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J.L. Perez-Gracia
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P1.07 - Immunology and Immunotherapy (ID 693)
- Event: WCLC 2017
- Type: Poster Session with Presenters Present
- Track: Immunology and Immunotherapy
- Presentations: 1
- Moderators:
- Coordinates: 10/16/2017, 09:30 - 16:00, Exhibit Hall (Hall B + C)
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P1.07-035 - Lymphocytes and Neutrophils Count After Two Cycles and TTF1 Expression as Early Outcome Predictors During Immunotherapy (ID 10308)
09:30 - 09:30 | Author(s): J.L. Perez-Gracia
- Abstract
Background:
Non-small cell lung cancer (NSCLC) therapeutic paradigm has dramatically changed with immune checkpoint blockers. The unconventional response patterns seen in patients treated with immunotherapy (IT) make it difficult to differentiate patients who respond from non-responders early on in treatment; and biomarkers predicting clinical benefit are still lacking. As previously shown in melanoma, changes in absolute lymphocytes and neutrophils count (ALC and ANC) during IT (PD-1/PD-L1 inhibitors) may be related to response in NSCLC (Nakamuta et al, Oncotarget 2016). TTF1 expression has been associated with PD-L1 expression (Vieira et al, Lung Cancer 2016). We aimed to investigate TTF1 expression and changes in ALC and ANC after 2 cycles and their potential association with clinical outcomes to IT.
Method:
We enrolled 32 consecutive patients with advanced NSCLC treated with IT at Clínica Universidad de Navarra (Spain) since 2015. Radiological response was evaluated according to RECIST v1.1. The potential correlation between ALC and ANC changes during the first two cycles and response to treatment [disease control rate (DCR) vs progression] was evaluated using Student’s T-test. Fisher’s exact test was used to study the association between changes in ALC (<1,000 vs >1,000) and ANC (<4,000 vs >4,000) after 2 cycles and response to IT. TTF1 expression was correlated with IT response. Overall survival (OS) was assessed with Kaplan-Meier analysis and Log-rank test according to ALC and ANC.
Result:
TTF1 tumor expression in adenocarcinoma histology (n= 18) was significantly associated with response to IT (88% vs 45%, p= 0.03). Patients with ANC <4,000 after two cycles showed a longer median OS (NR vs 4.9 months; p=0.02). An ALC increase after 2 cycles was associated with DCR compared to progression (147 vs -155; p=0.05). ALC >1,000 after 2 cycles seemed to be more frequent among patients with TTF1+ tumors (82% vs 45%; p= 0.05) and among those experiencing DCR compared to progression (73% vs 58%; p=0.30).
Conclusion:
Our results show that ALC and ANC changes during IT and TTF1 expression may act as early predictors of clinical benefit in stage IV NSCLC patients treated with PD1/PD-L1 blockers. Our results warrant further investigation in larger series.
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P2.02 - Biology/Pathology (ID 616)
- Event: WCLC 2017
- Type: Poster Session with Presenters Present
- Track: Biology/Pathology
- Presentations: 1
- Moderators:
- Coordinates: 10/17/2017, 09:30 - 16:00, Exhibit Hall (Hall B + C)
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P2.02-061 - Two Novel Protein-Based Prognostic Signatures Improve Risk Stratification of Early Lung ADC and SCC Patients (ID 9518)
09:30 - 09:30 | Author(s): J.L. Perez-Gracia
- Abstract
Background:
The development of robust, feasible and clinically useful molecular classifiers for early stage NSCLC patients to assess the risk of developing post-resection recurrence is an unmet medical need. Here we identified and validated the clinical utility of two different histotype-specific protein-based prognostic signatures to stratify the five-year risk of lung cancer recurrence or death in patients with either early lung adenocarcinoma (ADC) or early squamous cell carcinoma (SCC). The signatures are based on the immunohistochemical detection of three and five proteins, for ADC and SCC respectively
Method:
A total number of 562 lung cancer patients were included in this study (n=350 for ADC and n=212 for SSC). A training cohort was used to assess the value of the prognostic signatures based on immunohistochemical (IHC) detection (n=239 ADC and n=117 SSC). The prognostic signatures were developed by Cox regression analysis and were comprised of three and five proteins, respectively for ADC and SCC. Overfitting and optimism were quantified and calibrated by internal validation by applying shrinkage and bootstraping combination. The performance of the models was externally validated in a second cohort of 111 and 95 patients with stage I-II lung ADC and SCC, respectively.
Result:
The prognostic indexes (PIs) generated by the models were significant predictors of five-year outcome for disease-free survival: [P<0.001, HR=2.88 (95% CI, 1.77-4.69)] for ADC and [P<0.001; HR=2.97 (95% CI, 1.84-4.79)] for SCC; and overall survival: [P<0.001, HR=4.04 (95% CI, 2.30-7.10)] for ADC and [P=0.006; HR=1.86 (95% CI, 1.20-2.88)] for SCC, independently of other clinicopathological parameters. The prognostic ability of both PIs was externally validated in the second cohort of early stage lung cancer patients (P<0.05). The molecular classifiers added significant information to pathological stage. Combined models including both PIs and the pathological stage (CPIs) improved the risk stratification in both cases (P<0.001). Moreover, using the CPI value we were able to select the group of stage I-IIA patients who could obtain a benefit from platinum-based adjuvant chemotherapy treatment (P<0.05) in both histological subtypes.
Conclusion:
This study identifies and validates two protein-based prognostic signatures that accurately identify early lung cancer patients with high risk of recurrence or death. More importantly, the proposed models may be valuable tools to identify the subset of stage I-IIA patients for whom adjuvant chemotherapy could be beneficial.
