Review: AI in Precision Oncology: Multi-omics analysis for drug resistance mechanisms and personalized drug discovery. Evidence synthesis and status report · CheckPaper
Review by CheckPaper · Reviewed 4 Nov 2026
AI in Precision Oncology: Multi-omics analysis for drug resistance mechanisms and personalized drug discovery. Evidence synthesis and status report
A candid, unusually traceable synthesis whose quoted numbers almost all match their sources, but its headline resistance finding mislabels a second-line T790M-positive (AURA3) cohort as first-line, and its only clinical AI-discovery signal is a non-oncology (IPF) trial whose primary endpoint was safety. Both need correcting before the conclusions can be relied on.
Soundness
2/4
Presentation
3/4
Contribution
2/4
Confidence
4/5
✓ 13 confirmed✕ 3 discrepancy
Summary
The document is a 16-page written companion to a 35-slide deck. It summarises published evidence on AI and multi-omics in precision oncology and does not report new data or models. Its four claims are: (i) genome-matched therapy benefits a small minority of patients (about 5-7%) even though more patients are eligible; (ii) resistance to targeted therapy is common, runs through several routes, and in a 78-patient osimertinib plasma analysis 58% had no detectable mechanism; (iii) drug-response predictors are overstated, because a naive drug-mean plus cell-line-mean baseline already reaches R2=0.736 on a CTRPv2 random split and performance drops across datasets; (iv) AI-assisted discovery shortens hit finding to weeks and has produced one phase 2a signal (rentosertib).
The report also reports on its own process. Table 1 and Sec. 2 list what was done (141 Crossref-verified sources, 12 data charts, 13 deck QA gates) and what was not (no systematic-review protocol, no model-against-model chart, 29 of the planned queries run, nine sources read only at title level). It ends with concrete next steps (Sec. 5).
Strengths
Candid and largely accurate status reporting
Abstract (p. 2); Sec. 2 and Table 1 (pp. 6-8)
The abstract, Table 1 and the 'Limits of the evidence' bullets say plainly what was not done: no systematic-review protocol, no model-against-model chart, a hand-edited word in a concept figure, an interrupted literature search, nine title-level sources, and DrugCell full text unavailable. Few syntheses are this open about their limits. Most of these self-reported counts reproduce exactly from the uploaded workspace (check 15): 141 bibliography entries, 119 deck source rows with 7 notes-only rows, 29 successful queries before HTTP 429, 5 valid OA full texts plus the DrugCell error file, 13 deck gate checks, 35 deck pages and 12 chart scripts.
High numeric fidelity to the cited sources
Secs. 4.1-4.6 (pp. 8-12); Figs. 2-8
Over 70 quoted numbers were checked against the source abstracts or full texts, and almost all match verbatim or within rounding. These include the Marquart eligibility and benefit percentages, with recomputed ratios such as 28729/564830=5.086% against the claimed 5.09% (check 04); all 13 precision-trial figures (check 05); the KRAS G12C ORRs, e.g. 46/124= against 37.1% (check 06); the FLAURA PFS and OS values (check 10); the DrEval Table 1 values (check 03); and 21/21 secondary scale numbers (check 11).
Weaknesses
AURA3 (second-line, T790M-positive) cohort presented as first-line osimertinib resistance
major · Abstract (p. 2); Sec. 1 item 2 (p. 6); Sec. 4.2 (pp. 8-9); Fig. 3 caption (p. 9); Sec. 5 Step 2
Several places describe the resistance profile from [2] as 'after first-line osimertinib': Sec. 1 item 2, the Fig. 3 caption ('Mechanisms of acquired resistance after first-line osimertinib ... in the AURA3 analysis'), Sec. 4.2 and Sec. 5 Step 2. The source abstract describes 'second-line osimertinib (n = 78) in patients with EGFR T790M' (check 01). The report's own statement that 'half lost detectable T790M' only makes sense in that setting. Sec. 4.2 places the analysis directly after the FLAURA first-line PFS/OS, so readers will take it as the resistance profile of the first-line population, which is the standard of care the section discusses. The first-line FLAURA resistance analysis is a separate paper (Chmielecki et al., Nat Commun 2023, doi 10.1038/s41467-023-35961-y). The abstract's 'large osimertinib cohort' is 78 patients.
The percentages themselves are transcribed faithfully (check 02), so the qualitative point that many resistant cases show no plasma-detectable mechanism survives. The setting is wrong, though, and the mechanism mix after first-line treatment differs (no T790M, different C797X context). Fix: relabel every occurrence as second-line/T790M-positive, or replace it with the FLAURA resistance analysis. Drop 'large'.
The only clinical AI-discovery signal is a non-oncology, secondary-endpoint, best-arm result
major · Abstract (p. 2); Sec. 1 item 5 (p. 6); Sec. 4.5 and Figs. 7-8 (pp. 11-12)
The abstract and Sec. 1 item 5 present rentosertib's phase 2a as the 'one early clinical signal' of AI-assisted discovery, in a report about oncology. The trial is in idiopathic pulmonary fibrosis. Sec. 4.5 says so, but the abstract and executive summary do not. Its primary endpoint was the proportion of patients with treatment-emergent adverse events, and FVC was a secondary endpoint. The quoted +98.4 mL is the best of three active dose arms (18+18+18+17 = 71 patients; check 08). The approximate between-arm difference is 118.7 mL (95% CI about -11.1 to 248.5, p, normal approximation). Much of the speed evidence also comes from non-oncology targets: DDR1 is described by its source as a fibrosis target, the 170 M docking screen targeted AmpC/D4, and VirtualFlow targeted KEAP1 (check 09).
Questions for the authors
Was the AURA3 (second-line, T790M-positive) analysis [2] meant to represent resistance to first-line osimertinib? If you substitute the FLAURA resistance analysis (doi 10.1038/s41467-023-35961-y), does the 'most cases had no detectable mechanism' finding in the abstract still hold?
Which source and number support the abstract's statement that 'resistance follows in most who respond'?
Is there any clinical-stage signal from an AI-discovered molecule in oncology that met inclusion? If not, would you state that explicitly in the abstract and Sec. 1 item 5?
What inclusion and exclusion rules decided which of the 168 retrieved abstracts entered the 59 cited references? Was evidence favourable to matched therapy or to deep/foundation models deliberately searched for?
Which gate enforced the 0.85 title-similarity threshold in Table 1, given that the uploaded citation gates are configured at 0.55? Why do queries.txt (43 queries) and the text (42 planned) differ?
Would you add DrEval's unseen-cell-line and unseen-drug results (normalised R2<0.20, LDO ) to quantify the 'substantial drops' in Sec. 4.4?
Verification
Sixteen checks were run, each with a rerunnable script. Thirteen were confirmed and three found discrepancies. The checks string-matched or recomputed over 70 quoted numbers against the 168 Europe PMC abstracts and the 5 valid open-access full texts in the upload (Marquart counts, precision-trial ratios, KRAS ORRs, AURA3 counts, DrEval Table 1, FLAURA, the rentosertib arms and CIs, CDK20 values, scale numbers). They also checked the 59 references, the citation mapping, the figures against their vector originals, and the self-reported process counts. Numeric fidelity is high. The one substantive discrepancy concerns the headline resistance finding: its AURA3 cohort is second-line and T790M-positive, not first-line as the report says (check 01, major). The other two are minor. 86/327 is 26.3%, not the stated 26.0% (check 07, inherited from the source). Several process claims differ slightly from the workspace: 43 queries rather than 42, and a gate threshold of 0.55 rather than 0.85 (check 15). Neither minor discrepancy changes a conclusion. All 59 DOIs resolve, and the citation numbering maps one-to-one onto the deck's source table.
13Confirmed
3Discrepancy
✕DiscrepancyAbstract; Sec. 1 item 2; Sec. 4.2 (pp.8-9); Fig. 3 caption; Sec. 5 Step 2
[2] Chmielecki J, et al. Analysis of acquired resistance mechanisms to osimertinib in patients with EGFR-mutated advanced NSCLC (AURA3). Nature Communications, 2023.
Exists (check 13), but cited for a claim it does not support: the report (Abstract; Sec. 1 item 2; Sec. 4.2; Fig. 3 caption; Sec. 5) presents its mechanism percentages as resistance 'after first-line osimertinib', whereas the source abstract describes second-line osimertinib (n = 78) in EGFR T790M-positive patients (AURA3). The first-line (FLAURA) analysis is a different paper, doi 10.1038/s41467-023-35961-y. See work/verify/01_aura3_line_of_therapy.log.
[4] Xu Z, et al. A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial. Nature Medicine, 2025.
Exists, but used in an oncology report as the clinical signal for AI-discovered drugs (Abstract; Sec. 1 item 5; Fig. 8) without stating that the trial is in idiopathic pulmonary fibrosis, that its primary endpoint was safety (TEAEs) with FVC secondary, and that the quoted +98.4 mL is the best of three dose arms. See work/verify/08_rentosertib.log.
[58] Kudo R, et al. Long-term breast cancer response to CDK4/6 inhibition defined by TP53-mediated geroconversion. Cancer Cell, 2024.
Exists; the TP53 loss 27.6% and MDM2 amplification 6.4% figures are prevalences associated with lack of long-term disease control in the source, but Fig. 5 and Sec. 4.2 present them as 'genetic resistance routes' (acquired-resistance framing). See work/verify/07_brca_cdk46.log.
[107] Ren F, et al. AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor. Chemical Science, 2023.
Exists and supports the numbers, but the report mixes measures: the text cites 33.4 nM (an IC50) while Fig. 7 plots Kd 566.7 nM for the same compound, without labelling the difference. See work/verify/09_discovery.log.
[7] Tobalina L, et al. A meta-analysis of reversion mutations in BRCA genes ... Annals of Oncology, 2021.
Exists; minor arithmetic: 86 of 327 is 26.3%, the report (copying the source) states 26.0%. See work/verify/07_brca_cdk46.log.
Method named without a citation; the deck's source list (deck/source_rows.md row 27, Chaudhary et al.) has the supporting reference but it is not in the report's reference list. See work/verify/14_citation_mapping.log.
Suggestions
Relabel the AURA3 resistance data as second-line/T790M-positive everywhere (Abstract, Sec. 1, Sec. 4.2, Fig. 3, Sec. 5), or replace it with the FLAURA first-line resistance analysis. Drop 'large'.
Wherever rentosertib is cited, state the IPF indication, the safety primary endpoint, the FVC-secondary status and the arm selection. Say explicitly whether any oncology clinical signal exists.
Publish the full query list with dates and hit counts, the inclusion/exclusion rules and a per-claim evidence table (design, n, setting), or reword the general conclusions as illustrative.
Quantify the generalisation gap in Sec. 4.4 with DrEval's unseen-cell-line and unseen-drug results. Explain that between-drug variance dominates pooled R2.
Fix the Fig. 6 caption and axis label (standard error, not standard deviation); print 26.3% (86/327); name the metric (IC50 or Kd) in Sec. 4.5 and Fig. 7; add the FLAURA OS CI.
Retitle Fig. 5 or separate the panels, so that the Kudo prevalences are not read as acquired-resistance frequencies. In Fig. 2, state each bar's denominator explicitly (e.g. NCI-MATCH among successfully profiled specimens; the WINTHER primary-endpoint value).
Restrict the Sec. 4.3 statement about pretraining scale to the tasks actually benchmarked.
Reconcile the process claims with the workspace (query count, gate threshold, Marx provenance) and add the missing Chaudhary et al. reference for autoencoder survival models.
Ethics and integrity
No integrity concerns found. The report reproduces its sources faithfully and is transparent about what was not done. No text addressed to reviewers or attempting to influence the review was found in the paper or the uploaded workspace. The framing issues in weaknesses 1-2 appear to be errors, not attempts to mislead.
Scope of this review
The document is a narrative synthesis with no model, dataset or code to rerun. Checking therefore meant comparing quoted numbers with the 168 abstracts and 5 open-access full texts in the uploaded production workspace. The 'latex' upload is that workspace, not LaTeX source, so the PDF text was the primary source. Claims drawn from paywalled full texts, the nine title-level sources, and the DrugCell paper (its full-text file is an HTTP-500 error page) were checked only against abstracts or not at all. Partin et al.'s per-model results are inline images and could not be extracted. The deck itself was examined only for page count, gate log and source table. The output/ files listed in Table 3 were not in the upload. The recommendation weighs the confirmed framing discrepancy (check 01) heavily. The overall score sits at 5, above the cap of 4 for a confirmed discrepancy, because the qualitative finding (many osimertinib-resistant cases lack a plasma-detectable mechanism) survives in the correct setting and the fix is a relabelling. The supporting numbers themselves are accurate.
Every plotted value is recorded with its source key and location in survey/figure_data.json. The embedded charts match their vector originals, and the vector originals carry the recorded values (check 16). All 59 in-text citation numbers map one-to-one onto the reference list, and each reference's DOI equals the DOI of the same-numbered deck row (check 14). All 59 DOIs resolve on Crossref with matching title, first author, venue and year (check 13).
Careful caveats in figure captions
Captions of Figs. 2-5 and 8 (pp. 8-12)
Captions warn the reader where a naive reading would mislead: Fig. 2 notes that denominators differ between bars, Fig. 3 that categories overlap and bars do not sum to 100%, Fig. 4 that the sotorasib/adagrasib comparison is cross-trial, Fig. 5 that the cohorts differ, and Fig. 8 that the intervals are wide and overlap. The Fig. 8 caution holds up: the approximate between-arm difference is 118.7 mL with a 95% CI of about -11.1 to 248.5 mL (check 08).
Benchmark message is well anchored and actionable
Sec. 4.4, Table 2, Fig. 6 (pp. 10-11); Sec. 5 Step 1
The claim that drug-response prediction is overstated rests on a correctly transcribed table: DrEval Table 1, with 0.736/0.675/0.060/0.025 matching exactly (check 03). It is paired with qualitative statements from [3], [20] and [39] that match their sources nearly verbatim (check 12). The report turns this into a usable requirement: always report a naive mean baseline and a leave-dataset-out test.
≈
0.07
Fix: state the indication, the endpoint hierarchy and the arm selection wherever the signal is cited, and say explicitly that no oncology clinical signal from AI-discovered molecules is reported here.
No search, inclusion or appraisal method, so the synthesised conclusions are of unknown balance
major · Sec. 3 (p. 7); Abstract (p. 2); Table 1 (p. 7)
Sec. 3 lists four themes but gives no search strategy, inclusion/exclusion criteria, screening record or risk-of-bias appraisal. The report acknowledges this (Table 1, 'Systematic review: not done'), but it still states general conclusions, such as 'resistance follows in most who respond' (Abstract), for which no pooled number or source is given. A reader cannot tell whether contrary evidence, such as larger matched-therapy benefit in specific biomarker-defined settings, was searched for and excluded. Fix: publish the 43 queries with dates and hit counts, the inclusion rules, and a per-claim evidence table with study design and n. Alternatively, reword the conclusions as illustrative rather than general.
Generalisation gap asserted but not quantified, although the numbers were in hand
minor · Sec. 1 item 3 (p. 6); Sec. 4.4, Table 2, Fig. 6 (pp. 10-11)
Sec. 1 item 3 and Sec. 4.4 say performance 'falls substantially' on unseen datasets but give no number. The DrEval full text in the upload quantifies this: all models explain less than 20% of drug-sensitivity variance on unseen cell lines (normalised R2<0.20), LTO < 0.08, LDO R2≈0 (check 12). The report also does not explain why the drug mean alone reaches 0.675/0.736 = 91.7% of the mean-effects R2: between-drug variance dominates the pooled metric, which the source attributes to Simpson's paradox (check 03). Adding both would make the central benchmark argument much stronger.
Several small numeric and labelling slips
minor · Fig. 6 caption (p. 11); Sec. 4.2 and Fig. 5 (pp. 9-10); Fig. 3; Sec. 4.5 and Fig. 7 (pp. 11-12); Sec. 4.2 (p. 8)
(a) The Fig. 6 caption and axis say 'standard deviation at most 0.002'. The source reports standard error across 10 folds, which implies a fold-to-fold SD of about 0.006 (check 03). (b) BRCA reversion is given as '26.0%', but 86/327 = 26.30%; the slip is inherited from the source abstract (check 07). (c) 'No detectable mechanism' is given as 58%, but 46/78 = 58.97%; also inherited from the source (check 02). (d) For CDK20 compound ISM042-2-048 the text cites '33.4 nM' (IC50), while Fig. 7 plots the Kd of the same compound, 566.7 nM, a 17-fold difference, and neither names the metric (check 09). (e) The FLAURA OS HR 0.80 is quoted without its CI of 0.64-1.00, whose upper bound touches 1 (check 10).
Prevalences plotted as resistance routes; denominators blurred in Fig. 2
minor · Fig. 5 (p. 10); Fig. 2 and Sec. 4.1 (p. 8); Sec. 1 item 1 (p. 6)
Fig. 5 ('Genetic resistance routes') places Kudo's TP53 loss (27.6%) and MDM2 amplification (6.4%) on the same axis as the BRCA reversion rate. In the source these are prevalences 'associated with lack of long-term disease control', not acquired-resistance frequencies (check 07). The text in Sec. 4.2 is faithful, but the figure is not. In Fig. 2 and Sec. 4.1, NCI-MATCH 37.6% is computed among successfully profiled specimens rather than all 5,954 patients. WINTHER's 26.2% (SD ≥ 6 months or response) appears beside 'primary end point not met', whose actual value was 22.4% at PFS ratio > 1.5. MOSCATO's 63/193 is the evaluable subset of 199 treated (check 05). Sec. 1 item 1 says '4.90% of US patients with cancer', but the source population is advanced/metastatic patients and the figure is a model estimate (check 04).
Foundation-model claim generalised beyond its evidence
minor · Sec. 4.3 (p. 10); Sec. 1 item 4 (p. 6)
Sec. 4.3 says the scale of pretraining (Geneformer, scGPT, Prov-GigaPath, TITAN) 'is not evidence of downstream benefit, as the next section shows'. Sec. 4.4 tests cell-line drug-response baselines, and [39] tests transcriptome perturbation prediction (check 12). Neither tests pathology foundation models. Sec. 1 item 4 has the same over-reach in its heading. Fix: restrict the claim to the tasks actually benchmarked, or cite evidence for the other model classes.
Minor mismatches between process claims and the uploaded materials
minor · Sec. 2 and Table 1 (p. 7); Sec. 4.3 (p. 10); Sec. 6 Table 3 (p. 13)
(a) sources/queries.txt holds 43 non-empty queries, not the '42 planned', so 14 rather than 13 were not run. (b) Table 1 states a Crossref title-similarity threshold of 0.85, but the configured min_title_sim in the citation gates is 0.55. All actual scores are 1.0, so the outcome is unaffected. (c) Marx is listed as title-level but is absent from abstracts.json, so its provenance is not traceable (check 15). (d) Sec. 4.3 names 'autoencoder survival models' with no citation for them; the matching source (deck row 27, Chaudhary et al.) is missing from the reference list (check 14). (e) Table 3 lists files under output/ that are not in the upload.
R2≈0
First-line osimertinib median PFS 18.9 vs 10.2 months (HR 0.46); median OS 38.6 vs 31.8 months (HR 0.80).
All values match verbatim. PFS ratio 18.9/10.2 = 1.85 (the internal figure_data message 'doubled' is an overstatement but does not appear in the report); differences +8.7 and +6.8 months match the deck labels. The OS HR 0.80 has 95.05% CI 0.64 to 1.00 (P = 0.046) in the source; the report omits the CI, which matters because the upper bound touches 1. Minor.
String-matched values in the Soria 2018 and Ramalingam 2020 abstracts; computed ratio and differences.
21/21 numbers found in the cited abstracts with matching context (e.g. '1,644 tumour regions ... from the first 421 patients'; 'external validation cohort (75 patients) with an area under the curve of 0.87'; '222 devices approved in the USA and 240 devices in Europe'). Wording nuance: ctDNA 'about 200 mutations' is 'a median of 200' in the source. Numeric fidelity for these secondary numbers is high.
Regex search of each number in the cited source abstract with snippet printout.
Deep models barely beat the naive model and none beat tuned tree ensembles [3]; substantial drops on unseen datasets, no single best model, CTRPv2 best source [20]; five FMs + two deep models fail to beat simple baselines for perturbation prediction [39].
6/6 qualitative statements match their sources nearly verbatim. [39] concerns transcriptome perturbation prediction, which the report states correctly in Sec. 1 item 4, though the heading 'Large pretrained models are not yet a proven advantage' generalises beyond it. Missed opportunity: the DrEval full text in the upload quantifies the generalisation gap ('All models explain less than 20% of the drug sensitivity variance when predicting unseen cell lines (normalized R2 < 0.20)', LTO < 0.08, LDO R2 ~ 0; Random Forest 19%, DIPK 11%), but the report gives no number for the 'substantial drops' and none of these normalized/per-drug results. Partin's per-model values are inline images (205) and could not be extracted programmatically.
Printed matching sentences from the Bernett, Partin and Ahlmann-Eltze abstracts and full texts; tested whether quantitative generalisation numbers are available in the full texts.
All 59 listed references exist with the stated first author, title, venue and year (including 2026 refs [3] and [20]).
59/59 DOIs resolve on Crossref; all 59 parsed; lowest title similarity 0.984 ([24]); no first-author, venue or year mismatch. [3] Bernett et al., Nat Commun 2026 and [20] Partin et al. (Brief Bioinform) both resolve. Existence only - whether references support the attached claims is examined in checks 01-12 (one mis-framing: [2], check 01).
tools/check_refs.py (Crossref DOI lookup for all 59 DOIs), then a script comparing title similarity (difflib, threshold 0.85), first author, venue and year.
Every in-text citation resolves to a listed reference, every listed reference is cited, and numbering matches the deck's source table.
59 distinct cited numbers = 59 listed references; none cited-but-unlisted or listed-but-uncited; all 59 DOIs equal the DOI of the same-numbered deck row. One minor gap: Sec. 4.3 names 'autoencoder survival models' among integration methods with citations [24, 26, 28, 29, 30]; the matching source (deck row 27, Chaudhary et al. 2018) is neither cited nor listed in the report.
Parsed all [n] citations from the body, the Sec. 7 list, and the 119-row deck source table; compared DOIs for each shared number; inspected the Sec. 4.3 integration-methods sentence against the table rows.
Process/status claims (Sec. 2, Table 1, 'Limits of the evidence'): 141 verified sources, Crossref title similarity >= 0.85, 12 data charts, 35 slides, 13 gate checks, 29 of 42 planned queries, nine title-level sources, five OA full texts + DrugCell unavailable, seven notes-only sources, 119 deck rows.
Almost all process claims reproduce exactly: survey/bibliography.bib has 141 entries (report 141); citation gates checked=141 and 59, problems=0; deck table 119 rows (119), 7 'notes only' rows (seven); scholar/ has 29 result files and run_queries.log 29 'ok' lines before HTTP 429 (29); 5 valid OA full texts + PMC7737474 (DrugCell) a 150-byte error file (five + DrugCell unavailable); gate.log 13 ✓ (13); deck PDF Pages: 35 (35); 12 fig_*.py chart scripts (12). Minor discrepancies: (a) sources/queries.txt has 43 non-empty queries, not '42 planned', so 14 rather than 13 were not run; (b) the citation gates' configured min_title_sim threshold is 0.55, not 0.85 as Table 1 states (all actual scores are 1.0, so every entry did in fact exceed 0.85); (c) the nine empty-abstract entries in abstracts.json are the nine named sources except that partin2023eval is empty and Marx is absent from abstracts.json — Partin was read in full text, so the named list is defensible but the provenance of Marx being 'title-level' is not traceable in the upload. None changes a conclusion.
Counted entries/files/lines in the uploaded work directory (survey/bibliography.bib, qa/citation-gate.json, deck/source_rows.md, sources/queries.txt, sources/scholar/, run_queries*.log, abstracts.json, fulltext/, deck/gate.log, deck PDF via pdfinfo, figures/scripts).
Data figures (Figs 2-8) display the values in survey/figure_data.json and the captions; 'all values come from abstracts or open-access full text, none estimated' (Table 1).
Every figure_data value assigned to a chart appears as a label in that chart, except values the chart deliberately omits (NCI-MATCH actionable 37.6 in Fig 2; Sammut AUC 0.87 and n=168/75 in Fig 5). Caption strings found: Fig 3 'n = 78'; Fig 5 '86 of 327', '27.6', '6.4'; Fig 6 '0.736', '0.675'; Fig 7 '9.2', '566.7'; Fig 8 '+98.4', '-20.3'. Each embedded raster's best match is the expected chart, with MAD 5.4-9.6 against >=27.7 for the next-best chart (e.g. Fig 2 fig_06 MAD 9.0 vs 33.3; Fig 8 fig_11 MAD 5.4 vs 29.3); the first image on p. 8 matches no chart (Fig 1 concept figure, as expected). So the report's charts are faithful renderings of figure_data.json. Inherited issues already recorded elsewhere also appear in the charts: Fig 6 axis label 'SD ≤ 0.002' (source reports standard error; check 03), Fig 3 '58% (n=46)' (46/78 = 59.0%; check 02), Fig 5 plots Kudo prevalences under a 'resistance' framing (check 07), Fig 7 plots Kd 566.7 nM while the text cites IC50 33.4 nM (check 09). Whether the vector-PDF labels of Figs 2 and 4 belong to sources only partly read is outside this check.
Figures in inputs/paper.pdf are raster JPEGs, so labels were read from the vector originals figures/vector/fig*.pdf (pdftotext) and matched against figure_data.json values and caption strings; correspondence of each embedded raster to its vector chart tested by 96x48 grayscale thumbnails (mean absolute pixel difference, MAD) against all 12 charts.
Ref [2] (Chmielecki 2023, AURA3) reports resistance 'after first-line osimertinib' in 78 patients with plasma profiling, 58% with no detectable mechanism.
Report: 'After first-line osimertinib, 58% of 78 patients...' and Fig. 3 'Mechanisms of acquired resistance after first-line osimertinib ... in the AURA3 analysis [2]'. Source abstract: 'acquired resistance mechanisms to second-line osimertinib (n = 78) in patients with EGFR T790M advanced NSCLC from AURA3'; full text discussion: 'who received second-line osimertinib'. The first-line (FLAURA) resistance analysis is a separate paper that exists: Chmielecki et al., Nat Commun 2023, doi 10.1038/s41467-023-35961-y ('Candidate mechanisms of acquired resistance to first-line osimertinib...'). The report cites the AURA3 (second-line, T790M+) paper but labels it first-line, and places it right after FLAURA first-line PFS/OS.
Printed report sentences and the source abstract/full text (PMC9971022) sentences about line of therapy; Crossref query for the FLAURA first-line resistance paper.
All values are transcribed faithfully from the source (9/10 within 0.25 pp of count/78: e.g. MET 14/78 = 17.95% vs 18%; T790M loss 39/78 = 50.00% vs 'Half'). One inherited rounding slip: 'No detectable mechanism' 58% in both the source and the report, but 46/78 = 58.97% (would round to 59%); 32/78 = 41.03% with >=1 mechanism, 46+32 = 78. Minor; the report reproduces the source's figure. Line-of-therapy label is addressed in check 01.
Extracted counts from the source full text (PMC9971022), recomputed count/78, compared to report text and plotted values (tolerance 0.5 pp).
CTRPv2 random-split naive predictors R2 = 0.736 / 0.675 / 0.060 / 0.025; 'standard deviation at most 0.002'.
All four R2 values match exactly (report 0.736/0.675/0.060/0.025 = source 0.736/0.675/0.060/0.025, tolerance 5e-4). Minor labelling error: the source caption reads 'Mean R2 ± standard error across cross-validation folds' (k = 10), whereas the Fig. 6 caption says 'standard deviation at most 0.002'; with SE = SD/sqrt(10) the implied fold-to-fold SD is about 0.006. Drug mean alone reaches 0.675/0.736 = 91.7% of the mean-effects R2; the source attributes this to Simpson's paradox in pooled metrics, an explanation the report does not carry over.
Parsed Table 1 of the DrEval full text (PMC13168506.xml) and compared each value; read the dispersion label in the source caption.
28729/564830 = 5.086% (claimed 5.09%), 50811/609640 = 8.335% (8.33%), 59301/564830 = 10.499% (10.50%), 94157/609640 = 15.445% (15.44%); tolerance 0.005 pp; recomputed Wald CIs reproduce the source CIs. All eight values appear verbatim in source abstract, report and plotted data. Minor wording: Sec. 1 says '4.90% of US patients with cancer'; the source population is patients with advanced or metastatic cancer, and the benefit figures are model estimates.
Recomputed eligibility percentages and Wald 95% CIs from the counts in the Marquart abstract; string-matched all eight numbers in source, report and figure_data F6.
NCI-MATCH 37.6% actionable / 17.8% assigned of 5,954; MSK-IMPACT 11% of >10,000; MOSCATO 33% (63/193), 7% of screened; WINTHER 26.2% of 107, primary end point not met; I-PREDICT 49%; functional testing 56/143, 54% PFS ratio >1.3.
All 13 strings found verbatim; 63/193 = 32.64% (33%), 30/56 = 53.57% (54%), 56/143 = 39.16%, 107/303 = 35.31%. Denominator nuances (minor): NCI-MATCH 37.6% is reported after 'profiling was successful in 93.0% of specimens', so 'of 5,954 patients' may overstate the denominator; MOSCATO treated 199, the 63/193 is the evaluable subset; WINTHER's primary end point was PFS ratio >1.5 in 22.4%, while the report shows 26.2% (SD>=6 months or response) beside 'did not meet its primary end point' - accurate but could mislead in a bar chart.
String-matched every number in the source abstracts (Flaherty, Zehir, Massard, Rodon, Sicklick, Kornauth) and recomputed ratios from counts (tolerance 0.5 pp).
KRAS G12C: sotorasib ORR 37.1% (46/124), PFS 6.8, OS 12.5 mo; adagrasib ORR 42.9% (48/112), PFS 6.5 mo; adagrasib resistance mechanism in 17/38 (45%), 7 multiple; sotorasib alterations in 27/43.
46/124 = 37.10% (37.1%), 48/112 = 42.86% (42.9%), 17/38 = 44.74% (45%), 7/38 = 18.42%; all 10 strings found in the sources. Fig. 4 additionally plots adagrasib OS 12.6 months, which comes from a later data cut (15 Jan 2022) than the PFS/ORR (15 Oct 2021) - unremarked but minor. The Awad cohort mixes NSCLC (27), colorectal (10) and appendiceal (1) cancers, while Fig. 4 juxtaposes it with NSCLC efficacy data. The report correctly flags that the sotorasib/adagrasib comparison is cross-trial.
Recomputed percentages from counts; string-matched all values in the Skoulidis, Jänne, Awad and Zhao abstracts and in the plotted data.
BRCA reversion in 86 of 327 patients (26.0%), 269 events; TP53 loss 27.6% and MDM2 amplification 6.4% under CDK4/6 inhibition, shown as 'Genetic resistance routes'.
Report '26.0%' (Fig. 5 caption '26%'); 86/327 = 26.30% (difference 0.30 pp, tolerance 0.05 pp). The 26.0% is copied from the Tobalina abstract, so the inconsistency originates in the source; the report could print 26.3%. Minor. Kudo: 'we identify TP53 loss (27.6%) and MDM2 amplification (6.4%) to be associated with lack of long-term disease control' - these are prevalences in an HR+ metastatic breast cancer cohort, not rates of acquired resistance; the report text says 'associated with lack of long-term control' (faithful), but the Fig. 5 title 'Genetic resistance routes' plotting them on the same axis as the BRCA reversion rate invites a resistance-frequency reading.
Recomputed 86/327; string-matched the Tobalina and Kudo abstracts; printed the source wording for the Kudo percentages.
Rentosertib phase 2a: 71 patients; FVC +98.4 mL (95% CI 10.9 to 185.9) at 60 mg QD vs -20.3 mL (-116.1 to 75.6) placebo; 'intervals overlap widely'; presented as the one early clinical signal of AI-assisted discovery.
Arms 18+18+18+17 = 71 (report 71). CI midpoints 98.40 and -20.25 match the point estimates. Approximate difference 118.7 mL, 95% CI -11.1 to 248.5 (normal; z = 1.79, p ~ 0.07) or -1.6 to 239.0 with t-based SEs (p ~ 0.05): consistent with the report's caution that intervals overlap (overlap 10.9 to 75.6 mL). Framing issues the script documents: the primary endpoint was the proportion with >=1 treatment-emergent adverse event and FVC was a secondary endpoint (report never says so); the indication is idiopathic pulmonary fibrosis, stated in Sec. 4.5 but not in the abstract or Sec. 1 item 5 of an oncology report; the highlighted dose is the best of three active arms. The SE back-calculation assumes symmetric normal/t intervals and independent arms.
Summed arm sizes from the Xu abstract; checked CI symmetry; backed out SEs (normal, and t with n-1 df assuming the randomized n) and computed an approximate 95% CI and p-value for the between-arm difference; extracted primary endpoint and indication wording and checked the report text for them.
DDR1 inhibitors in 21 days with four actives; CDK20 hit within 30 days after 7 compounds, 'then a 33.4 nM compound'; Fig. 7 Kd 9.2 uM -> 566.7 nM; docking 170 million; VirtualFlow >1 billion.
All nine numbers appear in the sources (whitespace-normalised). Metric mixing (minor): for compound ISM042-2-048 the text quotes '33.4 nM', which is the source's biochemical IC50 (33.4 ± 22.6 nM), while Fig. 7 plots the Kd of the same compound (566.7 ± 256.2 nM), a 17-fold difference; neither the text nor the caption names the metric. Context the report does not give: DDR1 is described by the source as 'implicated in fibrosis', and the 170 M docking screen targeted AmpC and the D4 dopamine receptor, VirtualFlow targeted KEAP1 - i.e. the speed evidence is largely from non-oncology targets (CDK20/HCC is the exception).
String-matched every number in the Zhavoronkov, Ren, Lyu and Gorgulla abstracts and printed the assay context for each CDK20 value.