Browsing Over 164 Presentations
Welcome to MAP 2018 (ID 1)
- Fabrice André (Villejuif, FR)
Genomic characterisation of metastatic breast cancers (ID 3)
- François Bertucci (Marseille, FR)
Kidney cancer (ID 4)
- Samra Turajlic (London, GB)
Copy number alterations and breast cancers (ID 58)
- Carlos Caldas (Cambridge, GB)
Molecular epidemiology of metastatic cancer: Data from DRUP trial (ID 5)
- Emile E. Voest (Amsterdam, NL)
18O - Representative sequencing: Profiling extreme tumor diversity (ID 86)
- Kevin Litchfield (London, GB)
Abstract
Background
While next-generation sequencing (NGS) has been applied to thousands of solid tumors to date, there exists a fundamental undersampling bias inherent in current methodologies. This is caused by a biopsy input sample of fixed dimensions, which becomes grossly under-powered as tumor volume scales. Indeed, analysis of pan-cancer data reveals that current protocols sample on average only 1.5% of cancer cells, decreasing to 0.3% for stage IV tumors. Failure to address this bias risks undermining the clinical utility of genomic medicine in cancer.
Methods
Here we demonstrate Representative Sequencing (Rep-Seq), as a novel method to achieve unbiased sampling of solid tumor tissue. The Rep-Seq protocol comprises homogenization of all residual tumor material not taken for pathology into a well-mixed solution, coupled with NGS. Rep-Seq was implemented on a proof of concept basis in > 10 tumors, and benchmarked against single and multi-region sequencing approaches.
Results
Rep-Seq achieved a linear rate of novel variant discovery in whole-exome sequencing across 0 to 5,000x coverage, detecting four-fold more mutations as compared to multi-region sequencing at equivalent total read depth. All variants were validated using custom panel and Ion Torrent platforms. Targeted panel Rep-Seq at 50,000x showed sensitivity to detect extreme parallel evolution, with 16 independent mutations in the gene SETD2 observed in a single tumor. Clonal clustering analysis revealed rapid convergence of cancer cell fraction estimates in Rep-Seq towards true values, as validated in > 70 biopsies taken from a single tumor. As a consequence, 97% of variants were correctly classified as clonal by Rep-Seq, compared to > 85% in single biopsy sequencing. Finally, in a rapid autopsy setting Rep-Seq was able to accurately reconstruct the clonal phylogency of advanced stage disease, recovering a high proportion of all primary and metastatic variants, from deep sequencing of primary tissue alone.
Conclusions
Rep-Seq effectively implements unbiased tumor sampling, drawing DNA molecules from a well-mixed solution of the entire tumor mass, hence removing spatial bias inherent in current approaches. As a result, Rep-Seq detects more mutations, and achieves greater accuracy in determining clonal from subclonal variants.
Legal entity responsible for the study
The Francis Crick Institute.
Funding
Roche.
Disclosure
C. Swanton: Consulting and speaker fees from Boehringer Ingelheim, Eli Lilly, Novartis, and Roche; Research grants from Roche. S. Turajlic: Research grants from Roche. All other authors have declared no conflicts of interest.
Head and neck (ID 2)
- Kevin J. Harrington (London, GB)
Mutational processes (ID 59)
- Serena Nik-Zainal (Cambridge, GB)
Clonal evolution in lung cancers (ID 7)
- Nicholas McGranahan (London, GB)
APOBEC (ID 8)
- Reuben Harris (Minneapolis, US)
Single cell/PDX (ID 9)
- Sohrab Shah (Vancouver, CA)
59O - Preliminary results on mechanisms of resistance to ALK inhibitors: A prospective cohort from the MATCH-R study (ID 217)
- Gonzalo Recondo (Villejuif, FR)
Abstract
Background
ALK tyrosine kinase inhibitors (TKIs) have shown to be effective in the treatment of patients with ALK rearranged NSCLC. The clinical benefit is eventually limited by the acquisition of resistance to ALK TKIs by tumor cells. The study of the biological mechanisms implied in tumor progression can provide the rational for therapeutic strategies to overcome resistance.
Methods
In the MATCH-R prospective study, patients with unresectable or metastatic cancer were included upon acquired resistance to targeted therapies or immunotherapy, defined as progressive disease after partial or complete response or stable disease for 6 months. Upon progression tumor biopsy is performed and serial blood samples are collected. Targeted NGS, CGH, WES and RNAseq were performed on the tissue, and PDX models and patient derived cell lines were established to fully profile the underlying mechanisms of resistance.
Results
From June 29th 2015 and as of June 15th 2018, 309 patients were included of which 139 (45%) had advanced lung adenocarcinoma and n = 10 (7.2%) had tumors with ALK rearrangements. In 2 patients, multiple biopsies were taken upon progression to sequential treatments with ALK TKI. Evaluable tumor biopsies were taken upon progression to treatment with crizotinib (n = 3), ceritinib (n = 1), brigatinib (n = 2), lorlatinib (n = 5). PDX models/patient derived cell lines (n = 4) were developed from 2 patients. Mechanisms of resistance: Secondary mutations (n = 5), pathway bypass (n = 3), other non-genetic (n = 3). Novel compound mutations implied in resistance to lorlatinib were characterized by infecting BA/F3 cells with EML4-ALK V3 mutated lentiviral vectors, together with the characterization and reversion of a novel bypass mechanism. We also studied therapeutic strategies aiming at reverting resistance driven by EMT in patient derived cell lines treated with lorlatinib.
Conclusions
Mechanisms of resistance were identified in 9 out of 11 tumors from patients with ALK rearranged NSCLC. The development of patient derived cell lines provides further information on how to overcome the resistance to ALK inhibitors.
Clinical trial identification
NCT02517892.
Legal entity responsible for the study
Gustave Roussy Cancer Campus.
Funding
Natixis, European Research Council (ERC), Foundation Nelia et Amadeo Barletta.
Disclosure
F. André: Research grants from Novartis. J-C. Soria: Full-time employee of MedImmune. B. Besse: Institutional grants for clinical and translational research from AstraZeneca, BMS, Boehringer-Ingelheim, Inivata, Lilly, Loxo, OncoMed, Onxeo, Pfizer, Roche-Genentech, Sanofi-Aventis, Servier, and OSE Pharma. All other authors have declared no conflicts of interest.