Upfront 69-gene next-generation sequencing versus reflex PCR in BCR::ABL1-negative myeloproliferative neoplasms: Diagnostic yield, prognostic impact, and cost analysis in an Indian cohort.

Abstract

163 Background: Sequential PCR testing for JAK2, CALR, and MPL mutations remains standard in MPN evaluation but misses non-canonical drivers and high-molecular-risk (HMR) mutations that inform MIPSS70+ v2.0 and GIPSS prognostic scoring. No Indian study has quantified the combined clinical and economic impact of upfront NGS. Methods: We analyzed 195 consecutive patients with suspected BCR::ABL1-negative MPNs at an Indian tertiary referral center. All underwent 69-gene targeted NGS (Oncomine Myeloid GX v2, Genexus platform). Quality control excluded variants with VAF <2%, VUS with VAF <5%, and non-MPN genes. Prognostic reclassification was assessed by MIPSS70+ v2.0 and GIPSS. A Markov model compared reflex-PCR-then-NGS versus NGS-first strategies using real-world laboratory costs (PCR ₹3,500–5,000/assay; 69-gene NGS ₹18,000/panel). Results: Median age was 47 years (range 10–86); 66% male. Canonical drivers were identified in 86 patients (44.1%): JAK2 V617F in 70 (35.9%), CALR in 14 (7.2%), MPL in 1 (0.5%), JAK2 exon 12 in 1 (0.5%). Among 109 triple-negative patients, NGS detected non-canonical mutations in 41 (21.0% of cohort). HMR mutations (ASXL1, SRSF2, EZH2, IDH1/2) were present in 21 (10.8%); 11 (52%) were PCR-invisible. Applying MIPSS70+ v2.0 and GIPSS, 32 patients (16.4%) were reclassified to higher risk categories based solely on NGS-detected mutations, directly informing transplant eligibility. Specifically, standard PCR algorithms missed crucial HMR mutations co-occurring with canonical drivers in 10 patients, resulting in artificially underestimated MIPSS70+ risk scores prior to NGS. The most frequent cooperating genes were TET2 (11.7%), SRSF2 (6.6%), and ASXL1 (4.1%). Fifty-nine patients (30.3%) harbored two or more co-occurring mutations. Notably, canonical driver mutations exhibited significantly higher variant allele frequencies compared to non-canonical cooperating mutations (mean 46.3% vs. 27.3%, p<0.001). The NGS-first strategy was economically dominant (Table). Conclusions: Upfront NGS detected actionable mutations in 65.1% of patients, reclassified 16.4% to higher MIPSS70+ v2.0/GIPSS risk, and was both cheaper and faster than reflex PCR workflows. These data support NGS as first-line diagnostic standard for MPN evaluation in resource-conscious settings. Outcome NGS-First Reflex-then-NGS Canonical drivers detected, n (%) 86 (44.1) 86 (44.1) Non-canonical mutations detected, n (%) 41 (21.0) 0 (0) HMR mutations detected, n (%) 21 (10.8) 10 (5.1) Prognostic reclassification, n (%) 32 (16.4) 0 (0) Mean cost per patient (₹) 18,000 21,633 Mean turnaround time (days) 3.0 6.6 Cost saving per patient (₹) 3,633 Reference