- Email: [email protected]
OncOS: Scalable and accurate next-generation sequencing analytics for precision oncology and personalized patient care
abstracts
Annals of Oncology 1435P
A blinded comparison of patient treatments to therapeutic options presented by an artificial intelligence-based clinical decision-support system
S. Suwanvecho1, H. Suwanrusme1, T. Jirakulaporn1, N. Taechakraichana1, P. Lungchukiet1, N. Thanakarn1, W. Decha1, W. Boonpakdee1, A. Preininger2, I. DankwaMullan3, M. Solomon2, S. Wang2, G. Jackson2, V. Patel4, E.H. Shortliffe4, N. Kiatikajornthada1 1 Department of Oncology, Bunrungrad International Hospital, Bangkok, Thailand, 2 Watson Health, IBM Corporation, Cambridge, MA, USA, 3IBM Watson Health, IBM Corporation, Bethesda, MD, USA, 4Department of Biomedical informatics, Columbia University, New York, NY, USA
Table: 1435P Treatments
N (%) 228 Total
Treatments are identical Oncologists’ Evaluations Acceptable alternatives BIH Preferred WFO Preferred Both WFO and BIH-Rx unacceptable
174 (76.3%) 28 (12.3%) 9 (3.9%) 10 (4.4%) 7 (3.1%)
R ’s therapeutic options are at as least as Conclusions: This blinded study suggests WFOV good as (or are an acceptable alternative to) treatments in practice. Blinding evaluators to source of treatment may minimize bias in comparisons of CDS systems and decisions made in practice. Legal entity responsible for the study: Bumrungrad International Hospital. Funding: Bumrungrad International Hospital. Disclosure: All authors have declared no conflicts of interest.
1436P
OncOS: Scalable and accurate next-generation sequencing analytics for precision oncology and personalized patient care
J.S. Thompson, J.H.R. Farmery, H. Dobson, S. Frost, J.W. Cassidy, N. Patel, H. Thompson, H.W. Clifford Research and Development, Cambridge Cancer Genomics, Cambridge, UK Background: The advent of high-throughput next-generation sequencing (NGS) technologies has resulted in a deluge of data for a wide variety of clinical uses, with millions of samples sequenced to date. Data generated at an “-omics” level (genomics, transcriptomics, epigenomics, proteomics, etc) in cancer research and for clinical decision making is ushering in a new era of personalized cancer care. However this requires fast, accurate, and easily automatable bioinformatics pipelines capable of large scale analytics on big datasets without sacrificing accuracy. Here we present OncOS, a cloudbased auto-scaling architecture capable of performing highly accurate molecular profiling for personalized clinical insights. Methods: A flexible cloud architecture implements bioinformatics pipelines dynamically depending on the input data, including a range of possible sample types (FFPE, fresh frozen, plasma/cfDNA), and clinical insights (clinical trial matching, drug matching, and genomic insights such as mutation calls, copy number variant calls, and MSI/ TMB). This is powered by a pipeline scheduler and an elastic container service cluster that is capable of initializing a large number of elastic compute cloud instances for scalable and parallelized processing. Data is stored on HIPAA compliant and securely
Volume 30 | Supplement 5 | October 2019
ment: Cambridge Cancer Genomics. J.H.R. Farmery: Shareholder / Stockholder / Stock options: Cambridge Cancer Genomics. H. Dobson: Shareholder / Stockholder / Stock options, Full / Part-time employment: Cambridge Cancer Genomics. S. Frost: Shareholder / Stockholder / Stock options, Full / Part-time employment: Cambridge Cancer Genomics. J.W. Cassidy: Leadership role, Shareholder / Stockholder / Stock options, Full / Part-time employment, Officer / Board of Directors: Cambridge Cancer Genomics. N. Patel: Leadership role, Shareholder / Stockholder / Stock options, Full / Parttime employment: Cambridge Cancer Genomics. H. Thompson: Leadership role, Shareholder / Stockholder / Stock options, Full / Part-time employment: Cambridge Cancer Genomics. H.W. Clifford: Leadership role, Shareholder / Stockholder / Stock options, Full / Part-time employment: Cambridge Cancer Genomics.
1437P
The association between wearable device physical activity metrics and performance status in oncology: A systematic review
M. Kos1, E.N. Pijnappel1, C.S. Kampshoff2, L.M. Buffart3, H.W. Wilmink1, H.W.M. van Laarhoven1, M. van Oijen1 1 Dept. of Medical Oncology, Cancer Center Amsterdam, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands, 2Dept. of Medical Oncology, Cancer Center Amsterdam, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, Netherlands, 3Dept. of Medical Oncology and Dept. of Epidemiology and Biostatistics, Cancer Center Amsterdam, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, Netherlands Background: The expanding armamentarium of wearable devices offers new opportunities to supplement physician-assessed performance status (PS) with continuously acquired real-life patient data. It is relevant to identify and characterize the level of association between wearable device physical activity (PA) metrics and PS in cancer patients as a first step into evaluating their potential combined utility in evaluating treatment outcomes and clinical decisions. Therefore, we conducted a systematic review to examine the association between wearable device PA metrics and PS in cancer patients. Methods: We searched PubMed and EMBASE for studies that were conducted among adults with cancer, quantitatively assessed a relation between wearable device PA metrics and PS, and had a full text available in English. We extracted information on study design and population, wearable device type and PA metrics, outcome definitions, and results. Included studies were subjected to methodological quality assessment. Results: Nine studies with a total of 574 patients were included in this review. Eight studies had a prospective observational study design and all studies reported on a different combination of wearable device PA metrics including: steps per day (n ¼ 5), sedentary behavior (n ¼ 5), and PA volume/intensity (n ¼ 4). Much heterogeneity was observed regarding study population, wearable devices used, and reporting of results. None of the studies could be defined to be of ‘high methodological quality’ ( 70%): mean methodological quality was 47% and ranged from 40-60%. We found moderate evidence for a positive association between steps per day and PS, and for a negative association between sedentary behavior and PS. Conclusions: Much heterogeneity was identified between studies with regards to study population, reported PA metrics, and used devices. Nevertheless, results of this study indicate that higher daily step count is associated with better PS in cancer patients. Whereas sedentary behavior is associated with worse PS. The next step into determining their potential combined utility in evaluating treatment outcomes and clinical decisions is to investigate the association between wearable device PA metrics and cancer outcomes. Legal entity responsible for the study: The authors. Funding: Has not received any funding. Disclosure: H.W. Wilmink: Advisory / Consultancy: Shire; Advisory / Consultancy, Research grant / Funding (institution): Celgene; Research grant / Funding (institution): Servier; Research grant / Funding (institution): Halozyme; Research grant / Funding (institution): Novartis; Research grant / Funding (institution): AstraZeneca; Research grant / Funding (institution): Pfizer; Research grant / Funding (institution): Roche; Research grant / Funding (institution): Merck. H.W.M. van Laarhoven: Research grant / Funding (institution): Roche; Research grant / Funding (institution): Bayer; Advisory / Consultancy, Research grant / Funding (institution): BMS; Advisory / Consultancy, Research grant / Funding (institution): Celgene; Advisory / Consultancy, Research grant / Funding (institution): Lilly; Research grant / Funding (institution): Merck Serono; Research grant / Funding (institution): MSD; Advisory / Consultancy, Research grant / Funding (institution): Nordic; Research grant / Funding (institution): Philips. M. van Oijen: Research grant / Funding (institution): Roche; Research grant / Funding (institution): Lilly; Research grant / Funding (institution): Servier; Research grant / Funding (institution): Nordic; Research grant / Funding (institution): Amgen. All other authors have declared no conflicts of interest.
doi:10.1093/annonc/mdz257 | v583
Downloaded from https://academic.oup.com/annonc/article-abstract/30/Supplement_5/mdz257.031/5577003 by guest on 25 October 2019
Background: Comparison of options from clinical decision-support (CDS) systems and decisions made in practice may be biased towards the treating institution. In this retrospective study, bias was minimized by blinding evaluators to the source of treatR (WFOV R ) or treatments patients ment recommendations, either Watson for OncologyV R. received at Bumrungrad International Hospital (BIH), a user of WFOV Methods: Treatments given were compared to therapeutic options provided by R . Treatments that were identical to WFOV R “recommended” (green, acceptable) WFOV were not evaluated further. Paired treatments were evaluated independently in a blinded fashion by each oncologist before consensus ranking of each pair as either acceptable, acceptable alternatives, or unacceptable treatment. The consensus for each R , with WFOV R “for consideration” (yellow, accepttreatment was compared to WFOV able alternative), and “not recommended” (red, unacceptable). Chi-squared tests analyzed the association between risk factors and discordant recommendations. Results: Of 228 treatments given to patients with lung, colon, breast and rectal cancers, R acceptable (green) and not evaluated further; 54 non174 were identical to WFOV identical pairs were evaluated (Table). Overall, 88.6% of decisions were either the same or viewed as equally acceptable by oncologists; oncologists preferred 3.9% of BIH treatments and 4.4% of WFO treatments. In cases where reasons for discordance were provided, 70% were due to BIH oncologist preference, 20% to patient preference and 10% to WFO treatment availability. We found no association between discordant recommendations and patient age or stage of cancer.
encrypted databases and simple storage services, with key information relayed to a web app for use in a clinical setting. Results: OncOS has also been optimized for Positive Predictive Value (PPV), with testing on samples from the Multi-Center Mutation Calling in Multiple Cancers (MC3) project, a collaborative effort to provide a high confidence set of variants for patients in The Cancer Genome Atlas (TCGA). Benchmarking shows OncOS performs with a PPV of 87.4%, outperforming similar variant calling pipelines (BROAD institute 75.4%; MD Anderson 80.1%). Conclusions: OncOS is a precision oncology platform with a cloud architecture capable of processing a variety of sample types at scale, optimized for variant calling PPV and drawing of key clinical insights. Legal entity responsible for the study: The authors. Funding: Cambridge Cancer Genomics. Disclosure: J.S. Thompson: Shareholder / Stockholder / Stock options, Full / Part-time employ-
Annals of Oncology 1435P
A blinded comparison of patient treatments to therapeutic options presented by an artificial intelligence-based clinical decision-support system
S. Suwanvecho1, H. Suwanrusme1, T. Jirakulaporn1, N. Taechakraichana1, P. Lungchukiet1, N. Thanakarn1, W. Decha1, W. Boonpakdee1, A. Preininger2, I. DankwaMullan3, M. Solomon2, S. Wang2, G. Jackson2, V. Patel4, E.H. Shortliffe4, N. Kiatikajornthada1 1 Department of Oncology, Bunrungrad International Hospital, Bangkok, Thailand, 2 Watson Health, IBM Corporation, Cambridge, MA, USA, 3IBM Watson Health, IBM Corporation, Bethesda, MD, USA, 4Department of Biomedical informatics, Columbia University, New York, NY, USA
Table: 1435P Treatments
N (%) 228 Total
Treatments are identical Oncologists’ Evaluations Acceptable alternatives BIH Preferred WFO Preferred Both WFO and BIH-Rx unacceptable
174 (76.3%) 28 (12.3%) 9 (3.9%) 10 (4.4%) 7 (3.1%)
R ’s therapeutic options are at as least as Conclusions: This blinded study suggests WFOV good as (or are an acceptable alternative to) treatments in practice. Blinding evaluators to source of treatment may minimize bias in comparisons of CDS systems and decisions made in practice. Legal entity responsible for the study: Bumrungrad International Hospital. Funding: Bumrungrad International Hospital. Disclosure: All authors have declared no conflicts of interest.
1436P
OncOS: Scalable and accurate next-generation sequencing analytics for precision oncology and personalized patient care
J.S. Thompson, J.H.R. Farmery, H. Dobson, S. Frost, J.W. Cassidy, N. Patel, H. Thompson, H.W. Clifford Research and Development, Cambridge Cancer Genomics, Cambridge, UK Background: The advent of high-throughput next-generation sequencing (NGS) technologies has resulted in a deluge of data for a wide variety of clinical uses, with millions of samples sequenced to date. Data generated at an “-omics” level (genomics, transcriptomics, epigenomics, proteomics, etc) in cancer research and for clinical decision making is ushering in a new era of personalized cancer care. However this requires fast, accurate, and easily automatable bioinformatics pipelines capable of large scale analytics on big datasets without sacrificing accuracy. Here we present OncOS, a cloudbased auto-scaling architecture capable of performing highly accurate molecular profiling for personalized clinical insights. Methods: A flexible cloud architecture implements bioinformatics pipelines dynamically depending on the input data, including a range of possible sample types (FFPE, fresh frozen, plasma/cfDNA), and clinical insights (clinical trial matching, drug matching, and genomic insights such as mutation calls, copy number variant calls, and MSI/ TMB). This is powered by a pipeline scheduler and an elastic container service cluster that is capable of initializing a large number of elastic compute cloud instances for scalable and parallelized processing. Data is stored on HIPAA compliant and securely
Volume 30 | Supplement 5 | October 2019
ment: Cambridge Cancer Genomics. J.H.R. Farmery: Shareholder / Stockholder / Stock options: Cambridge Cancer Genomics. H. Dobson: Shareholder / Stockholder / Stock options, Full / Part-time employment: Cambridge Cancer Genomics. S. Frost: Shareholder / Stockholder / Stock options, Full / Part-time employment: Cambridge Cancer Genomics. J.W. Cassidy: Leadership role, Shareholder / Stockholder / Stock options, Full / Part-time employment, Officer / Board of Directors: Cambridge Cancer Genomics. N. Patel: Leadership role, Shareholder / Stockholder / Stock options, Full / Parttime employment: Cambridge Cancer Genomics. H. Thompson: Leadership role, Shareholder / Stockholder / Stock options, Full / Part-time employment: Cambridge Cancer Genomics. H.W. Clifford: Leadership role, Shareholder / Stockholder / Stock options, Full / Part-time employment: Cambridge Cancer Genomics.
1437P
The association between wearable device physical activity metrics and performance status in oncology: A systematic review
M. Kos1, E.N. Pijnappel1, C.S. Kampshoff2, L.M. Buffart3, H.W. Wilmink1, H.W.M. van Laarhoven1, M. van Oijen1 1 Dept. of Medical Oncology, Cancer Center Amsterdam, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands, 2Dept. of Medical Oncology, Cancer Center Amsterdam, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, Netherlands, 3Dept. of Medical Oncology and Dept. of Epidemiology and Biostatistics, Cancer Center Amsterdam, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, Netherlands Background: The expanding armamentarium of wearable devices offers new opportunities to supplement physician-assessed performance status (PS) with continuously acquired real-life patient data. It is relevant to identify and characterize the level of association between wearable device physical activity (PA) metrics and PS in cancer patients as a first step into evaluating their potential combined utility in evaluating treatment outcomes and clinical decisions. Therefore, we conducted a systematic review to examine the association between wearable device PA metrics and PS in cancer patients. Methods: We searched PubMed and EMBASE for studies that were conducted among adults with cancer, quantitatively assessed a relation between wearable device PA metrics and PS, and had a full text available in English. We extracted information on study design and population, wearable device type and PA metrics, outcome definitions, and results. Included studies were subjected to methodological quality assessment. Results: Nine studies with a total of 574 patients were included in this review. Eight studies had a prospective observational study design and all studies reported on a different combination of wearable device PA metrics including: steps per day (n ¼ 5), sedentary behavior (n ¼ 5), and PA volume/intensity (n ¼ 4). Much heterogeneity was observed regarding study population, wearable devices used, and reporting of results. None of the studies could be defined to be of ‘high methodological quality’ ( 70%): mean methodological quality was 47% and ranged from 40-60%. We found moderate evidence for a positive association between steps per day and PS, and for a negative association between sedentary behavior and PS. Conclusions: Much heterogeneity was identified between studies with regards to study population, reported PA metrics, and used devices. Nevertheless, results of this study indicate that higher daily step count is associated with better PS in cancer patients. Whereas sedentary behavior is associated with worse PS. The next step into determining their potential combined utility in evaluating treatment outcomes and clinical decisions is to investigate the association between wearable device PA metrics and cancer outcomes. Legal entity responsible for the study: The authors. Funding: Has not received any funding. Disclosure: H.W. Wilmink: Advisory / Consultancy: Shire; Advisory / Consultancy, Research grant / Funding (institution): Celgene; Research grant / Funding (institution): Servier; Research grant / Funding (institution): Halozyme; Research grant / Funding (institution): Novartis; Research grant / Funding (institution): AstraZeneca; Research grant / Funding (institution): Pfizer; Research grant / Funding (institution): Roche; Research grant / Funding (institution): Merck. H.W.M. van Laarhoven: Research grant / Funding (institution): Roche; Research grant / Funding (institution): Bayer; Advisory / Consultancy, Research grant / Funding (institution): BMS; Advisory / Consultancy, Research grant / Funding (institution): Celgene; Advisory / Consultancy, Research grant / Funding (institution): Lilly; Research grant / Funding (institution): Merck Serono; Research grant / Funding (institution): MSD; Advisory / Consultancy, Research grant / Funding (institution): Nordic; Research grant / Funding (institution): Philips. M. van Oijen: Research grant / Funding (institution): Roche; Research grant / Funding (institution): Lilly; Research grant / Funding (institution): Servier; Research grant / Funding (institution): Nordic; Research grant / Funding (institution): Amgen. All other authors have declared no conflicts of interest.
doi:10.1093/annonc/mdz257 | v583
Downloaded from https://academic.oup.com/annonc/article-abstract/30/Supplement_5/mdz257.031/5577003 by guest on 25 October 2019
Background: Comparison of options from clinical decision-support (CDS) systems and decisions made in practice may be biased towards the treating institution. In this retrospective study, bias was minimized by blinding evaluators to the source of treatR (WFOV R ) or treatments patients ment recommendations, either Watson for OncologyV R. received at Bumrungrad International Hospital (BIH), a user of WFOV Methods: Treatments given were compared to therapeutic options provided by R . Treatments that were identical to WFOV R “recommended” (green, acceptable) WFOV were not evaluated further. Paired treatments were evaluated independently in a blinded fashion by each oncologist before consensus ranking of each pair as either acceptable, acceptable alternatives, or unacceptable treatment. The consensus for each R , with WFOV R “for consideration” (yellow, accepttreatment was compared to WFOV able alternative), and “not recommended” (red, unacceptable). Chi-squared tests analyzed the association between risk factors and discordant recommendations. Results: Of 228 treatments given to patients with lung, colon, breast and rectal cancers, R acceptable (green) and not evaluated further; 54 non174 were identical to WFOV identical pairs were evaluated (Table). Overall, 88.6% of decisions were either the same or viewed as equally acceptable by oncologists; oncologists preferred 3.9% of BIH treatments and 4.4% of WFO treatments. In cases where reasons for discordance were provided, 70% were due to BIH oncologist preference, 20% to patient preference and 10% to WFO treatment availability. We found no association between discordant recommendations and patient age or stage of cancer.
encrypted databases and simple storage services, with key information relayed to a web app for use in a clinical setting. Results: OncOS has also been optimized for Positive Predictive Value (PPV), with testing on samples from the Multi-Center Mutation Calling in Multiple Cancers (MC3) project, a collaborative effort to provide a high confidence set of variants for patients in The Cancer Genome Atlas (TCGA). Benchmarking shows OncOS performs with a PPV of 87.4%, outperforming similar variant calling pipelines (BROAD institute 75.4%; MD Anderson 80.1%). Conclusions: OncOS is a precision oncology platform with a cloud architecture capable of processing a variety of sample types at scale, optimized for variant calling PPV and drawing of key clinical insights. Legal entity responsible for the study: The authors. Funding: Cambridge Cancer Genomics. Disclosure: J.S. Thompson: Shareholder / Stockholder / Stock options, Full / Part-time employ-