Search

Purpose: This study examines the impact of detecting cancer (interception) versus adenomas and cancer (prevention + interception) on clinical outcomes for a hypothetical colorectal cancer (CRC) screening test or multicancer early detection (MCED) test that includes CRC. Methods: CRC-MAPS™, a validated microsimulation model of the adenoma-carcinoma pathway that reproduced incidence reduction (IR) and mortality reduction (MR) consistent with CISNET models and a randomized controlled trial, was used to simulate perfect adherence to a hypothetical annual screening test among previously unscreened individuals free of diagnosed CRC. Four scenarios were examined: two cancer interception and two cancer prevention + interception. Individuals were screened from age 45 to 75. CRC IR and MR outcomes compared to no screening were aggregated from age 40 until death. Threshold analysis identified the ≥10mm adenoma sensitivity needed for a base case cancer interception test to yield CRC MR equivalent to a near-perfect cancer interception test (#2). Results: The base case scenario (#1) resulted in 15.0% CRC IR and 34.2% MR compared to 14.7% CRC IR and 38.3% MR for the near-perfect interception scenario (#2). In the threshold analysis, a modified base case cancer interception test (#5) yielded MR equivalent to a near-perfect interception test (#2) when ≥10mm adenoma sensitivity was increased from 1% to 1.94%. Due to adenoma detection, the cancer prevention + interception scenarios (#3, #4) resulted in outcomes more than twice as favorable as either cancer interception scenario. Conclusions: This analysis highlights that even small improvements in the detection of precancerous lesions for certain cancers (e.g., adenomas for CRC), which enable cancer prevention, can yield clinical benefits that meaningfully exceed those from tests that primarily detect cancer. Future studies will apply this approach to other cancers, such as ovarian and breast, to better understand the clinical utility of MCED tests. Table. Clinical outcomes for cancer interception and cancer prevention + interception scenarios Scenario Specificity Adenoma Sensitivity CRC Sensitivity CRC IR CRC MR 1. Cancer Interception (base case) 99% 1-5mm: 1% 6-9mm: 1% ≥10mm: 1% 60% 15.0% 34.2% 2. Cancer Interception (near-perfect) 99% 1-5mm: 1% 6-9mm: 1% ≥10mm: 1% 99% 14.7% 38.3% 3. Cancer Prevention (with FIT-like adenoma sensitivity) + Interception 99% 1-5mm: 5% 6-9mm: 10% ≥10mm: 20% 60% 73.5% 79.1% 4. Cancer Prevention (with improved FIT-like adenoma sensitivity) + Interception 99% 1-5mm:10% 6-9mm: 20% ≥10mm: 30% 60% 82.8% 86.2% 5. Threshold analysis 99% 1-5mm: 1% 6-9mm: 1% ≥10mm: 1.94% 60% 20.3% 38.3% Citation Format: Girish Putcha, Lauren N. Carroll, Signe Fransen, Tarun Chandra, Andrew Piscitello. Interception versus prevention in cancer screening: Results from the CRC-MAPS model [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 2240.

10546 Background: This study examines the impact of detecting cancer (interception) versus adenomas and cancer (prevention + interception) on clinical outcomes in a screen-naive Medicare cohort for a hypothetical colorectal cancer (CRC) screening test or a multicancer early detection (MCED) test that includes CRC. Methods: CRC-MAPS, a validated microsimulation model of the adenoma-carcinoma pathway that reproduced incidence reduction (IR) and mortality reduction (MR) consistent with CISNET models and a randomized controlled trial, was used to simulate perfect adherence to a hypothetical annual screening test among previously unscreened individuals free of diagnosed CRC. Four scenarios were examined: two cancer interception and two cancer prevention + interception. Individuals were screened from age 65 to 75. CRC IR and MR outcomes compared to no screening were aggregated from age 65 until death. Threshold analysis (#5) identified the ≥10mm adenoma sensitivity needed for a base-case cancer interception test (#1) to yield CRC MR equivalent to a near-perfect cancer interception test (#2). Results: The base-case interception scenario (#1) resulted in 5.6% CRC IR and 21.7% MR compared to 5.2% CRC IR and 25.9% MR for the near-perfect interception scenario (#2). The threshold analysis demonstrates that when the base-case interception scenario's ≥10mm adenoma sensitivity is increased from 1% to just 2.43% (#5), the resulting MR is equivalent to a near-perfect interception test. Accordingly, the cancer prevention + interception scenarios (#3, #4) resulted in CRC IR and MR outcomes 9.7-12.9x and 2.5-3.4x (respectively) as favorable as either cancer interception scenario due to adenoma detection. Conclusions: This analysis highlights that even small improvements in the detection of precancerous lesions for certain cancers (e.g., adenomas for CRC), which enable cancer prevention, can yield clinical benefits that meaningfully exceed those from tests that primarily detect cancer. Future studies will explore both benefits and burdens of different screening tests. Moreover, this approach will be applied to better understand the clinical utility of MCED tests. [Table: see text]

TPS208 Background: The USPSTF recently recommended colorectal cancer (CRC) screening for adults aged 45 to 49 years in addition to those aged 50 to 75 years. This guideline update, which increases the number of screen-eligible individuals by ̃19 million, is similar to the recommendation in 2018 by the American Cancer Society (ACS) and is based on the modeling studies that reflect rising CRC incidence rates in younger adults. Currently, only 67% of average-risk individuals over the age of 50 years are up-to-date on CRC screening, and adherence to screening is lower in younger individuals (e.g., 50-54 years). Despite the non-invasive nature of existing stool-based CRC tests, barriers remain to adoption, including a dislike for manipulating stool and a requirement for substantial navigational support. Blood tests may overcome these barriers through ease of sample collection and integration into routine blood work. Methods: Here we describe our prospective, multi-center registrational study for validating a blood-based multiomics test for average-risk CRC screening: PREEMPT CRC. Eligible participants include those aged 45-85 with no known history of CRC or colorectal adenomas who are undergoing CRC screening by colonoscopy. The target enrollment is 25,000 participants, and primary outcome measures are sensitivity for CRC and specificity for advanced colorectal neoplasia, which includes CRC and advanced adenomas. Secondary outcome measures include positive predictive value for CRC, negative predictive value for advanced colorectal neoplasia, and sensitivity for advanced adenomas. Novel recruitment methods have been implemented by combining traditional, site-based recruitment and virtual recruitment using an online web portal, coupled with mobile phlebotomy, to make participation broadly accessible, especially during the COVID-19 pandemic. Participants have been enrolled from 40 states as of August 2021, and virtual recruitment has enabled widespread participation, potentially from any zip code in the continental US. To ensure adequate representation of the intended use population, community organizations, federally qualified health centers (FQHCs), and universities have been engaged to reach underserved and minority patient populations. The study was initiated in May 2020 and to our knowledge will be the largest prospective, multi-center registrational validation study of an average-risk CRC screening test to date. Clinical trial information: NCT04369053.

43 Background: Blood-based screening tests for colorectal cancer (CRC) with high sensitivity and specificity are needed to improve adherence, facilitate early detection, and ultimately reduce mortality from CRC. Current stool-based tests have a sensitivity of 24-42% for colorectal advanced adenomas (AAs), while blood tests that rely on tumor-derived cell-free DNA (cfDNA) methylation signatures have shown limited sensitivity for AAs. Here we demonstrate the ability to detect AAs from blood using a multiomics test that incorporates both tumor- and immune-derived signatures, and compare it to the performance of a cfDNA methylation-only test. Methods: Participants enrolled in a prospective study (NCT03688906) were included in this analysis. The multiomics test includes signatures for cell-free nucleic acids based on next-generation sequencing, and for plasma proteins based on high-throughput multiplexed assays. Signatures are integrated computationally with a combination of convolutional neural networks and regularized logistic regression. We compared the multiomics test with one based on cfDNA methylation only. Results: This sub-study included 542 participants (AA: n = 122; colonoscopy-confirmed negative controls: n = 420). Participants with AA were 56% male with a mean age of 63 years, and colonoscopy-confirmed negative controls were 54% male with a mean age of 61 years. The multiomics test achieved a sensitivity of 41% (n = 50/122, 95% CI 34-48%) at 90% specificity (377/420). By contrast, the cfDNA methylation-only test achieved a sensitivity of 20% (24/122, 95% CI 15-25%) at 91% specificity (383/420). Performance was also analyzed by histological subtype and location, and superiority of the multiomics test to the cfDNA-methylation-only test was consistently observed. Conclusions: A novel multiomics blood test can detect colorectal AAs at a sensitivity and specificity comparable to existing stool-based tests. Combining signatures from both tumor- and immune-derived sources resulted in AA sensitivity greater than that of cfDNA-methylation alone.

BackgroundBlood-based methods using cell-free DNA (cfDNA) are under development as an alternative to existing screening tests. However, early-stage detection of cancer using tumor-derived cfDNA has proven challenging because of the small proportion of cfDNA derived from tumor tissue in early-stage disease. A machine learning approach to discover signatures in cfDNA, potentially reflective of both tumor and non-tumor contributions, may represent a promising direction for the early detection of cancer.MethodsWhole-genome sequencing was performed on cfDNA extracted from plasma samples (N=546 colorectal cancer and 271 non-cancer controls). Reads aligning to protein-coding gene bodies were extracted, and read counts were normalized. cfDNA tumor fraction was estimated using IchorCNA. Machine learning models were trained using k-fold cross-validation and confounder-based cross-validation to assess generalization performance.ResultsIn a colorectal cancer cohort heavily weighted towards early-stage cancer (80% stage I/II), we achieved a mean AUC of 0.92 (95% CI 0.91-0.93) with a mean sensitivity of 85% (95% CI 83-86%) at 85% specificity. Sensitivity generally increased with tumor stage and increasing tumor fraction. Stratification by age, sequencing batch, and institution demonstrated the impact of these confounders and provided a more accurate assessment of generalization performance.ConclusionsA machine learning approach using cfDNA achieved high sensitivity and specificity in a large, predominantly early-stage, colorectal cancer cohort. The possibility of systematic technical and institution-specific biases warrants similar confounder analyses in other studies. Prospective validation of this machine learning method and evaluation of a multi-analyte approach are underway.

9588 Background: Immune checkpoint inhibitors have shown promising results in many advanced cancers, but the response rate remains low. Various molecular and cellular biomarkers, such as elevated tumor-infiltrating cytotoxic T cells and Natural Killer (NK) cells at baseline, are associated with response. Blood-based biomarkers to predict or monitor response remain challenging to develop. Here we investigate the potential of cell-free DNA (cfDNA) biomarkers to predict response to the PD-1 immune checkpoint inhibitor nivolumab in patients with refractory metastatic non-small cell lung cancer (NSCLC). Methods: Plasma from stage IV NSCLC patients enrolled in ALCINA (NCT02866149) was collected before (baseline, BL, n = 30) and at week 8 (W8, n = 17) of nivolumab therapy. Response was determined using RECIST 1.1 (responders n = 5; non-responders n = 25). Whole-genome sequencing was performed to characterize cfDNA fragments. Tumor fraction (TF) was assessed using ichorCNA. Cellular composition was estimated by deconvolution of cfDNA co-fragmentation patterns, and transcription factor activity was estimated by measuring binding site accessibility across the genome. Results: Although estimated TF at baseline did not predict response to nivolumab, NK cell levels estimated by cell-mixture deconvolution were significantly higher in responders at BL (p < 0.05). Furthermore, estimated monocyte levels at W8 strongly correlated with overall survival (r = 0.75, p < 0.0005, HR = 15.02) and were significantly higher in responders (p < 0.05). By evaluating changes in transcription factor binding activity, we identified factors with greater accessibility in non-responders at baseline (DEAF1, THAP11) and W8 (DUX4, PDX-1). Conclusions: Plasma cfDNA signatures may be useful for response prediction and monitoring in NSCLC patients on immunotherapy. Our results suggest that changes in the immune system, as reflected by cellular composition and transcriptional activity inferred from cfDNA, may provide biological insights beyond TF alone that may benefit biomarker discovery and drug target identification.

Figure S1. Performance of confounding variable in k-fold and target CV procedure (ROC curves) for: a) Age and binned-age CV b) Batch and k-batch CV c) Processing date and ordered k-batch CV d) Institution and balanced k-batch CV. Note that stratification CVs (i.e., binned-age, k-batch, and ordered k-batch) perform at chance (mean AUC of 0.50) when the distribution of labels in the test covariate is even and would result in an ROC that is just a diagonal line. The variance seen in some of the target procedures is due to class imbalance in the test data. (DOCX 420 kb)

Figure S2. Percentage of colorectal cancer samples by stage and non-cancer controls in each range of estimated TF (based on observed CNV using an IchorCNA-based estimate). (DOCX 74 kb)

Figure S3. IchorCNA-based estimated TF alone to predict case status (i.e., cancer or control) achieved an AUC of 0.67 in the IU age range, meaningfully lower than results from the ML model under any analyzed CV procedure. (DOCX 101 kb)

Figure S4. Non-linear relationship between the total number of samples used for training and sensitivity at 85% specificity for colorectal cancer detection. The method was trained again with k-fold, except the number of training samples per fold was downsampled. The lower numbers are comparable to those available for balanced k-batch and were used to investigate decreased classifier performance due to smaller sample sizes in training. (DOCX 60 kb)

66 Background: Despite population screening efforts, screening rates for colorectal cancer (CRC) remain suboptimal. A non-invasive, blood-based screening test with high sensitivity and specificity in early-stage disease should improve adherence and ultimately reduce mortality; however, tests based only on tumor-derived biomarkers have limited sensitivity. Here we used a multiomic, machine learning platform to discover, refine, and combine tumor- and immune-derived signals to develop a blood test for the detection of early-stage CRC. Methods: Samples from 591 participants enrolled in a prospective study including average-risk screening and case-control cohorts (NCT03688906) were included in this analysis (CRC: n = 43; colonoscopy-confirmed CRC-negative controls: n = 548). Participants with CRC were 60% male with a mean age of 63, and controls were 55% male with a mean age of 60. Stage distribution was 54% early (I/II) and 34% late (III/IV) with 11% unknown. Plasma was analyzed by whole-genome sequencing, bisulfite sequencing, and protein quantification methods. Computational methods were used to assess and infer the performance of individual and combined assays. Results: For colorectal adenocarcinoma, which represents ~95% of all CRCs, our multiomic test achieved a mean sensitivity of 92% in early stage (n = 17) and 84% in late stage (n = 11) at a specificity of 90%. Across all CRC pathological subtypes, our test achieved a mean sensitivity of 80% in early stage (n = 19) and 83% in late stage (n = 12) at a specificity of 90%; the test detected the single squamous cell carcinoma but missed both neuroendocrine tumors. Individual assays achieved a mean sensitivity of 50% in early stage and 66% in late stage at a specificity of 90%. Conclusions: In a prospective cohort, we demonstrated high sensitivity and specificity for early-stage adenocarcinoma by combining tumor- and immune-derived signals from cfDNA, epigenetic, and protein biomarkers. While most CRCs are adenocarcinomas, detection of all pathological subtypes is required to maximize sensitivity in a screening population. Further analysis of molecular and pathological subtypes, as well as the entire ~3000 patient cohort, is underway. Clinical trial information: NCT03688906.

Introduction: Blood-based tests hold great promise as cancer diagnostics but until now have largely been restricted to the analysis of a single class of molecules (eg, circulating tumor DNA, platelet mRNA, circulating proteins). The ability to analyze multiple analytes simultaneously from the same biological sample may increase the sensitivity and specificity of such tests by exploiting independent information between signals. Here, we describe an experimental and analytical system that we developed and implemented for the integrated analysis of multiple analytes from a single blood sample, which revealed examples of both correlations and orthogonality among individual analytes. Methods: De-identified blood samples were obtained from healthy individuals, as well as individuals with pre-malignant conditions and stage I-IV colorectal cancer (CRC). After plasma separation, multiple types of analytes were assayed: cell-free DNA (cfDNA) content was assessed by low-coverage whole-genome sequencing (lcWGS) and whole-genome bisulfite sequencing (WGBS), cell-free microRNA (cf-miRNA) was assessed by small-RNA sequencing, and levels of circulating proteins were measured by quantitative immunoassay. Results: lcWGS of plasma cfDNA was able to identify CRC samples with high tumor fraction (>20%) on the basis of copy number variation (CNV) across the genome. High tumor fractions, while more frequent in late-stage cancer samples, were observed in some stage I and II patients. Aberrant signals in each of the three other analytes—cf-miRNA profiles discordant with those in healthy controls, genome-wide hypomethylation at LINE1 (long interspersed nuclear element 1) CpG loci, and elevated levels of circulating carcinoembryonic antigen (CEA) and cytokeratin fragment 21-1 (CYFRA 21-1) proteins—were also observed in cancer patients. Strikingly, aberrant profiles across analytes were indicative of high tumor fraction (as estimated from cfDNA CNV), rather than cancer stage. Conclusion: Our data suggest that tumor fraction is correlated with cancer stage but has a large potential range, even in early stage samples. Previous literature on blood-based screens for detection of cancer has displayed discordance in the claimed ability of different single analytes to detect early stage cancer. Tumor fraction may be able to explain the historical disagreement, as we found that aberrant profiles among cf-miRNA, cfDNA methylation, and circulating protein levels were more strongly associated with high tumor fraction than with late stage. These findings suggest that some positive “early stage” detection results may in fact be “high tumor fraction” detection results. Our results further demonstrate that assaying multiple analytes from a single sample may enable the development of classifiers that are reliable at low tumor fraction and for detecting pre-malignant or early-stage disease. Citation Format: Daniel Delubac, Eric Ariazi, Jonathan Berliner, Adam Drake, John Dulin, Riley Ennis, Erik Gafni, Kate Niehaus, Gabriel Otte, Jennifer Pecson, Girish Putcha, Corey Schaninger, Aarushi Sharma, Mike Singer, Abraham Tzou, Jill Waters, David Weinberg, Brandon White, Imran S. Haque. Multi-analyte profiling reveals relationships among circulating biomarkers in colorectal cancer [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 2227.

Binding of platelet-activating factor (PAF) was characterized in a human b lymphoblastoid cell line, ASK.0. [3H]PAF binding to these cells was time-dependent, reaching equilibrium at 60 minutes, and saturable. Scatchard analyses of saturation binding experiments revealed a single class of PAF binding sites (108,000 +/- 17,000 per cell) with a KD of 2.16 +/- 0.41 nM. That the binding sites were specific for PAF was demonstrated by competition studies. PAF was shown to increase the intracellular calcium concentrations of ASK.0 cells in a dose-dependent manner with an EC50 of 7 nM. We have, therefore, identified a B cell line expressing large numbers of functional PAF receptors.

The development of molecular assays designed to detect gene amplifications has largely been hampered by technical challenges such as limited DNA quantity and tumor heterogeneity, which demand methods of very high precision and sensitivity. The recent introduction of affordable digital PCR platforms, such as droplet digital PCR (ddPCR), that are capable of providing single molecule resolution of target abundances thus provides a unique opportunity to address this gap in molecular diagnostics. A ddPCR-based test was therefore developed under CLIA-CAP guidelines to determine the amplification status of 12 commonly amplified genes targeted by FDA approved drugs. Termed the Amplinome Test, this test was applied to 49 clinical samples received over a period of 6 months and compared to the results obtained from a commercially available clinical sequencing test applied that also reports copy number alterations (CNAs). An overall concordance rate of 90% (532/588 calls) was observed between the two tests across the entire gene set, 5 of which were identified as amplified. There were 56 discordant copy number amplifications between the two testing modalities. The vast majority (55/56) of discordant calls arose from gene amplifications identified by the Amplinome test, but not detected by sequencing, indicating an 11-fold increase in sensitivity in detecting amplifications. One discordant call (HER3) was identified as amplified via sequencing, but confirmed to be unamplified via FISH. At the patient level, the Amplinome test identified 5-fold more patients as having actionable amplifications in at least one of the 12 assayed genes versus clinical sequencing (29 vs. 6). Clinical management was altered in 14 of the 29 patients with an actionable CNA identified on the Amplinome test; those patients received targeted therapy directed against the amplified gene. The ddPCR-based Amplinome test thus provides a highly sensitive method for measuring gene amplifications in cancer that alters patient management, and suggests that the prevalence of actionable amplifications may be significantly underestimated by standard clinical next-generation sequencing tests. Citation Format: Austin P. So, Amy Wong, Jennifer Pecson, Girish Putcha, Gregory Jensen, Michael Lucero, Gary Stone, Jason Gillman, Pravin Mishra, David Loughmiller, Derrick S. Haslem, Lincoln Nadauld. The frequency of gene amplifications in cancer revealed by a droplet digital PCR (ddPCR) based pan-cancer gene panel test. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 625. doi:10.1158/1538-7445.AM2015-625

Supplemental Methods. (DOCX 26 kb)

Supplemental Methods. (DOCX 26 kb)

Table S1. Mean and standard deviation of the number of samples (both in and out of the IU age range) per fold for each CV procedure. SDâ =â standard deviation. (DOCX 16 kb)

Table S1. Mean and standard deviation of the number of samples (both in and out of the IU age range) per fold for each CV procedure. SDâ =â standard deviation. (DOCX 16 kb)