ipssm (js)

Javascript/Node Package for the Molecular International Prognostic Scoring System (IPSS-M) for Myelodysplastic Syndromes.

• For the R package, see papaemmelab/ipssm.

• For the Online Web Calculator, visit https://mds-risk-model.com.

Table of contents

• 📖 IPSS-M Publication
• 🚀 Installation instructions
• 💥 IPSS-M Usage
  • 🔥 Compute IPSS-M
  • ⚡️ IPSS-R and IPSS-R (Age adjusted)
  • 🎯 Annotating batch from CSV/Excel file
  • 🦾 Using the command line interface
• 🗒️ Input Variables Definition
• ❓ Question

inputs

:page_with_curl: 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.

:rocket: Installation instructions

# Using npm
npm install ipssm

:boom: IPSS-M Usage

:fire: Compute 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: {...}
  },
}

:zap: 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',
}

:dart: 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)

:mechanical_arm: Using the command line interface

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.

$  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]

:spiral_notepad: Input Variables Definition

| Category | Variable Explanation | Variable | Unit | Possible Value | |----------------------------|-------------------------------|--------------|------------------------------|-------------------------------------------------------------| | clinical | Hemoglobin | HB | numerical, in g/dL | [4-20] | | clinical | Platelets | PLT | numerical, in Giga/L | [0-2000] | | clinical | Bone Marrow Blasts | BM_BLAST | numerical, in % | [0-30] | | clinical (only for IPSS-R) | Absolute Neutrophil Count | ANC | numerical, in Giga/L | [0-15] | | clinical (only for IPSS-RA)| Bone Marrow Blasts | AGE | numerical, in years | [18-120] | | cytogenetics | Presence of del(5q) | del5q | binary | 0/1 | | cytogenetics | Presence of -7/del(7q) | del7_7q | binary | 0/1 | | cytogenetics | Presence of -17/del(17p) | del17_17p | binary | 0/1 | | cytogenetics | Complex karyotype | complex | binary | 0/1 | | cytogenetics | Cytogenetics Category | CYTO_IPSSR | categorical | Very Good/Good/Intermediate/Poor/Very Poor | | TP53 locus | Number of TP53 mutations | TP53mut | categorical | 0/1/2 or more | | TP53 locus | Maximum TP53 VAF | TP53maxvaf | numerical, between 0 and 1 | [0-1] | | TP53 locus | Loss of heterozygosity at TP53| TP53loh | binary | 0/1 | | MLL and FLT3 mutations | MLL PTD | MLL_PTD | binary | 0/1 | | MLL and FLT3 mutations | FLT3 ITD or TKD | FLT3 | binary | 0/1 | | gene main effect | ASXL1 | ASXL1 | binary | 0/1/NA | | gene main effect | CBL | CBL | binary | 0/1/NA | | gene main effect | DNMT3A | DNMT3A | binary | 0/1/NA | | gene main effect | ETV6 | ETV6 | binary | 0/1/NA | | gene main effect | EZH2 | EZH2 | binary | 0/1/NA | | gene main effect | IDH2 | IDH2 | binary | 0/1/NA | | gene main effect | KRAS | KRAS | binary | 0/1/NA | | gene main effect | NPM1 | NPM1 | binary | 0/1/NA | | gene main effect | NRAS | NRAS | binary | 0/1/NA | | gene main effect | RUNX1 | RUNX1 | binary | 0/1/NA | | gene main effect | SF3B1 | SF3B1 | binary | 0/1/NA | | gene main effect | SRSF2 | SRSF2 | binary | 0/1/NA | | gene main effect | U2AF1 | U2AF1 | binary | 0/1/NA | | gene residual | | BCOR | binary | 0/1/NA | | gene residual | | BCORL1 | binary | 0/1/NA | | gene residual | | CEBPA | binary | 0/1/NA | | gene residual | | ETNK1 | binary | 0/1/NA | | gene residual | | GATA2 | binary | 0/1/NA | | gene residual | | GNB1 | binary | 0/1/NA | | gene residual | | IDH1 | binary | 0/1/NA | | gene residual | | NF1 | binary | 0/1/NA | | gene residual | | PHF6 | binary | 0/1/NA | | gene residual | | PPM1D | binary | 0/1/NA | | gene residual | | PTPN11 | binary | 0/1/NA | | gene residual | | PRPF8 | binary | 0/1/NA | | gene residual | | SETBP1 | binary | 0/1/NA | | gene residual | | STAG2 | binary | 0/1/NA | | gene residual | | WT1 | binary | 0/1/NA |

:question: Question

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