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IPSS-M API and CLI

Compute IPSS-M and IPSS-M Risks on IWG-PM Cohort (Bernard et al, 2022 NJEM Evid) Can be run as CLI

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Important

You may use IPSS-M API, the underlying content, and any output therefrom for personal for academic and research and noncommercial purposes only. See LICENSE for more details.

API and CLI of the Molecular International Prognostic Scoring System (IPSS-M) for Myelodysplastic Syndromes.

⚡️ API Reference Available at: https://api.mds-risk-model.com

Table of contents

📃 IPSS-M Publication

Bernard E, Tuechler H, Greenberg PL, Hasserjian RP, Arango Ossa JE et al. Molecular International Prognostic Scoring System for Myelodysplastic Syndromes, NEJM Evidence 2022.

https://evidence.nejm.org/doi/full/10.1056/EVIDoa2200008

🗒️ Input Variables Definition

Note: Values for mutations:

  • 0 means wild-type (non-mutated)
  • 1 means mutated, and
  • NA means 'Not Asssesed'.
Category Variable Explanation Variable type, and Unit Possible Value
clinical Hemoglobin HB float, in g/dL [4-20]
clinical Platelets PLT float, in Giga/L [0-2000]
clinical Bone Marrow Blasts BM_BLAST float, in % [0-30]
clinical (only for IPSS-R) Absolute Neutrophil Count ANC float, in Giga/L [0-15]
clinical (only for IPSS-RA) Bone Marrow Blasts AGE float, in years [18-120]
cytogenetics Presence of del(5q) del5q integer 0/1
cytogenetics Presence of -7/del(7q) del7_7q integer 0/1
cytogenetics Presence of -17/del(17p) del17_17p integer 0/1
cytogenetics Complex karyotype complex integer 0/1
cytogenetics Cytogenetics Category CYTO_IPSSR string Very Good/Good/Intermediate/Poor/Very Poor
TP53 locus Number of TP53 mutations TP53mut string 0/1/2 or more
TP53 locus Maximum TP53 VAF TP53maxvaf float, between 0 and 1 [0-1]
TP53 locus Loss of heterozygosity at TP53 TP53loh integer: 0 or 1, string 'NA' 0/1
MLL and FLT3 mutations MLL PTD MLL_PTD integer: 0 or 1, string 'NA' 0/1 / NA
MLL and FLT3 mutations FLT3 ITD or TKD FLT3 integer: 0 or 1, string 'NA' 0/1
gene main effect ASXL1 ASXL1 integer: 0 or 1, string 'NA' 0/1/NA
gene main effect CBL CBL integer: 0 or 1, string 'NA' 0/1/NA
gene main effect DNMT3A DNMT3A integer: 0 or 1, string 'NA' 0/1/NA
gene main effect ETV6 ETV6 integer: 0 or 1, string 'NA' 0/1/NA
gene main effect EZH2 EZH2 integer: 0 or 1, string 'NA' 0/1/NA
gene main effect IDH2 IDH2 integer: 0 or 1, string 'NA' 0/1/NA
gene main effect KRAS KRAS integer: 0 or 1, string 'NA' 0/1/NA
gene main effect NPM1 NPM1 integer: 0 or 1, string 'NA' 0/1/NA
gene main effect NRAS NRAS integer: 0 or 1, string 'NA' 0/1/NA
gene main effect RUNX1 RUNX1 integer: 0 or 1, string 'NA' 0/1/NA
gene main effect SF3B1 SF3B1 integer: 0 or 1, string 'NA' 0/1/NA
gene main effect SRSF2 SRSF2 integer: 0 or 1, string 'NA' 0/1/NA
gene main effect U2AF1 U2AF1 integer: 0 or 1, string 'NA' 0/1/NA
gene residual BCOR BCOR integer: 0 or 1, string 'NA' 0/1/NA
gene residual BCORL1 BCORL1 integer: 0 or 1, string 'NA' 0/1/NA
gene residual CEBPA CEBPA integer: 0 or 1, string 'NA' 0/1/NA
gene residual ETNK1 ETNK1 integer: 0 or 1, string 'NA' 0/1/NA
gene residual GATA2 GATA2 integer: 0 or 1, string 'NA' 0/1/NA
gene residual GNB1 GNB1 integer: 0 or 1, string 'NA' 0/1/NA
gene residual IDH1 IDH1 integer: 0 or 1, string 'NA' 0/1/NA
gene residual NF1 NF1 integer: 0 or 1, string 'NA' 0/1/NA
gene residual PHF6 PHF6 integer: 0 or 1, string 'NA' 0/1/NA
gene residual PPM1D PPM1D integer: 0 or 1, string 'NA' 0/1/NA
gene residual PTPN11 PTPN11 integer: 0 or 1, string 'NA' 0/1/NA
gene residual PRPF8 PRPF8 integer: 0 or 1, string 'NA' 0/1/NA
gene residual SETBP1 SETBP1 integer: 0 or 1, string 'NA' 0/1/NA
gene residual STAG2 STAG2 integer: 0 or 1, string 'NA' 0/1/NA
gene residual WT1 WT1 integer: 0 or 1, string 'NA' 0/1/NA

🚀 API Usage

The API is available at: https://api.mds-risk-model.com

🧬 IPSS-M API Endpoint

Endpoint: POST /ipssm

curl \
    -X POST \
    -H "Content-Type: application/json" \
    -d '{"HB": 10, "PLT":150, "BM_BLAST":2, "CYTO_IPSSR": "Poor"}' \
    https://api.mds-risk-model.com/ipssm

# example response
{
  "patient": {
    "HB": 10,
    "PLT": 150,
    "BM_BLAST": 2,
    "CYTO_IPSSR": "Poor",
    "del5q": 0,
    "del7_7q": 0,
    "del17_17p": 0,
    "complex": 0,
    "TP53mut": "0",
    "TP53maxvaf": 0,
    "TP53loh": 0,
    "MLL_PTD": 0,
    "FLT3": 0,
    "ASXL1": 0,
    "CBL": 0,
    "DNMT3A": 0,
    "ETV6": 0,
    "EZH2": 0,
    "IDH2": 0,
    "KRAS": 0,
    "NPM1": 0,
    "NRAS": 0,
    "RUNX1": 0,
    "SF3B1": 0,
    "SRSF2": 0,
    "U2AF1": 0,
    "BCOR": 0,
    "BCORL1": 0,
    "CEBPA": 0,
    "ETNK1": 0,
    "GATA2": 0,
    "GNB1": 0,
    "IDH1": 0,
    "NF1": 0,
    "PHF6": 0,
    "PPM1D": 0,
    "PRPF8": 0,
    "PTPN11": 0,
    "SETBP1": 0,
    "STAG2": 0,
    "WT1": 0
  },
  "ipssm": {
    "means": {
      "riskScore": -0.35,
      "riskCat": "Moderate Low"
    },
    "worst": {
      "riskScore": -0.35,
      "riskCat": "Moderate Low"
    },
    "best": {
      "riskScore": -0.35,
      "riskCat": "Moderate Low"
    }
 }

🆎 IPSS-R API Endpoint

Endpoint: POST /ipssr

$ curl \
    -X POST \
    -H "Content-Type: application/json" \
    -d '{"HB": 10, "ANC": 1.8, "PLT": 150, "BM_BLAST": 2, "CYTO_IPSSR": "Poor", "AGE": 28}' \
    https://api.mds-risk-model.com/ipssr

# example response
{
  "patient": {
    "HB": 10,
    "ANC": 1.8,
    "PLT": 150,
    "BM_BLAST": 2,
    "CYTO_IPSSR": "Poor",
    "AGE": 28
  },
  "ipssr": {
    "IPSSR_SCORE": 3,
    "IPSSR_CAT": "Low",
    "IPSSRA_SCORE": 1.53,
    "IPSSRA_CAT": "Low"
  }
}

🤖 CLI Usage

You can use the command line interface to annotate a file with patients, where each row is a patient and each column is a variable.

# Without installing it using npx
npx ipssm --help
# With local installation using npm
npm install ipssm
ipssm --help
$  ipssm --help

Annotate a file of patients with IPSS-M and IPSS-R risk scores and categories.
It supports .csv, .tsv, .xlsx files.

Usage: ipssm <inputFile> <outputFile>

Positionals:
  inputFile   File to be annotated (rows: patients, columns: variables).[string]
  outputFile  Path for the annotated output file.                       [string]

Options:
      --version  Show version number                                   [boolean]
  -h, --help     Show help                                             [boolean]

🔥 Using it as a node/javascript package

💥 IPSS-M

Having a patient's data in a dictionary, you can compute the IPSS-M.

const { ipssm } from 'ipssm'

// Add patient data to an object with the following fields
const patientFields = {
  // Clinical Data
  BM_BLAST: 0,
  HB: 8.2,
  PLT: 239,
  // Optional IPSS-R fields
  ANC: 0.72,
  AGE: 63.5,
  // Cytogenetic Data 
  del5q: 0,
  del7_7q: 0,
  del17_17p: 0,
  complex: 0, 
  CYTO_IPSSR: 'Good',
  // Molecular Data
  TP53mut: 0,
  TP53maxvaf: 0,
  TP53loh: 0,
  MLL_PTD: 0,
  FLT3: 0,
  ASXL1: 1,
  CBL: 0,
  DNMT3A: 0,
  ETV6: 0,
  EZH2: 1,
  IDH2: 0,
  KRAS: 0,
  NPM1: 0,
  NRAS: 0,
  RUNX1: 1,
  SF3B1: 0,
  SRSF2: 0,
  U2AF1: 0,
  BCOR: 0,
  BCORL1: 0,
  CEBPA: 0,
  ETNK1: 0,
  GATA2: 0,
  GNB1: 0,
  IDH1: 0,
  NF1: 0,
  PHF6: 0,
  PPM1D: 0,
  PRPF8: 0,
  PTPN11: 0,
  SETBP1: 0,
  STAG2: 0,
  WT1: 0,
}

const ipssmResult = ipssm(patientFields)
console.log(ipssmResult)
// Result
{
  means: {
    riskScore: 0.09,
    riskCat: 'Moderate High',
    contributions: {...}
  },
  best: {
    riskScore: 0.09,
    riskCat: 'Moderate High',
    contributions: {...}
  },
  worst: {
    riskScore: 0.09,
    riskCat: 'Moderate High',
    contributions: {...}
  },
}

⚡ IPSS-R and IPSS-R (Age adjusted)

Additionally, you may find an implementation to compute the IPPS-R and IPSS-R (Age adjusted) in this module:

import { ipssr } from 'ipssm'

// using the same patient data
patientFields = {
  HB: 8.2,
  ANC: 0.72,
  PLT: 239,
  BM_BLAST: 0,
  CYTOVEC: 1,
  AGE: 63.5,
  ...
}

const ipssrResult = ipssr({
  hb: patientFields.HB,
  anc: patientFields.ANC,
  plt: patientFields.PLT,
  bmblast: patientFields.BM_BLAST,
  cytovec: patientFields.CYTOVEC,
  age: patientFields.AGE,
})

console.log(ipssrResult)

Which outputs a risk score (means), with a best and worst scenario risk score to account for missing genetic data.

// Result
{
    IPSSR_SCORE: 2.5,
    IPSSR: 'Low',
    IPSSRA_SCORE: 2.2563,
    IPSSRA: 'Low',
}

🎯 Annotating batch from CSV/Excel file

The following code will annotate a CSV file with the IPSS-M and IPSS-M Risks.

import { annotateFile } from 'ipssm'

const inputFile = './test/data/IPSSMexample.csv'
const outputFile = 'IPSSMexample.annotated.csv'

await annotateFile(inputFile, outputFile)

or with an excel file:

import { annotateFile } from 'ipssm'

const inputFile = './test/data/IPSSMexample.xlsx'
const outputFile = 'IPSSMexample.annotated.xlsx'

await annotateFile(inputFile, outputFile)

❓ Question

Any questions feel free to add an issue to this repo or to contact ElsaB.

👨🏻‍💻 Development

# Install dependencies
npm install

# Run the development server
npm run serve

# Run tests
npm test

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API and CLI for IPSS-M

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