{"doi":"10.1016/j.esmoop.2024.103044","title":"36P Multiplexed RNA-FISH-guided laser capture microdissection RNA sequencing improves breast cancer molecular subtyping and prognostic classification","abstract":"Multigene tests provide information that may guide the optimal treatment regimen for breast cancer (BCa) patients. However, assignment of an individual tumor to any subtype/prognostic risk group shows only moderate reproducibility depending on the assay, tumor composition, gene list and expression thresholds. This single-sample discordance impedes clinical use and raises important questions about which is the right test and whether multiple tests are better than one. We used multiplexed RNA fluorescent in situ hybridization of four BCa biomarkers, estrogen/progesterone/Her2/Ki67, to guide laser capture microdissection followed by RNAseq. This technique, called mFISHseq, ensures tumor purity, facilitates interrogation of tumor heterogeneity, permitting unbiased whole transcriptome analysis. To ascertain multigene test discordance, we applied mFISHseq on a cohort of 1,082 FFPE breast tumors with detailed clinicopathological data and derived molecular subtypes using research based PAM50, AIMS, and our own 293-gene subtyping classifier. We also assigned patients to prognostic risk groups using research based OncotypeDX, GENE70, risk of recurrence by subtype, and GGI. We observed considerable discordance with 24% and 61% of patients having at least one multigene test in disagreement for molecular subtyping and prognostic risk assignment, respectively. To improve single sample concordance, we implemented a simple voting scheme of the multigene classifiers to assign a consensus molecular subtype/risk group. Consensus subtyping reclassified 30% of patients into subtypes that better fit their transcriptomic risk and outcome, and further identified that 60% of these patients received suboptimal treatment. Likewise, our consensus prognostic risk approach mitigated discordance and provided prognostic insights for patients with high, low, and ultra-low risk. By leveraging spatially resolved, tumor enriched transcriptome profiling, mFISHseq alleviated sample-level discordance and assigned individuals to molecular subtypes/prognostic risk groups that better matched their outcome, thus resolving limitations to clinical adoption.","journal":"ESMO Open","year":2024,"id":497661,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9494,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1170709,"name":"Barbora Huraiová","orcid":"0000-0001-8784-8001","position":1,"is_corresponding":false},{"id":1170710,"name":"Natália Valková","orcid":"0000-0003-3898-7229","position":2,"is_corresponding":false},{"id":1344525,"name":"Natália Matyašovská","orcid":null,"position":3,"is_corresponding":false},{"id":1170712,"name":"Daniela Gábrišová","orcid":"0009-0001-3154-2369","position":4,"is_corresponding":false},{"id":1170713,"name":"Soňa Gubová","orcid":"0009-0005-4791-7056","position":5,"is_corresponding":false},{"id":1170714,"name":"Helena Ignačáková","orcid":"0000-0002-3854-1641","position":6,"is_corresponding":false},{"id":1170715,"name":"Tomáš Ondris","orcid":"0009-0009-2112-4006","position":7,"is_corresponding":false},{"id":1171070,"name":"Silvia Bendíková","orcid":null,"position":8,"is_corresponding":false},{"id":1170719,"name":"Daniel Lovíšek","orcid":"0000-0001-5654-3504","position":9,"is_corresponding":false},{"id":455352,"name":"Manish Gala","orcid":"0000-0002-3126-0783","position":10,"is_corresponding":false},{"id":1170721,"name":"Michaela Ristová","orcid":"0000-0003-1519-3805","position":11,"is_corresponding":false},{"id":1170722,"name":"Iñaki Comino‐Méndez","orcid":"0000-0003-1926-9432","position":12,"is_corresponding":false},{"id":226751,"name":"Pavel Morozov","orcid":"0000-0001-7085-9134","position":13,"is_corresponding":false},{"id":291726,"name":"Fresia Pareja","orcid":"0000-0003-3748-8049","position":14,"is_corresponding":false},{"id":110000,"name":"Jakob Nikolas Kather","orcid":"0000-0002-3730-5348","position":15,"is_corresponding":false},{"id":1170724,"name":"Pavol Čekan","orcid":"0000-0001-8806-9958","position":16,"is_corresponding":false},{"id":596150,"name":"Evan D. Paul","orcid":"0000-0002-7011-8050","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T02:09:34.764412Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}