Virtual Library

Abstract not provided

Abstract:
Epidermal growth factor receptor (EGFR) activating mutations in the tyrosine kinase domain serve as predictive biomarkers for EGFR-tyrosine kinase inhibitor (EGFR-TKI) treatment outcome for patients with advanced non-small cell lung cancer (NSCLC).[1] However, due to the invasive procedures required to obtain tumor tissues, not all patients can provide enough high-quality tissues for EGFR mutation analysis. Circulating free DNA (cfDNA) in plasma provides a noninvasive substitute for tumor tissues. Several studies have reported a concordance rate between tumor and plasma > 90%, even reaching 97%, demonstrating the feasibility of detecting EGFR mutations in cfDNA.[2-4]EGFR mutation status detection in cfDNA has been approved by the European Society for Medical Oncology and by China to be used with EGFR-TKI treatment for NSCLC.[5,6] In addition to providing pretreatment information, plasma-based EGFR mutation detection makes it possible to monitor dynamic changes in this mutation during treatment. Several studies have reported a quantitative change in EGFR mutations during EGFR-TKI treatment by comparing pre- and post-treatment plasma, in which various types of plasma EGFR mutations were found.[7,8] The quantity of the plasma EGFR mutation sometimes decreases, or sometimes decreases slowly or rapidly. Patients whose plasma EGFR mutations decrease rapidly usually exhibit a better response to EGFR-TKI treatment.[8] However, these studies were not based on prospective clinical trials, therefore the number of patients who had serial plasma specimens tested during EGFR-TKI treatments was limited, and very few plasma specimens were collected as part of a pre-planned schedule. The only recent study on plasma EGFR mutation changes based on a prospective clinical trial was reported by Mok et al.[9] In this phase III trial (FASTACT-2), patients received gemcitabine/platinum plus sequential erlotinib or placebo. EGFR mutation-specific cfDNA levels decreased at cycle 3 and increased at the time of disease progression. Positive plasma EGFR mutant DNA at cycle 3 predicted a worse clinical outcome. In this study, the treatment was chemotherapy plus EGFR-TKI or placebo, not EGFR-TKI, and there was no information on the plasma EGFR mutation at other time points except at baseline, cycle 3, and at disease progression. The dynamic changing types of plasma EGFR mutations during the whole course of EGFR-TKI treatment and its correlation with clinical outcomes were not determined. To measure changes of plasma EGFR L858R mutation during EGFR-TKI treatment, and to determine its correlation with the response and resistance to EGFR-TKI, we conducted a study. This study was a pre-planned exploratory analysis of a randomized phase III trial conducted from 2009 to 2014 comparing erlotinib with gefitinib in advanced NSCLC harboring EGFR mutations in tumor (CTONG0901). Serial plasma samples were collected as a pre-planned schedule. This trial was conducted in Guangdong Lung Cancer Institute, China. Totally, 256 patients were enrolled in CTONG0901. One hundred and eight patients harbored L858R mutation in tumors and 80 patients provided serial blood samples as pre-planned scheduled. Patients were randomized to receive erlotinib or gefitinib. Serial plasma L858R in 80 patients was detected using quantitative polymerase chain reaction. Changing types of plasma L858R were analyzed using Ward's Hierarchical Clustering Method. Progression-free survival (PFS) and overall survival (OS) were compared between different types. As a whole, the quantity of L858R decreased and reached the lowest level at the time of best response to EGFR-TKI. In 61 patients, L858R increased to its highest level when disease progressed (Ascend Type), while in 19 patients, L858R maintained a stable level when disease progressed (Stable Type). Median PFS was 11.1 (95%CI, 6.6–15.6) and 7.5 months (95%CI, 1.4–13.6) in patients with Ascend and Stable Types, respectively (P = .023). Median OS was 19.7 (95%CI, 16.5–22.9) and 16.0 months (95%CI, 13.4–18.5), respectively (P = .050). This is the first report finding two different changing types of plasma L858R mutation during EGFR-TKI treatment based on a prospective randomized study. Different changing types were correlated with benefits from EGFR-TKI. The impact of plasma L858R levels at disease progression on subsequent treatment strategy needs further exploration. This study was recently published in Journal of Hematology&Oncology.[10] In summary, liquid biopsy is very promising in monitoring dynamic changes of driver genes in advanced NSCLC, which promotes the development of precision medicine. References 1. Mok TS, Wu YL, Thongprasert S, et al. Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma. N. Engl. J. Med. 2009;361(10):947-957. 2. Kimura H, Suminoe M, Kasahara K, et al. Evaluation of epidermal growth factor receptor mutation status in serum DNA as a predictor of response to gefitinib (IRESSA). Br. J. Cancer. 2007;97(6):778-784. 3. Douillard JY, Ostoros G, Cobo M, et al. Gefitinib treatment in EGFR mutated caucasian NSCLC: circulating-free tumor DNA as a surrogate for determination of EGFR status. J. Thorac. Oncol. 2014;9(9):1345-1353. 4. Couraud S, Vaca-Paniagua F, Villar S, et al. Noninvasive diagnosis of actionable mutations by deep sequencing of circulating free DNA in lung cancer from never-smokers: a proof-of-concept study from BioCAST/IFCT-1002. Clin. Cancer Res. 2014;20(17):4613-4624. 5. European Medicines Agency. Summary of Product Characteristics 2014 [EB/OL], 10/14 update. http://www.ema.europa.eu/docs/en_GB/document_library/EPAR_-_Product_Information/human/001016/WC500036358.pdf. 6. Iressa 250mg Leaflet professional China. CN52-086A. 20150203. . 7. Sacher AG, Oxnard GR, Mach SL, et al. Prediction of lung cancer genotype noninvasively using droplet digital PCR (ddPCR) analysis of cell-free plasma DNA (cfDNA). Paper presented at: Journal Of Clinical Oncology 2014. 8. Marchetti A, Palma JF, Felicioni L, et al. Early Prediction of Response to Tyrosine Kinase Inhibitors by Quantification of EGFR Mutations in Plasma of NSCLC Patients. J. Thorac. Oncol. 2015;10(10):1437-1443. 9. Mok T, Wu YL, Lee JS, et al. Detection and Dynamic Changes of EGFR Mutations from Circulating Tumor DNA as a Predictor of Survival Outcomes in NSCLC Patients Treated with First-line Intercalated Erlotinib and Chemotherapy. Clin. Cancer Res. 2015;21(14):3196-3203. 10. Zhou Q, Yang JJ, Chen ZH, et al. Serial cfDNA assessment of response and resistance to EGFR-TKI for patients with EGFR-L858R mutant lung cancer from a prospective clinical trial. Journal of Hematology&Oncology. 2016;9:86.

Abstract not provided

Abstract not provided

Abstract not provided

Background:
To assess the ability of droplet digital PCR and ARMS technology to detect epidermal growth factor receptor (EGFR) T790M mutations from circulating tumor DNA (ctDNA) in advanced non-small cell lung cancer (NSCLC) patients with acquired EGFR-TKI resistance. A sensitive and convenient method for detecting T790M mutation would be desirable to direct patient sequential treatment strategy.

Methods:
To assess the ability of droplet digital PCR and ARMS technology to detect epidermal growth factor receptor (EGFR) T790M mutations from circulating tumor DNA (ctDNA) in advanced non-small cell lung cancer (NSCLC) patients with acquired EGFR-TKI resistance. A sensitive and convenient method for detecting T790M mutation would be desirable to direct patient sequential treatment strategy.To assess the ability of droplet digital PCR and ARMS technology to detect epidermal growth factor receptor (EGFR) T790M mutations from circulating tumor DNA (ctDNA) in advanced non-small cell lung cancer (NSCLC) patients with acquired EGFR-TKI resistance. A sensitive and convenient method for detecting T790M mutation would be desirable to direct patient sequential treatment strategy.

Results:
A total of 108 patients were enrolled in this study. 108 patient plasma samples were detected by ddPCR and 75 were detected by ARMS. And 16 patients experienced re-biopsy were detected T790M status by ARMS method. 43.7% (47/108) had acquired T790M mutation by ddPCR. In 75 patient plasma samples, comparing ddPCR with ARMS, the rates of T790M mutation were 46.7% (35/75) and 25.3% (19/75) by ddPCR and ARMS, respectively. Of all, 16 patients both had tumor and plasma samples, the T790M mutation rates were 56.3% (9/16) by ARMS in tissue and 50.5% (8/16) by ddPCR in plasma ctDNA. Among them, there were two ctDNA T790M mutations by ddPCR but T790M gene negative in tumor tissue by ARMS method. For all patients, the median PFS and OS were 12.3 months and 32.8 months, respectively. The patients with T790M-positive tumors had a longer time to disease progression after treatment with EGFR-TKIs (median, 13.1 months vs 10.8 months; P=0.010) and overall survival (median, 35.3 months vs 30.3 months; P=0.214) compared with those with T790M-negative patients.

Conclusion:
Our study demonstrates dPCR assay provide feasibility and sensitive method in detecting EGFR T790M status in plasma samples from NSCLC patients with acquired EGFR-TKI resistance.And T790M-positive patients have better clinical outcomes to EGFR-TKIs than patients with T790M negative.

Background:
Next generation sequencing (NGS) has been increasingly used in oncology practice but proven practically difficult when serial tumor specimens are needed. The objectives of this study were to determine feasibility and explore clinical utility of serial NGS analyses of circulating tumor DNA (ctDNA) in patients (pts) with advanced solid tumors undergoing treatment.

Methods:
ctDNA digital NGS was performed by a CLIA-certified lab (70-gene panel with mutant allele fraction (MAF) quantification). ctDNA results were retrospectively analyzed and decreases/increases/stability of molecular tumor load (MTL) defined here as MAFs of truncal driver mutations were correlated with clinical and radiographic response to treatment (response, progression, or stable disease, respectively).

Results:
From Jan 2015 to July 2016, 38 consecutive pts with advanced lung tumors (84% LUAD, 5% LUSC, 5% SCLC, 5% NOS) receiving treatment (Table) had serial ctDNA analyses (median 2, range 2-7). ctDNA alterations were detected at least once in 37 (97.4%) pts. Changes in MTL correlated with or predicted all (95% CI, 82.0-99.8%) radiological and/or clinical responses except for the patient with no genomic alteration detected. MTL results clarified response status when radiographic responses were difficult to assess in 9 (28%) of pts with either complex pleural disease (n=6), pneumonitis during PD-1 inhibitor therapy (2). Two MTL change patterns were observed: 1) clonal changes while receiving targeted therapy, including EGFR (12), ALK (3), MET (2), ERBB2 (2); 2) global changes to PD-1 inhibitors, chemotherapy or radiation. Representative tumor response maps will be presented. Table. Summary of tumor types and cancer treatment.

Cancer Type	Targeted Therapy	Immunotherapy	Chemotherapy	Radiation	TOTAL
LUAD	14	8	7	3	32
LUSC	1	1	0	0	2
SCLC	0	0	2	0	2
NOS	1	0	1	0	2
All	16	9	10	3	38

Conclusion:
Serial liquid biopsies and ctDNA digital NGS are feasible and clinically useful in monitoring MTL and genomic alterations during cancer treatment, especially in situations when radiographic responses are equivocal. Prospective evaluation of impact on clinical decision making is warranted.

Background:
Programmed death-ligand 1 (PD-L1) is known to be over-expressed in non-small cell lung cancer (NSCLC). However, the impact of chemotherapy on the altered status of PD-L1 expression has not been examined for NSCLC. The present study was intended to examine the impact of neoadjuvant chemotherapy on PD-L1 expression and its prognostic significance in lung squamous cell carcinoma (SCC).

Methods:
Matched tumor samples were obtained from SCC patients prior to and after neoadjuvant chemotherapy. The expression of PD-L1 was evaluated by immunohistochemistry. Survival analysis was performed by the Kaplan-Meier method.

Results:
A total of 76 eligible SCC patients were recruited. There were 51 males and 25 females with a median age of 60 (39-72) years. The smoking status was former (n=46) and never (n=34). Prior to neoadjuvant chemotherapy, PD-L1 expression was identified in 52.6% (40/76) of SCC patients while 61.8% (47/76) were positive for PD-L1 expression after neoadjuvant chemotherapy . Nine patients switched from negative to positive while another two patients’ samples showed the reverse of the above result. Multivariate analysis demonstrated that postoperative expression of PD-L1 was an independent prognostic factor for overall survival (HR=0.50, P=0.003), but not for PD-L1 expression prior to neoadjuvant chemotherapy.

Conclusion:
Neoadjuvant chemotherapy may up-regulate the expression of PD-L1. As compared with the status of PD-L1 expression prior to chemotherapy, the postoperative expression of PD-L1 is a better prognostic factor for overall survival in SCC.

Abstract not provided

Abstract not provided

Background:
Patients with advanced non-small-cell lung cancer (NSCLC) harboring sensitive epithelial growth factor receptor (EGFR) mutations invariably develop acquired resistance to EGFR tyrosine kinase inhibitors (TKIs). Although previous research have identified several mechanisms of resistance, the systematic evaluation using next generation sequencing (NGS) to establish the genomic mutation profiles at the time of acquired resistance has not been conducted.

Methods:
In our single center, we performed NGS of a pre-defined set of 416 cancer-related genes in a cohort of 97 patients with NSCLC harboring TKI-sensitive EGFR mutations at the time of acquired resistance to first-generation EGFR-TKIs between January 2015 to December 2015.

Results:
In 97 samples we found total 345 gene alterations (mean 3.6 mutations per patient, range 1-10). Fifty-six patients (57.7%) still exhibit EGFR-sensitive mutations as pretreatment, 93 patients (95.9%) exhibit at least one mutation except for previous existed EGFR-sensitive mutations. In all the 97 patients, most frequently mutated genes were TP53 (59.8%), T790M (28.9%), TET2 (11.3%), EGFR amplification (10.3%), PIK3CA (8.2%), BIM (8.2%), KRAS (7.2%), APC (7.2%), RB1 (7.2%), HER2 (6.2%), DNMT3A (6.2%) and MET (5.2%).

Conclusion:
NGS in this study uncovered many new genetic alterations potentially associated with EGFR TKI resistance and provided information for the further study of drug resistance and corresponding relevant tactics against the challenge of disease progression.

Background:
Pulmonary adenoid cystic carcinoma (PACC) is one of the rare malignancies, that primary from glandular tissues of lung. Currently, the treatment of PACC relies on surgery and local radiotherapy. However the therapy for advanced PACC patients is limited. A larger number of studies demonstrated that advanced PACC patients obtained little benefit from chemotherapy. Moreover, only a few case reports revealed PACC patients were appropriate for target therapy. Using high-flux and high-resolution techniques to detect the genomic alterations of PACC could provide theoretical foundation for the precision therapy of PACC.

Methods:
8 PACC patients who received surgical resection between January 2013 to December 2015 were enrolled. The tumor tissues from different locations and blood samples were collected. The oncoscreen[TM] panel by Illumina platform, which utilizing probe hybridization to gathering 287 exon regions and 22 intron regions, were used to detect the gene mutation status of PACC. And the embryonal system mutations were filtered by contrasting the gene mutation status of the leukocytes. The tumor heterogeneity was revealed by comparing the gene mutation status in different areas of the same PACC, and the phylogenetic relationships were analyzed to disclose the evolving and developing progression of PACC.

Results:
There were 69 gene mutations together among 8 patients including 29 samples. Each patient has 8.6 mutations averagely. The high-frequency mutations were PAK3-D219E, FBXW7-D112E, TET2-T418I, KAT6A-E796A, and MET-R1005Q. However, the common mutations in other NSCLC, like EGFR, KRAS, ALK, etc., weren’t happened in this group of PACC. In this study, the spatial heterogeneity was discovered in PACC, not only in the mutation site, but also in the mutant abundance. Moreover, the phylogenetic relationships revealed that the clonal evolution and development existed in PACC.

Conclusion:
The status of genomic alterations in PACC was different from the other non-small cell lung cancer (NSCLC). PACC showed obvious spatial heterogeneity and clonal evolution.

Background:
Amplification of the mesenchymal-epithelial transition factor (MET) gene plays a vital role in non-small cell lung cancer (NSCLC). The anti-MET therapeutic strategies are still unclear in epidermal growth factor receptor (EGFR) mutant patients and EGFR-naive patients. Aims of our study are to discuss role of MET amplification in Chinese NSCLC patients, and evaluate the antitumor activity of crizotinib (MET inhibitor) in Chinese NSCLC patients with MET gene amplification.

Methods:
From Jun 2015 to Jan 2016, we detected 11 metastatic NSCLC patients with MET amplification by fluorescence in situ hybridization (FISH). MET amplification was defined as gene focal amplification or high polysomy (at least 15% cells with ≥5 copy numbers). Patients with MET de novo amplification received crizotinib, patients with concomitant MET acquired amplification and EGFR mutation received combined therapy of EGFR-tyrosine kinase inhibitors (TKIs) (gefitinib, erlotinib, icotinib)and crizotinib. All enrolled subjects received tumor measurement according to RECIST1.1

Results:
The frequency of MET de novo amplification was 54.5%(6/11), and that of concomitant MET acquired amplification and EGFR mutation was 45.5%(5/11) respectively. 4 of 6 patients with MET de novo amplification received crizotinib, 2 patients had partial response (PR), 1 patient had stable disease (SD), 1 patient died due to heart disease. Response rate (RR) of crizotinib was 50%(2/4). Encouraging response was observed in one case, a CT scan performed 31 days after starting crizotinib revealed 42.2% decrease in tumor measurement, until now, a 7-month CT revealed 60.0% decrease. 3 of 5 patients with concomitant MET acquired amplification and EGFR mutation received the combined therapy of EGFR-TKIs and crizotinib. 1 patient achieved PR, 2 patients had SD. RR of combined therapy was 33.3%(1/3). Dramatic response was observed in one case with combined therapy, a 2-month CT revealed 31.0% decrease in tumor measurement.

Conclusion:
According to our study, patients with MET amplification benefited from crizotinib, and RR was inspiring. Patients with concomitant MET acquired amplification and EGFR mutation need combined targeted therapy.

Background:
A significant portion of patients with non-small cell lung cancer (NSCLC) develop brain metastasis. Patients with brain metastasis suffer from poor prognosis with a median survival of less than 6 months and low quality of life with limited treatment options. First generation EGFR tyrosine kinase inhibitors (EGFR TKIs) have demonstrated significant clinical benefit for patients with EGFR-mutant NSCLC. However, their effect on brain metastasis is limited due to poor drug penetration into the brain. Epitinib is an EGFR TKI designed to improve brain penetration. A Phase I dose escalation study on epitinib has been completed and the recommended Phase 2 dose (RP2D) determined (Y-L Wu, 2016 ASCO). This Phase I dose expansion study was designed to evaluate the efficacy and safety of epitinib in EGFR-mutant NSCLC patients with brain metastasis.

Methods:
This is an ongoing open label, multi-center Phase I dose expansion study. EGFR-mutant NSCLC patients with confirmed brain metastasis, either prior EGFR TKI treated or EGFR TKI treatment naïve, were enrolled to receive oral epitinib 160 mg per day. Patients with extra-cranial disease progression while on treatment with an EGFR TKI were excluded. Tumor response was assessed per RECIST 1.1.

Results:
As of 31 May, 2016, 27 patients (13 EGFR TKI pretreated, 14 EGFR TKI treatment naïve) have been enrolled and treated with epitinib. The most frequent adverse events (AEs) were skin rash (89%), elevated ALT (41%)/AST (37%), hyper-pigmentation (41%) and diarrhea (30%). The most frequent Grade 3/4 AEs were elevations in ALT (19%), gamma-GGT (11%), AST (7%), hyperbilirubinemia (7%) and skin rash (4%). There have been no Grade 5 AEs to date. Among the 24 efficacy evaluable patients (11 TKI pretreated, 13 TKI naïve), 7 (7/24, 29%) achieved a partial response (PR), including 1 unconfirmed PR. All PRs occurred in EGFR TKI treatment naïve patients (7/13, 53.8%). Of the 24 evaluable patients, 8 (5 EGFR TKI treatment naïve, 3 EGFR TKI pretreated) had measurable brain metastasis (lesion diameter>10 mm per RECIST 1.1) with 2 PRs (both EGFR TKI treatment naïve patients, 2/5, 40%).

Conclusion:
Epitinib 160mg per day treatment in EGFR-mutant NSCLC patients with brain metastasis demonstrated clinical activity both extra- and intra-cranial. Epitinib was well tolerated. The data to date appears encouraging and warrants further development of epitinib.

Background:
Recent studies have identified that the degree of tumor infiltrating lymphocyte (TIL) infiltration and PD-L1 expression in the tumor microenvironment (TME) are significantly correlated with the clinical outcomes of anti-PD-1/PD-L1 therapies. Here we conducted this study to verify the distribution of PD-L1/CD8[+] TIL expression and its clinical significance in non-small cell carcinoma (NSCLC). Potential mechanism predicted for PD-1 blockade was explored in depth as well.

Methods:
Immunohistochemistry was performed to detect PD-L1 and CD8 expression in NSCLC. The Kaplan–Meier (KM) survival curve was used to estimate disease free survival (DFS) and overall survival (OS). Gene Set Enrichment Analysis (GSEA) was used to determine potentially relevant gene expression signatures.

Results:
288 cases with stage I-IIIA NSCLC were evaluated for PD-L1 and CD8+ TIL staining. Dual positive PD-L1 and CD8 (PD-L1+/CD8+) represents a predominant subtype in NSCLC, accounting for 36.5% (105/288), followed by PD-L1-/CD8- (24.3%, 70/288), PD-L1-/CD8+ (26.0%, 75/288) and PD-L1+/CD8- (13.2%, 38/288). Survival analysis of DFS (p=0.031) and OS (p=0.002) showed a significant difference between four subgroups. Furthermore, we analyzed the correlation between expression types of PDL1/CD8 and mutation burden and angtigen presentation. We can identified dual positive PD-L1 and CD8 was significant with increased mutation burden (p<0.001), high frequency of mismatch repair (MMR) related gene mutation. More interestingly, tumor with dual positive PD-L1 and CD8 manifested a remarkable activated angtigen presentation and T cell receptor signature compared with other subgroups.

Conclusion:
Dual positive PD-L1 and CD8 was identified as a predominant subtype in NSCLC and correlates with increased immunogenicity. These findings provide the evidence that combined analysis of PD-L1 and CD8 in NSCLC may be a promising way to predict PD-1 blockade immunotherapy.

Background:
RET fusion gene is identified as a novel oncogene in a subset of non-small cell lung cancer (NSCLC). However, few data are available about the prevalence, clinicopathologic characteristics, genetic variability and therapeutic options in RET-positive lung adenocarcinoma patients.

Methods:
For 615 patients with lung adenocarcinoma, RET status was detected by reverse transcription-polymerase chain reaction (RT-PCR). Next-generation sequencing (NGS) and FISH were performed in positive cases. Thymidylate synthetase (TS) mRNA level was assayed by RT-PCR. Overall survival (OS) was evaluated by Kaplan-Meier method and compared with log-rank test.

Results:
Twelve RET-positive patients were identified by RT-PCR. However, one patient failed the detection of RET arrangement by FISH and NGS. Totally, 11 patients (1.8%) confirmed with RET rearrangements by three methods , including six females and five males with a median age of 54 years. The presence of RET rearrangement was associated with lepidic predominant lung adenocarcinoma subtype in five of 11 patients. RET rearrangements comprised of nine KIF5B–RET and two CCDC6–RET fusions. Four patients had concurrent gene variability by NGS detection,including EGFR(n=1),MAP2K1 (n=1), CTNNB1 (n=1) and AKT1 (n=1) . No survival difference existed between RET-positive and negative patients (58.1 vs. 52.0 months, P=0.504) . The median progression-free survival of first-line pemetrexed/platinum regimen was 7.5 months for four recurrent cases,and longer than RET-negative patients(7.5 vs.5.0 months, P=0.026). . The level of TS mRNA was lower in RET-positive patients than that in those RET-negative counterparts (239±188×10[-4] vs. 394±457×10[-4],P=0.019) .

Conclusion:
The prevalence of RET fusion is approximately 1.8% in Chinese patients with lung adenocarcinoma. RET arrangement is characterized by lepidic predominance and a lower TS level. RET-rearranged patients may benefit more from pemetrexed-based regimen.

Background:
ROS1 gene-rearrangement in non-small cell lung cancer (NSCLC) patients has recently been identified as a driver gene and benefited from crizotinib treatment. However, no data is available for ROS1-positivity NSCLC about chemotherapeutic options and prognostic data. We investigated pemetrexed-based treatment efficacy in ROS1 translocation NSCLC patients and determined the expression of thymidylate synthetase (TS) to provide a rationale for the efficacy results.

Methods:
We determined the ROS1 status of 1750 patients with lung adenocarcinoma. Patients’ clinical and therapeutic profile were assessed. In positive cases, thymidylate synthetase (TS) mRNA level was performed by RT-PCR. For comparison, we evaluated the TS mRNA status and pemetrexed-based treatment efficacy from 170 NSCLC patients with anaplastic lymphoma kinase (ALK) translocation(n=46), EGFR mutation (n=50), KRAS mutation (n=32) and wild-type of EGFR/ALK/ROS1/KRAS (n = 42).

Results:
Thirty-four ROS1 translocation patients were identified at two institutions. Among the 34 patients, twelve with advanced stage or recurrence were treated with pemetrexed-based first-line chemotherapy. The median progression-free survivals of pemetrexed-based first-line chemotherapy in ROS1 translocation, ALK translocation, EGFR mutation, KRAS mutation and EGFR/ALK/ROS1/KRAS wild-type patients were 6.8, 6.7, 5.2, 4.2 and 4.5 months, respectively (P=0.003). The TS mRNA level was lower in patients with ROS1-positive than ROS1-negative patients (264±469×10[-4] vs. 469 ± 615×10[-4] , P=0.03), but similar with ALK-positive patients (264±469×10-4 vs. 317±524×10[-4], P=0.64).

Conclusion:
Patients diagnosed with ROS1 translocation lung adenocarcinoma may benefit from pemetrexed-based chemotherapy. TS mRNA level enables the selection of therapeutic options for ROS1translocation patients.

Background:
According to the 2015 World Health Organization classification of lung tumors, pulmonary Large cell neuroendocrine carcinoma (PLCNC) is grouped with the small cell lung cancer (SCLC) and carcinoid as pulmonary neuroendocrine carcinoma(PNC) for the common features of neuroendocrine characteristics . Molecular profiles and prognosis of primary pulmonary neuroendocrine carcinoma(PNC) are not well investigated currently. We conducted present study to evaluate genomic abnormality and survivals in patients with primary PNC.

Methods:
Tumor samples of PNC after completely resection from Zhejiang Cancer Hospital were collected from 2008 to 2015. Nine driver genes including six mutation (EGFR, KRAS, NRAS, PIK3CA, BRAF, HER2) and three fusions (ALK, ROS1, RET) were evaluated by RT-PCR. Survival analysis was evaluated using the Kaplan-Meier method.

Results:
Totally, 108 patients with pathologic confirmed PNC were enrolled. Samples included 52 PLCNC, 44 small cell lung cancer (SCLC) and 12 carcinoid. Twelve patients were found to harbor genomic aberrations (11.1%). The most frequent gene abnormality was PIK3CA (n=5,4.6%),followed with EGFR (n=3,2.8%), KRAS (n=2,n=1.9%), ALK (n=1,0.9%), RET (n=1,0.9%). No ROS1,BRAF,NRAS and HER2 mutations were observed. The frequencies of gene aberrations in PLCNC, SCLC and carcinoid were 15.4%,6.8% and 8.3%,respectively. Sixty-seven patients were with recurrence or metastasis after surgery, including 32 PLCNC, 33 of SCLC, and two of carcinoid (both were atypical carcinoid). Among the 32 patients with PLCNC,none received molecular targeted treatment,28 received first-line chemotherapy,including 18 of etoposide/platinum regimen and 10 of other platinum-based treatment. The progression free survival in patients with etoposide/platinum regimen was longer than patients with non-etoposide/platinum treatment (4.8 vs.3.4 months,P=0.019) . Survival difference was observed among the PLCNC,SCLC and carcinoid group (37.0 vs. 34.0 vs.not reached, P=0.035), but no difference existed between the PLCNC and SCLC group (P=0.606).

Conclusion:
Common genomic abnormality is rare in PNC patients and most frequently observed in PLCNC. Patients with carcinoid had a superior survival than PLCNC and SCLC.

Background:
This study aimed to assess the ability of different technology platforms to detect epidermal growth factor receptor (EGFR) mutations including L858R, E19-dels, T790M, and G719X from circulating tumor DNA (ctDNA) in patients with non-small cell lung cancer (NSCLC).

Methods:
Plasma samples were collected from 20 patients with NSCLC including detailed clinical information along with data regarding treatment response. ctDNA was extracted from 10 mL plasma using the QIAamp Circulating Nucleic Acid Kit (Qiagen). Extracted ctDNA was analyzed using two real time-amplification refractory mutation system-quantitative PCR platforms (cobas® EGFR Mutation Test: cobas; and AmoyDx® EGFR 29 Mutations Detection Kit: ADx), one digital platform (Droplet Digital[TM] PCR, ddPCR: Bio-rad), and one next-generation sequencing platform (firefly NGS: Accuragen).

Results:
If a positive result was obtained from any one of the four platforms, the sample was categorized as positive. We identified 15 EGFR mutations in 20 patients with NSCLC using the four platforms, for which 7, 11, 10, and 12 mutations were detected by ADx, cobas, ddPCR, and firefly NGS, respectively. Among the 15 EGFR mutations, six and seven EGFRalterations demonstrated an allele frequency of more or less than 1% (group A or B, respectively), and two exhibited unknown allele frequency. In group A, 5, 5, 5, and 6 EGFR mutations were detected by ADx ,cobas, ddPCR, and firefly NGS, respectively. The positive coincidence rate of any two platforms ranged from 66.7% to 100% and the kappa value varied from 0.787 to 1.000 in group A. In group B, 1, 5, 5, and 6 EGFR mutations were detected and the positive coincidence rate of any two platforms ranged from 16.7% to 100% and the kappa value varied from 0.270 to 1.000. The output of cobas, ddPCR, and firefly NGS were highly correlated, whereas ADx displayed weak concordance with these three platforms in group B. In addition, we identified 75 wild-type loci when EGFR alleles identified as negative by one or more platforms were considered as negative. ADx, cobas, ddPCR, and firefly NGS uncovered 73, 69, 70, and 68 EGFR wild-type loci, respectively. The concordance and negative coincidence rates between any two platforms were over 90%.

Conclusion:
The detection rate and concordance were probably affected by the abundance of EGFR mutations and the sensitivity of different platforms. Three platforms, including cobas, ddPCR, and firefly NGS, exhibited higher positive coincidence and detection rates when the allele frequency was lower than 1%.

Background:
We aimed to investigate the feasibility of droplet digital PCR (ddPCR) for the detection of epidermal growth factor receptor (EGFR) mutations in circulating free DNA (cfDNA) from cerebrospinal fluid (CSF) and plasma of advanced Lung Adenocarcinoma (ADC) with brain metastases (BM).

Methods:
Fourteen advanced ADC patients with BM carrying activating EGFR mutations in tumour tissues were enrolled in this study, and their matched CSF and plasma samples were collected. EGFR mutations were detected by the Amplification Refractory Mutation System (ARMS) in tumour tissues. EGFR mutations, including 19del, L858R, and T790M were examined in cfDNA isolated from 2milliliter CSF or plasma by ddPCR assay. The clinical response was assessed according to Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 guidelines. Overall survival (OS) and progression free survival (PFS) after the diagnosis of BM were also evaluated.

Results:
Out of 14 patients, eleven were females and three males aged from 34 to 74 years old (median age of 55 years old). In all of cases, CSF cytology were negative. In ddPCR assays, EGFR mutations were detected in CSF of three patients (21.4%; one of 19del and two of L858R), and in plasma of six patients (42.9%; one of 19del, one of L858R, one of T790M, two of L858R&T790M, and one of 19del&T790M). All EGFR T790M mutations were found during or after EGFR-TKIs treatments. The three patients with activating EGFR mutations in CSF achieved partial response (PR) of BM after treated with combination of WBRT and EGFR-TKIs. The median OS and PFS after the diagnosis of BM were 18.0 months and 9.0 months, respectively.

Patient	Tissue EGFR	CSF EGFR	Plasma EGFR	Systematic Treatment	BM Treatment
1	19del	WT	T790M	Erotinib+Chemotherapy	WBRT+Gamma knife
2	19del	WT	19del	Erotinib+Chemotherapy	WBRT
3	L858R	L858R	L858R	Gefitinib+Chemotherapy	WBRT
4	L858R	WT	WT	Gefitinib+Chemotherapy	WBRT
5	19del	WT	WT	Gefitinib+Chemotherapy	WBRT
6	L858R	WT	L858R/T790M	Erotinib+Chemotherapy	WBRT
7	L858R	WT	WT	Gefitinib	WBRT
8	19del	19Del	19Del/T790M	Gefitinib	WBRT
9	L858R	WT	WT	Erotinib+Chemotherapy	NONE
10	19del	WT	WT	Erotinib+Chemotherapy	WBRT
11	19del	WT	WT	Icotinib+Chemotherapy	WBRT
12	L858R	WT	L858R/T790M	Chemotherapy	WBRT
13	L858R	L858R	WT	Icotinib	WBRT
14	19del	WT	WT	Gefitinib+Chemotherapy	WBRT

Conclusion:
It was feasible to test EGFR mutation in CSF. CSF may serve as liquid biopsy of advanced ADC with BM by enabling measurement of cfDNA within CSF to characterize EGFR mutations.

Background:
In patients with advanced non–small-cell lung cancer (NSCLC) harboring epidermal growth factor receptor (EGFR) activating mutations, EGFR-tyrosine kinase inhibitors (TKIs) are recommended as first-line treatment due to favorable clinical efficacy. However, acquired resistance inevitably develops after median progression-free survival (PFS) of 9-14 months. Among the mechanisms of acquired resistance, small-cell lung cancer (SCLC) transformation was reported to account for nearly 5%. However, the molecular details underlying this histological change and resistance to EGFR-TKI therapy remain unclear.

Methods:
15 out of 233 (6.4%) patients were confirmed to develop SCLC transformation after failure to EGFR-TKI. We analyzed the clinical parameters of these patients by using chi-square test and Kaplan-Meier analysis. To explore gene alterations that might contribute to SCLC transformation, next generation sequencing (NGS) was performed on four pairs of matched pre- and post-transformation tumor tissue samples. We further performed NGS on 11 matched circulating tumor DNA (ctDNA) to explore the potential mechanism of resistance to EGFR-TKI.

Results:
The median age of SCLC transformed patients was 53 years. 93.3% (14/15) patients harbored EGFR exon19 deletion. The median PFS and overall survival (OS) of SCLC-transformed patients treated with EGFR-TKI compared to those without transformation were 11.7 versus 11.9 months (P=0.473) and 29.4 versus 24.3 months (P=0.664), respectively. All 4 patients developed loss of heterozygosity of TP53/RB1 after transformation. Besides, increased copy number of five proto-oncogenes were identified in post-transformation tissue samples. Three patients developed EGFR T790M mutation in the post-transformation ctDNA rather than their tissue samples.

Conclusion:
SCLC transformation was commonly seen in patients harboring EGFR exon 19 deletion. The clinical outcomes of TKI and OS in SCLC transformed patients were similar to non-transformed patients. The loss of heterozygosity of TP53 and RB1along with increased copy number of proto-oncogenes may lead to the SCLC transformation. The mechanisms of acquired resistance to TKI during SCLC transformation might be the emergence of classic drug resistance mutations, which was undetectable due to the intra-tumor heterogeneity.

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Abstract:
Last decades have shown an impressive advance in terms of biological knowledge in cancer. Traditional way to bring new ideas/hypotesis into clinical trials was overcoming by this fact. New agents directed against specific molecular targets have important impact in terms of response rate (RR), response duration (RD), progression free survival and eventually overall survival (OS) as well as quality of life (QoL). If you have an interesting idea/hypotesis, today you have to take on account several points that can exclude it. Select population becomes a very important issue. How to do this? Selecting a target, a tumor, both, other conditions? Following the tradition of research phases, Phase I refers to measure safety and pharmacokinetics assesing maximum tolerated dose (MTD) but a number of new agents have a non reachable MTD because they have a low toxicity. On the other hand, phase II refers to the assesing of efficacy in a certain tumor as well as safety, but, in the case of new agents you may select a tumor (as ussual), a specific target no matter wath tumor carry it (basket), or other conditions. In this phase measurement of response is important as a precedent of next phase trials and the challenge is the method you will use to do it. New inmunotherapeutic agents probably need a different way to do this. Also, to have predictive biomarkers for most of these agent will help to select the potential population that will achieve the more benefit and avoid futile toxicity and a waste of time and resources. We have to remember that biological effects not always means clinical benefit. Breaking barriers, for phase III comparator selection, primary and secondary end points as well as inclusion and exclusion criteria become a very important point and are different in the traditional way and in a proposed new way. OS is the gold standard end point but there are many more very important like PFS, RR, DoR, QoL. Again, measurement methods are very important and may be different related with biological mechanism and length of response for different agents than chemotherapy. As phase III trials select (include and exclude) patients troughout very strict criteria and there are some late toxicities that can be as important as the acute and subacute toxicities, phase IV trials are very important because they represent better the daily patient we see at office practice and is a powerfull pharmacovigilance mechanism. Sanctuaries have to be consider as far as the prevalent tumors have a very frequent involvement of Central Nervous System and these patient are mostly excluded from clinical trials at the beggining. Ethics is a fundamental point as far as the most important objective is the patient safety and treatment accesibility. If we went troughout these restriction points and our idea/hypotesis has survive, we can follow the development of trials around wasting less time and resources.

Abstract:
Introduction: Published and officially approved medical research is based on evidence and subsequently, statistical methods are an essential part in proving the usefulness of results. The translation in statistical terms in most cases is to build hypotheses and their alternatives to be tested. Clearly, medical researchers need some sound understanding of statistical principles which can be taken, however, not as a matter of course. The aim of the contribution is to communicate among readers of medical journals and reports statistical matters focusing on basic statistical considerations to enable a better understanding. [1] Essentials of statistical analysis and reporting: (i) Making the information content of the research results visible in summarizing and prescinding them in tables, graphs, and figures. (ii) Assessing and quantifying any associations of reported measures like possible differences in the outcome of treatment actions etc., and using confidence intervals to express the uncertainty of those associations. (iii) Building hypotheses and their alternatives to prove that these associations have a real biomedical basis which is performed by statistical testing under a given level of significance (p-values). Important is the design of the research project: In randomized trials comparisons are an inherent part of those associations whereas in nonrandomized studies no direct conclusion can be driven that any association not due to chance indicates a causal relationship. Methods: Randomization is a process in which each of the patients has the same but not necessarily the equal chance to be assigned to predefined treatment arms ensuring that the treatment arms are comparable with respect to known or unknown risk factors. Hence, it is a method to remove selection and accidental bias and to guarantee the validity of statistical tests. Main design issues of studies are the formulation of the primary aim, the question of blinding, and the boundary conditions of sample size calculations. [2] Tables of baseline data and outcome events are part of most medical journal papers concerning treatments. Generally the first table displays the patients’ characteristics including some demographic variables and variables related to the primary aim. The main outcome events are forming the key table of every paper stratified by treatment groups. Categorical variables are shown as number and percent by group. Continuous variables can either be presented by mean and the standard deviation or by median and the interquartile range. Latter is preferred if the data are scattered and far from normal distribution with the implication that in the sequel non-parametric tests should be favored. For composite events like severe toxicities, progression of disease, and death the number of patients experiencing any of them plus the number in each component should be given, since we have the effect of multiple events. In focus are often variables displaying the time to the first event (e.g. progression of disease which can happen more than once during treatment history). For time driven events in the sequel analysis of general survival times are applied leading to special statistics and graphs. The Kaplan-Meier plot is the most used graph to show time-to-event outcomes as death, time to progression, disease free interval etc. In general the graph displays the steadily increasing difference in incidence rates of the outcome for two or more treatment arms. To make the process clearer, the numbers at risk in each group should be shown at regular time intervals in the time axis. Individuals who did not reach the endpoint are censored (e.g. still alive, lost to follow-up) and should be marked in the plot. The conditional probabilities of Kaplan-Meier statistics indicate the probability of experiencing the endpoint under consideration beyond a certain length of follow-up. Estimation of treatment effects is to measure the magnitude of the difference between treatments on patient outcomes. Normally this is done by a point estimate showing the actual difference observed. Inherent in this kind of statistics is that the bigger the trial, the more precise the point estimate will be. Such uncertainty is usually expressed by a 95% confidence interval in which this percentage of the sample will be found. The primary aim of the study determines the type of estimate required. Namely, there are three main types of outcomes: (a) Binary (dichotomous) response, e.g. dead or alive, progressive or non-progressive, success or failure, respectively. (b) Time to event outcome most measured in intervals, e.g. time from randomization to death, time of inclusion in the study to treatment failure. (c) Quantitative outcome as the reduction of a certain percentage of tumor loads at a given time point (e.g. a seen reduction of 30% after exactly 6 months). Estimates based in percentage are indicated if a binary outcome has to be judged in terms of absence or presence. Then a confidence interval of the proportion of interest can be given. Relative risks are the ratio of two percentages and can be converted to relative risk reduction. Alternatively relative odds can be applied which is a cross-product relationship and shows the relation of chance. Relative risk and relative odds are sometimes called risk ratio and odds ratio instead. The absolute difference in percentage is taken as a measure of absolute risk reduction. Estimates for time-to-event outcomes are used in all survival statistics as time to death, time to progression etc. The Kaplan-Meier plot depicts the first time of the occurrence of the event but does not in itself provide a simple estimate summarizing the treatment difference. The Kaplan-Meier estimate at the end of plotted time or at any other time between can be taken as cumulative rate of the leading event. That is only a time point estimate. Instead, the most common approach is to use a Cox proportional hazards model to obtain a hazard ratio and its 95% confidence interval. The hazard ratio can be thought of as the hazard rate in one group divided by the hazard rate in the other group averaged over the whole follow-up period. Examples from medical trials will be used to explain the statistical principles shown here. References [1] Pocock SJ, McMurray JJV, and Collier TJ: Making sense of statistics in clinical trial reports. J Am Coll Cardiol 2015; 66(23):2648-2662. [2] Pilz LR, Manegold C: Endpoints in lung cancer trials: Today's challenges for clinical statistics. MEMO 2013; 6(2): 92-97.

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Abstract:
Clinical trials in cancer have typically investigated agents or regimens in selected groups of patients based primarily on histology and clinical characteristics (e.g., tumor stage, performance status, prior treatment, etc). The major goal of those trials was to demonstrate statistically significant improvement in outcome with minimum p-value of 0.05, as compared with the control arm. In the majority of cases, this approach resulted in only small incremental improvements in overall survival. In some cases, even without any improvement in survival, a certain regimen became the foundation for adding novel targeted agents only based on the favorable toxicity profile and has been widely used in practice over the last two decades. More recently, targeted therapies administered to patients with biologically relevant biomarkers, such as activating EGFR mutations and ALK alternation, have produced substantial improvements in outcomes and rapidly changed the treatment paradigm of lung cancer. In addition, newer treatment modalities such as immune check-point inhibitors and antibody-drug conjugates are emerging as highly effective therapies that are providing improvements in patient outcome. In fact, between 2004 and 2015, 14 new drugs were approved by the FDA for NSCLC. However, the relevance of statistical significance has increasingly been challenged when the treatment effect is small. [1,2] To resolve this issue, there has been growing consensus to raise the bar of efficacy for approving new cancer drugs.[3,4] The critical question is what is clinically meaningful and how can this outcome be measured. The FDA considered OS to be the standard clinical benefit endpoint that should be used to establish efficacy of a treatment in patients with locally advanced or metastatic NSCLC.[5] The FDA also has recognized that PFS may be appropriate as the primary endpoint to establish efficacy for drug approval if the trial is designed to demonstrate a large magnitude for the treatment effect as measured by both the hazard ratio and absolute difference in median PFS and an acceptable risk-benefit profile of the drug is demonstrated. The remaining question is, “What is clinically meaningful?” Modest benefits could be considered worthwhile if associated with moderate costs and toxicity, whereas a new drug with a very high cost and/or substantial toxicity is worthwhile only if it produces sizeable clinical benefits. To address this issue, the ASCO Cancer Research Committee convened four disease-specific working groups, including the lung cancer working group. The Committee generally agreed that relative improvements in median OS of at least 20% are necessary to define a clinically meaningful improvement in outcome.[3] For lung cancer, it was recommended that one experimental agent in non-squamous NSCLC should be considered practice changing if it increases PFS by at least 4 months and OS by 3.5-4 months with a corresponding death risk reduction of 20-24%. Due to less favorable prognosis, the desired benefit in squamous NSCLC was 3 months increase in PFS and 2.5-3 months increase in OS with a death risk reduction of 20-23%.[3] Obviously, if a new treatment is to be introduced into clinical practice, it is not sufficient to demonstrate that it is "better than” or “non- interior to” the standard therapy. As cancer care costs continue to increase at an unsustainable rate, oncology professionals need to focus more on delivering value-based patient care rather than simply practicing evidence-based patient care. In addition, it has become increasingly clear that the traditional fee-for-service model will no longer serve the interest of all the parties involved, including the pharmaceutical company.[6] It seems to be a matter of time that the fee-for-service system will be replaced with the value-based reimbursement system. Reference 1. Sobrero A, Bruzzi P. Incremental advance or seismic shift? the need to raise the bar of efficacy for drug approval. J Clin Oncol 2009;27:5868–73. 2. Ocana A, Tannock IF. When are "positive" clinical trials in oncology truly positive? J Natl Cancer Inst 2011;103:16–20. 3. Ellis LM, Bernstein DS, Voest EE, Berlin JD, Sargent DJ, Cortazar P, et al. American Society of Clinical Oncology perspective: raising the bar for clinical trials by defining clinically meaningful outcomes. J Clin Oncol 2014;32:1277–80. 4. Sobrero AF, Pastorino A, Sargent DJ, Bruzzi P. Raising the bar for antineoplastic agents: How to choose threshold values for superiority trials in advanced solid tumors. Clin Cancer Res. 2015;21:1036-43. 5. United States, Department of Health and Human Services, Food and Drug Administration (FDA). Clinical Trial Endpoints for the Approval of Non-Small Cell Lung Cancer Drugs and Biologics Guidance for Industry (published April 2015) : Available online: http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM259421.pdf, 2015. 6. Eaton KD, Jagels B, Martins RG. Value-based care in lung cancer. Oncologist. 2016;21:903-6.

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Abstract:
Expectations from a Young Investigator Over the last two decades, research has pushed lung cancer investigations from the shallows of cancer treatment to one of the most innovative positions in oncology. The improvements in molecular diagnostics, in targeted therapy and immunotherapy with the linked creeping decline of traditional chemotherapy act as a model for many other tumor entities. Joined by this paradigm shift is a demographic change to young investigators who start their career in the innovative fields of lung cancer research instead of thinking in the traditional chemotherapy-based fashion. Nevertheless, in order to detect the needs and expectations from young investigators, even the definition of "young" is hard to handle, and subjective expectations might be biased by the socioeconomic background of the investigator. We therefore set out to find a way to present more robust and reliable data on the topic. We created an online questionnaire covering age, experiences, interests, and of course needs and expectations of young investigators. The expectations focus on research topics, treatment options, mentorships and social networking. The questionnaire will be forwarded to 20 investigators in the EU, Asia, South America and the US with link to the emerging fields of lung cancer research, in order to forward it to participants who they consider young in both clinical and preclinical investigations. For subgroup analyses, we will include students with interest in this field, too. Results will be analyzed by the presenters. The poll will be open until one week of the WCLCs Young Investigator's Scientific Mentoring Session, and results of this interim analysis will be presented by this talk. Nevertheless, all participants of the WCLC 2016 are invited to answer the questionnaire during the Conference, and a final data cut will be made at December 10th, 2016. We are aware of the potential biases in online polls. A valid e-mail address and the source of the online link (i. e., who was the "supervisor") are necessary. As an incentive to participate properly, we offer all participants to be part of the "WCLC young Investigator Expectations Network (WIEN)" which will coauthor the final manuscript. As we question the expectations of how lung cancer research will work in five years, it is intended to repeat the poll in a regular manner, maybe yearly. We expect a view on the expectations from young investigators worldwide and a feeling of their needs for the future.

Abstract:
All oncologists are part of the mentor-mentee relationship at some point of their career. Mentoring can be considered one of the critical factors in achieving a successful career. The importance of a good mentor is best described by the sentence of Robert S.Kerbel: “I have been extremely fortunate if not blessed, with having series of outstanding mentors [1].” In spite of the importance of mentoring, what makes good mentors and mentoring is often not well defined [2]. According to Nature’s guide for mentors, one of the most important characteristics of a good mentor is his/her orientation towards mentee’s long-term career development as a main focus of mentoring. In this way, the mentor becomes a “mentor for life” and not only temporary supervisor [2]. According to mentees, a good mentor has some distinct personal characteristics like enthusiasm, passion, positivity, compassion and understanding. Beside these, some others like appreciating individual differences, being respectful and unselfish are also very important. To properly advise and guide mentees in their work, mentors should be able to see their individual characteristics and support their personal strengths. Showing respect means that the protégée is not only seen as an workforce, but also as a genuine collaborator. Regarding unselfishness: letting the mentee be the first author of a common article, even if the mentor provided the initial idea is a good example [2]. Personal characteristics aside, abilities to become a good mentor can be gained by following some useful rules. Mentors should be generally available and have an “open door” policy instead of restricted and limited dedicated time. Availability should also be shown by quickly answering e-mails and phones calls. They should be inspirational and show optimism on every-day issues but – even more importantly - when facing failure. Mentors should find a balance between doing and letting do, should support mentees in analytical thinking and adapt to their needs according to the progress of the protégée (e.g. different mentoring at the beginning and the end of the PhD course). They should celebrate successes with the mentee [2, 3]. Scientific mentors have the obligation to teach, encourage and support students in some specific activities in which skills are essential in the world of science. Examples are writing and oral presentations. Supporting writing with fast and accurate reviews, while resisting the temptation of rewriting instead of the student is one of the main goals. Extensive mentoring regarding oral presentations is also needed due to the fact, that not many students have a natural gift for presenting. Mentors should also try to provide as many opportunities for oral presentations as possible. Involving students in mentors’ networking should also be a continuous process [2]. How to choose a good mentor is a question that should be carefully addressed. In selecting mentors, trainees should follow some recommendations. They should look for possible mentors online, see which mentors possibly have the same interests, e-mail previous mentees inquiring about their experience with the mentor and afterwards meet the potential mentor in person at work. Trainees should carefully look for signs of poor mentorship, like no available time for one-to-one conversation, repressed and stressed co-workers that show no respect for their head, the potential mentor [2, 4]. Even if some trials report objective data, evaluating mentorships can be challenging since it is a complex interpersonal interaction. In a trial reported by Badawi the majority (74%) of mentors and mentees report the experience as rewarding, worth their time and effort, many (58%) achieve their goals in a timely manner and plan to continue (89%) their collaboration after the mentorship period is finished [5]. High satisfaction with the mentorship experience is commonly reported in other surveys. DeCastro conducted a trial on 1708 clinicians-researchers and only 10% of them were not satisfied with the experience, without differences between male and female mentees [6]. Some surveys, like the one reported by Dhami, show the importance of formal mentorship. Satisfaction with the mentorship experience was greater in mentees included in formal mentorship compared to those who had an informal one (72% vs. 36%, p<0.01) [7]. Formal mentoring influences also on research productivity. In the survey of Riechelman, responders with mentors were more involved (more available time dedicated) in academic research compared to those without mentors [8]. A model mentor is involved in the fruitful career development of the mentee and broadly shares the knowledge, skills and expertise that the mentee needs. As the mentee advances and gains independence, a good mentor is able to guide him/her toward new opportunities and facilitates the mentee’s growth [9]. 1. Kerbel, R.S., Some guidelines for building a successful career in cancer research. Cancer Biol Ther, 2003. 2(1): p. 111-4. 2. Lee, A., C. Dennis, and P. Campbell, Nature's guide for mentors. Nature, 2007. 447(7146): p. 791-7. 3. Powers, P.J., Engaged mentors offer inspiration and open doors. Am J Med, 2006. 119(1): p. 3. 4. Purcell, E.P., et al., Research to reality (R2R) mentorship program: building partnership, capacity, and evidence. Health Promot Pract, 2013. 14(3): p. 321-7. 5. Badawy, S.M., et al., Early career mentoring through the American Society of Pediatric Hematology/Oncology: Lessons learned from a pilot program. Pediatr Blood Cancer, 2016. 6. DeCastro, R., et al., Mentoring and the career satisfaction of male and female academic medical faculty. Acad Med, 2014. 89(2): p. 301-11. 7. Dhami, G., et al., Mentorship Programs in Radiation Oncology Residency Training Programs: A Critical Unmet Need. Int J Radiat Oncol Biol Phys, 2016. 94(1): p. 27-30. 8. Riechelmann, R.P., et al., The influence of mentorship on research productivity in oncology. Am J Clin Oncol, 2007. 30(5): p. 549-55. 9. Gitlin, S.D. and M.L. Lypson, For Residents and Fellows: What to Look for in a Laboratory Research Mentor. J Cancer Educ, 2015.

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