---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"
compatibility:
- system: Python 3.10+
allowed-tools:
- run_shell_command
- read_file
- write_file
keywords:
- chromosomal-instability-agent
- automation
- biomedical
measurable_outcome: execute task with >95% success rate.
---"
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
CIN Scoring: Calculate comprehensive CIN scores from copy number data.
Aneuploidy Quantification: Measure arm-level and focal copy number alterations.
CIN Gene Expression: Analyze CIN70 and other transcriptional signatures.
Immune Correlation: Assess CIN-immune microenvironment relationships.
Therapeutic Vulnerability: Identify CIN-targeted treatment options.
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
Input: Copy number data (segments), gene expression, mutation data.
CNV Analysis: Calculate arm-level and focal alterations.
Signature Scoring: Compute CIN70 and other transcriptional signatures.
Integration: Combine DNA and RNA-based CIN metrics.
Immune Analysis: Correlate CIN with TME composition.
Vulnerability Assessment: Identify targetable dependencies.
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:
- Loss of tumor suppressors on chromosome arms
- Chronic inflammatory signaling
- Aneuploidy-induced stress responses
- 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
- Biomarker Development: CIN as predictive marker
- Drug Development: CIN-targeted therapy trials
- Evolution Studies: Track CIN changes over time
- Resistance Mechanisms: CIN and drug resistance
Author
AI Group - Biomedical AI Platform
1---2name: chromosomal-instability-agent3description: ---name: chromosomal-instability-agent4---5---name: chromosomal-instability-agent6description: AI-powered analysis of chromosomal instability (CIN) signatures for cancer prognosis, immunotherapy response prediction, and therapeutic vulnerability identification.7license: MIT8metadata:9 author: AI Group10 version: "1.0.0"11 created: "2026-01-19"12compatibility:13 - system: Python 3.10+14allowed-tools:15 - run_shell_command16 - read_file17 - write_file1819keywords:20 - chromosomal-instability-agent21 - automation22 - biomedical23measurable_outcome: execute task with >95% success rate.24---"2526# Chromosomal Instability Agent2728The **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.2930## When to Use This Skill3132* When assessing tumor aneuploidy and chromosomal instability levels.33* To predict prognosis based on CIN signatures.34* For identifying tumors vulnerable to CIN-targeted therapies (PARP, ATR, WEE1).35* When analyzing immune evasion mechanisms related to CIN.36* To stratify patients for immunotherapy based on CIN status.3738## Core Capabilities39401. **CIN Scoring**: Calculate comprehensive CIN scores from copy number data.41422. **Aneuploidy Quantification**: Measure arm-level and focal copy number alterations.43443. **CIN Gene Expression**: Analyze CIN70 and other transcriptional signatures.45464. **Immune Correlation**: Assess CIN-immune microenvironment relationships.47485. **Therapeutic Vulnerability**: Identify CIN-targeted treatment options.49506. **Prognostic Modeling**: Predict outcomes based on CIN signatures.5152## CIN Metrics5354| Metric | Calculation | Interpretation |55|--------|-------------|----------------|56| Aneuploidy score | Arm-level alterations | Chromosome-level CIN |57| SCNA burden | Total CNV alterations | Overall instability |58| Weighted GII | Fraction altered genome | Focal vs broad changes |59| CIN70 | 70-gene signature | Transcriptional CIN |60| WGII | Weighted genome instability | Comprehensive score |6162## CIN70 Signature Genes6364Core genes reflecting CIN phenotype:65- Mitotic checkpoint: BUB1, BUBR1, MAD2L166- Kinetochore: CENPA, CENPF, NDC8067- DNA replication: MCM2-7, ORC168- Cell cycle: CCNB1, CCNB2, CDK1, PLK169- Chromosome segregation: AURKB, KIF2C, KIF117071## Workflow72731. **Input**: Copy number data (segments), gene expression, mutation data.74752. **CNV Analysis**: Calculate arm-level and focal alterations.76773. **Signature Scoring**: Compute CIN70 and other transcriptional signatures.78794. **Integration**: Combine DNA and RNA-based CIN metrics.80815. **Immune Analysis**: Correlate CIN with TME composition.82836. **Vulnerability Assessment**: Identify targetable dependencies.84857. **Output**: CIN scores, prognosis, treatment recommendations.8687## Example Usage8889**User**: "Analyze chromosomal instability in this breast cancer sample and identify treatment vulnerabilities."9091**Agent Action**:92```bash93python3 Skills/Oncology/Chromosomal_Instability_Agent/cin_analyzer.py \94 --cnv_segments tumor_cnv.tsv \95 --expression rnaseq_tpm.tsv \96 --mutations somatic.maf \97 --tumor_type breast_cancer \98 --signatures cin70,cin25 \99 --output cin_report/100```101102## CIN and Immune Evasion103104**High CIN Associates With**:105- Reduced immune infiltration106- Lower checkpoint inhibitor response107- Increased immune evasion108- cGAS-STING activation (paradoxical)109110**Mechanisms**:1111. Loss of tumor suppressors on chromosome arms1122. Chronic inflammatory signaling1133. Aneuploidy-induced stress responses1144. Subclonal diversification115116## Therapeutic Vulnerabilities117118| Target | Agents | CIN Context |119|--------|--------|-------------|120| PARP | Olaparib, etc. | High CIN + HRD |121| ATR | Berzosertib | Replication stress |122| WEE1 | Adavosertib | G2/M dependency |123| CHK1 | Prexasertib | Cell cycle checkpoint |124| KIF11 | Ispinesib | Mitotic dependency |125| Aurora kinases | Alisertib | Mitotic errors |126127## CIN-Based Patient Stratification128129| CIN Level | Prognosis | ICI Response | Alternative Therapy |130|-----------|-----------|--------------|---------------------|131| Low | Better | Better | Standard care |132| Intermediate | Variable | Variable | Combination therapy |133| High | Poor | Poor | CIN-targeted agents |134| Extreme | Very poor | Immune desert | Chemotherapy |135136## AI/ML Components137138**CIN Score Prediction**:139- Random forest on CNV features140- Expression-based CIN inference141- Multi-modal integration142143**Prognosis Modeling**:144- Cox regression with CIN features145- Cancer-type specific models146- Integration with clinical variables147148**Therapeutic Matching**:149- GDSC/CCLE drug sensitivity150- CIN-drug response correlations151- Combination predictions152153## Pan-Cancer CIN Patterns154155| Cancer Type | Typical CIN Level | Driver Events |156|-------------|-------------------|---------------|157| Ovarian HGSOC | Very high | TP53, BRCA |158| Triple-neg breast | High | TP53, PI3K |159| Colorectal MSS | Moderate-high | APC, TP53 |160| Colorectal MSI | Low | MMR deficiency |161| Thyroid (PTC) | Low | BRAF, RAS |162| Melanoma | Moderate | BRAF, NRAS |163164## Prerequisites165166* Python 3.10+167* GISTIC2 or similar for CNV analysis168* Gene signature databases169* Survival analysis packages170171## Related Skills172173* HRD_Analysis_Agent - For HR-specific instability174* Pan_Cancer_MultiOmics_Agent - For pan-cancer context175* Tumor_Clonal_Evolution_Agent - For evolutionary dynamics176177## Research Applications1781791. **Biomarker Development**: CIN as predictive marker1802. **Drug Development**: CIN-targeted therapy trials1813. **Evolution Studies**: Track CIN changes over time1824. **Resistance Mechanisms**: CIN and drug resistance183184## Author185186AI Group - Biomedical AI Platform