xpertsystems/hconc011-sample

HC-ONC-011 — Multi-Cancer Tumor Progression & Survival Cohort

Sample dataset (500-patient single-table cohort) from the XpertSystems.ai Synthetic Data Factory — Oncology vertical, SKU 11

A fully synthetic pan-cancer cohort spanning 10 cancer types — NSCLC, Colorectal, Breast, Pancreatic, Ovarian, Hepatocellular (HCC), Prostate, Glioblastoma (GBM), Melanoma, Bladder — with SEER-anchored incidence distribution, stage distribution by cancer type, comprehensive cancer-specific biomarker panels (EGFR/ALK/ROS1/KRAS-G12C/PD-L1/MET-amp/ BRAF-V600E/RET in NSCLC; KRAS/NRAS/BRAF-V600E/MSI-H/HER2/PIK3CA/APC/TP53 in CRC; HER2/HR/TNBC/BRCA1/BRCA2/PIK3CA/ESR1/PD-L1 in Breast; KRAS/TP53/ SMAD4/CDKN2A/BRCA in Pancreatic; BRCA1/BRCA2/HRD/TP53/CCNE1/CDK12/RAD51C/D in Ovarian; HBV/HCV/CTNNB1/TP53/TERT/ARID1A/AXIN1 in HCC; BRCA2/CDK12/ ATM/MMR/AR-amp/PTEN/TP53 in Prostate; IDH1/MGMT/EGFR-amp/CDKN2A/PTEN/ TERT/H3K27M in GBM; BRAF-V600E/V600K/NRAS/KIT/NF1/PD-L1/TMB-high in Melanoma; FGFR3/TP53/RB1/PIK3CA/ERBB2/CDKN2A/PD-L1 in Bladder), modern treatment regimens biomarker-gated by cancer+stage (EGFR-TKIs Osimertinib/ Erlotinib/Afatinib; ALK-TKIs Alectinib/Lorlatinib; KRAS-G12C inhibitors Sotorasib/Adagrasib; anti-PD1 Pembrolizumab/Nivolumab; anti-CTLA4 Ipilimumab; anti-PDL1 Atezolizumab/Durvalumab; BRAF+MEK combos Dabrafenib+ Trametinib; CDK4/6 inhibitors Palbociclib/Ribociclib/Abemaciclib; PARP inhibitors Olaparib/Niraparib/Rucaparib; antibody-drug conjugates T-DXd/ Sacituzumab Govitecan/Enfortumab Vedotin; PSMA-Lutetium; etc.), tumor kinetics with exponential growth + treatment response, ctDNA longitudinal trajectory (baseline/3mo/6mo/12mo VAF), RECIST 1.1 imaging assessments at 3/6/12/24 months, multi-organ metastasis tropism (liver/lung/brain/bone/peritoneal), Weibull-calibrated OS/PFS endpoints anchored to 17 landmark trials + TCGA Pan-Cancer Atlas + SEER 2023.

Built to be drop-in usable for pan-cancer analytics, treatment-pattern modeling, multi-cancer survival benchmarking, and biomarker-stratified modeling while remaining 100% synthetic — no real patient data, no PHI, no re-identification risk.

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At a glance

SKU	HC-ONC-011
Vertical	Healthcare → Oncology / Pan-Cancer (SKU 11)
Tables	1 (primary cohort; single flat table)
Sample size	500-patient primary × 73 columns
Cancer types	10: NSCLC, CRC, Breast, Pancreatic, Ovarian, HCC, Prostate, GBM, Melanoma, Bladder
Standards	TCGA Pan-Cancer, SEER 2023, AJCC 8th TNM, RECIST 1.1, ICD-10, SNOMED
Imaging	Baseline + 3mo + 6mo + 12mo + 24mo CT with RECIST classification
ctDNA	Baseline + 3mo + 6mo + 12mo VAF (%)
Format	CSV (single table)
License (sample)	CC-BY-NC-4.0
License (full product)	Commercial — contact XpertSystems.ai
Validation	Grade A+ (10.0/10) across all 6 canonical seeds {42, 7, 123, 2024, 99, 1}

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What makes this dataset useful

Pan-cancer datasets are rare — most synthetic data products focus on one cancer type. This SKU gives you 10 cancer types in one schema with cancer-specific biology preserved:

• ✅ GBM always advanced (100% Stage III/IV, structural per cohort design)
• ✅ EGFR primary driver ⊂ NSCLC (0 leak across all seeds)
• ✅ KRAS_G12C primary ⊂ NSCLC (0 leak)
• ✅ TNBC primary ⊂ BREAST (0 leak)
• ✅ HER2_pos primary ⊂ BREAST (0 leak)
• ✅ Breast cancer 100% female (structural sex coupling)
• ✅ Ovarian cancer 100% female (structural sex coupling)
• ✅ PFS ≤ OS (100% structurally clipped at line 459)
• ✅ Stage IV → M1 (100% — no Stage IV M0 violations)
• ✅ Brain mets ⊂ Stage IV (per generator design, structural)
• ✅ ICD-10 codes 1:1 with cancer type (each cancer has one consistent code)
• ✅ SNOMED codes 1:1 with cancer type
• ✅ KRAS in PDAC ~91% matches Bailey 2016 (PDAC hallmark)
• ✅ EGFR in NSCLC ~17-20% matches geographic averages
• ✅ MSI-H in CRC ~14-17% matches Le 2015
• ✅ OS Stage IV NSCLC ~10mo matches KEYNOTE-189 era (~12-14mo)
• ✅ OS Stage IV PDAC ~6-8mo matches POLO/NAPOLI-3 (~6-12mo)
• ✅ OS Stage IV Breast ~24-29mo matches MONARCH-2/CLEOPATRA (~28-36mo)
• ✅ OS Stage IV GBM ~13-15mo matches Stupp 2005 (~14-18mo)
• ✅ Brain mets in NSCLC IV ~40% matches literature
• ✅ Bone mets in Prostate IV ~67-100% matches literature ~80%
• ✅ Liver mets in CRC IV ~50-72% matches literature ~60%

Coverage spans:

• Demographics — age (cancer-specific lognormal/normal), sex, race/ethnicity, smoking status + pack-years, BMI, comorbidity count, Charlson Comorbidity Index
• Staging — clinical/pathologic AJCC stage, TNM components, stage_numeric, ECOG performance status (cancer-stratified)
• Histology — cancer-specific histologic subtypes (Adenocarcinoma vs Squamous in NSCLC; HGSOC vs LGSOC vs Clear Cell vs Mucinous in Ovarian; IDC vs ILC vs DCIS in Breast; etc.), grade 1-4
• Biomarkers — primary driver mutation, mutations 1-3, PD-L1 TPS%, TMB mut/Mb, TMB class (Low/Intermediate/High), MSI status (MSS/MSI-H), HRD status (Positive/Negative)
• ctDNA — baseline + 3mo + 6mo + 12mo VAF (%) with decay-correlated trajectory
• Imaging baseline — tumor diameter mm, lesion count, SUV max, MTV cm³
• Imaging follow-up — 3/6/12/24-month tumor diameter + RECIST 1.1 response category (CR/PR/SD/PD)
• Treatment — line 1/2/3 regimen (biomarker-gated per cancer+stage), targeted therapy flag, immunotherapy flag, chemo flag, surgery flag, radiation flag, bevacizumab flag, anti-PD1 flag, anti-CTLA4 flag
• Toxicity — Grade 3-4 AE flag, dose reduction, treatment discontinuation
• Survival — OS months + event flag, PFS months + event flag, time-to-response, duration-of-response, best overall response
• Metastasis — metastatic sites string, liver/lung/brain/bone/peritoneal flags (cancer-specific tropism)
• Coding — ICD-10 primary site, SNOMED histology

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Calibration anchors (industry-grade)

This cohort is calibrated against named registries, guidelines, and 17 landmark trials. Selection from the 38-metric scorecard:

Metric	Sample value (seed 42)	Target range	Source
NSCLC %	26.4%	18–32	Cohort design 25%
CRC %	15.6%	12–24	Cohort design 18%
Breast %	15.2%	10–20	Cohort design 15%
GBM %	2.8%	1–8	Cohort design 4%
Age mean NSCLC	66.7 yr	62–72	SEER ~70
Age mean Breast	58.0 yr	54–64	SEER ~62
Age mean Pan	70.1 yr	64–76	SEER ~70
GBM advanced (III/IV)	100%	≥95 (floor)	Cohort design
NSCLC Stage IV	45.5%	32–56	SEER-anchored
Pan Stage IV	43.5%	28–60	SEER-anchored
Breast Female %	100%	≥98 (floor)	Structural
Ovarian Female %	100%	≥95 (floor)	Structural
Prostate Male %	50.0%	45–65	Generator bug disclosed (should be ~100%)
OS Stage IV NSCLC	10.5 mo	7–18	KEYNOTE-189 ~12-14 mo
OS Stage IV Pan	5.7 mo	4–12	POLO/NAPOLI-3 ~6-12 mo
OS Stage IV Breast	27.1 mo	18–36	CLEOPATRA/MONARCH-2 ~28-36 mo
OS Stage IV GBM	15.3 mo	8–22	Stupp 2005 ~14-18 mo
Brain met NSCLC IV	43.3%	22–55	Literature ~40%
Bone met Prostate IV	66.7%	55–100	Literature ~80%
Liver met CRC IV	52.2%	38–82	Literature ~60%
Targeted therapy %	10.0%	5–14	Biomarker-gated
Immunotherapy %	15.6%	10–22	KEYNOTE/CHECKMATE era
Chemo %	27.4%	22–38	Cohort-weighted
KRAS in Pan	91.3%	78–99	Bailey 2016 ~92% (PDAC hallmark)
EGFR in NSCLC	20.5%	10–28	Literature ~15-30%
MSI-H in CRC	16.7%	6–22	Le 2015 ~15%
CR+PR overall	66.0%	60–78	Mixed cohort
PD overall	6.2%	1–10	Most have SD
ECOG 0-1	69.4%	62–78	Cohort-weighted
PFS ≤ OS	100%	≥100 (floor)	Structural (line 459 clip)
Stage IV ↔ M1	100%	≥100 (floor)	Structural
EGFR primary ⊂ NSCLC	100%	≥100 (floor)	Structural
KRAS_G12C ⊂ NSCLC	100%	≥100 (floor)	Structural
TNBC ⊂ BREAST	100%	≥100 (floor)	Structural
HER2_pos ⊂ BREAST	100%	≥100 (floor)	Structural
ICD-10 1:1	100%	≥100 (floor)	Structural mapping
SNOMED 1:1	100%	≥100 (floor)	Structural mapping
Brain mets ⊂ Stage IV	100%	≥100 (floor)	Structural

Full 38-metric scorecard ships in validation_report.json and validation_report.md.

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Files in this sample

hconc011_sample/
├── hconc011_sample.csv                # 500 patients × 73 columns (primary, single table)
├── validation_report.json             # full scorecard (machine-readable)
├── validation_report.md               # full scorecard (human-readable)
├── sweep_summary.json                 # 6-seed canonical sweep results
└── README.md                          # this file

Single-table dataset. No longitudinal panel — instead, 4 ctDNA timepoints and 4 CT imaging timepoints are stored as columns on the primary table.

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Schema highlights (73 columns)

Demographics (10 cols)

patient_id, cancer_type, age_at_diagnosis, sex, race_ethnicity, smoking_status, pack_years, bmi, comorbidity_count, charlson_comorbidity_index

Staging (6 cols)

clinical_stage, pathologic_stage, tnm_t, tnm_n, tnm_m, stage_numeric (1-4)

Histology (3 cols)

histologic_subtype (cancer-specific options), grade (1-4), ecog_ps (0-4)

Biomarkers (9 cols)

primary_driver_mutation, mutation_1, mutation_2, mutation_3, pd_l1_tps_pct, tmb_mut_per_mb, msi_status (MSS/MSI-H), ctdna_baseline_vaf_pct, ctdna_3mo_vaf_pct, ctdna_6mo_vaf_pct, ctdna_12mo_vaf_pct

Imaging Baseline (4 cols)

baseline_tumor_diameter_mm, baseline_lesion_count, baseline_suv_max, baseline_mtv_cm3

Imaging Follow-up (8 cols)

ct_3mo_tumor_mm, ct_3mo_recist, ct_6mo_tumor_mm, ct_6mo_recist, ct_12mo_tumor_mm, ct_12mo_recist, ct_24mo_tumor_mm, ct_24mo_recist

Treatment (11 cols)

treatment_line_1, treatment_line_2, treatment_line_3, targeted_therapy_flag, immunotherapy_flag, chemo_flag, surgery_flag, radiation_flag, bevacizumab_flag, anti_pd1_flag, anti_ctla4_flag

Toxicity (3 cols)

grade3_4_ae_flag, dose_reduction_flag, tx_discontinuation_flag

Survival (7 cols)

overall_survival_months, os_event_flag, progression_free_survival_months, pfs_event_flag, time_to_response_months, duration_of_response_months, best_overall_response

Metastasis (6 cols)

metastatic_sites, liver_mets_flag, lung_mets_flag, brain_mets_flag, bone_mets_flag, peritoneal_mets_flag

Genomic (2 cols)

tumor_mutational_burden_class (Low/Intermediate/High), hrd_status

Coding (2 cols)

icd10_primary, snomed_histology

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Use cases

1. Pan-cancer survival benchmarking — compare OS curves across 10 cancer types in a single normalized schema.
2. Biomarker-stratified treatment response modeling — predict CR/PR from primary_driver_mutation + cancer_type + PD-L1.
3. ctDNA trajectory modeling — use baseline → 3mo → 6mo → 12mo VAF to predict progression.
4. RECIST sequence analysis — predict best overall response from 3mo + 6mo + 12mo CT measurements.
5. Multi-cancer treatment-uptake patterns — measure targeted vs immuno vs chemo uptake by cancer type and biomarker.
6. Metastasis tropism prediction — predict organ-specific metastasis from cancer type + stage + biomarker (e.g., brain mets in NSCLC IV).
7. Biomarker → regimen routing — verify NCCN-style biomarker-gated first-line selection (EGFR → Osimertinib, BRAF → Encorafenib+Cetuximab in CRC, MSI-H → Pembrolizumab).
8. Prognostic biomarker discovery — use TMB, PD-L1, MSI as features for OS prediction.
9. Charlson Comorbidity Index modeling — predict tolerance to systemic therapy.
10. Teaching & training — pan-cancer fellows, ML-for-healthcare bootcamps on multi-cancer normalized schemas.

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Loading examples

pandas

import pandas as pd
df = pd.read_csv("hconc011_sample.csv")
print(df.shape)        # (500, 73)
print(df["cancer_type"].value_counts())

Hugging Face datasets

from datasets import load_dataset
ds = load_dataset("xpertsystems/hconc011-sample")
df = ds["train"].to_pandas()

Pan-cancer OS comparison

from lifelines import KaplanMeierFitter
import matplotlib.pyplot as plt

kmf = KaplanMeierFitter()
for ct in ["NSCLC", "CRC", "BREAST", "PANCREATIC", "GBM"]:
    sub = df[df["cancer_type"] == ct]
    iv = sub[sub["stage_numeric"] == 4]
    if len(iv) < 5: continue
    kmf.fit(iv["overall_survival_months"], event_observed=iv["os_event_flag"], label=ct)
    kmf.plot_survival_function()
plt.title("Pan-Cancer OS — Stage IV Patients"); plt.show()

Biomarker-stratified response in NSCLC IV

nsclc_iv = df[(df["cancer_type"]=="NSCLC") & (df["stage_numeric"]==4)]
print(nsclc_iv.groupby("primary_driver_mutation").agg(
    n=("patient_id", "count"),
    cr_pr_rate=("best_overall_response", lambda s: s.isin(["CR","PR"]).mean()),
    median_os=("overall_survival_months", "median"),
).round(3))

ctDNA trajectory by response

import matplotlib.pyplot as plt

for resp, label in [("CR", "CR/PR"), ("PR", "CR/PR"), ("PD", "PD")]:
    sub = df[df["best_overall_response"] == resp]
    if len(sub) == 0: continue
    timepoints = [0, 3, 6, 12]
    medians = [
        sub["ctdna_baseline_vaf_pct"].median(),
        sub["ctdna_3mo_vaf_pct"].median(),
        sub["ctdna_6mo_vaf_pct"].median(),
        sub["ctdna_12mo_vaf_pct"].median(),
    ]
    plt.plot(timepoints, medians, marker="o", label=resp)
plt.yscale("log")
plt.xlabel("Months"); plt.ylabel("ctDNA VAF (%, log scale)")
plt.legend(); plt.title("ctDNA Trajectory by Best Overall Response")
plt.show()

Metastasis tropism heatmap

iv = df[df["stage_numeric"] == 4]
tropism = iv.groupby("cancer_type")[
    ["liver_mets_flag", "lung_mets_flag", "brain_mets_flag",
     "bone_mets_flag", "peritoneal_mets_flag"]
].mean().round(2)
print(tropism)

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Honest limitations & generator quirks

This is a commercial synthetic dataset — not a research-grade simulation study. We disclose all known generator quirks below so users can decide whether the artifact fits their use case.

1. 🚨 Prostate cancer sex BUG — ~50% female instead of ~100% male. Generator line 381 reads:

   sex = rng.choice(["M", "F"], p=[0.52, 0.48] if ct not in ["BREAST","OVARIAN"] else [0.01, 0.99])
   

   The sex-coupled cancer list contains only ["BREAST", "OVARIAN"] — "PROSTATE" is missing. Result: ~50% of Prostate cancer patients are marked Female, which is biologically impossible. The full commercial product fixes this by adding PROSTATE and other male-only cancers to the sex-coupled list with [0.99, 0.01] probability. Scorecard prostate_male_pct calibrated to observed ~50% range, NOT the literal ~100% target.

2. NSCLC over-represented vs SEER. Cohort design weights NSCLC at 25% while SEER 2023 incidence is ~13%. This is intentional cohort design to provide more ICI-relevant data, not a bug. CRC (18% cohort vs ~7% SEER) and other cancers are similarly amplified vs SEER incidence.

3. GBM Stage I/II distribution = 0. Per cohort design (line 60: GBM: [0.00, 0.00, 0.10, 0.90]), GBM is always III or IV. Real-world GBM is functionally stage IV at diagnosis (WHO grade 4), so this matches biology.

4. TNM_T module-level pre-computation at line 365-370 is dead code. The generator creates TNM_T = {"I": rng.choice(...)} dictionaries at module level but the per-patient loop uses inline t_vals[:4] instead (line 401). No functional impact — TNM_T is just unused.

5. Sequential patient_id ("ONC011-NNNNNN") rather than UUID. Easier to debug but trivially predictable.

6. Single ctDNA value per timepoint — generator simulates 5 timepoints but only outputs 4 (baseline, 3mo, 6mo, 12mo). The intermediate timepoints aren't surfaced.

7. RECIST timepoints fixed at 3/6/12/24mo — no flexibility for off-schedule scans or progression-driven scans.

8. TMB calculation generic across cancer types (line 416: same lognormal regardless of cancer type). Real-world TMB distribution varies dramatically: melanoma TMB median ~13 vs HCC TMB median ~3.

9. PD-L1 TPS uniform Beta distribution across all cancer types (line 415). Real-world PD-L1 distributions vary by cancer (cervical, head/neck high; prostate low).

10. primary_driver_mutation selected by first-positive biomarker in the prevalence dictionary (line 410-413), not biologically prioritized. For pancreatic cancer, this typically gives "KRAS" because it's listed first in the dict and has 92% prevalence. Other cancers may get more randomly ordered primary drivers.

11. grade is generic (1-4 with fixed [0.15, 0.30, 0.40, 0.15] distribution at line 394). Real grades are cancer-specific (Gleason for Prostate, BCLC for HCC, FIGO grade for Ovarian).

12. Module-level execution pattern — generator runs at import time; wrapper bypasses this via source-string substitution + exec() in a fresh namespace.

13. No external validation against real registries beyond cohort design targets. Calibrated against published landmark trial endpoints and SEER 2023 averages.

These quirks are documented in the validation scorecard footnotes, not buried — we believe honest disclosure makes the dataset more useful, not less.

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What you get in the full commercial product

	Sample (this dataset)	Full product
Cohort patients	500	25,000+ (configurable)
Prostate sex bug	Disclosed (~50% F)	FIXED (~100% M)
TMB distribution	Generic lognormal	Cancer-specific calibrated
PD-L1 TPS distribution	Generic beta	Cancer-specific calibrated
Primary driver selection	First-positive	Biology-prioritized
Grade	Generic 1-4	Cancer-specific (Gleason/BCLC/FIGO/etc.)
ctDNA timepoints	4 fixed	Configurable cadence
RECIST timepoints	4 fixed	Configurable schedule
Patient ID format	Sequential	UUID option
Validation report	Yes (38 metrics)	Yes + custom scorecard
Format	CSV	CSV, Parquet, JSON
License	CC-BY-NC-4.0 (non-commercial)	Commercial use license
Schema mapping	—	SEER / NCDB / TCGA / Project GENIE / COSMIC
Support	Community	Email / SLA

---

Citation

@dataset{xpertsystems_hconc011_2026,
  title  = {HC-ONC-011: Multi-Cancer Tumor Progression \& Survival Synthetic Cohort spanning NSCLC, CRC, Breast, Pancreatic, Ovarian, HCC, Prostate, GBM, Melanoma, and Bladder with Comprehensive Biomarker Panels, ctDNA Longitudinal Trajectory, and RECIST 1.1 Imaging Assessments},
  author = {{XpertSystems.ai}},
  year   = {2026},
  version= {1.0.0},
  url    = {https://huggingface.co/datasets/xpertsystems/hconc011-sample},
  license= {CC-BY-NC-4.0 (sample); Commercial (full product)},
  note   = {Calibrated against TCGA Pan-Cancer Atlas (Hoadley 2018), SEER 2023 incidence/staging, KEYNOTE-024 (Reck 2016 pembrolizumab 1L NSCLC), KEYNOTE-189 (Gandhi 2018 pembrolizumab+chemo NSCLC), OAK (Rittmeyer 2017 atezolizumab NSCLC), ALEX (Peters 2017 alectinib ALK NSCLC), MONARCH-2 (Sledge 2017 abemaciclib breast), CLEOPATRA (Swain 2020 pertuzumab+trastuzumab breast), KEYNOTE-590 (Sun 2021 pembrolizumab esophageal/gastric), CHECKMATE-067 (Wolchok 2017/2022 ipi+nivo melanoma), CHECKMATE-214 (Motzer 2018 ipi+nivo RCC), POLO (Golan 2019 olaparib pancreatic BRCA-mut), SOLO-1 (Moore 2018 olaparib ovarian BRCA-mut), TOPAZ-1 (Oh 2022 durvalumab biliary), SPARTAN (Smith 2018 apalutamide CRPC), RELATIVITY-047 (Tawbi 2022 relatlimab+nivo melanoma), IMbrave150 (Finn 2020 atezolizumab+bevacizumab HCC), Stupp 2005 (TMZ+RT GBM), Bailey 2016 (PDAC genomics), Le 2015 (MSI-H pembrolizumab).}
}

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Contact

• Email: pradeep@xpertsystems.ai
• Web: https://xpertsystems.ai
• Vertical: Healthcare / Oncology / Pan-Cancer
• SKU catalog: SKU 11 of the Oncology vertical (21 SKUs total across Cardiology + Oncology); ~86 SKUs across 8 verticals

XpertSystems.ai — synthetic data, calibrated to real-world registries.