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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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ipssm API and CLI

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

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

Table of contents

inputs

📃 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

🚀 API Usage

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

  • IPSS-M endpoint: /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 endpoint: /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)

🗒️ 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

❓ 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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