ipssm

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

📃 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.

🚀 Installation instructions

# Using npm
npm install ipssm

💥 IPSS-M Usage

🔥 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: {...}
  },
}

⚡ 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)

🦾 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]

🗒️ 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

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