Immunotherapy Response Prediction

Predict patient response to immune checkpoint inhibitors (ICIs) using multi-biomarker integration. Transforms a patient tumor profile (cancer type + mutations + biomarkers) into a quantitative ICI Response Score with drug-specific recommendations, resistance risk assessment, and monitoring plan.

KEY PRINCIPLES

:

Report-first approach

- Create report file FIRST, then populate progressively

Evidence-graded

- Every finding has an evidence tier (T1-T4)

Quantitative output

- ICI Response Score (0-100) with transparent component breakdown

Cancer-specific

- All thresholds and predictions are cancer-type adjusted

Multi-biomarker

- Integrate TMB + MSI + PD-L1 + neoantigen + mutations

Resistance-aware

- Always check for known resistance mutations (STK11, PTEN, JAK1/2, B2M)

Drug-specific

- Recommend specific ICI agents with evidence

Source-referenced

- Every statement cites the tool/database source

English-first queries

- Always use English terms in tool calls

When to Use

Apply when user asks:

"Will this patient respond to immunotherapy?"

"Should I give pembrolizumab to this melanoma patient?"

"Patient has NSCLC with TMB 25, PD-L1 80% - predict ICI response"

"MSI-high colorectal cancer - which checkpoint inhibitor?"

"Patient has BRAF V600E melanoma, TMB 15 - immunotherapy or targeted?"

"Compare pembrolizumab vs nivolumab for this patient profile"

Input Parsing

Required

Cancer type + at least one of: mutation list OR TMB value

Optional

PD-L1 expression, MSI status, immune infiltration data, HLA type, prior treatments, intended ICI

See

INPUT_REFERENCE.md

for input format examples, cancer type normalization, and gene symbol normalization tables.

Workflow Overview

Input: Cancer type + Mutations/TMB + Optional biomarkers (PD-L1, MSI, etc.)

Phase 1: Input Standardization & Cancer Context

Phase 2: TMB Analysis

Phase 3: Neoantigen Analysis

Phase 4: MSI/MMR Status Assessment

Phase 5: PD-L1 Expression Analysis

Phase 6: Immune Microenvironment Profiling

Phase 7: Mutation-Based Predictors

Phase 8: Clinical Evidence & ICI Options

Phase 9: Resistance Risk Assessment

Phase 10: Multi-Biomarker Score Integration

Phase 11: Clinical Recommendations

Phase 1: Input Standardization & Cancer Context

Resolve cancer type

to EFO ID via

OpenTargets_get_disease_id_description_by_name

Parse mutations

into structured format:

{gene, variant, type}

Resolve gene IDs

via

MyGene_query_genes

Look up cancer-specific ICI baseline ORR from the cancer context table (see

SCORING_TABLES.md

)

Phase 2: TMB Analysis

Classify TMB: Very-Low (<5), Low (5-9.9), Intermediate (10-19.9), High (>=20)

Check FDA TMB-H biomarker via

fda_pharmacogenomic_biomarkers(drug_name='pembrolizumab')

Apply cancer-specific TMB thresholds (see

SCORING_TABLES.md

)

Note: RCC responds to ICIs despite low TMB; TMB is less predictive in some cancers

Phase 3: Neoantigen Analysis

Estimate neoantigen burden: missense_count * 0.3 + frameshift_count * 1.5

Check mutation impact via

UniProt_get_function_by_accession

Query known epitopes via

iedb_search_epitopes

POLE/POLD1 mutations indicate ultra-high neoantigen load

Phase 4: MSI/MMR Status Assessment

Integrate MSI status if provided (MSI-H = 25 pts, MSS = 5 pts)

Check mutations in MMR genes: MLH1, MSH2, MSH6, PMS2, EPCAM

Check FDA MSI-H approvals via

fda_pharmacogenomic_biomarkers(biomarker='Microsatellite Instability')

Phase 5: PD-L1 Expression Analysis

Classify PD-L1: High (>=50%), Positive (1-49%), Negative (<1%)

Apply cancer-specific PD-L1 thresholds and scoring methods (TPS vs CPS)

Get baseline expression via

HPA_get_cancer_prognostics_by_gene(gene_name='CD274')

Phase 6: Immune Microenvironment Profiling

Query immune checkpoint gene expression for: CD274, PDCD1, CTLA4, LAG3, HAVCR2, TIGIT, CD8A, CD8B, GZMA, GZMB, PRF1, IFNG

Classify tumor: Hot (T cell inflamed), Cold (immune desert), Immune excluded, Immune suppressed

Run immune pathway enrichment via

enrichr_gene_enrichment_analysis

Phase 7: Mutation-Based Predictors

Resistance mutations

(apply PENALTIES): STK11 (-10), PTEN (-5), JAK1/2 (-10 each), B2M (-15), KEAP1 (-5), MDM2/4 (-5), EGFR (-5)

Sensitivity mutations

(apply BONUSES): POLE (+10), POLD1 (+5), BRCA1/2 (+3), ARID1A (+3), PBRM1 (+5 RCC only)

Check CIViC and OpenTargets for driver mutation ICI context

Check DDR pathway genes: ATM, ATR, CHEK1/2, BRCA1/2, PALB2, RAD50, MRE11

Phase 8: Clinical Evidence & ICI Options

Query FDA indications for ICI drugs via

FDA_get_indications_by_drug_name

Search clinical trials via

clinical_trials_search

or

search_clinical_trials

Search PubMed for biomarker-specific response data

Get drug mechanisms via

OpenTargets_get_drug_mechanisms_of_action_by_chemblId

See

SCORING_TABLES.md

for ICI drug profiles and ChEMBL IDs.

Phase 9: Resistance Risk Assessment

Check CIViC for resistance evidence via

civic_search_evidence_items

Assess pathway-level resistance: IFN-g signaling, antigen presentation, WNT/b-catenin, MAPK, PI3K/AKT/mTOR

Summarize risk: Low / Moderate / High

Phase 10: Multi-Biomarker Score Integration

TOTAL SCORE = TMB_score + MSI_score + PDL1_score + Neoantigen_score + Mutation_bonus + Resistance_penalty

TMB_score: 5-30 points MSI_score: 5-25 points

PDL1_score: 5-20 points Neoantigen_score: 5-15 points

Mutation_bonus: 0-10 points Resistance_penalty: -20 to 0 points

Floor: 0, Cap: 100

Response Likelihood Tiers

:

70-100 HIGH (50-80% ORR): Strong ICI candidate

40-69 MODERATE (20-50% ORR): Consider ICI, combo preferred

0-39 LOW (<20% ORR): ICI alone unlikely effective

Confidence

HIGH (all 4 biomarkers), MODERATE-HIGH (3/4), MODERATE (2/4), LOW (1), VERY LOW (cancer only)

Phase 11: Clinical Recommendations

ICI drug selection

using cancer-specific algorithm (see

SCORING_TABLES.md

)

Monitoring plan

CT/MRI q8-12wk, ctDNA at 4-6wk, thyroid/liver function, irAEs

Alternative strategies

if LOW response: targeted therapy, chemotherapy, ICI+chemo combo, ICI+anti-angiogenic, ICI+CTLA-4 combo, clinical trials

Output Report

Save as

immunotherapy_response_prediction_{cancer_type}.md

. See

REPORT_TEMPLATE.md

for the full report structure.

Tool Parameter Reference

BEFORE calling ANY tool

, verify parameters. See

TOOLS_REFERENCE.md

for verified tool parameters table.

Key reminders:

MyGene_query_genes

use

query

(NOT

q

)

EnsemblVEP_annotate_rsid

use

variant_id

(NOT

rsid

)

drugbank_*

tools: ALL 4 params required (

query

,

case_sensitive

,

exact_match

,

limit

)

cBioPortal_get_mutations

:

gene_list

is a STRING not array

ensembl_lookup_gene

REQUIRES species='homo_sapiens' Evidence Tiers Tier Description Source Examples T1 FDA-approved biomarker/indication FDA labels, NCCN guidelines T2 Phase 2-3 clinical trial evidence Published trial data, PubMed T3 Preclinical/computational evidence Pathway analysis, in vitro data T4 Expert opinion/case reports Case series, reviews References OpenTargets: https://platform.opentargets.org CIViC: https://civicdb.org FDA Drug Labels: https://dailymed.nlm.nih.gov DrugBank: https://go.drugbank.com PubMed: https://pubmed.ncbi.nlm.nih.gov IEDB: https://www.iedb.org HPA: https://www.proteinatlas.org cBioPortal: https://www.cbioportal.org