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Simultaneous Multi-Cancer Detection and Tissue of Origin (TOO) Localization Using Targeted Bisulfite Sequencing of Plasma Cell-Free DNA (Cfdna)

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Simultaneous Multi-Cancer Detection and Tissue of Origin (TOO) Localization Using Targeted Bisulfite Sequencing of Plasma Cell-Free DNA (Cfdna) Simultaneous Multi-cancer Detection and Tissue of Origin (TOO) Localization Using Targeted Bisulfite Sequencing of Plasma Cell-free DNA (cfDNA) 1 2 3 4 1 1 1 1 1 1 DDW 2020 Anne-Renee Hartman, MD ; Geoffrey R. Oxnard, MD ; Eric A. Klein, MD ; Michael V. Seiden, MD ; Earl Hubbell, PhD ; Oliver Venn, DPhil ; Arash Jamshidi, PhD ; Nan Zhang, PhD ; John F. Beausang, PhD ; Samuel Gross, PhD ; Kathryn N. Kurtzman, May 2020 MD1; Eric T. Fung, MD, PhD1; Brian Allen, MS1; Alexander P. Fields, PhD1; Hai Liu, PhD1; Mikkael A. Sekeres, MD3; Donald Richards, MD, PhD5; Peter P. Yu, MD6; Alexander M. Aravanis, MD, PhD1; Minetta C. Liu, MD7 Virtual Meeting 1GRAIL, Inc., Menlo Park, CA; 2Dana Farber Cancer Institute, Boston, MA; 3Cleveland Clinic, Cleveland, OH; 4US Oncology Research, The Woodlands, TX; 5Texas Oncology, Tyler, TX; 6Hartford HealthCare Cancer Institute, Hartford, CT; 7Mayo Clinic, Rochester, MN. ¡ Where a cancer signal was detected, cancer was localized to an anatomic site (ie, tissue type identified) for 96% INTRODUCTION RESULTS (344/359) of cases; of these (and consistent with training set analyses), the TOO call was correct in 93% (321/344) of cases (Figure 5). ¡ A noninvasive cell-free DNA blood test detecting multiple cancers at earlier stages (stages I–III) could decrease ¡ The trained classifier targeting specificity of >99% (see Methods) achieved specificity of 99.8% in the cross- cancer mortality. validated training set and 99.3% in the independent validation set (P=0.095). Figure 5. Validation Set Tissue of Origin Localization ¡ For a multi-cancer test to be effective at population scale, it should: ¡ Therefore, assay performance reflected a consistent false positive rate of <1%. Precision Anus 1 −− (1/1) ¡ Detect clinically significant cancers with a low false positive rate (ie, very high specificity [>99%]) to limit ¡ Importantly, the assay specificity and sensitivity were consistent between the cross-validated training set and Bladder/Urothelial 1 −− (1/1) overdiagnosis; independent validation set across stages (Figure 3), confirming that training data were not overfitted; this was also 1,2 Breast 1 1 1 40 93% (40/43) ¡ Identify a specific tissue origin to direct appropriate diagnostic work-up for detected cancers. consistent for all cancer types. Cervix 3 −− (3/3) ¡ In earlier discovery work, whole-genome bisulfite sequencing outperformed whole-genome and targeted sequencing Figure 3. Stage-specific Performance Consistency Between Training and Test Sets 1 38 97% (38/39) approaches for multi-cancer detection across cancer stages at high specificity3; targeted methylation was selected Colon/Rectum 1 15 2 83% (15/18) for further assay development, including training and internal cross-validation. 0.678 0.315 0.476 0.831 Head and Neck Kidney 3 −− (3/3) ¡ Presented here are data from a second pre-specified substudy of Circulating Cell-free Genome Atlas (CCGA; 100% 1 9 90% (9/10) NCT02889978), in which a multi-cancer detection and tissue-of-origin (TOO) localization using targeted bisulfite Liver/bile duct sequencing of plasma cfDNA to identify methylomic signatures was validated in preparation for returning results in a Lung 2 1 1 71 1 1 1 1 90% (71/79) Lymphoid Neoplasm 1 2 1 3 1 53 1 85% (53/62) clinical setting. 75% Melanoma −− Myeloid Neoplasm −− METHODS Non−cancer −− 50% Predicted TOO −− ¡ The primary analysis population used for this validation was comprised of 1,264 participants derived from the CCGA Other cancer types* 12 100% (12/12) and STRIVE study populations (Figure 1); CCGA is a multi-center, case-control, observational study with longitudinal Sensitivity Ovary follow-up (15,254 participants enrolled: 56% cancer, 44% non-cancer) and STRIVE is a multi-center, prospective, Pancreas/Gallbladder 1 30 97% (30/31) cohort study enrolling women undergoing screening mammography (99,259 participants enrolled). 25% Plasma Cell Neoplasm 10 100% (10/10) ¡ Importantly, to improve the resolution of the targeted high specificity (ie, >99%), non-cancer samples from the Prostate 10 100% (10/10) STRIVE study population were also analyzed. Sarcoma −− Training Set Validation Set −− ¡ Previously, we presented cross-validated results from a training set analysis of 3,583 participants derived from CCGA 0% Thyroid 4 I II III IV Upper GI† 17 1 1 1 2 77% (17/22) and STRIVE (CCGA: 1,530 cancer, 884 non-cancer; STRIVE: 1,169 non-cancer participants). (143 | 62) (142 | 62) (241 | 102) (301 | 130) 8 100% (8/8) Figure 1. Detail of Validation Cohort from Second Substudy of CCGA Uterus Clinical Stage † Lung Anus STRIVE Ovary Cervix Fraction Second CCGA Substudy Uterus Thyroid Kidney Breast SarcomaProstate 3,133 Training + 1,354 Validation 1,587 Training + 615 Validation Upper GI Melanoma 100% Non−cancer Plot reflects pre-specified list of cancer types: anal, bladder, colorectal, esophageal, head and neck, liver/bile-duct, lung, 3,133 randomized for training 1,587 randomized for training Liver/bile Headduct andColon/Rectum Neck 75% Myeloid Neoplasm Bladder/Urothelial Validation, n=1,354 Validation, n=615 lymphoma, ovary, pancreatic, plasma cell neoplasm, stomach. P values listed in the white boxes above (chi-squared test). Other cancer types* Lymphoid Neoplasm PlasmaPancreas/Gallbladder Cell Neoplasm 50% (775 cancer* + 579 non-cancer*) 2 (<1%) unlocked (All non-cancer) 9 (1.5%) unlocked 8 (<1%) ineligible/not evaluable Actual TOO 25% 14 (2.3%) ineligible/not evaluable 28 (2.1%) Unconfirmed cancer/tx ¡ At 99.3% specificity, the sensitivity (95% CI) for all cancer types was 55% (51–59%), and for the pre-specified status 0% Clinical Evaluable, n=1,316 Clinical Evaluable, n=592 cancer types was 76% (72-81%). 8 (<1%) assay not evaluable 2 (<1%) assay not evaluable Agreement between the true (x-axis) and predicted (y-axis) TOO per sample. Precision reported for cancer types with ¡ At each stage, cancer detection for all cancer types combined (sensitivity [95% CI]) was 18% (13–25%) in stage Analyzable, n=1,308 Analyzable, n=590 ≥10 samples. Matrix excludes 21 cancers not expected to be staged (lymphoid leukemia, myeloid neoplasm, brain), 9 of 276 (2 1.1%) reserved for future 152 (25.8%) reserved for future I (n=185), 43% (35–51%) in stage II (n=166), 81% (73–87%) in stage III (n=134), and 93% (87–96%) in stage IV analyses† analyses† which were detected (all lymphoid leukemia), and all 9 of which received the correct tissue of origin. Secondary Analysis Population Secondary Analysis Population (n=148) (Figure 4). n=1,032 n=438 *Upper GI combines esophageal and gastric cancers: diagnostic workup covers both cancer types. (659 cancer + 373 non-cancer) (438 non-cancer) ¡ **Other cancer types= skin cancer (not including basal cell carcinoma, squamous cell carcinoma, or melanoma), testis, seminoma, vagina, 5 (<1%) missing stage Among pre-specified high-signal cancer types, the stage-specific cancer detection was 39% (27–52%) in 101 (23.1%) follow-up not available 100 (9.7%) follow-up not available and vulva; these cancer types had too few samples to have a TOO trained. Primary Analysis Population Primary Analysis Population stage I (n=62), 69% (56–80%) in stage II (n=62), 83% (75–90%) in stage III (n=102), and 92% (86–96%) in n=927 n=337 stage IV (n=130). ¡ TOO detection rates were similar across stage and slightly higher at each stage among the prespecified cancers (654 cancer + 273 non-cancer) (337 non-cancer) compared to all cancers (Figure 6). Figure 4. Overall Cancer Detection by Stage *At enrollment, prior to confirmation of cancer versus non-cancer status. Figure 6. Consistently High Tissue of Origin Accuracy Across Stages †Samples reserved for future analysis include, for example, a cohort of participants recruited from hematology clinics meant to understand A. All Cancers* B. Pre-specified Cancers† ^† cfDNA signal in premalignant or other hematologic conditions. A. All Cancers* B. Pre-specified Cancers ¡ The validation set from the second substudy shown in Figure 1, was used to validate a trained and locked classifier for 100% 100% determining cancer versus non-cancer and TOO based on a targeted methylation sequencing approach. 90% ¡ Analysis followed a pre-specified statistical analysis plan, with clinical and assay data locked and blinded to 80% each other. 70% 75% ¡ This validation set of 1,264 evaluable samples included 610 non-cancer samples (273 from CCGA and 337 from 60% STRIVE), and 654 cancer samples (CCGA) from >50 cancers, which were grouped for reporting purposes; the 50% 50% pre-specified subset of cancers was: anal, bladder, colorectal, esophageal, head and neck, liver/bile-duct, lung, 40% Accuracy lymphoma, ovary, pancreatic, plasma cell neoplasm, stomach (356 cancer [all stages]). 30% Percent Detected ¡ The list of pre-specified high detection rate cancer types (ie, those with sensitivity >50% across stages I–III in 20% 25% training) in the validation set versus the cross-validated training set4 analyses differed by a single cancer type for 10% consistency with the validation set TOO analysis; specifically, this resulted in the addition of bladder cancer and the 0% removal of hormone-receptor negative breast cancer. 0% I (185) II (166) III (134) IV (148) I (62) II (62) III (102) IV (130) ¡ Plasma cfDNA was subjected to a cross-validated targeted methylation approach that included high-efficiency I (73 | 30) II (164 | 67) III (237 | 101) IV (310 | 137) I (49 | 22) II (107 | 41) III (202 | 82) IV (271 | 120) methylation chemistry to enrich for methylation targets and subsequent machine learning classifier for determining Clinical Stage (n) Clinical Stage (n) Clinical Stage Clinical Stage cancer status and TOO (Figure 2). Stage-specific sensitivity (achieved with 99.3% specificity) for (A) all examined cancer types* and (B) a pre-specified group ¡ Observed methylation fragments characteristic of cancer and TOO were combined across targeted regions and of cancer types†.
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  • Download.Php?File=/Images/ Publication/128393883472.Pdf, Accessed: May 2012] References 23
    Estimating the returns to UK publicly funded cancer-related research in terms of the net value of improved health outcomes Glover et al. Glover et al. BMC Medicine 2014, 12:99 http://www.biomedcentral.com/1741-7015/12/99 Glover et al. BMC Medicine 2014, 12:99 http://www.biomedcentral.com/1741-7015/12/99 RESEARCH ARTICLE Open Access Estimating the returns to UK publicly funded cancer-related research in terms of the net value of improved health outcomes Matthew Glover1, Martin Buxton1, Susan Guthrie2, Stephen Hanney1, Alexandra Pollitt2 and Jonathan Grant2,3* Abstract Background: Building on an approach developed to assess the economic returns to cardiovascular research, we estimated the economic returns from UK public and charitable funded cancer-related research that arise from the net value of the improved health outcomes. Methods: To assess these economic returns from cancer-related research in the UK we estimated: 1) public and charitable expenditure on cancer-related research in the UK from 1970 to 2009; 2) net monetary benefit (NMB), that is, the health benefit measured in quality adjusted life years (QALYs) valued in monetary terms (using a base-case value of a QALY of GB£25,000) minus the cost of delivering that benefit, for a prioritised list of interventions from 1991 to 2010; 3) the proportion of NMB attributable to UK research; 4) the elapsed time between research funding and health gain; and 5) the internal rate of return (IRR) from cancer-related research investments on health benefits. We analysed the uncertainties in the IRR estimate using sensitivity analyses to illustrate the effect of some key parameters.
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