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name: 'chromosomal-instability-agent' description: 'AI-powered analysis of chromosomal instability (CIN) signatures for cancer prognosis, immunotherapy response prediction, and therapeutic vulnerability identification.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

• read_file
• run_shell_command

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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.

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

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

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

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

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

CIN Metrics

| Metric | Calculation | Interpretation | |--------|-------------|----------------| | Aneuploidy score | Arm-level alterations | Chromosome-level CIN | | SCNA burden | Total CNV alterations | Overall instability | | Weighted GII | Fraction altered genome | Focal vs broad changes | | CIN70 | 70-gene signature | Transcriptional CIN | | WGII | Weighted genome instability | Comprehensive 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.

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

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

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

1. Immune Analysis: Correlate CIN with TME composition.

1. Vulnerability Assessment: Identify targetable dependencies.

1. 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

| Target | Agents | CIN Context | |--------|--------|-------------| | PARP | Olaparib, etc. | High CIN + HRD | | ATR | Berzosertib | Replication stress | | WEE1 | Adavosertib | G2/M dependency | | CHK1 | Prexasertib | Cell cycle checkpoint | | KIF11 | Ispinesib | Mitotic dependency | | Aurora kinases | Alisertib | Mitotic errors |

CIN-Based Patient Stratification

| CIN Level | Prognosis | ICI Response | Alternative Therapy | |-----------|-----------|--------------|---------------------| | Low | Better | Better | Standard care | | Intermediate | Variable | Variable | Combination therapy | | High | Poor | Poor | CIN-targeted agents | | Extreme | Very poor | Immune desert | Chemotherapy |

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 Type | Typical CIN Level | Driver Events | |-------------|-------------------|---------------| | Ovarian HGSOC | Very high | TP53, BRCA | | Triple-neg breast | High | TP53, PI3K | | Colorectal MSS | Moderate-high | APC, TP53 | | Colorectal MSI | Low | MMR deficiency | | Thyroid (PTC) | Low | BRAF, RAS | | Melanoma | Moderate | BRAF, 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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