IFN-signaling gene expression as a diagnostic biomarker for monogenic interferonopathies

IFN-signaling gene (ISG) expression scores are potential markers of inflammation with significance from cancer to genetic syndromes. In Aicardi Goutières Syndrome (AGS), a disorder of abnormal DNA and RNA metabolism, this score has potential as a diagnostic biomarker, although the approach to ISG calculation has not been standardized or validated. To optimize ISG calculation and validate ISG as a diagnostic biomarker, mRNA levels of 36 type I IFN response genes were quantified from 997 samples (including 334 AGS), and samples were randomized into training and test data sets. An independent validation cohort (n = 122) was also collected. ISGs were calculated using all potential combinations up to 6 genes. A 4-gene approach (IFI44L, IFI27, USP18, IFI6) was the best-performing model (AUC of 0.8872 [training data set], 0.9245 [test data set]). The majority of top-performing gene combinations included IFI44L. Performance of IFI44L alone was 0.8762 (training data set) and 0.9580 (test data set) by AUC. The top approaches were able to discriminate individuals with genetic interferonopathy from control samples. This study validates the context of use for the ISG score as a diagnostic biomarker and underscores the importance of IFI44L in diagnosis of genetic interferonopathies.

for a test based on the optimal cut-point (obtained using Youden Index) in the Test Data Set that contains the . **Negative Predictive Value, assuming prevalence (prev) of AGS = 10% (5%,15%); sensitivity (se), and specificity (sp) are as estimated for a test that classifies a sample as AGS if the classifier is >= each of the cutpoints in Table 3B.The negative predictive value =

Sensitivity = Specificity
The point on the ROC curve at which the sensitivity = specificity is the point on the ROC curve at which the product of sensitivity and specificity is maximized.
It is also the point of intersection between the ROC curve and the line that connects (0,1) and (1,0).

Maximum Youden's Index
The point on the ROC curve at which Youden's index (sensitivity + specificity -1) is maximized.This is the point on the ROC curve at which the sum of sensitivity and specificity is maximized.Since sensitivity = true-positive (TP) rate and 1 -specificity = false-positive (FP) rate, this is also the point at which the difference between the TP and FP rate is maximized.In addition, in the ROC plot of sensitivity versus 1-specificity, the 45-degree line represents an uninformative test.The vertical distance between any point on the ROC curve and a point on the 45-degree line is the distance between (1 -specificity, 1specificity) and (1-specificity, sensitivity) = (sensitivity + specificity -1)^2.
Therefore, the point on the ROC at which Youden's index is maximized is also the point that maximizes the vertical distance between the ROC curve and the 45-degree line.Since the points on the 45-degree line satisfy TP = FP, the 45degree line is the ROC curve for an uninformative test.The maximum value of the Youden's index is therefore the point on the ROC curve that has the greatest vertical distance from the ROC curve for an uninformative test.

Minimum
Positive, False Negative, Positive Predictive Value (PPV), Negative Predictive Value (PPV) for a test based on the optimal cut-point (obtained using Youden Index) in the Test Data Set that contains the AGS Cohort (N = 165) and Control Cohorts 1 and 2 combined (N = 574).(See Consort Diagram in Figure 1b.) TTG GAT CTT AGA AGA GAA TCA CTA ACC AGA GAC GAG ACT CAG TGA GTG AGC AGG TGT TTT GGA CAA TGG ACT GGT TGA GCC CAT CCC TAT T CD274 GGA TAC TTC TGA ACA AGG AGC CTC CAA GCA AAT CAT CCA TTG CTC ATC CTA GGA AGA CGG GTT GAG AAT CCC TAA TTT GAG GGT CAG TTC CTG CAG AAG T CXCL10 GCA GAG GAA CCT CCA GTC TCA GCA CCA TGA ATC AAA CTG CGA TTC TGA TTT GCT GCC TTA TCT TTC TGA CTC TAA GTG GCA TTC AAG GAG TAC CTC TCT C DDX60 TTC GAG ATG CTT ATC AAA AGA GTG GGG AAG TTT CTT GGA AGA GAG TTA CCC ATA TTT CCT GAT AGT TGC AGA CGA AGG CCT GAA CGA TCT ACA AAC ACA G GBP1 CCA GAT GAC CAG CAG TAG ACA AAT GGA TAC TGA GCA GAG TCT TAG GTA AAA GTC TTG GGA AAT ATT TGG GCA TTG GTC TGG CCA AGT CTA CAA TGT CCC A HERC5 CTG AAA GTT GGA ATG AAA GAG ACC CTA TAA GAG CAC TGA CAT GTT TCA GTG TCC TCT TCC TCC CTA AAT ATT CTA CAA TGG AAA CAG TTG AAG AAG CGC T HERC6 TCC ATC ACC CAG ATT TAT ACT TAG AGT CAG ACG AAG TCG CCT GGT TAA AGA TGC TCT GCG TCA ATT AAG TCA AGC TGA AGC TAC TGA CTT CTG CAA AGT A IDO1 CTA TTA TAA GAT GCT CTG AAA ACT CTT CAG ACA CTG AGG GGC ACC AGA GGA GCA GAC TAC AAG AAT GGC ACA CGC TAT GGA AAA CTC CTG GAC AAT CAG T IFI27 TCA CTG GGA GCA ACT GGA CTC TCC GGA TTG ACC AAG TTC ATC CTG GGC TCC ATT GGG TCT GCC ATT GCG GCT GTC ATT GCG AGG TTC TAC TAG CTC CCT G IFI44 TTT CCA AGG GCA TGT AAC GCA TCA GGC TTT GGT GGG CAC TAA TAC AAC TGG GAT ATC TGA GAA GTA TAG GAC ATA CTC TAT TAG AGA CGG GAA AGA TGG C IFI44L ATC TCT GCC ATT TAT GTT GTG TGA CAC TAT GGG GCT AGA TGG GGC AGA AGG AGC AGG ACT GTG CAT GGA TGA CAT TCC CCA CAT CTT AAA AGG TTG TAT G IFI6 GGG GTG GAG GCA GGT AAG AAA AAG TGC TCG GAG AGC TCG GAC AGC GGC TCC GGG TTC TGG AAG GCC CTG ACC TTC ATG GCC GTC GGA GGA GGA CTC GCA G IFIT1 GAG AAA GGC ATT AGA TCT GGA AAG CTT GAG CCT CCT TGG GTT CGT CTA CAA ATT GGA AGG AAA TAT GAA TGA AGC CCT GGA GTA CTA TGA GCG GGC CCT G IFIT2 TGC ATC CCA TAG AGG TTA GTC CTG CAT AGC CAG TAA TGT GCT AAG TTC ATC CAA AAG CTG GCG GAC CAA AGT CTA AAT AGG GCT CAG TAT CCC CCA TCG C IFIT3 CGC CTG CTA AGG GAT GCC CCT TCA GGC ATA GGC AGT ATT TTC CTG TCA GCA TCT GAG CTT GAG GAT GGT AGT GAG GAA ATG GGC CAG GGC GCA GTC AGC T IFIT5 GGG CTG TGT TCA TAC ACA GAA GGG GCC TGA GAT TTC TGC ACT TTA AAC AAG CTC CTC CTA GGT GAG GAT GCT GTG GCT GTT CTA ATT ACA TTT TGA GTA G IFNA2 GCC TAA GGT TTA GGC TCA CCC ATT TCA ACC AGT CTA GCA GCA TCT GCA ACA TCT ACA ATG GCC TTG ACC TTT GCT TTA CTG GTG GCC CTC CTG GTG CTC A ISG15 CCC GGC AGC ACG GTC CTG CTG GTG GTG GAC AAA TGC GAC GAA CCT CTG AGC ATC CTG GTG AGG AAT AAC AAG GGC CGC AGC AGC ACC TAC GAG GTA CGG C LAMP3 CAG CCA TCG TCA GTC AAG ACT GGA ATT TAT CAG GTT CTA AAC GGA AGC AGA CTC TGT ATA AAA GCA GAG ATG GGG ATA CAG CTG ATT GTT CAA GAC AAG G LY6E CTG CCC CAT CCC AGA AGG CGT CAA TGT TGG TGT GGC TTC CAT GGG CAT CAG CTG CTG CCA GAG CTT TCT GTG CAA TTT CAG TGC GGC CGA TGG CGG GCT G MX1 GCC TTT AAT CAG GAC ATC ACT GCT CTC ATG CAA GGA GAG GAA ACT GTA GGG GAG GAA GAC ATT CGG CTG TTT ACC AGA CTC CGA CAC GAG TTC CAC AAA T OAS1 TCT GAG GAA ACG AAA CCA ACA GCA GTC CAA GCT CAG TCA GCA GAA GAG ATA AAA GCA AAC AGG TCT GGG AGG CAG TTC TGT TGC CAC TCT CTC TCC TGT C OAS2 TGA AAA ACA ATT TCG AGA TCC AGA AGT CCC TTG ATG GGT TCA CCA TCC AGG TGT TCA CAA AAA ATC AGA GAA TCT CTT TCG AGG TGC TGG CCG CCT TCA A OAS3 GAG TGC CTT AGA CAG CCT GAC TCT CCA CAA ACC ACT GTT AAA ACT TAC CTG CTA GGA ATG CTA GAT TGA ATG GGA TGG GAA GAG CCT TCC CTC ATT ATT G OASL GGC GTT TCT GAG CTG TTT CCA CAG CTT CCA GGA GGC AGC CAA GCA TCA CAA AGA TGT TCT GAG GCT GAT ATG GAA AAC CAT GTG GCA AAG CCA GGA CCT G PLSCR1 TTT GAA AGC ACT GGC AGC CAG GAA CAA AAA TCA GGA GTG TGG TAG TGG ATT AGT GAA AGT CTC CTC AGG AAA TCT GAA GTC TGT ATA TTG ATT GAG ACT A RSAD2 ACC TTA TTC TGG ATG AAT ATA TGC GCT TTC TGA ACT GTA GAA AGG GAC GGA AGG ACC CTT CCA AGT CCA TCC TGG ATG TTG GTG TAG AAG AAG CTA TAA A RTP4 AGT AAT CCT GGA AGT GTC CCT GGA AGG ATC CCA TGA CAC AGC CAA TTG TGA GGC ATG CAC TTT GGG CAT CTG TGG ACA GGG CTT AAA AAG CTG CAT GAC A SAMD9 TGT GGG GGT GGT GAA AGG GAA GTA GAA CCG AAA CAA GAT TAG TCC TGA GTT AAC AAT GGC TGC AAG CTG GAT ACA TGG AAT TCA GCA CAC TTT TCT CCC T SIGLEC1 GCC AGA ATC TGT GAT GAC TCC AGC CTA TGA ATG TGA ATG AGG CAG TGT TGA GTC CTG CCC GCC TCT ACG AAA ACA GCT CTG TGA CAT CTG ACT TTT TAT G SOCS1 TTA ACT GTA TCT GGA GCC AGG ACC TGA ACT CGC ACC TCC TAC CTC TTC ATG TTT ACA TAT ACC CAG TAT CTT TGC ACA AAC CAG GGG TTG GGG GAG GGT C USP18 GGA AAT GCC CAA AAC CTT CAG AGA TTG ACA CGC TGT CAT TTT CCA TTT CCG TTC CTG GAT CTA CGG AGT CTT CTA AGA GAT TTT GCA ATG AGG AGA AGC A ALAS1 AGA AAG CAG GCA AAT CTC TGT TGT TCT ATG CCC AAA ACT GCC CCA AGA TGA TGG AAG TTG GGG CCA AGC CAG CCC CTC GGG CAT TGT CCA CTG CAG CAG T HPRT1 TGT GAT GAA GGA GAT GGG AGG CCA TCA CAT TGT AGC CCT CTG TGT GCT CAA GGG GGG CTA TAA ATT CTT TGC TGA CCT GCT GGA TTA CAT CAA AGC ACT G TBP ACA GTG AAT CTT GGT TGT AAA CTT GAC CTA AAG ACC ATT GCA CTT CGT GCC CGA AAC GCC GAA TAT AAT CCC AAG CGG TTT GCT GCG GTA ATC ATG AGG A TUBB TTC TAA GTA TGT CCA TTT CCC ATC TCA GCT TCA AGG GAG GTG TCA GCA GTA TTA TCT CCA CTT TCA ATC TCC CTC CAA GCT CTA CTC TGG AGG AGT CTG T Supplemental Table 13.Overview of Criteria used to Obtain Optimal Values on ROC Curves-Prevalence Cut-point Criteria* This point on the ROC curve is the point at which the distance from (0,1) (the point corresponding to FP = 0 and TP = 1) is minimized.This is the cut-point at which the Weighted Number Needed to Misdiagnose (NNM) is maximized, where the NNM represents the number of samples that need to be tested in order for one sample to be misdiagnosed.The probability of misdiagnosis = P( + test result | sample is non-AGS) x P(sample is non-AGS) + P(-test result | sample is AGS) x P(sample is AGS) = (1 -specificity)x (1 -prevalence) + (1 -sensitivity) x prevalence.If the probability of misdiagnosis is 0.5, the number of samples that need to be tested in order for one sample to be misdiagnosed is 2. We consider the Weighted Number Needed to Misdiagnose (proposed by Habibzadeh) because we are more concerned about the harm due to a false negative (FN) than the harm due to a false positive (FP).We let C = the harm due to FN divided by the harm due to FP.We assume C = 2 and obtain the cut-point at which the Weighted Number Needed to Misdiagnose is maximized, where the Weighted Number to Misdiagnose = 1/[C x FN + FP] = 1/[ (1 -specificity) x (1 -prevalence) + C x (1 -sensitivity) x prevalence].

Table 5 : Sensitivity, Specificity, and Number of samples needed to misdiagnose (NNM) for
a test based on the optimal cut-point (obtained using Youden Index) in the Test Data Set that contains the AGS Cohort (N = 165) and Control Cohort 1 (N = 77).(SeeConsortDiagram in Figure1b.) (1he values in this column are equal in Supplemental Tables5 and 6. **Number Samples Needed to Misdiagnose (NNM), assuming prevalence (prev) of AGS = 10% (5%, 15%); harm of false negative (C) is twice that of false positive results; and sensitivity (se), specificity (sp) are as estimated for a test that classifies a sample as AGS if the classifier is >= each of the cut-points in Table3A.The weighted number to misdiagnose(1

Table 10 .
* Nucleotide sequences for probe A

Table 11 .
Nucleotide sequences for probe B

Table 12 .
Synthetic DNA oligonucleotides used as a standard