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L. Roz



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    MINI 14 - Pre-Clinical Therapy (ID 119)

    • Event: WCLC 2015
    • Type: Mini Oral
    • Track: Biology, Pathology, and Molecular Testing
    • Presentations: 1
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      MINI14.11 - Establishment of a Lung Cancer Patient-Derived Xenografts Panel (ID 2607)

      10:45 - 12:15  |  Author(s): L. Roz

      • Abstract
      • Presentation
      • Slides

      Background:
      Studies based on cell lines were found to be poor predictors of clinical effects and therefore in many cases translation of the results into the clinics failed. A major determinant for the poor performance of cell lines is the observation that cell lines do not reflect the whole complexity and heterogeneity of primary tumors. A growing body of work suggests that Patient-Derived Xenografts (PDX) represent a more informative cancer model, providing a faithful representation of the patient’s original tumor.

      Methods:
      PDX were obtained by direct implants of small tumor fragments (30mm[3]) in previously anesthetized SCID mice, and were subsequently passaged as tissue explants. PDX metabolic in vivo imaging was performed using weekly [18F]FDG-PET and coronal and 3D-reconstruction at different days. Analysis of mutations and copy number alterations of PDX and human constitutive and tumoural DNA was performed by SALSA MLPA® probe mix X050-A1 Lung Cancer (MRC Holland).

      Results:
      Tumor samples from 95 lung cancer patients (66 AC, 16 SCC and 13 other lung cancer histotypes (OL)) have been implanted in the flanks of SCID mice. Overall, 36 samples (37,9%) successfully grafted and were propagated for at least 3 passages in immunocompromised mice. Take rate was 34,8 % (23/66) in AC, 43,8% (7/16) in SCC and 46.1% (6/13) in OL (2 large cell carcinomas, 1 sarcomatoid carcinoma and 3 small cell carcinomas). A detailed immunohistochemical analysis of 27 PDX, at different passage in mice, confirmed that tumor histology, expression of specific markers (TTF-1, p40, Vimentin, Ki64 and Synaptophysin) and the amount of specific tumor cell subpopulations (i.e. CD133[+] Cancer Initiating Cells) were generally maintained in PDX. In vivo animal PET imaging showed that also metabolic activity of PDX was strictly correlated with parental tumor’s features, especially for tumours with a SUV~max~ level higher than 8 (R[2]=0.67, p<0.05). Mutation and copy number analyses, performed on 29 biological samples belonging to 11 different engrafted models, showed that genetic changes were maintained in PDX that well recapitulated the frequency of the major changes involved in lung cancer development (66.7% TP53; 60% CDKN2A, 40% LKB1, 40% KEAP1, 38.4% KRAS, 20% SWI/SNF, 20% PTEN, 8% ERBB2). Furthermore, we developed a freeze/thawing procedure on samples derived from PDXs that allows for 100% successfully thawing and established a large collection of more than 200 frozen PDX samples for future preclinical studies.

      Conclusion:
      The deep characterization of our established PDX panel confirmed that these mouse models recapitulate the parental primary tumors in terms of tumor histology, cellular and mutation pattern, metabolic activity and expression of specific markers for several passages in mice. All these data support the use of these “human in mouse” models for functional studies, highlighting the relevance of our PDX panel as a valuable platform for preclinical studies.

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    ORAL 06 - Next Generation Sequencing and Testing Implications (ID 90)

    • Event: WCLC 2015
    • Type: Oral Session
    • Track: Biology, Pathology, and Molecular Testing
    • Presentations: 1
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      ORAL06.01 - Genomic Characterization of Large-Cell Neuroendocrine Lung Tumors (ID 1667)

      10:45 - 12:15  |  Author(s): L. Roz

      • Abstract
      • Slides

      Background:
      Neuroendocrine lung tumours account for 25% of all lung cancer cases, and they range from low-aggressive pulmonary carcinoids (PCA) to highly malignant small-cell lung cancer (SCLC) and large-cell neuroendocrine lung carcinoma (LCNEC). The last two are strongly associated with heavy smoking and are typically detected at a clinically advanced stage, having a poor survival. Comprehensive genomic analyses in lung neuroendocrine tumours are difficult because of limited availability of tissue. While more effort has been done in the context of SCLC, the detailed molecular features of LCNEC remain largely unknown.

      Methods:
      We conducted 6.0 SNP array analyses of 60 LCNEC tumours, exome sequencing of 55 tumor-normal pairs, genome sequencing of 11 tumour-normal pairs, transcriptome sequencing of 69 tumours, and expression arrays on 60 tumors. Data analyses were performed using in house developed and published pipelines.

      Results:
      Analyses of chromosomal gene copy number revealed amplifications of MYCL1, FGFR1, MYC, IRS2 and TTF1. We also observed deletions of CDKN2A and PTPRD. TTF1 amplifications are characteristic of lung adenocarcinoma (AD); CDKN2A deletions are frequent alterations in both AD and squamous-cell lung carcinoma (SQ); FGFR1 amplifications are found in SQ and, less frequently, in SCLC; and MYCL1 and IRS2 amplifications are frequent events in SCLC. Similar to the copy number data, we found patterns of mutations characteristic of other lung cancer subtypes: TP53 was the most frequently mutated gene (75%) followed by RB1 (27%), and inactivation of both TP53 and RB1, which is the hallmark of SCLC, occurred in 20% of the cases. Mutations in STK11 and KEAP1-NFE2L2 (frequently seen in AD and SQ) were found in 23% and 22% of the specimens, respectively. Interestingly, mutations in RB1 and STK11/KEAP1 occurred in a mutually exclusive fashion (p-value=0.016). Despite the heterogeneity observed at the mutation level, analysis of the pattern of expression of LCNEC in comparison with the other lung cancer subtypes (AD, SQ, SCLC, and PCA) points to LCNEC as being an independent entity. An average mutation rate of 10.7 mutations per megabase was detected in LCNEC, which is in line with the rate observed in other lung tumours associated with smoking. We found that, similar to SCLC, the mutation signatures associated with APOBEC family of cytidine deaminases, smoking, and age (based on Alexandrov et al 2013) were the predominant ones in LCNEC. However, the contribution of the individual SCLC and LCNEC samples to these three signatures was quite different, and we are currently exploring it.

      Conclusion:
      Taking into account somatic copy number and mutation data, we distinguished two well-defined groups of LCNEC: an SCLC-like group, carrying alterations in MYCL1, ISR2, and in both RB1 and TP53; and a group resembling AD and SQ, with alterations in CDKN2A, TTF1, KEAP1-NFE2L2, and STK11. Although these results suggest that LCNEC might be a mix of different lung cancer subtypes, mutation clonality and expression analyses show that they are likely to be a separate entity, sharing molecular characteristics with the other lung cancer subtypes. Their heterogeneity suggests that LCNEC might represent an evolutionary trunk that can branch to SCLC or AD/SQ.

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