Avoiding lead-time bias by estimating stage-specific proportions of cancer and non-cancer deaths

PURPOSE

Understanding how stage at cancer diagnosis influences cause of death, an endpoint that is not susceptible to lead-time bias, can inform population-level outcomes of cancer screening.

METHODS

Using data from 17 US Surveillance, Epidemiology, and End Results registries for 1,154,515 persons aged 50-84 years at cancer diagnosis in 2006-2010, we evaluated proportional causes of death by cancer type and uniformly classified stage, following or extrapolating all patients until death through 2020.

RESULTS

Most cancer patients diagnosed at stages I-II did not go on to die from their index cancer, whereas most patients diagnosed at stage IV did. For patients diagnosed with any cancer at stages I-II, an estimated 26% of deaths were due to the index cancer, 63% due to non-cancer causes, and 12% due to a subsequent primary (non-index) cancer. In contrast, for patients diagnosed with any stage IV cancer, 85% of deaths were attributed to the index cancer, with 13% non-cancer and 2% non-index-cancer deaths. Index cancer mortality from stages I-II cancer was proportionally lowest for thyroid, melanoma, uterus, prostate, and breast, and highest for pancreas, liver, esophagus, lung, and stomach.

CONCLUSION

Across all cancer types, the percentage of patients who went on to die from their cancer was over three times greater when the cancer was diagnosed at stage IV than stages I-II. As mortality patterns are not influenced by lead-time bias, these data suggest that earlier detection is likely to improve outcomes across cancer types, including those currently unscreened.

Impact of screening participation on modelled mortality benefits of a multi-cancer early detection test by socioeconomic group in England

BACKGROUND

Cancer burden is higher and cancer screening participation is lower among individuals living in more socioeconomically deprived areas of England, contributing to worse health outcomes and shorter life expectancy. Owing to higher multi-cancer early detection (MCED) test sensitivity for poor-prognosis cancers and greater cancer burden in groups experiencing greater deprivation, MCED screening programmes may have greater relative benefits in these groups. We modelled potential differential benefits of MCED screening between deprivation groups in England at different levels of screening participation.

METHODS

We applied the interception multi-cancer screening model to cancer incidence and survival data made available by the National Cancer Registration and Analysis Service in England to estimate reductions in late-stage diagnoses and cancer mortality from an MCED screening programme by deprivation group across 24 cancer types. We assessed the impact of varying the proportion of people who participated in annual screening in each deprivation group on these estimates.

RESULTS

The modelled benefits of an MCED screening programme were substantial: reductions in late-stage diagnoses were 160 and 274 per 100 000 persons in the least and most deprived groups, respectively. Reductions in cancer mortality were 60 and 99 per 100 000 persons in the least and most deprived groups, respectively. Benefits were greatest in the most deprived group at every participation level and were attenuated with lower screening participation.

CONCLUSIONS

For the greatest possible population benefit and to decrease health inequalities, an MCED implementation strategy should focus on enhancing equitable, informed participation, enabling equal participation across all socioeconomic deprivation groups.

TRIAL REGISTRATION NUMBER

NCT05611632.

Potential for Cure by Stage across the Cancer Spectrum in the United States

BACKGROUND

Cure fraction-the proportion of persons considered cured of cancer after long-term follow-up-reflects the total impact of cancer control strategies, including screening, without lead-time bias. Previous studies have not reported stage-stratified cure fraction across the spectrum of cancer types.

METHODS

Using a mixture cure model, we estimated cure fraction across stages for 21 cancer types and additional subtypes. Cause-specific survival for 2.4 million incident cancers came from 17 US Surveillance, Epidemiology, and End Results registries for adults 40 to 84 years at diagnosis in 2006 to 2015, followed through 2020.

RESULTS

Across cancer types, a substantial cure fraction was evident at early stages, followed by either a sharp drop from stages III to IV or a steady decline from stages I to IV. For example, estimated cure fractions for colorectal cancer at stages I, II, III, and IV were 62% (95% confidence interval: 59%-66%), 61% (58%-65%), 58% (57%-59%), and 7% (7%-7%), respectively. Corresponding estimates for gallbladder cancer were 50% (46%-54%), 24% (22%-27%), 22% (19%-25%), and 2% (2%-3%). Differences in 5-year cause-specific survival between early-stage and stage IV cancers were highly correlated with between-stage differences in cure fraction, indicating that survival gaps by stage are persistent and not due to lead-time bias.

CONCLUSIONS

A considerable fraction of cancer is amenable to cure at early stages, but not after metastasis.

IMPACT

These results emphasize the potential for early detection of numerous cancers, including those with no current screening modalities, to reduce cancer death.

Clinical performance and utility: A microsimulation model to inform the design of screening trials for a multi-cancer early detection test

OBJECTIVES

Designing cancer screening trials for multi-cancer early detection (MCED) tests presents a significant methodology challenge, as natural histories of cell-free DNA-shedding cancers are not yet known. A microsimulation model was developed to project the performance and utility of an MCED test in cancer screening trials.

METHODS

Individual natural history of preclinical progression through cancer stages for 23 cancer classes was simulated by a stage-transition model under a broad range of cancer latency parameters. Cancer incidences and stage distributions at clinical presentation in simulated trials were set to match the data from Surveillance, Epidemiology, and End Results program. One or multiple rounds of annual screening using a targeted methylation-based MCED test (GalleriⓇ) was conducted to detect preclinical cancers. Mortality benefit of early detection was simulated by a stage-shift model.

RESULTS

In simulated trials, accounting for healthy volunteer effect and varying test sensitivity, positive predictive value in the prevalence screening round reached 48% to 61% in 6 natural history scenarios. After 3 rounds of annual screening, the cumulative proportions of stage I/II cancers increased by approximately 9% to 14%, the incidence of stage IV cancers was reduced by 37% to 46%, the reduction of stages III and IV cancer incidences was 9% to 24%, and the reduction of mortality reached 13% to 16%. Greater reductions of late-stage cancers and cancer mortality were achieved by five rounds of MCED screening.

CONCLUSIONS

Simulation results guide trial design and suggest that adding this MCED test to routine screening in the United States may shift cancer detection to earlier stages, and potentially save lives.

Performance of a Cell-Free DNA-Based Multi-cancer Detection Test in Individuals Presenting With Symptoms Suspicious for Cancers

PURPOSE

A multi-cancer detection test using a targeted methylation assay and machine learning classifiers was validated and optimized for screening in prospective, case-controlled Circulating Cell-free Genome Atlas (ClinicalTrials.gov identifier: NCT02889978) substudy 3. Here, we report test performance in a subgroup of participants with symptoms suspicious for cancer to assess the test's ability to potentially facilitate efficient diagnostic evaluation in symptomatic individuals.

METHODS

We evaluated test performance (sensitivity, specificity, and accuracy of cancer signal origin [CSO] prediction accuracy) in participants with clinically presenting cancers (CPCs) and noncancer with underlying medical conditions and among two subgroups (65 years and older and GI cancers). Overall survival (OS) of participants who had a cancer signal detected/not detected was compared with SEER-based expected survival.

RESULTS

A total of 2,036 cancer and 1,472 noncancer participants were included. Specificity was high in all noncancer participants (99.5% [95% CI, 98.4 to 99.8]). In participants with CPCs, the overall sensitivity was 64.3% (95% CI, 62.2 to 66.4) and the overall accuracy of CSO prediction in true positives was 90.3%. For GI cancers, the overall sensitivity was 84.1% (95% CI, 80.6 to 87.1). In participants 65 years and older, test performance was similar to that of all participants. Individuals with cancers not detected had a significantly better OS than that expected from SEER (P < .01).

CONCLUSION

This test detected a cancer signal with high specificity and CSO prediction accuracy and moderate sensitivity in symptomatic individuals, with especially high performance in participants with GI cancers. The survival analysis implied that the cancers not detected were less clinically aggressive than cancers detected by the test, providing prognostic insights to physicians. This multi-cancer detection test could facilitate efficient workup and stratify cancer risk in symptomatic individuals.

Modelled mortality benefits of multi-cancer early detection screening in England

BACKGROUND

Screening programmes utilising blood-based multi-cancer early detection (MCED) tests, which can detect a shared cancer signal from any site in the body with a single, low false-positive rate, could reduce cancer burden through early diagnosis.

METHODS

A natural history ('interception') model of cancer was previously used to characterise potential benefits of MCED screening (based on published performance of an MCED test). We built upon this using a two-population survival model to account for an increased risk of death from cfDNA-detectable cancers relative to cfDNA-non-detectable cancers. We developed another model allowing some cancers to metastasise directly from stage I, bypassing intermediate tumour stages. We used incidence and survival-by-stage data from the National Cancer Registration and Analysis Service in England to estimate longer-term benefits to a cohort screened between ages 50-79 years.

RESULTS

Estimated late-stage and mortality reductions were robust to a range of assumptions. With the least favourable dwell (sojourn) time and cfDNA status hazard ratio assumptions, we estimated, among 100,000 screened individuals, 67 (17%) fewer cancer deaths per year corresponding to 2029 fewer deaths in those screened between ages 50-79 years.

CONCLUSION

Realising the potential benefits of MCED tests could substantially reduce late-stage cancer diagnoses and mortality.

Modeled residual current cancer risk after clinical investigation of a positive multicancer early detection test result

BACKGROUND

Positive results of a multi-cancer early detection (MCED) test require confirmatory diagnostic workup. Here, residual current cancer risk (RR) during the process of diagnostic resolution, including situations where the initial confirmatory test does not provide resolution, was modeled.

METHODS

A decision-tree framework was used to model conditional risk in a patient's journey through confirmatory diagnostic options and outcomes. The diagnostic journey assumed that cancer signal detection (a positive MCED test result) had already led to a transition from screening to diagnosis and began with an initial positive predictive value (PPV) from the positive result. Evaluation of a most probable (top) predicted cancer signal origin (CSO) and then a second-most probable predicted CSO followed. Under the assumption that the top- and second-predicted CSOs were each followed by a targeted confirmatory test, the RR was estimated for each subsequent scenario.

RESULTS

For an initial MCED test result with typical performance characteristics modeled (PPV, 40%; top-predicted CSO accuracy, 90%), after a negative initial confirmatory test (sensitivity, 70%, 90%, or 100%) the RR ranged from 6% to 20%. A second-predicted CSO (accuracy, 50%), after a negative second confirmatory test, still provided a significant RR (3%-18%) in comparison with the National Institute for Health and Care Excellence-recommended cancer risk threshold warranting investigation in symptomatic individuals (3%). With a 40% PPV for an MCED test and 90% specificity for a confirmatory test, the risk of incidental findings after one or two confirmatory tests was 6% and 12%, respectively.

CONCLUSIONS

These results may illustrate the impact of a positive MCED test on follow-up decision-making.

Surgical resection as a predictor of cancer-specific survival by stage at diagnosis and cancer type, United States, 2006-2015

BACKGROUND

When solid tumors are amenable to definitive resection, clinical outcomes are generally superior to when those tumors are inoperable. However, the population-level cancer survival benefit of eligibility for surgery by cancer stage has not yet been quantified.

METHODS

Using Surveillance, Epidemiology and End Results data allowing us to identify patients who were deemed eligible for and received surgical resection, we examined the stage-specific association of surgical resection with 12-year cancer-specific survival. The 12-year endpoint was selected to maximize follow-up time and thereby minimize the influence of lead time bias.

RESULTS

Across a variety of solid tumor types, earlier stage at diagnosis allowed for surgical intervention at a much higher rate than later-stage diagnosis. At every stage, surgical intervention was associated with a substantially higher rate of 12-year cancer-specific survival, with absolute differences of up to 51% for stage I, 51% for stage II, and 44% for stage III cancer, and stage-specific mortality relative risks of 3.6, 2.4, and 1.7, respectively.

CONCLUSIONS

Diagnosis of solid cancers in early stages often enables surgical resection, which reduces the risk of death from cancer. Receipt of surgical resection is an informative endpoint that is strongly associated with long-term cancer-specific survival at every stage.

Performance of a targeted methylation-based multi-cancer early detection test by race and ethnicity

Disparities in cancer screening and outcomes based on factors such as sex, socioeconomic status, and race and ethnicity in the United States are well documented. A blood-based multi-cancer early detection (MCED) test that detects a shared cancer signal across multiple cancer types and also predicts the cancer signal origin was developed and validated in the Circulating Cell-free Genome Atlas study (CCGA; NCT02889978). CCGA is a prospective, multicenter, case-control, observational study with longitudinal follow-up (overall N = 15,254). In this pre-specified, exploratory, descriptive analysis, test performance was evaluated among racial and ethnic groups. Overall, 4077 participants comprised the independent validation set with confirmed cancer status (cancer: n = 2823; non-cancer: n = 1254). Participants were stratified into the following racial/ethnic groups: Black (non-Hispanic), Hispanic (all races), Other (non-Hispanic), Other/unknown and White (non-Hispanic). Cancer and non-cancer participants were predominantly White (n = 2316, 82.0% and n = 996, 79.4%, respectively). Across groups, specificity for cancer signal detection ranged from 98.1% [n = 103; 95% CI: 93.2-99.5%] to 100% [n = 85; 95% CI: 95.7-100.0%]. The sensitivity for cancer signal detection across groups ranged from 43.9% [n = 57; 95% CI: 31.8-56.7%] to 63.0% [n = 192; 95% CI: 56.0-69.5%] and generally increased with clinical stage. The MCED test had consistently high specificity and similar sensitivity across racial and ethnic groups, though results are limited by sample size for some groups. Results support the broad applicability of this MCED test and clinical implementation on a population scale as a complement to standard screening.

Evaluation of cell-free DNA approaches for multi-cancer early detection

In the Circulating Cell-free Genome Atlas (NCT02889978) substudy 1, we evaluate several approaches for a circulating cell-free DNA (cfDNA)-based multi-cancer early detection (MCED) test by defining clinical limit of detection (LOD) based on circulating tumor allele fraction (cTAF), enabling performance comparisons. Among 10 machine-learning classifiers trained on the same samples and independently validated, when evaluated at 98% specificity, those using whole-genome (WG) methylation, single nucleotide variants with paired white blood cell background removal, and combined scores from classifiers evaluated in this study show the highest cancer signal detection sensitivities. Compared with clinical stage and tumor type, cTAF is a more significant predictor of classifier performance and may more closely reflect tumor biology. Clinical LODs mirror relative sensitivities for all approaches. The WG methylation feature best predicts cancer signal origin. WG methylation is the most promising technology for MCED and informs development of a targeted methylation MCED test.

Multi-cancer early detection test sensitivity for cancers with and without current population-level screening options

There are four solid tumors with common screening options in the average-risk population aged 21 to 75 years (breast, cervical, colorectal, and, based on personalized risk assessment, prostate), but many cancers lack recommended population screening and are often detected at advanced stages when mortality is high. Blood-based multi-cancer early detection tests have the potential to improve cancer mortality through additional population screening. Reported here is a post-hoc analysis from the third Circulating Cell-free Genome Atlas substudy to examine multi-cancer early detection test performance in solid tumors with and without population screening recommendations and in hematologic malignancies. Participants with cancer in the third Circulating Cell-free Genome Atlas substudy analysis were split into three subgroups: solid screened tumors (breast, cervical, colorectal, prostate), solid unscreened tumors, and hematologic malignancies. In this post hoc analysis, sensitivity is reported for each subgroup across all ages and those aged ⩾50 years overall, by cancer, and by clinical cancer stage. Aggregate sensitivity in the solid screened, solid unscreened, and hematologic malignancy subgroups was 34%, 66%, and 55% across all cancer stages, respectively; restricting to participants aged ⩾50 years showed similar aggregate sensitivity. Aggregate sensitivity was 27%, 53%, and 60% across stages I to III, respectively. Within the solid unscreened subgroup, aggregate sensitivity was >75% in 8/18 cancers (44%) and >50% in 13/18 (72%). This multi-cancer early detection test detected cancer signals at high (>75%) sensitivity for multiple cancers without existing population screening recommendations, suggesting its potential to complement recommended screening programs.

Clinical trial identifier: NCT02889978.

Abstract 2239: Detecting cancer when it can be cured: The potential for cure across all stageable cancers

Introduction: Early detection of cancer may reduce cancer mortality by providing access to treatments with the potential to cure cancer at early stages. A mixture cure model divides cancer cases into two populations: one where cancer is likely to severely impact mortality (not-cured) and one where long-term survival with low risk is possible (cured). Previous work on such models has concentrated on estimating cure for either many cancer types without regard to stage (public health arena), or single cancers by stage at diagnosis (screening arena). A gap in the current literature is an estimation of likelihood of cure in many cancer types at all stages.

Methods: We estimate a mixture-cure model for all stages simultaneously for stageable cancers using the fact that cancer is a progressive disease (worse by stage) as a constraint on model parameters. We applied this estimation procedure to 21 cancer classes with standard American Joint Committee on Cancer (AJCC) staging using 12 years of cancer-specific survival data in 50-79 year old individuals using Surveillance, Epidemiology and End Results (SEER) program data from 2006-2015 followed to 2018. For each stage, we recover the fraction of those cured (“cure fraction”, i.e., long-term survivors with minimal excess hazard) and not-cured (severe acute mortality due to cancer, modeled as a Weibull distribution).

Results: Cure fractions varied between cancer classes. Two important types of cancer behavior are illustrated by colorectal and gallbladder cancers. Colorectal cancer had a good potential for cure at any stage before metastasis, with a precipitous drop from 63% (95% CI: 62-64%) cure at stage III to 7% (6-7%) cure at stage IV. In contrast, gallbladder cancer exhibited a systematic decrease at each stage, with 47% (43-52%) cure fraction at stage I, 22% (20-24%) at stage II, 20% (17-22%) at stage III, and 2% (2-3%) at stage IV. Differences in 5-year survival between earlier stages and metastasis were highly correlated to differences in cure fraction (r^2=0.97), suggesting differences in 5-year survival are a proxy for differences in cure.

Conclusions: Long-term survivors were evident at early stages for all 21 cancer types examined. These survival fractions were all greatly reduced by the time cancer reached metastasis. This indicates early-stage cancers do not differ from late-stage cancers simply by lead time and provides statistical evidence that detection of cancer in early stages may result in long-term survival for many stageable cancer types.

Citation Format: Earl Hubbell, Christina Clarke. Detecting cancer when it can be cured: The potential for cure across all stageable cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2239.

Racial/Ethnic Differences in Cancer Diagnosed after Metastasis: Absolute Burden and Deaths Potentially Avoidable through Earlier Detection

BACKGROUND

Racial/ethnic disparities in cancer mortality are well described and are partly attributable to later stage of diagnosis. It is unclear to what extent reductions in the incidence of late-stage cancer could narrow these relative and absolute disparities.

METHODS

We obtained stage- and cancer-specific incidence and survival data from the Surveillance, Epidemiology, and End Results Program for persons ages 50 to 79 years between 2006 and 2015. For eight hypothetical cohorts of 100,000 persons defined by race/ethnicity and sex, we estimated cancer-related deaths if cancers diagnosed at stage IV were detected earlier, by assigning them outcomes of earlier stages.

RESULTS

We observed a 3-fold difference in the absolute burden of stage IV cancer between the group with the highest rate (non-Hispanic Black males, 337 per 100,000) and the lowest rate (non-Hispanic Asian/Pacific Islander females, 117 per 100,000). Assuming all stage IV cancers were diagnosed at stage III, 32-80 fewer cancer-related deaths would be expected across subgroups, a relative reduction of 13%-14%. Assuming one third of metastatic cancers were diagnosed at each earlier stage (I, II, and III), 52-126 fewer cancer-related deaths would be expected across subgroups, a relative reduction of 21%-23%.

CONCLUSIONS

Across population subgroups, non-Hispanic Black males have the highest burden of stage IV cancer and would have the most deaths averted from improved detection of cancer before metastasis.

IMPACT

Detecting cancer before metastasis could meaningfully reduce deaths in all populations, but especially in non-Hispanic Black populations. See related commentary by Loomans-Kropp et al., p. 512.

Prognostic Significance of Blood-Based Multi-cancer Detection in Plasma Cell-Free DNA

PURPOSE

We recently reported the development of a cell-free DNA (cfDNA) targeted methylation (TM)-based sequencing approach for a multi-cancer early detection (MCED) test that includes cancer signal origin prediction. Here, we evaluated the prognostic significance of cancer detection by the MCED test using longitudinal follow-up data.

EXPERIMENTAL DESIGN

As part of a Circulating Cell-free Genome Atlas (CCGA) substudy, plasma cfDNA samples were sequenced using a TM approach, and machine learning classifiers predicted cancer status and cancer signal origin. Overall survival (OS) of cancer participants in the first 3 years of follow-up was evaluated in relation to cancer detection by the MCED test and clinical characteristics.

RESULTS

Cancers not detected by the MCED test had significantly better OS (P < 0.0001) than cancers detected, even after accounting for other covariates, including clinical stage and method of clinical diagnosis (i.e., standard-of-care screening or clinical presentation with signs/symptoms). Additionally, cancers not detected by the MCED test had better OS than was expected when data were adjusted for age, stage, and cancer type from the Surveillance, Epidemiology, and End Results (SEER) program. In cancers with current screening options, the MCED test also differentiated more aggressive cancers from less aggressive cancers (P < 0.0001).

CONCLUSIONS

Cancer detection by the MCED test was prognostic beyond clinical stage and method of diagnosis. Cancers not detected by the MCED test had better prognosis than cancers detected and SEER-based expected survival. Cancer detection and prognosis may be linked by the underlying biological factor of tumor fraction in cfDNA.

Abstract LB058: Performance of a cell-free DNA-based multi-cancer detection test as a tool for diagnostic resolution of symptomatic cancers

Introduction: A test that detects cancer signal across multiple cancer types and predicts signal (tissue) origin (SO) could aid in more efficient diagnostic workup and shorten time to cancer diagnosis in individuals with signs and symptoms.Methods: The Circulating Cell-free Genome Atlas (CCGA; NCT02889978) study is a prospective, longitudinal, multicenter, case-control study to develop and validate a multi-cancer detection test. The 2nd CCGA substudy utilized a targeted methylation-based cell-free DNA assay and machine learning algorithm and included assessment of test performance (sensitivity and SO prediction accuracy) in a subgroup of participants with clinically presenting cancers (CPCs) that were undiagnosed prior to blood draw. Specificity was assessed in the noncancer group and subgroups with confounding (nonmalignant) conditions (CCs; eg, cirrhosis) and noncancer participants enrolled in hematology clinics (HCs).Results: Specificity was 99.5% (95% confidence interval: 98.2-99.9%; 396/398), 93.8% (71.7-99.7%; 15/16), and 99.3% (96.0-100.0%; 136/137) for the noncancer group, CCs subgroup, and HCs subgroup, respectively. Overall sensitivity among those with CPCs was 66.4% (62.2-70.3%; 344/518). Sensitivity of cancer signal detection increased with increasing clinical stage (Table). SO prediction accuracy was 91.7% (88.3-94.3%; 300/327) among CPC participants with cancers detected, excluding those with multiple or unknown primaries. The test demonstrated prognostic value as detected cancer participants had worse survival probability than those not detected. Conclusions: This multi-cancer detection test detected cancer signals and predicted SO in individuals with CPCs with high specificity. These findings support further clinical development of this multi-cancer detection test that could accelerate the diagnostic resolution of symptomatic cancers.

Table. Sensitivity by Clinical Stage Across Cancer Type in Clinically Presenting CancersClinical StagePositive Test/Total Cancer; Sensitivity (95% CI)All*344/518; 66.4% (62.2-70.3%)I33/122; 27.0% (20.0-35.5%)II60/102; 58.8% (49.1-67.9%)III103/121; 85.1% (77.7-90.4%)IV136/147; 92.5% (87.1-95.8%)Not expected to be staged9/21; 42.9% (24.5-63.5%)Non-informative2/4; 50.0% (15.0-85.0%)CI, confidence interval.*One participant who had a positive test result had multiple primaries with clinical stage I and not-expected-to-be-staged.

Citation Format: Alan H. Bryce, Minetta C. Liu, Michael V. Seiden, David D. Thiel, Donald Richards, Carlos Becerra, Kathryn N. Kurtzman, Xiaoji Chen, Tony Wu, Quan Zhang, Jingjing Gao, Nan Zhang, Earl Hubbell, Arash Jamshidi, Eric T. Fung, Eric A. Klein. Performance of a cell-free DNA-based multi-cancer detection test as a tool for diagnostic resolution of symptomatic cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr LB058.

Performance of a targeted methylation-based multi-cancer early detection test by race/ethnicity

3071

Background: Disparities in cancer screening and outcomes based on factors such as gender, socioeconomic status, and race/ethnicity are well documented.1 The Circulating Cell-free Genome Atlas study (CCGA; NCT02889978) was designed to develop and validate a blood-based multi-cancer early detection (MCED) test analyzing plasma cell-free DNA (cfDNA) to detect cancer signals across multiple cancer types and simultaneously predict cancer signal origin. Findings stratified by race/ethnicity from the third and final CCGA validation sub-study are reported. Methods: CCGA is a prospective, multicenter, case-control, observational study with longitudinal follow-up (overall N = 15,254). In this pre-specified exploratory analysis from the third substudy, key objectives were to evaluate test performance for cancer signal detection (specificity, overall sensitivity, and sensitivity by clinical stage) among racial/ethnic groups. Plasma cfDNA from evaluable samples was analyzed using a targeted methylation bisulfite sequencing assay and a machine learning approach. Overall, 4077 participants comprised the independent validation set with confirmed status (cancer: n = 2823; non-cancer: n = 1254). The groups stratified by race/ethnicity were White Non-Hispanic, Black Non-Hispanic, Other Non-Hispanic (including but not limited to Asian, Native Hawaiian, Pacific Islander, American Indian, Alaska Native), Hispanic (all races), and Other/unknown. The study was not powered to detect statistical differences between groups. Results: Cancer and non-cancer groups were predominantly White (2316/2823, 82.0% and 996/1254, 79.4%, respectively). Across racial/ethnic groups, specificity for cancer signal detection was 99.6% (White Non-Hispanic: 992/996, 95% confidence interval [99.0-99.8%]), 100.0% (Black Non-Hispanic: 85/85 [95.7-100.0%]), 100.0% (Other Non-Hispanic: 33/33 [89.6-100.0%]), 98.1% (Hispanic: 101/103 [93.2-99.5%]), and 100% (Other/unknown: 37/37 [90.6-100.0%]). Despite slight differences in cancer type and staging across racial/ethnic groups, overall sensitivity for cancer signal detection among groups ranged from 43.9% to 63.0% (White Non-Hispanic: 50.5%, 1169/2316 [48.4-52.5%], Black Non-Hispanic: 53.9%, 104/193 [46.8-60.8%], Other Non-Hispanic: 43.9%, 25/57 [31.8-56.7%], Hispanic: 63.0%, 121/192 [56.0-69.5%], and Other/unknown: 52.3%, 34/65 [40.4-64.0%]). For all racial/ethnic groups, sensitivity generally increased with clinical stage (with limited exceptions at Stage IV in some groups with small sample sizes). Conclusions: The MCED test demonstrated consistent specificity and sensitivity across racial/ethnic groups, though results are limited by sample size for some groups. These findings indicate broad applicability and support clinical implementation of this MCED test on a population scale. 1. Zavela et al. Brit J Cancer 2021. Clinical trial information: NCT02889978.

Modeled Reductions in Late-stage Cancer with a Multi-Cancer Early Detection Test

BACKGROUND

Cancer is the second leading cause of death globally, with many cases detected at a late stage when prognosis is poor. New technologies enabling multi-cancer early detection (MCED) may make "universal cancer screening" possible. We extend single-cancer models to understand the potential public health effects of adding a MCED test to usual care.

METHODS

We obtained data on stage-specific incidence and survival of all invasive cancers diagnosed in persons aged 50-79 between 2006 and 2015 from the US Surveillance, Epidemiology, and End Results (SEER) program, and combined this with published performance of a MCED test in a state transition model (interception model) to predict diagnostic yield, stage shift, and potential mortality reductions. We model long-term (incident) performance, accou.

Abstract 139: cfDNA methylation profiling distinguishes lineage-specific hematologic malignancies

Introduction: Hematologic (heme) malignancies and their precursor conditions are highly prevalent. They are also diverse in biology, clinical presentation, and outcomes, underlining the importance of differentiating them. Previously, we demonstrated that a blood-based targeted methylation assay detected multiple cancer types across stages. Here, we examined test performance on various heme cancers, identifying specific methylation signatures.

Methods: From the second substudy (training set) of the Circulating Cell-free Genome Atlas (CCGA) study (NCT02889978), we evaluated 325 participants from 17 different heme disease subtypes and 3,211 non-cancer controls enrolled without a cancer diagnosis. A cross-validated mutual information-based algorithm was used to identify features that discriminated heme subtypes. The resulting feature distribution was visualized using uniform manifold approximation and projection (UMAP) dimensionality reduction on held-out data. In cross validation with feature selection, we then trained a multinomial classifier to distinguish from among the major heme cancers and non-cancer and correlated the model's class probabilities to positions in feature space.

Results: Dimensionality reduction and visualization of input features demonstrated that heme malignancies separated into five major clusters reflecting developmental lineages and disease ontogeny: myeloid, circulating lymphomas, hodgkin lymphomas, non-hodgkin lymphomas, and plasma cell neoplasm. The position of samples within each heme cluster correlated with the cancer signal strength. At 99.4% specificity [95% CI: 99.1, 99.7], heme cancer detection was 74.5% [69.4, 79.1] overall, 67.7% [41.1, 87.8] for myeloid, 77.9% [66.3, 86.9] for circulating lymphomas, 90.7% [73.2, 98.4] for hodgkin lymphomas, 68.6% [60.4, 76.1] for other non-hodgkin lymphomas, and 78.8% [67.0, 87.9] for plasma cell neoplasms. Of 18 non-cancer participants who were classified as having heme cancers, 4 were predicted as myeloid, 6 as circulating lymphoid, and 8 as other non-hodgkin lymphoid neoplasms (&lt;1% false positive rate).

Conclusion: Methylation features of cfDNA in patients with heme malignancies delineated five major clusters that reflected disease ontogeny and heme lineage. Lineage-specific signals followed a gradient suggestive of variation in disease-related methylation or tumor DNA shedding. These findings contribute to the understanding of biological signals that arise from various heme conditions. Since in general, most cfDNA arises from blood lineages, this knowledge will guide further efforts towards removing interfering biological signals from cfDNA-based cancer detection assays and achieving even more sensitive detection of multiple cancer types.

Citation Format: Qinwen Liu, Rita Shaknovich, Xiaoji Chen, Zhao Dong, M. C. Maher, Samuel Gross, Alexander P. Fields, Jan Schellenberger, Kathryn N. Kurtzman, Eric T. Fung, Anne-Renee Hartman, Earl Hubbell, Arash Jamshidi, Alexander M. Aravanis, Oliver Venn. cfDNA methylation profiling distinguishes lineage-specific hematologic malignancies [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 139.

Abstract 2114: HPV-driven cancers show distinct methylation signatures in cell-free DNA (cfDNA)

Accurate tissue of origin (TOO) prediction is crucial for effective clinical follow-up in early cancer detection from blood. In the second substudy of the Circulating Cell-free Genome Atlas (CCGA; NCT02889978), we trained logistic regression classifiers under cross-validation to detect and localize cancer. Input features were methylation states from a targeted cfDNA assay of 2023 participants. TOO classification accuracy was 89% across 20 pre-specified prediction classes. We subsequently sought to understand the causes of the remaining TOO errors. 45% of the errors fell into clusters reflecting similarities in developmental biology, histology, or oncological drivers. Here, we analyzed tissues that may be affected by HPV-driven cancers; these accounted for 21% of TOO errors. The original classifier, which used only human epigenetic states as input, demonstrated cross-scoring between likely HPV-driven cancers of the anus (N = 14) and cervix (N = 11), as well as confirmed HPV-positive head & neck (H&N) cancers (37/62). We also observed HPV-associated vulva (N = 9) and penis (N = 1) cancers, which were not directly trained as TOO classes, were assigned high H&N scores. To test the hypothesis of HPV-driven TOO confusion, we assessed HPV cfDNA and HPV-driven methylation in human peripheral blood cfDNA, and used a specialist classifier restricted to HPV-associated cancers to resolve errors. We corroborated putative HPV-positive participants using targeted sequencing of HPV16 and HPV18 cfDNA fragments, and showed that the number of unique HPV-derived fragments in a sample matched with expected cancer localizations, HPV subtypes, and HPV status. Consistent with the literature, we found little evidence of HPV viremia in non-cancer participants despite the high prevalence of transient HPV infections in the US population. At 99.8% specificity, a cross-validated cutoff on the number of HPV cfDNA fragments in a sample achieved sensitivities of 78.6% (11/14), 36.3% (4/11), 66.6% (6/9), 100% (1/1), and 81.0% (30/37), for anus, cervix, vulva, penis, and confirmed HPV-positive H&N cancers, respectively. These sensitivities were similar to those achieved by the epigenetic classifier. Finally, we trained a cross-validated specialist classifier using the same features as the TOO classifier, but restricted to HPV-driven cancers. This improved TOO accuracy for detected anal cancers from 11% (1/9) to 100% (9/9), with little effect on other classes. These data support that HPV presence may explain observed cross-scoring patterns between H&N, cervix, and anus TOO prediction classes, which were driven by recurrent epigenomic changes in participants with HPV-positive cancers as detected by this assay. This suggests that modelling axes of shared biology across cancer types can be useful for accurate cfDNA TOO classification, which is critical to direct diagnostic work-up of diverse cancer types in a multi-cancer early detection test.

Citation Format: Robert Calef, Oliver Venn, M. Cyrus Maher, John F. Beausang, Earl Hubbell, Aman Patel, Alexander P. Fields, Joerg Bredno, Arash Jamshidi, Alexander M. Aravanis. HPV-driven cancers show distinct methylation signatures in cell-free DNA (cfDNA) [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2114.

Abstract CT021: Prediction of cancer and tissue of origin in individuals with suspicion of cancer using a cell-free DNA multi-cancer early detection test

Background: The Circulating Cell-free Genome Atlas study (NCT02889978) is a multi-center, case-control, observational study with longitudinal follow-up (n=15,254; 56% cancer, 44% non-cancer) to support development of a cell-free DNA (cfDNA) multi-cancer early detection test. Previously, we reported that a targeted methylation assay detected and localized &gt;20 cancer types at &gt;99% specificity in individuals with cancer.1,2 Here, we report prediction of cancer (presence/absence) and tissue of origin (TOO) in individuals enrolled with clinical suspicion of cancer but without pathologic diagnosis or treatment at time of enrollment. Methods: Plasma cfDNA from blood samples collected prior to clinical diagnosis was subjected to targeted methylation sequencing. Samples were divided into a training set and an independent validation set to train and validate a machine learning classifier to assess cancer and predict TOO. Performance was assessed in a subset of participants enrolled with suspicion of cancer; subsequently, cancer was confirmed by evaluating a pathologic specimen. Results: Participants being evaluated for suspicion of cancer were classified as confirmed cancer (&gt;20 cancer types; n=164 in training, n=75 in validation) or confirmed non-cancer (n=49 training, n=15 validation). In the confirmed non-cancer group, all training and validation samples were correctly predicted as non-cancer (100% specificity). In the confirmed cancer group, cancer detection across all stages was 40.2% (66/164; 95% confidence interval [CI], 32.7-48.2%) in training and 46.7% (35/75; 95% CI, 35.1-58.6%) in validation. Excluding stage I renal cancers (where detection/tumor fraction is low in plasma and which comprised 20% of participants in this subset) detection across stages was 50.4% (66/131; 95% CI, 41.5-59.2%) and 59.3% (35/59; 95% CI, 45.7-71.9%), respectively. In stages II and above, detection was 70.7% (58/82; 95% CI, 59.6-80.3%) and 78.9% (30/38; 95% CI, 62.7-90.4%), respectively. For detected cancers, TOO was predicted in 93.9% (62/66) samples in training and 100% (35/35) in validation. Of those with a TOO call, accuracy was 85.5% (53/62; 95% CI, 74.2-93.1%) and 97.1% (34/35; 95% CI, 85.1-99.9%), respectively. Conclusion: A cfDNA multi-cancer detection test has shown the potential to predict cancer and TOO in individuals with suspicion of cancer ahead of histologic diagnosis with performance comparable to those with confirmed cancer at the time of blood collection. This was achieved with high specificity and TOO accuracy. The high specificity suggests that the false positive rate could be comparable in populations with average versus higher risk (suspicion) of cancer. These findings suggest that a cfDNA multi-cancer detection test could accelerate the diagnostic resolution of suspicion of cancer. References: 1. Oxnard GR, et al. ASCO Breakthrough Meeting 2019; Abstract 44. 2. Oxnard GR, et al. ESMO Annual Meeting 2019; Abstract 5639.

Citation Format: David D. Thiel, Xiaoji Chen, Kathryn N. Kurtzman, Jessica Yecies, Tony Wu, Quan Zhang, Hai Liu, Nan Zhang, Eric T. Fung, Michael V. Seiden, Minetta C. Liu, Geoffrey R. Oxnard, Earl Hubbell, Alexander M. Aravanis, Anne-Renee Hartman, Eric A. Klein. Prediction of cancer and tissue of origin in individuals with suspicion of cancer using a cell-free DNA multi-cancer early detection test [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr CT021.

Alignment-free filtering for cfNA fusion fragments

Motivation

Cell-free nucleic acid (cfNA) sequencing data require improvements to existing fusion detection methods along multiple axes: high depth of sequencing, low allele fractions, short fragment lengths and specialized barcodes, such as unique molecular identifiers.

Results

AF4 was developed to address these challenges. It uses a novel alignment-free kmer-based method to detect candidate fusion fragments with high sensitivity and orders of magnitude faster than existing tools. Candidate fragments are then filtered using a max-cover criterion that significantly reduces spurious matches while retaining authentic fusion fragments. This efficient first stage reduces the data sufficiently that commonly used criteria can process the remaining information, or sophisticated filtering policies that may not scale to the raw reads can be used. AF4 provides both targeted and de novo fusion detection modes. We demonstrate both modes in benchmark simulated and real RNA-seq data as well as clinical and cell-line cfNA data.

Availability and implementation

AF4 is open sourced, licensed under Apache License 2.0, and is available at: https://github.com/grailbio/bio/tree/master/fusion.

Abstract IA02: Multicancer detection of early-stage cancers with simultaneous tissue localization using a plasma cfDNA-based targeted methylation assay

Background: Earlier cancer detection could identify tumors when they are more treatable and thus may improve clinical outcomes. A blood-based multicancer early detection test using circulating tumor cell-free DNA (cfDNA) offers the potential to address this unmet need. Our previous discovery work identified whole-genome bisulfite sequencing as outperforming whole-genome and targeted sequencing approaches for multicancer detection across stages. We thus developed a targeted methylation (TM) assay for multicancer detection and tissue-of-origin (TOO) localization.

Methods: Participants were from the Circulating Cell-free Genome Atlas (CCGA; NCT02889978) and STRIVE (NCT03085888) studies, both prospective, multicenter, observational studies with longitudinal follow-up. cfDNA samples spanning &gt;20 cancer types of all stages were divided into a cross-validated training set and an independent validation set; there were 3,583 evaluable samples in training (1,530 cancer; 2,053 noncancer) and 1,803 in validation (678 cancer; 1,125 noncancer). Performance in a prespecified set of 12 high-signal cancers was also reported (anus, bladder, colon/rectum, esophagus, head and neck, liver/bile-duct, lung, lymphoma, ovary, pancreas, plasma cell neoplasm, stomach). This multicancer early detection test evaluated cfDNA for the presence of cancer and localization of TOO; the TM classifier was trained to target &gt;99% specificity. Precision was defined as the fraction of correct calls.

Results: Participants with and without cancer were similar in age. Specificity across all cancer types in the training and validation sets was 99.8% (95% CI, 99.2-99.9%) and 99.3% (98.3-99.8%), respectively, which reflects a consistent (P=0.292) false positive rate of &lt;1%. Across all cancer types, aggregate sensitivity was also consistent between the training and validation sets (55% [53-58%] and 55% [51-59], respectively; P=0.897). In the validation set, aggregate sensitivity in the prespecified group was 76% (73-78%); detection was 39% (27–52%) in 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 stage IV (n=130). Overall, the classifier assigned a TOO across &gt;20 cancer types in 96% of samples (344/359); of these, the TOO was correct in 93% (321/344) of cases, which was consistent with training set analyses.

Conclusions: This multicancer early detection test detected cancer signal across &gt;20 cancer types with a single, fixed, low false positive rate and highly accurate TOO localization. Importantly, results in the independent validation set were consistent with the training set, demonstrating the robustness of machine learning classifier training, and confirms that data were not overfitted. These data support the feasibility of a single blood-based test that can detect multiple cancers, supporting further clinical development in preparation for the return of results.

Citation Format: Anne-Renee Hartman, Geoffrey Oxnard, Eric Klein, Michael Seiden, Earl Hubbell, Oliver Venn, Arash Jamshidi, Nan Zhang, John Beausang, Samuel Gross, Kathryn Kurtzman, Eric Fung, Brian Allen, Alexander Fields, Hai Liu, Mikkael Sekeres, Donald Richards, Peter Yu, Alexander Aravanis, Minetta Liu. Multicancer detection of early-stage cancers with simultaneous tissue localization using a plasma cfDNA-based targeted methylation assay [abstract]. In: Proceedings of the AACR Special Conference on Advances in Liquid Biopsies; Jan 13-16, 2020; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(11_Suppl):Abstract nr IA02.

Tumor methylation patterns to measure tumor fraction in cell-free DNA

3052

Background: Cell-free DNA (cfDNA) tumor fraction (TF), the proportion of tumor molecules in a cfDNA sample, is a direct measurement of signal for cfDNA cancer applications. The Circulating Cell-free Genome Atlas study (CCGA; NCT02889978) is a prospective, multi-center, observational, case-control study designed to support development of a methylation-based, multi-cancer detection test in which a classifier is trained to distinguish cancer from non-cancer. Here we leveraged CCGA data to examine the relationship between cfDNA containing tumor DNA methylation patterns, TF, and cancer classification performance. Methods: The CCGA classifier was trained on whole-genome bisulfite sequencing (WGBS) and targeted methylation (TM) sequencing data to detect cancer versus non-cancer. 822 samples had biopsy WGBS performed; of those, 231 also had cfDNA targeted methylation (TM) and cfDNA whole-genome sequencing (WGS). Biopsy WGBS identified somatic single nucleotide variants (SNV) and methylation variants (MV; defined as methylation patterns in sequenced DNA fragments observed commonly in biopsy but rarely [ < 1/10,000] in the cfDNA of non-cancer controls [n = 898]). Observed tumor fragment counts (SNV in WGS; MV in TM), were modeled as a Poisson process with rate dependent on TF. TF and classifier limits of detection (LOD) were each assessed using Bayesian logistic regression. Results: Across biopsy samples, a median of 2,635 MV was distributed across the genome, with a median of 86.8% shared with ≥1 participant, and a median of 69.3% targeted by the TM assay. TF LOD from MV was 0.00050 (95% credible interval [CI]: 0.00041 - 0.00061); MV and SNV estimates were concordant (Spearman’s Rho: 0.820). MV TF estimates explained classifier performance (Spearman’s Rho: 0.856) and allowed determination of the classifier LOD (0.00082 [95% CI: 0.00057 - 0.00115]). Conclusions: These data demonstrate the existence of methylation patterns in tumor-derived cfDNA fragments that are rarely found in individuals without cancer; their abundance directly measured TF, and was a major factor influencing classification performance. Finally, the low classifier LOD (~0.1%) motivates further clinical development of a methylation-based assay for cancer detection. Clinical trial information: NCT02889978 .

Projected Reductions in Absolute Cancer-Related Deaths from Diagnosing Cancers Before Metastasis, 2006-2015

BACKGROUND

New technologies are being developed for early detection of multiple types of cancer simultaneously. To quantify the potential benefit, we estimated reductions in absolute cancer-related deaths that could occur if cancers diagnosed after metastasis (stage IV) were instead diagnosed at earlier stages.

METHODS

We obtained stage-specific incidence and survival data from the Surveillance, Epidemiology, and End Results Program for 17 cancer types for all persons diagnosed ages 50 to 79 years in 18 geographic regions between 2006 and 2015. For a hypothetical cohort of 100,000 persons, we estimated cancer-related deaths under assumptions that cancers diagnosed at stage IV were diagnosed at earlier stages.

RESULTS

Stage IV cancers represented 18% of all estimated diagnoses but 48% of all estimated cancer-related deaths within 5 years. Assuming all stage IV cancers were diagnosed at stage III, 51 fewer cancer-related deaths would be expected per 100,000, a reduction of 15% of all cancer-related deaths. Assuming one third of metastatic cancers were diagnosed at stage III, one third diagnosed at stage II, and one third diagnosed at stage I, 81 fewer cancer-related deaths would be expected per 100,000, a reduction of 24% of all cancer-related deaths, corresponding to a reduction in all-cause mortality comparable in magnitude to eliminating deaths due to cerebrovascular disease.

CONCLUSIONS

Detection of multiple cancer types earlier than stage IV could reduce at least 15% of cancer-related deaths within 5 years, affecting not only cancer-specific but all-cause mortality.

IMPACT

Detecting cancer before stage IV, including modest shifts to stage III, could offer substantial population benefit.

Tumor area and microscopic extent of invasion to determine circulating tumor DNA fraction in plasma and detectability of colorectal cancer (CRC)

243

Background: Circulating Cell-free Genome Atlas (CCGA; NCT02889978) is a multi-center, case-control, observational study with longitudinal follow-up to develop a cfDNA assay in which classifiers were trained on whole-genome bisulfite sequencing (WGBS) and targeted methylation (TM) sequencing data for detection of multiple cancer types. Previously, we showed that the fraction of ctDNA fragments (TF) was a stronger predictor of cancer detection than clinical stage and an equivalent predictor for survival. Given that CRC tumors can be described via surface area (TSA) and microscopic tumor extent (microinvasion), CRC was used as a model to examine the biophysical determinants of TF. Methods: Detection of multiple cancers with WGBS at 98% and TM at > 99% specificity, and methods for determining TF, were previously reported. A model to predict the presence of detectable cfDNA fragments for CRC adenocarcinomas of stages I, II, and III included TSA and microinvasion beyond the subserosa. Predictors were combined assuming a linear increase of cfDNA shedding with tumor size, with scaling factors depending on microinvasion. Model parameters were determined for 27 participants (7, 11, 9 for stages I, II, III, resp.) with WGBS and applied to 40 participants (12, 15, 13 for I, II, III, resp.) with TM assay and information on tumor size and microinvasion. Results: CRC detection at stages I/II/III was 33/46, 61/73, 57/74% for WGBS/TM. TF predicted detection with AUC = 97.6. The model predicted TF as TSA multiplied by 3.81*10−6 / mm2 for tumors that invaded beyond the subserosa (p < 0.001). This was 4.4x higher than estimates for tumors below the subserosa. The model trained on the WGBS assay predicted CRC detection in the TM assay with an AUC of 0.844. Conclusions: This model used TSA (number of tumor cells) and microinvasion (bloodstream access) to predict the fraction of CRC ctDNA fragments in blood without needing to account for stage. Tumors not penetrating the subserosa had low ctDNA shedding that likely limited detection. These findings may generalize to other cancer types, providing principles to predict ctDNA shedding and thus cancer detectability based on microinvasion and surface area. Clinical trial information: NCT02889978.

Simultaneous multi-cancer detection and tissue of origin (TOO) localization using targeted bisulfite sequencing of plasma cell-free DNA (cfDNA)

44

Background: A noninvasive cfDNA blood test detecting multiple cancers at earlier stages could decrease cancer mortality. In earlier discovery work, whole-genome bisulfite sequencing outperformed whole-genome and targeted sequencing approaches for multi-cancer detection across stages at high specificity. Here, multi-cancer detection and TOO localization using bisulfite sequencing of plasma cfDNA to identify methylomic signatures was evaluated in preparation for clinical validation, utility, and implementation studies. Methods: 2301 analyzable participants (1422 cancer [ > 20 tumor types, all stages], 879 non-cancer) were included in this prespecified substudy from the Circulating Cell-free Genome Atlas (CCGA) (NCT02889978) study - a prospective, multi-center, observational, case-control study with longitudinal follow-up. Plasma cfDNA was subjected to a targeted methylation sequencing assay using high-efficiency methylation chemistry to enrich for methylation targets, and a machine learning classifier determined cancer status and tissue of origin (TOO). Observed methylation fragments characteristic of cancer and TOO were combined across targeted regions and assigned a relative probability of cancer and of a specific TOO. Results: Performance is reported at 99% specificity (ie, a combined false positive rate across all cancer types of 1%), a level required for population-level screening. Across cancer types, sensitivity ranged from 59 to 86%. Combined cancer detection (sensitivity [95% CI]) was 34% (27-43%) in stage I (n = 151), 77% (70-83%) in stage II (n = 171), 84% (79-89%) in stage III (n = 204), and 92% (88-95%) in stage IV (n = 281). TOO was provided for 94% of all cancers detected; of these, TOO was correct in > 90% of cases. Conclusions: Detection of multiple deadly cancers across stages using methylation signatures in plasma cfDNA was achieved with a single, fixed, low false positive rate, and simultaneously provided accurate TOO localization. This targeted methylation assay is undergoing validation in preparation for prospective clinical investigation as a cancer detection diagnostic. Clinical trial information: NCT02889978.

Abstract 3372: Cell-free DNA (cfDNA) fragment length patterns of tumor- and blood-derived variants in participants with and without cancer

Previous studies on transplanted tissue or single cancers indicated that cfDNA variant fragment lengths reflect their respective source. The Circulating Cell-free Genome Atlas (NCT02889978) study provides an opportunity to examine cfDNA variant fragment lengths across tumor types and describe the nature of cfDNA variants derived from different sources.

Blood samples (N=1406) were evaluated from participants with (n=845) and without (n=561) cancer; cancer samples included 339 breast, 118 lung, 69 prostate, 45 colorectal, 27 uterine, 26 pancreas, 26 renal, 24 esophageal, 22 lymphoma, 19 head/neck, and 17 ovarian (113 remaining samples represented cancers with ≤15 samples each). cfDNA and genomic DNA from white blood cells (WBC) were subjected to a high-intensity targeted panel (507 genes, 60000X) with error-corrected sequencing; 533 samples also had matched tumor biopsy tissue subjected to whole-genome sequencing (30X).

Somatic single-nucleotide variants (SNVs; that passed noise filters) were identified and classified using the sequencing results into one of four categories: tumor biopsy-matched (TBM; present in cfDNA and biopsy), WBC-matched (WM; present in cfDNA and WBC), non-matched (NM; low probability [P&lt;0.01] of being WBC-derived), or ambiguous (AMB; unidentifiable source). Fragment lengths of reference and SNV alleles were recorded. A statistical model based on fragment lengths was built to predict the likelihood that an SNV belonged to a WBC-like source without using the WBC sequencing results.

A total of 21604 SNVs were identified. The proportion of SNVs from each category were: 4% TBM, 68% WM, 19% NM, and 8% AMB. The number of samples (non-mutually exclusive) that had each SNV category were 152 TBM, 1338 WM, 499 NM, and 761 AMB.

Across categories, the median (SD) length of fragments containing the reference allele was 167 (16.3). Median (SD) fragment lengths of TBM, WM, NM, and AMB were 156 (22.2), 169 (14.8), 158 (20.8), and 165 (17.8), respectively. AMB and WM median SNV fragment lengths were similar to that of the reference allele, suggesting that fragment length shifts are minimal in SNVs derived from clonal hematopoiesis (CH). Fragment lengths of TBM and NM SNVs were similar; further, most NM SNVs came from cfDNA samples in the cancer cohort, suggesting that NM SNVs may be tumor-derived. As expected in a population with a median (SD) age of 61 (12.2), most SNVs occurred in the WM category.

The prediction model distinguished TBM from WM SNVs with an AUC of 0.87. However, at a specificity of 98% (to match filtering based on WBC sequencing), false-negative rates were 35% (TBM) and 52% (NM).

Together, these data suggest that source prediction based on fragment length alone is less robust than source assignment using individual-matched WBC sequencing, highlighting the importance of accounting for CH-derived SNVs when using targeted cfDNA-based approaches for cancer detection.

Citation Format: Earl Hubbell, Tara Maddala, Oliver Venn, Eric Scott, Susan Tang, Archana Shenoy, Alex Aravanis. Cell-free DNA (cfDNA) fragment length patterns of tumor- and blood-derived variants in participants with and without cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3372.

Prognostic significance of blood-based cancer detection in plasma cell-free DNA (cfDNA): Evaluating risk of overdiagnosis

1545

Background: Screening tests for early cancer detection are often criticized due to risk of overdiagnosis—detection of good prognosis cancers which may not require immediate treatment. We recently reported development of cfDNA sequencing approaches for cancer detection; longitudinal follow-up (F/U) data were utilized here to evaluate prognostic significance of cancer detection using cfDNA. Methods: Plasma cfDNA samples were subjected to whole-genome bisulfite sequencing (WGBS, 30X) as part of a previously-reported Circulating Cell-free Genome Atlas (CCGA; NCT02889978) substudy. This exploratory analysis evaluated the overall survival (OS) of training and test set participants (pts) with cancer (20 cancer types, any stage I-IV). Combining train and test set pts, univariate and multivariate analyses (Cox proportional hazards) assessed OS association with WGBS result (cancer detected vs not detected, set at 98% specificity), clinical stage (IV vs I-III), diagnostic method (symptom- vs screen-detected), sex, age, and histologic grade. Results: Of 827 pts from the training set with F/U (median 12.2 mo), 334 (40.4%) had WGBS-detected cancer. Among 127 (15.4%) pts with cancer that died during F/U, cancer was detected in 104 (81.9%). Results were similar in the test set. In univariate analyses all variables were associated with prognosis, including WGBS result (HR 7.7 p<0.001). In multivariate analyses accounting for other covariates, the three variables that most significantly remained prognostic were WGBS (HR 3.0, p<0.001), clinical stage (HR 3.3, p<0.001), and diagnostic method (HR 3.0, p<0.001). Validation of these findings is ongoing in an independent cohort of ~5,000 cancer pts from CCGA using an optimized assay; updated performance results will be reported. Conclusions: Cancers detected using WGBS of cfDNA had a worse prognosis than cancers not detected. WGBS cancer detection carried comparable prognostic significance as clinical stage. By preferentially detecting higher risk cancers, cancer detection using plasma cfDNA may avoid some of the overdiagnosis that has been seen with some existing cancer screening methods. Clinical trial information: NCT02889978.

The Circulating Cell-free Genome Atlas (CCGA) Study: Follow-up (F/U) on non-cancer participants with cancer-like cell-free DNA signals

5574

Background: A noninvasive cell-free DNA (cfDNA)-based cancer detection assay offers the hope of a blood test that might reduce morbidity and mortality of cancers, particularly those without recommended screening tests (eg, some gynecologic cancers). CCGA (NCT02889978) is a prospective, multi-center, longitudinal, case-control study evaluating models for discriminating cancer versus non-cancer. Here, we report F/U of control participants (pts) who demonstrated a cancer-signal in CCGA. Methods: Clinically evaluable samples (N = 2508) from pts enrolled without a cancer diagnosis (dx; NC) and treatment-naive pts with newly diagnosed cancer (C) were divided into training (n = 1564; 580 NC, 984 C) and test (n = 944; 368 NC, 576 C) sets. Classification performance (cancer/non-cancer) was assessed via 3 prototype assays: whole-genome bisulfite (WGBS), whole-genome (WGS), and targeted (507 gene) sequencing. Notable outlier NC pts were identified with cancer-like scores in either ≥2 assay classification results or by the presence of known cancer drivers with ≥1 assay classification result suggesting cancer. All pts are currently in F/U in accordance with study protocol (to date: 80% with > 10 mo and 15% with > 22 mo F/U). Results: Among training and test sets, 8 ( < 1%) NC pts were identified with a cancer-like signal. To-date, 2 have been diagnosed with a gynecologic malignancy: 1 stage IIIc clear cell endometrial carcinoma and 1 stage IIIc ovarian cancer, 3 and 2 months (mo) post-enrollment [PE], respectively. Among C pts in the study, sensitivity (at 98% specificity; WGBS) in these cancer types was: uterine/endometrial: 11% (n = 27 train) and 22% (n = 9 test); ovarian: 82% (n = 17) and 71% (n = 7). In addition, a third NC pt was diagnosed with a stage IV lung cancer 15 mo PE. Conclusions: This cfDNA-based assay detected a cancer-like signal that anticipated a clinical presentation of cancer in undiagnosed pts as early as 15 months prior to the actual dx. High specificity ( > 99%) requires accounting for undiagnosed cancers in study design and analysis. Together, these data suggest that this prototype assay may have high performance detecting a variety of gynecological and other cancers. Clinical trial information: NCT02889978.

Abstract LB-343: Development of plasma cell-free DNA (cfDNA) assays for early cancer detection: first insights from the Circulating Cell-Free Genome Atlas Study (CCGA)

CCGA [NCT02889978] is the largest study of cfDNA-based early cancer detection; the first CCGA learnings from multiple cfDNA assays are reported here. This prospective, multi-center, observational study has enrolled 10,012 of 15,000 demographically-balanced participants at 141 sites. Blood was collected from participants with newly diagnosed therapy-naive cancer (C, case) and participants without a diagnosis of cancer (noncancer [NC], control) as defined at enrollment. This preplanned substudy included 878 cases, 580 controls, and 169 assay controls (n=1627) across 20 tumor types and all clinical stages. All samples were analyzed by: 1) Paired cfDNA and white blood cell (WBC)-targeted sequencing (60,000X, 507 gene panel); a joint caller removed WBC-derived somatic variants and residual technical noise; 2) Paired cfDNA and WBC whole-genome sequencing (WGS; 35X); a novel machine learning algorithm generated cancer-related signal scores; joint analysis identified shared events; and 3) cfDNA whole-genome bisulfite sequencing (WGBS; 34X); normalized scores were generated using abnormally methylated fragments. In the targeted assay, non-tumor WBC-matched cfDNA somatic variants (SNVs/indels) accounted for 76% of all variants in NC and 65% in C. Consistent with somatic mosaicism (i.e., clonal hematopoiesis), WBC-matched variants increased with age; several were non-canonical loss-of-function mutations not previously reported. After WBC variant removal, canonical driver somatic variants were highly specific to C (e.g., in EGFR and PIK3CA, 0 NC had variants vs 11 and 30, respectively, of C). Similarly, of 8 NC with somatic copy number alterations (SCNAs) detected with WGS, 4 were derived from WBCs. WGBS data revealed informative hyper- and hypo-fragment level CpGs (1:2 ratio); a subset was used to calculate methylation scores. A consistent “cancer-like” signal was observed in &lt;1% of NC participants across all assays (representing potential undiagnosed cancers). An increasing trend was observed in NC vs stages I-III vs stage IV (nonsyn. SNVs/indels per Mb [Mean±SD] NC: 1.01±0.86, stages I-III: 2.43±3.98; stage IV: 6.45±6.79; WGS score NC: 0.00±0.08, I-III: 0.27±0.98; IV: 1.95± 2.33; methylation score NC: 0±0.50; I-III: 1.02±1.77; IV: 3.94±1.70). These data demonstrate the feasibility of achieving &gt;99% specificity for invasive cancer, and support the promise of cfDNA assay for early cancer detection. Additional data will be presented on detected plasma:tissue variant concordance and on multi-assay modeling.

Citation Format: Alexander A. Aravanis, Geoffrey R. Oxnard, Tara Maddala, Earl Hubbell, Oliver Venn, Arash Jamshidi, Ling Shen, Hamed Amini, John A. Beausang, Craig Betts, Daniel Civello, Konstantin Davydov, Saniya Fazullina, Darya Filippova, Sante Gnerre, Samuel Gross, Chenlu Hou, Roger Jiang, Byoungsok Jung, Kathryn Kurtzman, Collin Melton, Shivani Nautiyal, Jonathan Newman, Joshua Newman, Cosmos Nicolaou, Richard Rava, Onur Sakarya, Ravi Vijaya Satya, Seyedmehdi Shojaee, Kristan Steffen, Anton Valouev, Hui Xu, Jeanne Yue, Nan Zhang, Jose Baselga, Rosanna Lapham, Daron G. Davis, David Smith, Donald Richards, Michael V. Seiden, Charles Swanton, Timothy J. Yeatman, Robert Tibshirani, Christina Curtis, Sylvia K. Plevritis, Richard Williams, Eric Klein, Anne-Renee Hartman, Minetta C. Liu. Development of plasma cell-free DNA (cfDNA) assays for early cancer detection: first insights from the Circulating Cell-Free Genome Atlas Study (CCGA) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr LB-343.

Genome-wide sequencing for early stage lung cancer detection from plasma cell-free DNA (cfDNA): The Circulating Cancer Genome Atlas (CCGA) study

LBA8501

Background: Plasma cfDNA genomic analysis is used widely for the care of advanced lung cancer, but its suitability for early stage lung cancer detection is not well established. CCGA (NCT02889978) is a prospective, multi-center, observational study launched for the development of a noninvasive assay for cancer detection. Methods: Blood was prospectively collected (N = 1627) from 749 controls (no cancer diagnosis) and 878 participants (pts) with newly-diagnosed untreated cancer in this preplanned substudy, including 127 pts with lung cancer. Three prototype sequencing assays were performed: paired cfDNA and white blood cell (WBC) targeted sequencing (507 genes, 60,000X) for single nucleotide variants/indels; paired cfDNA and WBC whole genome sequencing (WGS) for copy number variation (30X); and cfDNA whole genome bisulfite sequencing (WGBS) for methylation (30X). For each assay, a classification model using 10-fold cross-validation was developed for all pts with cancer, then evaluated in the pts with lung cancer; sensitivity was estimated at 95% specificity. Results: We evaluated pts with lung cancer (127) and a subset of controls (580) with similar ages (mean±SD yrs: 67±9, 60±13), 85% and 43% were ever-smokers, and 46% and 22% were men, respectively. Of 3055 nonsynonymous mutations detected across 122 evaluable pts with lung cancer, > 50% were detected in WBC consistent with clonal hematopoiesis (CH). Accounting for CH, sensitivity in 63 stage I-IIIA pts evaluable across all 3 assays was 48% (35-61, targeted), 54% (41-67, WGS), and 56% (43-68, WGBS); in 54 stage IIIB-IV pts it was 85% (73-93, targeted), 91% (80-97, WGS), and 93% (82-98, WGBS) . Similar sensitivities were observed across histological subtypes (adenocarcinoma, squamous cell, small cell). Comparison to tumor WGS and multi-assay classification will be reported. Conclusions: Early stage lung cancers are detectable in cfDNA using a genome-wide sequencing approach. For lung cancer detection using targeted assays, CH must be accounted for to minimize false positives. Assay optimization is ongoing to allow further clinical development in the intended use population. Clinical trial information: NCT02889978.

Cell-free DNA (cfDNA) mutations from clonal hematopoiesis: Implications for interpretation of liquid biopsy tests

11526

Background: A large fraction of cfDNA fragments are derived from hematopoietic sources. Somatic alterations in cfDNA can be tumor-derived but also could represent somatic changes associated with clonal hematopoiesis. We performed deep sequencing of both plasma cfDNA and matched white blood cell (WBC) genomic DNA (gDNA) to determine the contribution of clonal hematopoiesis to the variants observed in cfDNA. Four cohorts were investigated: metastatic breast (BC), non-small cell lung (NSCLC), castration-resistant prostate cancer (CRPC), and non-cancer participants (pts). Methods: Metastatic cancer pts with de novo or progressive disease were prospectively enrolled. Non-cancer pts were blood bank donors. Plasma cfDNA and matched WBC gDNA were sequenced using a targeted 508-gene panel (2 Mb) to > 60,000X raw depth. Variant calling used a novel pipeline that employed molecular barcoding for error suppression followed by de novo assembly and graph-based variant calling. Results: Of 151 metastatic cancer pts (48 BC, 49 NSCLC, 54 CRPC), median age was 64 (30-87) with 53% female and 33% treatment naive. Of 47 non-cancer pts, median age was 61 (20-78) with 51% female. Analysis of cfDNA identified 1072 variants (AF > 0.1%, > 2 mutant reads, passing bioinformatic quality filters) which were also detected in WBC gDNA as non-germline ( < 35% allele frequency [AF]) non-synonymous variants. For these cfDNA variants, AF ranged from 0.1-14.4% and correlated with AF in WBC gDNA (r2 = 0.47, p < 0.001). Mutated genes were consistent with clonal hematopoiesis, with the most frequently mutated genes being DNMT3A, TET2, PPM1D, and TP53 (215, 77, 45, and 36 variants, respectively). For both cancer and non-cancer pts (age > 45), median number of overlapping variants was 5 per pt (range 0-22). The number of WBC gDNA and cfDNA variants per individual was positively associated with age (p < 0.001) in both cancer and non-cancer pts (interaction p = 0.08). Conclusions: Somatic cfDNA variants are frequently derived from clonal hematopoiesis and increase with age. Accurate assessment of somatic alterations in cfDNA should account for this phenomenon to distinguish between tumor-derived and WBC-derived variants.

Next generation genome-wide association tool: design and coverage of a high-throughput European-optimized SNP array

The success of genome-wide association studies has paralleled the development of efficient genotyping technologies. We describe the development of a next-generation microarray based on the new highly-efficient Affymetrix Axiom genotyping technology that we are using to genotype individuals of European ancestry from the Kaiser Permanente Research Program on Genes, Environment and Health (RPGEH). The array contains 674,517 SNPs, and provides excellent genome-wide as well as gene-based and candidate-SNP coverage. Coverage was calculated using an approach based on imputation and cross validation. Preliminary results for the first 80,301 saliva-derived DNA samples from the RPGEH demonstrate very high quality genotypes, with sample success rates above 94% and over 98% of successful samples having SNP call rates exceeding 98%. At steady state, we have produced 462 million genotypes per week for each Axiom system. The new array provides a valuable addition to the repertoire of tools for large scale genome-wide association studies.

Resolving deconvolution ambiguity in gene alternative splicing

Background

For many gene structures it is impossible to resolve intensity data uniquely to establish abundances of splice variants. This was empirically noted by Wang et al. in which it was called a "degeneracy problem". The ambiguity results from an ill-posed problem where additional information is needed in order to obtain an unique answer in splice variant deconvolution.

Results

In this paper, we analyze the situations under which the problem occurs and perform a rigorous mathematical study which gives necessary and sufficient conditions on how many and what type of constraints are needed to resolve all ambiguity. This analysis is generally applicable to matrix models of splice variants. We explore the proposal that probe sequence information may provide sufficient additional constraints to resolve real-world instances. However, probe behavior cannot be predicted with sufficient accuracy by any existing probe sequence model, and so we present a Bayesian framework for estimating variant abundances by incorporating the prediction uncertainty from the micro-model of probe responsiveness into the macro-model of probe intensities.

Conclusion

The matrix analysis of constraints provides a tool for detecting real-world instances in which additional constraints may be necessary to resolve splice variants. While purely mathematical constraints can be stated without error, real-world constraints may themselves be poorly resolved. Our Bayesian framework provides a generic solution to the problem of uniquely estimating transcript abundances given additional constraints that themselves may be uncertain, such as regression fit to probe sequence models. We demonstrate the efficacy of it by extensive simulations as well as various biological data.

Integrated genotype calling and association analysis of SNPs, common copy number polymorphisms and rare CNVs

Accurate and complete measurement of single nucleotide (SNP) and copy number (CNV) variants, both common and rare, will be required to understand the role of genetic variation in disease. We present Birdsuite, a four-stage analytical framework instantiated in software for deriving integrated and mutually consistent copy number and SNP genotypes. The method sequentially assigns copy number across regions of common copy number polymorphisms (CNPs), calls genotypes of SNPs, identifies rare CNVs via a hidden Markov model (HMM), and generates an integrated sequence and copy number genotype at every locus (for example, including genotypes such as A-null, AAB and BBB in addition to AA, AB and BB calls). Such genotypes more accurately depict the underlying sequence of each individual, reducing the rate of apparent mendelian inconsistencies. The Birdsuite software is applied here to data from the Affymetrix SNP 6.0 array. Additionally, we describe a method, implemented in PLINK, to utilize these combined SNP and CNV genotypes for association testing with a phenotype.

Integrated detection and population-genetic analysis of SNPs and copy number variation

Dissecting the genetic basis of disease risk requires measuring all forms of genetic variation, including SNPs and copy number variants (CNVs), and is enabled by accurate maps of their locations, frequencies and population-genetic properties. We designed a hybrid genotyping array (Affymetrix SNP 6.0) to simultaneously measure 906,600 SNPs and copy number at 1.8 million genomic locations. By characterizing 270 HapMap samples, we developed a map of human CNV (at 2-kb breakpoint resolution) informed by integer genotypes for 1,320 copy number polymorphisms (CNPs) that segregate at an allele frequency >1%. More than 80% of the sequence in previously reported CNV regions fell outside our estimated CNV boundaries, indicating that large (>100 kb) CNVs affect much less of the genome than initially reported. Approximately 80% of observed copy number differences between pairs of individuals were due to common CNPs with an allele frequency >5%, and more than 99% derived from inheritance rather than new mutation. Most common, diallelic CNPs were in strong linkage disequilibrium with SNPs, and most low-frequency CNVs segregated on specific SNP haplotypes.

DNA copy number analysis in gastrointestinal stromal tumors using gene expression microarrays

We report a method, Expression-Microarray Copy Number Analysis (ECNA) for the detection of copy number changes using Affymetrix Human Genome U133 Plus 2.0 arrays, starting with as little as 5 ng input genomic DNA. An analytical approach was developed using DNA isolated from cell lines containing various X-chromosome numbers, and validated with DNA from cell lines with defined deletions and amplifications in other chromosomal locations. We applied this method to examine the copy number changes in DNA from 5 frozen gastrointestinal stromal tumors (GIST). We detected known copy number aberrations consistent with previously published results using conventional or BAC-array CGH, as well as novel changes in GIST tumors. These changes were concordant with results from Affymetrix 100K human SNP mapping arrays. Gene expression data for these GIST samples had previously been generated on U133A arrays, allowing us to explore correlations between chromosomal copy number and RNA expression levels. One of the novel aberrations identified in the GIST samples, a previously unreported gain on 1q21.1 containing the PEX11B gene, was confirmed in this study by FISH and was also shown to have significant differences in expression pattern when compared to a control sample. In summary, we have demonstrated the use of gene expression microarrays for the detection of genomic copy number aberrations in tumor samples. This method may be used to study copy number changes in other species for which RNA expression arrays are available, e.g. other mammals, plants, etc., and for which SNPs have not yet been mapped.

Genotyping over 100,000 SNPs on a pair of oligonucleotide arrays

We present a genotyping method for simultaneously scoring 116,204 SNPs using oligonucleotide arrays. At call rates >99%, reproducibility is >99.97% and accuracy, as measured by inheritance in trios and concordance with the HapMap Project, is >99.7%. Average intermarker distance is 23.6 kb, and 92% of the genome is within 100 kb of a SNP marker. Average heterozygosity is 0.30, with 105,511 SNPs having minor allele frequencies >5%.

Dynamic model based algorithms for screening and genotyping over 100K SNPs on oligonucleotide microarrays

MOTIVATION

A high density of single nucleotide polymorphism (SNP) coverage on the genome is desirable and often an essential requirement for population genetics studies. Region-specific or chromosome-specific linkage studies also benefit from the availability of as many high quality SNPs as possible. The availability of millions of SNPs from both Perlegen and the public domain and the development of an efficient microarray-based assay for genotyping SNPs has brought up some interesting analytical challenges. Effective methods for the selection of optimal subsets of SNPs spanning the genome and methods for accurately calling genotypes from probe hybridization patterns have enabled the development of a new microarray-based system for robustly genotyping over 100,000 SNPs per sample.

RESULTS

We introduce a new dynamic model-based algorithm (DM) for screening over 3 million SNPs and genotyping over 100,000 SNPs. The model is based on four possible underlying states: Null, A, AB and B for each probe quartet. We calculate a probe-level log likelihood for each model and then select between the four competing models with an SNP-level statistical aggregation across multiple probe quartets to provide a high-quality genotype call along with a quality measure of the call. We assess performance with HapMap reference genotypes, informative Mendelian inheritance relationship in families, and consistency between DM and another genotype classification method. At a call rate of 95.91% the concordance with reference genotypes from the HapMap Project is 99.81% based on over 1.5 million genotypes, the Mendelian error rate is 0.018% based on 10 trios, and the consistency between DM and MPAM is 99.90% at a comparable rate of 97.18%. We also develop methods for SNP selection and optimal probe selection.

AVAILABILITY

The DM algorithm is available in Affymetrix's Genotyping Tools software package and in Affymetrix's GDAS software package. See http://www.affymetrix.com for further information. 10 K and 100 K mapping array data are available on the Affymetrix website.