Cancer Research and Treatment

Lung and Thoracic cancer

Comparison of the Predictive Power of a Combination versus Individual Biomarker Testing in Non–Small Cell Lung Cancer Patients Treated with Immune Checkpoint Inhibitors: Hyojin Kim, Hyun Jung Kwon, Eun Sun Kim, Soohyeon Kwon, Kyoung Jin Suh, Se Hyun Kim, Yu Jung Kim, Jong Seok Lee, Jin-Haeng Chung; Cancer Res Treat. 2022;54(2):424-433. Published online July 7, 2021; DOI: https://doi.org/10.4143/crt.2021.583

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Purpose
Since tumor mutational burden (TMB) and gene expression profiling (GEP) have complementary effects, they may have improved predictive power when used in combination. Here, we investigated the ability of TMB and GEP to predict the immunotherapy response in patients with non–small cell lung cancer (NSCLC) and assessed if this combination can improve predictive power compared to that when used individually.
Materials and Methods
This retrospective cohort study included 30 patients with NSCLC who received immune checkpoint inhibitors (ICI) therapy at the Seoul National University Bundang Hospital. programmed cell death-ligand-1 (PD-L1) protein expression was assessed using immunohistochemistry, and TMB was measured by targeted deep sequencing. Gene expression was determined using NanoString nCounter analysis for the PanCancer IO360 panel, and enrichment analysis were performed.
Results
Eleven patients (36.7%) showed a durable clinical benefit (DCB), whereas 19 (63.3%) showed no durable benefit (NDB). TMB and enrichment scores (ES) showed significant differences between the DCB and NDB groups (p=0.044 and p=0.017, respectively); however, no significant correlations were observed among TMB, ES, and PD-L1. ES was the best single biomarker for predicting DCB (area under the curve [AUC], 0.794), followed by TMB (AUC, 0.679) and PD-L1 (AUC, 0.622). TMB and ES showed the highest AUC (0.837) among other combinations (AUC [TMB and PD-L1], 0.777; AUC [PD-L1 and ES], 0.763) and was similar to that of all biomarkers used together (0.832).
Conclusion
The combination of TMB and ES may be an effective predictive tool to identify patients with NSCLC patients who would possibly benefit from ICI therapies.

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Systems Biology Approaches to Decoding the Genome of Liver Cancer: Ju-Seog Lee, Ji Hoon Kim, Yun-Yong Park, Gordon B. Mills; Cancer Res Treat. 2011;43(4):205-211. Published online December 27, 2011; DOI: https://doi.org/10.4143/crt.2011.43.4.205

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Molecular classification of cancers has been significantly improved patient outcomes through the implementation of treatment protocols tailored to the abnormalities present in each patient's cancer cells. Breast cancer represents the poster child with marked improvements in outcome occurring due to the implementation of targeted therapies for estrogen receptor or human epidermal growth factor receptor-2 positive breast cancers. Important subtypes with characteristic molecular features as potential therapeutic targets are likely to exist for all tumor lineages including hepatocellular carcinoma (HCC) but have yet to be discovered and validated as targets. Because each tumor accumulates hundreds or thousands of genomic and epigenetic alterations of critical genes, it is challenging to identify and validate candidate tumor aberrations as therapeutic targets or biomarkers that predict prognosis or response to therapy. Therefore, there is an urgent need to devise new experimental and analytical strategies to overcome this problem. Systems biology approaches integrating multiple data sets and technologies analyzing patient tissues holds great promise for the identification of novel therapeutic targets and linked predictive biomarkers allowing implementation of personalized medicine for HCC patients.

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ERRATUM: Correction for Incorrect Citation of Reference and Wording in a Table: Cancer Res Treat. 2011;43(3):204-204. Published online September 30, 2011; DOI: https://doi.org/10.4143/crt.2011.43.3.204

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