Characteristics of Myelodysplastic Syndromes with Idiopathic Pulmonary Fibrosis
Bibliographic record
Abstract
Introduction The co-occurrence of myelodysplastic syndromes (MDS) and idiopathic pulmonary fibrosis (IPF) is highly specific of an underlying short telomere syndrome (STS), most of which harbor a TERT germline mutation. The understanding of germline MDS predisposition is increasing, however, the only evidence in this context is reported by Papiris et al., describing 5 patients co-affected with IPF and MDS. Most STS are diagnosed after the sixth decade, with IPF usually being the primary manifestation. This particular scenario is of relevance, as comorbidities like IPF alongside with other known predispositions may play a crucial role in hematologic malignancy management. The study aims to describe the molecular profile, clinical and pulmonary characteristics, and outcomes of a cohort of 141 patients with MDS and pulmonary fibrosis (PF) diagnosed in our center from November 2008 to February 2022. Methods This retrospective single-center study included patients diagnosed with MDS and PF from November 2008-February 2022. For mutational analysis, 28 or 81-gene NGS panel covering TERT and TERT but no other STS-related genes were used. Responses were assessed using the IWG2023 criteria. Chest CT images and functional respiratory tests at IPF diagnosis were individually reviewed by the Pulmonary Team of our center. Results Of the 141 patients identified with MDS and PF, 75 were diagnosed with IPF, and of those, 61 had available molecular data. The median age at MDS diagnosis was 71 years (52-91), and 75% were male. Fifty-one (84%) had MDS, 2 (4%) MDS/MPN, and 7 (12%) CMML. Based on IPSS-M, 19 (40%) were classified as very high-risk, 10 ( 21%) high-risk, 2 (4%) moderate-high, 4 (9%) moderate-low, 8 (17%) low and 4 (9%) as a very low. CPSS-Mol on the CMML patients stratified 3 (44%), 2 (28%), 2 (28%) to high, intermediate-2, and intermediate-1 risk categories, respectively. Twenty patients (33%) progressed to AML, and 13 (20%) underwent an HSCT. Results from the 81-gene panel, which includes TERT were available in 46 patients, 13 of whom harbored a TERT germinal mutation. This led to an incidence of suspected STS of 28.3% in individuals co-affected with IPF and MDS. Overall Response Rate (ORR) (IWG2023 criteria) was 62.9% (first line), 39.4% (second line), and 50% (third line). The median follow-up of the cohort was 39.01 months, and the median Overall Survival (OS) was 30.6 months (95% CI 22.4 - 48.4, n=61). TERT-mutated patients were diagnosed with MDS younger (median 69 vs. 73 years, p=0.033). Five (36%) of them had MDS-IB2, 3 (21%) MDS-biTP53, and 3 (21%) MDS-SF3B1 and MDS-LB, respectively (none CMML or MDS/MPS). Mutations in TERT were significantly associated with co-mutations in SRSF2 (p=0.043), without differences with other splicing genes. TERT-mutated patients also presented an earlier age of IPF diagnosis (p=0.039) and carried with more severe interstitial lung disease (ILD) (p=0.001). No differences were identified in smoke history, fibrogenic exposure, patterns on CT at IPF diagnosis, pulmonary function tests, or respiratory symptoms. Patients with wildtype TERT did not exhibit any cutaneous (p=0.000), hepatic involvement (p=0.024), nor premature aging signs (p=0.088). First-grade family history was significant for IPF (p=0.003). TERT and wildtype TERT mutated groups received equivalent MDS treatments; no differences were observed in responses. OS was not shorter for TERT-mutated patients (wildtype 29.2 months (95% CI 22.4 - 66.3) vs. TERT-mutated 19.7 months (95% CI 7.9 - NA); p=0.92). However, harboring a TERT mutation conferred a shorter OS only in very-high IPSS-M patients (median OS 20.3 months (95%CI 15.5-NA; n=9) vs. 8 months (95% CI 6.14 - NA, n=5); p=0.038). Patients with TERT mutation died significantly more of non-infectious respiratory causes (p=0.005). Univariate analysis for OS was significant for IPSS-M (p=0.000), 2022 WHO classification (p=0.001), and TP53 mutation (p=0.000). TERT mutations and pulmonary characteristics had no impact on survival. Conclusion The incidence of suspected STS in patients with concomitant MDS and IPF is 28.3%. MDS and IPF present at younger age in those with TERT mutation and may impact survival if they present with high-risk MDS. Larger cohorts are needed to validate these results. Nevertheless, this study has profound implications for clinical decisions, family counseling, and early advice on avoiding smoke and other potential fibrogenic exposures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".