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Poster session 06

1681P - Testing the generalizability of cfDNA fragmentomic features across different studies for cancer early detection

Date

10 Sep 2022

Session

Poster session 06

Topics

Translational Research;  Primary Prevention;  Multi-Disciplinary and Multi-Professional Cancer Care

Tumour Site

Non-Small Cell Lung Cancer

Presenters

Shu Su

Citation

Annals of Oncology (2022) 33 (suppl_7): S758-S771. 10.1016/annonc/annonc1078

Authors

S. Su1, Y. Xuan2, X. Fan3, H. Bao3, H. Tang3, X. Lv1, W. Ren4, F. Chen1, X. Wu5, Y. Shao3, T. Wang6, L. Wang1

Author affiliations

  • 1 Department Of Medical Oncology, Nanjing Drum Tower Hospital Affiliated to Nanjing University Medical School, 210008 - Nanjing/CN
  • 2 Department Of Thoracic And Cardiovascular Surgery, Nanjing Drum Tower Hospital Affiliated to Nanjing University Medical School, Nanjing/CN
  • 3 Research And Development, Nanjing GENESEEQ Technology Inc., 210032 - Nanjing/CN
  • 4 Department Of Medical Oncology, Nanjing Drum Tower Hospital Affiliated to Nanjing University Medical School, Nanjing/CN
  • 5 Geneseeq Research Institute, Nanjing Geneseeq Technology Inc., 210000 - Nanjing/CN
  • 6 Department Of Thoracic And Cardiovascular Surgery, Nanjing Drum Tower Hospital Affiliated to Nanjing University Medical School, 210008 - Nanjing/CN
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Abstract 1681P

Background

The use of cfDNA fragmentomic features has recently shown a strong surge in cancer early detection models. Although many individual studies have demonstrated successful applications in cancer detection, their generalizability remains unclear due to the lack of cross-study validations. A generalized model across studies will allow for robust diagnosis of high-risk individuals.

Methods

This study evaluated the window-level cfDNA size summary (WINDOW-FSS) feature from the commonly used method profiling the short (100 -150bp) and long (151-220 bp) cfDNA fragments and our in-house developed feature mapping chromosome arm-level fragment size distribution (ARM-FSD). The two features were analyzed uniformly in the lung cancer and pan-cancer models. The performance of the two models was also cross-study evaluated in two external cohorts. For pan-cancer, we built the models on an online pan-cancer dataset and assessed their performance using two independent cohorts across studies.

Results

The lung cancer models implementing ARM-FSD and WINDOW-FSS reached the area under the curve (AUC) of 0.99 and 0.86 in our internal validation cohort. The ARM-FSD lung cancer model outperformed the WINDOW-FSS model by ∼10% when tested in two external cohorts (AUC: 0.97 vs 0.86; 0.87 vs 0.76). Dimension reduction of features showed that ARM-FSD will produce even better results when denoised using a non-linear algorithm, such as autoencoder, while performance of WINDOW-FSS derived models did not benefit from this. Using the online pan-cancer cohort for modeling, our ARM-FSD and WINDOW-FSS pan-cancer models achieved the AUCs of 0.91 and 0.93, respectively. The performance of the ARM-FSD pan-cancer model is consistently higher than the WINDOW-FSS model (0.88 vs 0.75, 0.98 vs 0.63) in two cross-study validation cohorts.

Conclusions

Our cross-study analysis revealed performance variation of models implementing different cfDNA fragmentomic features. The ARM-FSD-based models have consistently demonstrated higher generalizability and robustness in cohorts from diverse sources, highlighting the necessity of cross-study feature verification for future predictive model development.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Jiangsu Province Health Planning Commission Medical Research Project; National Natural Science Foundation of China.

Disclosure

X. Fan, H. Bao, H. Tang, X. Wu, Y. Shao: Financial Interests, Personal, Full or part-time Employment: Nanjing Geneseeq Technology Inc. All other authors have declared no conflicts of interest.

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