- Email: [email protected]
Genomic correlates of response to anti-PDL1 atezolizumab in non-small cell lung cancer OAK and POPLAR trials
abstracts
Annals of Oncology
employment, Officer / Board of Directors: NantHealth. All other authors have declared no conflicts of interest.
1236P
Insights into the tumour immune microenvironment using tissue phenomics to drive cancer immunotherapy
M. Groher1, J. Zimmermann1, H. Musa2, A. Ackermann2, M. Surace3, J. RodriguezCanales3, M. Rebelatto3, K. Steele3, A. Kapil1, N. Brieu1, L. Rognoni1, F. Segerer1, A. Spitzmu¨ller1, T-H. Tan1, A. Sch€ape1, G. Schmidt1 1 Definiens AG, Munich, Germany, 2Bo¨hringer-Ingelheim GmbH & Co KG, Biberach An Der Riss, Germany, 3AstraZeneca LLC, Gaithersburg, MD, USA Background: The tumor immune microenvironment (TIME) may hold critical information for developing and optimizing immuno-therapeutic approaches, identifying predictive signatures, and selecting the most adequate treatment option for a given patient. Tissue phenomics facilitates the use of the TIME to derive predictive conclusions. The visual information content in histological sections is systematically converted into numerical readouts using artificial intelligence (AI). Resulting quantitative descriptors, phenes, of detected structures are mined to yield local expression profiles; this spatial data aggregation detects categories of local environments, which are correlated to clinical, genomic or other -omics data to identify relevant cohort subpopulations. Methods: Exploration of this technology is illustrated by various examples on different cohorts of NSCLC patients: A categorization of n ¼ 45 non-IO-treated patients with respect to local immune profiles learned via AI in a hypothesis-free scenario was examined. A deep learning based PD-L1 scoring was compared to 3 pathologist’s scoring on n ¼ 40 durvalumab-treated patients using the cutoff 25% of tumor cells staining positive for PD-L1 at any intensity. The predictive value of a digital signature combining cell densities of PD-L1 and CD8þ was tested on n ¼ 163 durvalumab-treated and n ¼ 199 non-IO-treated samples. Results: A categorization into biologically interpretable classes learned by AI illustrates the exploratory benefits of tissue phenomics. The scoring algorithm could reproduce survival prediction when compared to pathologist’s visual scoring.The digital signature suggests a predictive value for patient stratification into responders and non-responders for durvalumab, while no prognostic value could be found on the non-IO-treated patients. Kaplan-Meier plots for the 2 latter examples will be presented in the poster. Conclusions: Tissue phenomics facilitates the quantitative assessment of the tumor geography and may lead to improved tools for biomarker analysis and diagnosis. Analysis on larger and prospective datasets are to be conducted in the future to strengthen the findings. Clinical trial identification: All of these results have been generated retrospectively from samples unrelated to a trial or related to the durvalumab-trial NCT01693562. Legal entity responsible for the study: The authors. Funding: Boehringer Ingelheim, MedImmune, Definiens AG. Disclosure: M. Groher: Full / Part-time employment: Definiens AG. J. Zimmermann: Shareholder / Stockholder / Stock options: AstraZeneca; Full / Part-time employment: Definiens AG. H. Musa: Full / Part-time employment: Boehringer Ingelheim. A. Ackermann: Full / Part-time employment: Boehringer Ingelheim. M. Surace: Shareholder / Stockholder / Stock options, Full / Part-time employment: AstraZeneca. J. Rodriguez-Canales: Shareholder / Stockholder / Stock options, Full / Part-time employment: AstraZeneca. M. Rebelatto: Shareholder / Stackeholder / Stock options: AstraZenec LLC; Full / Part-time employment: AstraZeneca LLC. K. Steele: Shareholder / Stockholder / Stock options, Full / Part-time employment: AstraZeneca; Spouse / Financial dependant: Arcellx LLC. A. Kapil: Full / Part-time employment: Definiens AG. N. Brieu: Shareholder / Stockholder / Stock options, Full / Part-time employment: Definiens AG. L. Rognoni: Full / Part-time employment: Definiens AG. F. Segerer: Full / Part-time employment: Definiens AG. A. Spitzmu¨ller: Full / Part-time employment: Definiens AG. T. Tan: Full / Part-time employment: Definiens AG. A. Sch€ape: Full / Part-time employment: Definiens AG. G. Schmidt: Full / Part-time employment: Definiens AG; Shareholder / Stockholder / Stock options: AstraZeneca.
Volume 30 | Supplement 5 | October 2019
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Immune competent somatic mosaic model of colorectal cancer
S. Napolitano1, F. Carbone2, D.G. Menter1, T. Troiani3, G. Genovese2, S. Kopetz1 Department of Gastrointestinal Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA, 2Department of Genitourinary Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA, 3Medicina di Precisione, Universit a degli studi della Campania "Luigi Vanvitelli", Naples, Italy
1
Background: Historically, immunotherapies have been tested in syngeneic mouse models and until now only limited CRC syngeneic cells are available. Animal models with functioning human immune systems are critically needed to more accurately evaluate checkpoint blockers delivery, therapeutic response, and to better define biomarker expression in the presence of a competent immune system. Methods: To better address the efficacy of immune checkpoint therapy specifically in relation to presence of a BRAFmutation, we developed a novel platform for the generation of somatic mosaic models of CRC enabling (i) high-throughput generation of genetically complex syngeneic models of cancer, (ii) tracing studies through fluorescence reporters. Results: Cdx2Cre/þ mice, expressing the Cre recombinaseunder the control of a human Cdx2 promoter/enhancer sequence have been crossed with the R26LSL-Cas9-Gfp strain to generate models allowing for tissue specific activation of Cas9 and Gfpreporter only in CDX2 positive cells. This strain has been crossed with BRAFFSF-V600E mice to generate the final model. 6-8 weeks old mice have then been transduced with AAV constructs expressing the FLPO recombinasethat can be activated by Cre recombinase and sgRNAs targeting APC, TP53, MLH1, MSH2and ARID1Aalone or combined, in order to model MSI CRC (APC-TP53-MLH1and ARID1A-MLH1conditional mosaic knockouts) and MSS CRC (APC-TP53 conditional mosaic knock-out). Viral particles have been surgically delivered via subserosal cecal injection and mice monitored for tumor formation by IVIS imaging. 35 mice injected with 3 combinations of tumor suppressors provide a diverse immunology repetoire. Cell lines isolated from genetically modified mice will provide a physiologic relevant and feasible means to study the mechanisms of response and resistance to immunotherapies and to understand biological and molecular differences between BRAFmutated MSI and MSS tumors. Conclusions: This project focuses on the identification and characterization of the functional drivers of CMS1CRC tumors leveraging in vivomosaic genome editing technologies and functional genetic labeling of malignant sub-populations emerging during disease progression and in response to therapy. Legal entity responsible for the study: The authors. Funding: MD Anderson Cancer Center. Disclosure: All authors have declared no conflicts of interest.
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Genomic correlates of response to anti-PDL1 atezolizumab in non-small cell lung cancer OAK and POPLAR trials
H. Singhal1, O. Ekinci2, C. Alcorn1, D.R. Gandara3 Diagnostics Information Solutions, Roche Molecular Systems, Belmont, CA, USA, 2 Roche Diagnostics Information Solutions, Roche Molecular Systems, Santa Clara, CA, USA, 3Internal Med: Hematology-Oncology, University of California Davis Cancer Center, Sacramento, CA, USA
1
Background: Limitations of integrating biological knowledgebases with genomics impedes the development of predictive correlates that would help in personalization of immunotherapy. Non-small cell lung cancer (NSCLC) patients treated with atezolizumab, an antiprogrammed death-ligand 1 (PDL1) antibody, have better overall survival when compared to patients receiving docetaxel chemotherapy in Poplar (Lancet, 2016) and Oak (Lancet, 2017). We hypothesized that patterns in the mutations of immune signatures would correlate with the immunotherapeutic effect of atezolizumab in NSCLC. Methods: Sequencing data from Poplar (n ¼ 277 patients) and Oak (n ¼ 725 patients) trials was analyzed for understanding genomic alterations in relation to patient outcomes. Signatures from publicly-available knowledgebases were integrated with the genomics and clinicopathological data into an algorithm for identifying patterns correlative of immunotherapeutic response. Results: Patients benefitting from atezolizumab were more likely to have mutations in oncoimmunity-related genes which were significantly overlapping between the two trials. These overlapping genes were used to develop a de novo signature comprising of CDKN1A, ERRFI1, JAK2, NOTCH2, ACVR1B, NFKBIA, GNA13, MERTK, BTG1, CDKN1B, FOXP1, PDK1, ETV6, MLL2, SMAD3, DICER1 and BRCA2. Mutations in any of the genes within the signature identified patient subpopulations with higher immunotherapeutic response to Atezolizumab in both Oak (HR ¼ 0.3, P ¼ 1.8e-6 versus HR ¼ 0.76, P ¼ 5.5e-3 for entire cohort) and Poplar (HR ¼ 0.18 and P ¼ 3.6e-2 versus HR ¼ 0.71, P ¼ 0.023 for all patients) trials. Prediction efficiency of the signature was significantly better than established biomarkers such as PDL1 staining (Nature, 2014) in both Oak (HR: 0.58 and P ¼ 1.8e-6) and Poplar (HR: 0.58 and P ¼ 0.04) trials indicating that genomic correlates could add significant value to existing modalities in predicting immunotherapy response.
doi:10.1093/annonc/mdz253 | v505
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molecules (IRM) was analyzed between mt vs. wt. To ensure observed significant associations were not confounded by tumor-type, differential IRM expression within mtenriched tumor-types was compared to that of mt vs. wt. Results: 19/50 gene mutations were found to be significantly associated with 1 IRM expression. This included elevated CTLA4in CDKN2A mt (adj. p ¼ 1.9e-9), elevated IDO1in FBXW7 mt (adj. p ¼ 0.007), and decreased PDL1 in APCmt (adj, p ¼ 0.02). In many, the mt effect-size was larger than that of tumor-type; e.g. head & neck carcinomas (HNSCC) are highly enriched for CDKN2Amt (OR ¼ 4.9, p ¼ 4.3e-9), yet CDKN2Amt are more associated with CTLA4expression than HNSCC location (t ¼ 7.0 vs. 5.4). Similarly, FBXW7mt are more associated with high IDO1 than colorectal adenocarcinoma (CRC) (t ¼ 4.3 vs. 0.9), and APC mt are more associated with low PDL1 (t¼-4.1 vs. -3.2) than CRC. In total, 15 strong mt-gene/immune-regulator associations were identified. Conclusions: The presented differential checkpoint expression patterns are strongly associated with mutation status and are not primarily driven by tissue type. NGS data continues to drive agnostic approvals while immunotherapeutic efforts work to replace chemotherapy providing better efficacy with milder toxicities. This data hopes to shed light on the future studies that may analyze optimization of concomitant versus sequential therapies in various genomic-driven targeted therapies combined with immunotherapy trials. Legal entity responsible for the study: The authors. Funding: NantWorks. Disclosure: C.W. Szeto: Full / Part-time employment: NantCell. S.K. Reddy: Full / Part-time
abstracts Conclusions: In summary, integration of biological knowledgebases with genomics suggests that a rheostat of mutational burden in non-overlapping genesets is associated with immunotherapeutic effect and identifies novel genomic correlates for response to Atezolizumab. Clinical trial identification: NCT02008227; NCT01903993. Legal entity responsible for the study: Roche. Funding: Roche. Disclosure: D.R. Gandara: Research grant: Bristol-Myers Squibb, Roche-Genentech, Novartis, Merck; Consultancy: AstraZeneca, Celgene, CellMax Life, FujiFilm, Roche-Genentech, Guardant Health, Inviata, IO Biotech, Lilly, Liquid Genomics, Merck, Samsung Bioepis, Pfizer. All other authors have declared no conflicts of interest.
High level of activity of nivolumab anti-PD-1 immunotherapy and favorable outcome in metastatic/refractory MSI-H non-colorectal cancer: Results of the MSI cohort from the French AcSe´ program
C. Tournigand1, A. Flechon2, S. Oudard3, E. Saada-Bouzid4, D. Pouessel5, C. Le Tourneau6, P. Augereau7, M. Beylot-Barry8, J.J. Grob9, B. Chibaudel10, J-C. Soria11, C. Simon12, D. Couch12, N. Hoog-Labouret13, C. Tiffon14, S. Chevret15, T. Andre16, A. Marabelle17 1 Medical Oncology, Centre Hospitalier Universitaire Henri-Mondor, Cre´teil, France, 2 Medical Oncology, Centre Le´on Be´rard, Lyon, France, 3Immunothe´rapie et Traitement Antiangioge´nique en Pathologie Cance´rologique, Hopital European George Pompidou, Paris, France, 4Medical Oncology, Hopital Lacassagne, Nice, France, 5Medical Oncology, Institut Claudius Re´gaud, Toulouse, France, 6Department of Drug Development and Innovation (D3i), Institut Curie, Paris, France, 7Medical Oncology, Institut de Cance´rologie de l’Ouest, Angers, France, 8Unite´ de Dermatologie Cance´rologique, Hoˆpital Saint-Andre´ - CHU de Bordeaux, Bordeaux, France, 9Department of dermatology, Hoˆpital de la Timone, Marseille, France, 10Medical Oncology, Institut Hospitaliser Franco-Britannique, Levallois-Perret, France, 11Unite´ des Me´dicaments Innovants en Oncologie, MedImmune, Gaithersburg, MD, USA, 12R&D, Unicancer, Paris, France, 13Poˆle Recherche et Innovation, Institut National du Cancer (INCa), Boulogne-Billancourt, France, 14De´partement Biologie, Transfert et Innovations, Institut National du Cancer (INCa), Boulogne-Billancourt, France, 15Service de Biostatistique et Information Medical, Hoˆpital Saint Louis, Paris, France, 16Medical Oncology, Hopital Saint-Antoine, Paris, France, 17Drug Development Department, Institut Gustave Roussy, Villejuif, France Background: Microsatellite instability-high (MSI-H) is observed in a large variety of cancer types. Immune checkpoint targeted therapies against PD-1 and CTLA-4 have demonstrated significant activity in metastatic colorectal cancer (mCRC) with nivolumab þ/- ipilimumab. We aimed to demonstrate a clinical benefit of nivolumab in nonCRC MSI patients. Methods: The AcSe´ immunotherapy program launched by the French National Cancer Institute (INCa) and sponsored by the French network of comprehensive cancer centers (Unicancer) is a nationwide exploratory program which has allowed access to antiPD-1 therapies outside of their current approvals. A phase II, single arm, AcSe´-nivolumab trial has been conducted to investigate the efficacy and tolerance of nivolumab in patients with metastastic/refractory rare tumor types. Here we report the results of the MSI cohort. Nivolumab (240 mg IV) was administered q2w for a max of 2 years or until disease progression (PD), or toxicity. The primary endpoint was the objective response rate (ORR) assessed by RECIST v1.1 at 12 weeks. Results: From July 2017 to October 2018, 50 pts (mean age 63 years) were included. Primary locations were endometrial adenoCa (17), gastric (10), small bowel (7), pancreas (5), biliary (4), urothelial (2), ovary (2), and breast (2). 15 patients (30%) had a Lynch syndrome. All patients were pre-treated (mean of previous lines: 1.6) and had a MSI status locally determined by IHC and/or PCR (IHC 15 pts, PCR 4, both 31). The mean number of cycles/patients was 12.9. The ORR at 12 weeks was 38% (95%CI 24.6 to 52.8) and the best ORR at any time was 42% (95%CI, 28.2 to 56.8) with median time to response of 14 weeks. CR: n ¼ 2; PR: n ¼ 17; SD: n ¼ 16; DCR¼74%. Median PFS was not reached with a 6-mo PFS at 58.9% (95%CI, 46.5 to 74.6). At the date of analysis, 15 patients died (PD 13, drug related death 1, other 1), with a 6-mo OS rate at 80.3% (95%CI, 69.5 to 92.8). No unexpected adverse event of nivolumab has been observed. Conclusions: Nivolumab as monotherapy is highly active in non-colorectal MSI patients, outperforming results of classical chemotherapy in this heavily pre-treated population. Clinical trial identification: NCT03012581, EudraCT 2016-002257-37. Legal entity responsible for the study: R&D UNICANCER. Funding: La Ligue Nationale Contre le Cancer, Institut National du Cancer (INCa), BMS La Ligue Contre le Cancer, INCa, BMS.
v506 | Immunotherapy of Cancer
Disclosure: C. Tournigand: Honoraria (self), Advisory / Consultancy: Bayer; Honoraria (institution), Travel / Accommodation / Expenses: MSD; Advisory / Consultancy: Roche; Honoraria (self), Advisory / Consultancy: Sanofi; Honoraria (institution), Travel / Accommodation / Expenses: BMS. E. Saada-Bouzid: Advisory / Consultancy: BMS; Advisory / Consultancy: AstraZeneca; Advisory / Consultancy: Merck Serono. D. Pouessel: Advisory / Consultancy, Travel / Accommodation / Expenses: AstraZeneca; Advisory / Consultancy: Sanofi; Advisory / Consultancy, Travel / Accommodation / Expenses: Pfizer; Advisory / Consultancy: Astellas; Advisory / Consultancy, Research grant / Funding (institution): Janssen; Research grant / Funding (institution): MSD; Research grant / Funding (institution): Roche; Research grant / Funding (institution): Incyte; Research grant / Funding (institution): A2; Speaker Bureau / Expert testimony: Ipsen; Speaker Bureau / Expert testimony: BMS. C. Le Tourneau: Advisory / Consultancy: MSD; Advisory / Consultancy: BMS; Advisory / Consultancy: Merck Serono; Advisory / Consultancy: AstraZeneca; Advisory / Consultancy: Nanobiotix; Advisory / Consultancy: Roche; Advisory / Consultancy: Amgen; Advisory / Consultancy: GSK. P. Augereau: Advisory / Consultancy: Pfizer; Advisory / Consultancy: AstraZeneca. J. Soria: Advisory / Consultancy, Shareholder / Stockholder / Stock options, Full / Parttime employment: AstraZeneca; Shareholder / Stockholder / Stock options: Gritstone; Advisory / Consultancy: Astex; Advisory / Consultancy: Clovis; Advisory / Consultancy: GSK; Advisory / Consultancy: GammaMabs; Advisory / Consultancy: Lilly; Advisory / Consultancy: MSD; Advisory / Consultancy: Mission Therapeutics; Advisory / Consultancy: Merus; Advisory / Consultancy: Pfizer; Advisory / Consultancy: PharmaMar; Advisory / Consultancy: Pierre Fabre; Advisory / Consultancy: Roche/Genentech; Advisory / Consultancy: Sanofi; Advisory / Consultancy: Servier; Advisory / Consultancy: Symphogen; Advisory / Consultancy: Takeda. A. Marabelle: Advisory / Consultancy, Speaker Bureau / Expert testimony, Research grant / Funding (self): BMS; Research grant / Funding (self): Boehringer Ingelheim; Research grant / Funding (self): Fondation MSD Avenir; Advisory / Consultancy: Oncovir; Advisory / Consultancy, Speaker Bureau / Expert testimony: Merck Serono; Advisory / Consultancy: eTheRNA; Advisory / Consultancy, Research grant / Funding (self): Lytix pharma; Advisory / Consultancy: Kyowa Kirin Pharma; Advisory / Consultancy: Novartis, Seattle Genetics, Molecular Partners; Advisory / Consultancy: Symphogen, Bayer, Partner Therapeutics; Advisory / Consultancy: Genmab, RedX pharma, OSE Immunotherapeutics; Advisory / Consultancy, Speaker Bureau / Expert testimony: Amgen, Sanofi, Servier; Advisory / Consultancy: Biothera, Gritstone; Advisory / Consultancy: Nektar, Pierre Fabre; Advisory / Consultancy: GSK; Advisory / Consultancy: Oncosec; Advisory / Consultancy, Research grant / Funding (self): Pfizer; Speaker Bureau / Expert testimony, Research grant / Funding (self): MSD; Advisory / Consultancy, Speaker Bureau / Expert testimony, Research grant / Funding (self): AstraZeneca/MedImmune; Speaker Bureau / Expert testimony, Research grant / Funding (self): Roche/Genentech. All other authors have declared no conflicts of interest.
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TP53 and ATM co-mutation predicts response to immune checkpoint inhibitors in non-small cell lung cancer
Y. Chen1, G. Chen2, J. Li3, C. Huang4, Y. Li4, J. Lin1, L.Z. Chen1, J.P. Lu2, Y.Q. Wang3, C.X. Wang3, L.K. Pan5, X.F. Xia3, X. Yi3, C.B. Chen4, X.W. Zheng2, Z.Q. Guo1, J.J. Pan4 1 Department of Medical Oncology, Fujian Medical University Cancer Hospital, Fuzhou, China, 2Pathology, Fujian Cancer Hospital, Fuzhou, China, 3Geneplus-Beijing Institute, Beijing, China, 4Department of Radiation Oncology, Fujian Medical University Cancer Hospital, Fuzhou, China, 5Hui Xian Medical Center, Macao, China Background: Immune checkpoint inhibitors (ICIs) elicits durable responses in non– small cell lung cancer (NSCLC), but only a fraction of patients responded. TP53 and ATM co-mutation may lead to genomic instability and hypermutation. However, the prevalence and utility of a TP53/ATM co-mutation as a biomarker to ICIs are not fully understood. Methods: This was a multiple cohort pooled study, 2020 NSCLC samples from Geneplus Institute, 1031 samples from TCGA, 1567 samples from MSKCC, 853 samples from POLAR/OAK databases were statically analyzed. Next-generation sequencing assays were performed in the Geneplus Institute. Genomic, transcriptomic and clinical data were obtained from the TCGA, MSKCC, POLAR/OAK databases. Comprehensive profiling was performed to determine the prevalence of TP53/ATM co-mutation and correlation with the prognosis and the response to ICIs. Results: TP53/ATM co-mutation sites were found to be scattered throughout the genes and we did not observe any significant difference in TP53/ATM co-mutated frequency within the histologic subtypes and driver genes. In five independent NSCLC cohorts, TP53/ATM co-mutation contributed to significantly higher tumor mutation burden (TMB) compared to both the sole mutation and both wild type groups. Furthermore, in the MSKCC-IO cohort, a TP53/ATM co-mutation was associated with better OS than sole mutation and both wild type groups, especially in pan-cancer (P¼.241, NSCLC and P<.0001, Pan-cancer). Similar results were shown in the POLAR/OAK cohort, the disease control benefit rate, and progression free survival (PFS) and OS were all greater in patients with the TP53/ATM co-mutation compared with the other three groups. GSEA showed that IFN-a response, IFN-c response, IL6/JAK/STAT3 signaling, and TNF-a signaling pathways had higher levels of activation and there was greater PD-L1 expression in the co-mutated group, compared with solely mutated and both wild type group. Conclusions: Our findings suggest that patients with TP53/ATM co-mutation comprise a special subgroup of NSCLC patients and correlate with increasing TMB and responses to ICIs. It may have implications for potential predictive biomarker in guiding ICIs immunotherapy. Legal entity responsible for the study: Jian Ji Pan. Funding: National Natural Science Foundation of China (Grant No. U1705282). Disclosure: All authors have declared no conflicts of interest.
Volume 30 | Supplement 5 | October 2019
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Annals of Oncology
Annals of Oncology
employment, Officer / Board of Directors: NantHealth. All other authors have declared no conflicts of interest.
1236P
Insights into the tumour immune microenvironment using tissue phenomics to drive cancer immunotherapy
M. Groher1, J. Zimmermann1, H. Musa2, A. Ackermann2, M. Surace3, J. RodriguezCanales3, M. Rebelatto3, K. Steele3, A. Kapil1, N. Brieu1, L. Rognoni1, F. Segerer1, A. Spitzmu¨ller1, T-H. Tan1, A. Sch€ape1, G. Schmidt1 1 Definiens AG, Munich, Germany, 2Bo¨hringer-Ingelheim GmbH & Co KG, Biberach An Der Riss, Germany, 3AstraZeneca LLC, Gaithersburg, MD, USA Background: The tumor immune microenvironment (TIME) may hold critical information for developing and optimizing immuno-therapeutic approaches, identifying predictive signatures, and selecting the most adequate treatment option for a given patient. Tissue phenomics facilitates the use of the TIME to derive predictive conclusions. The visual information content in histological sections is systematically converted into numerical readouts using artificial intelligence (AI). Resulting quantitative descriptors, phenes, of detected structures are mined to yield local expression profiles; this spatial data aggregation detects categories of local environments, which are correlated to clinical, genomic or other -omics data to identify relevant cohort subpopulations. Methods: Exploration of this technology is illustrated by various examples on different cohorts of NSCLC patients: A categorization of n ¼ 45 non-IO-treated patients with respect to local immune profiles learned via AI in a hypothesis-free scenario was examined. A deep learning based PD-L1 scoring was compared to 3 pathologist’s scoring on n ¼ 40 durvalumab-treated patients using the cutoff 25% of tumor cells staining positive for PD-L1 at any intensity. The predictive value of a digital signature combining cell densities of PD-L1 and CD8þ was tested on n ¼ 163 durvalumab-treated and n ¼ 199 non-IO-treated samples. Results: A categorization into biologically interpretable classes learned by AI illustrates the exploratory benefits of tissue phenomics. The scoring algorithm could reproduce survival prediction when compared to pathologist’s visual scoring.The digital signature suggests a predictive value for patient stratification into responders and non-responders for durvalumab, while no prognostic value could be found on the non-IO-treated patients. Kaplan-Meier plots for the 2 latter examples will be presented in the poster. Conclusions: Tissue phenomics facilitates the quantitative assessment of the tumor geography and may lead to improved tools for biomarker analysis and diagnosis. Analysis on larger and prospective datasets are to be conducted in the future to strengthen the findings. Clinical trial identification: All of these results have been generated retrospectively from samples unrelated to a trial or related to the durvalumab-trial NCT01693562. Legal entity responsible for the study: The authors. Funding: Boehringer Ingelheim, MedImmune, Definiens AG. Disclosure: M. Groher: Full / Part-time employment: Definiens AG. J. Zimmermann: Shareholder / Stockholder / Stock options: AstraZeneca; Full / Part-time employment: Definiens AG. H. Musa: Full / Part-time employment: Boehringer Ingelheim. A. Ackermann: Full / Part-time employment: Boehringer Ingelheim. M. Surace: Shareholder / Stockholder / Stock options, Full / Part-time employment: AstraZeneca. J. Rodriguez-Canales: Shareholder / Stockholder / Stock options, Full / Part-time employment: AstraZeneca. M. Rebelatto: Shareholder / Stackeholder / Stock options: AstraZenec LLC; Full / Part-time employment: AstraZeneca LLC. K. Steele: Shareholder / Stockholder / Stock options, Full / Part-time employment: AstraZeneca; Spouse / Financial dependant: Arcellx LLC. A. Kapil: Full / Part-time employment: Definiens AG. N. Brieu: Shareholder / Stockholder / Stock options, Full / Part-time employment: Definiens AG. L. Rognoni: Full / Part-time employment: Definiens AG. F. Segerer: Full / Part-time employment: Definiens AG. A. Spitzmu¨ller: Full / Part-time employment: Definiens AG. T. Tan: Full / Part-time employment: Definiens AG. A. Sch€ape: Full / Part-time employment: Definiens AG. G. Schmidt: Full / Part-time employment: Definiens AG; Shareholder / Stockholder / Stock options: AstraZeneca.
Volume 30 | Supplement 5 | October 2019
1237P
Immune competent somatic mosaic model of colorectal cancer
S. Napolitano1, F. Carbone2, D.G. Menter1, T. Troiani3, G. Genovese2, S. Kopetz1 Department of Gastrointestinal Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA, 2Department of Genitourinary Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA, 3Medicina di Precisione, Universit a degli studi della Campania "Luigi Vanvitelli", Naples, Italy
1
Background: Historically, immunotherapies have been tested in syngeneic mouse models and until now only limited CRC syngeneic cells are available. Animal models with functioning human immune systems are critically needed to more accurately evaluate checkpoint blockers delivery, therapeutic response, and to better define biomarker expression in the presence of a competent immune system. Methods: To better address the efficacy of immune checkpoint therapy specifically in relation to presence of a BRAFmutation, we developed a novel platform for the generation of somatic mosaic models of CRC enabling (i) high-throughput generation of genetically complex syngeneic models of cancer, (ii) tracing studies through fluorescence reporters. Results: Cdx2Cre/þ mice, expressing the Cre recombinaseunder the control of a human Cdx2 promoter/enhancer sequence have been crossed with the R26LSL-Cas9-Gfp strain to generate models allowing for tissue specific activation of Cas9 and Gfpreporter only in CDX2 positive cells. This strain has been crossed with BRAFFSF-V600E mice to generate the final model. 6-8 weeks old mice have then been transduced with AAV constructs expressing the FLPO recombinasethat can be activated by Cre recombinase and sgRNAs targeting APC, TP53, MLH1, MSH2and ARID1Aalone or combined, in order to model MSI CRC (APC-TP53-MLH1and ARID1A-MLH1conditional mosaic knockouts) and MSS CRC (APC-TP53 conditional mosaic knock-out). Viral particles have been surgically delivered via subserosal cecal injection and mice monitored for tumor formation by IVIS imaging. 35 mice injected with 3 combinations of tumor suppressors provide a diverse immunology repetoire. Cell lines isolated from genetically modified mice will provide a physiologic relevant and feasible means to study the mechanisms of response and resistance to immunotherapies and to understand biological and molecular differences between BRAFmutated MSI and MSS tumors. Conclusions: This project focuses on the identification and characterization of the functional drivers of CMS1CRC tumors leveraging in vivomosaic genome editing technologies and functional genetic labeling of malignant sub-populations emerging during disease progression and in response to therapy. Legal entity responsible for the study: The authors. Funding: MD Anderson Cancer Center. Disclosure: All authors have declared no conflicts of interest.
1238P
Genomic correlates of response to anti-PDL1 atezolizumab in non-small cell lung cancer OAK and POPLAR trials
H. Singhal1, O. Ekinci2, C. Alcorn1, D.R. Gandara3 Diagnostics Information Solutions, Roche Molecular Systems, Belmont, CA, USA, 2 Roche Diagnostics Information Solutions, Roche Molecular Systems, Santa Clara, CA, USA, 3Internal Med: Hematology-Oncology, University of California Davis Cancer Center, Sacramento, CA, USA
1
Background: Limitations of integrating biological knowledgebases with genomics impedes the development of predictive correlates that would help in personalization of immunotherapy. Non-small cell lung cancer (NSCLC) patients treated with atezolizumab, an antiprogrammed death-ligand 1 (PDL1) antibody, have better overall survival when compared to patients receiving docetaxel chemotherapy in Poplar (Lancet, 2016) and Oak (Lancet, 2017). We hypothesized that patterns in the mutations of immune signatures would correlate with the immunotherapeutic effect of atezolizumab in NSCLC. Methods: Sequencing data from Poplar (n ¼ 277 patients) and Oak (n ¼ 725 patients) trials was analyzed for understanding genomic alterations in relation to patient outcomes. Signatures from publicly-available knowledgebases were integrated with the genomics and clinicopathological data into an algorithm for identifying patterns correlative of immunotherapeutic response. Results: Patients benefitting from atezolizumab were more likely to have mutations in oncoimmunity-related genes which were significantly overlapping between the two trials. These overlapping genes were used to develop a de novo signature comprising of CDKN1A, ERRFI1, JAK2, NOTCH2, ACVR1B, NFKBIA, GNA13, MERTK, BTG1, CDKN1B, FOXP1, PDK1, ETV6, MLL2, SMAD3, DICER1 and BRCA2. Mutations in any of the genes within the signature identified patient subpopulations with higher immunotherapeutic response to Atezolizumab in both Oak (HR ¼ 0.3, P ¼ 1.8e-6 versus HR ¼ 0.76, P ¼ 5.5e-3 for entire cohort) and Poplar (HR ¼ 0.18 and P ¼ 3.6e-2 versus HR ¼ 0.71, P ¼ 0.023 for all patients) trials. Prediction efficiency of the signature was significantly better than established biomarkers such as PDL1 staining (Nature, 2014) in both Oak (HR: 0.58 and P ¼ 1.8e-6) and Poplar (HR: 0.58 and P ¼ 0.04) trials indicating that genomic correlates could add significant value to existing modalities in predicting immunotherapy response.
doi:10.1093/annonc/mdz253 | v505
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molecules (IRM) was analyzed between mt vs. wt. To ensure observed significant associations were not confounded by tumor-type, differential IRM expression within mtenriched tumor-types was compared to that of mt vs. wt. Results: 19/50 gene mutations were found to be significantly associated with 1 IRM expression. This included elevated CTLA4in CDKN2A mt (adj. p ¼ 1.9e-9), elevated IDO1in FBXW7 mt (adj. p ¼ 0.007), and decreased PDL1 in APCmt (adj, p ¼ 0.02). In many, the mt effect-size was larger than that of tumor-type; e.g. head & neck carcinomas (HNSCC) are highly enriched for CDKN2Amt (OR ¼ 4.9, p ¼ 4.3e-9), yet CDKN2Amt are more associated with CTLA4expression than HNSCC location (t ¼ 7.0 vs. 5.4). Similarly, FBXW7mt are more associated with high IDO1 than colorectal adenocarcinoma (CRC) (t ¼ 4.3 vs. 0.9), and APC mt are more associated with low PDL1 (t¼-4.1 vs. -3.2) than CRC. In total, 15 strong mt-gene/immune-regulator associations were identified. Conclusions: The presented differential checkpoint expression patterns are strongly associated with mutation status and are not primarily driven by tissue type. NGS data continues to drive agnostic approvals while immunotherapeutic efforts work to replace chemotherapy providing better efficacy with milder toxicities. This data hopes to shed light on the future studies that may analyze optimization of concomitant versus sequential therapies in various genomic-driven targeted therapies combined with immunotherapy trials. Legal entity responsible for the study: The authors. Funding: NantWorks. Disclosure: C.W. Szeto: Full / Part-time employment: NantCell. S.K. Reddy: Full / Part-time
abstracts Conclusions: In summary, integration of biological knowledgebases with genomics suggests that a rheostat of mutational burden in non-overlapping genesets is associated with immunotherapeutic effect and identifies novel genomic correlates for response to Atezolizumab. Clinical trial identification: NCT02008227; NCT01903993. Legal entity responsible for the study: Roche. Funding: Roche. Disclosure: D.R. Gandara: Research grant: Bristol-Myers Squibb, Roche-Genentech, Novartis, Merck; Consultancy: AstraZeneca, Celgene, CellMax Life, FujiFilm, Roche-Genentech, Guardant Health, Inviata, IO Biotech, Lilly, Liquid Genomics, Merck, Samsung Bioepis, Pfizer. All other authors have declared no conflicts of interest.
High level of activity of nivolumab anti-PD-1 immunotherapy and favorable outcome in metastatic/refractory MSI-H non-colorectal cancer: Results of the MSI cohort from the French AcSe´ program
C. Tournigand1, A. Flechon2, S. Oudard3, E. Saada-Bouzid4, D. Pouessel5, C. Le Tourneau6, P. Augereau7, M. Beylot-Barry8, J.J. Grob9, B. Chibaudel10, J-C. Soria11, C. Simon12, D. Couch12, N. Hoog-Labouret13, C. Tiffon14, S. Chevret15, T. Andre16, A. Marabelle17 1 Medical Oncology, Centre Hospitalier Universitaire Henri-Mondor, Cre´teil, France, 2 Medical Oncology, Centre Le´on Be´rard, Lyon, France, 3Immunothe´rapie et Traitement Antiangioge´nique en Pathologie Cance´rologique, Hopital European George Pompidou, Paris, France, 4Medical Oncology, Hopital Lacassagne, Nice, France, 5Medical Oncology, Institut Claudius Re´gaud, Toulouse, France, 6Department of Drug Development and Innovation (D3i), Institut Curie, Paris, France, 7Medical Oncology, Institut de Cance´rologie de l’Ouest, Angers, France, 8Unite´ de Dermatologie Cance´rologique, Hoˆpital Saint-Andre´ - CHU de Bordeaux, Bordeaux, France, 9Department of dermatology, Hoˆpital de la Timone, Marseille, France, 10Medical Oncology, Institut Hospitaliser Franco-Britannique, Levallois-Perret, France, 11Unite´ des Me´dicaments Innovants en Oncologie, MedImmune, Gaithersburg, MD, USA, 12R&D, Unicancer, Paris, France, 13Poˆle Recherche et Innovation, Institut National du Cancer (INCa), Boulogne-Billancourt, France, 14De´partement Biologie, Transfert et Innovations, Institut National du Cancer (INCa), Boulogne-Billancourt, France, 15Service de Biostatistique et Information Medical, Hoˆpital Saint Louis, Paris, France, 16Medical Oncology, Hopital Saint-Antoine, Paris, France, 17Drug Development Department, Institut Gustave Roussy, Villejuif, France Background: Microsatellite instability-high (MSI-H) is observed in a large variety of cancer types. Immune checkpoint targeted therapies against PD-1 and CTLA-4 have demonstrated significant activity in metastatic colorectal cancer (mCRC) with nivolumab þ/- ipilimumab. We aimed to demonstrate a clinical benefit of nivolumab in nonCRC MSI patients. Methods: The AcSe´ immunotherapy program launched by the French National Cancer Institute (INCa) and sponsored by the French network of comprehensive cancer centers (Unicancer) is a nationwide exploratory program which has allowed access to antiPD-1 therapies outside of their current approvals. A phase II, single arm, AcSe´-nivolumab trial has been conducted to investigate the efficacy and tolerance of nivolumab in patients with metastastic/refractory rare tumor types. Here we report the results of the MSI cohort. Nivolumab (240 mg IV) was administered q2w for a max of 2 years or until disease progression (PD), or toxicity. The primary endpoint was the objective response rate (ORR) assessed by RECIST v1.1 at 12 weeks. Results: From July 2017 to October 2018, 50 pts (mean age 63 years) were included. Primary locations were endometrial adenoCa (17), gastric (10), small bowel (7), pancreas (5), biliary (4), urothelial (2), ovary (2), and breast (2). 15 patients (30%) had a Lynch syndrome. All patients were pre-treated (mean of previous lines: 1.6) and had a MSI status locally determined by IHC and/or PCR (IHC 15 pts, PCR 4, both 31). The mean number of cycles/patients was 12.9. The ORR at 12 weeks was 38% (95%CI 24.6 to 52.8) and the best ORR at any time was 42% (95%CI, 28.2 to 56.8) with median time to response of 14 weeks. CR: n ¼ 2; PR: n ¼ 17; SD: n ¼ 16; DCR¼74%. Median PFS was not reached with a 6-mo PFS at 58.9% (95%CI, 46.5 to 74.6). At the date of analysis, 15 patients died (PD 13, drug related death 1, other 1), with a 6-mo OS rate at 80.3% (95%CI, 69.5 to 92.8). No unexpected adverse event of nivolumab has been observed. Conclusions: Nivolumab as monotherapy is highly active in non-colorectal MSI patients, outperforming results of classical chemotherapy in this heavily pre-treated population. Clinical trial identification: NCT03012581, EudraCT 2016-002257-37. Legal entity responsible for the study: R&D UNICANCER. Funding: La Ligue Nationale Contre le Cancer, Institut National du Cancer (INCa), BMS La Ligue Contre le Cancer, INCa, BMS.
v506 | Immunotherapy of Cancer
Disclosure: C. Tournigand: Honoraria (self), Advisory / Consultancy: Bayer; Honoraria (institution), Travel / Accommodation / Expenses: MSD; Advisory / Consultancy: Roche; Honoraria (self), Advisory / Consultancy: Sanofi; Honoraria (institution), Travel / Accommodation / Expenses: BMS. E. Saada-Bouzid: Advisory / Consultancy: BMS; Advisory / Consultancy: AstraZeneca; Advisory / Consultancy: Merck Serono. D. Pouessel: Advisory / Consultancy, Travel / Accommodation / Expenses: AstraZeneca; Advisory / Consultancy: Sanofi; Advisory / Consultancy, Travel / Accommodation / Expenses: Pfizer; Advisory / Consultancy: Astellas; Advisory / Consultancy, Research grant / Funding (institution): Janssen; Research grant / Funding (institution): MSD; Research grant / Funding (institution): Roche; Research grant / Funding (institution): Incyte; Research grant / Funding (institution): A2; Speaker Bureau / Expert testimony: Ipsen; Speaker Bureau / Expert testimony: BMS. C. Le Tourneau: Advisory / Consultancy: MSD; Advisory / Consultancy: BMS; Advisory / Consultancy: Merck Serono; Advisory / Consultancy: AstraZeneca; Advisory / Consultancy: Nanobiotix; Advisory / Consultancy: Roche; Advisory / Consultancy: Amgen; Advisory / Consultancy: GSK. P. Augereau: Advisory / Consultancy: Pfizer; Advisory / Consultancy: AstraZeneca. J. Soria: Advisory / Consultancy, Shareholder / Stockholder / Stock options, Full / Parttime employment: AstraZeneca; Shareholder / Stockholder / Stock options: Gritstone; Advisory / Consultancy: Astex; Advisory / Consultancy: Clovis; Advisory / Consultancy: GSK; Advisory / Consultancy: GammaMabs; Advisory / Consultancy: Lilly; Advisory / Consultancy: MSD; Advisory / Consultancy: Mission Therapeutics; Advisory / Consultancy: Merus; Advisory / Consultancy: Pfizer; Advisory / Consultancy: PharmaMar; Advisory / Consultancy: Pierre Fabre; Advisory / Consultancy: Roche/Genentech; Advisory / Consultancy: Sanofi; Advisory / Consultancy: Servier; Advisory / Consultancy: Symphogen; Advisory / Consultancy: Takeda. A. Marabelle: Advisory / Consultancy, Speaker Bureau / Expert testimony, Research grant / Funding (self): BMS; Research grant / Funding (self): Boehringer Ingelheim; Research grant / Funding (self): Fondation MSD Avenir; Advisory / Consultancy: Oncovir; Advisory / Consultancy, Speaker Bureau / Expert testimony: Merck Serono; Advisory / Consultancy: eTheRNA; Advisory / Consultancy, Research grant / Funding (self): Lytix pharma; Advisory / Consultancy: Kyowa Kirin Pharma; Advisory / Consultancy: Novartis, Seattle Genetics, Molecular Partners; Advisory / Consultancy: Symphogen, Bayer, Partner Therapeutics; Advisory / Consultancy: Genmab, RedX pharma, OSE Immunotherapeutics; Advisory / Consultancy, Speaker Bureau / Expert testimony: Amgen, Sanofi, Servier; Advisory / Consultancy: Biothera, Gritstone; Advisory / Consultancy: Nektar, Pierre Fabre; Advisory / Consultancy: GSK; Advisory / Consultancy: Oncosec; Advisory / Consultancy, Research grant / Funding (self): Pfizer; Speaker Bureau / Expert testimony, Research grant / Funding (self): MSD; Advisory / Consultancy, Speaker Bureau / Expert testimony, Research grant / Funding (self): AstraZeneca/MedImmune; Speaker Bureau / Expert testimony, Research grant / Funding (self): Roche/Genentech. All other authors have declared no conflicts of interest.
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TP53 and ATM co-mutation predicts response to immune checkpoint inhibitors in non-small cell lung cancer
Y. Chen1, G. Chen2, J. Li3, C. Huang4, Y. Li4, J. Lin1, L.Z. Chen1, J.P. Lu2, Y.Q. Wang3, C.X. Wang3, L.K. Pan5, X.F. Xia3, X. Yi3, C.B. Chen4, X.W. Zheng2, Z.Q. Guo1, J.J. Pan4 1 Department of Medical Oncology, Fujian Medical University Cancer Hospital, Fuzhou, China, 2Pathology, Fujian Cancer Hospital, Fuzhou, China, 3Geneplus-Beijing Institute, Beijing, China, 4Department of Radiation Oncology, Fujian Medical University Cancer Hospital, Fuzhou, China, 5Hui Xian Medical Center, Macao, China Background: Immune checkpoint inhibitors (ICIs) elicits durable responses in non– small cell lung cancer (NSCLC), but only a fraction of patients responded. TP53 and ATM co-mutation may lead to genomic instability and hypermutation. However, the prevalence and utility of a TP53/ATM co-mutation as a biomarker to ICIs are not fully understood. Methods: This was a multiple cohort pooled study, 2020 NSCLC samples from Geneplus Institute, 1031 samples from TCGA, 1567 samples from MSKCC, 853 samples from POLAR/OAK databases were statically analyzed. Next-generation sequencing assays were performed in the Geneplus Institute. Genomic, transcriptomic and clinical data were obtained from the TCGA, MSKCC, POLAR/OAK databases. Comprehensive profiling was performed to determine the prevalence of TP53/ATM co-mutation and correlation with the prognosis and the response to ICIs. Results: TP53/ATM co-mutation sites were found to be scattered throughout the genes and we did not observe any significant difference in TP53/ATM co-mutated frequency within the histologic subtypes and driver genes. In five independent NSCLC cohorts, TP53/ATM co-mutation contributed to significantly higher tumor mutation burden (TMB) compared to both the sole mutation and both wild type groups. Furthermore, in the MSKCC-IO cohort, a TP53/ATM co-mutation was associated with better OS than sole mutation and both wild type groups, especially in pan-cancer (P¼.241, NSCLC and P<.0001, Pan-cancer). Similar results were shown in the POLAR/OAK cohort, the disease control benefit rate, and progression free survival (PFS) and OS were all greater in patients with the TP53/ATM co-mutation compared with the other three groups. GSEA showed that IFN-a response, IFN-c response, IL6/JAK/STAT3 signaling, and TNF-a signaling pathways had higher levels of activation and there was greater PD-L1 expression in the co-mutated group, compared with solely mutated and both wild type group. Conclusions: Our findings suggest that patients with TP53/ATM co-mutation comprise a special subgroup of NSCLC patients and correlate with increasing TMB and responses to ICIs. It may have implications for potential predictive biomarker in guiding ICIs immunotherapy. Legal entity responsible for the study: Jian Ji Pan. Funding: National Natural Science Foundation of China (Grant No. U1705282). Disclosure: All authors have declared no conflicts of interest.
Volume 30 | Supplement 5 | October 2019
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Annals of Oncology