Displaying One Session

14:15 - 15:30 (1h 15m)
Date
09.11.2019
Time And Duration
14:15 - 15:30 (1h 15m)
Biomarkers and checkpoint inhibitor Education session

Predictive biomarkers for the efficacy of IO: which biomarkers to use for daily use?

Lecture Time
14:15 - 14:35
Speakers
  • John B. Haanen, Amsterdam, Netherlands, Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital (NKI-AVL)
Location
Fleming room, Queen Elizabeth II Centre, London, United Kingdom
Date
09.11.2019
Time
14:15 - 15:30
Biomarkers and checkpoint inhibitor Education session

Predictive biomarkers for the efficacy of IO: What’s in the pipeline

Lecture Time
14:35 - 14:55
Speakers
  • Daniela S. Thommen, Amsterdam, Netherlands, Netherlands Cancer Institute/Antoni van Leeuwenhoek hospital (NKI-AVL)
Location
Fleming room, Queen Elizabeth II Centre, London, United Kingdom
Date
09.11.2019
Time
14:15 - 15:30
Biomarkers and checkpoint inhibitor Education session

Discussion

Lecture Time
14:55 - 15:10
Location
Fleming room, Queen Elizabeth II Centre, London, United Kingdom
Date
09.11.2019
Time
14:15 - 15:30
Biomarkers and checkpoint inhibitor Education session

6O - Comprehensive genomic profiling and outcomes among metastatic melanoma patients (pts) treated with first-line cancer immunotherapy (CIT) in a real-world setting

Lecture Time
15:10 - 15:20
Speakers
  • Yibing Yan, South San Francisco, CA, United States of America, Genentech Inc. - Roche - USA
Location
Fleming room, Queen Elizabeth II Centre, London, United Kingdom
Date
09.11.2019
Time
14:15 - 15:30

Abstract

Background

Distinctive genomic subtypes (BRAF-mutated [BRAFmut], NRASmut, NF1mut, and triple wild type [wt]) have been identified in melanoma, but little is known about their distribution and association with outcomes outside of clinical trials. By linking longitudinal electronic health records (EHR) and comprehensive genomic profiling we aim to describe characteristics and outcomes among CIT-treated pts in a real-world setting.

Methods

Metastatic melanoma pts in the de-identified Flatiron Health (FH)-Foundation Medicine (FMI) Clinico-Genomic Database (CGDB), in which EHR-derived data from FH are linked to genomic data from FMI, who received first-line pembrolizumab (pembro), nivolumab (nivo), ipilimumab (ipi), or ipi + nivo between Jan 1, 2011, and Nov 30, 2018, were analyzed. Median OS was calculated from the start of first-line therapy and was evaluated according to BRAF status and genomic subtypes.

Results

Of 656 melanoma pts in CGDB, 236 received first-line CIT. The majority of pts (69%, n = 165) were BRAFwt and the rest (31%, n = 71) were BRAFmut. Median age was 62.9 yrs (57.3 yrs for BRAFmut pts and 65.6 yrs for BRAFwt pts), 89% were white, and 97% were treated in community clinics. Among BRAFwt pts, 40% (n = 66) were NRASmut, 32% (n = 53) were NF1mut, and 28% (n = 46) were triple wt. Pembro was used in 36% of pts, followed by ipi + nivo (25%), nivo (20%), and ipi (18%). Almost half (47%) of the pts received subsequent therapy; of these pts, 53% (n = 59) received CIT, 23% (n = 26) received targeted therapy, and 24% (n = 27) received other therapies. Median OS for BRAFwt and BRAFmut pts after first-line CIT was 38.6 mo (95% CI 20.2–not estimable [NE]) and 28.9 mo (95% CI 23.3–NE), respectively. Among BRAFwt pts, median OS was 44.9 mo (95% CI 28.9–NE) for NRASmut pts, 27.1 mo (95% CI 19.4–NE) for NF1mut pts, and 19.8 mo (95% CI 11.8-NE) for triple wt pts.

Conclusions

Our study demonstrated that outcomes among melanoma pts receiving first-line CIT in real-world setting vary based on genomic subtypes and revealed, for the first time, differences in outcomes of major genomic subtypes in BRAFwt pts. Continued investigation of the association between specific genomic subtypes and survival in a real-world setting is needed.

Editorial acknowledgement

Melanie Sweetlove, MSc (ApotheCom, Yardley, PA, USA).

Legal entity responsible for the study

Genentech, a member of the Roche Group.

Funding

Genentech, a member of the Roche Group.

Disclosure

N. Sadetsky: Shareholder / Stockholder / Stock options, Full / Part-time employment: Genentech, a member of the Roche Group. P. Lambert: Shareholder / Stockholder / Stock options, Full / Part-time employment: Genentech, a member of the Roche Group. C. Julian: Shareholder / Stockholder / Stock options, Full / Part-time employment: Genentech, a member of the Roche Group. J. Chen: Shareholder / Stockholder / Stock options, Full / Part-time employment: Genentech, a member of the Roche Group. Y. Yan: Shareholder / Stockholder / Stock options, Full / Part-time employment: Genentech, a member of the Roche Group.

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Biomarkers and checkpoint inhibitor Education session

7O - Contrasting the drivers of response to immunotherapy across solid tumour types: Results from analysis of > 1000 cases

Lecture Time
15:20 - 15:30
Speakers
  • Kevin Litchfield, London, United Kingdom, The Francis Crick Institute
Location
Fleming room, Queen Elizabeth II Centre, London, United Kingdom
Date
09.11.2019
Time
14:15 - 15:30

Abstract

Background

Multiple genomic and transciptomic biomarkers have been associated with response to immune checkpoint inhibitor (CPI) therapy. Emerging evidence suggests that each solid tumour type has a unique mix of factors determining CPI response, reflecting the subtle differences in antigen repertoire and immune microenvironment across histologies. Compiling large-scale sequencing datasets of patients treated with CPI therapy, from a range of solid tumour types, allows detailed comparison of the contrasting immune drivers per histology. Understanding these differences enhances our understanding of the pathways influencing CPI response, which may be of utility for therapeutic and biomarker development.

Methods

We compiled data from 13 CPI treated cohorts, across 6 solid tumour types, encompassing 1,453 patients (n = 1,453 with exome data, n = 674 with RNAseq data). All raw data was accessed, and reprocessed through a standardised state of the art bioinformatics pipeline. A comphrehensive range of genomic & transcriptomic biomarker metrics were derived across the cohort. A combined predictive model was constructred encompassing all biomarkers, & the importance weighting was calculated for each biomarker, in each tumour type.

Results

TMB was found to be a universal predictor of response across all tumour types, except for renal cell carcinoma (RCC). Instead CPI response in RCC appears to be strongly driven by expression of human endogeneuos retroviruses (hERV). In malignant melanoma, while TMB (nsSNVs) was associated with CPI response, the number of expressed indel mutations was found to be a stronger predictor. Shared antigen expression also demonstrated tumour specific predictive patterns. A signature of high immune inflitatation was found to be another universal predictor of response across multiple tumour types, however differences in the varying importance of immune cell subsets across histologies was observed. The rate of HLA LOH, and other immune evasion mechanisms also varied dramatically by cancer type.

Conclusions

The determinants of immunotherapy response vary across solid tumour types, offering unique insight into both tumour intrinsic and extrinsic drivers of immunogenicity.

Legal entity responsible for the study

The Francis Crick Institute.

Funding

Has not received any funding.

Disclosure

All authors have declared no conflicts of interest.

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