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M.B. Schabath
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P1.01 - Poster Session with Presenters Present (ID 453)
- Event: WCLC 2016
- Type: Poster Presenters Present
- Track: Epidemiology/Tobacco Control and Cessation/Prevention
- Presentations: 2
- Moderators:
- Coordinates: 12/05/2016, 14:30 - 15:45, Hall B (Poster Area)
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P1.01-041 - Quantitative Imaging Features Predict Response of Immunotherapy in Non-Small Cell Lung Cancer Patients (ID 5729)
14:30 - 14:30 | Author(s): M.B. Schabath
- Abstract
Background:
Although immunotherapy has revolutionized the field of cancer treatment, response rates are only ~20% in non-small cell lung cancer (NSCLC) patients and cost of this therapy is high. Predictive biomarkers are needed to identify patients likely to benefit. Converting digital medical images into high-dimensional data (‘Radiomics’) contains information that reflects underlying pathophysiology and that can be revealed via quantitative analyses. We extracted radiomic imaging features from baseline CT scans (prior to initiation of immunotherapy) and identified features that predict response to immunotherapy in NSCLC patients. This work is an initial test of the hypothesis that radiomic data may predict who will respond favorably and who will not.
Methods:
We curated a subset of data and images from 13 different institutional immunotherapy clinical trials. Patients were stage III/IV NSCLC and received PD-1, PD-L1, or doublet checkpoint inhibitors. All target nodules were identified on the CT prior scan prior to initiation of immunotherapy. RECIST guidelines 1.1 were used to measure patient response from baseline to last follow-up scan. Based on last follow-up, 43 patients had progressive disease (PD) and 28 patients with partial response (PR) or complete response (CR). Since we focused on extreme responses, stable disease (SD) patients were not included in the current analyses. We extracted 219 radiomic features including size, shape, location, and texture information from a total of 210 target nodules (lung, lymph nodes, or other). Backward-elimination analyses were utilized to generate parsimonious radiomic models associated with objective responses (PD vs. PR/CR) and post estimation computed performance statistics.
Results:
There were no significant differences for the patient characteristics between patients with PD vs. CR/PR. Analysis of the radiomic features for all target nodules to differentiate PD patients vs. PR/CR patients resulted in a final model containing 2 features that provided an AUROC of 0.64 (95% CI 0.56–0.72). When we analyzed features for only lung target nodules, we identified a final model with 4 features that produced an AUROC of 0.79 (95% CI 0.68–0.89). When we analyzed the imaging features for lymph node target nodules, we found that a final model with 1 feature yielded an AUROC of 0.67 (95% CI 0.51–0.82).
Conclusion:
Radiomic features of lung target nodules have better performance statistics for predicting response to immune therapies compared to target nodules from other organ sites. With this model, cutoffs can be chosen to reduce non-responders with high confidence. Change feature analyses following therapy are underway.
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P1.01-042 - Molecular Epidemiology of Programmed Cell Death 1-Ligand 1 (PD-L1) Protein Expression in Non-Small Cell Lung Cancer (ID 4746)
14:30 - 14:30 | Author(s): M.B. Schabath
- Abstract
Background:
Expression of programmed death-ligand 1 (PD-L1) in non-small cell lung cancer (NSCLC) patients might identify patients who would benefit from PD-L1 blocking antibodies. In a retrospective cohort of NSCLC patients, we characterized PD-L1 expression and other biomarkers to determine if PD-L1 expression is a prognostic biomarker and whether patient characteristics could be identified to determine those associated with high expression.
Methods:
This was a retrospective analysis of 136 NSCLC patients diagnosed between 1997 and 2015 with stage IIIB and IV disease and treated at Moffitt Cancer Center and affiliated institutions. All patients had at least 2 lines of standard of care chemotherapy and sufficient archival tumor tissue for PD-L1 testing by the Ventana SP263 validated assay and mutation status testing by targeted DNA sequencing with the TumorCare Panel. High PD-L1 expression was defined as ≥ 25% of tumor cells with membrane positivity for PD-L1 at any intensity above background staining. Statistical analyses were performed comparing PD-L1 expression by patient characteristics. Survival analyses were performed using Kaplan-Meier survival curves and the log-rank statistic. All statistical tests were two-sided; P-value of less than .05 was considered statistically significant.
Results:
Of the 136 tissues tested for PD‐L1 expression, 116 (85.3%) were collected by surgical resection and 20 (14.7%) were collected by biopsy. Mean sample age was 7.2 years (SD=2.8 years). 82 of the 136 samples also underwent targeted DNA sequencing. In this patient cohort, 51.5% were male, 83.1% were ever smokers, 90.4% were White, 39% were stage IV at time of tissue collection, 71.3% had adenocarcinoma, 28.7% had four or more lines of therapy, and 24.2% had high-expression for PD‐L1. There were no statistically significant differences with respect to PD-L1 expression for patient characteristics, overall survival (OS), or progression-free survival (PFS). Additionally, there were no statistically significant differences with respect to PD-L1 expression by EGFR (WT/PD-L1<25% = 74.4% vs. WT/PD-L1≥ 25% = 88.5%), KRAS (WT/PD-L1<25% = 74.7% vs. WT/PD-L1≥ 25% = 69.2%), and ALK status (Neg/PD-L1<25% = 98.5% vs. Neg/PD-L1≥ 25% = 100%). However, mutation load (total number of non-synonymous mutations) was statistically significantly correlated with PD-L1 expression (correlation coefficient = 0.23; P = 0.03).
Conclusion:
In this study of NSCLC patients treated with 2 or more lines of standard of care chemotherapy, PD-L1 expression (high vs. low and as a continuous covariate) was not statistically significantly associated with OS or PFS. We did observe a novel positive correlation between PD-L1 expression and mutational load.
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P1.03 - Poster Session with Presenters Present (ID 455)
- Event: WCLC 2016
- Type: Poster Presenters Present
- Track: Radiology/Staging/Screening
- Presentations: 1
- Moderators:
- Coordinates: 12/05/2016, 14:30 - 15:45, Hall B (Poster Area)
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P1.03-063 - Quantitative Imaging Features Predict Incidence Lung Cancer in Low-Dose Computed Tomography (LDCT) Screening (ID 5669)
14:30 - 14:30 | Author(s): M.B. Schabath
- Abstract
Background:
Although the NLST demonstrated a benefit of LDCT screening for reducing lung cancer and all-cause mortality, LDCT screening identifies large numbers of indeterminate pulmonary nodules and there are limited clinical decision tools that predict probability of cancer development. Using data and images from the NLST, we extracted quantitative imaging features from nodules of baseline positive screens (T0) and delta features from T0 to first follow-up (T1) and performed analyses to identify imaging features that predict incidence lung cancer. Analyses were stratified by nodule size since guidelines in the U.S. have increased the size threshold for positivity to 6 mm.
Methods:
We extracted 438 features from T0 nodules, and delta features from T0 to T1, including size, shape, location, and texture information. Nodules were identified for 170 cases that were diagnosed with incidence lung cancer at the first (T1) or second (T2) follow-up screen and for 328 controls that had three consecutive positive screens (T0 to T2) not diagnosed as lung cancer. The cases and controls were split into a training cohort and a testing cohort and classifier models (Decision tree-J48, Rule Based Classier-JRIP, Naive Bayes, Support Vector Machine, Random Forests) that were stratified by nodule size (< 6 mm, 6 to 16 mm, ≥ 16 mm) were used to identify the most predictive feature sets.
Results:
The training cohort consisted of 83 cases and 172 controls and a testing cohort of 77 cases and 135 controls. Within and across each cohort, there were no significant differences in demographic and clinical covariates. Training and testing was first performed using three difference nodule size groups (< 6 mm, 6 to 16 mm, and ≥ 16 mm) which revealed for < 6 mm a final model of 5 features (AUROC=0.75), 6 to 16 mm a final model with 10 features (AUROC=0.73), and ≥ 16 mm a final model with 10 features (AUROC=0.83). Finally, we combined the two larger groups and found for ≥ 6 mm a final model with 10 features with an AUROC of 0.84 (95% CI 0.8-0.87), Sensitivity of 60% (95% CI 0.50-0.71), and Specificity of 95% (95% CI 0.92-0.99).
Conclusion:
In this analysis we revealed a set of highly informative imaging features that predicts subsequent development of incidence lung cancer among individuals presenting with a ≥ 6 mm nodule. These imaging features could be scored in the clinical setting to improve nodule management of current size-based screening guidelines.
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P3.05 - Poster Session with Presenters Present (ID 475)
- Event: WCLC 2016
- Type: Poster Presenters Present
- Track: Palliative Care/Ethics
- Presentations: 1
- Moderators:
- Coordinates: 12/07/2016, 14:30 - 15:45, Hall B (Poster Area)
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P3.05-010 - Developing Tools for a Successful Thoracic Rapid Tissue Donation Program (ID 3722)
14:30 - 14:30 | Author(s): M.B. Schabath
- Abstract
Background:
Advances in cancer treatment have been made through the use of human tumor tissues from patients with refractory disease. Rapid Tissue Donation (RTD) provides an opportunity to gain insight into treatment-resistant cancers by analyzing tissue from primary tumors and metastasis within 24 hours following a patient’s death. The discussion of participation is a delicate process that must consider inherent communication challenges. Prospective patients may perceive their physician’s recruitment efforts for RTD as a sign of loss of hope. Companions may be distressed by the offer. This study examined the decision making of participating in a RTD program for patients with advanced stage lung cancer and their companion.
Methods:
After a physician-guided introduction of the RTD program, participants with stage 4 lung cancer (n=9) and their companions (n=8) were consented to participate in a qualitative, semi-structured interview assessing their decision making process and barriers and benefits of enrolling in the program, perceptions of the RTD brochures and satisfaction with the recruitment process. Companions participated in independent and joint interviews assessing their perceptions of patients’ decision to enroll in the program. Coders reviewed the verbatim transcripts of the interviews and applied qualitative thematic analysis to identify emerging themes.
Results:
The majority of patients chose to enroll in the RTD program as an opportunity to give back to science and upon learning organ donation was not an option for them. All patients had good relationships with their physician and this was a deciding factor for participation. Patients had limited concerns about participation and wanted to be sure their loved ones were not burdened by the process. Companions had more concerns about logistics but all supported patients’ decisions. All participants were comfortable with the recruiting process and their physician’s initiation and subsequent discussion of the program. Several patients indicated that they did not plan to inform extended family members. Two companions reported feeling distressed during a clinical discussion concerning the patients’ participation. Patients and their companions approved of the brochure’s content, including references to death, but often objected to the use of language depicting cancer as a “battle” or “fight”.
Conclusion:
Implementation of an RTD program requires monitoring of the complex communication processes that occur at both interpersonal and institutional levels. Additional research during the ongoing accrual process will continue to assess physician perspectives and seek methods honoring the wishes of patients and companions. R21 CA 194932-01 (NCI)
