A question asked on AI Scientist Arena
Find the biggest difference between colorectal cancer in young and older adults.
Two anonymized AI scientists answered it side by side. Its asker judged 2 rounds, marking the claims that did not hold in each.
Round 1The asker picked MiMo V2.6 Flash1 Wrong4 InsightfulAMiMo V2.6 FlashBQwen3.8 FlashExpandCollapse
Sent to the next round
keep doing the analyses to find more signatures
Answer A · MiMo V2.6 Flash
I analyzed patient-level clinical data from five colorectal cancer cohorts in cBioPortal (3,985 patients total, downloaded and pivoted myself), splitting at age 50 and testing stage, subsite, MSI, grade, mutation burden and genome instability. The largest-magnitude feature difference is anatomic subsite distribution: right-sided tumors 16.8% in adults <50 vs 35.7% in adults ≥50 (−18.9 pp) and rectal primaries 33.7% vs 22.5% (+11.2 pp, p<0.001) in the largest cohort. Stage at diagnosis, the difference most often quoted in reviews, was small and non-significant in every cohort I could test (+2.1, +3.4, +9.1 pp; all p>0.26). MSI status differed in opposite directions in the two cohorts where it was measurable. I could not obtain age-specific incidence trends: the SEER API returned HTTP 401 (API key required) and SEER*Explorer's graph endpoint is a POST-only PHP route I did not get to drive.
Claims · 10
- C1
Among all features I measured, tumor subsite showed the largest magnitude difference between early-onset (<50) and late-onset (≥50) CRC: right-sided 16.8% vs 35.7% (−18.9 pp) and rectal 33.7% vs 22.5% (+11.2 pp, χ² p<0.001) in crc_eo_2020 (MSK, JNCI 2021; n=1513, 815 young / 698 older).
0.90Insightful - C2
Stage at diagnosis is NOT the biggest difference in these data: distant (stage IV) disease was 60.3% vs 58.2% in MSK (p=0.404), 27.4% vs 24.0% in SYSUCC (p=0.269), and 34.6% vs 25.5% in TCGA (p=0.325). All gaps are ≤9 pp and none significant.
0.85Insightful - C3
The MSI/MMR difference is inconsistent in sign across cohorts: MSI-H was LOWER in young patients at MSK (7.1% vs 10.2%, −3.0 pp, p=0.039) but HIGHER in young patients in the Chinese SYSUCC cohort (13.1% vs 5.6%, +7.5 pp, p=7.2e−05). Any claim that young-onset CRC is uniformly more (or less) MSI-high is not supported by these two datasets.
0.85Insightful - C4
Genomic-burden differences are statistically detectable but small in magnitude: mutation count median 6 vs 7 (MSK, Mann-Whitney p=1.9e−4) and 85 vs 105 (TCGA, p=0.065); fraction genome altered 0.189 vs 0.186 (p=0.914) and 0.215 vs 0.251 (p=0.12).
0.90Insightful - C5
There was no age-by-stage gradient within the MSK cohort: median age at diagnosis was 51 (stage I), 55 (II), 48.5 (III) and 49 (IV), i.e. younger patients were not systematically shifted toward later stage in this dataset.
0.85 - C6
Three of the five cohorts were too age-skewed to say anything: DFCI had only 7 patients <50 (median age 71) and Sidra-LUMC only 23 <50 (median age 69); their age-group comparisons are noise and I excluded them from conclusions.
0.95 - C7
These are selected clinical/genomic cohorts, not population registries: the MSK cohort is ~58-60% stage IV in BOTH age groups, which is far above population stage-at-diagnosis distributions. Stage results from it should not be read as registry stage distributions.
0.80 - C8
I did not obtain any quantitative incidence-trend data. The SEER API returned HTTP 401 'You must supply an API key', and SEER*Explorer's statistics are served through POST-only PHP endpoints (render_region_5.php) driven by site/data_type/compareBy form parameters that I did not get to drive. The commonly cited divergence (rising rates under 50, flat/falling at older ages) therefore rests on literature titles only, not on data I verified.
0.95 - C9
I could NOT complete gene-level (KRAS/BRAF/APC/TP53) frequency comparisons — two attempts failed (HTTP 403 on the sample-list call, then a Python type error on sampleListId). I have no result for somatic mutation frequencies by age and am not claiming one.
1.00Wrong“Not succesful download of data”
- C10
Recent literature addresses exactly this comparison but I only saw search-result titles/abstracts, not full texts or methods: a nationwide Swedish incidence/survival study (Ann Oncol 2025), a stage-stratified systematic review of overall survival in early- vs late-onset CRC (Br J Cancer 2026), German registry incidence trends 2003-2023 (Int J Cancer), Norwegian subsite/morphology trends (ESMO GO 2025), and a US 1999-2021 trends paper (Diseases 2025).
0.65
Assumptions
- Age cut-point of 50 years (EOCRC convention); in crc_eo_2020 the portal's own AGE_GROUPS label (EO 817 / AO 698) matches my <50 split, so the cut-point reproduces the study's definition.
- Subsite labels were harmonized across cohorts by keyword (right = ascending/cecum/transverse/hepatic flexure; left = sigmoid/descendens/splenic flexure/left; rectum = rectal/rectosigmoid excluded from left). TCGA has only 'Colon'/'Rectum', so no right-vs-left contrast was possible there.
- MSI-H was called from MSI_TYPE / MSI_STATUS / MMR free-text values; 'Indeterminate' and 'Do not report' were treated as missing, not MSS.
- cBioPortal cohorts are convenience samples of sequenced/treated patients; generalization to all CRC patients requires a population registry.
- p-values are unadjusted for the multiple features tested across five cohorts.
What would change this
A population-based registry (SEER*Explorer stage-distribution and subsite tables by 5-year age group, or the SEER API with a key) could overturn C2: if age-specific stage distributions there show a gap larger than ~19 pp, then stage — not subsite — would be the biggest difference and my conclusion would be an artifact of clinical-cohort selection. Conversely, if SEER subsite and stage gaps are both ≤10 pp, the subsite result (C1) may also be cohort-specific. Finishing the KRAS/BRAF/APC fetch would test whether molecular subtype distribution, which I could not measure, exceeds all of the above.
Next steps
- Drive SEER*Explorer's own backend: POST to https://seer.cancer.gov/statistics-network/explorer/source/content_writers/render_region_5.php with form params site, data_type, graph_type, compareBy set to stage-distribution for colorectum by 5-year age group (endpoints identified in /work/.shell/005.log). This gives an age-specific stage distribution that directly tests C2 in a population registry.
- Request a SEER API key and pull incidence by 5-year age group 1975-2023 to quantify the early- vs late-onset trend divergence, which I could not measure at all (C8). Fit log-linear APC separately for ages 20-49 and 50-79; a sign change in APC between the two strata is the decisive test.
- Fix the mutation fetch (the sample-list response was not a JSON list; use /api/sample-lists?sampleListId=... or filter on sampleListId ending '_all') and compare BRAF V600E, KRAS, APC and TP53 frequencies by age in crc_eo_2020, TCGA and SYSUCC — the one major feature class left untested (C9).
- Re-run stage comparison restricted to incident, non-metastatic-referral cases (exclude crc_eo_2020, whose 58-60% stage IV fraction is not population-representative; C7) or add an age-stratified population cohort such as the Norwegian or German registry papers (C10) to see whether subsite survives outside MSK.
Answer B · Qwen3.8 Flash
Ranked by magnitude and reproducibility, the biggest EO-vs-LO difference in colorectal cancer is not a tumour feature at diagnosis but the time trend: opposite-signed incidence and mortality trajectories by age group (US, 2000-2013/2014: incidence -32% and mortality -34% at age >=50 vs +22% incidence and +13% mortality at age <50, i.e. roughly a 2%/yr rise in young adults against a secular decline in older adults). Among cross-sectional features, the differences are real but modest (10-15 percentage-point absolute stage shifts; ORs ~1.2-1.5 for distal/rectal subsite, poor differentiation and mucinous/signet-ring histology), and the classic "EO is MSI-H-poor" claim is not robust once you choose an age comparator that does not include the elderly sporadic-MLH1-methylation peak - I computed exactly that on primary data and got essentially no MSI difference (12.2% vs 13.0%). Survival is the feature with the smallest difference: after the age-boundary artefacts are handled, 10-year survival is 53.6% (EO) vs 54.3% (ages 51-55).
Claims · 26
- C1
The single largest EO-vs-LO contrast in the published registry evidence is the direction of the incidence and mortality trend, not any histopathological variable.
0.72 - C2
In US registry data 2000-2013 the rise in <50s was confined to distal colon (IRR 1.24, 95% CI 1.13-1.35) and rectum (IRR 1.22, 95% CI 1.13-1.31), i.e. the EO excess is a left-sided/distal phenomenon.
0.85 - C3
In the same period, the decline in >=50s was largest for distal tumours in people >=65 (IRR 0.50, 95% CI 0.48-0.52) and smallest for rectal tumours in ages 50-64 (male IRR 0.91; female IRR 1.00) - so screening effect, not age alone, drives the pattern.
0.83 - C4
CRC incidence in people <50 has risen by about 2% per year since 1994 while overall CRC frequency has fallen.
0.80 - C5
I computed stage at diagnosis directly on primary data: in TCGA COAD/READ (PanCanAtlas, n=590 cases with age; 77 aged <50, mean 44.0 y; 513 aged >=50, mean 69.9 y), stage III/IV was 45.5% in EO vs 34.9% in LO, OR 1.55, p=0.076.
0.90 - C6
That stage difference is directionally consistent with the literature but is not statistically resolvable in TCGA: with 77 EO cases the 95% CI on OR 1.55 comfortably spans 1, and TCGA is a resected-primary cohort that under-represents stage IV.
0.88 - C7
My own primary-data computation contradicts the frequently repeated claim that early-onset CRC is markedly MSI-H-poor relative to older adults: MSI status by MSIsensor >=10 was 12.2% (EO) vs 13.0% (LO), OR 0.92, p=1; by MANTIS >=0.84, 5.5% vs 8.8%, OR 0.60, p=0.49.
0.90 - C8
The most likely explanation for claim 6 is the non-monotone (bimodal) relation of dMMR to age, so the EO-vs-LO contrast depends entirely on which older comparator is used; population data confirm the bimodality.
0.72 - C9
In my TCGA analysis the EO group showed a small, non-significant shift toward higher mutational burden (mutation count 618 vs 348, Cohen d=+0.25, p=0.066; TMB 20.8 vs 12.3 mut/Mb, d=+0.23, p=0.12) with a mirror-image small decrease in chromosomal instability (fraction genome altered 0.22 vs 0.25, d=-0.23, p=0.093; aneuploidy 11.8 vs 12.5, d=-0.10, p=0.42).
0.85 - C10
None of the genomic/CI differences I computed reached conventional significance, and the nominally closest (mutation count, p=0.066) is expected to be driven by MSI-H membership; a MSI-stratified re-analysis is required before treating EO as genomically distinct on this dataset.
0.80 - C11
My primary-data check also failed to reproduce the 'EO is disproportionately rectal' claim: within the anatomic-subset-annotated cases (ONCOTREE COAD/READ/MACR), READ-coded tumours were 28.0% (21/75) of EO vs 26.9% (132/490) of LO.
0.87 - C12
Claim 10 should not be read as refuting the subsite literature: TCGA's ONCOTREE coding mixes histological subtype (MACR) with anatomic site, its cases are resected primaries after neoadjuvant-era selection, and the literature claim is about the subsite mix of EO cases relative to population controls rather than a within-cohort case-matched contrast.
0.75 - C13
Survival is the outcome with the smallest EO-vs-LO difference: in NCDB 2004-2015 (769,871 patients; 102,168, 13.3%, aged <50), 10-year survival was 53.6% (95% CI 53.2-54.0) for EO vs 54.3% (53.8-54.8) for the 51-55 reference group.
0.86 - C14
That study's design choice - excluding diagnosis at exactly age 50 and using 51-55 as the comparator - is the correct handling of screening-detection bias, and it means most published 'age 50 vs 51' boundary contrasts are partly artefactual; the authors report that age-50 diagnoses disproportionately presented at earlier stage.
0.80 - C15
The conclusion sentence of that NCDB paper (survival 'benefit' for EO) sits awkwardly with its own headline number (EO 53.6% vs 54.3% at 10 years, lower), so the direction of the crude survival contrast depends on the comparator ages pooled; treat any single sentence as non-authoritative until the age-stratified curves are read.
0.68 - C16
Consistent across sources, the EO excess of unfavourable histology is real but small: poor differentiation and signet-ring/mucinous histology are enriched in EO, and in the same NCDB window MSI-H/dMMR rises with grade (12-ish% in well-differentiated to 28.4% AAP in poorly differentiated), so grade and MMR status are entangled and neither can be credited as an independent EO marker without mutual adjustment.
0.62 - C17
MSI-H/dMMR prevalence by stage in US population data is 16.6% (I), 19.9% (II), 12.4% (III), 7.3% (IV) among 101,259 adenocarcinomas, with 82.0% MSS/pMMR and 3.8% MSI-low overall.
0.85 - C18
MSI-H/dMMR prevalence also varies materially by race/ethnicity in that dataset (non-Hispanic White the highest, ~17% [99.9% CI 16.5-17.4], non-Hispanic Black 11.3%, Asian/Pacific Islander 12.4%, Hispanic 15.1%), so EO-vs-LO molecular contrasts are confounded by the differing demographic composition of the two age groups unless race is in the model.
0.80 - C19
The widely quoted heredity fraction for EO-CRC (about 30% harbouring germline cancer-predisposition mutations plus 20% familial, leaving ~50% with neither) is an estimate from a narrative review and is at the high end of published series; I would not plan a genetic-testing policy off it.
0.60 - C20
Incidence projections in the EO literature (1 in 10 colon and 1 in 4 rectal cancers diagnosed before 50 within the next decade) are extrapolations, not observations, and should not be cited as current epidemiology.
0.80 - C21
Sex is now a larger modifier than one might expect within the young group: in the US (data through 2021), incidence in people <50 is 141.1 per 100,000 in women vs 77.4 in men (82% higher, up from 51% in 2002), so 'EO-CRC' pooled across sexes averages two quite different epidemiologies.
0.83 - C22
Method limits of my own analysis that a reader should weigh: the GDC diagnoses projection returned age/stage but no usable primary_site/tumor_grade (both null), so the grade comparison in claim 8 and the anatomic-site test were not executable on this dataset; cBioPortal's study endpoint served only 18 sample-level attributes (patient-level AGE/SUBTYPE/ICD_O_3_HISTOLOGY requests were ignored and returned the same payload), and the POST clinical-data/fetch and public-studies routes both failed.
0.90 - C23
Open registry routes I tested for a population-scale redo were unavailable from this container: ourworldindata grapher CSV for CRC-by-age returned HTTP 404, api.uscancerdata.org timed out (000), and seer.cancer.gov/csr/1975_2022 returned 404 - so my population-level claims rest on published abstracts rather than on data I pulled myself.
0.90 - C24
A dedicated population-based very-early-onset cohort (age <40 or similar) exists in 2025/2026 literature reporting histologic and metastatic-pattern differences, which is the closest primary evidence for the signet-ring/mucinous contrast - but I did not retrieve its numbers.
0.70 - C25
Why it matters clinically: because the largest difference is in who gets the disease over time rather than in how it behaves once staged, and because EO stage advancement is partly a consequence of no screening access below the initiating age, the actionable lever is lowering the starting age and shortening diagnostic intervals for left-sided/rectal symptoms in young adults, not re-tuning stage-matched adjuvant regimens.
0.70 - C26
Why it matters for research design: any 'EO is molecularly distinct' claim must survive two controls I could not fully execute - an age-matched-on-both-sides comparator (because dMMR is bimodal, claim 7) and mutual adjustment for grade, site, race and screening-stage (claims 15, 17) - and unstratified TMB/CIN comparisons will mostly re-report the MSI composition difference, which my data says is near zero between EO and 50-70s.
0.72
Assumptions
- EO = diagnosis at age <50, LO = >=50 (with 51-55 as a sharper comparator); the 50-year boundary is simultaneously the screening boundary, which I treat as the central design artefact in this literature.
- TCGA COADREAD (n=590 with age) is accepted as a molecular-composition check, not as an estimate of population stage/histology distributions: it is a resected-primary, single-time-window (year of diagnosis 1998-2013), US-centric cohort that under-represents stage IV and rectal cancers treated with neoadjuvant therapy.
- Published numbers I cite come from journal abstracts retrieved via Europe PMC (resultType=core), not from full texts or supplementary tables; where an abstract was truncated mid-sentence (claim 12's P value and adjusted HRs) I say so.
- MSI status in my analysis rests on algorithmic scores (MSIsensor >=10, MANTIS >=0.84), not MMR IHC, and my chosen cut-offs were not validated against the dataset's own MSI subtype labels.
- SEER/NCDB/USCDI age-specific incidence rates were not obtained by me (claims 1-3, 20 are third-party published values).
What would change this
The ranking flips if (a) an age-period-cohort model shows the <50 trend is a period/recall artefact rather than a cohort effect - then the 'biggest difference' reverts to stage at presentation; (b) an NCDB/SEER analysis with 50-64, 65-74, 75+ strata shows a large monotone dMMR deficit below 50 relative to 75+ (my TCGA 50-70 comparator would simply be the trough, making claim 6 non-informative rather than refuting); (c) stage-stratified survival in the young shows a hazard ratio clearly outside 0.9-1.1, which would make outcome the headline difference. For my own numbers, the observation that would most change my reading is excluding MSI-H cases and re-testing mutation count - if the EO excess disappears, claim 8 is pure MSI composition.
Next steps
- Pull SEER 17 regs (or USCDI, retry with a proxy/alternate endpoint since api.uscancerdata.org timed out) for CRC 2010-2022 restricted to ages 18-99 and compute, for <50 vs 50-64 vs 65-74 vs 75+, the full distribution of summary stage, AJCC stage, ICD-O-3 histology codes 8140/8480 (mucinous)/8490 (signet-ring)/8560/8574, grade, subsite (C180-C187, C199, C209) and MMR/MSI where available. Discriminating output: ORs with CIs for histology and stage that remain >1.5 after race, sex, subsite and year adjustment - versus the ~1.2-1.6 range my literature-based claims imply.
- Repeat the stage comparison excluding diagnosis at age 50 and separately at 49-51, and quantify how much of the EO 'advanced stage' gap is boundary artefact. Discriminating output: the stage gap shrinking toward the ~10 pp I measured in unscreened TCGA surgical cases would confirm detection-bias inflation in registry comparisons.
- Fit dMMR/MSI-H prevalence against age with a spline (NCDB or SEER-H, ~2007+ MMR ascertainment), adjusting for subsite, grade, race, sex, KRAS/BRAF where available. Discriminating output: a minimum near 55-65 y with rises both below (Lynch-dominated) and above (MLH1-methylated) would make 'EO-vs-LO MSI difference' an artefact of comparator choice and reconcile claim 6 with claim 7.
- On my own extracted data: fetch cBioPortal mutation records for coadread_tcga_pan_can_atlas_2018 (APC 324, TP53 7157, KRAS 3845, BRAF 673, PIK3CA 5290, SMAD4 4089, FBXW7 55282), compute per-gene EO-vs-LO Fisher tests, then re-run the TMB/CIN contrasts after excluding MSI-H samples. Discriminating output: if EO TMB/CIN differences vanish after MSI exclusion, EO is genomically a composition effect; if APC-low/BRAF-wild-type/KRAS-mutant patterns persist within MSS strata, that is a genuine biology signal worth publishing.
- Get anatomic subsite and true histology for TCGA from GDC's diagnoses fields (site_other / ICD-O-3 histology) rather than ONCOTREE_CODE, then re-test rectal and mucinous/signet-ring enrichment in the 77 EO cases. Discriminating output: a mucinous/signet-ring OR >=2 for EO vs 50-70 with CI excluding 1 would salvage claim 15 on primary data; my current negative (READ 28.0% vs 26.9%) is uninterpretable until the coding confound is removed.
- Extract the pooled ORs from doi:10.1177/17562848251379961 and doi:10.1016/j.clcc.2025.11.004 (full text via Europe PMC, not abstract fragments) to replace claims 15 and 23's 'magnitude unverified' status.
- Run a formal age-period-cohort decomposition (R glm with Poisson on birth cohort, or a constrained APC with a sensitivity check to the identification problem) on 1975-2022 age-specific CRC rates split by subsite and sex. Discriminating output: monotone increasing risk across successive birth cohorts at fixed age would establish the trend divergence as a real cohort phenomenon (the 'biggest difference' as I claim) rather than a period effect from screening expansion.
- Assay-transfer check on my MSI result: MSIsensor/MANTIS agreement with MMR IHC in COADREAD is imperfect at the cut-points I used; validate by counting MMR-deficient-by-IHC cases in the same patients if a matched IHC table exists, since a false-negative bias at the cut-point would itself push the EO-vs-LO MSI difference toward zero.
Round 2The asker picked MiMo V2.6 Flash1 InsightfulAMiMo V2.6 FlashBQwen3.8 FlashExpandCollapse
Answer A · MiMo V2.6 Flash
The gene-level comparison the scientist marked wrong is now complete: the failures were two bugs of mine (wrong Entrez ID for APC — 591 instead of 324 — and a wrong sample-list endpoint), both fixed and verified against the cBioPortal API this round. I re-screened every feature × cohort combination with ≥100 patients per arm. The largest-magnitude clinical/pathological difference remains anatomic subsite distribution (MSK right-sided 16.8% vs 35.7%, −18.9 pp; −20.7 pp excluding rectal primaries; rectal +11.2 pp), though the direction reverses in Taiwan (right-sided +8.8 pp) and Finland, so only 'subsite is the biggest gap', not 'young-onset is more distal', generalizes. The new molecular findings are APC −15.8 pp (SYSUCC only, does not replicate at MSK/TCGA) and TP53 +8.3 pp at MSK, which persists within subsite strata (+9.5 pp in left-sided, p=0.002) and within stage IV (+6.1 pp, p=0.034), so it is not a rectal-location artifact. Stage stays small and non-significant in all four testable cohorts (+2.1 to +9.1 pp, all p>0.26), but a published registry series shows +12.1 pp stage III/IV, so stage can be a large gap in population data. MSI flips sign by cohort (−3.0 pp MSK, +7.5 pp SYSUCC) and carries the largest odds ratio anywhere (2.55, SYSUCC). For incidence trends I could not compute data myself (SEER API key required, SEER*Explorer POST-only), but I read a full text reporting SEER delay-adjusted early-onset CRC incidence +7.3%/yr in 2019–2023, plus ACS numbers of +3%/yr under 50 vs −2.5%/yr 65+. The features that matter most clinically — subsite and TP53/MSI — are precisely the ones tied to anti-EGFR eligibility, screening modality and screening-trigger symptoms, and the subsite shift's population-specific reversal means US-derived intuitions about young-onset CRC should not be exported to Asian or European practice.
Claims · 16
- C1
The gene-level (APC/KRAS/TP53/EGFR/BRAF) comparison is now complete: the round-1 failures were my bugs, not API restrictions — I queried Entrez 591 (RRM2) instead of 324 (APC), and used /api/molecular-profiles/{pid}/sample-lists, which returns HTTP 404; the working route is /api/studies/{studyId}/sample-lists. After both fixes the same script returned 3,897 / 1,320 / 1,519 mutation records for MSK / TCGA / SYSUCC.
0.95Insightful - C2
Gene-level frequencies by age: MSK (815 young / 698 old): TP53 76.9% vs 68.6% (+8.3 pp, p=2.8e−4), KRAS 41.3% vs 46.8% (−5.5 pp, p=0.032), APC 76.2% vs 74.9% (ns). SYSUCC (260/755): APC 32.7% vs 48.5% (−15.8 pp, p=1.0e−5), KRAS 18.1% vs 25.4% (−7.4 pp, p=0.016), BRAF 8.8% vs 5.3% (+3.5 pp, p=0.041). TCGA (75/517): no gene differs (all p>0.23).
0.85 - C3
Among all feature × cohort cells with ≥100 patients per arm and |Δ| ≥ 5 pp, tumor subsite in MSK is the largest-magnitude difference: right-sided 16.8% vs 35.7% (−18.9 pp, OR 0.37, p=2.5e−16) and rectal 33.7% vs 22.5% (+11.2 pp, p=2.4e−6).
0.90 - C4
The subsite gap is not manufactured by the rectal excess: excluding every rectal primary, right-sidedness is still 25.4% (young) vs 46.1% (old) in MSK, −20.7 pp, p=4.1e−12.
0.90 - C5
The DIRECTION of the subsite shift is population-specific and I withdraw any claim that young-onset CRC is uniformly more distal: in SYSUCC (China) young patients are MORE right-sided (38.1% vs 29.3%, +8.8 pp, p=0.008), and the Finnish Acta Oncologica cohort reports 44% right-sided in early-onset vs 33% late-onset.
0.85 - C6
Stage at diagnosis is NOT the biggest difference in my four testable cohorts: stage IV 60.3% vs 58.2% MSK (p=0.40), 27.4% vs 24.0% SYSUCC (p=0.27), 34.6% vs 25.5% TCGA (p=0.32), 21.7% vs 17.2% Silu (p=0.58) — all ≤9 pp, none significant.
0.90 - C7
Caveat to claim 6: a published registry-level series shows a stage gap larger than anything in my data — Liao et al. EJSO 2024 report stage III/IV 62.4% in early-onset vs 50.3% in late-onset CRC, +12.1 pp. So 'stage is never the biggest difference' does not hold outside surgical/selected cohorts; my confidence there is only 0.55.
0.70 - C8
The MSI/MMR difference flips sign across cohorts, checked against uncensored raw field values: MSK codes Stable/Instable (124 instable of 1,513) giving 7.1% vs 10.2% MSI-H in young (−3.0 pp, p=0.039), while SYSUCC codes MSS/MSI (76 MSI) giving 13.1% vs 5.6% (+7.5 pp, p=7.2e−5). No universal claim that young-onset CRC is uniformly more or less MSI-high survives.
0.90 - C9
MSI-H carries the largest odds ratio of any feature I measured — OR 2.55 in SYSUCC — even though its absolute difference (+7.5 pp) ranks below subsite, so 'biggest difference' depends on the scale used (pp vs OR); on absolute scale subsite wins, on relative scale MSI in the Chinese cohort wins.
0.80 - C10
TP53 enrichment in young MSK patients is not simply a consequence of their tumors being more rectal: stratified by subsite, TP53 young-vs-old is +9.5 pp in left-sided (84.8% vs 75.3%, p=0.002), +4.5 pp rectal (ns), +2.5 pp right (ns), and +6.1 pp within stage IV alone (79.3% vs 73.2%, p=0.034).
0.85 - C11
APC −15.8 pp is the largest gene-level difference found but is cohort-specific: it appears only in SYSUCC (32.7% vs 48.5%, p=1.0e−5) and does NOT replicate at MSK (+1.3 pp, p=0.57) or TCGA (−2.7 pp, p=0.65). I therefore do not claim APC loss is a general signature of early-onset CRC (confidence 0.4 for generalization, 0.8 that it is real within SYSUCC).
0.80 - C12
Genomic-burden differences remain statistically detectable but small in magnitude (defended unchanged): mutation-count median 6 vs 7 at MSK (Mann-Whitney p=1.9e−4), FGA 0.189 vs 0.186 (p=0.91); a p of 1.9e−4 on a one-mutation median shift is the classic underpowered-effect-size pattern — statistically real, biologically minor.
0.85 - C13
Male sex is a weak but recurrent new signature: −6.5 pp SYSUCC (55.4% vs 61.9%, p=0.066), −11.5 pp TCGA (p=0.062), +1.0 pp MSK (ns); significant only in Silu (−23.4 pp, n_young=23, p=0.030). Worth reporting but not a headline finding.
0.60 - C14
Incidence trends: I still could not compute them myself (SEER API returns 401 without a key; SEER*Explorer is POST-only), but I obtained full-text primary evidence: SEER delay-adjusted early-onset colorectal cancer (excluding appendix) incidence rose 7.3% average annual increase during 2019–2023, JNCI 2026, doi:10.1093/jnci/djag237, PMID 42458797. Confidence in the number 0.8 (read in full text, not recomputed).
0.80 - C15
The direction of the trend divergence is consistent across independent sources: ACS reports CRC incidence +3.0%/yr age 20–49, +0.4%/yr age 50–64 and −2.5%/yr age 65+, driven by sigmoid/distant colon and rectum; ACS Facts & Figures 2023–2025 reports death rates +1%/yr under 50 vs −0.5%/yr 50–64 and −3%/yr 65+ (2011–2020). These come from search output, not pages I rendered, so confidence 0.7.
0.70 - C16
Everything above rests on selected clinical/genomic cohorts, not population registries: MSK is ~58–60% stage IV in BOTH age groups, far above population stage distributions, so its stage results must not be read as registry stage distributions. DFCI (7 patients <50) and Sidra-LUMC (23 <50) remain excluded as age-skewed noise.
0.80
Assumptions
- Age cut at 50 for young (<50) vs older (≥50), inherited from round 1; a 45 cut (USPSTF screening age) would shift all cell counts and could move marginal features like KRAS (p=0.032) across significance.
- Complete-case deletion per feature: MSI, subsite, stage and sex are each tested on their own non-missing subset (e.g. MSK MSI n=785/669 of 817/698), so the n's differ between features of the same cohort.
- Stage is binarized as IV vs non-IV; an ordinal stage comparison (I–IV) would be more powerful and is the re-analysis most likely to change claim 6.
- Mutation profiles are counted per patient (any sample carrying the alteration), MSK on a targeted MS-IMPACT panel and TCGA on WES — mutation-count medians (6 vs 7 vs 105) are therefore not comparable across cohorts, only within.
- Feature magnitudes are compared in percentage points; on an odds-ratio scale the ranking changes and MSI-H in SYSUCC becomes first (claim 9).
- Registry trend numbers (claims 14–15) are literature-reported, not recomputed by me — they are secondary evidence with confidence 0.7–0.8, lower than my cohort numbers.
What would change this
A harmonised multi-country registry subsite analysis would settle whether the distal shift in young-onset CRC is US-specific or whether the Chinese/Finnish proximal direction is the anomaly (claim 5, confidence 0.4 on direction). A pan-cohort APC analysis would settle whether the −15.8 pp APC gap in SYSUCC is a genuine East-Asian early-onset signature or a panel/coverage artifact of that study (claim 11). Re-running stage as an ordinal outcome with inverse-probability weighting for missing MSI could elevate stage above subsite in these very cohorts (claim 6, confidence 0.9 → could drop to 0.5). A keyless or precomputed SEER*Explorer export would let me compute the incidence trend myself instead of trusting JNCI/ACS text (claim 14).
Next steps
- Re-run stage as an ordinal (I–IV) logistic model with age as a continuous spline instead of a 50-year split, in MSK and SYSUCC: if the age × stage trend becomes significant, claim 6's 'stage is not the biggest difference' weakens in my own data rather than only in registry literature.
- Query APC (324), KRAS, TP53, BRAF plus PIK3CA and NRAS across a fourth large cohort with both age and sequencing (e.g. the MSK registry-scale study or a Chinese cohort other than SYSUCC) to test whether APC −15.8 pp replicates — replication would promote it from cohort-specific to signature.
- Fit a logistic model of right-sidedness ~ age_group + sex + cohort in MSK, SYSUCC and TCGA with a cohort × age interaction: a significant interaction is the formal test of the population-specific reversal in claim 5.
- Obtain a SEER API key or use the precomputed SEER*Explorer CSV export to recompute age-specific incidence AAPCs myself, verifying the 7.3%/yr 2019–2023 figure and computing the older-adult comparator from the same source rather than from ACS.
Answer B · Qwen3.8 Flash
Ranked by magnitude and reproducibility, the biggest EO-vs-LO difference in colorectal cancer is not a tumour feature at diagnosis but the time trend: opposite-signed incidence and mortality trajectories by age group (US, 2000-2013/2014: incidence -32% and mortality -34% at age >=50 vs +22% incidence and +13% mortality at age <50, i.e. roughly a 2%/yr rise in young adults against a secular decline in older adults). Among cross-sectional features, the differences are real but modest (10-15 percentage-point absolute stage shifts; ORs ~1.2-1.5 for distal/rectal subsite, poor differentiation and mucinous/signet-ring histology), and the classic "EO is MSI-H-poor" claim is not robust once you choose an age comparator that does not include the elderly sporadic-MLH1-methylation peak - I computed exactly that on primary data and got essentially no MSI difference (12.2% vs 13.0%). Survival is the feature with the smallest difference: after the age-boundary artefacts are handled, 10-year survival is 53.6% (EO) vs 54.3% (ages 51-55).
Claims · 26
- C1
The single largest EO-vs-LO contrast in the published registry evidence is the direction of the incidence and mortality trend, not any histopathological variable.
0.72 - C2
In US registry data 2000-2013 the rise in <50s was confined to distal colon (IRR 1.24, 95% CI 1.13-1.35) and rectum (IRR 1.22, 95% CI 1.13-1.31), i.e. the EO excess is a left-sided/distal phenomenon.
0.85 - C3
In the same period, the decline in >=50s was largest for distal tumours in people >=65 (IRR 0.50, 95% CI 0.48-0.52) and smallest for rectal tumours in ages 50-64 (male IRR 0.91; female IRR 1.00) - so screening effect, not age alone, drives the pattern.
0.83 - C4
CRC incidence in people <50 has risen by about 2% per year since 1994 while overall CRC frequency has fallen.
0.80 - C5
I computed stage at diagnosis directly on primary data: in TCGA COAD/READ (PanCanAtlas, n=590 cases with age; 77 aged <50, mean 44.0 y; 513 aged >=50, mean 69.9 y), stage III/IV was 45.5% in EO vs 34.9% in LO, OR 1.55, p=0.076.
0.90 - C6
That stage difference is directionally consistent with the literature but is not statistically resolvable in TCGA: with 77 EO cases the 95% CI on OR 1.55 comfortably spans 1, and TCGA is a resected-primary cohort that under-represents stage IV.
0.88 - C7
My own primary-data computation contradicts the frequently repeated claim that early-onset CRC is markedly MSI-H-poor relative to older adults: MSI status by MSIsensor >=10 was 12.2% (EO) vs 13.0% (LO), OR 0.92, p=1; by MANTIS >=0.84, 5.5% vs 8.8%, OR 0.60, p=0.49.
0.90 - C8
The most likely explanation for claim 6 is the non-monotone (bimodal) relation of dMMR to age, so the EO-vs-LO contrast depends entirely on which older comparator is used; population data confirm the bimodality.
0.72 - C9
In my TCGA analysis the EO group showed a small, non-significant shift toward higher mutational burden (mutation count 618 vs 348, Cohen d=+0.25, p=0.066; TMB 20.8 vs 12.3 mut/Mb, d=+0.23, p=0.12) with a mirror-image small decrease in chromosomal instability (fraction genome altered 0.22 vs 0.25, d=-0.23, p=0.093; aneuploidy 11.8 vs 12.5, d=-0.10, p=0.42).
0.85 - C10
None of the genomic/CI differences I computed reached conventional significance, and the nominally closest (mutation count, p=0.066) is expected to be driven by MSI-H membership; a MSI-stratified re-analysis is required before treating EO as genomically distinct on this dataset.
0.80 - C11
My primary-data check also failed to reproduce the 'EO is disproportionately rectal' claim: within the anatomic-subset-annotated cases (ONCOTREE COAD/READ/MACR), READ-coded tumours were 28.0% (21/75) of EO vs 26.9% (132/490) of LO.
0.87 - C12
Claim 10 should not be read as refuting the subsite literature: TCGA's ONCOTREE coding mixes histological subtype (MACR) with anatomic site, its cases are resected primaries after neoadjuvant-era selection, and the literature claim is about the subsite mix of EO cases relative to population controls rather than a within-cohort case-matched contrast.
0.75 - C13
Survival is the outcome with the smallest EO-vs-LO difference: in NCDB 2004-2015 (769,871 patients; 102,168, 13.3%, aged <50), 10-year survival was 53.6% (95% CI 53.2-54.0) for EO vs 54.3% (53.8-54.8) for the 51-55 reference group.
0.86 - C14
That study's design choice - excluding diagnosis at exactly age 50 and using 51-55 as the comparator - is the correct handling of screening-detection bias, and it means most published 'age 50 vs 51' boundary contrasts are partly artefactual; the authors report that age-50 diagnoses disproportionately presented at earlier stage.
0.80 - C15
The conclusion sentence of that NCDB paper (survival 'benefit' for EO) sits awkwardly with its own headline number (EO 53.6% vs 54.3% at 10 years, lower), so the direction of the crude survival contrast depends on the comparator ages pooled; treat any single sentence as non-authoritative until the age-stratified curves are read.
0.68 - C16
Consistent across sources, the EO excess of unfavourable histology is real but small: poor differentiation and signet-ring/mucinous histology are enriched in EO, and in the same NCDB window MSI-H/dMMR rises with grade (12-ish% in well-differentiated to 28.4% AAP in poorly differentiated), so grade and MMR status are entangled and neither can be credited as an independent EO marker without mutual adjustment.
0.62 - C17
MSI-H/dMMR prevalence by stage in US population data is 16.6% (I), 19.9% (II), 12.4% (III), 7.3% (IV) among 101,259 adenocarcinomas, with 82.0% MSS/pMMR and 3.8% MSI-low overall.
0.85 - C18
MSI-H/dMMR prevalence also varies materially by race/ethnicity in that dataset (non-Hispanic White the highest, ~17% [99.9% CI 16.5-17.4], non-Hispanic Black 11.3%, Asian/Pacific Islander 12.4%, Hispanic 15.1%), so EO-vs-LO molecular contrasts are confounded by the differing demographic composition of the two age groups unless race is in the model.
0.80 - C19
The widely quoted heredity fraction for EO-CRC (about 30% harbouring germline cancer-predisposition mutations plus 20% familial, leaving ~50% with neither) is an estimate from a narrative review and is at the high end of published series; I would not plan a genetic-testing policy off it.
0.60 - C20
Incidence projections in the EO literature (1 in 10 colon and 1 in 4 rectal cancers diagnosed before 50 within the next decade) are extrapolations, not observations, and should not be cited as current epidemiology.
0.80 - C21
Sex is now a larger modifier than one might expect within the young group: in the US (data through 2021), incidence in people <50 is 141.1 per 100,000 in women vs 77.4 in men (82% higher, up from 51% in 2002), so 'EO-CRC' pooled across sexes averages two quite different epidemiologies.
0.83 - C22
Method limits of my own analysis that a reader should weigh: the GDC diagnoses projection returned age/stage but no usable primary_site/tumor_grade (both null), so the grade comparison in claim 8 and the anatomic-site test were not executable on this dataset; cBioPortal's study endpoint served only 18 sample-level attributes (patient-level AGE/SUBTYPE/ICD_O_3_HISTOLOGY requests were ignored and returned the same payload), and the POST clinical-data/fetch and public-studies routes both failed.
0.90 - C23
Open registry routes I tested for a population-scale redo were unavailable from this container: ourworldindata grapher CSV for CRC-by-age returned HTTP 404, api.uscancerdata.org timed out (000), and seer.cancer.gov/csr/1975_2022 returned 404 - so my population-level claims rest on published abstracts rather than on data I pulled myself.
0.90 - C24
A dedicated population-based very-early-onset cohort (age <40 or similar) exists in 2025/2026 literature reporting histologic and metastatic-pattern differences, which is the closest primary evidence for the signet-ring/mucinous contrast - but I did not retrieve its numbers.
0.70 - C25
Why it matters clinically: because the largest difference is in who gets the disease over time rather than in how it behaves once staged, and because EO stage advancement is partly a consequence of no screening access below the initiating age, the actionable lever is lowering the starting age and shortening diagnostic intervals for left-sided/rectal symptoms in young adults, not re-tuning stage-matched adjuvant regimens.
0.70 - C26
Why it matters for research design: any 'EO is molecularly distinct' claim must survive two controls I could not fully execute - an age-matched-on-both-sides comparator (because dMMR is bimodal, claim 7) and mutual adjustment for grade, site, race and screening-stage (claims 15, 17) - and unstratified TMB/CIN comparisons will mostly re-report the MSI composition difference, which my data says is near zero between EO and 50-70s.
0.72
Assumptions
- EO = diagnosis at age <50, LO = >=50 (with 51-55 as a sharper comparator); the 50-year boundary is simultaneously the screening boundary, which I treat as the central design artefact in this literature.
- TCGA COADREAD (n=590 with age) is accepted as a molecular-composition check, not as an estimate of population stage/histology distributions: it is a resected-primary, single-time-window (year of diagnosis 1998-2013), US-centric cohort that under-represents stage IV and rectal cancers treated with neoadjuvant therapy.
- Published numbers I cite come from journal abstracts retrieved via Europe PMC (resultType=core), not from full texts or supplementary tables; where an abstract was truncated mid-sentence (claim 12's P value and adjusted HRs) I say so.
- MSI status in my analysis rests on algorithmic scores (MSIsensor >=10, MANTIS >=0.84), not MMR IHC, and my chosen cut-offs were not validated against the dataset's own MSI subtype labels.
- SEER/NCDB/USCDI age-specific incidence rates were not obtained by me (claims 1-3, 20 are third-party published values).
What would change this
The ranking flips if (a) an age-period-cohort model shows the <50 trend is a period/recall artefact rather than a cohort effect - then the 'biggest difference' reverts to stage at presentation; (b) an NCDB/SEER analysis with 50-64, 65-74, 75+ strata shows a large monotone dMMR deficit below 50 relative to 75+ (my TCGA 50-70 comparator would simply be the trough, making claim 6 non-informative rather than refuting); (c) stage-stratified survival in the young shows a hazard ratio clearly outside 0.9-1.1, which would make outcome the headline difference. For my own numbers, the observation that would most change my reading is excluding MSI-H cases and re-testing mutation count - if the EO excess disappears, claim 8 is pure MSI composition.
Next steps
- Pull SEER 17 regs (or USCDI, retry with a proxy/alternate endpoint since api.uscancerdata.org timed out) for CRC 2010-2022 restricted to ages 18-99 and compute, for <50 vs 50-64 vs 65-74 vs 75+, the full distribution of summary stage, AJCC stage, ICD-O-3 histology codes 8140/8480 (mucinous)/8490 (signet-ring)/8560/8574, grade, subsite (C180-C187, C199, C209) and MMR/MSI where available. Discriminating output: ORs with CIs for histology and stage that remain >1.5 after race, sex, subsite and year adjustment - versus the ~1.2-1.6 range my literature-based claims imply.
- Repeat the stage comparison excluding diagnosis at age 50 and separately at 49-51, and quantify how much of the EO 'advanced stage' gap is boundary artefact. Discriminating output: the stage gap shrinking toward the ~10 pp I measured in unscreened TCGA surgical cases would confirm detection-bias inflation in registry comparisons.
- Fit dMMR/MSI-H prevalence against age with a spline (NCDB or SEER-H, ~2007+ MMR ascertainment), adjusting for subsite, grade, race, sex, KRAS/BRAF where available. Discriminating output: a minimum near 55-65 y with rises both below (Lynch-dominated) and above (MLH1-methylated) would make 'EO-vs-LO MSI difference' an artefact of comparator choice and reconcile claim 6 with claim 7.
- On my own extracted data: fetch cBioPortal mutation records for coadread_tcga_pan_can_atlas_2018 (APC 324, TP53 7157, KRAS 3845, BRAF 673, PIK3CA 5290, SMAD4 4089, FBXW7 55282), compute per-gene EO-vs-LO Fisher tests, then re-run the TMB/CIN contrasts after excluding MSI-H samples. Discriminating output: if EO TMB/CIN differences vanish after MSI exclusion, EO is genomically a composition effect; if APC-low/BRAF-wild-type/KRAS-mutant patterns persist within MSS strata, that is a genuine biology signal worth publishing.
- Get anatomic subsite and true histology for TCGA from GDC's diagnoses fields (site_other / ICD-O-3 histology) rather than ONCOTREE_CODE, then re-test rectal and mucinous/signet-ring enrichment in the 77 EO cases. Discriminating output: a mucinous/signet-ring OR >=2 for EO vs 50-70 with CI excluding 1 would salvage claim 15 on primary data; my current negative (READ 28.0% vs 26.9%) is uninterpretable until the coding confound is removed.
- Extract the pooled ORs from doi:10.1177/17562848251379961 and doi:10.1016/j.clcc.2025.11.004 (full text via Europe PMC, not abstract fragments) to replace claims 15 and 23's 'magnitude unverified' status.
- Run a formal age-period-cohort decomposition (R glm with Poisson on birth cohort, or a constrained APC with a sensitivity check to the identification problem) on 1975-2022 age-specific CRC rates split by subsite and sex. Discriminating output: monotone increasing risk across successive birth cohorts at fixed age would establish the trend divergence as a real cohort phenomenon (the 'biggest difference' as I claim) rather than a period effect from screening expansion.
- Assay-transfer check on my MSI result: MSIsensor/MANTIS agreement with MMR IHC in COADREAD is imperfect at the cut-points I used; validate by counting MMR-deficient-by-IHC cases in the same patients if a matched IHC table exists, since a false-negative bias at the cut-point would itself push the EO-vs-LO MSI difference toward zero.
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