Original product specification supplied by the project owner on 2026-09-08. The repository CLAUDE.md is the condensed operational version.

# CLAUDE.md — CancerIndex.io

# 0. PROJECT IDENTITY

Project: CancerIndex.io Product type: Global cancer intelligence platform, database, search engine, ranking system, knowledge graph, analytics platform, and AI research interface. Primary language: English. Domain: cancerindex.io Mission: Build the most comprehensive, structured, searchable, continuously updated and transparently sourced cancer intelligence database possible.

CancerIndex.io must aim to become:

The global index of cancer.

Think of the product as a combination of:

Bloomberg Terminal for oncology
IMDb/Wikipedia-style entity coverage
Our World in Data for cancer epidemiology
ClinicalTrials.gov explorer
cBioPortal genomic exploration
PubMed intelligence layer
drug/biomarker intelligence platform
cancer knowledge graph
transparent ranking engine
research assistant
cancer statistics terminal

The product must not merely list the 20–50 most common cancers.

CancerIndex must attempt to model every identifiable malignant disease, histological subtype, molecular subtype, rare cancer, hematologic malignancy, pediatric malignancy, tumor family, anatomical site and recognized cancer entity that can be responsibly mapped from authoritative taxonomies.

This includes cancers that may affect only a tiny number of people annually.

Do not hardcode a simplistic cancer list.

Build an evolving oncology ontology.

# 1. THE CORE PRINCIPLE

CancerIndex must answer questions such as:

What cancers exist?
How common is each cancer?
Which cancers kill the most people?
Which cancers have the highest mortality rate?
Which cancers have the poorest survival?
Which cancers are increasing fastest?
Which cancers affect younger populations?
Which cancers have the most treatments?
Which cancers have the fewest treatments?
Which cancers receive the most research?
Which cancers receive the least research relative to burden?
Which cancers currently have the most clinical trials?
Which cancers have the most actionable mutations?
Which cancers have the largest unmet need?
Which cancers are associated with which genes?
What variants occur in those genes?
Which therapies target those abnormalities?
Which drugs are approved?
Which drugs are experimental?
Which biomarkers predict response?
Which trials are recruiting?
Which publications support each relationship?
Which countries have the highest incidence?
How has incidence changed through time?
How does survival differ by stage?
How does age influence incidence?
How does sex influence incidence?
Which risk factors are associated with each malignancy?
Which cancers can be screened for?
Which cancers can be prevented?
Which cancers are becoming more survivable?
Where are the largest gaps in oncology research?

Everything must be explorable from the web interface and eventually through an API.

# 2. ABSOLUTE RULE: PROVENANCE FIRST

CancerIndex must never display an important scientific number without knowing where it originated.

Every imported fact should support metadata such as:

interface Provenance {
  sourceId: string
  sourceName: string
  sourceRecordId?: string
  sourceUrl?: string

  dataset?: string
  datasetVersion?: string

  publicationId?: string
  pmid?: string
  doi?: string

  retrievedAt: string
  publishedAt?: string
  updatedAt?: string

  geography?: string
  population?: string
  cohortSize?: number

  methodology?: string

  evidenceType:
    | "registry"
    | "clinical_trial"
    | "meta_analysis"
    | "systematic_review"
    | "cohort"
    | "case_control"
    | "case_series"
    | "case_report"
    | "preclinical"
    | "regulatory"
    | "guideline"
    | "expert_curation"
    | "database"
    | "computed"

  accessLevel:
    | "open"
    | "registration_required"
    | "controlled"
    | "licensed"

  confidence?: number

  license?: string
}

Every derived statistic must also be reproducible.

Example:

json

{
  "metric": "mortality_to_incidence_ratio",
  "value": 0.73,
  "computed": true,
  "formula_version": "ci-mir-v1",
  "inputs": [
    "CINDEX-METRIC-102991",
    "CINDEX-METRIC-102992"
  ]
}

Never destroy raw source information during normalization.

Architecture:

text

RAW
 ↓
NORMALIZED
 ↓
CANONICAL
 ↓
DERIVED
 ↓
RANKED
 ↓
AI SYNTHESIS

These layers must remain separable.

# 3. SCIENTIFIC SAFETY

CancerIndex is a research/information platform.

It is NOT:

a physician
a diagnostic system
a treatment prescriber
a replacement for professional medical care

Never produce treatment recommendations based only on an AI-generated inference.

Clearly distinguish:

text

OBSERVED DATA
PUBLISHED EVIDENCE
CURATED EVIDENCE
REGULATORY STATUS
CLINICAL GUIDELINE
COMPUTED METRIC
AI-GENERATED SYNTHESIS

Never silently merge these categories.

# 4. COVERAGE GOAL — EVERY CANCER WE CAN MODEL

CancerIndex needs a hierarchical disease model.

A simple table:

text

lung cancer
breast cancer
brain cancer
...

is unacceptable.

Cancer must be represented through a hierarchy.

Example:

text

Cancer
└── Solid Tumor
    └── Lung Cancer
        └── Non-Small Cell Lung Cancer
            └── Lung Adenocarcinoma
                ├── EGFR-mutated LUAD
                ├── KRAS-mutated LUAD
                ├── ALK-positive LUAD
                ├── ROS1-positive LUAD
                └── RET-positive LUAD

Another:

text

Cancer
└── Hematologic Malignancy
    └── Leukemia
        └── Acute Leukemia
            └── Acute Myeloid Leukemia
                ├── AML with NPM1 mutation
                ├── AML with CEBPA mutation
                ├── APL
                └── therapy-related AML

Another:

text

Cancer
└── CNS Tumor
    └── Glioma
        └── Diffuse Glioma
            └── Glioblastoma

The hierarchy needs multiple dimensions.

Do NOT force every entity into only one parent tree.

Support:

text

anatomical hierarchy
histological hierarchy
molecular hierarchy
WHO-style disease classification
ICD hierarchy
ICD-O morphology
ICD-O topography
NCI Thesaurus
Disease Ontology
OncoTree
SEER classification
pediatric classification
hematologic classification

# 5. CANONICAL CANCER ENTITY

Create:

CancerEntity

Example schema:

interface CancerEntity {
  id: string
  slug: string

  canonicalName: string
  shortName?: string

  aliases: string[]
  abbreviations: string[]

  entityType:
    | "cancer"
    | "cancer_family"
    | "histology"
    | "subtype"
    | "molecular_subtype"
    | "hematologic_malignancy"
    | "precursor_condition"
    | "other"

  parentIds: string[]
  childIds: string[]

  anatomyIds: string[]
  histologyIds: string[]

  ncitCodes: string[]
  icd10Codes: string[]
  icdoTopographyCodes: string[]
  icdoMorphologyCodes: string[]
  doidCodes: string[]
  oncotreeCodes: string[]
  umlsCodes: string[]
  meshIds: string[]
  mondoIds: string[]

  malignant: boolean
  solidTumor: boolean
  hematologic: boolean
  pediatricRelevant: boolean
  rareCancer: boolean

  description?: string

  epidemiology?: CancerEpidemiologySummary
  survival?: CancerSurvivalSummary

  geneAssociations?: string[]
  biomarkerAssociations?: string[]
  drugAssociations?: string[]
  trialAssociations?: string[]

  ranking?: CancerRanking

  provenanceIds: string[]

  createdAt: string
  updatedAt: string
}

# 6. CANCERINDEX IDENTIFIERS

CancerIndex needs its own stable ID namespace.

Examples:

text

CI-CAN-00000001
CI-CAN-00000002
CI-GENE-00000001
CI-VAR-00000001
CI-DRUG-00000001
CI-TRIAL-00000001
CI-PUB-00000001
CI-BIO-00000001
CI-STUDY-00000001
CI-METRIC-00000001
CI-SOURCE-00000001
CI-ORG-00000001
CI-TRT-00000001

Never expose database auto-increment integers as public identifiers.

IDs must remain stable forever.

# 7. ENTITY UNIVERSE

CancerIndex should eventually contain first-class entities for:

# Cancer

text

Cancer
Cancer subtype
Histology
Molecular subtype
Tumor family
Precancerous condition where relevant
Metastatic disease state

# Anatomy

text

Organ
Tissue
Anatomical site
Primary site
Metastatic site

# Genes

text

Gene
Transcript
Protein
Pathway
Gene family

# Genetic alterations

text

SNV
MNV
Insertion
Deletion
Indel
Fusion
Rearrangement
Amplification
Deletion/CNA
Loss of heterozygosity
Promoter mutation
Splice alteration
Expression change
Epigenetic alteration
Structural variant

# Biomarkers

text

Gene mutation
Protein expression
Hormone receptor
PD-L1
MSI
TMB
HRD
ctDNA
methylation
gene signature
expression signature
cell surface marker
immune marker

# Drugs

text

small molecule
monoclonal antibody
ADC
bispecific antibody
CAR-T
cell therapy
gene therapy
cancer vaccine
radiopharmaceutical
chemotherapy
hormonal therapy
immunotherapy
targeted therapy

# Treatment concepts

text

drug
drug combination
surgery
radiotherapy
brachytherapy
proton therapy
transplantation
cell therapy
watchful waiting
active surveillance

# Clinical research

text

clinical trial
trial arm
intervention
cohort
endpoint
study
publication
investigator
institution
sponsor

# Population

text

country
territory
state/province
region
registry
age group
sex
calendar year

# 8. THE CONNECTOR PHILOSOPHY

CancerIndex must be built around connectors.

Do not manually populate the platform except for explicitly curated metadata.

Every external system must have an independent connector.

Structure:

text

/connectors
  /nci
  /gdc
  /seer
  /iarc
  /clinicaltrials
  /pubmed
  /clinvar
  /cbioportal
  /civic
  /hgnc
  /ensembl
  /chembl
  /opentargets
  /dgidb
  /fda
  ...

Every connector implements something similar to:

interface Connector {
  id: string
  name: string

  discover(): Promise<void>
  fetch(): Promise<void>
  normalize(): Promise<void>
  reconcile(): Promise<void>
  validate(): Promise<void>
  persist(): Promise<void>

  healthCheck(): Promise<ConnectorHealth>

  getCursor(): Promise<ConnectorCursor>
  setCursor(cursor: ConnectorCursor): Promise<void>
}

# 9. CONNECTOR MANIFEST

Every connector needs:

yaml

id:
name:
organization:
category:

access:
  type: api|bulk|rss|ftp|graphql|rest|scrape|manual
  auth: none|api_key|oauth|account|controlled

license:
terms_reviewed:
commercial_use_status:

update_frequency:
expected_latency:

supports_incremental_sync:

entities:
metrics:

rate_limits:

retry_policy:

raw_retention:

schema_version:

documentation_verified_at:

owner:
status:

# 10. CONNECTOR PRIORITY TIERS

# TIER 0 — FOUNDATIONAL

These must be implemented first.

# 10.1 NCI Enterprise Vocabulary Services

Purpose:

cancer terminology
canonical disease names
disease aliases
drug concepts
biomarkers
terminology mappings
controlled oncology concepts

Use NCI terminology as one of the anchors of the CancerIndex normalization system.

Store NCI identifiers on entities.

# 10.2 NCI Genomic Data Commons — GDC

Use for:

TCGA
TARGET
CPTAC where available through the platform
case metadata
tumor metadata
genomic files
mutation information
copy-number information
expression
molecular features
survival-related analyses
cancer cohorts

Connector must support:

text

projects
cases
files
annotations
genes
mutations
CNV
metadata
manifests

Do not ingest controlled-access patient-identifiable/low-level data without the proper authorization model.

Prioritize open, aggregated and de-identified data.

# 10.3 SEER

Purpose:

US cancer incidence
mortality where available/appropriate
survival
age
sex
race/ethnicity where datasets permit
staging
disease classification
registry geography
trends

Use SEER for American cancer burden and survival analytics.

Never represent a SEER population estimate as a global estimate.

# 10.4 IARC Global Cancer Observatory / GLOBOCAN

This should be the major global epidemiology layer.

Capture when permitted:

text

incidence
mortality
prevalence
age-standardized rates
sex
country
region
cancer type
year

Build CancerIndex country and global rankings from this layer.

Terms and redistribution rights MUST be reviewed before automated bulk ingestion.

Do not assume that because data are publicly viewable they can automatically be republished wholesale.

# 10.5 ClinicalTrials.gov

Build an extremely robust ClinicalTrials.gov connector.

Capture:

text

NCT ID
official title
brief title
study type
phase
status
conditions
interventions
arms
sponsor
collaborators
eligibility
sex
age
enrollment
locations
countries
investigators
primary outcomes
secondary outcomes
start date
completion date
study results
references
last update

CancerIndex must map free-text conditions to canonical cancer IDs.

CancerIndex must map:

text

trial → cancer
trial → drug
trial → biomarker
trial → gene
trial → institution
trial → country

Incrementally synchronize changed records.

# 10.6 PubMed

Massive literature connector.

Capture:

text

PMID
title
abstract
authors
affiliations
journal
publication date
publication types
MeSH
DOI
references where accessible
retractions/corrections

Build mappings:

text

publication → cancer
publication → gene
publication → variant
publication → biomarker
publication → drug
publication → trial

Do not make LLM entity extraction authoritative.

LLM extraction creates candidate relationships.

Those candidates must be labeled accordingly until validated.

# 10.7 ClinVar

Use for:

text

variants
clinical significance
conditions
review status
submitter information
variation IDs
HGVS
genes
citations
drug response

Map cancer-associated ClinVar records into the graph.

# 10.8 cBioPortal

Use for cancer cohort/genomics exploration.

Import where licensing permits:

text

studies
patients
samples
mutations
CNA
expression
clinical attributes
survival
molecular profiles

CancerIndex should preserve original study IDs.

# 10.9 CIViC

Extremely important for curated clinical interpretation of cancer variants.

Map:

text

gene
variant
molecular profile
disease
therapy
evidence item
assertion
publication
evidence level
evidence direction
clinical significance

Do not flatten CIViC evidence into a binary:

text

works / doesn't work

Preserve its structured evidence.

# 11. TIER 1 — MOLECULAR INTELLIGENCE

Implement these after foundational ingest.

# HGNC

Canonical human gene nomenclature.

Capture:

text

HGNC ID
approved symbol
approved name
aliases
previous symbols
chromosomal location
cross references

HGNC should be authoritative for canonical human gene symbol reconciliation.

# Ensembl

Capture:

text

genes
transcripts
variants
coordinates
assemblies
regulatory information
homology where useful

Always retain genome assembly.

Never store a coordinate without:

text

assembly
chromosome
position
reference
alternate

# NCBI Gene

Use as additional cross-reference and annotation source.

# dbSNP

Variant identifiers and genomic cross references.

# dbVar

Structural variation.

# Sequence Ontology

Normalize variant types.

# Gene Ontology

Functional annotation.

# UniProt

Protein entities and protein annotation.

# Reactome

Pathways.

Build:

text

gene → pathway
protein → pathway
drug target → pathway
cancer → dysregulated pathway

# WikiPathways

Secondary pathway source.

# Protein Data Bank

Connect cancer proteins and drug targets to experimental structures.

# AlphaFold Protein Structure Database

Optional structural biology layer.

Do not imply predicted structure equals experimental structure.

# 12. TIER 2 — DRUG INTELLIGENCE

# ChEMBL

Capture:

text

molecules
mechanisms
targets
assays
activities
indications
development phase

# Open Targets

Use as a disease-target-drug evidence layer.

Map:

text

target ↔ disease
drug ↔ target
evidence source
association score

Never convert another database's association score directly into a CancerIndex evidence score without documenting transformation.

# DGIdb

Drug-gene interactions.

Preserve contributing source information.

# DrugBank

Potential connector.

Important: licensing must be verified before implementation or redistribution.

Do not scrape or reproduce licensed content without permission.

# PubChem

Use for:

text

compound IDs
structures
synonyms
chemical identifiers

# DrugCentral

Candidate drug information source.

Verify current terms and downloadable datasets.

# DailyMed

Structured FDA label information.

Potential fields:

text

drug label
indications
contraindications
warnings
dose language
adverse reactions
manufacturer
label version

Never paraphrase a drug label and then present the paraphrase as the legal label.

# OpenFDA

Use where useful for structured FDA data.

Potential:

text

labels
adverse event aggregates
drug metadata

Adverse-event reports must include strong caveats.

Spontaneous reporting cannot be treated as incidence or causal proof.

# 13. TIER 3 — REGULATORY INTELLIGENCE

Build country-aware regulatory status.

Never use:

text

approved = true

alone.

Use:

interface DrugApproval {
  drugId: string
  cancerId?: string
  biomarkerIds: string[]

  jurisdiction:
    | "US"
    | "CA"
    | "EU"
    | "UK"
    | "AU"
    | "JP"
    | "OTHER"

  authority:
    | "FDA"
    | "Health Canada"
    | "EMA"
    | "MHRA"
    | "TGA"
    | "PMDA"
    | string

  indication: string

  lineOfTherapy?: string
  diseaseStage?: string

  approvalType?: string
  accelerated?: boolean
  conditional?: boolean

  approvalDate?: string
  withdrawalDate?: string

  status:
    | "approved"
    | "conditional"
    | "accelerated"
    | "withdrawn"
    | "superseded"

  sourceId: string
}

# FDA Oncology

Connect:

oncology approval announcements
Oncology Center of Excellence
Project Confirm
accelerated approvals
withdrawn accelerated approvals
verified clinical benefit
Project Orbis
labeling
regulatory reviews

CancerIndex should have an:

Oncology Approval Timeline

# Health Canada

CancerIndex is global; Canada must be properly represented.

Potential datasets:

Drug Product Database
Notice of Compliance
Summary Basis of Decision
Product Monographs
Project Orbis-related approvals

Review redistribution and API availability before implementation.

# EMA

Capture:

text

European Public Assessment Reports
indications
marketing authorization
authorization dates
withdrawals
safety changes

# MHRA

UK regulatory layer.

# TGA

Australia regulatory layer.

# PMDA

Japan regulatory layer.

# Swissmedic

Swiss layer.

# 14. TIER 4 — GLOBAL EPIDEMIOLOGY

Additional country sources should augment IARC rather than blindly override it.

Possible connectors:

# WHO

Global health statistics and relevant cancer-related datasets.

# CDC

US cancer statistics where useful.

# Statistics Canada

Canadian mortality/population data.

# Canadian Cancer Statistics

Evaluate licensing and machine-readable availability.

# Canadian Cancer Registry

Integrate permitted aggregated data where accessible.

# European Cancer Information System

European epidemiology.

# EUROCARE

European survival research where permitted.

# National cancer registries

Build country-specific connectors where high-quality public data exist.

Examples:

text

UK
Australia
New Zealand
Nordic countries
France
Germany
Netherlands
Japan
South Korea
Singapore
Canada
United States

Do NOT mix incompatible epidemiological definitions without harmonization.

# 15. TIER 5 — LITERATURE

# PubMed

Primary.

# Europe PMC

Use as a complementary literature graph.

Potential:

text

abstracts
full-text availability
citations
references
grants
preprints

# Crossref

DOI and publication metadata.

# OpenAlex

Useful for:

text

citation graph
institutions
authors
topics
research trends

Verify licensing/current API conditions.

# Semantic Scholar

Potential secondary citation/AI-literature layer.

Review API conditions.

# bioRxiv

Preprints.

# medRxiv

Preprints.

Always label preprints prominently.

Never rank a preprint as equivalent to peer-reviewed evidence.

# 16. TIER 6 — CLINICAL GUIDELINES

Potential sources:

text

NCI
ASCO
ESMO
NCCN
Cancer Care Ontario
NICE
other national oncology organizations

CRITICAL:

Guideline copyright and licensing vary substantially.

CancerIndex must NOT automatically scrape and reproduce paid/copyrighted guidelines.

For restricted sources:

store only permitted:

text

citation
title
publication date
organization
external reference
metadata

unless licensing permits more.

# 17. TIER 7 — PRECISION ONCOLOGY

Potential integrations:

text

CIViC
ClinVar
OncoKB
Cancer Genome Interpreter
JAX-CKB
MolecularMatch
My Cancer Genome

But:

Licensing must be checked individually.

Never assume commercial reuse.

Build the CancerIndex precision oncology layer first from sources with clear reuse rights.

# 18. TIER 8 — CANCER CELL LINES AND PRECLINICAL DATA

Potential sources:

# DepMap

text

cell lines
gene dependencies
CRISPR screens
drug sensitivity
molecular features

# Cancer Cell Line Encyclopedia

Integrate where licensing permits.

# GDSC

Genomics of Drug Sensitivity in Cancer.

# Cell Model Passports

Cancer model information.

# PDX resources

Patient-derived xenograft data where publicly available.

Keep:

text

PRECLINICAL

clearly separated from human clinical evidence.

# 19. TIER 9 — IMMUNO-ONCOLOGY

Model:

text

immune checkpoints
immune cell populations
neoantigens
HLA
PD-1
PD-L1
CTLA-4
LAG-3
TIGIT
TIM-3
TMB
MSI
immune gene signatures

Sources can include:

text

GDC
cBioPortal
CIViC
clinical trials
publications
Open Targets

# 20. TIER 10 — PEDIATRIC ONCOLOGY

CancerIndex must NOT treat pediatric cancers as simply adult cancers in younger people.

Build dedicated pediatric taxonomy.

Sources could include:

text

TARGET
NCI
SEER
IARC pediatric resources
St. Jude public resources
pediatric clinical trials
literature

Add:

text

age at diagnosis
pediatric incidence
AYA incidence
survival
molecular subtype
treatment landscape
late effects evidence

# 21. TIER 11 — RARE CANCERS

Rare cancers are a core differentiator.

CancerIndex should attempt to index cancers even when:

text

incidence < 1 / 100,000

Do not hide them because data are sparse.

Create:

Rare Cancer Explorer

Metrics:

text

estimated incidence
number of known cases/cohorts
number of publications
number of clinical trials
number of approved therapies
number of targeted therapies
available genomic studies
research activity

Data scarcity itself should be displayed.

# 22. CONNECTOR FALLBACK SYSTEM

Preferred connector hierarchy:

text

1. official API
2. official bulk download
3. official structured feed
4. official database export
5. official static dataset
6. compliant website extraction
7. publication extraction
8. manual curator review

Do NOT start by scraping if an API exists.

# 23. FIRECRAWL + SCRAPFLY

CancerIndex may use Firecrawl and Scrapfly for sources without suitable APIs.

Architecture:

text

official API
    ↓ unavailable
official bulk
    ↓ unavailable
Firecrawl
    ↓ blocked / inadequate
Scrapfly

Firecrawl is primarily useful for:

text

documentation
regulatory pages
research institution pages
structured public pages
public tables
release notes

Scrapfly should be a fallback for technically difficult public pages when use is permitted.

Do NOT use anti-bot tooling to bypass:

authentication
paywalls
explicit access restrictions
licensing controls
patient privacy protections
controlled genomic datasets

Store source terms/compliance status per connector.

# 24. CONNECTOR OBSERVABILITY

Every connector receives an admin dashboard.

Display:

text

status
last successful sync
last attempt
duration
records fetched
records created
records updated
records rejected
schema drift
HTTP failures
rate limit events
validation failures
freshness

Example:

text

GDC                HEALTHY       11 min ago
ClinicalTrials     HEALTHY        4 min ago
PubMed             HEALTHY       8 min ago
SEER               HEALTHY       2 hr ago
FDA                DEGRADED      37 min ago
IARC               REVIEW       license check

# 25. SCHEMA DRIFT DETECTION

External APIs change.

Every connector must detect:

text

new fields
removed fields
changed enum values
changed types
unexpected nullability
pagination behavior changes
authentication changes

When drift occurs:

text

DO NOT silently discard data.

Alert the administrator.

# 26. RAW DATA LAKE

Every source payload should be retained when licensing allows.

Use object storage:

text

/raw/{source}/{date}/{entity}/{id}.json

or compressed batch files.

Benefits:

auditability
reproducibility
reprocessing
parser upgrades
debugging
historical snapshots

# 27. CANONICAL DATA MODEL

Core relational tables:

text

cancers
cancer_aliases
cancer_hierarchy
cancer_codes

anatomical_sites

genes
gene_aliases
proteins
transcripts

variants
variant_coordinates
variant_aliases

biomarkers

drugs
drug_aliases
drug_targets
drug_indications
drug_approvals

treatments
treatment_regimens

clinical_trials
trial_conditions
trial_interventions
trial_locations
trial_outcomes
trial_eligibility

publications
authors
institutions

studies
cohorts

epidemiology_observations
survival_observations

cancer_gene_edges
cancer_variant_edges
cancer_biomarker_edges
cancer_drug_edges
drug_gene_edges
drug_variant_edges
trial_cancer_edges
publication_entity_edges

sources
source_records
provenance

rankings
ranking_snapshots

# 28. KNOWLEDGE GRAPH

CancerIndex must be graph-native conceptually, even if PostgreSQL remains the primary transactional database.

Graph:

text

Cancer
 ├── HAS_SUBTYPE → Cancer
 ├── OCCURS_IN → Anatomy
 ├── ASSOCIATED_WITH → Gene
 ├── HAS_VARIANT → Variant
 ├── HAS_BIOMARKER → Biomarker
 ├── TREATED_BY → Drug
 ├── STUDIED_IN → Trial
 ├── DESCRIBED_BY → Publication
 └── OBSERVED_IN → Cohort

Gene
 ├── HAS_VARIANT → Variant
 ├── ENCODES → Protein
 ├── MEMBER_OF → Pathway
 └── TARGETED_BY → Drug

Variant
 ├── OCCURS_IN → Cancer
 ├── PREDICTS_RESPONSE_TO → Drug
 ├── CONFERS_RESISTANCE_TO → Drug
 └── SUPPORTED_BY → Evidence

Drug
 ├── TARGETS → Gene
 ├── APPROVED_FOR → Cancer
 ├── INVESTIGATED_FOR → Cancer
 └── USED_IN → Trial

Relationships need provenance.

# 29. EDGE MODEL

Never store:

text

EGFR mutation → osimertinib

without context.

Use:

interface KnowledgeEdge {
  id: string

  sourceEntityId: string
  targetEntityId: string

  relationshipType: string

  cancerContextIds?: string[]

  predictive?: boolean
  prognostic?: boolean
  diagnostic?: boolean
  predisposing?: boolean

  direction?:
    | "supports"
    | "resistance"
    | "sensitivity"
    | "neutral"
    | "unknown"

  evidenceLevel?: string
  evidenceScore?: number

  provenanceIds: string[]

  firstSeenAt: string
  lastSeenAt: string
}

# 30. CANCER RANKING ENGINE

This is one of the signature features.

CancerIndex must rank every eligible cancer across MANY metrics.

There must never be one unexplained “danger ranking.”

# 31. RANKING DIMENSIONS

Every cancer can potentially have:

# Burden

text

global incidence count
global mortality count
global prevalence
age-standardized incidence
age-standardized mortality
DALYs if source available
YLL if source available

# Lethality

text

mortality / incidence ratio
1-year survival
5-year survival
10-year survival
stage IV survival
median OS where meaningful

# Trend

text

incidence CAGR
mortality CAGR
survival improvement
age-adjusted incidence trend

# Rarity

text

global incidence rank
incidence per 100k
estimated annual patients

# Treatment Landscape

text

number of approved therapies
number of targeted therapies
number of immunotherapies
number of biomarker-directed therapies
number of treatment classes

# Clinical Research

text

active trials
recruiting trials
phase I trials
phase II trials
phase III trials
interventional trial count
trial enrollment

# Research Activity

text

publications last 12 months
publications last 5 years
publication growth
citations
research institutions

# Molecular Knowledge

text

known recurrent genes
actionable variants
validated biomarkers
genomic studies
sequenced cohorts

# Unmet Need

Derived carefully from:

text

mortality burden
poor survival
few approved therapies
few active trials
low research activity
lack of actionable biomarkers

# 32. RANK EVERY CANCER BY DEFAULT

Cancer detail pages should display:

text

Global incidence rank
Global mortality rank
5-year survival rank
Lethality rank
Research activity rank
Clinical trial rank
Treatment availability rank
Genomic knowledge rank
Unmet need rank
CancerIndex composite rank

Example:

text

Pancreatic Adenocarcinoma

Mortality burden       #7
Incidence burden      #14
Lethality              #3
Five-year survival     #4 poorest
Research activity     #11
Active trials         #15
Treatment options     #82
Unmet need             #5
CancerIndex Impact     #8

These are examples only.

Never hardcode examples as actual statistics.

# 33. RANKING SCOPE

Rankings need scope.

Example:

text

WORLD
CANADA
UNITED STATES
EUROPE
QUEBEC
MALE
FEMALE
CHILDREN
AYA
AGE 65+
2024
2025
historical

A rank is meaningless without a population and reference year.

Schema:

interface Ranking {
  cancerId: string

  metricId: string

  rank: number
  eligibleEntities: number

  percentile: number

  geography: string
  sex?: string
  ageGroup?: string
  year?: number

  value: number
  unit: string

  sourceIds: string[]

  formulaVersion?: string

  generatedAt: string
}

# 34. COMPOSITE CANCERINDEX SCORE

Create an optional composite metric.

Do NOT present it as biological truth.

Possible conceptual model:

text

CancerIndex Impact Score

0–100.

Possible components:

text

25% mortality burden
20% lethality
15% incidence burden
15% unmet treatment need
10% adverse trend
10% research deficit
5% clinical trial deficit

Weights must be:

visible
versioned
configurable
documented

Example:

text

CancerIndex Impact Score v1.0

Display:

text

Score: 87.4 / 100
Rank: 6 / 412

and a breakdown.

Never show only 87.4.

Show:

text

Mortality burden       93
Lethality              97
Incidence              78
Treatment deficit      84
Research deficit       63
Trend                   71

# 35. UNCERTAINTY

Rankings must account for uncertainty.

A rare cancer may have:

text

n = 22

Do not rank survival estimates derived from tiny datasets as equivalent to huge registry datasets.

Store:

text

sample size
confidence interval
standard error
estimate method
source quality
data completeness

Optionally display:

text

Ranking confidence

HIGH
MEDIUM
LOW
INSUFFICIENT DATA

# 36. DATA COMPLETENESS SCORE

Every cancer gets a completeness profile.

Example:

text

Epidemiology      92%
Survival          81%
Genomics          97%
Trials            100%
Therapies         93%
Biomarkers        88%
Literature        100%
Pathology         76%

This is separate from scientific confidence.

# 37. RESEARCH GAP INDEX

Create a major CancerIndex innovation:

# Research Gap Index

Question:

Which cancers have a large burden but disproportionately little research?

Possible formula:

text

burden percentile
÷
research activity percentile

More sophisticated version:

text

expected research activity =
f(
  incidence,
  mortality,
  years_of_life_lost,
  lethality
)

research gap =
expected activity - observed activity

Display:

Most Under-Researched Cancers

This could be extremely compelling.

# 38. TRIAL GAP INDEX

Another ranking:

text

disease burden
vs
active interventional trials

Identify:

High-burden cancers with few active trials.

# 39. TREATMENT GAP INDEX

Rank cancers based on:

text

mortality
survival
approved drug count
effective targeted treatment count
biomarker-directed therapies

Again, label as CancerIndex-derived metric.

# 40. PROGRESS INDEX

Create:

Cancer Progress Index

Track over 5/10/20 years:

text

mortality improvement
survival improvement
treatment approvals
trial growth
biomarker growth
research growth

Show:

text

Most rapidly improving cancers
Least improving cancers

# 41. MOMENTUM INDEX

Short-term research momentum.

Components:

text

new trials
new publications
new drugs
new FDA approvals
new biomarkers
new genomic studies

Windows:

text

30 days
90 days
1 year
5 years

# 42. CANCER ENTITY DETAIL PAGE

Route:

text

/cancer/{slug}

Example layout:

text

┌─────────────────────────────────────────────┐
│ Pancreatic Ductal Adenocarcinoma            │
│ PDAC                                        │
│ CI-CAN-0000342                              │
└─────────────────────────────────────────────┘

CancerIndex Score
89.2

Global Rank
#5

Tabs:
Overview
Statistics
Survival
Stages
Genomics
Genes
Variants
Biomarkers
Treatments
Drugs
Trials
Research
Publications
Risk Factors
Screening
Prevention
Countries
Trends
Sources

# 43. CANCER OVERVIEW HERO

Show:

text

Global annual cases
Global annual deaths
5-year survival
median diagnosis age
male/female distribution

Impact rank
Mortality rank
Lethality rank
Research rank
Unmet need rank

Never display unsupported values.

# 44. CANCER SUMMARY

AI-generated summary should contain:

text

What it is
Where it originates
Major subtypes
Epidemiology
Typical molecular features
Major treatment modalities
Current research landscape

Every paragraph needs citations.

AI summaries must be cached with:

text

model
prompt version
source snapshot
generation date

# 45. GLOBAL CANCER RANKING PAGE

Route:

text

/rankings

Filters:

text

metric
year
country
region
sex
age
cancer category
minimum cases
data confidence

Columns:

text

Rank
Cancer
Score/value
Cases
Deaths
Mortality/incidence
5-year survival
Active trials
Publications
Trend

# 46. RANKING PRESETS

Routes or presets:

text

/rankings/incidence
/rankings/mortality
/rankings/lethality
/rankings/survival
/rankings/research
/rankings/trials
/rankings/treatment-gap
/rankings/research-gap
/rankings/momentum
/rankings/progress
/rankings/rare-cancers

# 47. COUNTRY PAGES

Route:

text

/country/canada
/country/united-states
/country/france

Display:

text

population
annual cancer cases
annual cancer deaths
ASIR
ASMR

Top cancers by incidence
Top cancers by mortality
male
female

historical trend
age distribution

Map visualization.

# 48. GLOBAL CANCER MAP

Interactive world map.

Filters:

text

cancer
incidence
mortality
ASR
sex
year
age

Click country → country dashboard.

# 49. GENE PAGES

Route:

text

/gene/TP53

Display:

text

gene overview
HGNC identity
chromosome
protein
pathways

cancers
variants
mutation frequencies
biomarkers
therapies
clinical trials
publications

# 50. VARIANT PAGES

Example:

text

/variant/BRAF-V600E

Display:

text

gene
HGVS
protein change
coordinates by assembly
ClinVar
CIViC
cancers
frequencies
drug sensitivity evidence
drug resistance evidence
clinical trials
publications

Separate evidence by cancer.

BRAF V600E in one cancer must not automatically inherit evidence from another cancer.

# 51. BIOMARKER PAGES

Examples:

text

PD-L1
MSI-H
TMB-high
HER2
HRD
ER
PR
PSMA
ctDNA

Display:

text

definition
measurement method
cancers
therapies
FDA-approved indications
clinical evidence
trials
publications

# 52. DRUG PAGES

Route:

text

/drug/osimertinib

Hero:

text

Generic name
Brand names
Drug class
Targets
Mechanism
Developer
First approval
Current jurisdictions

Tabs:

text

Overview
Mechanism
Targets
Cancer indications
Biomarkers
Approvals
Clinical trials
Publications
Combinations
Resistance
Safety
Sources

# 53. DRUG COMBINATION ENTITY

Do not treat:

text

Drug A + Drug B

as two unrelated drugs.

Create:

text

TreatmentRegimen

Examples:

text

FOLFOX
FOLFIRINOX
R-CHOP
ABVD
drug A + drug B

# 54. CLINICAL TRIAL PAGES

Route:

text

/trial/NCT...

Display:

text

status
phase
title
cancers
biomarkers
interventions
enrollment
sponsor
locations
eligibility
dates
outcomes
publications
results

# 55. TRIAL MATCH EXPLORER

Research use only.

Filters:

text

cancer
stage
gene
variant
biomarker
drug
phase
country
recruiting status
age
sex

Do not claim a patient is eligible solely from automated filtering.

Use:

Potentially relevant trials — verify full eligibility criteria with the study team.

# 56. PUBLICATION PAGES

Route:

text

/publication/{pmid}

Show:

text

title
authors
journal
date
abstract
DOI
publication type

linked cancers
linked genes
linked variants
linked drugs
linked trials

AI:

text

structured research summary

Only where legally permitted from available text.

# 57. RESEARCHER PAGES

Optional later stage:

text

/researcher/{id}

Metrics:

text

oncology publications
cancers studied
genes studied
clinical trials
citations
institutions

Avoid misleading researcher ranking based on raw citation count alone.

# 58. INSTITUTION PAGES

Examples:

text

MD Anderson
Memorial Sloan Kettering
Dana-Farber
Princess Margaret
Mayo Clinic
Gustave Roussy

Automatically derived from:

text

trials
authors
affiliations
publications

Rank institutions by transparent criteria, not prestige claims.

# 59. CANCER RESEARCH DASHBOARD

Route:

text

/research

Show:

text

publications/year
trials/year
new drugs/year
new targets/year
new biomarkers/year
research funding when reliable data exists

# 60. RESEARCH TRENDS

Detect emerging topics.

Examples:

text

KRAS G12D
T-cell engagers
ADC
ctDNA
personalized vaccines
radioligand therapy
CAR-T in solid tumors

Do not hardcode trends.

Calculate from publication/trial growth.

# 61. CANCER NEWS

Potential later connector layer:

text

FDA
NCI
NIH
major journals
cancer centers
regulators
clinical trial updates

Use original source and publication timestamp.

AI clustering:

text

multiple reports → one story cluster

# 62. AI — ASK CANCERINDEX

CancerIndex should contain a research assistant.

Route:

text

/ask

Example questions:

text

Which cancers have the highest mortality-to-incidence ratio?

What are the most frequent genomic alterations in LUAD?

Compare KRAS G12C in lung and colorectal cancer.

Which recruiting Phase III trials are testing therapies for pancreatic cancer?

Which rare cancers have the fewest active clinical trials relative to incidence?

What cancers have seen the largest improvement in survival over 20 years?

# 63. AI MUST QUERY STRUCTURED DATA FIRST

Never do:

text

question
↓
LLM general knowledge
↓
answer

Do:

text

question
↓
intent parser
↓
CancerIndex query plan
↓
SQL / graph / search
↓
source records
↓
LLM synthesis
↓
citations

# 64. AI ANSWER CONTRACT

Every answer returns:

json

{
  "answer": "...",
  "entities": [],
  "citations": [],
  "data_as_of": "...",
  "confidence": "...",
  "limitations": []
}

# 65. AI CITATIONS

Every important assertion must point to:

text

source
dataset
publication
or regulatory record

Click citation → source drawer.

Source drawer:

text

Source
Organization
Dataset
Version
Record
Retrieved
Raw value
Normalized value
Transformation

# 66. AI MODEL PROVIDER ABSTRACTION

Do not couple the application to one LLM.

Interface:

interface LLMProvider {
  generate()
  stream()
  structuredOutput()
  embed()
}

Support configurable providers.

Possible:

text

OpenAI
Anthropic
Google
xAI
local OpenAI-compatible endpoint

CancerIndex should operate without requiring AI for core database functionality.

# 67. EMBEDDINGS

Generate embeddings for:

text

cancer descriptions
publication abstracts
trial descriptions
drug mechanisms
biomarker descriptions
evidence summaries

Use pgvector initially.

Store embedding model/version.

Never mix embeddings generated by incompatible models in one vector column without model metadata.

# 68. SEARCH ENGINE

Global search should support:

text

cancers
subtypes
genes
variants
biomarkers
drugs
trials
publications
institutions
researchers

Examples:

text

panc
→ Pancreatic Cancer
→ Pancreatic Ductal Adenocarcinoma

G12C
→ KRAS G12C

HER2 low
→ HER2-low Breast Cancer
→ HER2-low biomarker concept

Implement:

text

exact
alias
prefix
fuzzy
semantic
cross-entity

# 69. ENTITY RECONCILIATION ENGINE

This is one of the hardest parts.

Example source names:

text

NSCLC
Non-small-cell lung cancer
Non Small Cell Lung Carcinoma
non-small cell carcinoma of lung

must resolve appropriately.

Use:

text

exact IDs
ontology mappings
canonical aliases
normalized strings
context
LLM only as fallback candidate generator
human review

Never merge two cancer entities solely because embeddings are similar.

# 70. ENTITY MERGE QUEUE

Admin system:

text

Possible duplicate

Cancer A
Cancer B

Evidence:
name similarity: 0.94
NCIt match: yes
OncoTree match: yes

[MERGE]
[KEEP SEPARATE]
[REVIEW]

Every merge must be auditable and reversible.

# 71. TEMPORAL DATA

Every observation is time-aware.

Never overwrite:

text

incidence 2022

with:

text

incidence 2024

Store both.

Core observation:

interface EpidemiologyObservation {
  cancerId: string

  geographyId: string

  year: number

  sex?: string
  ageGroup?: string

  metric:
    | "incidence_count"
    | "incidence_rate"
    | "as_incidence_rate"
    | "mortality_count"
    | "mortality_rate"
    | "as_mortality_rate"
    | "prevalence"

  value: number
  unit: string

  lowerCI?: number
  upperCI?: number

  sourceId: string
}

# 72. SURVIVAL DATA MODEL

Survival requires context.

Never store simply:

text

survival = 32%

Use:

interface SurvivalObservation {
  cancerId: string
  geographyId?: string

  stage?: string
  sex?: string
  ageGroup?: string
  diagnosisPeriod?: string

  survivalType:
    | "overall"
    | "relative"
    | "cause_specific"
    | "progression_free"
    | "disease_free"

  durationMonths: number
  probability?: number
  medianMonths?: number

  cohortSize?: number

  lowerCI?: number
  upperCI?: number

  sourceId: string
}

# 73. STAGING

CancerIndex must support multiple staging systems.

Do not pretend all cancers use identical Stage I–IV systems.

Model:

text

AJCC/TNM
FIGO
Ann Arbor
Lugano
Durie-Salmon
ISS/R-ISS
Binet
Rai
disease-specific systems

Licensing must be checked before reproducing proprietary staging definitions.

# 74. RISK FACTORS

Risk factor entities:

text

smoking
alcohol
UV
obesity
infection
occupational exposure
radiation
genetic predisposition
hormonal factors
age

Relations require evidence.

Example:

text

RiskFactor → ASSOCIATED_WITH → Cancer

Store:

text

relative risk
odds ratio
hazard ratio
population attributable fraction
confidence interval
study

Do not translate association into causality automatically.

# 75. HEREDITARY CANCER

Dedicated hereditary layer.

Entities:

text

germline gene
syndrome
variant
cancer risk
penetrance estimate

Examples conceptually:

text

BRCA1
BRCA2
Lynch syndrome
TP53/Li-Fraumeni
APC/FAP
VHL

Use trusted genetic sources.

Strong warning:

CancerIndex must not interpret a user's personal germline result as medical advice.

# 76. SCREENING

Store:

text

screening method
eligible population
cancer
country
organization
recommendation date
evidence level

Guidelines are geography and organization specific.

Never display a universal screening recommendation when there isn't one.

# 77. PREVENTION

Represent prevention evidence separately.

Potential:

text

vaccination
smoking cessation
UV protection
risk-reducing surgery
screening
infection prevention
occupational exposure reduction

# 78. PATHOLOGY

Future module.

Data:

text

histology
pathology images
stains
IHC
morphology
grade

Use public datasets with explicit image usage rights.

# 79. RADIOLOGY

Future module.

Potential public datasets:

text

TCIA and other properly licensed collections

Separate:

text

CT
MRI
PET
X-ray
ultrasound

Do not expose patient-identifiable DICOM metadata.

# 80. CANCER INDEX API

Public API:

text

api.cancerindex.io

Version:

text

/v1/

Possible endpoints:

text

GET /v1/cancers
GET /v1/cancers/{id}
GET /v1/cancers/{id}/statistics
GET /v1/cancers/{id}/survival
GET /v1/cancers/{id}/genes
GET /v1/cancers/{id}/variants
GET /v1/cancers/{id}/drugs
GET /v1/cancers/{id}/trials
GET /v1/cancers/{id}/publications

GET /v1/genes
GET /v1/genes/{symbol}

GET /v1/variants/{id}

GET /v1/drugs
GET /v1/drugs/{id}

GET /v1/trials/{nct}

GET /v1/rankings

# 81. GRAPHQL

Consider later:

text

/graphql

Example conceptual query:

graphql

cancer(id: "CI-CAN-...") {
  name
  ranking {
    incidence
    mortality
  }
  genes {
    gene {
      symbol
    }
    frequency
  }
  trials(status: RECRUITING) {
    nctId
    phase
  }
}

# 82. MCP SERVER

CancerIndex should eventually expose an MCP server.

Purpose:

Allow AI agents to query CancerIndex directly.

Tools:

text

search_cancers
get_cancer
rank_cancers
get_epidemiology
get_survival
get_gene
get_variant
get_drug
search_trials
search_publications
query_knowledge_graph

Read-only initially.

# 83. BULK DATA

Eventually provide permitted CancerIndex-derived datasets.

Formats:

text

CSV
JSON
JSONL
Parquet

Never redistribute restricted upstream source material.

# 84. ARCHITECTURE

Recommended:

text

Next.js
TypeScript
React

PostgreSQL
pgvector

ClickHouse
Redis

OpenSearch or Elasticsearch

MinIO/S3

Python ingestion workers
FastAPI scientific services

Temporal or durable job orchestration

Graph:

Start with relational edge tables.

Introduce Neo4j/Memgraph only if graph workloads justify operational complexity.

Do not add infrastructure merely because it sounds sophisticated.

# 85. POSTGRESQL

Primary store for:

text

canonical entities
relationships
users
API metadata
source registry
provenance
admin
ranking snapshots

# 86. CLICKHOUSE

Use for large analytical observations:

text

epidemiology
variant frequencies
publication timelines
trial timelines
ranking datasets
event logs

# 87. OPENSEARCH

Use for global full-text search.

Indexes:

text

cancers
genes
variants
drugs
trials
publications

# 88. OBJECT STORAGE

Use for:

text

raw connector snapshots
bulk source archives
large datasets
export files
images where licensed

# 89. REDIS

Use for:

text

hot cache
rate limiting
jobs
distributed locks
temporary AI streams

Do not use Redis as canonical storage.

# 90. INGESTION JOB SYSTEM

Every ingest must be restartable.

Use:

text

connector
↓
discovery
↓
fetch
↓
raw persist
↓
parse
↓
validate
↓
normalize
↓
reconcile
↓
canonical persist
↓
index
↓
derived metrics
↓
ranking recompute

# 91. IDEMPOTENCY

Running a connector twice must not duplicate data.

Use source-native IDs.

Example:

text

source = PubMed
source_record_id = 12345678

Unique constraint.

# 92. SOFT DELETION

Sources can retract or remove records.

Never immediately hard-delete.

Use:

text

active
deprecated
retracted
withdrawn
source_missing

Retain history.

# 93. PUBLICATION RETRACTIONS

CancerIndex must track retracted publications when data permit.

Relationships based only on retracted evidence should be flagged.

# 94. DATA FRESHNESS

Every page needs:

text

Data updated
Source updated
CancerIndex synchronized

Example:

text

Clinical trials updated: today
Genomics updated: Aug 2026
Global incidence dataset: 2024 estimate

# 95. SOURCE PAGE

Route:

text

/source/{source}

Display:

text

provider
description
dataset
access method
last sync
records
coverage
license status
data version
connector health

Transparency is a feature.

# 96. CHANGE HISTORY

Every entity should support change history.

Example:

text

Aug 19:
FDA approval added

Aug 14:
3 new trials

Aug 10:
GDC mutation frequency refreshed

Aug 03:
CancerIndex score changed 84.1 → 84.7

# 97. CANCER WATCH

Users can follow:

text

cancer
gene
variant
drug
trial

Notifications:

text

new clinical trial
trial status change
FDA approval
publication
new genomic finding
ranking change

# 98. USER ACCOUNTS

Account system:

text

email
password
email verification
password reset
session management

Optional:

text

Google
Apple
ORCID

Do not store sensitive health profiles by default.

# 99. RESEARCH WORKSPACE

Users can save:

text

cancers
genes
variants
drugs
trials
papers
queries
charts

Create collections:

text

"My KRAS research"
"Rare sarcomas"
"Pancreatic cancer trials"

# 100. COMPARISON ENGINE

Route:

text

/compare

Compare up to several cancers.

Example:

text

Pancreatic cancer
Glioblastoma
Lung adenocarcinoma
Melanoma

Compare:

text

incidence
mortality
survival
trends
genes
biomarkers
treatments
trials
research

# 101. VISUALIZATION SYSTEM

CancerIndex should be visually exceptional.

Visualizations:

text

ranked bar charts
time-series
survival curves
heatmaps
world maps
genomic frequency plots
co-occurrence matrices
oncoprints
trial timelines
drug approval timelines
knowledge graphs
Sankey charts
bubble plots

Charts need:

text

source
unit
population
time period
download

# 102. KNOWLEDGE GRAPH UI

Users can start from:

text

KRAS

and visually explore:

text

KRAS
├── G12C
│   ├── NSCLC
│   ├── colorectal cancer
│   ├── therapies
│   └── trials
├── G12D
├── G12V
└── pathways

Click nodes dynamically.

Avoid rendering thousands of nodes at once.

# 103. DESIGN SYSTEM

CancerIndex must NOT look like a generic SaaS dashboard.

Target aesthetic:

text

scientific
editorial
premium
institutional
modern
high-information-density
trustworthy

Think:

text

Nature
Bloomberg
Our World in Data
high-end scientific visualization

Avoid:

text

giant gradients everywhere
dozens of rounded cards
cartoon health icons
generic AI sparkle graphics

# 104. COLOR

Base:

text

off-white / white
deep charcoal
muted scientific neutrals

Cancer-specific color coding can exist but must not compromise accessibility.

Never rely on color alone.

# 105. HOME PAGE

Hero:

text

CancerIndex

The global index of cancer.

Explore every cancer.
Rank global burden.
Follow treatments.
Search genomics.
Track clinical research.

Global search immediately visible.

Below:

text

Cancer burden today
Global rankings
Fastest-rising cancers
Highest mortality
Poorest survival
Most active research
Largest treatment gaps
Rare cancers
Latest oncology approvals
New clinical trials

# 106. LIVE DATA TICKER

Tasteful top-line statistics:

text

Cancer entities indexed
Genes indexed
Variants indexed
Clinical trials
Publications
Drug indications
Countries
Sources

Values must come from database counts.

# 107. "ALL CANCERS" EXPLORER

Route:

text

/cancers

Do not show only a few dozen cards.

Build a powerful explorer.

Filters:

text

anatomical system
histology
solid/hematologic
adult/pediatric
rare/common
molecular subtype
incidence
mortality
survival
research level
trial count
treatment availability

Support thousands of entities.

# 108. TAXONOMY EXPLORER

Tree/browser:

text

Blood
Breast
CNS
Digestive
Endocrine
Gynecologic
Head & Neck
Lung
Skin
Soft tissue
Urinary
...

Also:

text

histology view
molecular view
WHO view
NCI view

# 109. RARE CANCER DISCOVERY

Feature:

Random Rare Cancer

Useful for discovery.

Shows:

text

what it is
annual incidence
known cases/data
research count
trials
genes
treatments

# 110. DATA QUALITY ENGINE

Every normalized record runs validation.

Examples:

text

incidence >= 0
deaths >= 0
survival between 0 and 1
year reasonable
country valid
gene symbol canonical
variant syntax valid where possible
trial phase enum recognized

# 111. CROSS-SOURCE CONFLICTS

Sources will disagree.

Never silently average everything.

Store each observation.

Example:

text

Source A:
5-year survival = 31%

Source B:
5-year survival = 36%

CancerIndex may compute a harmonized estimate only with a documented method.

Show:

text

Why estimates differ

# 112. EVIDENCE ENGINE

Create CancerIndex evidence hierarchy.

Possible dimensions:

text

study design
sample size
replication
publication quality
clinical relevance
regulatory validation
expert curation
recency

Do NOT reduce all scientific truth to one score.

Use multi-dimensional evidence badges.

# 113. CLINICAL EVIDENCE LABELS

Example:

text

REGULATORY APPROVED
GUIDELINE SUPPORTED
PHASE III
PHASE II
PHASE I
RETROSPECTIVE CLINICAL
CASE SERIES
CASE REPORT
PRECLINICAL
COMPUTATIONAL

# 114. STATISTICAL INTEGRITY

Never calculate survival by dividing unrelated values.

Never compare crude incidence with age-standardized incidence without labeling.

Never mix:

text

incidence
prevalence
mortality
case fatality
overall survival
relative survival

Every metric needs precise definition.

# 115. CANCER "DEADLINESS"

Avoid an undefined “deadliest cancer” metric.

The interface should let users choose:

text

Most deaths
Highest mortality rate
Highest mortality/incidence ratio
Lowest 5-year survival
Highest CancerIndex Impact

This distinction is important.

# 116. AGE STANDARDIZATION

For international comparison prioritize appropriately standardized rates.

Store standard population used if source provides it.

Do not present crude rates as directly comparable across countries with radically different age structures.

# 117. GEOGRAPHIC NORMALIZATION

Canonical geography entity:

text

ISO country
ISO subdivision
region
continent
WHO region
IARC region if appropriate

Keep source geography separately.

# 118. CURRENCY

Not central initially.

If later adding:

text

drug cost
economic burden
research funding

always store:

text

currency
year
country
nominal/real
source

# 119. RESEARCH FUNDING

Future innovation:

Connect:

text

NIH RePORTER
CIHR
EU grants
UKRI
other public grants

Then create:

text

funding by cancer
funding per annual death
funding per incident case

Potential:

Funding Gap Index

But methodology must be transparent.

# 120. NIH REPORTER CONNECTOR

Potential high-priority future connector.

Map grant:

text

project
principal investigator
institution
funding amount
year
cancer
gene
topic
publication

# 121. PATENTS

Potential future module.

Sources:

text

USPTO
EPO
Google Patents metadata where appropriate

Use to map therapeutic innovation.

Not required for MVP.

# 122. COMPANY PIPELINE

Potential future module:

text

biotech
pharma
drug candidate
target
phase
indication

Sources must be verified.

Public company claims should not override trial registries/regulatory sources.

# 123. DRUG DEVELOPMENT PIPELINE

Statuses:

text

preclinical
Phase I
Phase I/II
Phase II
Phase II/III
Phase III
submitted
approved
discontinued
withdrawn

Status may be disease-specific.

# 124. FAILURE DATABASE

Extremely valuable.

Track oncology programs that fail or stop.

Sources:

text

ClinicalTrials.gov status
regulatory documents
company releases
publications

Create:

text

Drug → Cancer → Development outcome

Avoid inferring failure solely from stale trial status.

# 125. RESISTANCE DATABASE

Track mechanisms:

text

primary resistance
acquired resistance

Relations:

text

Variant → confers resistance → Drug
Pathway → resistance mechanism → Drug

Evidence-backed only.

# 126. METASTASIS DATABASE

Map:

text

primary cancer
→ common metastatic locations

Store frequency only with cohort context.

Do not generalize from small cohorts.

# 127. MULTI-OMICS

Future coverage:

text

genome
transcriptome
epigenome
proteome
metabolome
single-cell
spatial

GDC and other public research repositories can seed this layer.

# 128. SINGLE-CELL CANCER DATA

Future connector candidates:

text

CELLxGENE
Human Tumor Atlas Network resources
public scRNA-seq studies

Must support:

text

study
sample
cell type
cancer
gene expression

Large matrices should not live in PostgreSQL.

# 129. HUMAN TUMOR ATLAS

Potential high-value research connector where datasets and terms permit.

# 130. PROTEOMICS

CPTAC-related data should connect:

text

cancer
protein
phosphoprotein
genomic alteration
clinical outcome

# 131. MICROBIOME / CANCER

Future experimental research category.

Clearly label exploratory evidence.

# 132. ENVIRONMENTAL EXPOSURES

Possible integration:

text

IARC carcinogen classifications
occupational exposure datasets
air pollution data

Do not infer personal cancer risk.

# 133. CARCINOGEN ENTITY

Create:

text

Carcinogen

Relations:

text

Carcinogen → evidence of association → Cancer

Store classification authority.

# 134. INFECTIOUS ONCOLOGY

Entities:

text

HPV
HBV
HCV
EBV
H. pylori
HHV-8
etc.

Map to cancer evidence.

# 135. CANCER PREVALENCE FORECASTS

Can later model forecasts.

But clearly label:

text

OBSERVED
ESTIMATED
PROJECTED

Never make projections visually indistinguishable from observed registry data.

# 136. FORECAST ENGINE

Potential:

text

incidence forecast
mortality forecast
trial activity forecast
research momentum

Version each model.

Display uncertainty intervals.

# 137. DATA SNAPSHOTS

Monthly immutable snapshots:

text

CancerIndex 2026-09
CancerIndex 2026-10

Allows reproducibility.

# 138. DATA RELEASES

Publish:

text

CancerIndex Data Release 1

With:

text

new sources
updated sources
entity changes
ranking methodology changes
known limitations

# 139. API VERSIONING

Never break existing clients casually.

Use:

text

/v1
/v2

Data release version separate from API version.

# 140. ADMIN CONTROL CENTER

Route:

text

/admin

Sections:

text

Overview
Connectors
Ingestion
Entities
Reconciliation
Rankings
Evidence
Sources
Licensing
Users
AI
Jobs
Search
System

# 141. CONNECTOR ADMIN

For every connector:

text

Run now
Pause
Resume
Backfill
Incremental sync
Dry run
View raw records
View parser
View errors
View schema changes

# 142. LICENSE REGISTRY

Create internal table:

text

source_license

Fields:

text

source
license
commercial use
redistribution
derivative works
attribution requirements
API terms
review date
notes
approved for production

No new connector becomes public until licensing status is reviewed.

# 143. SOURCE PRIORITY

When sources conflict, do not blindly implement a global precedence.

Precedence depends on field.

Examples:

text

Gene official symbol → HGNC
Clinical trial registration → ClinicalTrials.gov
US regulatory status → FDA
global burden estimate → selected IARC dataset
US registry survival → SEER
variant clinical curation → retain multiple curated sources

# 144. ENTITY LINEAGE

Every canonical field may need:

text

derivedFromSourceRecordIds

Example:

text

canonical name:
"Lung Adenocarcinoma"

supported by:
NCIt
SEER
OncoTree
GDC

# 145. CACHING

Cache expensive:

text

rankings
global aggregates
country dashboards
AI answers
knowledge graph layouts

Invalidation should be event-driven when possible.

# 146. PERFORMANCE

Targets:

text

homepage < 2 sec meaningful render
search suggestions < 200 ms cached target
common API reads < 300 ms target
ranking query < 500 ms target

Do not block page rendering on AI generation.

# 147. SEO

CancerIndex has enormous programmatic SEO potential.

Pages:

text

/cancer/{cancer}
/cancer/{cancer}/survival
/cancer/{cancer}/statistics
/cancer/{cancer}/genes
/cancer/{cancer}/trials

/gene/{gene}
/drug/{drug}
/variant/{variant}
/country/{country}

Every generated page must contain substantive sourced information.

No thin spam pages.

# 148. STRUCTURED DATA

Use relevant Schema.org structured metadata where appropriate:

text

MedicalCondition
Drug
Dataset
ScholarlyArticle
Organization

Verify current specifications before implementation.

# 149. ACCESSIBILITY

WCAG-minded implementation.

Requirements:

text

keyboard navigation
screen reader labels
contrast
chart text alternatives
color-independent state
reduced motion

# 150. INTERNATIONALIZATION

English first.

Architecture must support:

text

French
Spanish
German
Portuguese
Japanese
etc.

Canonical scientific entity remains language-independent.

Translations are attributes.

# 151. LOCALIZATION

Important distinction:

text

language != geography

French Canadian user can view Canadian data.

French user can view France data.

# 152. TESTING REQUIREMENTS

Claude must create:

text

unit tests
integration tests
connector fixture tests
schema tests
ranking tests
reconciliation tests
API contract tests
UI tests
end-to-end tests

# 153. CONNECTOR FIXTURES

Never run all connector tests against production APIs.

Store sanitized fixtures.

Test:

text

normal response
empty response
pagination
rate limit
server error
schema change
malformed record
duplicate record

# 154. SCIENTIFIC REGRESSION TESTS

Create invariant tests.

Examples:

text

survival >= 0
survival <= 1

incidence >= 0
mortality >= 0

lowerCI <= estimate
estimate <= upperCI

# 155. RANKING TESTS

Given fixed fixture inputs, ranking output must be deterministic.

Snapshot:

text

ranking methodology version
input snapshot
output

# 156. RECONCILIATION TESTS

Known aliases:

text

NSCLC
non-small cell lung cancer

should behave correctly.

Known distinct diseases must never merge accidentally.

Build a large gold-standard mapping fixture.

# 157. AI EVALUATION SUITE

Create fixed questions:

text

What is PDAC?
Compare LUAD and SCLC.
What cancers are associated with BRAF V600E?
What recruiting trials exist for X?

Evaluate:

text

citation correctness
entity correctness
numerical correctness
unsupported statements
source freshness

# 158. HALLUCINATION DEFENSE

AI must say:

text

CancerIndex does not currently have sufficient sourced data to answer this.

instead of guessing.

# 159. NO SILENT FALLBACK TO MODEL KNOWLEDGE

If CancerIndex retrieval finds no evidence:

do not silently answer using model memory.

Model knowledge may only be used as clearly labeled supplementary context if product policy explicitly allows it.

Default:

database-grounded answers only.

# 160. SECURITY

Protect:

text

API keys
database credentials
connector credentials
LLM keys
admin routes
worker endpoints

Use environment variables/secrets.

Never commit secrets.

# 161. USER PRIVACY

CancerIndex does not require personal health data to be useful.

Avoid collecting:

text

diagnosis
genetic results
treatment history
medical documents

unless a future clearly separated healthcare feature has proper privacy architecture.

# 162. ANALYTICS PRIVACY

Do not log sensitive search queries unnecessarily.

Provide privacy-preserving analytics.

# 163. AUTHORIZATION

Roles:

text

USER
RESEARCHER
CURATOR
ADMIN
SUPERADMIN

Curators can edit scientific metadata.

Every curator action is logged.

# 164. CURATION PLATFORM

Allow expert curators to:

text

merge entities
split entities
add aliases
correct mappings
flag evidence
resolve conflicts
add citations
approve AI extractions

# 165. AI CURATION QUEUE

LLM pipeline may discover candidate:

text

publication → gene
publication → cancer
publication → drug

Confidence:

text

>0.98 auto-accept only for low-risk deterministic mappings
0.80–0.98 review
<0.80 reject/manual

Thresholds must be evaluated empirically.

Do not use these exact numbers blindly.

# 166. EXTRACTION ENGINE

For publications:

text

abstract
↓
NER
↓
ontology mapping
↓
relationship extraction
↓
confidence
↓
validation
↓
graph edge

Prefer deterministic identifiers when present.

# 167. PDF INGESTION

Some sources publish PDFs.

Pipeline:

text

PDF
↓
native text extraction
↓
layout understanding
↓
table extraction
↓
OCR only when necessary
↓
structured JSON
↓
validation

Store page-level citations.

# 168. TABLE EXTRACTION

AI-extracted numeric tables must pass validations.

Do not accept:

text

OCR number → production statistic

without confidence checks.

# 169. DATA DIFFS

On source refresh:

text

previous snapshot
vs
current snapshot

Generate:

text

new records
removed records
changed values
new enums

Store diff.

# 170. ALERTS

Internal alerts:

text

connector failure
stale source
ranking anomaly
mass entity deletion
schema drift
unexpected record drop
license review due

# 171. ANOMALY DETECTION

Example:

text

GDC records yesterday: 2,430,000
today: 214

Do NOT publish a destructive update.

Pause ingest and alert.

# 172. BACKUPS

Automated:

text

PostgreSQL backups
object storage versioning
search reindex ability
configuration backups

Test restore process.

# 173. INFRASTRUCTURE / CLUSTER DEPLOYMENT

CancerIndex should be containerized.

Use:

text

Docker

Services should be independently deployable.

Suggested:

text

web
api
worker-ingest
worker-ai
worker-ranking
postgres
redis
clickhouse
opensearch
minio

If deploying to an existing cluster, keep configuration portable.

# 174. DOMAIN

Production:

text

www.cancerindex.io
cancerindex.io
api.cancerindex.io

Optional:

text

status.cancerindex.io
docs.cancerindex.io

# 175. OBSERVABILITY

Use:

text

structured logs
metrics
distributed traces
error monitoring
job monitoring

Every request gets correlation ID.

Every ingest gets run ID.

# 176. INGEST RUN ID

Example:

text

ING-CLINICALTRIALS-20260908-000019

Every created/updated record can reference ingest run.

# 177. CANCERINDEX SCORE VERSIONING

Example:

text

CI-IMPACT-v1.0
CI-RESEARCH-GAP-v1.0
CI-TRIAL-GAP-v1.0
CI-PROGRESS-v1.0
CI-MOMENTUM-v1.0

Never silently change formula.

# 178. METHODOLOGY PAGE

Route:

text

/methodology

Explain:

text

sources
normalization
ranking
age standardization
survival
research metrics
trial metrics
composite indexes
uncertainty
limitations

The methodology page should be exceptionally detailed.

# 179. PUBLIC REPRODUCIBILITY

For each ranking:

button:

text

Methodology

Display formula.

Potential later:

text

Download input dataset
Download ranking dataset

where licensing permits.

# 180. DATA SOURCE BADGES

On values:

text

IARC
SEER
GDC
FDA
ClinicalTrials.gov
CIViC

Hover → metadata.

# 181. CITATION UX

Citation:

text

[1]

click opens side panel rather than sending user away immediately.

Panel:

text

Source
Original title
Dataset
Record
Date
Method
Open source

# 182. CONFIDENCE UX

Examples:

text

High confidence
Moderate confidence
Limited evidence
Sparse data

Do not hide uncertainty.

# 183. "WHY THIS RANK?"

Every CancerIndex rank gets:

text

Why #4?

Click:

text

Mortality burden     +23.3
Lethality            +18.9
Treatment gap        +14.1
Research gap          +9.7
Trend                 +8.2
...

# 184. HISTORICAL RANKS

Store ranking snapshots.

Graph:

text

Important: methodology consistency must be maintained or explicitly annotated.

# 185. USER-CUSTOM RANKINGS

Advanced feature.

Allow users to set weights:

text

Mortality      40%
Incidence      20%
Survival       20%
Research gap   20%

Generate:

text

Custom Cancer Index

Do not overwrite official CancerIndex ranking.

# 186. DATA EXPLORER

Advanced SQL-like analytics UI without exposing raw SQL.

Dimensions:

text

cancer
country
year
sex
age

Measures:

text

cases
deaths
ASIR
ASMR
survival
trials
publications

# 187. CHART BUILDER

Users choose:

text

X = year
Y = mortality
Group = cancer
Country = Canada

Generate shareable chart.

# 188. EMBEDDABLE CHARTS

Future:

text

embed.cancerindex.io/chart/{id}

Attribution required.

# 189. SHAREABLE RESEARCH CARDS

Generate beautiful cards:

text

Pancreatic Cancer
#3 Lethality
#7 Global Mortality
5-year survival ...

Always include date/source.

# 190. PUBLIC DATA API KEYS

API account:

text

free
research
pro
institutional

Do not monetize third-party data contrary to source licenses.

Value can come from CancerIndex aggregation, normalization and infrastructure where permitted.

# 191. RATE LIMITING

API:

text

anonymous
authenticated
paid/institutional

Return standard rate-limit headers.

# 192. DEVELOPER PORTAL

Route:

text

/developers

Include:

text

API docs
OpenAPI
authentication
examples
schema
changelog
status

# 193. DATA DOWNLOAD CENTER

Route:

text

/data

List datasets CancerIndex is legally allowed to redistribute.

# 194. SOURCE LICENSE AUTOMATION

Crawler can periodically detect source terms changes.

But:

AI cannot make final legal determination.

Flag for human review.

# 195. RELEASE BOT

Weekly report:

text

CancerIndex Weekly Data Report

+43 cancers/subtypes
+12,328 publications
+184 trials
+2 FDA approvals
+91,224 variant relations

3 connector warnings

# 196. FRONT PAGE DAILY UPDATE

Show:

text

Updated X minutes ago

only for sources actually refreshed that recently.

Do not imply global dataset freshness because ClinicalTrials updated today.

# 197. CANCERINDEX DAILY

Potential editorial product:

CancerIndex Daily

Automatically identify:

text

important approvals
practice-changing trials
major publications
new trial openings
large dataset releases

AI summarizes with citations.

# 198. TREND DETECTOR

Calculate abnormal increases in:

text

publication volume
trial creation
drug development
gene mentions

Potential:

text

"KRAS G12D research activity +74% YoY"

Only publish after methodology validation.

# 199. TOPIC GRAPH

Search:

text

ADC

Graph:

text

ADC
→ HER2
→ TROP2
→ HER3
→ cancers
→ drugs
→ trials
→ publications

# 200. RELATIONSHIP TEMPORALITY

Relationships evolve.

Store:

text

first evidence
most recent evidence
current status

A therapy-cancer relationship may change from:

text

experimental
→ Phase III
→ approved

# 201. REAL-WORLD EVIDENCE

Future module.

Possible sources:

text

public registries
regulatory RWE reports
published cohorts

Do not attempt to ingest private medical records casually.

# 202. PATIENT-REPORTED OUTCOMES

When published:

text

quality of life
symptom burden
functional outcomes

Store separately from survival.

# 203. ENDPOINT ENTITY

Clinical endpoints should become structured concepts:

text

OS
PFS
DFS
EFS
ORR
DOR
pCR
MRD
QoL

Map trial results.

# 204. TRIAL RESULTS EXTRACTION

When results are available:

capture structured registry results first.

Publication-derived results must cite paper.

Store:

text

endpoint
population
arm
estimate
CI
p-value
follow-up

# 205. TREATMENT EFFECT MODEL

Do not store:

text

Drug X improves survival by 40%

Store:

text

endpoint
effect measure
HR/RR/OR
estimate
CI
population
comparator
trial
follow-up

# 206. CROSS-CANCER ANALYSIS

Enable questions:

text

Which cancers share KRAS mutations?

Which cancers have HER2 amplification?

Which cancers respond to tissue-agnostic therapies?

Which cancers share immune biomarkers?

# 207. TUMOR-AGNOSTIC INDICATIONS

Support cancer-agnostic drug approvals.

Drug indication entity may reference:

text

biomarker
without single cancer restriction

Do not force every approval to one cancer ID.

# 208. CANCER OF UNKNOWN PRIMARY

Include CUP properly.

Do not force primary anatomical site where unknown.

# 209. BENIGN / BORDERLINE TUMORS

CancerIndex may index clinically relevant nonmalignant/borderline tumors if useful for taxonomy.

They must be clearly marked:

text

malignant = false

Never count them in cancer rankings unless methodology explicitly includes them.

# 210. SKIN CANCER COUNTING

Be careful with:

text

non-melanoma skin cancers

Some global datasets treat them differently.

Ranking engine must preserve inclusion/exclusion rules.

# 211. HEMATOLOGIC MALIGNANCIES

Do not model solely by anatomical organ.

Dedicated structure for:

text

leukemia
lymphoma
myeloma
myelodysplastic neoplasms
myeloproliferative neoplasms

# 212. SARCOMAS

Build fine-grained taxonomy.

Examples categories:

text

soft tissue
bone
GIST
leiomyosarcoma
liposarcoma
angiosarcoma
synovial sarcoma
Ewing sarcoma
osteosarcoma

Do not group all rare sarcomas when subtype data exists.

# 213. BRAIN/CNS TUMORS

Molecular classification is essential.

Model modern molecular subtypes.

Taxonomies change over time.

Store classification version.

# 214. BREAST CANCER

Support:

text

histology
ER
PR
HER2
HER2-low where applicable
triple negative
molecular subtypes
germline context

Do not collapse all breast cancers.

# 215. LUNG CANCER

Support:

text

SCLC
NSCLC
adenocarcinoma
squamous
large cell
molecular alterations

# 216. COLORECTAL CANCER

Support:

text

colon
rectal
left/right sided context where evidence requires
MSI
RAS
BRAF
HER2

# 217. PRECISION TAXONOMY

CancerIndex needs overlapping labels.

One patient cohort may conceptually be:

text

lung
adenocarcinoma
metastatic
EGFR-mutated
exon 19 deletion

Do not create a unique canonical cancer entity for every arbitrary combination.

Use attributes/biomarker cohort definitions appropriately.

# 218. COHORT ENTITY

Create:

CohortDefinition

Example:

text

Metastatic EGFR exon 19 deletion lung adenocarcinoma

This is not necessarily a globally recognized cancer taxonomy node.

# 219. ONTOLOGY VERSIONING

Taxonomies evolve.

Store:

text

ontology
version
concept
valid_from
valid_to

Never lose historical mappings.

# 220. CROSSWALK TABLES

Build:

text

NCIt ↔ ICD-O
NCIt ↔ ICD-10
NCIt ↔ OncoTree
NCIt ↔ Disease Ontology
NCIt ↔ MONDO
SEER ↔ canonical CancerIndex

Mappings may be:

text

exact
broader
narrower
related
ambiguous

# 221. MATCH CONFIDENCE

Entity mapping:

text

EXACT_IDENTIFIER
CURATED_EXACT
ONTOLOGY_EXACT
CURATED_BROADER
CURATED_NARROWER
ALIAS
PROBABILISTIC
UNRESOLVED

# 222. UNRESOLVED ENTITY QUEUE

Never discard unknown disease labels.

Store:

text

source text
source ID
context
count

Admin can map later.

# 223. DATA DISCOVERY AGENT

Build an AI-assisted internal agent that searches for:

text

new official APIs
new dataset releases
schema changes
new registries
new cancer ontologies

It produces proposals.

It cannot automatically onboard sources into production without compliance review.

# 224. CONNECTOR DOCUMENTATION REQUIREMENT

Before Claude implements ANY connector:

locate current official documentation;
verify API/bulk mechanism;
verify authentication;
inspect pagination;
inspect rate limits;
inspect license/terms;
inspect update schedule;
inspect identifiers;
save source schema;
create tests.

Do not implement an API from memory.

# 225. CURRENT-DOC REQUIREMENT

Because CancerIndex depends on external systems:

Claude MUST always verify current documentation before coding an integration.

Do not trust:

text

old blog posts
random GitHub examples
Stack Overflow
cached knowledge

Prefer:

text

official documentation
official repositories
official OpenAPI specs
official release notes

# 226. CONNECTOR SOURCE TEST

Before production:

text

curl/API smoke test
↓
small fixture
↓
parser
↓
normalization
↓
reconciliation
↓
integration test
↓
full sync

# 227. HUGE IMPORT SAFETY

Never begin a million-record import before proving the pipeline on:

text

10
100
1,000

records.

# 228. BULK-FIRST STRATEGY

For massive datasets:

prefer bulk downloads over millions of API calls when terms and official access support it.

# 229. RATE LIMIT RESPECT

Implement:

text

token bucket
exponential backoff
Retry-After
jitter
max concurrency

Source-specific.

# 230. CHECKSUMS

Bulk file:

text

SHA-256

Store:

text

source URL
timestamp
checksum
size

# 231. ETL LANGUAGE

Use Python heavily for scientific ETL.

TypeScript can orchestrate web/application systems.

Do not force complex bioinformatics normalization into TypeScript if mature Python packages are appropriate.

# 232. DATAFRAMES

For large ETL:

consider:

text

Polars
PyArrow
DuckDB

instead of blindly using pandas for everything.

# 233. PARQUET

Use Parquet for large analytical snapshots.

Partition by sensible dimensions.

Example:

text

source
year
entity type

# 234. BIOINFORMATICS LIBRARIES

Before choosing packages:

verify active maintenance/current documentation.

Potential functionality:

text

HGVS normalization
VCF parsing
genomic liftover
sequence handling

Never implement complex genomics standards from scratch unless necessary.

# 235. GENOME BUILD

Canonical support:

text

GRCh37
GRCh38

Where available.

Never silently convert coordinates.

Store original + normalized.

# 236. LIFTOVER

If performing liftover:

text

original assembly
original coordinate
target assembly
converted coordinate
tool/version
status

# 237. VARIANT NORMALIZATION

Store:

text

genomic HGVS
coding HGVS
protein HGVS
gene
transcript
assembly
dbSNP
ClinVar ID
CIViC ID

Not every variant will have all identifiers.

# 238. FUSIONS

Dedicated structure:

text

5' gene
3' gene
breakpoint
fusion name
orientation

Do not model only as free text.

# 239. COPY NUMBER

Model:

text

amplification
gain
loss
deep deletion

Keep source-specific thresholds.

# 240. EXPRESSION

Keep units/platform.

Never compare raw expression values from incompatible platforms directly.

# 241. BIOMARKER THRESHOLDS

Example PD-L1.

Store:

text

assay
clone
scoring system
threshold
cancer
indication

Do not reduce to positive/negative globally.

# 242. TMB

Store:

text

assay
unit
threshold
panel
cancer

# 243. MSI

Map:

text

MSI-H
MSS
MSI-L
dMMR
pMMR

but preserve differences.

# 244. EVIDENCE CROSS-CANCER CONTEXT

A variant may be:

text

predictive in cancer A
prognostic in cancer B
unknown in cancer C

Relationship context is mandatory.

# 245. RANKING DATA ELIGIBILITY

For a cancer to enter a ranking:

define explicit inclusion rules.

Example survival ranking:

text

minimum cohort size
accepted survival type
accepted diagnosis period
geography
minimum source quality

Do not rank sparse estimates unfairly.

# 246. PARENT VS SUBTYPE RANKING

Avoid double-counting.

If global incidence gives:

text

Lung Cancer = 2.4M

and subtype estimates separately:

text

LUAD
SCC

do not sum all three.

Rank scope must define entity level.

Allow:

text

Top-level cancer ranking
Histology ranking
Subtype ranking
Rare entity ranking

# 247. GLOBAL MASTER RANKING

Default broad global ranking should use mutually exclusive or carefully defined top-level cancer categories.

Fine-grained ranking is separate.

# 248. CANCERINDEX COVERAGE COUNT

Homepage may say:

text

4,812 cancer entities indexed

only if entity model genuinely contains them.

Do not market every alias as a separate cancer.

# 249. DUPLICATE CONTROL

Alias count ≠ cancer count.

Subtype count ≠ top-level cancer count.

Be explicit.

# 250. METRIC CATALOG

Create first-class:

text

MetricDefinition

Fields:

text

id
name
description
formula
unit
higherIsWorse
aggregation
validDimensions
sources
methodologyVersion

# 251. FORMULA ENGINE

Derived metrics should not live as random code functions.

Create versioned formulas.

Example:

yaml

id: CI-METRIC-MIR
name: Mortality-to-Incidence Ratio
formula: mortality_count / incidence_count
version: 1.0

# 252. DATA LINEAGE GRAPH

Every ranked score should be traceable:

text

CancerIndex score
↓
component
↓
normalized metric
↓
source observation
↓
raw source record

# 253. ADMIN "TRACE VALUE"

Admin button:

text

TRACE

For any number.

Shows full lineage.

This will save massive debugging time.

# 254. CANCERINDEX LABS

Experimental section:

text

/labs

For:

text

forecasting
experimental indexes
novel network analysis
AI research tools

Clearly separate experimental metrics from main product.

# 255. NETWORK CENTRALITY

Interesting research feature:

rank genes by:

text

number of cancers
number of actionable variants
number of approved drugs
number of trials
network centrality

Do not imply biological importance solely from graph centrality.

# 256. DRUG TARGET LANDSCAPE

Visual:

text

targets × cancers

Heatmap:

text

approved
clinical
preclinical

# 257. ONCOLOGY PIPELINE MAP

Interactive:

text

Cancer
→ Target
→ Drug
→ Phase
→ Company

# 258. BIOMARKER LANDSCAPE

Interactive matrix:

text

Cancer × Biomarker

Color:

text

frequency
clinical actionability

Different toggles.

# 259. CANCER GENOMIC LANDSCAPE

Cancer page:

text

Top mutated genes
CNAs
fusions
pathways

Allow study selection.

Do not merge frequencies from incompatible cohorts without method.

# 260. COHORT SELECTOR

Example:

text

TCGA
MSK cohort
CPTAC
study X

User can switch data source.

# 261. FREQUENCY DENOMINATORS

Every genomic frequency must include denominator.

Example:

text

KRAS mutation: 31.4%
214 / 681 profiled samples

Never show 31.4% without cohort context.

# 262. MISSINGNESS

Genomic studies frequently have different profiling coverage.

Store:

text

tested
not tested
unknown

Do not assume missing = wild type.

# 263. SURVIVAL CURVES

Where permissible/raw aggregate data allow:

Kaplan-Meier visualization.

Display:

text

n at risk
CI
censoring
cohort
endpoint

Do not fabricate curves from summary survival percentages.

# 264. INCIDENCE TREND CHART

Use:

text

annual estimates
ASIR
confidence intervals when available

# 265. GLOBAL BURDEN BUBBLE CHART

Axes:

text

X = incidence
Y = mortality/incidence
bubble = deaths

Great discovery visualization.

# 266. RESEARCH GAP QUADRANT

Axes:

text

X = disease burden
Y = research activity

Quadrants:

text

high burden / high research
high burden / low research
low burden / high research
low burden / low research

# 267. CLINICAL TRIAL MAP

World map of recruiting trial sites.

Filters:

text

cancer
drug
phase
biomarker

# 268. FACILITY ENTITY

Normalize trial locations.

Challenge:

text

same hospital with slightly different names

Use:

text

name
address
city
country
geocode
organization ID

# 269. ORGANIZATION RESOLUTION

Potential IDs:

text

ROR
GRID historical mappings
OpenAlex institution

Use ROR where appropriate.

# 270. RESEARCHER RESOLUTION

Use ORCID when explicitly linked.

Never assume identical author names are same person.

# 271. PUBLICATION ENTITY EXTRACTION

Use pipeline:

text

dictionary match
ontology mapping
NER
LLM structured extraction
cross validation

Store extraction method.

# 272. EVIDENCE SENTENCE

When permitted, keep sentence-level evidence reference.

Do not violate publication copyright.

Store small extracted evidence snippets within legal limits where appropriate, otherwise store location/reference only.

# 273. FULL-TEXT RIGHTS

Open access ≠ automatically unrestricted redistribution in every context.

Track article license.

# 274. AUTOMATED CITATION

Generated text should prefer primary source where possible.

Example:

trial result:

prefer:

text

peer-reviewed trial publication

plus registry.

Regulatory claim:

prefer regulator.

# 275. SOURCE TRUST DOES NOT MEAN UNIVERSAL AUTHORITY

A source can be excellent for one question and inappropriate for another.

Example:

text

FDA → US approval
SEER → US registry statistics
IARC → global estimates
HGNC → gene symbols

# 276. NO SOURCE MONOCULTURE

Important cancer facts should ideally cross-reference multiple sources where appropriate.

# 277. DATA FUSION

Do not create a fused value unless scientifically justified.

Often the right UX is:

text

SEER estimate
IARC estimate
Canadian estimate

side by side.

# 278. DOCUMENT EVERYTHING

Repository:

text

/docs
  architecture.md
  data-model.md
  ranking-methodology.md
  source-policy.md
  connectors.md
  reconciliation.md
  evidence.md
  ai.md
  security.md

# 279. ADRs

Use Architecture Decision Records.

Example:

text

ADR-001 PostgreSQL as canonical database
ADR-002 pgvector
ADR-003 source-native raw retention
ADR-004 CancerIndex identifiers

# 280. CLAUDE WORKFLOW

Before editing code:

text

1. inspect repository
2. inspect CLAUDE.md
3. inspect current architecture
4. inspect existing tests
5. inspect database schema
6. inspect connector framework
7. verify current external documentation
8. create plan
9. implement
10. test
11. verify
12. document

# 281. DO NOT FAKE FEATURES

Never create UI with hardcoded fake metrics merely to make screenshots look complete.

If backend data doesn't exist:

show:

text

Data not yet available

or use explicitly labeled fixtures only in tests/dev.

# 282. NO MOCK DATA IN PRODUCTION

Absolutely no hidden mock values.

# 283. SOURCE AVAILABILITY

If a source connector fails:

CancerIndex continues using last known valid snapshot.

Display freshness.

Do not replace missing real data with AI guesses.

# 284. DATABASE MIGRATIONS

Every schema change:

text

migration
rollback strategy
test

Never manually mutate production schema without migration.

# 285. QUERY OPTIMIZATION

Use indexes on:

text

canonical IDs
external IDs
slugs
gene symbol
NCT ID
PMID
DOI
year
geography
metric

# 286. PARTITIONING

For huge observations:

partition or use ClickHouse.

Do not create billions of rows in poorly indexed PostgreSQL tables.

# 287. ENTITY COUNTERS

Precompute:

text

trial count
publication count
gene count
drug count

where necessary.

But source them from canonical relations and refresh deterministically.

# 288. RANKING MATERIALIZATION

Rankings should be precomputed per common scope.

Do not run huge window functions for every homepage request.

# 289. INCREMENTAL RANK REBUILD

When ClinicalTrials changes:

recalculate trial-dependent ranks only.

Do not recompute global genomic rankings unnecessarily.

# 290. EVENT BUS

Useful events:

text

CancerUpdated
TrialUpdated
DrugApprovalAdded
PublicationAdded
ConnectorCompleted
RankingInvalidated

# 291. STATIC + DYNAMIC

Use Next.js rendering strategy intelligently.

SEO pages can be statically regenerated.

Live trials/rankings can refresh dynamically.

# 292. MOBILE

Mobile is first-class.

High-density information must remain usable.

Use:

text

sticky tabs
collapsible sources
horizontal ranking tables
full-screen charts

# 293. DESKTOP

Desktop should feel like a professional research terminal.

Allow:

text

split panels
dense tables
compare mode
persistent filters
keyboard search

# 294. COMMAND PALETTE

Shortcut:

text

⌘K

Search any entity/action.

# 295. SHAREABLE URL STATE

Filters reflected in URL.

Example:

text

/rankings?metric=mortality&year=2024&sex=all

# 296. DOWNLOAD CHART DATA

Every chart:

text

Download CSV
Download PNG/SVG where appropriate
Copy citation

subject to source redistribution terms.

# 297. CITATION EXPORT

Support:

text

BibTeX
RIS
plain citation

for publications.

# 298. RESEARCH NOTEBOOK

Future:

Users can create notes linking CancerIndex entities.

Not needed for initial release.

# 299. COMPLIANCE CHECKLIST

Before new source goes live:

text

[ ] official source verified
[ ] access method verified
[ ] license reviewed
[ ] attribution requirements stored
[ ] rate limits implemented
[ ] parser tests
[ ] raw snapshot
[ ] entity mappings
[ ] quality tests
[ ] production health monitoring

# 300. PHASE 1

Build foundation.

Deliver:

text

Cancer taxonomy
NCI EVS
GDC
ClinicalTrials.gov
PubMed
ClinVar
SEER
IARC integration plan/compliance
HGNC

Cancer pages
Gene pages
Trial pages
Publication pages
Search
Provenance
Basic rankings

# 301. PHASE 2

Add:

text

cBioPortal
CIViC
ChEMBL
Open Targets
DGIdb
FDA
DailyMed
Ensembl

Drug pages
Variant pages
Biomarker pages
Knowledge graph
Advanced rankings

# 302. PHASE 3

Add:

text

global country data
European sources
Canadian sources
additional regulators
research funding
research gap
trial gap
treatment gap

# 303. PHASE 4

Add:

text

DepMap
cell lines
preclinical
single cell
pathology
imaging
multi-omics

# 304. PHASE 5

Add:

text

CancerIndex AI
MCP
advanced analytics
custom rankings
developer ecosystem
public data releases

# 305. INITIAL CONNECTOR TARGET

Do not stop at five connectors.

Long-term target:

text

50+ high-quality connectors

But quality > arbitrary connector count.

One reliable bulk/API integration is worth more than ten fragile scrapers.

# 306. CANCER COVERAGE TARGET

Do not set a fixed target such as:

text

200 cancers

Instead:

Index every distinct malignant disease entity that can be mapped from selected authoritative oncology classifications.

Then expose:

text

top-level cancers
families
histologies
subtypes
molecular subtypes

separately.

# 307. PUBLIC SOURCE CATALOG

CancerIndex should publicly list connectors.

Example:

text

NCI GDC           Genomics
SEER              Epidemiology
IARC              Global burden
ClinicalTrials    Trials
PubMed            Literature
ClinVar           Variants
cBioPortal        Cancer genomics
CIViC             Clinical variants
HGNC              Genes
ChEMBL            Drugs
FDA               Regulation
...

# 308. SOURCE COVERAGE MATRIX

Table:

text

                    Epidemiology Genomics Trials Drugs Research
GDC                      ·          ✓       ·      ·      ✓
SEER                     ✓          ·       ·      ·      ·
ClinicalTrials           ·          ·       ✓      ✓      ✓
PubMed                   ·          ✓       ✓      ✓      ✓
CIViC                    ·          ✓       ·      ✓      ✓

# 309. CANCER DATA CARD

Every cancer card should contain at minimum:

text

name
parent category
annual burden if available
mortality
survival
CancerIndex rank
research activity
data confidence

# 310. CANCER INDEX BADGES

Examples:

text

Rare
Pediatric
Hematologic
High Mortality
Rapidly Rising
High Research Activity
Low Trial Activity
Treatment Gap

Generated from rules, not editorial opinion.

# 311. RANKING CHANGE EXPLANATION

If rank changes:

text

#12 → #8

show why:

text

2024 global mortality estimate updated
+4 new active trials
methodology unchanged

# 312. SEARCH RESULT EXPLANATION

Search result should show type:

text

EGFR
GENE

EGFR L858R
VARIANT

EGFR-mutated NSCLC
MOLECULAR COHORT

Osimertinib
DRUG

# 313. SYNONYM MANAGEMENT

Alias examples:

text

GBM
glioblastoma
glioblastoma multiforme [historical usage]

Preserve historical terminology without necessarily using it as preferred current name.

# 314. DEPRECATED TERMINOLOGY

Fields:

text

deprecated
deprecated_reason
replacement_entity
classification_version

# 315. VERSION-SENSITIVE MEDICINE

Cancer classification changes.

Never rewrite historical publication terminology as if author used modern classification.

Map:

text

source_term
→ modern CancerIndex entity

while preserving original term.

# 316. DATA CITATION

CancerIndex itself should produce dataset citations:

text

CancerIndex Data Release YYYY-MM

for researchers.

# 317. ABOUT PAGE

Explain:

text

what CancerIndex is
what it is not
where data comes from
how rankings work
limitations

# 318. TRUST CENTER

Route:

text

/trust

Include:

text

data provenance
methodology
AI policy
privacy
security
source policy
corrections

# 319. CORRECTIONS

Public correction mechanism.

Researchers can report:

text

wrong mapping
outdated statistic
incorrect citation
entity duplication

Corrections tracked publicly where appropriate.

# 320. SCIENTIFIC ADVISORY MODEL

Future:

expert contributors can have verified profiles.

No anonymous modification of clinical evidence without review.

# 321. API SOURCE ATTRIBUTION

API responses include:

json

{
  "data": {},
  "sources": [],
  "dataRelease": "..."
}

# 322. AI SOURCE SNAPSHOT

AI answer stores exactly which record versions were used.

This allows reproducibility after database updates.

# 323. NO BLACK-BOX SCORE

CancerIndex's major promise:

Every score can be decomposed.

No proprietary mystery scoring methodology.

# 324. RESEARCH GAP ETHICS

Low research activity does not inherently mean neglect.

Reasons can include:

text

rarity
disease biology
classification changes
small populations
successful prevention

Display as quantitative signal, not accusation.

# 325. SURVIVAL ETHICS

Population survival statistics do not predict an individual's outcome.

Always show contextual note.

# 326. EPIDEMIOLOGY ESTIMATES

Global cancer statistics are frequently estimates, not literal complete counts.

Label:

text

estimated cases
estimated deaths

where appropriate.

# 327. CANCERINDEX "TODAY"

Avoid saying:

text

today X people have cancer

unless the methodology genuinely supports it.

Prefer annual/latest dataset statistics.

# 328. CANCERINDEX DISCOVERY HOMEPAGE

Interesting modules:

text

Most common
Most lethal
Most researched
Most under-researched
Most trials
Largest treatment gaps
Fastest improving
Fastest rising
Rare cancer spotlight

# 329. "CANCER UNIVERSE"

Create a visual:

text

Cancer Universe

Thousands of cancer/subtype nodes arranged by anatomical/histological family.

Size could represent:

text

incidence

Color could represent:

text

survival

Filterable.

Potential signature visualization.

# 330. "CANCER MATRIX"

Rows:

text

cancers

Columns:

text

genes

Cell:

text

alteration frequency

Filter by study.

# 331. "DRUG MATRIX"

Rows:

text

cancers

Columns:

text

drugs

Cell:

text

approved
clinical
preclinical

# 332. "TRIAL PULSE"

Live research activity chart.

text

new oncology trials / week

Break down by:

text

cancer
phase
country
target

# 333. "RESEARCH PULSE"

text

new publications / week

Topic clustering.

# 334. "APPROVAL PULSE"

Timeline of oncology regulatory decisions.

# 335. "GENOMIC PULSE"

Newly curated variant/drug evidence from authoritative sources.

# 336. DATA QUALITY BADGE

Pages can show:

text

Data Quality: High

Derived from:

text

source coverage
freshness
agreement
sample size
missingness

Document formula.

# 337. SPARSE CANCER UX

For ultra-rare cancers:

do NOT show empty giant sections.

Instead:

text

Very limited epidemiological data are currently available.

What CancerIndex knows:
- 7 publications
- 1 active trial
- 2 reported genomic associations

Sparse-data visibility is valuable.

# 338. ENTITY DISCOVERY PRIORITY

Nightly job:

find source disease labels not mapped to canonical CancerIndex cancer.

Rank by frequency.

This continuously expands coverage.

# 339. AUTOMATIC CANCER DISCOVERY

Candidate disease discovery:

text

NCIt additions
SEER updates
ClinicalTrials condition strings
GDC disease terms
CIViC diseases
PubMed MeSH

Candidate → curator queue.

# 340. NEVER LET LLM INVENT A NEW CANONICAL DISEASE

LLM can suggest.

Canonical entity creation requires:

text

recognized ontology
trusted source
or human curator approval

# 341. SOURCE COUNT IS NOT EVIDENCE QUALITY

Displaying:

text

14 sources

does not mean stronger evidence if all sources repeat one paper.

Track evidence lineage and primary evidence.

# 342. DUPLICATE PUBLICATION DETECTION

Resolve:

text

PubMed
DOI
Crossref
Europe PMC

into one publication entity.

# 343. TRIAL ↔ PUBLICATION RESOLUTION

Use:

text

NCT IDs in publication
registry references
PubMed links

Then probabilistic matching only as fallback.

# 344. DRUG SYNONYMS

Drug naming is messy.

Support:

text

generic
brand
development code
salt
active moiety
combination

Do not treat brand names as separate molecules.

# 345. TARGET SYNONYMS

Resolve gene/protein target nomenclature carefully.

# 346. CANCERINDEX INTERNAL ONTOLOGY

Do not reinvent all external ontologies.

CancerIndex ontology should function primarily as a stable reconciliation layer.

# 347. EXTERNAL IDS ARE FIRST-CLASS

Never place IDs in an unstructured JSON dump only.

Create searchable cross-reference tables.

# 348. AUDIT LOG

Record:

text

who
what
before
after
when
why

for curator/admin changes.

# 349. SOFT LAUNCH CRITERIA

CancerIndex is not ready merely because home page looks good.

Minimum:

text

credible taxonomy
multiple foundational connectors
provenance
search
cancer pages
ranking methodology
ranking reproducibility
connector monitoring
scientific disclaimers

# 350. LAUNCH DATA QUALITY TARGETS

Before launch:

no unexplained metrics
no production mock data
no duplicate canonical cancers from obvious aliases
no broken source links
no AI answers without citations
no ranking without scope/year/source
no unsupported treatment claims

# 351. PRIORITY IMPLEMENTATION ORDER

Claude should execute approximately:

text

1. repository foundation
2. database
3. source/provenance framework
4. canonical IDs
5. cancer ontology
6. NCI terminology connector
7. HGNC
8. GDC
9. ClinicalTrials
10. PubMed
11. ClinVar
12. SEER
13. IARC compliance/integration
14. cBioPortal
15. CIViC
16. FDA
17. ChEMBL
18. Open Targets
19. ranking engine
20. web experience
21. AI

Exact order may change based on API/data dependencies.

# 352. FIRST RANKINGS TO SHIP

Ship these first:

text

Global Incidence
Global Mortality
Age-standardized Incidence
Age-standardized Mortality
Mortality/Incidence Ratio
5-Year Survival where comparable
Active Trial Count
Research Publication Count
CancerIndex Research Gap
CancerIndex Trial Gap

Then composite score.

# 353. COMPOSITE SCORE MUST COME LATER

Do not launch composite score until individual metrics are correct.

Composite rankings amplify bad data.

# 354. DOCUMENT CONNECTORS AS CODE

Connector documentation should be generated/validated from manifests where practical.

# 355. REPOSITORY STRUCTURE

Suggested:

text

/apps
  /web
  /api
  /admin

/services
  /ingestion
  /ranking
  /ai
  /search

/packages
  /database
  /ontology
  /connectors
  /schemas
  /ui
  /analytics
  /provenance

/connectors
  /nci-evs
  /gdc
  /seer
  /iarc
  /clinicaltrials
  /pubmed
  /clinvar
  /cbioportal
  /civic
  /hgnc
  /ensembl
  /chembl
  /opentargets
  /dgidb
  /fda

/docs

/infra

/tests

# 356. TYPES

Share canonical TypeScript/Python schemas where possible.

Use generated JSON Schema/OpenAPI contracts to prevent divergence.

# 357. OPENAPI

API documentation generated from real API definitions.

Never manually maintain docs that drift from endpoints.

# 358. DATABASE SEEDS

Only seed:

text

system roles
configuration
metric definitions
source definitions

Scientific data comes from connectors.

# 359. DEVELOPMENT FIXTURES

Label dev fixtures clearly.

Environment must make production fixture insertion impossible.

# 360. FINAL PRODUCT STANDARD

CancerIndex.io should feel like a serious piece of scientific infrastructure.

Not:

"Here are 30 cancers and some AI summaries."

But:

"Here is a continuously updated, provenance-aware, globally ranked ontology of cancer connecting epidemiology, genomics, biomarkers, therapies, trials, regulatory evidence and scientific literature."

The user should be able to start at any node:

text

a cancer
a gene
a mutation
a drug
a clinical trial
a paper
a country

and move through the entire oncology knowledge graph.

# 361. NORTH STAR

The ultimate CancerIndex graph should conceptually support:

text

ALL CANCERS
    ↓
ALL RECOGNIZED SUBTYPES
    ↓
EPIDEMIOLOGY
    ↓
GENES
    ↓
VARIANTS
    ↓
BIOMARKERS
    ↓
PATHWAYS
    ↓
DRUGS
    ↓
TREATMENTS
    ↓
APPROVALS
    ↓
CLINICAL TRIALS
    ↓
PUBLICATIONS
    ↓
RESEARCHERS
    ↓
INSTITUTIONS
    ↓
COUNTRIES

Every connection:

text

traceable
versioned
source-backed
queryable
rankable

# 362. NON-NEGOTIABLE CLAUDE INSTRUCTION

Claude must NOT rush this project.

Before building substantial portions of CancerIndex:

read this entire CLAUDE.md;
inspect all existing code;
research the latest official documentation;
identify source licensing constraints;
construct the canonical ontology carefully;
test every connector independently;
retain raw source provenance;
build reconciliation before massive ingestion;
validate all scientific computations;
ensure ranking formulas are reproducible;
never fabricate missing data;
never substitute AI memory for missing database evidence;
preserve uncertainty;
optimize the product for both ordinary users and serious researchers.

Whenever there is a choice between:

text

faster implementation

and:

text

scientifically defensible implementation

choose the scientifically defensible implementation.

Whenever there is a choice between:

text

more connectors

and:

text

reliable connectors with provenance

choose reliability first — then expand aggressively.

Whenever there is a choice between:

text

one broad cancer category

and:

text

accurately modeling recognized subtypes

preserve the detailed taxonomy.

Whenever an upstream source provides an identifier:

keep it.

Whenever an upstream source provides provenance:

keep it.

Whenever an upstream source provides uncertainty:

keep it.

Whenever CancerIndex computes something:

make the formula visible.

Whenever CancerIndex ranks something:

explain the ranking.

Whenever CancerIndex AI says something:

cite the evidence.

# 363. FINAL VISION

CancerIndex.io should eventually make it possible to ask:

Rank every indexed cancer in the world by unmet need, weighting global mortality, 5-year survival, number of approved systemic therapies, active Phase II/III trials and publication activity.

and receive a reproducible result.

Or:

Show every known cancer subtype associated with BRAF V600E, the frequency reported in each major cohort, regulatory-approved treatments by jurisdiction, active trials and supporting clinical evidence.

Or:

Which rare cancers have fewer than five recruiting trials worldwide despite high mortality?

Or:

What malignancies experienced the largest improvement in survival during the last two decades?

Or:

Which cancer research areas are accelerating fastest this year?

Or:

Show the entire treatment-development landscape for KRAS G12D.

CancerIndex should answer these from structured, sourced data rather than model memory.

That is the standard.

# 364. BUILD PRINCIPLE

Do not build a cancer website.

Build:

the structured global intelligence layer for cancer.

That is CancerIndex.io.