The marine toxintetrodotoxin (TTX) poses a great risk to publichealth safety due to its severe paralytic effects after ingestion.Seafood poisoning caused by the consumption of contaminated marinespecies like pufferfish due to its expansion to nonendemic areas hasincreased the need for fast and reliable detection of the toxin toeffectively implement prevention strategies. Liquid chromatography-massspectrometry is considered the most accurate method, although competitiveimmunoassays have also been reported. In this work, we sought to developan aptamer-based assay for the rapid, sensitive, and cost-effectivedetection of TTX in pufferfish. Using capture-SELEX combined withnext-generation sequencing, aptamers were identified, and their bindingproperties were evaluated. Finally, a highly sensitive and user-friendlyhybrid antibody–aptamer sandwich assay was developed with superiorperformance compared to several assays reported in the literatureand commercial immunoassay kits. The assay was successfully appliedto the quantification of TTX in pufferfish extracts, and the resultsobtained correlated very well with a competitive magnetic bead-basedimmunoassay performed in parallel for comparison. This is one of thevery few works reported in the literature of such hybrid assays forsmall-molecule analytes whose compatibility with field samples isalso demonstrated. Even though it was initially believed that TTXwas produced by the pufferfish itself, marine bacterial species havebeen postulated to be able to produce TTX,9 suggesting that symbiotic marine bacteria could be the primary sourceof TTX that bioaccumulates in pufferfish and other marine speciesand finally reaches humans through the food chain. As recently reported,there are more than 30 different bacteria genera capable of producingTTX that have been isolated, among which the most common is the Vibrio sp.10 To date, however,there is still some discussion regarding the TTX production/biosynthesisas well as the pathway of TTX bioaccumulation in marine ecosystems.11Tetrodotoxin (TTX)is a very potent neurotoxin produced by marinebacteria, and it is associated with severe seafood poisoning afterconsumption of pufferfish (Tetraodontidae family).12 includingThailand,5 Taiwan,13 Singapore,14 Cambodia,15 Bangladesh,16 and India.18 However, toxic pufferfish species have expanded to other regions,and there have been an increasing number of reports of incidencesin the Mediterranean Sea, which has been attributed to the openingof the Suez Canal (the “Lessepsian migration”), whichresulted in the migration of species from the Red Sea to colonizethe Mediterranean Sea,22 the Aegean Sea,23 the Adriatic Sea,24 and Oman,25 and therehave also been reports of the incidence of tetrodotoxin in Australia26 and the United States,27 highlighting the widespread distribution of the toxin.Pufferfish poisoning is typical of warmwaters and was regardedas a problem confined to Asian countries,28 Portugal,30 Greece,31 the Netherlands,32 Spain,33 Italy,34 and France,35 although usually at very low concentrations.Nevertheless, it is now considered that TTX may pose a food safetyrisk even in nonendemic areas.Additionally,TTX has been recently found in shellfish, particularlyin European countries such as the United Kingdom,36 In Japan, where pufferfish is considered a delicacyand highly consumed despite its potential toxicity, a limit of 2 mgof TTX equiv/kg has been used as a criterion to judge the acceptabilityof pufferfish as food,37 and a guide withthe edible parts and species of pufferfish that are allowed for consumptionhas been published.38 In the USA, strongrestrictions exist for the importation of pufferfish.39 In Europe, fish of the family Tetraodontidae and productsderived from them must not be placed on the markets.41 Regarding shellfish, no regulations exist. Nevertheless, the EuropeanFood Safety Authority (EFSA) has recently published that concentrationsbelow 44 μg of TTX equiv/kg shellfish meat do not result inadverse effects in humans.36TTX is highly toxic. Pufferfishpoisonings have revealed that ingestionof 0.18–0.2 mg of TTX might be near the minimum dose for developingTTX symptoms, and 2 mg is a lethal dose. However, the levels of TTXthat result in acute toxicity or death in humans are still unclear,with some reports of human cases suggesting that acute poisoning canoccur from doses of 4–42 mg/kg body weight or higher.42 Toxicityequivalency factors (TEFs) for these TTX analogues are essential forthe evaluation of relative risk, but, unfortunately, information onthe relative potencies of TTXs is limited. Although the use of differentcell lines in toxicity assays has been questioned, it is evident thatmost analogues are much less toxic than TTX44 Additionally, the parent TTX is usually the most abundant.36Thereare about 30 TTX analogues.45 Ethical concernsand low specificity of the mouse bioassay, the most frequently usedmethod, encouraged the development of alternative strategies. Liquidchromatography coupled with mass spectrometry (LC-MS/MS)31 has been widely exploited for TTX detection, and it provides moresensitive, specific, and accurate results than bioassays. However,instrumental analysis techniques are expensive, time-consuming, andlabor-intensive and require sample pretreatment, trained personnel,and significant laboratory infrastructure. Immunological methods suchas ELISA using specific TTX antibodies can provide quantitative andsensitive detection,47 and commercial ELISA kits arereadily available. The small size of TTX requires the use of hapten-carrierprotein bioconjugates for antibody development, requiring carefulconsideration in the preparation of these bioconjugates. The developmentof antibody pairs for sandwich assay development is hindered by thesmall size of the TTX, thus requiring the design of competitive assays.49 Competitive immunoassays are more difficult to optimize, and thepreparation of toxin-reporter molecule conjugates required for sometypes of immunoassays can also be challenging.45 Nevertheless, antibody-based biosensors have been reportedand are particularly useful for rapid screening purposes.52Bioassays, instrumental analysis, andimmunological methods aretypically employed to detect TTXs in field samples, based on the toxiceffects, physicochemical properties, and antigenic specificity ofthe toxin, respectively.55 Aptamers are single-stranded synthetic oligonucleotidesthat can bind their target molecule with high affinity and specificityowing to the specific structural conformations they adopt. Systematicevolution of ligands by exponential enrichment, commonly known asSELEX, was developed for the generation of aptamers, and it is basedon iterative rounds of binding, partitioning, and amplification.57 Compared to antibodies, aptamers show several advantages for biosensingapplications, including in vitro selection, the possibilityto bind any kind of target, high affinity and specificity, reproduciblechemical synthesis, stability at various environmental conditions,reversible denaturation, and easy site-directed modification.55Aptamers are biorecognition molecules considered as an alternativeto antibodies that are suitable for the detection of virtually anytype of target.58 One of the main hurdles is target immobilizationon a solid matrix to allow selection through traditional SELEX approaches.Altering the native structure of the target to facilitate immobilizationcan prevent the aptamer from binding to the target in solution inits natural form, and an absence of functional groups can completelyhinder immobilization as the small size of the targets also limitsthe availability of binding sites. An alternative selection strategy,termed capture-SELEX, based on library immobilizationand use of the target in solution, was first reported by Stoltenburget al.59 This approach is ideal for smallmolecules since the target molecule can be used in solution, and thepotential structure-switching properties of the selected aptamerscan be exploited for characterization and assay development.58 Capture-SELEX strategy has been successfully used for several targets,including aminoglycoside antibiotics,59 cadmium,60 penicillin,61 quinolone,62 and lipopolysaccharides.63The development of aptamers for small molecules is a challengingtask.64 However,the trial-and-error nature of the process of generating split aptamers,possibly resulting in lower binding affinities of the individual fragmentsand further requirements for modifications, is among the factors discouragingresearchers from undertaking this complex and costly task. In fact,to date, split aptamers have only been reported for 15 small molecules.64 Hybrid antibody–aptamer sandwich systems,on the other hand, have emerged as an attractive alternative offeringthe best of both antibody and aptamer biorecognition molecules, togetherwith the advantages of sandwich assays.65 Even though several examples have been reported for the detectionof protein targets using such hybrid systems, only a handful of examplesexist for small molecules, including trinitrotoluene,66 tetracycline,67 and aflatoxinB1.68The path from aptamer discovery to assaydevelopment for smallmolecules is not trivial. The usual format is competitive assays,which can be difficult to develop as discussed earlier in the caseof antibodies. Sandwich assays are hindered by the small size of thetargets, and to the best of our knowledge, no sandwich aptamer assayshave been reported for small molecules. Alternatively, split aptamerscan be generated and have been exploited in a sandwich format forthe detection of small molecules.69 who did not provide details regarding the selectionprocess or the aptamer affinity, and the second by Gu et al.,70 who used a variation of the capture-SELEX strategywith magnetic reduced graphene oxide to immobilize the ssDNA libraryand identified a TTX aptamer with high affinity (KD of 44 nM). In this work, we sought to apply the capture-SELEXstrategy to develop TTX-binding aptamers and apply them for the detectionof the toxin in pufferfish. Two selections were performed in parallel,using two different types of streptavidin magnetic beads to facilitatelibrary immobilization. Next-generation sequencing of various poolsfrom the selections enabled the identification of aptamer candidates,and different approaches were used to evaluate their binding properties.Finally, a highly sensitive hybrid antibody–aptamer sandwichassay was developed and successfully exploited for the detection ofTTX in pufferfish.Two TTX aptamers have beenreported, the first one by Shao et al.,Prorocentrum concavum, 11-amino-1-undecanethiolhydrochloride (MUAM), cysteamine, l-arginine, 1,6-anhydro-β-d-mannopyranose, and streptavidin-horseradish peroxidase (SA-HRP)were purchased from Merck (Spain). Maleimide-activated magnetic beadswere purchased from Cube Biotech (Germany) and SiMAG-streptavidinmagnetic beads  fromChemicell (Germany). Streptavidin-polyHRP80 (SA-pHRP) was purchasedfrom Bionova (Spain) and the TMB Super Sensitive One Component HRPMicrowell Substrate from Surmodics. All oligonucleotides were synthesizedby Biomers.net (Germany).Tetrodotoxinof 98.8% purity (TTX) was purchasedfrom Tocris Bioscience  and Latoxan ,and standard solutions at 1 mg/mL were prepared in 0.1 M sodium acetatebuffer pH 4.8. Certified reference materials of saxitoxin (STX) anddomoic acid (DA) were obtained from the National Research Councilof Canada . The mouse monoclonal anti-TTX antibody was obtained from DeltaclonS.L. (Spain). Sulfo-NHS-acetate, maleimide-activated microplate stripwells, Dynabeads M-270 streptavidin magnetic beads , DreamTaq DNA polymerase, and lambda exonucleasewere purchased from Fisher Scientific (Spain). The DNA purificationkits (Oligo Clean & Concentrator kit and DNA Clean & Concentratorkit) were purchased from Ecogen (Spain). Okadaic acid potassium salt(OA) from 34 The docking site was flanked by two random regions of 10 and 40 nucleotides. Libraryimmobilization on streptavidin magnetic beads (SA-MB) was achievedvia hybridization of a docking probe  with the docking site of the library. Two differenttypes of SA-MB beads were used for two parallel selections, the DynabeadsM-270 streptavidin and the SiMAG-streptavidin. The binding bufferused was PBS with 1.5 mM MgCl2. A total of 23 selectionrounds were performed using the TTX precursors l-arginineand 1,6-anhydro-β-d-mannopyranose as counter-selectionmolecules during the last six rounds. A detailed description of theselections performed can be found in the Supporting Information.The libraryused for the selectionwas based on a previous report (5′-ATACCA GCTTAT TCAATT-N10-TGAGGCTCGATC-N40-AGATAG TAAGTG CAATCT-3′).https://usegalaxy.org/), and thequality of the data was evaluated with the “FASTQC”tool, which also provided general statistics. The format of the datawas converted to FASTA, and datasets containing only library-lengthsequences (90–110 bp) were created. Each dataset was finallycollapsed to identify unique sequences within the first megabyte ofdata. The 100 most abundant sequences from all of the datasets werecompared to identify the ones preferentially enriched in the targetpools. Multiple sequence alignments were performed with Clustal Omega to determine sequence families, while sequence motif analysis wasperformed using MEME (https://meme-suite.org/meme/tools/meme). Ten aptamer candidateswere finally selected, five from each selection, for further characterization.Different rounds from the selections were chosenfor NGS. Targetelution fractions from rounds 6, 9, 16, 23, and counter elution fractionfrom round 23 for both selections were individually amplified withdifferent forward primers (containing distinct barcode sequences)and a common reverse primer. The resulting dsDNA for each round wascolumn-purified and sequenced using Ion Torrent NGS. The FASTQ rawdata were imported into the Galaxy web server  in combination withunmodified aptamer sequences. The preparation of the TTX-beads isdescribed in the Supporting Information. For the binding studies, 50 μL of different concentrationsof each aptamer (up to 600 nM in binding buffer) was incubated with2 μL of the TTX-beads for 30 min under rotation at an ambienttemperature. The supernatants were discarded, the beads were washedthree times with 100 μL of PBS with 0.05% v/v Tween-20 (PBST),and finally resuspended with 20 μL of binding buffer. Boundsequences were detected after PCR amplification using library-specificprimers and agarose gel electrophoresis. Analysis was performed induplicate for each concentration. The intensity of the DNA bands wasestimated with ImageJ software and the gel analysis option, plottedagainst aptamer concentration using GraphPad Prism 6 software, andthe KD of each aptamer was finally determinedusing the “One site Specific binding with Hill slope”model.TheAPAA was carriedout as previously described with minor modifications specific to TTX.72 TTX-beads were used in combinationwith 5′-biotin-modified aptamers. TTX-beads (2 μL) weremixed with different concentrations of each biotinylated aptamer inbinding buffer (50 μL of up to 450 nM) and incubated for 30min at an ambient temperature under rotation. The supernatants werediscarded, and the beads were washed three times with 100 μLof PBST. Next, 50 μL of 50 ng/mL SA-pHRP in PBST was added andincubated for 20 min. After a final washing step (five times with100 μL of PBST), 50 μL of the TMB substrate was added,and following a brief incubation at room temperature, an equal volumeof 1 M H2SO4 was added to stop color development.The supernatants were separated from the beads using a magnet, transferredto a 96-well microtiter plate, and the absorbance was recorded at450 nm. The KD of the aptamers was calculatedas described above. All measurements were carried out in duplicate.The APAAwas carried out as previously described with minor modifications specificto TTX.bottom). The precision ofthe assay was evaluated using duplicate measurements of differentconcentrations of TTX analyzed on four different days. The interassaycoefficients of variation (% CV) were calculated as the standard deviationfor each measurement divided by the average. The cross-reactivityof the assay with possibly interfering marine toxins such as domoicacid, okadaic acid, and saxitoxin was finally studied under the conditionsdetailed above using each toxin at 40 ng/mL.A sandwich assay was developed using an antibody forcapture and an aptamer for detection of TTX. Specifically, 50 μLof 5 μg/mL anti-TTX monoclonal antibody in 50 mM carbonate bufferpH 9.4 was used to coat the wells of a MaxiSorp immunoassay plateovernight at 4 °C. The wells were washed three times with 200μL of PBST, followed by blocking with 200 μL of 1% w/vBSA in PBST for 30 min. The wells were washed again and incubatedwith 50 μL of different concentrations of TTX in PBS for 1 h.After washing, 50 μL of 500 nM biotinylated aptamer in bindingbuffer was added and left to incubate for 1 h, followed by washing.Fifty microliters of 100 ng/mL SA-HRP in PBST was then added, followedby a final incubation of 30 min and washing. The TMB substrate (50μL) was added, and color development proceeded for 6 min. Sulfuricacid (50 μL of 1 M) was added to stop color development, andthe absorbance was recorded at 450 nm. All incubation steps were performedat an ambient temperature (22–25 °C) unless stated otherwise.All five aptamer candidates were initially screened in combinationwith the antibody at a constant TTX concentration (32 μg/mL= 100 μM), and the aptamer providing the highest signal waschosen for the final assay. A calibration curve was constructed usingserial twofold dilutions of TTX in the range of 0.039–40 ng/mL(0.12–125 nM). Duplicate measurements were performed, and thedata were fitted to a four-parameter sigmoidal model using GraphPadPrism 6 software. The limit of detection (LOD) was interpolated fromthe curve as the bottom of the fitted curve plus three times its standarddeviation   and one silver-cheeked toadfish were caught in 2014 in Alicante (Spain).Pufferfishes were dissected, and the gonads, liver, skin, and musclewere retrieved. A double TTX extraction was performed with 0.1% v/vacetic acid, as previously described.73 Extracts were obtained at a tissue concentration of 200 mg equiv/mL.Fish extracts wereobtained from previous work.L. lagocephalus pufferfish organs  were spiked with TTX at 1.5 ng/mL, and recoverieswere calculated after interpolation in the TTX calibration curve constructedin PBS buffer as detailed above. The L. sceleratus TTX-containing fish extracts were then analyzed. The amount of TTXin these extracts was calculated after interpolation in the calibrationcurve constructed in PBS and also in calibration curves constructedin parallel using the respective extracts from the TTX-free L. lagocephalus pufferfish. The extracts were diluted1/1000 with PBS for all experiments. For comparison, the extractswere also analyzed with a magnetic bead-based competitive immunoassay,as detailed in the Supporting Information.The compatibility ofthe hybrid sandwich assay with field sample analysis was initiallyevaluated with a spiking experiment. The TTX-free extracts from the 59 Besides the primerannealing sites, the library contained two random regions separatedby a docking sequence, which provided an immobilization site to streptavidinmagnetic beads through its hybridization with a complementary biotinylateddocking probe. Previous studies exploiting the capture-SELEX strategyreported the use of Dynabeads M-270 SA-MB62 and the library design from the original study.59 Different affinity media like streptavidin agarose beadsand homemade avidin-magnetic beads were reported in other studies,in combination with libraries containing only one random region whoseimmobilization was achieved via a biotinylated complementary to oneof the primer annealing sites.63 The distribution ofthe random sequences on the SA-MB, which could be partially determinedby the availability of immobilization sites on the beads and the specificthree-dimensional structures of the sequences, could potentially affectthe evolution of a selection based on the capture-SELEX strategy.In this work, two different types of streptavidin magnetic beads wereused to perform two parallel selections. Even though Dynabeads andSiMAG SA-MB differ in size , theirmaximum binding capacity is almost identical. Taking into considerationthe higher cost of the Dynabeads SA-MB as compared to the SiMAG ones,selections with both bead types were performed in an effort to reducethe overall selection costs and investigate the effect of the propertiesof the beads on their performance for capture-SELEX applications.TTX is a very small moleculewith only one(amine) functional group 1A. Its cTable S1 (SI). Starting with500 μM TTXand 2 h incubation steps (background and target elution steps), thegradual decrease of the TTX concentration and duration of the incubationsteps led to the completion of the selections after 23 rounds using50 μM TTX and 30 min incubations. TTX precursors l-arginineand 1,6-anhydro-β-d-mannopyranose74 were added as counter-selection molecules during the lastseven selection rounds to improve the specificity of the selectedsequences  in the target pool datasets from rounds 23was monitored, and their distribution in the pools from rounds 16,23, and 23-counter is shown in Figure S1). The opposite wasobserved for the SiMAG-based selection . Notably, sequences selected with one type of beads werenot found in the pools from the selection conducted with the othertype of beads. Despite theoretically starting from the same initiallibrary and using the same selection conditions, each of the SELEXevolved differently, resulting in different sequences being selected,depending on the beads used for library immobilization. This can beexplained, in part, to be due to the fact that even though the startingaliquots are taken from the same initial library, each aliquot cancontain a different combination of diverse sequences. Additionally,the size and nature of the beads can affect the number of dockingprobes and thus the individual sequences of the immobilized librarycaptured on its surface, and this can affect the accessibility ofthe target to the individual sequences.A 7–89-fold enrichment was observed forthe sequences selectedwith Dynabeads SA-MB, which was calculated as the ratio of abundancein round 23 to round 16. The selection performed with the SiMAG beadsexhibited 2–73-fold enrichment. This data again demonstratesthat the Dynabead-based selection appears to be more successful witha higher enrichment of selected sequences. A direct comparison ofthe datasets from the last selection rounds with TTX and the counter-selectionmolecules revealed that the top 20 sequences were slightly more abundantin the counter-selection dataset than in the target dataset when Dynabeadswere used . The second and third most abundant sequences, identified as sequences2 and 3, were encountered at lower percentages and did not appear to belong to any family. Only one major sequencefamily was also observed in the dataset from the SiMAG beads selection, containing the second mostabundant sequence (sequence 2 at 1.8%). The first and third most enrichedsequences (2.3 and 1.3%) do not appear to belong to any cluster.Multiple sequence alignmentof the 100 most abundant sequencesin rounds 23 from both selections was also performed to identify possiblesequence families. As can be seen in Table S2 and Figures S3 and S4) were also selected and wereannotated as D4, D5, C4, and C5. The sequences of all aptamer candidatesare shown in Table S3.The three most enriched sequences from the two selections wereultimately chosen for further characterization. These were annotatedas D1, D2, and D3 for the Dynabeads and C1, C2, and C3 for the SiMAGselections. Additionally, two sequences identified in the two datasetsfrom rounds 23 with preferential abundance in the target pools comparedto the counter target pools . Moreover, the ssDNA-folding was observed in thepredicted structures of the five selected TTX aptamers shown in Figure S7, using the M-fold program (http://www.unafold.org/mfold/applications/dna-folding-form.php.).The 10 selectedaptamer candidates were initially evaluated under conditions mimickingthe selection process to choose the most promising ones for furtheranalysis. Each aptamer was immobilized on SA-MB via hybridizationto a biotinylated docking probe. Aptamer displacing to the solutionafter incubation with TTX was detected after PCR amplification andagarose gel electrophoresis, as detailed in the 75 including microscale thermophoresis77 and isothermal titration calorimetry,51 but these require specialized equipment. Our group has previouslyreported the use of magnetic beads for the immobilization of small-moleculetargets and the detection of aptamer binding by PCR and colorimetry.77 We have developed microtiterplate-based assays using long-chain crosslinkers to spatially separatethe target from the plate surface and facilitate aptamer binding72 and also used gold nanoparticle aggregation assays.78 These methods are easy to perform and require materialand equipment found in almost any laboratory.Characterizationof the binding properties of aptamers for small-molecular-weight targetslike TTX using classical methods is usually hindered by the size ofthe molecules. A variety of approaches have been reported for affinitystudies,KD values are shown in Figure S6. For APAA, TTX was immobilized on magneticbeads, whereas bound unmodified aptamer was detected after PCR amplificationand gel electrophoresis. All aptamers demonstrated similar bindingaffinities with affinity dissociation constants in the range of 73–114nM. Aptamers C2 and C3 selected using the SiMAG SA-MB showed slightlybetter KD values compared to the onesselected with the Dynabeads SA-MB . Biotinylated aptamerswere used for bead-ELAA in combination with TTX immobilized on magneticbeads. Colorimetric detection of bound aptamers was achieved usingSA-pHRP and the TMB substrate. As with APAA, all KD values determined with bead-ELAA were calculated inthe low nanomolar range (7–89 nM). Given the low nanomolaraffinity constants obtained for the five aptamers, all of these werethen tested to identify the best aptamer for use in a sandwich assaywith the antibody.For the TTX aptamers,three of these methods were exploited. The calculated 71 or microplate wells,72 and here, we pursued a robust hybrid antibody–aptamersandwich microtiter plate assay. It was hypothesized that the uniquecagelike structure of TTX could potentially allow the formation ofan antibody–TTX–aptamer complex, enabling the detectionof TTX with a sandwich assay. Even though hybrid antibody–aptamerassays have been reported before for high-molecular-weight targetslike proteins and cells,65 examples forsmall-molecule targets are rare. Nevertheless, these assays are veryattractive because they combine the advantages of both types of biorecognitionelements while at the same time providing the sensitivity/specificityof sandwich assay formats. Using a monoclonal anti-TTX IgG antibodyto coat the wells of a microtiter immunoplate, the five TTX aptamerswere initially screened to choose the most suitable one for sandwichassay development. Indeed, all aptamers were able to form a sandwichwith the antibody and allow the detection of TTX . Aptamer D3, however, was by far the most successfulone, leading to more than twofold higher signal compared to the signalsobtained with the other aptamers, and it was chosen for final assaydevelopment. The sensitivity of the hybrid assay employing the monoclonalTTX antibody for capture and the D3 aptamer for detection was thenevaluated at concentrations of TTX ranging from 39 pg/mL to 40 ng/mL,equivalent to 122 pM to 125 nM. The assay was very sensitive witha LOD of 310 pg/mL (970 pM) and EC50 of 1.1 ng/mL or 3.4 nM (Table S4).Finally, the high specificity of the assay was exhibited by the absenceof interference from other marine toxins such as domoic acid (DA),okadaic acid (OA), or saxitoxin (STX), the latter sometimes simultaneouslypresent in pufferfish79 or shellfish34  and in therespective tissue extract from the TTX-free pufferfish. As expected,TTX contents with both strategies were very similar. High TTX levelswere observed, especially in the gonads and liver tissues where TTXusually bioaccumulates (SI), which was previously developed and exploited a different monoclonalantibody.83 Some differences were observed, which mayderive from the specificity of the assays toward the different TTXanalogues. It is necessary to take into account that the cross-reactivityfactors for the different TTX analogues may vary according to thebiorecognition molecule (which in the case of the hybrid sandwichassay are both the antibody and the aptamer) and also the format ofthe assay. Nevertheless, comparable results were obtained with bothmethods. A very good correlation was also observed with a previousanalysis carried out with LC-MS/MS;73 theTTX contents trend in the different tissues are the same: gonads >liver > skin > mussel (Table S6).The establishmentof the cross-reactivity factors for the different TTX analogues presentin these tissues would facilitate the comparison with LC-MS/MS results.However, pure TTX analogues are not commercially available, and theirproduction is not a facile task. The isolation of the TTX analoguesand the elucidation of the toxicity equivalence factors, alongsidethe cross-reactivity factors, which ideally should be similar, ispart of ongoing work.Thehybrid antibody–aptamer sandwich assay was finally employedfor the analysis of field samples. Extracts from different tissues of umulates 3. The TTKD valuesin the low nanomolar range. The specific properties of the streptavidinmagnetic beads used to immobilize the library and perform the twoparallel selections appeared to affect the speed of evolution andthe enrichment achieved, although the binding properties of the selectedaptamers were not significantly affected. Finally, a simple hybridantibody–aptamer sandwich assay was demonstrated with highsensitivity, precision, and specificity. Its sensitivity was superioror at least comparable to commercial kits based on competitive immunoassaysand other existing aptamer and antibody-based assays and biosensors.The excellent performance of the assay was further demonstrated bythe reliable determination of TTX levels in pufferfish with an excellentdegree of correlation with measurements obtained with a competitivemagnetic bead-based immunoassay and liquid chromatography-mass spectrometry.This is the first demonstration of an assay employing an aptamer forthe detection of TTX in pufferfish, and, in general, is one of thevery few examples reported in the literature of such hybrid antibody–aptamersandwich assay for small-molecular-weight analytes. The sandwich formatof the assay is particularly attractive, and ongoing work is focusedon its transfer to a lateral flow assay to allow the rapid and facileanalysis of samples at the point of need. The evaluation of cross-reactivityfactors for different TTX analogues with this hybrid antibody–aptamerassay, as well as its applicability to the analysis of shellfish,is also in progress.TTXhas emerged as a major food hazard because of its high neurotoxicityand its presence in seafood found not only in Asian but also Europeanwaters. Traditionally, bioassays have been used to detect TTX; however,instrumental analysis using liquid chromatography in combination withmass spectrometry is currently employed for monitoring field samples.Microplate immunoassays and antibody-based biosensors can also providethe required sensitivity and specificity, provided that highly specificantibodies are used. Aptamers are cost-effective alternatives to monoclonalantibodies, and since their discovery in the early 1990s, they havebeen used for the detection of not only large targets such as cellsand proteins but also small-molecular-weight targets like toxins.To date, only two TTX-specific aptamers have been reported and havebeen exploited for the development of fluorescence and electrochemicalassays, which are quite elaborate and are not compatible with therapid and facile on-site analysis and have not been employed for theanalysis of field samples. In this work, capture-SELEX technologyin combination with high-throughput NGS analysis was exploited forthe discovery of TTX aptamers. Assays using magnetic beads were developedfor the verification of the binding properties of the selected aptamercandidates, which exhibited


In December 2019, the COVID-19 outbreak started in China and rapidly spread around the world. Many studies have been conducted to understand the clinical characteristics of COVID-19, and recently postinfection sequelae of this disease have begun to be investigated. However, there is little consensus on the longitudinal changes of lasting physical or psychological symptoms from prior COVID-19 infection.This study aims to investigate and analyze public social media data from Reddit to understand the longitudinal impact of COVID-19 symptoms before and after recovery from COVID-19.We collected 22,890 Reddit posts that were generated by 14,401 authors from March 14 to December 16, 2020. Using active learning and intensive manual inspection, 292 (2.03%) active authors, who were infected by COVID-19 and frequently reported disease progress on Reddit, along with their 2213 (9.67%) longitudinal posts, were identified. Machine learning tools to extract biomedical information were applied to identify COVID-19 symptoms mentioned in the Reddit posts. We then examined longitudinal changes in individual physiological and psychological characteristics before and after recovery from COVID-19 infection.In total, 58 physiological and 3 psychological symptoms were identified in social media before and after recovery from COVID-19 infection. From the analyses, we found that symptoms of patients with COVID-19 lasted 2.5 months. On average, symptoms appeared around a month before recovery and remained for 1.5 months after recovery. Well-known COVID-19 symptoms, such as fever, cough, and chest congestion, appeared relatively earlier in patient journeys and were frequently observed before recovery from COVID-19. Meanwhile, mental discomfort or distress, such as brain fog or stress, fatigue, and manifestations on toes or fingers, were frequently mentioned after recovery and remained as intermediate- and longer-term sequelae.In this study, we showed the dynamic changes in COVID-19 symptoms during the infection and recovery phases of the disease. Our findings suggest the feasibility of using social media data for investigating disease states and understanding the evolution of the physiological and psychological characteristics of COVID-19 infection over time. COVID-19 is a pandemic viral infectious disease that has quickly spread worldwide. The clinical presentation of COVID-19 has been well defined from active clinical and basic scientific research. Risk factors and commonly observed symptoms at the diagnosis of COVID-19 and throughout the acute disease course have been well documented . SeveralSince early 2021, researchers have found emerging evidence of long-term sequelae in a considerable proportion of patients who have recovered from COVID-19. Several systematic reviews and cohort-based studies have suggested a set of symptoms from which COVID-19 survivors have partially recovered or those they have retained ,8. Most To better understand the longitudinal changes in the physiological and psychological characteristics of the COVID-19 patient population throughout the patient journey, we systematically investigated Reddit public social media data from individuals who had previously been infected with COVID-19 and have recovered. Social media provides an efficient method of gathering large amounts of real-world data on the general public, which are scalable and convenient for users at any time of day, especially from remote or unattended regions. In addition, the development of sophisticated machine learning methods has been used to collect high-quality medical information from social media ,11. For Reddit was used to collect social media posts of COVID-19 survivors. Reddit is a large user-generated content website and text-based social media platform (without the limitation of text length) enabling us to extract detailed information about a given topic. Reddit is also a community-based social media platform providing sections devoted to topic-specific discussions (subreddits). Authors express their opinions or concerns on subreddit forums. Subreddit defines its characteristics and eligible members. Therefore, people who are really interested in each topic can participate in a subreddit community. Furthermore, Reddit provides a publicly accessible Application Programming Interface (API) so that researchers can collect and analyze anonymized posts for their own purpose. Such clear definitions of topics and member eligibility, as well as anonymized data access through the API, made Reddit a reliable and effective social media platform to find the appropriate target audience for this study.For this analysis, we collected posts that were generated from the moment the United States declared COVID-19 a national emergency  until the Food and Drug Administration (FDA) issued an emergency use authorization to both Pfizer-BioNTech’s and Moderna’s COVID-19 vaccines . After the World Health Organization (WHO) declared COVID-19 a pandemic and the United States declared COVID-19 a national emergency, it was logical to assume that the general population started to become aware of the presence of the COVID-19 virus and be interested in symptoms and their health conditions. In addition, they were exposed to the risk of COVID-19 contraction before the vaccine was available. During this period, people could actively discuss COVID-19-related issues and share their personal experience. We especially considered posts generated in “COVID-19Positive” and “CoronavirusSurvivors” subreddits. “COVID-19Positive” was self-described as “a place for people who came back positive for COVID-19 to share your stories, experiences, answer questions and vent!” “CoronavirusSurvivor” was self-described as a community for survivors of the coronavirus. At the time the conversations analyzed for this study were downloaded, the “COVID-19Positive” subreddit had about 81,000 members and the “CoronavirusSurvivors” subreddit had about 1000 members.The procedure to process and analyze social media data is described in To identify active authors’ authentic COVID-19-related posts, we performed active learning. After the automated active learning procedure, manual inspection was performed to examine the reliability of post labeling A. DetailWe observed some authors asking questions about COVID-19 or posting about symptoms, or about a family member, rather than themselves. It was apparent after reading many posts that not all authors were COVID-19 positive. This observation necessitated distinguishing between true patients with COVID-19 and authors who had not tested positive for COVID-19. Furthermore, we wanted to identify those authors that had discussed recovery from COVID-19 in their posts. We were also interested to know how different physical and psychological symptoms were discussed within Reddit posts.We first generated an unlabeled data set of 4762 posts that were written by 734 authors who had posted at least 4 times in our COVID-19-related subreddits A. We desTo do this, we implemented an active learning method  to captuUsing active learning, we built a classifier for each of the label classes; the classifier was periodically updated with new evidence, as saved by a human assessor . This waA labeling interface was built to capture labels using our active learning method . An asseWe recruited 13 well-trained assessors for the labeling task; the number of assessors varied across label classes. The goal for assessors was to find and label as many label-relevant posts as fast as possible. To do this, we provided 2 hours of training sessions so that accessors could find relevant posts generated by reliable patients with COVID-19. Labeled data completed by accessors were then manually inspected by the authors in this study to confirm that labeling was done correctly . The assWe selected active authors who were infected by COVID-19 and described their experiences and COVID-19 symptoms across the entire patient journey: from the confirmation of COVID-19 infection to after recovery from COVID-19. To do this, we examined the median time duration between the first post and the last post that a given user generated , and selWe found that symptoms usually take 5-6 days to appear after COVID-19 exposure (incubation period of COVID-19) . We alsoTo identify the physical or psychological symptoms that were mentioned in Reddit posts, we applied 2 automated symptom extraction methods. Using the Amazon Comprehend Medical tool (Amazon Web Services), we extracted medical entities. The Amazon Comprehend Medical tool uses machine learning to extract health-related information from text automatically. For this study, we considered medical entities “symptoms” and “signs” as COVID-19 symptoms. In addition to the Amazon Comprehend Medical tool, we also built a model to identify medical entities using Scispacy (v.0.4.0). Scispacy is a Python package for handling scientific documents and extracting medical and clinical terminology . We consIn total, 1634 posts generated by 290 active authors mentioned at least 1 COVID-19 symptom A. Next, Since the number of before- and after-posts was different, identified COVID-19 symptoms were observed with different frequencies. For a fair comparison of symptom frequency between before and after COVID-19 recovery, we normalized the frequency of each symptom to 100 posts . Depending on this frequency, acute and chronic symptoms were determined. Acute symptoms developed rapidly and were mentioned more frequently in before-posts. Chronic symptoms developed gradually and were slow to resolve, remaining as sequelae, and were mentioned more frequently after recovery from COVID-19 (after-posts).To identify symptoms that commonly appear together, we performed association rule analysis . We measTo understand how patients perceive and respond to COVID-19 symptoms, negation analysis was performed using the Amazon Comprehend Medical negation model  and manuP<.05 was considered statistically significant. For the visualization of analyses, the BPG library (v.6.0.1) in R was used [To compare the duration of symptoms before and after COVID-19, Spearman rank correlation coefficient measurement was performed using the programming language Python (v.3.8.1) and SciPy package (v.1.6.2). was used .To understand longitudinal changes in physical and psychological characteristics during the COVID-19 patient journey, we decided to collect patients' self-generated posts from Reddit. Reddit is a community-based online forum where patients can share their clinical journey, symptoms, and experiences from diagnosis to postrecovery. We identified 292 active authors who continuously and voluntarily discussed their physiological and psychological symptoms from the beginning of COVID-19 contraction to the after-recovery stage of COVID-19  program to find COVID-19 symptoms in a comprehensive manner. In total, 1634 (73.84%) posts mentioned at least 1 COVID-19 symptom A. From tTo understand the dynamic development of physiological and psychological symptoms throughout the COVID-19 patient journey, we compared how often individual symptoms were mentioned in before- and after-posts. Of 61 symptoms, 33 (54%) were acute symptoms. They were mentioned more frequently before recovery from COVID-19 compared to after recovery. The most known COVID-19 symptoms, such as weakness, body aches, cough, fever, chest tightness, and loss of smell and taste, were mentioned frequently in before-posts. We observed that these symptoms were relieved or improved after recovery. For example, in 100 posts, loss of smell and taste was mentioned 63 times in before-posts. However, after recovery, it was 32.89% less frequently mentioned (42 times in after-posts). Fever, cough, and throat discomfort (dry or sore throat) were approximately 40% less frequently mentioned after recovery A. ChillsFurthermore, 28 46%) of 61 symptoms were mentioned more frequently after recovery from COVID-19 (chronic symptoms). We observed that patients with COVID-19 mainly complained of psychological symptoms during this time. Mental discomfort and distress, including brain fog, stress, and panic, were mentioned 18.27% more often, and confusion/fluster was mentioned 8.63% more after recovery. Constitutional symptoms, such as fatigue (25.96%), and physiological symptoms, including problems in toes or fingers , renal-related problems , and immunodeficiency , were also frequently mentioned after recovery B. These % of 61 sIt has been shown that COVID-19 can cause multiple symptoms during early infection and progression . To undeOur data set was composed of posts that followed time courses, enabling us to perform temporal analyses to understand dynamic changes over time. We examined the duration of symptoms across the COVID-19 patient journey. On average, each symptom persisted for 83 days. On average, symptoms appeared 37 days before recovery A and remP=.28×10-4; Spearman correlation coefficient, We found that there was a weak correlation between frequency of symptoms and symptom duration  appeared earlier in patient journeys and were mentioned more frequently before recovery from COVID-19. These common COVID-19 symptoms were easily recognizable and evaluated by ordinary people without any screening tools. In addition, they were basic indicators used in clinical and medical research. Accumulated COVID-19 research defined them as well-characterized diagnostic indicators for COVID-19 . FurtherInterestingly, common symptoms were relieved or disappeared after recovery from COVID-19 (acute symptoms) based on our analyses of symptom negation and the duration over this entire period. Fever was considered a beneficial response to infection. Increased temperature reduces pathogens’ survival and increases mobilization of immune cells . Cough iCurrently, there is limited information about nonhospitalized or initially asymptomatic patients with COVID-19 who have had persistent and chronic symptoms after their recovery. In addition, there is a lack of information about longitudinal changes in COVID-19 symptoms due to the limited methods or accessibility to identify COVID-19 survivors on a global level. Our study explored dynamic changes in COVID-19 symptoms throughout patient journeys using social media data. Although social media may lack some depth of patient information, it provides an effective method of collecting a wide breadth of data. Social media data can be easily gathered across the world 24 hours a day, without the need for a clinician visit, and is an extremely efficient method  for rapiThe presence and rapid diffusion of misinformation in online communities is a growing concern for patients and health care providers . We suspIt could be possible that all the symptoms extracted from Reddit would not cover the entire literature of COVID-19 symptoms or would not necessarily be COVID-19 symptoms. Moreover, there is an inherent uncertainty in social media analysis about the accuracy of temporal directions and the potential delays that might occur between the actual event and the time of posting, which is challenging to detect. A closely related limitation is our assumption of a duration of 20 days for the COVID-19 patient journey, which is derived through the available data (Reddit posts).Natural language processing (NLP) could assist in identifying the temporal mentions and adjusting the outcomes. We might be able to improve our recovery time estimates using NLP, helping to improve temporal accuracy. As we work beyond social media surveillance and integrate with other data sets in the future, we are likely to find better alignment regarding the patient journey and related timelines.We believe that the integration of various social media data sets and the accumulation of data or systematic surveys of COVID-19 survivors as well as customized machine learning algorithms to capture COVID-19 symptoms would help to decide which symptoms are manifestations of COVID-19 infection and provide new and untapped insights into understanding the longitudinal evolution of symptoms throughout the entire COVID-19 patient journey. This also could be a novel means of assessing the fraction of COVID-19 survivors with persistent symptoms.In this observational study, we demonstrated the extensive variability of physiological and psychological impacts of COVID-19 infection and their variability during the acute infection and recovery phases of illness. We also demonstrated the usefulness of gathering social media data as an effective and alternative way to understanding the patient journey from diagnosis through recovery. Our findings show the practicality and feasibility of employing social media data for investigating disease states and understanding the evolution of physiological and psychological characteristics of disease over time. These practices could be incorporated into routine procedures for COVID-19 patient care, providing appropriate treatment and long-term care after recovery from COVID-19.


Classifying pan-cancer samples using gene expression patterns is a crucial challenge for the accurate diagnosis and treatment of cancer patients. Machine learning algorithms have been considered proven tools to perform downstream analysis and capture the deviations in gene expression patterns across diversified diseases. In our present work, we have developed PC-RMTL, a pan-cancer classification model using regularized multi-task learning (RMTL) for classifying 21 cancer types and adjacent normal samples using RNASeq data obtained from TCGA. PC-RMTL is observed to outperform when compared with five state-of-the-art classification algorithms, viz. SVM with the linear kernel (SVM-Lin), SVM with radial basis function kernel (SVM-RBF), random forest (RF), k-nearest neighbours (kNN), and decision trees (DT). The PC-RMTL achieves 96.07% accuracy and 95.80% MCC score for a completely unknown independent test set. The only method that appears as the real competitor is SVM-Lin, which nearly equalizes the accuracy in prediction of PC-RMTL but only when complete feature sets are provided for training; otherwise, PC-RMTL outperformed all other classification models. To the best of our knowledge, this is a significant improvement over all the existing works in pan-cancer classification as they have failed to classify many cancer types from one another reliably. We have also compared gene expression patterns of the top discriminating genes across the cancers and performed their functional enrichment analysis that uncovers several interesting facts in distinguishing pan-cancer samples. The advancement of recent next-generation RNA Sequencing technology (RNASeq) lay the foundation for The Cancer Genome Atlas (TCGA), a multi-platform cancer data repository with more than 11,000 patients and 33 cancer types. This massive amount of data instigate the uncovering of tumorigenic features by examining the difference between tumorous and non-tumorous data. It also makes it possible to reveal key features that differentiate among the cancer types and comprehend poorly examined cancers.Cancer is a generic term that indicates a broad array of disorders that may impact any region of our human body. It is the second major cause of death all over the globe. In cancer, malignant tumors or abnormal cells grow abundantly with the potential to infect other cells of the body through the bloodstream. Throughout the last few decades, there has been continuous development in cancer research. Researchers applied numerous methodologies for screening out preliminary cancer stages to determine cancer types even before they become symptomatic. The evolution of modern technologies in bioinformatics brings forth a massive surge in the collection and availability of cancer and other diversified diseases’ data to scientists. Applications of machine learning algorithms possess the immense capability to analyze such a massive amount of data10. Danaee et al.11 applied Stacked Denoising Autoencoder (SDAE) to discern genes most relevant for diagnosing breast cancer. SDAE was used here for feature selection, identifying deeply connected genes (DCG), i.e., genes having significant interactions among themselves from the RNASeq gene expressions dataset in breast cancer. They utilized single-layer ANN, SVM with the linear kernel (SVM-Lin), SVM with radial basis function kernel (SVM-RBF) to distinguish tumor samples from normal samples. In this binary classification problem, the best classification was achieved using SVM-RBF, which yields 94.78% accuracy. In12, the genetic algorithm was used to detect several subsets of 20 genes for pan-cancer classification of 32 cancer types using RNASeq data by the k-nearest neighbors (kNN) classifier. However, their technique was computationally expensive as they ran the GA/kNN thousand iterations to discover the gene sets, and classification accuracy was inconsistent across the cancers. Kim et al.13 performed classification of 21 cancer types using SVM-Lin, SVM-RBF, kNN, random forest (RF), and neural network (NN) classifiers with 300 most significant differentially expressed (DE) genes detected by applying ANOVA on RNASeq gene expressions data collected from TCGA. They attained the best results with NN that yields accuracy: 0.9 and Matthews Correlation Coefficient (MCC): 0.89. Nevertheless, with the NN classification model, several cancer types were incorrectly classified as one another, especially in classifying squamous cells carcinomas , subtypes of lung cancer , cancers in kidney tissues , CHOL, COAD, READ, ESCA, PAAD, STAD, and BLCA.Many researchers in the recent past have utilized the classification of cancer types using transcriptomic profiles to overcome the limitations of diagnostic capabilities of conventional clinical and morphological approaches14. Although less studied than other machine learning models, MTL is extremely powerful in several data-intensive applications where training samples of the learning tasks have high degrees of similarity. MTL has been utilized in diversified fields including image processing and computer vision15, web search ranking16, deep learning-based natural language processing17, text classification18 and biomedical researches21.Multi-task learning (MTL) mines association among a set of related tasks to improve classification performance22 and developed PC-RMTL, a classification model for the pan-cancer classification task. Here, we have initially applied DESeq2 to evaluate the differential gene expression to identify highly significant differentially expressed (DE) genes and perform dimensionality reduction for each cancer. The union-set of all those DE genes has been used to perform pan-cancer classifications among 21 cancer types and adjacent normal samples using SVM-Lin, SVM-RBF, kNN, RF, decision tree (DT), and PC-RMTL. All the classifiers’ performance has been compared through precision, recall, f1-score, Matthews correlation coefficient (MCC), ROC curve, precision–recall curve, and logistic loss. Despite the highly imbalanced nature of the pan-cancer dataset (due to significant differences in the number of samples for each cancer type), PC-RMTL outperforms all the other five classifiers achieving 96.07% accuracy and 95.80% MCC score. PC-RMTL performs exceptionally well in distinguishing all of the pan-cancer samples except the READ. SVM-Lin and SVM-RBF have shown highly comparable prediction results with PC-RMTL. We have compared all the competing methods’ classification performance with a small group of features (genes) that have been detected using coefficients (weights) of the trained linear SVM (SVM-Lin) and a widely used independent features selection algorithm called ‘minimum redundancy maximal relevance' (MRMR)23. PC-RMTL again outperformed all the competing classifiers for all those small sets of selected features. We have further analyzed the discriminating capability of the selected features (genes) using functional enrichment analysis. It has been discovered that almost all of the top discriminating genes are highly associated with cancer-related gene ontology (GO) terms and pathways.In the present article, we leveraged the landmark advantages of regularized multi-task learning (RMTL)Summary of contributions In this work, we have made the following novel contributions: (1)We have explored the first multi-task learning model to classify 21 pan-cancers and adjacent normal samples of TCGA data. We have employed the regularized version of multi-task learning (MTL) that has a significant advantage over single-task learning, enabling us to learn several tasks  simultaneously. RNASeq expression data of multiple cancers has been utilized in a mutual context via a notion of relatedness of all the tasks. Here, the objective of each task is to learn the RNASeq gene expression patterns of the tumor samples of a particular cancer type.(2)Our approach is the first to explicitly address how to learn the feature representation of multiple cancer types’ samples simultaneously. We have raised an objective function see Eq.  to minim(3)Our framework (with tuned parameters) can classify unseen RNASeq expression data with utmost accuracy. Classifying hitherto unclassified cancer samples is crucial for early diagnosis of the disease. We present our framework to be effective in this case. We demonstrate that the model can classify completely unseen test samples with high accuracy.(4)It is challenging to obtain essential regulatory genes that can be utilized to discriminate different cancer samples. Here, we demonstrate 75 genes having useful discriminating features.24 has been utilized to retrieve the raw count data from the GDC cancer data portal. Figure p value 25, for each cancer type by comparing the tumor and normal samples. Next, we have obtained 1055 highly significant genes (HUGO gene symbols) from the union set of those top 75 DE genes selected in the previous step. Thus the identified genes are differentially expressed in at least one of the cancer types. Mapping from Ensembl gene ids to HUGO gene symbols was performed via the biomaRt26 R/Bioconductor package. Variance stabilizing transformation (VST)27 has been used to obtain normalized gene-expression values from the raw transcript abundance counts. Here, we have considered only primary tumor and adjacent normal samples of all the 21 cancer types for the pan-cancer classification task. Thus, among the 7839 collected samples with 22 sample classes, 21 classes correspond to 21 different primary tumor samples and one normal sample class that includes all the normal samples from each cancer type  Computing the common set of DE genes We have identified the differentially expressed (DE) genes for each cancer type using the R/Bioconductor package DESeq225. DESeq2 finds out the DE genes by comparing the transcript abundance between the primary tumor and the normal samples. We have identified 1055 highly significant genes (HUGO gene symbols) by taking a union among the individual sets of the top 75 DE genes detected for each cancer type.(2) Classifying samples using PC-RMTL and other state-of-the-art classifiers PC-RMTL and other state-of-the-art classifiers are trained with the VST normalized gene expressions of the identified DE genes to classify 21 cancer types and adjacent normal samples. PC-RMTL utilizes a regularized multi-task learning (RMTL) model with (3) Identifying the top discriminating DE genes We have identified the top key discriminating features (genes) using the coefficients (weights) of the trained linear SVM (SVM-Lin). Once an SVM-Lin model is trained with the training samples, the coefficients (weights) of the fitted model can be obtained that represent the vector coordinates orthogonal to the hyperplane, and their direction determines the predicted class. By comparing the absolute size of the coefficients, it is possible to discover the importance of all the features of a classification model. Moreover, to make a fair comparison, we have also utilized a widely used independent features (genes) selection algorithm called ‘minimum redundancy maximal relevance' (MRMR)23, for comparing the performance of PC-RMTL with other competing methods in smaller datasets . We have gradually identified the top 75, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000 discriminating features (genes) accountable for pan-cancer classification.(4) Functional enrichment analysis of the top discriminating DE genes After identifying the essential discriminating DE genes in pan-cancer classification using SVM-Lin, we have compared gene expressions of those top discriminating genes and performed their functional enrichment analysis.(5) Classification with small features (genes) We have also performed the classification task using several small sets of top discriminating DE genes discovered from the SVM-Lin classifier and the MRMR23 feature selection algorithm. It has been observed that the PC-RMTL outperforms all other classification models with these small sets of features.(6) 28 to choose an optimal hypothesis space from a family of hypothesis spaces. In29, the notion of the “extended VC dimension” was introduced to compute the bounds on the average error of T tasks. The same framework was utilized in28 to model the amount of information needed to learn a task using Bayesian and information theory arguments. In30, the extended VC dimension was utilized to derive strict tighter bounds for each task, assuming that the learning tasks are related in a particular way.The statistical learning approach for multi-task learning (MTL) was first introduced in the articleIn general, MTL deals with multiple learning tasks, including general learning such as supervised tasks , unsupervised tasks , reinforcement learning tasks, semi-supervised tasks, etc. Among all these, it is assumed that all tasks or a subset of the tasks are related. The main advantage of MTL is that it can jointly learn tasks that can leads to much performance improvement compared with individual learning tasks. Hence, the main aim of applying MTL is to improve the generalization performance of multiple related tasks.T learning tasks, assuming that data for all the tasks come from the same space of Y: t) are related, where X is the explanatory variable of all T tasks. In particular, given t learning tasks: n data points: t functions t. The other scenario may be the case of having the same output 31.The idea behind the methodologies of multi-task learning is that given 22, a regularization-based approach is proposed to solve the MTL problem, where the regularization functions are minimized analogously to SVM used in single-task learning. All the MTL algorithms more or less try to minimize the following objective function:W. Here, F is the Frobenius norm. W and the vector In34:i indices tasks and k indices samples in each task. Therefore k in task i, while n refer to the number of subjects in task i. Once the objective function defined in Eq. , incorporating the entclass1pt{minimaentclass1pt{minimaT). There are 22 types of samples (classes) in our work, 21 different cancer types, and one type of sample comprising all the normal tissue samples adjacent to each type of cancer. In this work, we have formulated the pan-cancer RNASeq gene expression dataset for classification in such a way that share the identical predictor matrices: i, p represents the number of selected differentially expressed (DE) genes from RNASeq expression data.In our PC-RMTL model, the goal of each task is to learn the expression matrices  for M classes and is defined as:k truly occurs, k is predicted, To classify unknown samples, PC-RMTL yields the probability of a sample to be present in a positive class In the case of multi-class classification, the minimum value of MCC ranges between M binary indicator matrix M, i.e., j has label m taken from a set of M labels; P denotes the probability estimator matrix, with We have also compared logistic regression loss (log loss) or cross-entropy loss for all the classification models to evaluate the probability outputs of the classifiers instead of their discrete predictions. A perfect model with all the samples correctly predicted has a log loss of 0. Let the actual labels for a set of samples be encoded as a 1-of-p value”) gene ontology terms , KEGG pathways, and disease-genes associations of those top discriminating genes have been identified through the Enrichr35 and the DisGeNET36.We have identified the key discriminating DE genes in the pan-cancer classification task using the coefficients (weights) of the trained SVM-Lin model. These top discriminating genes are then utilized to classify the samples again. We have also compared gene expressions of the top discriminating genes and performed their functional enrichment analysis. The top 10 most significant  identified through the coefficients (weights) of the trained SVM-Lin and the MRMR feature selection algorithm. It provides sound evidence that PC-RMTL can be utilized in the classification task when the expression of a small number of genes is available. Finally, our study describes a comprehensive comparative analysis of the six classification methods to classify cancer samples. The following sections describe the results of the performed experiments.We have observed that PC-RMTL outperforms all the state-of-the-art classifiers. It can be seen from the Fig. The confusion matrix in Fig. Figure In our present work, we have observed that although the PC-RMTL outperforms all the state-of-the-art classifiers, SVM-Lin and SVM-RBF have also been able to classify the pan-cancer samples well. This fact tempted us to identify the key discriminating features (genes) in SVM-Lin for pan-cancer samples classification. Once an SVM-Lin model is trained with the training samples, the coefficients (weights) of the fitted model can be obtained that represent the vector coordinates orthogonal to the hyperplane, and their direction determines the predicted class. By comparing the absolute size of the coefficients, it is possible to discover the importance of all the features of a classification model.37. Similarly, ‘TERT’ gene can be treated as a useful marker in the diagnosis and prognosis of various cancers38. ‘TERT’ expression/telomerase activity can be detectable in up to 90% of primary cancers39. Considerable evidence has been reported that ‘TERT’ contributes to cancer development and progression via multiple activities39. We have gradually identified the top 75, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000 features (genes) accountable for the pan-cancer classification using SVM-Lin. The list of the top 75 genes and their expression pattern across all the samples types are shown in Fig. 23 for comparing the performance of PC-RMTL with other competing methods in smaller datasets . The supplementary table Furthermore, we have also performed pan-cancer classification using gene expressions of all those sets of the top discriminating genes discovered from SVM-Lin and repeatedly observed the performance of all the classification models. We have seen that with only 75 and 100 top discriminating genes, respectively, 91.84% and 93.69% pan-cancer classification accuracy have been obtained through the PC-RMTL. Figure 40. We have also observed that regulation of cellular senescence, stem cell proliferation, extracellular matrix organization, replicative senescence, and DNA damage checkpoint are significant GO terms associated with these genes. Senescence, an essential activity in a cell to protect against malignant transformation, also promotes cancer growth by generating a pro-tumorigenic microenvironment through a senescence-associated secretory phenotype (SASP) by accelerating extracellular matrix remodeling and inflammation41. The extracellular matrix is a leading structural component for the tumor microenvironment. Cancer development is closely associated with self-renewal and multipotent differentiation of stem cells. Cancer stem cells initiate cancers and have self-renewal and proliferation, drug-resistant capability, and thus are the driving force of carcinogenesis42. DNA damage checkpoints have a critical role in damage repair and act as crucial control points in the cell cycle. Daughter cells lose genomic integrity and accumulate genetic damage due to the loss of checkpoint functions, which are considered the primary reasons for DNA aberrations in cancer. It has also been observed that the top discriminating genes are associated with several cancer-related pathways along with human cytomegalovirus infection, Wnt, calcium, and GnRH signaling, which are key regulators of pathways critical in cancer progression. Human cytomegalovirus infection accelerates damage in the chromosome, thereby promotes genetic instability, which is a major driver in cancer progression43. Wnt signaling is a conserved regulatory pathway that controls several normal cellular and developmental processes from an evolutionary perspective. However, aberrant Wnt signaling has also been observed as a significant pathway in many cancers44. From Fig. We have also examined the functional roles of the top 75 discriminating genes selected from the SVM-Lin classifier using gene ontology (GO) terms, pathways, and disease-genes associations Fig. . Figure Classification of cancer types using machine learning algorithms leverages diagnostic capabilities of conventional clinical and morphological approaches. This article employed regularized multi-task learning (RMTL) to perform pan-cancer classification using RNASeq gene expressions data. We have prepared the pan-cancer classification dataset by accumulating gene expressions of highly significant differentially expressed (DE) genes for each cancer.We have compared the performance of our proposed PC-RMTL model with SVM-Lin, SVM-RBF, kNN, RF, and DT through precision, recall, f1-score, MCC, ROC curve, precision–recall curve, and logistic loss. Even though the pan-caner dataset is highly imbalanced due to considerable differences in the number of samples across the cancer types, the PC-RMTL outperforms all the other five classifiers (PC-RMTL achieves 96.07% accuracy and 95.80% MCC score). We have observed that PC-RMTL performs exceptionally well in distinguishing all of the pan-cancer samples except the READ. To the best of our knowledge, this is a significant improvement over the existing works in pan-cancer classification as they have failed to classify many cancer types from one another reliably.Furthermore, we have identified the key discriminating features (genes) in pan-cancer classification using the coefficients (weights) of the trained SVM-Lin classifier and an independent features (genes) selection algorithm: MRMR. Pan-cancer classification with all the classification models using those small sets of discriminating features has also been carried out. Interestingly, the PC-RMTL again outperformed all the classifiers with these small feature sets. The top discriminating genes are evaluated using functional enrichment analysis. It has been discovered that almost all of the top discriminating genes are highly associated with cancer-related gene ontology (GO) terms and pathways.Since our classification model has reliably classified all cancer types, it can be utilized to design diagnostic kits for classifying cancer samples from liquid biopsy, prognosis, and forecasting of cancers. A thorough study of the cancer-specific features and their correlation across several cancer types will certainly unveil path-breaking insights into the understanding, prognosis, efficient therapeutics for cancers.Supplementary Tables.


The management of advanced colorectal cancer (CRC) has been greatly improved with integrated strategies including stereotactic radiation therapy (SRT). It is a safe and effective option, particularly in oligo-metastatic (om) CRC patients. Interestingly, it has been demonstrated that SRT can induce regression of tumors in non-irradiated regions  through stimulation of anti-tumor immune effects (“radiation-induced immunity”). We have recently shown that lung-limited omCRC is characterized by regression of tumor clones bearing specific key driver gene mutations. The aim of the PRELUDE-1 study is to assess the genetic and immunologic evolutions on tumor cancer/host cells induced by SRT in lung-limited omCRC through liquid biopsies and Next Generation Sequencing of tumor exome, HLA repertoire assessment, peripheral immune cells, and cytokine dynamics characterizations. An important secondary objective is the first prospective evaluation of the abscopal effect. The PRELUDE-1 results will help to identify subsets of patients more prone to show the abscopal effect. The PRELUDE-1 trial was registered into the clinicaltrials.gov registry on 22 April 2021, with identifier NCT04854213.0 = 0.0077, with α = 0.05 and 1-β = 0.60, the final sample size is 25 patients.Background: in recent years, the management of advanced colorectal cancer (CRC) has been greatly improved with integrated strategies including stereotactic radiation therapy (SRT). The administration of SRT has been demonstrated, particularly in oligo-metastatic (om) CRC, to be a safe and effective option. Interestingly, it has been demonstrated that SRT can induce regression of tumors in non-irradiated regions  through stimulation of anti-tumor immune effects (“radiation-induced immunity”). We have recently shown that lung-limited omCRC is characterized by regression of tumor clones bearing specific key driver gene mutations. Aims: to assess the genetic evolution on tumor cancer cells induced by SRT in lung-limited omCRC. Secondary objectives included descriptions of the abscopal effect, responses’ duration, toxicity, and progression-free survival. A translational research will be performed to evaluate tumor genetic evolution (through liquid biopsies and Next Generation Sequencing), HLA class I repertoire, peripheral immune cells, and cytokine dynamics. Methods: PRELUDE-1 is a prospective translational study. SRT will be administered only to the largest nodule (with a maximum diameter ≤ 25 mm) in omCRC with two or three radiologically evident lesions. The sample size is based on the innovative hypothesis that radiation-induced immunity could induce regression of tumor clones bearing KRAS oncogene mutations. According to the binomial test, considering the frequency of KRAS mutations and assuming a probability of mutant KRAS→wild type KRAS of p Colorectal cancer (CRC) represents the third most frequent cancer in both sexes. Unfortunately, more than 30% of patients are diagnosed with metastatic and unresectable disease mostly involving the liver . Lungs aIn recent years, the following considerations prompted the use of SRT in the treatment of omCRC: (i) the opportunity to delay the start of more toxic therapeutic approaches; (ii) the radio-sensitivity of colorectal cancer; (iii) the amelioration of the technique ; and (iv) the increasing availability of the technique. The “abscopal effect” (from Latin abscopus) is the anti-tumor effect elicited away from the target. In fact, high-dose radiotherapy  can stimulate anti-tumor immune effects: “radiation-induced immunity” ,14,15,16Soluble mediators  and effector/regulatory lymphocytes participate in the orchestration of anti-tumor immunity through a dynamic balance between activation and inhibition of the immune response. In fact, many innovative drugs act by revitalizing the anti-tumor response . The hypT cells’activation is based on sequential phases starting into specialized lymphoid organs. T cell precursors recognize tumor-derived peptide epitopes/HLA complexes on APCs through their T-cell receptors (TCRs) ,47,48,49The cancer genome is highly heterogeneous and dynamic. Mutation gains in some genes prompt cancer progression and resistance to therapy. Study of these changes is crucial to understand cancer biology and to propose innovative treatments. The most direct method to assess cancer genetics relies on the sampling of tumor tissues and its molecular characterization through whole genome sequencing techniques ,56. Howede novo mutations) with concordance rates  ranging from 0 to 100% [Identifying the genetic and immunologic evolutions of cancer, in time and space, is a new and challenging issue. The introduction of high-throughput genetic assessments including NGS has enormously prompted this aspect of cancer research. Such an approach is providing novel and unexpected insights in cancer biology and suggesting new therapeutic strategies. Many studies in pmCRC have revealed heterogeneous results in the genetics of primary tumors (PT) and “matched” metastatic lesions  tumor tissues compared to the primary ones (KRAS was mutated in primary tumor but not in subsequent metastases) . These gThe hypothesis on the elimination by the immune system of KRAS mutant clones is under intensive investigation by our and other groups. A complete description of factors involved in the interaction between immune and cancer cells is beyond the scope of this article. However, it has been demonstrated that T cells can recognize peptide epitopes presented by HLA class I on the surface of tumor cells. In fact, DNA mutations can lead to the formation of altered proteins and related peptides absent from the normal proteome. These peptides are called “neoantigens.” Many studies demonstrated that mutation-derived neoantigens can trigger specific tumor clone elimination ,72,73. IThe primary objective is to verify that SRT can induce regression of KRAS mutant clones into the entire metastatic mass in a clean model of oligo-metastatic CRC. The most important secondary objective is the description of eventual radiologic responses in non-irradiated disease . It will be measured through total-body computed tomography (CT) with iv contrast (if contraindicated: chest-computed tomography without iv contrast).Other secondary objectives are the responses’ duration, progression-free survival (PFS), and toxicity (see beyond for response and toxicity assessment criteria). The response duration will be measured from the time of documented objective response until documented tumor progression. PFS will be determined from the data of treatment start until progression. Overall survival is generally long in this clinical setting so that it cannot be evaluated in the short time of the study. 18F-deoxyglucose positron emission tomography (FDG-PET) metabolic responses, HLA repertoire, and immune regulatory cells.Tertiary and correlative objectives consist in exploratory and descriptive studies of the tumor immune microenvironment in primary tumors, PRELUDE-1 is a prospective, translational, and proof-of-principle study to detect regression of KRAS mutations in CRC induced by SRT. It will be conducted at the academic hospital Istituto Nazionale Tumori di Napoli, IRCCS “G. Pascale” in Naples . The sample size of the PRELUDE-1 study is based on the innovative hypothesis that radiation-induced immunity could induce regressive trajectories in metastases bearing KRAS oncogene mutations . 0 = 0.0077 [a priori success rate, and X = the number of patients with mutKRAS→wtKRAS, the final sample size is 25 patients. With the algorithm According to the binomial test, considering a frequency of KRAS mutations of 50.0% (the study is not restricted to KRAS mutated patients) and assuming a probability of mutKRAS→wtKRAS of p= 0.0077 , with α The protocol has been developed according to the Good Clinical Practice guidelines of the International Conference on Harmonization (ICH) and of the Declaration of Helsinki principles. The Ethical Committee of the National Cancer Institute of Naples, Italy approved it with document no. 88/20. Written informed consent will be obtained before starting the collection of blood samples and SRT. The structure that has the responsibility for registration, collection, and management of personal data will protect the privacy of patients included in the PRELUDE-1 study. A progressive numerical code will be attributed to the patients to manage documents, electronic data systems, or communications. A list with numerical codes and associated patients’ names will be kept exclusively at the secretariat of the PRELUDE-1 study. Only the Ministry of Health or Ethics Committees can know patients’ names (as required by the current legislation) for inspection and control purposes. The choice to treat two or three nodules with SRT was based on scientific, practical, and ethical considerations that deserve to be briefly described. The mainstay treatment of one nodule is surgery; moreover, abscopal effect cannot be detected in this context. On the other hand, four nodules are not consistent with the definition of oligo-metastatic disease we adopted ,5; in th®  tubes and centrifuged at 1800× g for 10min. Thereafter, plasma will be further centrifuged at 3000× g for 10min, aliquoted, and stored at −80°C prior to analysis. The QIAamp DNA Blood Mini Kit  will be used to extract circulating free (cfDNA). Formalin-fixed and paraffin-embedded (FFPE) tissue specimens of primary lesions will be collected and ten μM serial sections cut from each tissue specimen for microdissection of tumor cells under morphological control. DNA isolation will be performed through the MGF03-Genomic DNA FFPE One-Step Kit . Tumor DNA quality from PEFF tissues will be established using the FFPE QC Kit according to the manufacturer’s protocol . Concentration, quantity, and integrity of cfDNA will be assessed through a 2100 BioAnalyzer  to determine the size distribution of cfDNA fragments. DNA libraries for tumor genetic evaluations will be obtained with TruSigt Oncology (TSO) 500 kit, based on gene-target enrichment that analyzes 523 cancer-relevant genes  results will be depicted through the arithmetic averages + 2 standard deviations (SD). Immunohistochemistry (IHC) will be applied to analyze T cell subsets in the tumor microenvironment. Briefly, formalin-fixed, paraffin-embedded 4-μm tissue sections of primary CRC will be stained according to a biotin-streptavidin-peroxidase method  previously described . PrimaryHLA haplotypes at loci A, B, C, and DRB1 will be assessed centrally at the University of Campania “L. Vanvitelli” through a low-medium-resolution reverse SSO DNA typing assays  on genomic DNA extracted according to the kit manufacturer from whole blood or peripheral blood mononuclear cells. Comparisonof the results with our database bank including healthy bone marrow donors  will be performed.Blood samples will be collected from a peripheral vein at the decided times during the treatment courses and kept on ice. Serum will be aliquoted and stored at −80 °C until analyzed after centrifugation (3000 r.p.m. for 10 min at 4 °C). An ELISA-based immunoassay, using monoclonal antibodies specific for the different target proteins will be used. Multiple soluble cytokines will be assessedwith either commercially or customized kits: Interferon (IFN)γ, TNFα, IL-4, IL-8, IL-10, IL-12, IL-17, vascular endothelial growth factor (VEGF), granulocyte colony-stimulating factor (G-CSF), angiopoietin-2, and hegapocyte growth factor(HGF). Each experiment will be performed in duplicate. Serum levels of all proteins will be determined using a multiplate reader , and the substance concentration will be assessedthrougha standard curve, with software provided by the manufacturer .http://www.ncbi.nlm.nih.gov/projects/genome/assembly/grc/human/index.shtml) (accessed on 22 June 2021) through the Burrows–Wheeler Aligner with default parameters [An IlluminaTruSigth Oncology bioinformatics pipeline will be used to analyze DNA sequencing results. Number of reads and coverage in the target regions will be required to be above the manufacturer’s suggested thresholds. Sequence data will be aligned to the human reference genome GRCh37 .Active or uncontrolled infections.Cardiovascular diseases including coronary artery disease (CAD), inadequately controlled hypertension, ischemic or hemorrhagic stroke, moderate/sever arrhythmias, aortic aneurysm requiring surgical repair, recent deep vein thrombosis with or without pulmonary embolisms, moderate/sever valvular heart diseases, recent arterial thrombosis.Other uncontrolled or uncompensated diseases .Refusal or inability to provide informed consent.Impossibility to guarantee follow-up.SRT will be administered according to a risk-adapted protocol; size and location of the tumor will drive doses and fractionations . Regardless of the dose-fractionation regimen, the treatment will be delivered on alternate days. For all patients, a 4-D CT simulation scan will be acquired. Respiratory gating will be used in cases with thorax motion > 7 mm in any direction. The gross tumor volume (GTV) will be defined as the tumor visible on CT and PET imaging, and an internal GTV will encompass the GTV from all phases of respiration. A planning target volume (PTV) margin of 5 mm will be applied. The prescription point will be approximately the 80% isodose line surrounding the PTV, with the requirement that 95% of the PTV will be covered by 100% of the prescription dose. Timings and types of procedures are synthesized in Screening phase. Inclusion and exclusion criteria will be evaluated during the screening phase.Baseline phase. Thereafter, after signing the informed consent, a venous blood sample (10 mL) for the liquid biopsy (LB) will be collected coinciding with the routine assessment before the SRT start. Baseline visit  and exams ) will also be carried out within 14 days before the SRT start. Blood count and clinical biochemistry will be performed at our local laboratories. The following parameters will be assessed: blood count with leukocyte formula, hemoglobin, platelets, total bilirurbin, AST, ALT, alkaline phosphatase, serum creatinine, total proteins, sodium, potassium, calcium, urea, lactic dehydrogenase, creatinine clearance, CEA, and CA19.9. Patient’s heart rhythm and conduction will be evaluated with ECG. The ventricular ejection fraction will be assessed with cardiac ultrasonography.All potentially childbearing women musthave a negative pregnancy test during the screening phase. If requested by the local ethics committee, the test can also be repeated during treatment. Barrier methods for anti-conception must be used by all patients throughout the duration of the study. FDG-PET and total-body computed tomography with iv contrast or, if contraindicated, magnetic resonance (MRI) abdomen and high-definition chest-computed tomography without iv contrast will be performed. Treatment phase. Clinical examination and evaluation of vital signs will be performed at each radiotherapy visit. Cardiological evaluation, CEA, CA19.9, and assessment of response to treatment will be performed after 40 days and 2 months from the radiotherapy start and, thereafter, every 3 months until progression. “Interval” phase. LB-T2 will be performed 40 days after major nodule irradiation coinciding with post-RT cell blood count and biochemical assessment along with CT total-body-scan and FDG-PET. Thereafter, the disease will be monitored every three months, and non-irradiated nodules will be treated in case of volumetric progression according to clinical practice in oligo-metastatic disease.End-of-study phase. The study ends after LB-T2 assessment. Follow-up. Additional radiotherapy, surgery, or systemic treatment lines in case of appearance of new nodules or specific follow-up procedures will be applied at the discretion of the clinician responsible for the medical treatment. https://recist.eortc.org/recist-1-1-2/) (accessed on 22 June 2021) will be applied to classify responses. An independent and blinded data monitoring committee (DMC) will review radiologic responses. The DMC will also review the PFS data. Toxicity will be evaluatedaccording to the CTCAE, version 4.0 (https://www.eortc.be/services/doc/ctc/CTCAE_4.03_2010-06-14_QuickReference_5x7.pdf) (accessed on 22 June 2021). The presence of eventual adverse events will be evaluated at each study visit. Patients will also notify the investigator by telephone in case of adverse events occurring between one visit and the next. The maximum grade per patient will be reported for each adverse event. A toxic effect of any grade on multiple occasions will be counted only once.Response will be assessed through total-body computed tomography (CT) with iv contrast (if contraindicated: abdomen MRI and high-definition chest-computed tomography without iv contrast), CEA, CA19.9, and PET-FDG after 40 days from the end of SRT. A CT scan will be repeated at two months and, thereafter, every three months until progression. RECIST v. 1.1 (Complete metabolic response (CMR), complete resolution of all metabolically active target and non-target lesions, and no new lesions;Partial metabolic response (PMR), 20% or greater decrease in SUV of target lesions with or without decrease in number/size of nontarget lesions, and no new lesions;Progressive metabolic disease (PMD), one or more new lesions, 20% or greater increase in SUV of target lesions, and/or unequivocal increase in FDG activity of nontarget lesions;Stable metabolic disease (SMD): not qualifying as CMR, PMR, or PMD.FDG-PET will be performed after 40 days from the treatment end to early monitor untreated nodules. In this case, tumor metabolic changes will be evaluated according to the European Organization for Research and Treatment of Cancer (EORTC) criteria ,82. Metamonitoraggioscc@istitutotumori.na.it). Audits and other monitoring procedures will be planned by the Director of Scientific Monitoring and Research Quality Assurance (Dr. Gianfranco De Feo). The Principal Investigator will be responsible to clarify or respond to any eventual queries coming from the sanitary authorities or any component of the scientific community. The clinical database will be locked and analyzed at the end of enrollment. Clinical data will be registered in electronic case report forms (eCRFs). From a formal point of view, institutional standard operating procedures (SOPs) have been used to write and present this protocol to the ethical committee. Patient safety and integrity of the data are guaranteed by the strict adherence to SOPs (integral SOPs can be requested to Patients’ withdrawals from study will be registered in eCRFs. The information will include the date and the reason for cessation. However, considering the high translational nature, the short duration of the study, its small sample size, and the good safety profile of SRT, withdrawals are expected to be extremely low. Patients off study therapy will be followed-up until death. Patients stopping the treatment early for any reason will not be excluded from the progression-free survival analysis; all patients will be included in the descriptive statistics.Final and “interim” results of the study will be shown at national and international congresses. Final results will be submitted for publication to open access peer-reviewed scientific journals. Furthermore, upon Scientific Director authorization, the results will be disseminated by press releases by the Istituto Nazionale Tumori di Napoli, IRCCS “G. Pascale.”First described in 1953 , in receThe hypothesis that SRT could induce genetic regressive trajectories in omCRC is fascinating and deserves a scientific effort to be verified. Therefore, we included in the translational section of the PRELUDE-1 study the assessment of genetic trajectories of KRAS, which is also the primary objective of the study and that is the main reason for its sample size. The dynamism of tumor clones in space and time is a crucial issue for future research in oncology. The main forces influencing the molecular and biologic shape of neoplastic populations are still largely unknown. Is the selection of tumor clones on stochastic/evolutionistic pushing? Does immunologic selection concur to modify the neoplastic population during progression or treatments? Oligo-metastatic disease may provide an interesting clinical model to study some aspects of these questions. We previously found that “regressive genetic trajectories” of KRAS, a key driver gene, could account for the good prognosis in the omCRC setting, and we are evaluating the hypothesis that an immunological negative selection of KRAS-mutated aggressive tumor clones could be responsible for this effect.It has already been described that, in addition to direct damage to neoplastic cells, a radiation-induced immunity, based on HLA expression, APC and CTLs recruitment and activation, and cytokines release, exists. Some of these immunologic effects become systemic and potentially responsible for anti-tumor effects on the entire neoplastic progeny. In this context, the study of regulatory lymphocytes is of paramount interest. Notably, MDSCs and Tregs can suppress anti-tumor immunity, and their assessment could help to identify subsets of patients more prone to show the abscopal effect. Study of the HLA-I genotype although exploratory will help to interpret the probability of both neo-antigen and immune recognition of tumors in case of the abscopal effect. PRELUDE-1 is the first prospective and translational study that, with its mono-institutional nature, will contribute to describe and clarify biologic and immunologic correlates of the SRT-induced abscopal effect through an innovative and high-throughput analytical approach.