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Biomarker pairs for predicting preterm birth

A technology of biomarkers, biological samples, applied in the field of biomarker pairs for predicting preterm birth, can solve problems such as not leading to clinically useful tests

Active Publication Date: 2018-08-24
SERA PROGNOSTICS
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] Despite numerous studies identifying women at risk, PTB prediction algorithms based solely on clinical and demographic factors or using measured serum or vaginal biomarkers have not resulted in clinically useful tests

Method used

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  • Biomarker pairs for predicting preterm birth
  • Biomarker pairs for predicting preterm birth
  • Biomarker pairs for predicting preterm birth

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 2

[0270] Example 2 provides an example of such an analysis.

[0271] In one embodiment, hierarchical clustering is implemented in the derivation of the predictive model, using Pearson correlation as a clustering measure. One approach is to consider the preterm birth data set as a "learning sample" in a "supervised learning" problem. CART is standard in medical applications (Singer, Recursive Partitioning in the Health Sciences, (1999)) and can be modified by: converting any qualitative feature into a quantitative feature; Classification of the level of significance achieved using method evaluation; and suitable application of the lasso method. Indeed, by properly using the Gini classification criterion in regression quality assessment, the prediction problem can be transformed into a regression problem without losing the sight of prediction.

[0272] This method leads to the so-called FlexTree (Huang, Proc.Nat.Acad.Sci.U.S.A 101:10529-10534 (2004)). FlexTree works well when...

Embodiment 1

[0289] Example 1. Establishment of a sample set for the discovery and validation of preterm biomarkers

[0290] Establishment of standard protocols governing the conduct of proteomic evaluation of preterm birth risk (PAPR) clinical studies. Samples were obtained from women at 11 Institutional Review Board (IRB)-approved sites throughout the United States. After providing informed consent, serum and plasma samples were obtained, along with relevant information on patient demographics, past medical and pregnancy history, current pregnancy history, and concurrent drug therapy. After delivery, data are collected on maternal and infant conditions and complications. Serum and plasma samples were processed according to protocols requiring standardized refrigerated centrifugation, aliquoting of samples into 2-D barcoded freezing vials and subsequent freezing at -80°C.

[0291] After delivery, preterm birth cases were reviewed individually to determine whether their status was sponta...

Embodiment 3

[0361] Example 3. Correlation of mass spectrometry and immunoassay data

[0362] This example shows the results of a Myriad RBM screen identifying IBP4 as well as other individual biomarkers of sPTB at early, mid and late gestational age acquisition windows, (2) correlation of MS and immunoassay results for SHBG / IBP4, and ( 3) Clinical data related to SHBG as a biomarker of sPTB.

[0363] RBM data

[0364] Briefly, RBM assayed 40 cases and 40 controls from PAPR (20 / 20 from early window, 10 / 10 from mid window, 10 / 10 from late window). RBM used Human Discovery MAP 250+v2.0 (Myriad RBM, Austin, TX). The goal of these analyzes was to develop a multivariate model using multiple analytes to predict PTB. We used four modeling methods: random forest (rf), boosting, lasso and logistic (logit) methods. We performed a first round of variable selection, where each method independently selected the best 15 variables for that method. From these 15 variables, the area under the ROC curv...

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Abstract

The disclosure provides a pair of isolated biomarkers selected from the group consisting of IBP4 / SHBG, IBP4 / PSG3, IBP4 / LYAM1, IBP4 / IGF2, CLUS / IBP3, CLUS / IGF2, CLUS / LYAM1, INHBC / PSG3, INHBC / IGF2, PSG2 / LYAM1, PSG2 / IGF2, PSG2 / LYAM1, PEDF / PSG3, PEDF / SHBG, PEDF / LYAMl, CD14 / LYAM1, and APOC3 / LYAM1, wherein the pair of biomarkers exhibits a change in reversal value between pregnant females at risk for pre-term birth and term controls. Also provided is a method of determining probability for preterm birth in a pregnant female, the method comprising measuring in a biological sample obtained from the pregnant female a reversal value for at least one pair of biomarkers selected from the group consisting of IBP4 / SFIBG, IBP4 / PSG3, IBP4 / LYAM1, IBP4 / IGF2, CLUS / IBP3, CLUS / IGF2, CLUS / LYAM1, INHBC / PSG3, INHBC / IGF2, PSG2 / LYAM1, PSG2 / IGF2, PSG2 / LYAM1, PEDF / PSG3, PEDF / SHBG, PEDF / LYAMl, CD14 / LYAM1, and APOC3 / LYAM1 to determine the probability for preterm birth in the pregnant female.

Description

[0001] The present application claims U.S. Provisional Patent Application No. 62 / 290,796 filed on February 3, 2016, U.S. Provisional Patent Application No. 62 / 387,420 filed on December 24, 2015, and U.S. Provisional Patent Application No. 62 / 387,420 filed on June 19, 2015. Priority to Patent Application No. 62 / 182,349, each of which is hereby incorporated by reference in its entirety. [0002] The present invention relates generally to the field of precision medicine, and more particularly, to compositions and methods for determining the probability of preterm birth in a pregnant female animal. Background of the invention [0003] According to the World Health Organization, an estimated 15 million babies are born prematurely (before the 37th full week of pregnancy) each year. According to reliable data, the rate of preterm births is increasing in almost all countries. See, World Health Organization; March of Dimes; The Partnership for Maternal, Newborn & Child Health; Save th...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N33/68C07K14/47C12Q1/6883
CPCG01N33/689G01N33/68C12Q1/6883C12Q2600/158G01N2333/4713G01N2333/4745G01N2333/47G01N2800/368C07K14/47
Inventor 约翰·杰伊·博尼费斯特蕾西·弗莱舍安德鲁·加斯曼杰夫·弗利克查德·布拉德福德阿输迦·波尔比蒂亚都尔林·爱德华·希科克保罗·科尔尼格雷戈里·查尔斯·克里驰菲尔德
Owner SERA PROGNOSTICS
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