Abstract 939: Genomic characterization of PMBCL, cHL and DLBCL utilizing tissue and liquid biopsies
Bibliographic record
Abstract
Abstract Background: Primary mediastinal large B-cell lymphoma (PMBCL), arising from thymic medullary B cells, shares molecular features with classic Hodgkin lymphoma (cHL), including activation of JAK/STAT and Nuclear Factor Kappa B (NF-ƙB) pathways, as well as PD-L1 mediated immune evasion. Much of the molecular characterization relied on availability of tissue samples. Cell-free DNA (cfDNA) has emerged as a promising non-invasive approach for molecular profiling in lymphomas. In this study, we characterized the molecular features of PMBCL and cHL in the Chinese population utilizing both tissue-based and liquid biopsies. Methods: Tissue and/or plasma samples from a total of 35 PMBCL patients, 52 cHL patients and 81 DLBCL patients that were subjected to targeted next-generation sequencing (NGS) using a 475 cancer-related gene panel were included in the analysis. Results: Analysis of matched tumor and plasma samples from 38 cHL patients revealed 152 (28.10%) overlapping genetic alterations, with a higher number of mutations detected in the plasma compared to tissue (Total=472 vs. 221; Median=11 vs. 3 per patient, P<0.01). Of these, 320 (59.15%) were unique to the plasma, whereas 69 (12.15%) were only found in the tissue. Mutant allele frequencies (MAFs) were also lower in the tissue than in the plasma, with a median of 1.71% vs 2.99% (P<0.01). In 18 PMBCL patients, who had paired plasma and tumor samples, similar number of genetic alterations were detected (Total=347 vs. 411; Median=19.5 vs. 21 per patient, P=0.09). Of these, 273 (56.29%) alterations were shared variants, 74 (15.28%) were unique to the plasma sample and 138 (28.45%) were unique to the tissue. MAFs were higher in the tissue compared with the plasma (23.35% vs. 8.78%, P<0.01). Comparisons of mutational profiles among PMBCL, cHL, and DLBCL showed similar mutational profiles between PMBCL and cHL. The most frequently detected genetic alterations in PMBCL and cHL were STAT6 (68.57% vs. 40.38%), SOCS1 (57.14% vs. 57.69%), ACTB (48.57% vs. 23.08%), B2M (48.57% vs. 42.31%), and TNFAIP3 (34.29% vs. 40.38%). On the contrary, the frequencies of STAT6 (68.57% vs. 6.17%, P <0.01), SOCS1 (57.14% vs. 18.52%, P<0.05), and ACTB (48.57% vs. 9.88%, P<0.05) were markedly different comparing PMBCL and DLBCL. Meanwhile, the more common genetic alterations in DLBCL, such as MYD88 (33.33% vs. 0.00%, P<0.01), BCL6 (33.33% vs. 11.43%, P=0.06), BCL2 (20.99% vs. 2.86%, P<0.05), and CDKN2A (24.69% vs. 2.86%, P<0.05) were rarely detected in PMBCL patients. Conclusion: Our findings support that ctDNA in PMBCL and cHL may be an attractive source of genetic materials to assess tumor genomics and guide treatment decisions, especially in cHL with only 1% Hodgkin and Reed/Sternberg (HRS) cells. We also demonstrate for the first time in the Chinese population that PMBCL and cHL shared comparable mutational profiles, which were different from that of DLBCL. Citation Format: Xiaonan Wang, Lu Shen, Liuqing Zhu, Jiani C. Yin, Haimeng Tang, Yang Shao. Genomic characterization of PMBCL, cHL and DLBCL utilizing tissue and liquid biopsies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 939.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 0.000 |
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".