{"apiVersion":"1","version":"sha256:faade27e0b75b2aca6311142ff65347b4dacc153e8d28cbdf0e8a237319331f9","data":{"id":"S7587","title":"DRUG-NEM: Optimizing drug combinations using single-cell perturbation response to account for intratumoral heterogeneity","authors":["Benedict Anchang","Kara L. Davis","Harris G. Fienberg","Brian D. Williamson","Sean C. Bendall","Loukia G. Karacosta","Robert Tibshirani","Garry P. Nolan","Sylvia K. Plevritis"],"publishedDate":"2018-04-13","format":"Paper","url":"https://www.ebi.ac.uk/europepmc/webservices/rest/PMC5939057/fullTextXML","summary":"DRUG-NEM ranks drug combinations using selected single-cell signaling responses. Its short-term surrogates and fixed-subpopulation assumptions limit interpretation. Repeated analyses are not new patients, and cell-line validation does not establish clinical benefit. The supplied main text is read; supporting information remains missing.","subjects":["Single-cell modeling","Drug-combination research","Research limitations"],"people":["Garry Nolan"],"review":{"label":"Supplied main text and mathematics read; apparatus partial","scope":"All75,234 decoded main XML characters were read in ten complete returns, including six captions, roles, disclosures and40 references. All139 native MathML expressions were additionally read structurally in two complete returns, including five displayed equations. Root independently matched the character/mathematical unions and nine native authors. Six supplied main JPEG figures were inspected, including37 graphical marker items; fine ticks and overlapping labels remain partly unresolved. Publication is April13,2018 electronically and May1 in print, with native PMC version1. The original ten-page main layout, SI text, supplementary FiguresS1–S10 and DatasetS1 were not received. Repository GIF/JPEG pairs are renditions of the same figures. Public publisher/supporting routes that failed remain closed.","gaps":["Thirty diagnostic samples produced1080 dependent analyses. Combination selection in39% of runs differs from60% patient-modal predictions; about45% of runs selected one drug. These figures are not clinical response probabilities or uniform combination benefit.","Desired signaling effects are assumed short-term surrogates. Marker choice, nonapoptotic filtering, fixed subgroups, priors, nested graphs, conditional independence and OR combination logic affect results. Longer treatment may change subpopulations.","HeLa and three cell-line viability assays do not validate clinical outcomes for thirty patients. Relative controls, tied rankings, single-drug exceptions and point interaction scores without uncertainty remain explicit; no medical advice follows.","Figure6C prints one patient code twice and omits another; intended correction is unknown. Other model-name, pathway spelling and mathematical-normalization inconsistencies remain literal. Original fine glyphs, SI, raw FCS, records and code were not reviewed or executed.","Nolan’s stated role includes research and analytic tools, rather than algorithm development or manuscript authorship. A Stanford patent application is disclosed. No independent replication, clinical safety/efficacy or UAP relevance is established."]},"familyId":"DOI-10.1073-PNAS.1711365115","citationUrl":"https://bioparity.io/sources/S7587","citation":"Benedict Anchang, Kara L. Davis, Harris G. Fienberg, Brian D. Williamson, Sean C. Bendall, Loukia G. Karacosta, Robert Tibshirani, Garry P. Nolan, Sylvia K. Plevritis. DRUG-NEM: Optimizing drug combinations using single-cell perturbation response to account for intratumoral heterogeneity. April 13, 2018. BioParity S7587. https://bioparity.io/sources/S7587"}}