabrics withsimilar characteristics as they both show comparable ζ-potentialvalues. This observation suggests that all HECs, irrespective of thecomposition of their substituents, are able to impart polyester fabricswith similar oil-repellent finishes as a result of similar levelsof deposition. Supplementary investigations are needed to furtherunderstand the deposition process of HEC ethers onto fabrics.Overall, our results highlight a clear relation between modifiedHEC composition and their applicability as soil-release agent. Thelength of hydrophobic appendages and their degree of substitutionare key factors that need to be finely tuned to deliver significantbenefits on textiles. Cationic moieties should be avoided in the designof new soil-release agents as these were found to compromise the modifiedHEC anti-redeposition performance with particulate soil.


We demonstrated that the vaporized Zn and S were solidified in different shapes of nanostructures with the temperatures solely. To our knowledge, this is the first demonstration of synthesizing heterogeneous nanostructures, consisting of a nanowire from the vapor–liquid–solid and then nanoparticles from the vapor–solid grown mechanism by in situ temperature control. The obtained hierarchically-assembled ZnS nanostructures were characterized by various TEM technologies, verifying the crystal growth mechanism. Lastly, electron tomography and 3D printing enabled the nanoscale structures to visualize with centimeter scales. The 3D printing from randomly fabricated nanomaterials is rarely performed to date. The collaborating work could offer a better opportunity to fabricate advanced and sophisticated nanostructures.Nanomaterials have gained enormous interest in improving the performance of energy harvest systems, biomedical devices, and high-strength composites. Many studies were performed fabricating more elaborate and heterogeneous nanostructures then the structures were characterized using TEM tomographic images, upgrading the fabrication technique. Despite the effort, intricate fabrication process, agglomeration characteristic, and non-uniform output were still limited to presenting the 3D panoramic views straightforwardly. Here we suggested in situ synthesis method to prepare complex and hierarchically-assembled nanostructures that consisted of ZnS nanowire core and nanoparticles under Ag However, the manmade nanostructures preparation requires intricate fabrication steps to control the overall shape and precise position control of the nanomaterials on the targeting substrate. Correspondingly, there is an escalating need for novel visualization tools, which address rapid progress in developing various manmade nanoscale matters nowadays12. For visualization, the intricate structural details of such nanoscale matters should be directly inspected and verified at length scales relevant to their intrinsic near-atomic dimensions. Simultaneously, the obtained structural information and knowledge must be readily translated at much larger scales where additional assisting characterization tools are unnecessary for clarification and understanding.Nanomaterials are of intense interest due to their enhanced surface properties, thus the nanomaterials can be used for high-performance energy storage and redox reaction platform, in vivo targeting drug delivery, additives for mechanical strength enhancement, and plasmonic light direction controller18. Then different types of nanostructures have been developed in the shape of the nanowire, nanoribbon, nanosheets, and nanoparticles. Although nanostructural fabrication technologies have been drastically developed, the prepared nanomaterials tend to agglomerate amongst the nanoscale structures, attenuating the intrinsic performance of the nanomaterials23. Thus, heterogeneous structures, such as core/shell structure and surface modification, have been studied to maintain and further enhance the functionality of nanomaterials27. Various nanostructures and their fabrication methods are continuously evolving in advanced heterogeneous structures, the methods however require complicated and exquisite processes. Transmission electron microscopy (TEM) is a typical tool to characterize near-atomic scale structures, capturing two-dimensional (2D) projected nanomaterials. The measuring technology helps the nanomaterials inspection to be more closely to atomic scales, hence the use of the technology would be a complementary strategy to fabricate advanced nanostructures29.Many studies have been conducted to prepare nanostructures from various methods such as chemical vapor deposition (CVD), thermal evaporation, and thermo-solution methods32. The tomography operates based on accumulating and reconstructing 2D TEM images from matters of interest that are systematically rotated at various tilt angles inside the TEM device35. While its advantage for visualizing sophisticated structural details unattainable by the conventional tools is obvious, such information that is an accumulation of individual 2D TEM images assigned at specific tilt angles still presents a gap with truly 3D panoramic views. Also, grabbing and accessing the true 3D structures of nanoscale entities is not quite straightforward with 3D TEM tomography solely, particularly for those not familiar with TEM crystallographic analysis.Although the TEM measurement broadened the nanomaterial preparation technique, the conventional TEM measurement often needs very sophisticated sample preparation and instrument operations for accessing spatial areas of specific interest. Three-dimensional (3D) TEM tomography has emerged as a powerful characterization tool owing to its unique ability to access structural details of nanoscale entities from all angles, which can grab details overlooked with the conventional approach40. First, a hierarchically-assembled nanostructure was fabricated, consisting of a zinc sulfide (ZnS) nanowire (~ 50 nm diameter) core, ZnS nanoparticles on the surface of the ZnS nanowire, and Ag2S nanoscale catalyst on the tip of the ZnS nanowire. ZnS-based nanostructures fabrication has been widely studied with different dimensions due to its wide direct bandgap43. This characteristic enables the ZnS-based nanostructures to be used as optoelectronic applications such as light emitting diodes (LEDs), electroluminescent devices (ELDs), versatile sensors, and infrared windows49. The hierarchically-assembled nanostructures were prepared from simple, in situ synthesis by metal–organic chemical vapor deposition (MOCVD). Since the vapored or liquefied Zn and S can be solidified in the different shapes of nanostructures with temperatures (VLS pathway), the Zn and S are assembled into nanowire under Ag catalyst and then nanoparticles on the surface of the nanowire by the temperature control from vaporized Zn and S (VS pathway). To our knowledge, this is the first demonstration to synthesize heterogeneous nanostructures by VLS then VS grown mechanism from in situ temperature control solely.Here we suggest a method to prepare a complex and heterogeneous nanostructure using an in situ synthesis. The nanowire-based core–shell structures have been suggested in various research fields, such as photovoltaic devices, biomedical implantable electrodes, energy harvesting active layers, thermoelectric additives, and light absorption and reflection studies2) where their direct inspection is possible without additional characterization efforts52. Furthermore, the successive nanostructure inspection by 3D TEM tomography and visualization by 3D printing promotes elaborate nanofabrication, resulting in the enhanced and modified performance of the products55. Although many studies have been reported to materialize sophisticated structure using 3D printing from intentionally coordinating work58, rare study has been performed to print nanostructures from randomly fabricated materials.The assembled nanostructures were monitored by various TEM techniques then characterized by 3D TEM tomography. Further, the 3D TEM tomography was combined with 3D printing technology to visualize the complex nanostructures. We suggest a collaborating characterization approach that directly visualizes 3D structural details of nanoscale entities and translates them to a much larger length  The temperature of the quartz tubing inside was decreased after 20 min when ZnS was sufficiently dissolved into the Ag2S droplet. Then the supersaturated ZnS is precipitated in a solid phase to form nanowires at the nucleation temperature. Then ZnS nanowire growth was monitored with temperatures at 800 °C  The growth of the highly supersaturated ZnS nanowires  and the length of ~ 1 μm, respectively Fig. .2S catalyst was shown as a dark elliptical form on the end of the hierarchically-assembled ZnS nanostructure. TEM elemental mapping analysis of individual hierarchically-assembled ZnS nanostructure displayed that Zn and S elements are dominant in nanowire and nanoparticles  TEM image of the hierarchically-assembled ZnS nanostructure revealed distinctly irregular nanoparticles attached to the surface of the ZnS nanowire Fig. . The Ag2hod Fig. .Figure 2The hierarchically-assembled ZnS nanostructure was further inspected using the high-resolution TEM (HR-TEM) images, examining the detailed crystal structures of both ZnS nanowire and nanoparticles Fig. c; this sxy, and the mapping data clearly showed that strain is not induced (less than 1%) between nanowire and nanoparticle.The FIB technique performed cross-sectioning of the ZnS nanostructures. High-resolution annular dark-field STEM (HR ADF-STEM) image of the specific area marked with a red square Fig. d was wel3D tomography analysis of the hierarchically-assembled ZnS nanostructure with a lacey carbon grid was performed inside the TEM, obtaining better insight into the specific nanostructure. Several sets of tilted BF TEM images were collected at tilt intervals of multi-degrees from − 75° to 75° using the rotation holder  and formed ZnS nanoparticles. The in situ method controlled by temperature solely should open up the numerous possibilities of fabricating heterogeneous hierarchically-assembled nanostructures. Various TEM measurements demonstrated the hierarchically-assembled ZnS nanostructures and crystal growth direction directly. In addition, 3D tomography helped the nanostructures to reveal their specific features. Lastly, the nanostructures were visualized by the 3D printing technique on large scales (> 10 cm2). This study would enable the nanostructure fabrication to be more precise, resulting in high-performance nanomaterial products.The hierarchically-assembled ZnS nanostructures were prepared by VLS and VS growing methods. Vaporized Zn and S dissolved in liquefied AgThe hierarchically-assembled ZnS nanostructures were prepared in horizontal quartz tubing with a gold furnace system see Fig. . First, The morphologies and microstructures of the as-synthesized products were systematically characterized. The crystalline structure characteristics and the morphology of the synthesized hierarchically assembled ZnS nanostructure were investigated using an X-ray diffractometer , field-emission scanning electron microscopy , and Cs-corrected transmission electron microscopy (Cs-TEM JEOL ARM200F) operating 200 kV. The chemical composition of the hierarchically assembled ZnS nanostructure was analyzed using an energy dispersive X-ray spectrometer  attached to the Cs-TEM. A focused ion beam  was used to observe the cross-section of the hierarchically assembled ZnS nanostructure. A geometric phase analysis (GPA) script installed on a Gatan Digital Micrograph was used to calculate the strain generated at the interface between the ZnS nanowire and the ZnS nanoparticle.TEM tomography was performed using the automated tilt-series acquisition software TEMography on a Cs-TEM operated at 200 kV. The first step in TEM tomography is recording TEM images while tilting the sample over a wide range of angles in small increments. For the ZnS nanowires coated with nanoparticles, JEOL rotation holder was employed to collect bright field (BF) images in a Cs-TEM. The tilt of the JEOL rotation is motor-driven from 0 to 360°. 160 BF images were collected for each reconstruction at 1° tilt intervals in a tilt range of − 80° to + 80°. Here lacey carbon grid, which was used as a template for TEM analysis, was included to visualize the whole nanostructures more sophisticatedly. Composer (TEMography.com) performs complete automatic acquiring of sequential tilted TEM images series essential for tomographical 3D image reconstruction. Algorithms corresponding to inherent problems, such as correction of shifted position when specimen tilted and maintenance of focus, are applied automatically. After data acquisition, all BF TEM images are aligned with respect to a common origin and tilt axis. In the next step, a 3D reconstruction of the imaged sample is computed using specialized algorithms such as filtered back-projection (FBP) and the simultaneous iterative reconstruction technique (SIRT). The 3D reconstructed result and Amira software (ThermoFischer Scientific) provide the basis to visualize the ZnS nanostructure morphology. The hierarchically-assembled ZnS nanostructure forms were fabricated by a 3D printing approach with the polylactic acid (PLA) filament. The used custom-made 3D printing system consisted of a fused-filament-fabrication (FFF) head with a 300 μm nozzle, a movable printing bed on a three-axis, and an optical monitoring module; the monitoring module consisted of an optical lens (10 ×) and a charge-coupled device (CCD) camera (Blackfly USB3 FLIR). The template of the hierarchically assembled ZnS nanostructure form was obtained with TEM tomography and exported as stereolithography (.stl) file into TEMography . The 3D structures, which were formed in .stl format, were fabricated in a layer-by-layer 3D printing manner. After printing, the support part that maintained the shape of the printed structures during the printing process was removed.Supplementary Figures.Supplementary Movie S1.Supplementary Movie S2.Supplementary Movie S3.


IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, one class composed of highly aggressive SMARCB1-deficient carcinomas and another class with tumors that represent potentially previously misclassified adenoid cystic carcinomas. Our findings can aid in improving the diagnostic classification of sinonasal tumors and could help to change the current perception of SNUCs.The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply a machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by  Sinonasal tumour diagnosis can be complicated by the heterogeneity of disease and classification systems. Here, the authors use machine learning to classify sinonasal undifferentiated carcinomas into 4 molecular classe with differences in differentiation state and clinical outcome. The complexity of these tumors presents a major challenge for histopathological diagnosis, even for trained head and neck pathologists2. In fact, tumors of the sinonasal region have been reported to show the highest rate of conflicting diagnoses among all head and neck tumors3.Although tumors of the sinonasal region only account for a small fraction of head and neck tumors, they encompass a diverse spectrum of epithelial, mesenchymal and neuroectodermal neoplasms4. For diagnostic evaluation, a variety of other entities have to be excluded, such as poorly differentiated carcinomas or high-grade olfactory neuroblastomas. Histologically, SNUCs by definition lack squamous or glandular differentiation but may show subtle neuroendocrine features and thus may focally resemble neuroendocrine carcinomas7. In recent years, molecular analyses of SNUCs have revealed a high rate of IDH2 mutations or alterations of the switch/sucrose non-fermentable (SWI/SNF) complex leading to SMARCB1 or SMARCA4 deficiency11. These distinct molecular patterns as well as the occasional morphological and immunohistochemical resemblance to neuroendocrine carcinoma are challenging the current definition of SNUC as a single entity.Sinonasal undifferentiated carcinomas (SNUC) represent an especially challenging diagnosis. SNUCs are aggressive carcinomas that lack a definite lineage-specific differentiation12. Although epigenetic alterations represent one of the hallmarks of cancer development, the global DNA methylation signature of tumor cells is thought to contain substantial information about the cell of origin, making DNA methylation an ideal tool for tumor classification13. From a technical perspective, methylated DNA is highly robust , enabling the retrospective analysis of formalin-fixed and paraffin embedded (FFPE) samples, almost irrespective of sample age. Using this approach, significant cohorts of even exceedingly rare tumors can be assembled. For these reasons, DNA methylation has shown promising results in the classification of a growing number of malignancies18. DNA methylation profiling of olfactory neuroblastomas and a cohort of sinonasal carcinomas showed that IDH2 mutated and SMARCB1 deficient carcinomas likely represent epigenetically distinct classes19. Furthermore, it has been suggested that IDH2 mutated neuroendocrine carcinomas and IDH2 mutated SNUCs may represent the same entity, due to their epigenetic similarity20.DNA methylation is an epigenetic modification of the DNA which regulates gene expression. It plays a significant role in the differentiation of different cell types and it has been shown that DNA methylation patterns are highly tissue-specificFor this study, we collected a cohort of 395 sinonasal tumors and relevant differential diagnoses encompassing 18 different histologically defined entities as well as normal sinonasal control tissue to elucidate the epigenetic landscape of these tumors. Within this dataset we identified highly robust DNA methylation-based tumor classes. By further integrating mutational profiling and mass spectrometry-based proteomics, we provide sound evidence that tumors with SNUC morphology consist of four distinct epigenetic subclasses which are supported by different driver mutations landscape and protein expression profiles. Furthermore, we provide a machine learning-based algorithm for reliable classification of diagnostic samples which may improve the histopathological diagnosis of challenging cases.4. The workflow for the compilation of the reference set is also summarized in Supplementary Fig. To test if DNA methylation-based tumor classification for SNUCs was applicable, we obtained a cohort of 429 high quality DNA methylation profiles of sinonasal tumors and normal tissue. A t-distributed stochastic neighbor embedding (t-SNE) dimensionality reduction and unsupervised clustering of the 20,000 most variable CpG sites was used to assess the optimal number of classes and for best partition. The distribution of the CpG sites selected for class separation showed a uniform chromosomal distribution and did not show any enrichment in the functionally relevant promotor regions when compared to the overall array design  contained tumors that had initially been diagnosed as either SNUCs, olfactory neuroblastomas, neuroendocrine carcinomas or adenocarcinomas  consisted of histologically diagnosed SNUCs, neuroendocrine carcinomas, poorly differentiated carcinomas and atypical teratoid/rhabdoid tumors of adults in the sellar region  and subsequent loss of INI1 protein expression all cases with available tissue  including cases where the chromosomal SMARCB1 loss was not identifiable  mainly contained conventional adenoid cystic carcinomas , but also tumors that were initially diagnosed as adenocarcinomas, poorly differentiated carcinomas and SNUCs by means of conventional diagnostic criteria  mainly consisted of tumors that had been diagnosed as SNUCs, neuroendocrine carcinomas and olfactory neuroblastomas, but also single adenocarcinomas, poorly differentiated carcinomas and squamous cell carcinomas  and NEC-like SMARCA4/ARID1A class (FDR < 0.001) as well as olfactory neuroblastomas (FDR < 0.001) was reflected in their similarity with pulmonary neuroendocrine cells (PNEC). Furthermore, olfactory neuroblastomas (FDR 0.004) and tumors from the NEC-like IDH2 group (FDR < 0.001) showed similarity to a class of Undefined Rare Cells, which likely represent progenitor cells of epithelial and neuroendocrine cells22. Tumors from the ACC class mostly resembled serous cells of submucosal glands . As expected, squamous cell carcinoma profiles were closely related to squamous cells .To identify potential cells of origin, differentially expressed proteins from all tumor classes in comparison to normal sinonasal tissue were subjected to overrepresentation analysis using cell type-specific gene sets from previously published single cell RNA sequencing data of the mucosal lining of upper and lower human airways Fig. 22,23. ThAdditionally, we performed a differential protein expression analysis comparing the ACC, NEC-like IDH2 and NEC-like SMARCA4/ARID1A tumor classes against each other. Protein lists were subjected to functional pathway analysis  or NGS panel sequencing (n = 10) of 19 tumors from this group. We observed relatively low median tumor mutational burden with 3.7 mutations per megabase  and ARID1A . Early clinical data suggests that patients with these alterations might benefit from treatment with PD1 inhibitors24. Notably, we observed one case of a young patient in which a SMARCA4 frameshift mutation (p.Q306Rfs*12) was detected in tumor and in adjacent normal tissue, suggesting a germline or mosaic origin for this mutation. Additional recurrent alterations in this tumor class comprised PIK3CA mutations , including classical hotspot mutations such as p.H1047 or p.E545, which are known predictive markers for treatment with PIK3 pathway inhibitors in breast cancer26. Other known pathogenic driver mutations included CTNNB1 , TP53  and TSC2 .As a clear driver for the NEC-like SMARCA4/ARID1A molecular tumor class was not apparent in the available retrospective data, we performed whole exome , NEC-like SMARCA4/ARID1A (p < 0.001) or ACC class (p = 0.004). Best survival rates were seen in the NEC-like SMARCA4/ARID1A group, although there was no significant difference in comparison with the other two classes . In the ACC methylation class, we observed no significant difference in survival between tumors that were classified as adenoid cystic carcinoma or as SNUC by conventional histopathology (p = 0.5).To further evaluate the clinical importance of the DNA methylation-based classes, we compared disease-specific survival between the four SNUC classes Fig. . SMARCB1As described above, we used t-SNE and hierarchical clustering to define epigenetic classes. However, t-SNE is not a reliable tool to classify new cases as the position of individual data points can change over different iterations and highly depends on selected parameters and the composition of the cohort. For convenient and rapid classification of raw DNA methylation data in a diagnostic setting, we used the data from the reference set to develop a machine learning algorithm that assigns a given sample to one of the DNA methylation classes. We also implemented a supervised outlier detection designed to recognize and prevent the classification of samples with divergent DNA methylation profiles, such as distant metastases from other organs or entities that have not been included in the development of the classifier. For this, we collected a set of 8065 tumor and normal samples, covering eight different categories  including 197 different exact diagnoses . A subset of 400 cases from this cohort was used for the training of an additional Unknown class. The performance of the classifiers was validated using an independent test set consisting of 52 sinonasal tumors as well as the remaining 7665 non-sinonasal tumors.29 and random forest30 machine learning algorithms, the latter being the current gold standard for DNA methylation-based tumor classification31. In a hypothetical diagnostic setting, the potential hazard of a false classification is higher than the hazard of an unsuccessful classification. While the random forest achieved higher sensitivity values, the results of the support vector machine were superior with regards to specificity and accuracy. Therefore, the support vector machine will be further described in detail. For comparison, the results of the random forest classification are shown in Supplementary Table To explore the most suitable technique for this classification task, we compared support vector machineNUTM1 fusion analysis, respectively. Furthermore, the sinonasal tumor set also contained six SNUC specimens. Five of these were classified as NEC-like IDH2 and subsequent mutational analysis revealed the presence of an IDH2 R172 mutation in all cases. The remaining SNUC specimen was assigned to the NEC-like SMARCA4/ARID1A class and DNA sequencing confirmed a truncating SMARCA4 mutation (p.Q611*). Furthermore, all six samples showed strong expression of UCHL1. Thus, the molecular workup confirmed the DNA methylation-based diagnosis in all reclassified cases. The classifier, therefore, achieved an accuracy of 1.0 on the sinonasal validation cohort and lead to a revision or refinement of the initial diagnosis in 17% of cases  to correctly assign non-sinonasal tumor specimens to the Unknown class. We observed some variation in the specificity in different categories of the non-sinonasal test set Fig. . The lowses Fig. .Fig. 5Clwww.aimethylation.com.A web platform which provides convenient access to the classification algorithm can be accessed at In this study, we provide a resource of DNA methylation profiles from a diverse cohort of sinonasal tumors and present a machine learning algorithm for a robust classification of these diagnostically challenging tumors. Using DNA methylation profiling, DNA sequencing, copy number analysis and mass spectrometry-based proteomics, we show that tumors with SNUC morphology are not as undifferentiated as their current terminology suggests, but rather consist of four different molecularly distinct entities.32. While earlier studies only covered a fraction of the sinonasal cancer spectrum with rather limited total case numbers, the current study includes the whole spectrum of diagnostically relevant tumor classes and has clearly increased the total numbers of samples. This allowed to identify previously unrecognized DNA methylation-based tumor classes among sinonasal tumors. During the assembly of the reference cohort, 34 specimens were excluded, as they could not be stably assigned to an epigenetic tumor class. There are several aspects that could explain this. First, some of the excluded cases encompassed diagnoses with insufficient number of cases to form a separate and stable class . These entities could be included in future versions of the classifier, if additional cases can be acquired. Second, slightly divergent DNA methylation profiles could also be caused by array quality or by technical variations between different analyses. Third, some of these cases could also be of non-sinonasal origin, such as advanced tumors from neighboring anatomic regions  with continuous infiltration of sinonasal structures or distant metastases from an unrecognized primary site. Of note, none of these tumors were used for the development of the classifier as they did not correspond to a stable epigenetic class and were therefore excluded from further analyses. Finally, other cases of these non-clustering samples could correspond to hitherto unrecognized, even rarer tumor classes that require additional investigation. The last two points could also explain the large proportion of neuroendocrine carcinomas and SNUCs in the noise point category. Non-sinonasal tumors would be prone to be histologically classified as SNUCs due to their unusual morphology. Furthermore, expression of neuroendocrine markers is not uncommon in advanced and potentially dedifferentiated carcinomas, making the classification as neuroendocrine carcinoma more likely. To facilitate the further characterization of potentially unrecognized classes, we provide the unprocessed DNA methylation data for these cases along with the data of the reference cohort.A cohort of the clinically relevant spectrum of sinonasal tumors and associated neoplasms was surveyed for DNA methylation classification. In line with previous studies from other fields, we were able to demonstrate that most established tumor entities show characteristic DNA methylation signatures which can be used for reliable clinical classification and differentiationUsing an independent test set, we were able to show that the DNA methylation-based classification algorithm can reliably subtype samples with SNUC morphology without the need for additional molecular testing. Furthermore, the classifier correctly reclassified two samples initially diagnosed as sinonasal squamous cell carcinomas as lymphoepithelial carcinoma and NUT midline carcinoma, respectively. This reclassification is of profound clinical importance, as lymphoepithelial carcinomas show improved response to radiotherapy while NUT midline carcinomas are associated with very poor prognosis. We also describe the implementation of a supervised outlier detection for enhancing DNA methylation-based tumor classification. Machine learning algorithms typically assign anomalous profiles to the next, most similar class, potentially leading to spurious classification results. In the context of DNA methylation-based classification, entities that have not been used for the training of the algorithm such as distant metastases from other organs would thus either go unnoticed or be assigned to a wrong class. Crucially, both errors can be avoided by incorporating outlier detection in the machine learning pipeline. Although this approach may slightly compromise sensitivity, it reduces the risk of misclassifying non-sinonasal tumors, which is crucial for application in a potential diagnostic setting. Copy number profiles derived from DNA methylation data revealed different recurrent copy number alterations between the tumor classes. This information may provide additional confidence to the DNA methylation-based classification, although the sensitivity and specify of these alterations for classificatory purposes seem limited.A further focus of our study was to investigate different DNA methylation signatures and molecular alterations in the diagnostically highly challenging group of SNUCs. Our results indicate that what is currently summarized as SNUCs likely represent a heterogeneous group of tumors comprising at least four different molecular classes with different molecular drivers and different clinical course. A summary of the most important characteristics of the four classes is provided in Fig. IDH2 mutations and highly recurrent copy number alterations. Similar to acute myeloid leukemia and gliomas, IDH2 mutations induce a CpG island hypermethylation phenotype in sinonasal carcinomas, resulting in a highly distinct DNA methylation signature and significant hypermethylation of various tumor-related genes33. The value of IDH2-specific inhibitors in the treatment of patients with sinonasal tumors is currently unknown34. In line with previous reports, patients with sinonasal tumors with IDH2 mutations have a comparably favorable prognosis35.Specimens from the NEC-like IDH2 class were characterized by SMARCA4 or ARID1A mutations, which are part of the SWI/SNF chromatin remodeling complex. This also included one case with a SMARCA4 loss of function mutation in tumor-free normal tissue, potentially representing a germline or mosaic mutation. Germline mutations of SMARCA4 have previously been described to be associated with Rhabdoid tumor predisposition syndrome 2, leading to highly aggressive and early-onset tumors such as small cell carcinoma of the ovary, hypercalcemic type36. Although our data is clearly limited in this aspect, our findings suggest that NEC-like SMARCA4/ARID1A tumors may also occur in the context of tumor predisposition syndromes. Furthermore, we also observed a remarkably high rate of activating and potentially actionable PIK3CA mutations in almost one-third of NEC-like SMARCA4/ARID1A tumors. Comparably high mutation rates of PIK3CA are observed in breast carcinomas but have so far not been detected in other types of cancer26. There have been previous studies which observed activating PIK3CA mutations in SNUCs at low frequency, which indicates that these alterations are likely enriched in NEC-like SMARCA4/ARID1A tumors11. Functional studies or clinical trials will be required to evaluate whether these tumors may be responsive to treatment with PIK3 pathway inhibitors. Overall, the prognosis of patients from this tumor class is relatively favorable and comparable to IDH2 mutated tumors.Most cases from the NEC-like SMARCA4/ARID1A class showed IDH2 mutations  or recurrent SMARCA4/ARID1A alterations . However, it must be noted that this concept may change the treatment of patients with tumors of SNUC morphology. Therefore, a careful clinical evaluation and confirmation in further studies is crucial before drawing any clinical conclusions from our study.In mass spectrometry-based proteomics, we observed relatively similar global protein expression profiles for NEC-like IDH2 and NEC-like SMARCA4/ARID1A tumors. In both classes, we identified overexpression of several proteins that are specific to neurons or cells of the diffuse neuroendocrine system, and gene set enrichment analysis also indicated a high similarity with neuroendocrine cells. The identified markers are not routinely established in histopathological laboratories, ENO2 being a possible exception, and have therefore not been investigated in previous studies or used in routine diagnostics. Importantly, routinely used diagnostic markers such as chromogranin A or synaptophysin were not among the highly enriched markers in our proteomics analysis and may thus fail to identify the neuroendocrine differentiation of these cancers. This might explain why a substantial proportion of tumors from this class were diagnosed as SNUCs or even as adenocarcinomas or squamous cell carcinomas. It should further be mentioned that in our extensive reference cohort of sinonasal tumors no other class of neuroendocrine carcinomas could be identified. Based on our findings, we therefore, propose that these tumors should be regarded as “neuroendocrine carcinoma related”, either characterized by With regards to routine histopathological workup, we identified KRT18 in combination with UCHL1 as potential immunohistochemical markers to differentiate NEC-like IDH2 and NEC-like SMARCA4/ARID1A tumors from adenoid cystic carcinomas, olfactory neuroblastomas and squamous cell carcinomas. The combinational use of these markers could be of high diagnostic value when DNA methylation analysis is not available or not feasible.IDH1/2, disrupting the citric acid cycle which is located in the inner mitochondrial membrane and producing the oncometabolite 2-hydroxyglutarate37. For NEC-like SMARCA4/ARID1A tumors, we observed a general association with translational processes, however, no specifically disrupted pathways were observed.For tumors from the NEC-like IDH2 class, functional analysis of proteomic data revealed alterations in mitochondrial processes, including the citric acid cycle. This is in line with the well-known oncogenic mechanism of mutated SMARCB1 deficiency, which has recently been identified among SNUCs19. Our data further substantiates that sinonasal tumors with this alteration represent a distinct entity, including a broad range of histological morphologies and should therefore be identified by molecular testing. Although we were not able to include tumors from this class in our proteomics study due to insufficient quantities of tumor tissue, we did not detect immunohistochemical expression of the neuroendocrine and neuronal markers that were upregulated in NEC-like IDH2 and NEC-like SMARCA4/ARID1A tumors. This suggests a different cell of origin for these cancers and further studies are required to clarify their origin. Interestingly, SMARCB1-deficient sinonasal carcinomas show a remarkable epigenetic similarity to adult sellar atypical teratoid/rhabdoid tumors, although they tended to aggregate slightly separate in t-SNE analysis. It remains unclear if this is due to batch effects or if these tumors actually represent two distinct tumor types sharing the same driving alteration.Cases from the SMARCB1 class were characterized by 38) of adenoid cystic carcinomas and most likely represent high-grade adenoid cystic carcinomas. In several such tumors, we also detected focal adenoid cystic differentiation on histological reexamination. In addition, mass spectrometry-based proteomics revealed similarities of these tumors with serous cells of submucosal glands, further supporting the reclassification. Functional analysis of proteomic data revealed evidence of MAPK-pathway activation as a key mechanism which is in line with other reports40. Previous studies recognized that solid variants of adenoid cystic carcinomas can be mistaken for SNUCs and that close histomorphological investigation and adequate sampling is crucial41. A major benefit of DNA methylation-based classification is that it does not require the analysis of a tumor area with a certain differentiation or growth pattern. Therefore, a classification is also possible if only high-grade tumor areas are available .Samples from the ACC class shared the molecular profile  or tandem mass tag (TMT) labeling as well phosphoproteomic profiling. Therefore, the results from our functional analyses should be interpreted with caution and should be further investigated in future studies.Third, we did not perform central histopathological review of the cases included in this study. Therefore, the quality of the given conventional diagnoses might differ between the providing institutions due to different expertise in the diagnosis of sinonasal tumors.Furthermore, our outcome analysis should be interpreted with caution, as there was only very limited data available on other outcome associated clinical factors such as local tumor stage or metastatic stage.IDH2 mutant, (2) sinonasal neuroendocrine carcinoma-like, SMARCA4/ARID1A enriched, (3) sinonasal carcinoma, SMARCB1 altered and (4) poorly differentiated adenoid cystic carcinoma.In summary, we provide a DNA methylation-based algorithm, which could serve as a valuable tool in the diagnosis of sinonasal tumors, preventing misclassifications and supporting the workup of challenging cases. In addition, we clarify the molecular heterogeneity of tumors with SNUC morphology. We demonstrate that tumors with SNUC morphology can be segregated to four distinct tumor types, including (1) sinonasal neuroendocrine carcinoma-like, This research project has been approved by the ethics committee of the Charité – Universitätsmedizin Berlin. Retrospective investigation of left-over diagnostic samples for research purposes was covered by the general treatment agreement of the respective hospitals. No compensations were provided.14. Detailed parameters for exclusion of low-quality samples and cases that did not correspond to a stable DNA methylation class are listed in the DNA methylation analysis section. The investigators were not blinded to allocation during experiments and outcome assessment, but the algorithms that assigned cases to DNA methylation classes were agnostic to the conventional histopathological diagnosis.Statistical analysis was performed in RStudio Version 1.3.1093. For the reference cohort, minimum sample size per histopathological entity was set at 6, similarly to previously published work21. 32 samples were excluded after quality control, including 20 samples with poor DNA methylation analysis quality metrics as well as 12 specimens with low tumor cell content. 429 cases were used for subsequent analyses. Access to FFPE blocks to reproduce or validate the findings described in this manuscript can be obtained if sufficient material is left for further analyses.For the identification of DNA methylation-based tumor classes and the development of corresponding machine learning classifiers, we compiled a reference cohort of 495 samples from sinonasal tumors  automated slide stainer according to the manufacturer’s instructions. Sections were incubated with primary antibody against UCHL1  and KRT18 . Antibodies were validated using adequate positive controls, including human neural tissue for UCHL1 and human cancer tissue for KRT18. Expression was scored using an H-score which was calculated by multiplying the staining intensity  by the respective percentage of tumor cells43 using the MYB Dual Color Break Apart Probe (Zytovision). In brief, 4 µm sections were deparaffinized dehydrated and incubated in pretreatment solution  at 95–99 °C for 10 min. Following immersion in pepsin solution for 3–6 min at 37 °C, slides were washed, dehydrated and air dried. DNA probes were applied and the sections were sealed and denaturalized in humidified atmosphere at 82 °C for 5 min. Sections hybridized at 45 °C overnight. After washing, slides were counterstained with 4′,6-diamidino-2-phenylindole (DAPI).Fluorescence in-situ hybridization (FISH) was performed as described previouslySilver-enhanced in-situ hybridization for EBV analysis was done using the BOND Epstein-Barr virus-encoded small RNA (EBER) Probe (Leica) on the BOND-MAX automated slide stainer (Leica).Representative tumor areas were identified using light microscopy of hematoxylin and eosin-stained sections. Semi-automated DNA extraction was performed on the Maxwell RSC Instrument using the Maxwell RSC FFPE Plus DNA Purification Kit . Extracted total DNA quantities were measured using the Qubit™ HS DNA Assay (Thermo Fisher Scientific).We used the Illumina Infinium HD FFPE DNA Restore Kit for DNA restoration of FFPE samples. Subsequent bisulfite conversion was performed using the EpiTect Bisulfite Kit (Qiagen). The bisulfite-converted DNA was analyzed using the Illumina Infinium HumanMethylation450 or MethylationEPIC BeadChip.44. The pfilter (with perc = 5) and rmSNPandCH functions from the wateRmelon and DMRcate packages were used to exclude low-quality samples and to filter CpGs with low quality, reported cross-reactivity or association with SNPs or sex chromosomes46. The 20,000 most variant CpG sites were selected for further analysis. The combineArrays function of the minfi package was used to merge EPIC and 450k data. T-SNE was done using the RTSNE package, using a perplexity of 20 and 4000 iterations47. Density-based spatial clustering of applications with noise (DBSCAN) with the minPts parameter set at 6 was used to determine the optimal number of classes based on t-SNE coordinates and to assign individual cases to their respective class. Cases that were labeled as noise points were excluded from further analysis. Comparison of the number of classes and the assignment of the non-outlier cases to these classes revealed no differences before and after exclusion of the outlier samples. Robustness of tumor classes derived from t-SNE analysis was tested using iterative random down-sampling to 80% of the total cohort, as described previously14. The Pearson’s correlation coefficient of the x and y coordinates for all samples were calculated after 300 iterations. Tumor purity was estimated using the predict_purity_betas function48.Raw DNA methylation data were processed in RStudio Version 1.3.1093 using the minfi package49.We developed two separate machine learning classifiers based on a support vector machine and a random forest model that predict the tumor class of sinonasal tumor samples from their DNA methylation profile. In addition to these classes, a single non-sinonasal class for other tumor entities was introduced to detect outliers. Outlier detection has several modes of operation, namely unsupervised (where no outlier labels are required), semi-supervised (where only a few outlier labels are available) and supervised outlier classification. The latter was used in this study to distinguish sinonasal tumors from all other non-sinonasal tumorsn = 395) and 5% of the samples of each category from the non-sinonasal cohort (n = 400), which were randomly selected. This resulted in a combined dataset of 795 samples.The models were developed on a training set composed of all samples from the sinonasal reference cohort  as hyperparameters. Further, both models were configured to return scores for each methylation class.For the development of support vector machine models, we used the R package e1071. Linear and radial basis function kernels, gamma values of γ = 231. In detail, we used the cv.glmnet function of the R package glmnet on the scores on the training set resulting from the previous cross-validation which correspond to the selected hyperparameters. For prediction of the calibrated scores, the λ parameter with minimum mean cross-validated error was chosen. The class with the highest calibrated score was then determined as the final prediction for each sample.In order to make these scores more readily interpretable as probabilities, we developed calibration models based on ridge-penalized multinomial logistic regression, resembling previously described procedures but accounting for the challenge of a lower number of samples heren = 52) and the samples from the non-sinonasal cohort that had not been included in the training set before . In order to assess the outlier detection, all predictions of sinonasal tumor classes were retrospectively combined in one class and sensitivity and specificity were computed for the binary differentiation of sinonasal and non-sinonasal samples (outlier detection specificity and outlier detection sensitivity). For the evaluation of the sinonasal methylation class prediction, only samples from the sinonasal test cohort without classification as Unknown were considered . Five repetitions of the classifier development and evaluation led to similar results as with our final classifier, confirming the stability of the procedure.The resulting classification procedure was then evaluated on the sinonasal test cohort , the proteins were alkylated using iodoacetamide (IAA) for 30 minutes and blocked with an excess of 1,4-dithiothreitol (DTT). They were then bound to a 1:1 mixture of hydrophilic and hydrophobic magnetic beads at a high ACN (acetonitrile) concentration (>70%) and a beads to protein ratio of 10:1 by weight (10 µg beads:1 µg protein). After washing of the beads with 70% ethanol, the proteins were digested in solution in 50 mM HEPES (pH 8) with trypsin and LysC (enzyme-to-substrate ratio 1:50) overnight at 37 °C. The eluted peptides were then acidified using 100% formic acid  and desalted using AssayMAP tips on the Bravo robot. The final peptide concentration was determined by BCA assay.Representative 1.0 or 1.5 mm punch biopsy needle tissue cores were subjected to sonication using a Covaris LE220Rsc Focused-ultrasonicator  in a denaturing buffer containing 1.5% SDS and 2.5 mM DTT in 25 mM Tris, pH 8.0. Lysates were separated from cell debris and remaining paraffin by centrifugation at 20.000 x 6 ion count target and maximum injection time of 10 ms, followed by 20 MS2 scans with 45 K resolution, 1 x 105 ion count target, and maximum injection time of 86 ms.Mass spectrometric data acquisition was performed on a Q Exactive HF-X instrument coupled to an easy nanoLC 1200 system . One microgram of peptides was injected per run and the separation was performed using an in-house packed reverse-phase column  with a 110 min gradient from 3% to 60% (v/v) ACN, 0.1% (v/v) formic acid in water. The Q Exactive HF-X was operated in data-dependent mode with 60 K MS1 resolution, 3 x 1054. For the database searches, Oxidation (M) and acetylation (N-term) were included as variable modifications; carbamidomethyl cysteine was included as a fixed modification. Peptides of a minimum length of seven amino acids were included in the search. The FDR was set to 0.01 for peptide and protein identifications. The Match-Between-Runs (MBR) feature was used for the analysis. We excluded proteins that were flagged by MaxQuant in the Reverse and Only identified by site column as well as proteins with less than three peptides. Contaminants of non-human proteins were identified manually and removed from the data set. After excluding samples with less than 1000 detected proteins, 51 specimens remained for further analysis. Further downstream analysis was performed using R Studio using base functions and the lmfit and eBayes function from the limma package to perform differential expression analysis for groupwise comparisons comparing the different tumor classes to normal sinonasal tissue55. Proteins with a log2 fold change >1.5 and an FDR < 0.05 were considered as differentially expressed. The list of significantly overexpressed genes in each tumor class compared to normal sinonasal tissue was subjected to overrepresentation analysis using the WebGestaltR package. All identified proteins that remained after filtering as described above were used as a reference gene list. Two gene sets of normal respiratory cell types were used to identify potential cells of origin for the respective tumor classes23. The WebGestaltR pipeline uses the Fisher’s exact test to test for significance and the FDR method was used to adjust for multiple testing.Raw data were processed using the MaxQuant software Version 1.6.17.0 and the human reference proteome Functional analysis of proteomic data was performed using Cytoscape Version 3.9.1 and ClueGO Version 2.5.9. Differentially expressed proteins between the three proposed SNUC subtypes ACC, NEC-like IDH2 and NEC-like SMARCA4/ARID1A were used as input markers. Functional analysis of Reactome pathway terms was performed using a hypergeometric test, followed by Bonferroni step down correction. The results were visualized as functionally grouped networks using prefuse force directed layout.T-SNE plots were generated as described above using a perplexity of 10 and 2000 iterations.56.The Ion AmpliSeq Library Kit 2.0 (Thermo Fisher Scientific) was used to perform library preparation of 10 ng of genomic DNA using the Ion AmpliSeq Cancer Hotspot Panel v2 . The final library was quantified with the Ion Library Quantitation Kit (Thermo Fisher Scientific). Samples were multiplexed and amplified on Ion Spheres Particles with Ion 540 Kit-Chef and were sequenced using Ion 540 Chip (Thermo Fisher Scientific) with an adapted standard protocolSelected samples were processed using the TruSight Oncology 500 Panel . 100 base pairs were sequenced in paired-end mode on an Illumina NextSeq 550 machine. The raw data was demultiplexed and analyzed using the TruSight Oncology 500 v2.2 Local App Docker. Briefly, demultiplexed reads were alignment to the GRCh37 (hg19) genome using the Burrows-Wheeler Aligner, mapped reads were collapsed, re-aligned and stitched. Pisces software was used for somatic variant calling.57. Somatic variants were called in comparison to matched normal tissue, requiring a coverage of at least 30 in both sequencings as well as an allele frequency of at least 0.05 in the tumor specimen. Tumor mutational burden was calculated using the parameters established for the Illumina TSO500 panel58.DNA exome sequencing was performed at the CeGaT laboratory (Tübingen) using the Twist Human Core Exome Plus Kit  on a NovaSeq 6000 sequencer (Illumina) to generate 2 × 100 bp reads. Sequence data were aligned to GRCh37 (hg19) genome using the Burrows-Wheeler-Aligner (Version 0.7.17)Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4