Early onset of severe lymphopenia during definitive radiotherapy correlates with mean body dose and predicts poor survival in cervical cancer

Pelvic bone marrow dose-volume predictors of late lymphopenia following pelvic lymph node radiation therapy for prostate cancer

BACKGROUND AND PURPOSE

Given the substantial lack of knowledge, we aimed to assess clinical/dosimetry predictors of late hematological toxicity on patients undergoing pelvic-nodes irradiation (PNI) for prostate cancer (PCa) within a prospective multi-institute study.

MATERIALS AND METHODS

Clinical/dosimetry/blood test data were prospectively collected including lymphocytes count (ALC) at baseline, mid/end-PNI, 3/6 months and every 6 months up to 5-year after PNI. DVHs of the Body, ileum (BMILEUM), lumbosacral spine (BMLS), lower pelvis (BMPELVIS), and whole pelvis (BMTOT) were extracted. Current analysis focused on 2-year CTCAEv4.03 Grade ≥ 2 (G2+) lymphopenia (ALC < 800/μL). DVH parameters that better discriminate patients with/without toxicity were first identified. After data pre-processing to limit overfitting, a multi-variable logistic regression model combining DVH and clinical information was identified and internally validated by bootstrap.

RESULTS

Complete data of 499 patients were available: 46 patients (9.2 %) experienced late G2+ lymphopenia. DVH parameters of BMLS/BMPELVIS/BMTOT and Body were associated to increased G2+ lymphopenia. The variables retained in the resulting model were ALC at baseline [HR = 0.997, 95 %CI 0.996-0.998, p < 0.0001], smoke (yes/no) [HR = 2.9, 95 %CI 1.25-6.76, p = 0.013] and BMLS-V ≥ 24 Gy (cc) [HR = 1.006, 95 %CI 1.002-1.011, p = 0.003]. When acute G3+ lymphopenia (yes/no) was considered, it was retained in the model [HR = 4.517, 95 %CI 1.954-10.441, p = 0.0004]. Performances of the models were relatively high (AUC = 0.87/0.88) and confirmed by validation.

CONCLUSIONS

Two-year lymphopenia after PNI for PCa is largely modulated by baseline ALC, with an independent role of acute G3+ lymphopenia. BMLS-V24 was the best dosimetry predictor: constraints for BMTOT (V10Gy < 1520 cc, V20Gy < 1250 cc, V30Gy < 850 cc), and BMLS (V24y < 307 cc) were suggested to potentially reduce the risk.

Radiation Therapy and Myeloid-Derived Suppressor Cells: Breaking Down Their Cancerous Partnership

Radiation therapy (RT) has been a primary treatment modality in cancer for decades. Increasing evidence suggests that RT can induce an immunosuppressive shift via upregulation of cells such as tumor-associated macrophages (TAMs) and myeloid-derived suppressor cells (MDSCs). MDSCs inhibit antitumor immunity through potent immunosuppressive mechanisms and have the potential to be crucial tools for cancer prognosis and treatment. MDSCs interact with many different pathways, desensitizing tumor tissue and interacting with tumor cells to promote therapeutic resistance. Vascular damage induced by RT triggers an inflammatory signaling cascade and potentiates hypoxia in the tumor microenvironment (TME). RT can also drastically modify cytokine and chemokine signaling in the TME to promote the accumulation of MDSCs. RT activation of the cGAS-STING cytosolic DNA sensing pathway recruits MDSCs through a CCR2-mediated mechanism, inhibiting the production of type 1 interferons and hampering antitumor activity and immune surveillance in the TME. The upregulation of hypoxia-inducible factor-1 and vascular endothelial growth factor mobilizes MDSCs to the TME. After recruitment, MDSCs promote immunosuppression by releasing reactive oxygen species and upregulating nitric oxide production through inducible nitric oxide synthase expression to inhibit cytotoxic activity. Overexpression of arginase-1 on subsets of MDSCs degrades L-arginine and downregulates CD3ζ, inhibiting T-cell receptor reactivity. This review explains how radiation promotes tumor resistance through activation of immunosuppressive MDSCs in the TME and discusses current research targeting MDSCs, which could serve as a promising clinical treatment strategy in the future.

Radiation-induced lymphopenia and the survival of women with cervical cancer: a meta-analysis

The current systematic analysis and meta-analysis was aimed to evaluate the association between radiation-induced lymphopenia (RIL) and survival of women with cervical cancer (CC). PubMed, Embase, Web of Science, and Cochrane Library were searched for relevant cohort studies comparing survival between women with CC who developed versus not developed RIL after radiotherapy. We pooled the results using a random-effects model that incorporates heterogeneity. In the meta-analysis, 952 women with CC were included from eight cohort studies. Overall, 378 (39.7%) of them had RIL after radiotherapy. During a median follow-up duration of 41.8 months, pooled results showed that RIL was independently associated with poor overall survival (hazard ratio [HR]: 2.67, 95% confidence interval [CI]: 1.81 to 3.94, p < 0.001; I² = 20%) and progression-free survival (HR: 2.17, 95% CI: 1.58 to 2.98, p < 0.001; I² = 0%). Predefined subgroup analyses showed similar results in patients with grade 3-4 and grade 4 RIL, in patients with RIL diagnosed during or after the radiotherapy, and in studies with quality score of seven or eight points (p values for subgroup effect all < 0.05). In conclusion, women with RIL were associated with poor survival after radiotherapy for CC.

A review on lymphocyte radiosensitivity and its impact on radiotherapy

It is well known that radiation therapy causes lymphopenia in patients and that this is correlated with a negative outcome. The mechanism is not well understood because radiation can have both immunostimulatory and immunosuppressive effects. How tumor dose conformation, dose fractionation, and selective lymph node irradiation in radiation therapy does affect lymphopenia and immune response is an active area of research. In addition, understanding the impact of radiation on the immune system is important for the design and interpretation of clinical trials combining radiation with immune checkpoint inhibitors, both in terms of radiation dose and treatment schedules. Although only a few percent of the total lymphocyte population are circulating, it has been speculated that their increased radiosensitivity may contribute to, or even be the primary cause of, lymphopenia. This review summarizes published data on lymphocyte radiosensitivity based on human, small animal, and in vitro studies. The data indicate differences in radiosensitivity among lymphocyte subpopulations that affect their relative contribution and thus the dynamics of the immune response. In general, B cells appear to be more radiosensitive than T cells and NK cells appear to be the most resistant. However, the reported dose-response data suggest that in the context of lymphopenia in patients, aspects other than cell death must also be considered. Not only absolute lymphocyte counts, but also lymphocyte diversity and activity are likely to be affected by radiation. Taken together, the reviewed data suggest that it is unlikely that radiation-induced cell death in lymphocytes is the sole factor in radiation-induced lymphopenia.

Lymphocyte sparing normal tissue effects in the clinic (LymphoTEC): A systematic review of dose constraint considerations to mitigate radiation-related lymphopenia in the era of immunotherapy

BACKGROUND

Radiation-related lymphopenia has been associated with suboptimal tumor control rates leading to inferior survival outcomes. To date, no standardized dose constraints are available to limit radiation dose to resident and circulating lymphocyte populations. We undertook this systemic review of the literature to provide a synopsis of the dosimetric predictors of radiation-related lymphopenia in solid malignancies.

METHODOLOGY

A systematic literature review of PubMed (National Institutes of Health), Cochrane Central (Cochrane collaboration), and Google Scholar was conducted with the following keywords: "radiation", "lymphopenia", "cancer", "dosimetric predictors" with an inclusion deadline of May 31, 2022. Studies that met prespecified inclusion criteria were designated either Good, Fair, or Poor Quality based on the Newcastle-Ottawa quality assessment. The dosimetric parameters derived from Good Quality studies were tabulated as LymphoTEC dose constraints. Dosimetric parameters derived from Fair and Poor-quality studies were grouped as optional.

RESULTS

An initial systematic search of the literature yielded 1,632 articles. After screening, a total of 48 studies met inclusion criteria and were divided into the following categories: central nervous system (CNS, 6), thoracic (11), gastrointestinal (26), gynecologic (2), head and neck, breast, and genitourinary (one each) cancers. Lung mean dose, heart mean dose, brain V25, spleen mean dose, estimated dose to immune cells, and bone marrow V10 were among the strongest predictors for severe lymphopenia related to radiotherapy.

CONCLUSION

Optimizing the delivery of radiation therapy to limit dose to lymphocyte-rich structures may curb the negative oncologic impact of lymphocyte depletion. The dose constraints described herein may be considered for prospective validation and future use in clinical trials to limit risk of radiation-related lymphopenia and possibly improve cancer-associated outcomes.

Establishment of a prognostic nomogram for patients with locoregionally advanced nasopharyngeal carcinoma incorporating clinical characteristics and dynamic changes in hematological and inflammatory markers

Background

This study aims to investigate the prognostic value of changes in hematological and inflammatory markers during induction chemotherapy (IC) and concurrent chemo-radiation (CCRT), thus construct nomograms to predict progression free survival (PFS) of patients with locally advanced nasopharyngeal carcinoma (LANPC).

Methods

130 patients were included in this prospective analysis. Univariate and multivariate cox regression analyses were conducted to identify prognostic factors. Three multivariate analyses integrating different groups of variables were conducted independently. Concordance indexes (c-index), calibration plots and Kaplan-Meier curves were used to evaluate the nomograms. Bootstrap validation was performed to determine the accuracy of the nomogram using 1000 resamples. The performances of proposed nomograms and TNM staging system were compared to validate the prognostic value of hematological and inflammatory markers.

Results

Pretreatment gross tumor volume of nodal disease (GTVn), Δe/bHGB (hemoglobin count at end of treatment/baseline hemoglobin count), and stage were selected as predictors for 3-year PFS in first multivariate analysis of clinical factors. The second multivariate analysis of clinical factors and all hematological variables demonstrated that ΔminLYM (minimum lymphocyte count during CCRT/lymphocyte count post-IC), pretreatment GTVn and stage were associated with 3-year PFS. Final multivariate analysis, incorporating all clinical factors, hematological variables and inflammatory markers, identified the following prognostic factors: pretreatment GTVn, stage, ΔmaxPLR (maximum platelet-to-lymphocyte ratio (PLR) during CCRT/PLR post-IC), and ΔminPLT (minimum platelet count during CCRT/platelet count post-IC). Calibration plots showed agreement between the PFS predicted by the nomograms and actual PFS. Kaplan–Meier curves demonstrated that patients in the high-risk group had shorter PFS than those in the low-risk group (P ≤ 0.001). The c-indexes of the three nomograms for PFS were 0.742 (95% CI, 0.639-0.846), 0.766 (95% CI, 0.661-0.871) and 0.815 (95% CI,0.737-0.893) respectively, while c-index of current TNM staging system was 0.633 (95% CI, 0.531-0.736).

Conclusion

We developed and validated a nomogram for predicting PFS in patients with LANPC who received induction chemotherapy and concurrent chemo-radiation. Our study confirmed the prognostic value of dynamic changes in hematological and inflammatory markers. The proposed nomogram outperformed the current TNM staging system in predicting PFS, facilitating risk stratification and guiding individualized treatment plans.

A machine learning model for grade 4 lymphopenia prediction during pelvic radiotherapy in patients with cervical cancer

Background/purpose

Severe lymphopenia during pelvic radiotherapy (RT) predicts poor survival in patients with cervical cancer. However, the risk of severe lymphopenia has not been well predicted. We developed a machine learning model using clinical and dosimetric information to predict grade 4 (G4) lymphopenia during pelvic RT in patients with cervical cancer.

Methods

This retrospective study included cervical cancer patients treated with definitive pelvic RT ± induction/concurrent chemotherapy. Clinical information and a set of dosimetric parameters of external beam radiotherapy plan were collected. G4 lymphopenia during RT, which was also referred to as G4 absolute lymphocyte count (ALC) nadir, was defined as ALC nadir <0.2 × 10⁹ cells/L during RT according to Common Terminology Criteria for Adverse Events (CTCAE) v4.03. Elastic-net logistic regression models were constructed for the prediction of G4 lymphopenia during pelvic RT using a repeated cross-validation methodology.

Results

A total of 130 patients were eligible, and 43 (33.1%) patients had G4 lymphopenia during RT. On multivariable analysis, G4 ALC nadir was associated with poor overall survival (OS) [hazard ratio (HR), 3.91; 95% confidence interval (CI), 1.34–11.38, p = 0.01]. Seven significant factors [Eastern Cooperative Oncology Group (ECOG) performance score, pre-RT hemoglobin, pre-RT lymphocytes, concurrent chemotherapy, gross tumor volume of regional lymphadenopathy (GTV_N volume), body volume, and maximum dose of planning target volume receiving at least 55 Gy (PTV_5500 Dmax)] were obtained by elastic-net logistic regression models and were included in the final prediction model for G4 ALC nadir. The model’s predicting ability in test set was area under the curve (AUC) = 0.77 and accuracy = 0.76. A nomogram of the final predicting model was constructed.

Conclusions

This study developed and validated a comprehensive model integrating clinical and dosimetric parameters by machine learning method, which performed well in predicting G4 lymphopenia during pelvic RT for cervical cancer and will facilitate physicians to identify patients at high risk of G4 lymphopenia who might benefit from modified treatment approaches.