Simultaneous Multi- 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 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 test detecting multiple 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% −− 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, /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. −− 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 , 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 , 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 , , or melanoma), testis, , 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 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 and the 0% removal of hormone-receptor negative . 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†. Stage-specific accuracy that includes localization to the top TOO calls for (A) all examined cancer types and (B) a pre- assigned a relative probability of cancer and of a specific TOO; precision was defined as the fraction of correct calls. *Plot excludes unstaged cancers: lymphoid leukemia, myeloid neoplasm, brain. †Includes anal, bladder, colorectal, esophageal, head and specified group of cancer types.† ¡ neck, liver/bile-duct, lung, lymphoma, ovary, pancreatic, plasma cell neoplasm, stomach Classifier was trained and locked, including decision thresholds, targeting above 99% specificity with some *Plot excludes unstaged cancers. †Includes anal, bladder, colorectal, esophageal, head and neck, liver/bile-duct, lung, lymphoma, ovary, allowance for statistical variability. ¡ For the most common cancer types (that also have the most samples) breast, lung, and colorectal, cancer detection pancreatic, plasma cell neoplasm, stomach. Figure 2. Methylation Database: Target Selection and Machine Learning Algorithm was 39% (30–50%; n=104), 66% (56–75%; n=111), and 77% (64–88%; n=53), respectively.

Machine Learning Algorithm Tissue of Origin (TOO) Targets A large methylation 4000 CCGA Cancer Pre-specified TOO classes cfDNA Methylation Lung sequence database of CONCLUSIONS Sequence Data Non-Cancer Lymphoid Neoplasm References Disclosures (+ 1000 Tissue) Plasma Cell Neoplasm cancer and non-cancer Ovary 1. Institute of and National Research Council. 2003. Fulfilling the Study funded by GRAIL, Inc. GRO is an advisory board member and consultant ¡ 20+ Cancer Types Bladder/Urothelial was generated to enable Across stages, multiple deadly cancer types that currently have no screening paradigm were detected, and Early and Late Stage Colon/Rectum Potential of Cancer Prevention and Early Detection. Washington, DC: The for Inivata Ltd.; an honorarium recipient from Guardant Health, Inc., Sysmex Upper GI* target selection for a National Academies Press. https://doi.org/10.17226/10263. Corporation, and Bio-Rad Laboratories, Inc.; and a consultant for DropWorks, Inc., simultaneously accurately localized to a TOO, using methylation signatures in plasma cfDNA. Liver/Bile-duct** AstraZeneca plc, and GRAIL, Inc. EAK is a consultant for GRAIL, Inc. and Cellanyx, Colon Pancreas/Gallbladder*** single test able to classify 2. Aravanis AM, et al. Cell. 2017;168(4):571-574. doi:10.1016/j.cell.2017.01.030 2000 CCGA Non-Cancer Head and Neck LLC. MVS is an employee of, and shareholder in, McKesson Corporation. EH, OV, ¡ This was achieved with trained thresholds that resulted in a single, fixed, low false positive rate (<1%) in an 3. Liu MC, Jamshidi A, Venn O, et al. Genome-wide cell-free DNA (cfDNA) cfDNA Methylation Anus to cancer/non-cancer for AJ, NZ, JFB, SG, KNK, ETF, JY, RS, APF, ANA, and A-RH are employees of GRAIL, Sequence Data Cervix methylation signatures and effect on tissue of origin (TOO) performance. J Clin independent validation set. Breast multiple cancer types at Inc., with equity in the company. AJ and KNK hold stock in Illumina, Inc. The Mayo Oncol 2019;37(15_suppl):3049. Matched on age distribution Lung Uterus Clinic was compensated for MCL’s advisory board activities for GRAIL, Inc. Kidney ¡ by gender in CCGA1 high specificity and identify 4. Liu MC, Oxnard GR, Klein GA, CCGA Consortium, Swanton C, Seiden, MV. Importantly, results in the independent validation set were indistinguishable from the training set, demonstrating Discovery cohort Prostate Melanoma - TOO. Sensitive and specific pan-cancer detection and localization using methylation Acknowledgements Thyroid robustness of machine learning classifier training, and no evidence of overtraining. Non-Cancer Conditions: Public Sequence Data Myeloid Neoplasm signatures in cell-free DNA. Ann Oncol., in press. The authors would like to acknowledge Lori Zhang, Tony Wu, and Metabolic, Hematological, Cancer (TCGA) Inflammatory, Auto-immune Sarcoma ¡ Oncoviruses Indeterminate localization Hai Liu, PhD (GRAIL, Inc.) for statistical support, data analysis and This validation supports the feasibility of a single blood-based test that can simultaneously detect multiple figure generation. *Upper GI combines esophageal and gastric cancers: diagnostic workup covers both cancer types. **Liver/bile duct includes liver and cancers and supports further clinical development for the preparation of returning results. intrahepatic bile duct. ***Pancreas/gallbladder includes pancreas, gallbladder, and extrahepatic bile ducts.