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Xiaochun Zhang
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P1.01 - Advanced NSCLC (ID 757)
- Event: WCLC 2017
- Type: Poster Session with Presenters Present
- Track: Advanced NSCLC
- Presentations: 2
- Moderators:
- Coordinates: 10/16/2017, 09:30 - 16:00, Exhibit Hall (Hall B + C)
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P1.01-042 - Dynamic ctDNA Assay by Next Generation Sequencing to Guide Targeted Therapy in Advanced Non-Small Cell Lung Cancer (ID 10387)
09:30 - 09:30 | Presenting Author(s): Xiaochun Zhang
- Abstract
Background:
It is difficult to identify tumor driving genes in advanced non-small cell lung cancer (NSCLC) patients especially for those who were unable to obtain cancer tissue samples and had developed treatment resistance. Circulating tumor DNA (ctDNA) has garnered much excitement over the past few years for its potential clinical utility as a tumor therapy monitoring tool. Our study aims to evaluate the usefulness of ctDNA for serial monitoring actionable genetic alterations in NSCLC patients.
Method:
34 pairs of matched cancer tissue and plasma samples were collected from patients with advanced NSCLC and applied to capture-based next generation sequencing (NGS) using the lung panel, consisting of critical exons and introns of 168 genes. Additional, serial monitoring of ctDNA was conducted in 11 NSCLC patients with actionable genetic alterations using the above NGS assay.
Result:
ctDNA yielded a close agreement of 85.3% (29/34) on actionable genetic alteration status when compared to cancer tissue at baseline in this study. Analysis of ctDNA at different time points showed a strong correlation to treatment efficacy. Interestingly, secondary genetic alterations such as EGFR mutation T790M were detected in 45.5% (5/11) of the patients during monitoring.
Conclusion:
ctDNA may be a potential alternative to conventional primary tissue based NGS assay. ctDNA assay by NGS could be clinically used to monitor the efficiency of personalized target therapy for NSCLC patients.
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P1.01-069 - Clinical Experience with IBM Watson for Oncology (WFO) Cognitive System for Lung Cancer Treatment in China (ID 9774)
09:30 - 09:30 | Presenting Author(s): Xiaochun Zhang
- Abstract
Background:
IBM Watson for Oncology (WFO) is a Memorial Sloan Kettering-trained cognitive computing system. Watson provides evidence-based treatment options and ranks them into three categories for oncologist decision making as "Recommended ","For Consideration" and "Not Recommended". We examined the concordance of treatment options in Lung cancer patients between WFO and tumor board in the Affiliated Hospital of Qingdao University, China.
Method:
33patients with stage I-IV lung cancer were enrolled in this study. By tumor stage, 15.1% (5 of 33) patients are Phase I or II, 15.1% (5 of 33) are Phase III and the rest of the patients (23 of 33) are Phase IV. By histology, 18.2% (6 of 33) are small cell lung cancer (SCLC) and 81.8% (27 of 33) are non-small cell lung cancer (NSCLC). Options were considered concordant when it was included in the “Recommended” or “For Consideration” categories.
Result:
Overall, treatment recommendations were concordant in 96.9% (32 of 33) of cases. By histology, 100% of SCLC patients’ therapy regimes were concordant with the recommended one, and treatment recommendations were concordant in 96.3% of NSCLC patients. By tumor stage, treatment recommendations were concordant in 100% of I- III and 96% of Phase IV lung cancer. Of the whole cases, 69.7% were recommended and 27.3% were for consideration (figure1). The majority reason for choosing consideration option is that the immunotherapy drugs targeted PD-1 or PD-L1 such as Nivolumab, Pembrolizumab and Atezolizumab recommended by Watson for Oncology had not been launched in China.Figure 1
Conclusion:
Treatment recommendations made by the Affiliated Hospital of Qingdao University and Watson for Oncology were highly concordant in lung cancer. We’ll make further analysis for this cognitive computing system in more cases and other types of carcinomas.
