Is Programmed Death Ligand 1(PD-L1) Expression in Vulvar Cancer Prognostic for Locoregional Control?

PURPOSE/OBJECTIVE(S)

Vulvar cancer is a rare female genital neoplasm in which surgery and radiotherapy play an integral role in the treatment paradigm; however, locoregional recurrence remains the predominant pattern of failure. Little is known about the impact of PD-L1 status in vulvar cancer and its value for clinical outcomes and response prediction to immunotherapy. We sought to explore clinical outcomes of patients with positive PD-L1 expression in vulvar cancer.

MATERIALS/METHODS

Single-institution retrospective analysis of patients with surgically resectable invasive vulvar carcinoma from 2001-2021 was performed. Patients with locally advanced disease not amendable to upfront surgery or de novo metastatic disease were excluded. Immunohistochemical PD-L1 expression was assessed using the Combined Positive Score (CPS) with positive expression defined as ≥1, and Tumor Proportion Score (TPS) with positive expression defined as ≥1%. Survival and disease control outcomes were calculated using the Kaplan-Meier Method with log-rank t-test. Multivariable analysis was conducted using a parsimonious cox regression analysis using forward conditional selection.

RESULTS

A total of 85 patients were identified with a median age of 69 years old (IQR: 59-78), 54% (n = 46) FIGO stage I-II, 97% (n = 82) squamous cell carcinoma histology, 41% (n = 35) p16 positive status, 74% (n = 63) without a history of lichen sclerosis, 40% (n = 34) without co-existing vulvar intraepithelial neoplasm (VIN), and 49% (n = 42) treated with surgery alone. There were 72% (n = 61) with positive PD-L1 TPS (≥1%), and 81% (n = 69) with positive PD-L1 CPS (≥1) expression. The median follow up was 49 months (IQR: 21-75 months). The 5-year OS was 79% (95% CI, 70%-89%), DFS 55% (95% CI, 43%-67%), local control (LC) 59% (95% CI, 47%-72%), regional control (RC) 86% (95% CI, 78%-94%), and distant metastasis (DM) 96% (95% CI, 92%-100%). PD-L1 expression was associated with lower LC and DFS by TPS ≥1%. The 5-year LC of 82% (95% CI, 65%-98%) for PD-L1 negative versus 50% (95% CI, 34%-65%) positive disease (p = 0.03). The 5-year DFS was 77% (95% CI, 59%-95%) for PD-L1 negative versus 46% (95% CI, 31%-61%) positive disease (p = 0.03). No significant DFS or LC difference was noted by CPS levels ≥1. No significant difference was observed for RC, DM, or OS. On multivariable analysis, PD-L1 TPS remained a significant predictor for LC (HR = 3.01, 95% CI, 1.07-8.95, p = 0.04). No significant difference in DFS was observed for PD-L1 TPS on multivariable analysis.

CONCLUSION

PD-L1 expression is associated with higher rates of local recurrence and may represents a potentially important actionable target independent of p16 status to improve the predominant pattern of relapse in this uncommon malignancy.

Conformality Indices and Brain Dose Objective for Multi-Lesion Brain Single Isocenter Linac-Based Stereotactic Radiosurgery

PURPOSE/OBJECTIVE(S)

The lack of recommendation on Linac-based stereotactic radiosurgery (SRS) and radiotherapy (SRT) plan quality for multi-lesion brain metastasis results in planning heterogeneity. The use of MLCs in the delivery is expected to add extra leakage when multi-lesions are treated simultaneously, and no recommendations availability in literature. We aimed to identify conformality indices and plan objectives that would guide multi-brain metastasis SRS & SRT MATERIALS/METHODS: We performed a retrospective analysis of clinically accepted brain metastasis radiotherapy plans treated using SRS and SRT at our department. Each plan was assessed for four metrics: Two conformality indices, including R50% referring to the ratio of the 50% dose cloud to the PTV, and %D1cm referring to the percentage of dose at 1cm away from PTV in any direction. One dose objective, V12 Gy referring to the brain volume receiving 12 Gy, and one delivery efficiency parameter, the modulation factor, defined as the ratio of the total MU to the prescribed dose. Each parameter was evaluated as a function of total PTV volume and number of lesions.

RESULTS

A total of 107 plans were analyzed. The analyzed plans included pre- and post-operative treated brain metastasis with 86% with ≤ 4 lesions (median is 2, IQR is 3), 80% of the plans received single fraction and 43% of plans have a total PTV volumes of ≤ 1cc (median is 2.2cc and IQR is 7.5cc). We used a two tail T-test to check for correlation. The 50% relative dose gradient, R50%, was found to be dependent on PTV volume (P < 0.001), and a power law was used to fit the data. The brain volume, V12 Gy, was also found to be dependent on total PTV volume (P < 0.0001) and a linear curve was used to fit V12 Gy vs PTV. We did not find correlation between the number of lesions and R50% and or V12 Gy (p-value = 0.154 and 0.736, respectively). The dose gradient at 1cm away in any direction from PTV, %D1cm, has a median, average and a S.D. of 32%, 33% and 9%, respectively. 85% of all plans have %D1cm<42% which falls within 1 S.D. In addition, we found %D1cm to be dependent on both PTV volume and number of lesions (P < 0.001 for both). The modulation factor (MF) has a median, average and a S.D. of 2.9, 3.0 and 1.5, respectively. We found that 81% of all plans has MF < 4.5, which is within the 1 S.D. It has a positive correlation of 0.628 with the number of lesions.

CONCLUSION

We found a clear correlation between the total PTV volumes and R50% and V12 Gy, and a fit line was used to relate each parameter to the total PTV volume. As for %D1cm and MF, we found that the average value within 1 S.D. is adequate to include > 80% of the plan. We propose these four parameters to fill the gap for plan quality for Linac-based SRS and SRT. Further evaluation and validation of our results on larger cohort is needed to allow generalizability and applicability of the results and correlation with clinical outcomes to guide clinical decision making.

A spatial neighborhood methodology for computing and analyzing lymph node carcinoma similarity in precision medicine

Precision medicine seeks to tailor therapy to the individual patient, based on statistical correlates from patients who are similar to the one under consideration. These correlates can and should go beyond genetics, and in general, beyond tabular or array data that can be easily represented computationally and compared. For example, in many types of cancer, cancer treatment and toxicity depend in large measure on the spatial disease spread-e.g., metastasizes to regional lymph nodes in head and neck cancer. However, there is currently a lack of methodology for integrating spatial information when considering patient similarity. We present a novel modeling methodology for the comparison of cancer patients within a cohort, based on the spatial spread of the lymph nodes affected in each patient. The method uses a topological map, bigrams, and hierarchical clustering to group patients based on their similarity. We compare this approach against a nonspatial (categorical) similarity approach where patients are binned solely by their affected nodes. We present similarity results on a 582 head and neck cancer patient cohort, along with two visual abstractions for analysis of the results, and we present clinician feedback. Our novel methodology partitions a patient cohort into clinically meaningful groups more susceptible to treatment side-effects. Such spatially-aware similarity approaches can help maximize the effectiveness of each patient's treatment.

Cohort-based T-SSIM Visual Computing for Radiation Therapy Prediction and Exploration

We describe a visual computing approach to radiation therapy (RT) planning, based on spatial similarity within a patient cohort. In radiotherapy for head and neck cancer treatment, dosage to organs at risk surrounding a tumor is a large cause of treatment toxicity. Along with the availability of patient repositories, this situation has lead to clinician interest in understanding and predicting RT outcomes based on previously treated similar patients. To enable this type of analysis, we introduce a novel topology-based spatial similarity measure, T-SSIM, and a predictive algorithm based on this similarity measure. We couple the algorithm with a visual steering interface that intertwines visual encodings for the spatial data and statistical results, including a novel parallel-marker encoding that is spatially aware. We report quantitative results on a cohort of 165 patients, as well as a qualitative evaluation with domain experts in radiation oncology, data management, biostatistics, and medical imaging, who are collaborating remotely.