{"id":"https://openalex.org/W7152084012","doi":"https://doi.org/10.48550/arxiv.2604.05478","title":"Transcriptomic Models for Immunotherapy Response Prediction Show Limited Cross-cohort Generalisability","display_name":"Transcriptomic Models for Immunotherapy Response Prediction Show Limited Cross-cohort Generalisability","publication_year":2026,"publication_date":"2026-04-07","ids":{"openalex":"https://openalex.org/W7152084012","doi":"https://doi.org/10.48550/arxiv.2604.05478"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.05478","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05478","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.05478","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101295462","display_name":"Yuheng Liang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang, Yuheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Chhuo, Lucy","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chhuo, Lucy","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038653363","display_name":"Ahmadreza Argha","orcid":"https://orcid.org/0000-0002-8276-9774"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Argha, Ahmadreza","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034404707","display_name":"Nona Farbehi","orcid":"https://orcid.org/0000-0001-8461-236X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Farbehi, Nona","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133203099","display_name":"Lu Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Lu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133169620","display_name":"Roohallah Alizadehsani","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alizadehsani, Roohallah","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111177476","display_name":"Mehdi Hosseinzadeh","orcid":"https://orcid.org/0000-0001-9488-369X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hosseinzadeh, Mehdi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133163676","display_name":"Amin Beheshti","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Beheshti, Amin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133164519","display_name":"Thantrira Porntaveetusm","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Porntaveetusm, Thantrira","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024500256","display_name":"Youqiong Ye","orcid":"https://orcid.org/0000-0001-8332-4710"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye, Youqiong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133193922","display_name":"Hamid Alinejad-Rokny","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alinejad-Rokny, Hamid","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10158","display_name":"Cancer Immunotherapy and Biomarkers","score":0.6269000172615051,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10158","display_name":"Cancer Immunotherapy and Biomarkers","score":0.6269000172615051,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11289","display_name":"Single-cell and spatial transcriptomics","score":0.2160000056028366,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11297","display_name":"Ferroptosis and cancer prognosis","score":0.1168999969959259,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/transcriptome","display_name":"Transcriptome","score":0.663100004196167},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6262999773025513},{"id":"https://openalex.org/keywords/biomarker","display_name":"Biomarker","score":0.41940000653266907},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.39800000190734863},{"id":"https://openalex.org/keywords/immunotherapy","display_name":"Immunotherapy","score":0.37860000133514404},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.33889999985694885},{"id":"https://openalex.org/keywords/immune-checkpoint","display_name":"Immune checkpoint","score":0.3352000117301941},{"id":"https://openalex.org/keywords/immune-system","display_name":"Immune system","score":0.3312999904155731}],"concepts":[{"id":"https://openalex.org/C162317418","wikidata":"https://www.wikidata.org/wiki/Q252857","display_name":"Transcriptome","level":4,"score":0.663100004196167},{"id":"https://openalex.org/C70721500","wikidata":"https://www.wikidata.org/wiki/Q177005","display_name":"Computational biology","level":1,"score":0.6413999795913696},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6262999773025513},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4596000015735626},{"id":"https://openalex.org/C2781197716","wikidata":"https://www.wikidata.org/wiki/Q864574","display_name":"Biomarker","level":2,"score":0.41940000653266907},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.40149998664855957},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.39800000190734863},{"id":"https://openalex.org/C2777701055","wikidata":"https://www.wikidata.org/wiki/Q1427096","display_name":"Immunotherapy","level":3,"score":0.37860000133514404},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.33889999985694885},{"id":"https://openalex.org/C2780851360","wikidata":"https://www.wikidata.org/wiki/Q21686041","display_name":"Immune checkpoint","level":4,"score":0.3352000117301941},{"id":"https://openalex.org/C8891405","wikidata":"https://www.wikidata.org/wiki/Q1059","display_name":"Immune system","level":2,"score":0.3312999904155731},{"id":"https://openalex.org/C60644358","wikidata":"https://www.wikidata.org/wiki/Q128570","display_name":"Bioinformatics","level":1,"score":0.3190000057220459},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.31470000743865967},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.3034999966621399},{"id":"https://openalex.org/C2780674031","wikidata":"https://www.wikidata.org/wiki/Q2012719","display_name":"Cancer immunotherapy","level":4,"score":0.30300000309944153},{"id":"https://openalex.org/C2777002142","wikidata":"https://www.wikidata.org/wiki/Q5031432","display_name":"Cancer biomarkers","level":3,"score":0.30250000953674316},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.3005000054836273},{"id":"https://openalex.org/C124535831","wikidata":"https://www.wikidata.org/wiki/Q4915074","display_name":"Biomarker discovery","level":4,"score":0.295199990272522},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2930000126361847},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.2879999876022339},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27630001306533813},{"id":"https://openalex.org/C3019816032","wikidata":"https://www.wikidata.org/wiki/Q2575340","display_name":"Cancer treatment","level":3,"score":0.2689000070095062},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26820001006126404},{"id":"https://openalex.org/C8415881","wikidata":"https://www.wikidata.org/wiki/Q6839217","display_name":"Microarray analysis techniques","level":4,"score":0.2572000026702881},{"id":"https://openalex.org/C2780030458","wikidata":"https://www.wikidata.org/wiki/Q7041828","display_name":"Nivolumab","level":4,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.05478","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05478","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.05478","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05478","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.49540337920188904,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Immune":[0],"checkpoint":[1],"inhibitors":[2],"(ICIs)":[3],"have":[4],"transformed":[5],"cancer":[6],"therapy;":[7],"yet":[8,45],"substantial":[9],"proportion":[10],"of":[11,49,174],"patients":[12],"exhibit":[13],"intrinsic":[14],"or":[15,101],"acquired":[16],"resistance,":[17],"making":[18],"accurate":[19],"pre-treatment":[20],"response":[21,61],"prediction":[22,51,178],"a":[23,38],"critical":[24],"unmet":[25],"need.":[26],"Transcriptomics-based":[27],"biomarkers":[28],"derived":[29],"from":[30],"bulk":[31,64,96,136],"and":[32,71,73,80,119,144,171,189],"single-cell":[33],"RNA":[34],"sequencing":[35],"(scRNA-seq)":[36],"offer":[37],"promising":[39],"avenue":[40],"for":[41,183],"capturing":[42],"tumour-immune":[43],"interactions,":[44],"the":[46,147,167,181],"cross-cohort":[47,169],"generalisability":[48],"existing":[50],"models":[52,66,76,98,110,138],"remains":[53],"unclear.We":[54],"systematically":[55],"benchmark":[56],"nine":[57],"state-of-the-art":[58],"transcriptomic":[59,176],"ICI":[60,161,177],"predictors,":[62],"five":[63],"RNA-seq-based":[65,137],"(COMPASS,":[67],"IRNet,":[68],"NetBio,":[69],"IKCScore,":[70],"TNBC-ICI)":[72],"four":[74],"scRNA-seq-based":[75,126],"(PRECISE,":[77],"DeepGeneX,":[78],"Tres":[79],"scCURE),":[81],"using":[82],"publicly":[83],"available":[84],"independent":[85],"datasets":[86],"unseen":[87],"during":[88],"model":[89,192],"development.":[90],"Overall,":[91],"predictive":[92],"performance":[93],"was":[94],"modest:":[95],"RNA-seq":[97],"performed":[99],"at":[100],"near":[102],"chance":[103],"level":[104],"across":[105,123],"most":[106,148],"cohorts,":[107],"while":[108],"scRNA-seq":[109],"showed":[111],"only":[112],"marginal":[113],"improvements.":[114],"Pathway-level":[115],"analyses":[116],"revealed":[117],"sparse":[118],"inconsistent":[120],"biomarker":[121],"signals":[122],"models.":[124],"Although":[125],"predictors":[127],"converged":[128],"on":[129],"immune-related":[130,150],"programs":[131],"such":[132],"as":[133],"allograft":[134],"rejection,":[135],"exhibited":[139],"little":[140],"reproducible":[141],"overlap.":[142],"PRECISE":[143],"NetBio":[145],"identified":[146],"coherent":[149],"themes,":[151],"whereas":[152],"IRNet":[153],"predominantly":[154],"captured":[155],"metabolic":[156],"pathways":[157],"weakly":[158],"aligned":[159],"with":[160],"biology.":[162],"Together,":[163],"these":[164],"findings":[165],"demonstrate":[166],"limited":[168],"robustness":[170],"biological":[172],"consistency":[173],"current":[175],"models,":[179],"underscoring":[180],"need":[182],"improved":[184],"domain":[185],"adaptation,":[186],"standardised":[187],"preprocessing,":[188],"biologically":[190],"grounded":[191],"design.":[193]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-09T00:00:00"}
