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Chromosomal Instability Agent

---name: chromosomal-instability-agent description: AI-powered analysis of chromosomal instability (CIN) signatures for cancer prognosis, immunotherapy response prediction, and therapeutic vulnerability identification. license: MIT metadata: author: AI Group version: "1.0.0" created: "2026-01-19"...

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Chromosomal Instability Agent

The Chromosomal Instability Agent analyzes CIN signatures to predict cancer prognosis, immunotherapy response, and therapeutic vulnerabilities. It integrates copy number alterations, aneuploidy scores, and CIN-related gene expression for comprehensive genomic instability assessment.

When to Use This Skill

  • When assessing tumor aneuploidy and chromosomal instability levels.
  • To predict prognosis based on CIN signatures.
  • For identifying tumors vulnerable to CIN-targeted therapies (PARP, ATR, WEE1).
  • When analyzing immune evasion mechanisms related to CIN.
  • To stratify patients for immunotherapy based on CIN status.

Core Capabilities

  1. CIN Scoring: Calculate comprehensive CIN scores from copy number data.

  2. Aneuploidy Quantification: Measure arm-level and focal copy number alterations.

  3. CIN Gene Expression: Analyze CIN70 and other transcriptional signatures.

  4. Immune Correlation: Assess CIN-immune microenvironment relationships.

  5. Therapeutic Vulnerability: Identify CIN-targeted treatment options.

  6. Prognostic Modeling: Predict outcomes based on CIN signatures.

CIN Metrics

MetricCalculationInterpretation
Aneuploidy scoreArm-level alterationsChromosome-level CIN
SCNA burdenTotal CNV alterationsOverall instability
Weighted GIIFraction altered genomeFocal vs broad changes
CIN7070-gene signatureTranscriptional CIN
WGIIWeighted genome instabilityComprehensive score

CIN70 Signature Genes

Core genes reflecting CIN phenotype:

  • Mitotic checkpoint: BUB1, BUBR1, MAD2L1
  • Kinetochore: CENPA, CENPF, NDC80
  • DNA replication: MCM2-7, ORC1
  • Cell cycle: CCNB1, CCNB2, CDK1, PLK1
  • Chromosome segregation: AURKB, KIF2C, KIF11

Workflow

  1. Input: Copy number data (segments), gene expression, mutation data.

  2. CNV Analysis: Calculate arm-level and focal alterations.

  3. Signature Scoring: Compute CIN70 and other transcriptional signatures.

  4. Integration: Combine DNA and RNA-based CIN metrics.

  5. Immune Analysis: Correlate CIN with TME composition.

  6. Vulnerability Assessment: Identify targetable dependencies.

  7. Output: CIN scores, prognosis, treatment recommendations.

Example Usage

User: "Analyze chromosomal instability in this breast cancer sample and identify treatment vulnerabilities."

Agent Action:

python3 Skills/​Oncology/​Chromosomal_Instability_Agent/​cin_analyzer.py \
    --cnv_segments tumor_cnv.tsv \
    --expression rnaseq_tpm.tsv \
    --mutations somatic.maf \
    --tumor_type breast_cancer \
    --signatures cin70,cin25 \
    --output cin_report/

CIN and Immune Evasion

High CIN Associates With:

  • Reduced immune infiltration
  • Lower checkpoint inhibitor response
  • Increased immune evasion
  • cGAS-STING activation (paradoxical)

Mechanisms:

  1. Loss of tumor suppressors on chromosome arms
  2. Chronic inflammatory signaling
  3. Aneuploidy-induced stress responses
  4. Subclonal diversification

Therapeutic Vulnerabilities

TargetAgentsCIN Context
PARPOlaparib, etc.High CIN + HRD
ATRBerzosertibReplication stress
WEE1AdavosertibG2/​M dependency
CHK1PrexasertibCell cycle checkpoint
KIF11IspinesibMitotic dependency
Aurora kinasesAlisertibMitotic errors

CIN-Based Patient Stratification

CIN LevelPrognosisICI ResponseAlternative Therapy
LowBetterBetterStandard care
IntermediateVariableVariableCombination therapy
HighPoorPoorCIN-targeted agents
ExtremeVery poorImmune desertChemotherapy

AI/​ML Components

CIN Score Prediction:

  • Random forest on CNV features
  • Expression-based CIN inference
  • Multi-modal integration

Prognosis Modeling:

  • Cox regression with CIN features
  • Cancer-type specific models
  • Integration with clinical variables

Therapeutic Matching:

  • GDSC/​CCLE drug sensitivity
  • CIN-drug response correlations
  • Combination predictions

Pan-Cancer CIN Patterns

Cancer TypeTypical CIN LevelDriver Events
Ovarian HGSOCVery highTP53, BRCA
Triple-neg breastHighTP53, PI3K
Colorectal MSSModerate-highAPC, TP53
Colorectal MSILowMMR deficiency
Thyroid (PTC)LowBRAF, RAS
MelanomaModerateBRAF, NRAS

Prerequisites

  • Python 3.10+
  • GISTIC2 or similar for CNV analysis
  • Gene signature databases
  • Survival analysis packages

Related Skills

  • HRD_Analysis_Agent - For HR-specific instability
  • Pan_Cancer_MultiOmics_Agent - For pan-cancer context
  • Tumor_Clonal_Evolution_Agent - For evolutionary dynamics

Research Applications

  1. Biomarker Development: CIN as predictive marker
  2. Drug Development: CIN-targeted therapy trials
  3. Evolution Studies: Track CIN changes over time
  4. Resistance Mechanisms: CIN and drug resistance

Author

AI Group - Biomedical AI Platform

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