Tumor Purity-Related Genes for Predicting the Prognosis and Drug Sensitivity of DLBCL Patients

  1. Zhenbang Ye
  2. Ning Huang
  3. Yongliang Fu
  4. Rongle Tian
  5. Wenting Huang

  • Curated by eLife

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    eLife assessment

    The findings in this study are useful and may have practical implications for predicting DLBCL risk subject to further validating the bioinformatics outcomes. We found the approach and data analysis solid. However, some concerns regarding the drug sensitivity prediction and the links between the selected genes for the risk scores have been raised that need to be addressed by further functional works.

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Abstract

Background

Diffuse large B-cell lymphoma (DLBCL) is the predominant type of malignant B-cell lymphoma. Although various treatments have been developed, the limited efficacy calls for more and further exploration of its characteristics.

Methods

Datasets from Gene Expression Omnibus (GEO) database were used for identifying the tumor purity of DLBCL. Survival analysis was employed for analyzing the prognosis of DLBCL patients. Immunohistochemistry was conducted to detect the important factor that influenced the prognosis. Drug sensitive prediction was performed to evaluate the value of the constructed model.

Results

VCAN, CD3G and C1QB were identified as three key genes that impacted the outcome of DLBCL patients both in GEO datasets and samples from our center. Among them, VCAN and CD3G+ T cells were correlated with favorable prognosis, and C1QB was correlated with worse prognosis. The ratio of CD68+ macrophages and CD8+ T cells was associated with better prognosis. In addition, CD3G+ T cells ratio was significantly correlated with CD68+ macrophages, CD4+ T cells and CD8+ T cells ratio, indicating it could play an important role in the anti-tumor immunity in DLBCL. The riskScore model constructed based on the RNASeq data of VCAN, C1QB and CD3G work well in predicting the prognosis and drug sensitivity.

Conclusion

VCAN, CD3G and C1QB were three key genes that influenced the tumor purity of DLBCL, and could also exert certain impact on drug sensitivity and prognosis of DLBCL patients.

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  1. Version published to 10.1101/2023.10.04.23296534v2 on medRxiv
  2. Version published to 10.7554/elife.92841.1 on eLife
  3. Version published to 10.7554/elife.92841 on eLife
  4. eLife

    eLife assessment

    The findings in this study are useful and may have practical implications for predicting DLBCL risk subject to further validating the bioinformatics outcomes. We found the approach and data analysis solid. However, some concerns regarding the drug sensitivity prediction and the links between the selected genes for the risk scores have been raised that need to be addressed by further functional works.

  5. eLife

    Reviewer #1 (Public Review):

    Summary:
    Ye et al. identified a novel tumour microenvironment (TME) signature that can help to prognosticate DLBCL. They first interrogated a publicly available dataset to identify tumour purity-related genes (TPGs) and found these TPGs were associated with extracellular matrix organisation and immune response. Protein-protein interaction analysis identified hub genes that were associated with prognosis, and 3 genes (VCAN, CD3G, C1QB) were selected to construct a prognosis model. The authors attempted to validate the findings on immunohistochemistry (IHC) and showed prognostication using an IHC assay. Finally, they showed a possible prediction of drug sensitivity using the novel signature in DLBCL.

    Strengths:
    This study investigated both immune and non-immune TME related to tumour purity. Tumour purity has not been thoroughly investigated in DLBCL. Hence, the prognostic significance of tumour purity demonstrated in this paper brought into light another potential area of research in DLBCL. Similarly, the investigation into non-immune TME was novel and thought-provoking, as most research in DLBCL TME has mostly been in the immune microenvironment.

    The bioinformatics approach in identifying the key TPGs was well conducted, such as the GO and KEGG enrichment analysis which supported the role of these TPGs in the modulation of the microenvironment. The findings were also validated in another dataset, which increased the confidence in this model. However, it was not clear to me why the authors chose VCAN, CD3G, and C1QB out of the 9 intersection genes that they found. It would perhaps be useful to show the statistical justification in the Supplementary Results section.

    The possible translation of these findings into clinical practice by immunohistochemistry (IHC) was a useful tool to make the findings applicable in the clinical setting. However, as stated by the authors, the real-life clinical application of these findings may be more challenging as these antigens seemed to be expressed in a continuum, rather than in a discrete manner. For example, in Figure 5A, even the low VCAN status still demonstrated strong cytoplasmic staining. Similarly, in Figure 5C, it seemed to be difficult to differentiate strong from background staining. This means pre-analytical variables may affect the staining and standardisation among different laboratories may be difficult to achieve without external controls.

    Weaknesses:
    Though the rationale behind choosing the TPG genes and its correlation with non-immune TME was clear, the justification for investigating CD68+ macrophages, CD4+ T cells, and CD8+ T cells was not as strong. This was done in a subsection that was supposed to investigate the prognostic values of IHC staining in VCAN, CD3G, and C1QB. Hence, the analysis of the immune compartment of the TME was rather superficial. For example, it would be insufficient to correlate CD4+ and CD8T+ T cells without understanding their deeper phenotypes such as regulatory vs memory or exhausted vs activated. An attempt was made to subtype the macrophages by bioinformatics approach but it was not further investigated with IHC.

    Similarly, the investigation into drug sensitivity was only done in-silico. This investigation was adequate for hypothesis generation. However, it was not enough to substantiate the claim that TPGs can be used to predict drug sensitivity. This claim requires functional in-vitro experiments to validate the bioinformatics approach, or even correlation with clinical data when the identified drugs were used in DLBCL, for example in the ReMODL-B cohort that used bortezomib.

  6. eLife

    Reviewer #2 (Public Review):

    In this study, Zhenbang Ye and colleagues investigate the links between microenvironment signatures, gene expression profiles, and prognosis in diffuse large B-cell lymphoma (DLBCL). They show that increased tumor purity (ie, a higher proportion of tumor cells relative to surrounding stromal components) is associated with a worse prognosis. They then show that three genes associated with tumor purity (VCAN, CD3G, and C1QB) correlate with patterns of immune cell infiltration and can be used to create a risk-scoring system that predicts prognosis, which can be replicated by immunohistochemistry (IHC), and response to some therapies.

    1. The two strengths of the study are its relatively large sample size (n = 190) and the strong prognostic significance of the risk-scoring system. It is worth noting that the validation of this scoring with IHC, a simple technique already routinely used for the diagnosis and classification of DLBCL, increases the potential for clinical translation. However, the correlative nature of the study limits the conclusions that can be drawn in regard to links between the risk scoring system, the tumor microenvironment, and the biology of DLBCL.

    2. The tumor microenvironment has been extensively studied in DLBCL and a prognostic implication has already been established (for instance, Steen et al., Cancer Cell, 2021). In addition, associations have already been established in non-Hodgkin lymphoma between prognosis and expression of C1QB (Rapier-Sharman et al., Journal of Bioinformatics and Systems Biology, 2022), VCAN (S. Hu et al., Blood, 2013), and CD3G (Chen et al., Medical Oncology, 2022). Nevertheless, one of the strengths and novelty aspects of the study is the combination of these 3 genes into a risk score that is also valid by immunohistochemistry (IHC), which substantially facilitates a potential clinical translation.

    3. Figures 1A-B: tumor purity is calculated using the ESTIMATE (Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data) algorithm (Yoshihara et al., Nature Communications, 2013). The ESTIMATE algorithm is based on two gene signatures ("stromal" and "immune"). It is therefore expected that tumor purity measured by the ESTIMATE algorithm will correlate with the expression of multiple genes. Importantly, C1QB is included in the stromal signature of the ESTIMATE algorithm meaning that, by definition, it will be correlated with tumor purity in that setting.

    4. Figure 2A: as established in Figure 1C, high tumor purity is associated with worse prognosis. Later in the manuscript, it is also shown that C1QB expression is associated with a worse prognosis. However, Figure 2A shows that C1QB is associated with decreased tumor purity. It therefore makes it less likely that the prognostic role of C1QB expression is related to its impact on tumor purity. The prognostic impact could be related to different patterns of immune cell infiltration, as shown later. However, the evidence presented in the study is correlative and natural and not sufficient to draw this conclusion.

    5. Figure 3G: although there is a strong prognostic implication of the risk score on prognosis, the correlation between the risk score and tumor purity is significant but not very strong (R = 0.376). It is therefore likely that other important biological factors explain the correlation between the risk score and prognosis.

    6. Figure 6: the drug sensitivity analysis includes a wide range of established and investigational drugs with varied mechanisms of action. Although the difference in sensitivity between tumors with low and high-risk scores shows statistical significance for certain drugs, the absolute difference appears small in most cases and is of unclear biological significance. In addition, even though the risk score is statistically related to drug sensitivity, there is no direct evidence that the differences in drug sensitivity are directly related to tumor purity.

  7. Version published to 10.1101/2023.10.04.23296534v1 on medRxiv