xpertsystems/hconc014-sample

HC-ONC-014 — Metastatic Cancer Cohort

Sample dataset (500-patient single-table cohort) from the XpertSystems.ai Synthetic Data Factory — Oncology vertical, SKU 14 (FINAL)

🎉 This is the fourteenth and final SKU in the XpertSystems Oncology vertical, closing out a 14-SKU catalog covering Breast, Lung, Prostate, Colorectal, Pancreatic, Liver/HCC, Leukemia, Lymphoma, Melanoma, Ovarian, Multi-Cancer Tumor Progression, Chemotherapy Response, Immunotherapy/CPI Response, and now Metastatic Cancer.

A fully synthetic metastatic cancer cohort spanning 13 metastatic cancer types — breast_met 20%, nsclc_met 18%, crc_met 14%, prostate_met 12%, melanoma_met 8%, pancreatic_met 6%, renal_met 6%, gastric_met 5%, ovarian_met 5%, bladder_met 2%, hcc_met 2%, sclc_met 1%, sarcoma_met 1% — with 12-organ tropism matrices (liver, lung, bone, brain, adrenal, peritoneum, pleura, distant lymph nodes, skin, spinal, pericardium, bone marrow) calibrated to Disibio 2008 autopsy series and cancer-specific biology (Bubendorf 2000 mPC bone, Coleman 2001 mBC bone, Damsky 2014 mMel CNS), synchronous vs metachronous metastasis (de novo Stage IV vs late recurrence), comprehensive biomarker panels by cancer type (EGFR/ALK/KRAS-G12C/PD-L1 in NSCLC; HER2/HR/BRCA/PD-L1-CPS in breast; RAS/BRAF/MSI/HER2 in CRC; BRAF-V600E/NRAS in melanoma; AR splice variants/ PSA in prostate; HRD/CA-125/BRCA in ovarian; IMDC risk/VHL in RCC), 4-line treatment sequences with biomarker-routed regimens (Palbociclib/Ribociclib/Abemaciclib for HR+HER2- mBC, Trastuzumab- Deruxtecan/Tucatinib for HER2+ mBC, Sacituzumab/Pembro+Chemo for TNBC, Osimertinib/Alectinib for EGFR/ALK NSCLC, Sotorasib/Adagrasib for KRAS-G12C, Enzalutamide/Abiraterone/Lu-177-PSMA for mCRPC, Dabrafenib+Trametinib/ Nivo+Ipi for mMel, FOLFIRINOX/NALIRIFOX for mPDAC, Cabozantinib/ Pembro+Axitinib for mRCC, EV+Pembro/Erdafitinib for mUC, Atezo+Bev for HCC, Atezo-EP for SCLC), acquired resistance mechanisms (EGFR T790M, ESR1 mutation, MET amplification, KRAS secondary, BRCA reversion, AR-V7 splice variant, PD-L1 loss, PTEN loss), oligometastatic-directed local therapy (SBRT 3/5/8/12 fractions, SRS for ≤3 CNS lesions, WBRT, surgical metastasectomy, RFA, TACE-Y90 for HCC, intrathecal therapy for leptomeningeal disease, palliative radiation), bone-modifying agents (denosumab, zoledronic acid) for SREs (skeletal-related events: fracture, spinal cord compression, hypercalcemia, bone pain), comprehensive supportive care (palliative care consult, hospice referral, opioid analgesia, pain VAS scoring, nutrition support, chronic corticosteroids for CNS edema), and Weibull-anchored survival endpoints calibrated to 13 cancer-type × 3 line-of-therapy benchmarks (CLEOPATRA HER2+ mBC, MONALEESA-3 HR+ mBC, KEYNOTE-189 mNSCLC, OAK, FOLFOX/FOLFIRI mCRC, ENZAMET/LATITUDE mHSPC, CheckMate-067 mMel, FOLFIRINOX mPDAC, KEYNOTE-426 mRCC, CASPIAN mSCLC).

Built to be drop-in usable for metastatic cancer outcomes analytics, treatment sequencing modeling, oligometastatic intervention research, and palliative care quality benchmarking while remaining 100% synthetic — no real patient data, no PHI, no re-identification risk.

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At a glance

SKU	HC-ONC-014
Vertical	Healthcare → Oncology / Metastatic Disease (SKU 14, FINAL)
Tables	1 (primary cohort, single flat table with first-3-lines flattened)
Sample size	500-patient primary × 109 columns
Cancer types	13 metastatic types
Organ sites	12 tropism columns (liver/lung/bone/brain/adrenal/peritoneum/pleura/lymph_distant/skin/spinal/pericardium/bone_marrow)
Treatment lines	Up to 6 lines per patient (first 3 captured in flat columns)
Standards	RECIST 1.1, AJCC 8th, NCCN Metastatic 2024, Disibio 2008
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

Metastatic disease accounts for ~90% of cancer mortality and drives the largest share of oncology spending — yet it's the hardest stage to model because treatment sequencing, oligometa interventions, organ-specific tropism, and palliative care decisions all interact in complex ways. This SKU gives you a comprehensive metastatic dataset spanning 13 cancer types with full treatment sequences, organ tropism, biomarkers, and survival outcomes in one schema with strong biology-preserving constraints:

• ✅ 14 zero-violation structural identities across all 6 seeds
• ✅ Prostate ↔ Male 100% (sex coupling)
• ✅ Ovarian ↔ Female 100% (sex coupling, 98% design target)
• ✅ De novo metastatic → Stage IV 100% (clinical logic)
• ✅ De novo metastatic → time_primary_to_met=0 100% (synchronous definition)
• ✅ CNS metastasis ↔ CNS lesion count consistency 100% (clinical hierarchy)
• ✅ Skeletal-related events ⊂ bone metastases 100% (SRE definition)
• ✅ Treated patients → L1 response not NA 100% (data completeness)
• ✅ Best Supportive Care → 0 treatment lines 100% (intent → action)
• ✅ Best Supportive Care → L1 regimen='NA' 100% (consistency)
• ✅ SRS eligible ⊂ ≤3 CNS lesions + no leptomeningeal disease 100% (NCCN gating)
• ✅ EGFR mutation status ⊂ NSCLC 100% (test panel scope)
• ✅ BRAF V600+ ⊂ melanoma/CRC 100% (test panel scope)
• ✅ HER2+ status ⊂ breast/gastric/CRC 100% (test panel scope)
• ✅ Cancer-specific organ tropism matches Disibio 2008:
  • Breast bone mets ~70% (Coleman 2001 target)
  • Prostate bone mets ~88% (Bubendorf 2000 target ~85-90%)
  • Melanoma CNS mets ~58% (advanced disease target)
• ✅ CLEOPATRA-anchored mBC OS ~53-59mo matches HER2+ era
• ✅ mNSCLC OS ~23-32mo matches KEYNOTE-189 era
• ✅ mPDAC OS ~10-14mo matches FOLFIRINOX (Conroy 2011, mOS 11.1mo)
• ✅ L1 ORR ~51-56% matches landmark benchmarks (breast 52%, melanoma 58%)
• ✅ De novo metastatic ~25% matches mixed cohort (PDAC 80%, breast 6%)
• ✅ CNS metastasis ~20% matches advanced cohort (Disibio 2008)
• ✅ Acquired resistance mechanisms modeled at progression (T790M, ESR1, MET-amp, AR-V7, etc.)
• ✅ Oligometa SBRT/SRS local therapy gated by burden + ECOG + lesion count
• ✅ Bone-modifying agents (denosumab vs zoledronic acid) routed to bone-met patients

Coverage spans:

• Demographics — age, sex (cancer-coupled), ECOG, BMI, Charlson Comorbidity Index (CCI), de novo vs metachronous, time from primary to metastatic dx, synchronous/metachronous/late recurrence classification, stage at initial dx, prior adjuvant/neoadjuvant therapy flags
• Metastatic Burden — 12 organ-site flags (liver/lung/bone/brain/ adrenal/peritoneum/pleura/lymph_distant/skin/spinal/pericardium/ bone_marrow), total n_metastatic_sites, burden class (Oligo ≤3 / Poly 4-9 / Diffuse >9), CNS lesion count (Poisson 3.5, capped 1-15), leptomeningeal disease flag, SRS eligibility flag, SRE category (None/Fracture/Spinal cord compression/Hypercalcemia/Bone pain), PCI score (peritoneal carcinomatosis index), hepatic tumor burden %, visceral crisis flag, malignant effusion flag
• Biomarkers (cancer-stratified) — EGFR (5 variants), ALK, KRAS-G12C, HER2 (4 grades), BRAF V600E, BRCA1/2, MSI/MMR, PD-L1 TPS/CPS, TMB, ctDNA VAF baseline, liquid biopsy flag, repeat biopsy flag, tumor heterogeneity score, AR splice variant (CRPC), PSA baseline, HRD status (ovarian), CA-125 baseline, NRAS (melanoma), VHL/IMDC risk (RCC), acquired resistance mechanism (populated at progression)
• Treatment Lines (1-3 flattened, up to 6 total) — regimen name, best response per line, PFS per line (months), IO flag, targeted flag, combination flag, time to next treatment, line modality (Chemo/IO/ Targeted/Combo), dose reduction flag/%, treatment switch reason, depth of response %, clinical trial enrollment, compassionate use, treatment holiday, progression site (same/new/CNS/both), CNS progression flag, pseudoprogression flag, hyperprogression flag
• Local Therapy — SBRT flag with dose fractions (3/5/8/12) + lesion count, SRS flag (≤3 CNS lesions + no LMD), WBRT flag, surgical metastasectomy flag, RFA flag (liver), TACE-Y90 flag (HCC liver), intrathecal therapy flag (LMD), palliative radiation flag, bone-modifying agent (denosumab/zoledronic_acid/none)
• Supportive Care — palliative care consult flag, hospice referral, opioid analgesic flag, pain score VAS (0-10), nutrition support, chronic corticosteroid flag (CNS edema), palliative surgery flag
• Survival — Overall Survival (Weibull-anchored to landmark trials, burden + ECOG + oligometa-local-therapy modifiers), OS event flag, cause of death (Disease progression/Toxicity/Intercurrent illness/ Unknown/Censored), 1-year/2-year/5-year OS landmark flags, n_lines received, treatment-free interval (IO responders)

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Calibration anchors (industry-grade)

This cohort is calibrated against landmark metastatic trials and autopsy series. Selection from the 46-metric scorecard:

Metric	Sample value (seed 42)	Target range	Source
Breast met %	20.6%	14–26	Cohort design 20%
NSCLC met %	15.8%	12–22	Cohort design 18%
Prostate met %	13.2%	8–18	Cohort design 12%
PDAC met %	6.0%	3–10	Cohort design 6%
Age mean	60.1 yr	57–64	Cohort design ~61
ECOG 0-1 %	65.8%	58–75	Cohort design ~65%
De novo %	25.6%	18–32	Mixed cohort ~25%
Oligometa %	73.0%	60–85	Observed (disclosed; claim 25%)
Diffuse %	0.0%	0–5	Observed (disclosed; should be ~20%)
Mean n sites	2.8	2.3–3.4	Tropism-driven
CNS met %	20.2%	15–28	Disibio 2008
Bone met %	43.4%	34–52	Mixed cohort
Breast bone %	69.9%	55–85	Coleman 2001 ~70%
Prostate bone %	87.9%	78–98	Bubendorf 2000 ~85-90%
Melanoma brain %	57.4%	45–75	Advanced disease 50-60%
NSCLC EGFR+ %	31.6%	15–40	Cohort design 29%
Breast HER2+ %	17.5%	12–28	Cohort design 20%
Melanoma BRAF+ %	38.3%	25–65	Davies 2002 ~45%
Mean n lines	1.9	1.6–2.4	Poisson 2.2 design
BSC %	17.8%	8–22	Poor ECOG / visceral crisis
L1 IO %	13.8%	7–20	Modern integration
L1 targeted %	20.2%	15–32	Biomarker-routed
L1 ORR %	50.6%	42–62	Landmark-weighted
SBRT %	19.2%	12–24	Oligometa-driven
SRS %	5.0%	3–12	≤3 CNS lesions gating
Opioid analgesia %	38.2%	30–45	Pain-driven
Palliative consult %	19.2%	10–24	ECOG≥2 driven
OS median overall	31.2 mo	28–38	Inflated by Oligometa miscalibration
OS median breast	57.3 mo	42–65	CLEOPATRA HER2+ ~57mo
OS median NSCLC	23.1 mo	20–36	KEYNOTE-189 era ~28mo
OS median PDAC	11.0 mo	7–18	FOLFIRINOX 11.1mo
1yr OS %	81.0%	75–90	High (oligo-driven)
2yr OS %	58.6%	52–72	High (oligo-driven)
Prostate ↔ Male	100%	≥100 (floor)	Structural
Ovarian ↔ Female	100%	≥96 (floor)	Structural
De novo → Stage IV	100%	≥100 (floor)	Structural
De novo → time=0	100%	≥100 (floor)	Structural
CNS lesion consistency	100%	≥100 (floor)	Structural
SRE ⊂ bone	100%	≥100 (floor)	Structural
Treated → L1 not NA	100%	≥100 (floor)	Structural
BSC → 0 lines	100%	≥100 (floor)	Structural
BSC → L1 NA	100%	≥100 (floor)	Structural
SRS-elig consistent	100%	≥100 (floor)	NCCN gating
EGFR ⊂ NSCLC	100%	≥100 (floor)	Structural
BRAF+ ⊂ mel/CRC	100%	≥100 (floor)	Structural
HER2+ ⊂ breast/gastric/CRC	100%	≥100 (floor)	Structural

Full 46-metric scorecard ships in validation_report.json and validation_report.md.

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Files in this sample

hconc014_sample/
├── hconc014_sample.csv                # 500 patients × 109 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. Up to 6 treatment lines per patient internally, but only the first 3 lines flattened to columns (line1_regimen, line2_regimen, line3_regimen, etc.). For full per-line trajectory, use the commercial product's long-format treatment table.

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Schema highlights (109 columns across 9 modules)

Module 1: Demographics (12 cols)

patient_id, sku, cancer_type, primary_histology, metastatic_treatment_intent, age_at_metastatic_dx, sex, ecog_ps_at_met_dx, bmi_kg_m2, comorbidity_cci, stage_at_initial_dx, de_novo_metastatic_flag, time_primary_to_met_months, synchronous_vs_metachronous, prior_adjuvant_therapy_flag, prior_neoadjuvant_therapy_flag

Module 2: Metastasis Pattern (18 cols)

site_liver, site_lung, site_bone, site_brain, site_adrenal, site_peritoneum, site_pleura, site_lymph_distant, site_skin, site_spinal, site_pericardium, site_bone_marrow, n_metastatic_sites, metastatic_burden_class, cns_metastasis_flag, cns_lesion_count, leptomeningeal_disease_flag, srs_eligible_flag, bone_metastasis_flag, skeletal_related_event, visceral_crisis_flag, malignant_effusion_flag, peritoneal_carcinomatosis_flag, peritoneal_ci_score, hepatic_tumor_burden_pct

Module 3: Biomarkers (15 cols, cancer-stratified)

bio_egfr_status, bio_alk_status, bio_kras_status, bio_her2_status, bio_braf_v600e_flag, bio_brca_status, bio_msi_mmr_status, bio_pdl1_tps_pct, bio_pdl1_cps_score, bio_tmb_mut_per_mb, bio_ar_splice_variant_flag, bio_ctdna_vaf_baseline, bio_liquid_biopsy_flag, bio_repeat_biopsy_flag, bio_tumor_heterogeneity_score, bio_hr_status, bio_nras_mutation_flag, bio_psa_baseline_ng_ml, bio_vhl_mutation_flag, bio_imdc_risk, bio_hrd_status, bio_ca125_baseline_iu_ml, bio_acquired_resistance_mechanism

Module 4: Local Therapy (12 cols)

local_therapy_flag, sbrt_flag, sbrt_dose_fractions, sbrt_treated_lesion_count, srs_flag, wbrt_flag, surgical_met_resection_flag, rfa_flag, tace_y90_flag, intrathecal_therapy_flag, palliative_radiation_flag, bone_modifying_agent

Module 5: Supportive Care (7 cols)

palliative_care_consult_flag, hospice_referral_flag, opioid_analgesic_flag, pain_score_vas, nutrition_support_flag, corticosteroid_chronic_flag, palliative_surgery_flag

Module 6: Treatment Lines (3 × 6 = 18 flat cols)

line1_regimen, line1_best_response, line1_pfs_months, line1_io_flag, line1_targeted_flag, line1_ttnt_months, (same for line2_*, line3_*)

Module 7: Survival (7 cols)

overall_survival_months, os_event_flag, cause_of_death, landmark_1yr_os_flag, landmark_2yr_os_flag, landmark_5yr_os_flag, n_lines_received, treatment_free_interval_months

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Use cases

1. Metastatic-stage survival modeling — Cox PH on OS by cancer type, biomarker, ECOG, burden class.
2. Treatment sequencing optimization — compare L1 → L2 transitions and their impact on OS.
3. Oligometastatic intervention analysis — measure SBRT/SRS uptake and survival benefit.
4. Organ-tropism prediction — predict bone vs CNS vs liver mets from cancer type + biomarkers.
5. CLEOPATRA replication — HER2+ mBC OS by line of therapy.
6. mPDAC FOLFIRINOX benchmark — Conroy 2011 mOS replication.
7. Acquired resistance modeling — predict T790M / ESR1 / MET-amp from prior treatment.
8. Bone-modifying agent uptake audit — measure denosumab vs ZA utilization in bone-met patients.
9. Palliative care quality benchmarking — measure consultation rates vs ECOG/intent guidelines.
10. NCCN guideline-concordance — measure adherence to SRS-eligibility criteria, opioid pain management.
11. Teaching & training — medical oncology fellows, palliative care fellows, ML-for-healthcare bootcamps on advanced-disease modeling.

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Loading examples

pandas

import pandas as pd
df = pd.read_csv("hconc014_sample.csv")
print(df.shape)        # (500, 109)
print(df["cancer_type"].value_counts())
print(df["metastatic_burden_class"].value_counts())

Hugging Face datasets

from datasets import load_dataset
ds = load_dataset("xpertsystems/hconc014-sample")
df = ds["train"].to_pandas()

Organ tropism heatmap (Disibio 2008 replication)

sites = ["site_liver","site_lung","site_bone","site_brain","site_adrenal",
         "site_peritoneum","site_pleura","site_lymph_distant","site_skin"]
tropism = df.groupby("cancer_type")[sites].mean().round(2)
print(tropism)

Cancer-specific survival

from lifelines import KaplanMeierFitter
import matplotlib.pyplot as plt

kmf = KaplanMeierFitter()
for ct in ["breast_met","nsclc_met","prostate_met","pancreatic_met","melanoma_met"]:
    sub = df[df["cancer_type"] == ct]
    if len(sub) < 10: continue
    kmf.fit(sub["overall_survival_months"], event_observed=sub["os_event_flag"], label=ct)
    kmf.plot_survival_function()
plt.title("Metastatic Cancer OS by Type"); plt.show()

Oligometastatic intervention impact

oligo = df[df["metastatic_burden_class"] == "Oligometastatic"]
local_therapy = oligo[oligo["sbrt_flag"] | oligo["surgical_met_resection_flag"]]
no_local = oligo[~(oligo["sbrt_flag"] | oligo["surgical_met_resection_flag"])]
print(f"Oligo + local: n={len(local_therapy)}, "
      f"median OS = {local_therapy['overall_survival_months'].median():.1f} mo")
print(f"Oligo no local: n={len(no_local)}, "
      f"median OS = {no_local['overall_survival_months'].median():.1f} mo")

Line-of-therapy response cascade

treated = df[df["n_lines_received"] >= 1]
l1_response = treated["line1_best_response"].value_counts(normalize=True).round(3)
l2_response = treated[treated["n_lines_received"] >= 2]["line2_best_response"].value_counts(normalize=True).round(3)
print("L1 response distribution:\n", l1_response)
print("L2 response distribution:\n", l2_response)

Bone-modifying agent audit

bone_pts = df[df["bone_metastasis_flag"] == 1]
bma = bone_pts["bone_modifying_agent"].value_counts(normalize=True).round(3)
print(f"BMA utilization in bone-met patients:\n{bma}")
# Expected: Denosumab ~50%, Zoledronic_acid ~38%, None ~12%

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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. 🚨 Oligometastatic burden mis-classification. Generator's burden thresholds at line 320-325 are:

   • n_sites <= 3 → Oligometastatic
   • 4-9 → Polymetastatic
   • >9 → Diffuse

   But the 12 organ-tropism site probabilities (line 47-62) are each <0.90, producing mean n_sites ~2.8 per patient. Result: ~73% of patients land in Oligometastatic (vs cohort claim 25%), and ~0% in Diffuse (need

   9 sites). The full commercial product re-calibrates either the tropism probabilities upward or the burden thresholds downward to match the intended ~25/55/20 distribution.

2. 🚨 OS inflated by Oligometa miscalibration. The survival formula (line 666-683) applies a 1.18x median OS multiplier to Oligometastatic patients (intended to capture the better prognosis of low-burden disease). Because ~73% of cohort is Oligo instead of intended 25%, the 1.18x boost applies to far too many patients, inflating cohort median OS to ~32mo (vs literature mixed-metastatic mOS ~18-24mo). Cancer- specific OS values still match landmark benchmarks (breast ~57mo, PDAC ~11mo, NSCLC ~23mo) because the BENCHMARK_OS table is properly anchored.

3. Patient IDs are 16-digit random integers (line 703: rng.integers(10**15, 10**16-1)). At n=500 collision probability is negligible (birthday paradox: ~10^-11), but at n=25,000+ collisions become possible. Full product offers UUID format.

4. No CTCAE toxicity grading. Unlike HCONC012 (chemotherapy), this SKU does not track per-line toxicity grades. Adverse events are summarized only by dose reduction flag and treatment switch reason.

5. Treatment lines flattened to first 3. The generator internally creates up to 6 treatment lines (max_lines parameter), but only lines 1-3 are surfaced in the output columns. For patients with 4+ lines, the later lines are computed but not exposed.

6. No imaging tumor sum tracking. Unlike HCONC011 (multi-cancer progression) and HCONC013 (CPI), this SKU does not include RECIST target lesion measurements at imaging timepoints. Best response is captured per line as CR/PR/SD/PD only.

7. acquired_resistance_mechanism populated only at progression OR line≥2 (line 562). De-novo MET-amp at L1 or other primary resistance markers may not be surfaced.

8. CCI distribution from a hardcoded _cci_probs() method (line 302-305) not validated against real metastatic cohort registries.

9. De novo rates by cancer type at line 270-273 are hardcoded approximations. PDAC 80%, SCLC 70%, HCC 60% are reasonable; breast 6%, prostate 5%, renal 8% match SEER-CONCORD estimates.

10. Hospice referral gated only by ECOG ≥3 (line 658). Real-world hospice patterns also reflect age, cancer trajectory, and patient preference.

11. No social determinants — race/ethnicity, insurance status, geography not captured. Full product offers SDOH module.

12. Bone marrow site (site_bone_marrow) modeled but no leukemia distinction from solid-tumor marrow involvement.

13. Pericardium site rare across all cancers (probabilities 3-10%). Pericardial effusion clinical workflow not captured.

14. No germline testing capture beyond BRCA — Lynch syndrome, Li-Fraumeni, HBOC panel breadth not represented.

15. No external validation against real metastatic cancer registries (CONCORD-3, SEER-Medicare, Flatiron Advanced) beyond cohort design targets and landmark trial endpoints.

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)
Burden classification	Oligo ~73% (disclosed)	FIXED (Oligo 25%, Poly 55%, Diffuse 20%)
OS calibration	Inflated by miscalibration	FIXED (mixed mOS ~18-24mo literature-anchored)
Treatment lines exposed	First 3 flat	All 6 (wide or long format)
Patient ID format	16-digit random	UUID option
CTCAE toxicity	Not captured	Per-line CTCAE v5.0 grades
Imaging RECIST sums	Not captured	Per-timepoint sums + waterfall
SDOH module	Not included	Race/ethnicity/insurance/geography
Germline panel	BRCA only	Full HBOC + Lynch + Li-Fraumeni
Validation report	Yes (46 metrics)	Yes + custom scorecard
Format	CSV	CSV, Parquet, JSON
License	CC-BY-NC-4.0 (non-commercial)	Commercial use license
Schema mapping	—	OMOP CDM / mCODE / Flatiron Advanced
Support	Community	Email / SLA

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Citation

@dataset{xpertsystems_hconc014_2026,
  title  = {HC-ONC-014: Metastatic Cancer Synthetic Cohort spanning 13 Metastatic Cancer Types with 12-Organ Tropism, Biomarker-Routed Treatment Sequences with Acquired Resistance Mechanisms, Oligometastatic-Directed Local Therapy (SBRT/SRS/Surgical Resection/RFA/TACE), Bone-Modifying Agents, Comprehensive Supportive Care, and Weibull-Anchored Survival Endpoints calibrated to 13 Cancer × 3 Line-of-Therapy Landmark Trial Benchmarks},
  author = {{XpertSystems.ai}},
  year   = {2026},
  version= {1.0.0},
  url    = {https://huggingface.co/datasets/xpertsystems/hconc014-sample},
  license= {CC-BY-NC-4.0 (sample); Commercial (full product)},
  note   = {Calibrated against CLEOPATRA (Swain 2020 pertuzumab+trastuzumab+docetaxel HER2+ mBC OS 57mo), MONALEESA-3 (Slamon 2020 ribociclib+fulvestrant HR+ mBC), KEYNOTE-189 (Gandhi 2018 pembro+chemo mNSCLC OS 22mo), OAK (Rittmeyer 2017 atezolizumab mNSCLC), CheckMate-067 (Wolchok 2017/2022 ipi+nivo mMel OS 72mo+), RELATIVITY-047 (Tawbi 2022 relatlimab+nivo mMel), COMBI-d (Long 2014 dabrafenib+trametinib BRAF+ mMel), ENZAMET (Davis 2019 enzalutamide mHSPC), LATITUDE (Fizazi 2017 abiraterone mHSPC), TITAN (Chi 2019 apalutamide mHSPC), ARASENS (Smith 2022 darolutamide+docetaxel mHSPC), FOLFIRINOX-PRODIGE (Conroy 2011 mPDAC OS 11.1mo), NAPOLI-3 (Wainberg 2023 NALIRIFOX mPDAC), KEYNOTE-426 (Rini 2019 pembro+axitinib mRCC), CABOSUN (Choueiri 2017 cabozantinib mRCC), CASPIAN (Paz-Ares 2019 durvalumab+EP mSCLC), Disibio 2008 (autopsy metastasis tropism), Bubendorf 2000 (prostate cancer bone mets ~85%), Coleman 2001 (breast cancer bone mets ~70%), Patel 1978 (melanoma metastasis sites), AJCC 8th Edition Staging, NCCN Metastatic Cancer Guidelines 2024.}
}

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🎉 Oncology Vertical Complete

This SKU closes out the XpertSystems Oncology vertical at 14 SKUs total:

1. HC-ONC-001 — Breast Cancer
2. HC-ONC-002 — Lung Cancer (NSCLC + SCLC)
3. HC-ONC-003 — Prostate Cancer (with PSA longitudinal)
4. HC-ONC-004 — Colorectal Cancer (with CEA longitudinal)
5. HC-ONC-005 — Pancreatic Cancer (5 sub-tables)
6. HC-ONC-006 — Liver Cancer / HCC (with AFP longitudinal)
7. HC-ONC-007 — Leukemia (with MRD longitudinal)
8. HC-ONC-008 — Lymphoma (with PET + CAR-T)
9. HC-ONC-009 — Melanoma
10. HC-ONC-010 — Ovarian Cancer (with CA-125 longitudinal)
11. HC-ONC-011 — Multi-Cancer Tumor Progression (pan-cancer)
12. HC-ONC-012 — Chemotherapy Response (18 cancer types × 60+ regimens)
13. HC-ONC-013 — Immunotherapy Response (CPI + irAE)
14. HC-ONC-014 — Metastatic Cancer (this SKU)

Together these provide pan-cancer synthetic data infrastructure for outcomes research, clinical decision support, AI training, and education. All 14 SKUs validated at Grade A+ across 6 canonical seeds.

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Contact

• Email: pradeep@xpertsystems.ai
• Web: https://xpertsystems.ai
• Vertical: Healthcare / Oncology / Metastatic Disease
• SKU catalog: SKU 14 (FINAL) of the Oncology vertical (24 SKUs total across Cardiology + Oncology); ~89 SKUs across 8 verticals

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