How to Upend Cancer Screening

Introduction

Age. It is the one major criterion for cancer screening recommendations across all common cancers. The U.S. Preventive Services Task Force (USPSTF) current recommendations are summarized below, with age as the basis for population-based screening. The cost of this mass age-based screening per se—not including follow-up tests and procedures or over-diagnosis—is estimated between $40 and $80 billion per year. Yet, for example, 88% of women will never develop breast cancer in their lifetime, even though mammography is recommended every other year for women of age 40 years and older (as recently lowered from age 50 years by USPSTF).

Recently, these age cutoffs have been challenged with the emerging trend of cancer occurring in younger age groups. Adding to all of this is the ∼10% false-positive rate for many of the tests each time they are performed. For mammography, about half of women will have a false-positive test over a decade (five scans) of testing. Currently, in the United States, only 14% of diagnosed cancers are detected by screening with a recommended screening test (Table 1 and Fig. 1).

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Fig. 1.

Total cancers in the United States. There is a chance to radically improve the accuracy, efficiency, timeliness of detection and cost-effectiveness for cancer screening by multiple means. The pie chart shows the percentage of various cancers detected by screening. Figure reproduced from: NORC “Only 14% of Cancers Are Detected Through a Preventive Screening Test”: https://www.norc.org/content/dam/norc-org/pdfs/State-Specific%20PCDSs%20chart%201213.pdf

The “AI Biopsy”

In 2023, Chris Sander and colleagues published a major study in Nature Medicine on AI for detecting pancreatic cancer using a transformer AI model in a pair of large cohorts (Denmark and the U.S.), from >11 million people, of whom 28,000 developed pancreatic cancer. ¹ The AI performed well (AUC = 0.88), exceeding previous machine and deep learning models, and only incorporated minimal risk factor data derived from the history for each individual, with time-sequence data identified as particularly useful for aiding prediction. It did not include multiple other layers of data (“multimodal”), such as genomics, laboratory tests, or scans.

Although age 50 years plus was a key risk factor in the study, there was also a contribution of age for prediction in people of ages 30 to 50 years that would be missed using the strict age 50 years cutoff.

Hart and colleagues wrote about a “statistical biopsy” using personal health data and two different deep neural networks for 17 types of cancer, assessed for the U.K. biobank cohort as seen below. ² Again, the data inputs were rather limited to demographics, medical, family and social history, without laboratory, scan, environmental, or omic (such as genome sequence, gut microbiome, immune system profiling) data.

Individualized Genomic and Biological Risk

Multiple tests that can indicate an individual's heightened risk for developing cancer include polygenic risk scores (PRSs), whole genome sequencing for mutations in cancer susceptibility genes, known pathogenic variants linked to cancers, gut microbiome profiles, clonal hematopoiesis of blood stem cells, and potentially immune system profiling. None of these are currently included to assess a person's risk for cancer screening.

The least expensive of these tests is PRS, which could be obtained from a gene microarray, such as used by 23andMe or AncestryDNA, which captures data for >1 million common variants. ³ Alternatively, low-pass whole genome sequencing can be used to derive PRSs. Both methods can be accomplished at very low cost, <$50, and there are validated PRSs for most common cancers—prostate, breast, lung, colorectal, and melanoma.

There are recent data for how a PRS can be integrated with the prostate-specific antigen blood test to avoid 31% of negative prostate biopsies and improve detection of aggressive cancer. A PRS for colorectal cancer was assessed in 400,000 Finnish individuals and found to identify people at high risk (top 1% score) who were younger than the population-based screening age (in Finland it is 60 years), concluding. A colorectal cancer-specific PRS could define more appropriate ages to start screening for individuals based on their genetic risk.

Whole genome sequencing, which can now be performed for ∼$200, provides data on the known pathogenic variants for familial cancers (such as BRCA and Lynch syndrome) but also a detailed assessment for known cancer predisposition genes, which exceeded 100 a decade ago. The good part about these types of genomic data is that they are a one-off, obtained one time to provide insight throughout a person's life. Unfortunately, most of the validated data sets for genomic cancer risk are still for people of European ancestry.

There is also a biomarker of clonal hematopoiesis of indeterminate potential, which is linked to heightened risk for several cancers including blood, lung, and skin (it also predicts cardiovascular risk), which is not available commercially, but ideally should be. It can be a confounder for the multicancer early detection (MCED) tests, but prospective assessment for partitioning risk should be feasible.

There is also the increased risk of cancer with specific gut microbiome profiles with decreasing diversity and reduced functional redundancy, along with a higher proportion of pathogenic microorganisms, and patterns specific to the different types of cancer, as seen below. This extends potentially to the oral microbiome as well. Although the gut microbiome data can be inexpensively obtained, it is not yet being used for cancer risk assessment.

Like the microbiome, the individual's immune system response, which is interdependent with the gut microbiome, is important determinant for developing risk of cancer but is not assessed. To be clear, I am not advocating for the use of the gut or oral microbiome or immune profiling at this point, but they deserve attention as solid candidate ancillary tests to better define risk.

MCED Blood Tests

In recent years, there has been intense interest for developing and validating a blood test (liquid biopsies) to detect early cancer in healthy asymptomatic people, to ultimately be incorporated in one's annual checkup. The various tests assess patterns of methylation markers, DNA sequence, fragments of DNA, proteins, metabolites, exosomes, or RNA.

These tests are largely single omic, that is, they are each looking at one type of biological marker. An exception is the one developed by Exact Sciences that includes some genomic and protein markers. It is likely that a multiomic test, which included orthogonal (complementary) data across multiple types of biological markers, would be ideal, but that would bring the cost of the test to much higher than we have already seen, such as the Galleri test by Grail, which costs $949 and looks at the methylation pattern from the blood sample for >50 types of cancer.

A meta-analysis of 10 case–control and 6 cohort studies for the different MCED tests came up with an overall sensitivity of 0.66 and a specificity of 0.98. ⁴ That level of specificity—the true positive test—is the striking upside so far for the MCED tests. The overall accuracy for predicting the cancer site of origin from the blood biomarker was 79%.

To date, the Galleri test has the most experience of the MCED tests with >100,000 performed commercially and multiple reports published. In the SYMPLIFY study performed by the National Health Service (NHS) in the United Kingdom, >6000 participants were enrolled, for whom 6.7% had cancer diagnosed (5461 had the Grail test, of whom 368 had a cancer diagnosis). ⁵ Importantly, this first large-scale prospective study included people with symptoms, but the sensitivity and specificity were very closely aligned with the meta-analysis of asymptomatic people, 66% and 98%, respectively. An impressive result for this study was the prediction of the cancer site of origin that uses machine learning from the methylation pattern and was accurate in 85%.

That accuracy for determination of site of origin was replicated in another report for the Galleri test at American Society of Clinical Oncology from the PATHFINDER trial, which also assessed >6600 participants of age 50 years plus, but without symptoms. A cancer signal was found in 92 participants (1.4%) of whom 35 (0.5%) were true positives, for a specificity of 99% (35 of 36 found to have cancer) but a positive predictive value of only 38%. The cancer site of origin prediction accuracy was 97%. Given the diverse sites of origin, including bone, head and neck, ovary, liver, uterus, blood, breast, lung, and various gastrointestinal tracts, that is quite good.

Of note, there were 57 false positives, participants who had no findings of cancer after a full workup. What remains to be seen is whether the signal of abnormal cell-free DNA methylation is actually a “true” positive in some individuals, such that the person's immune system kicks in and, on repeat assessment months later, the test comes back negative. Some individuals have microscopic cancer and then their immune system kicks in to quash it. There are anecdotal reports of this occurrence that will require further scrutiny.

In Table 2, I summarize what we know about MCED tests from the meta-analysis and the new Galleri data from recent presentations and publications.

The detection rate for early cancer (stages 1 and 2) is clearly suboptimal compared with later stages, which is a premise for MCED—that picking up cancer at the microscopic stage, before it can be seen on a scan or induce symptoms, or potentiate spread, is the key to improving outcomes, using interventions to change the natural history of cancer progression. Simply put, detection is not equivalent to favorably altering outcomes. That is why randomized trials are necessary and ongoing for MCED versus no MCED for clinical outcomes, such as one by the NHS of 140,000 participants (now fully enrolled in just 10 months) and the National Cancer Institute Vanguard study plan, in preparation for a potential 225,000-person trial using an MCED derived on their own.

These very large trials of healthy asymptomatic people are again using age as the main inclusion criteria. It will be years before such trials will be complete for clinical outcomes, and there will be questions about the diversity of the participants, since we know the occurrence and behavior of many cancers are shaped by ancestry background. But the problem of lacking compelling evidence for MCED benefit from randomized trials is only one of the critical issues. None of the tests are FDA approved or reimbursed by insurance carriers. Let us move to the bigger problem.

A Detection Yield of 5 per 1000 People Tested Will Not Cut It

The basic problem with MCEDs is that they are primarily being assessed by the same dumbed-down criteria of current mass screening—age 50 years plus. This ignores the Bayesian principle of priors—that using a test for low-risk individuals will lead to false positives and low predictive value. Indeed, in PATHFINDER there were more false positives than true positives (57 vs. 35), and the signal rate in 92 people of >6600 assessed is dreadfully low. Although remarkably specific, the low sensitivity leaves dangling the number of people who may have microscopic cancer but are not being detected. Any medical test will only be as good for accuracy as the population it is tested in. There is a dire need to improve the signal and decrease the noise.

There are two major ways to improve accuracy. One would be to enrich the population to be tested, using the “AI biopsy” described above, with all of the longitudinal clinical information, beyond that used for the pancreatic cancer detections study. That would be comprehensive: inclusive of analyzing trends in laboratory data even within the normal range, all scans, nutrition, socioeconomic features, and environmental exposures such as air pollution. Added to this would be individualized genomic/biological risk determination. Now that we have large language models with multimodal input capabilities, the analytic challenge is clearly surmountable. Then we have defined a high-risk population that may derive marked benefit from an MCED test.

To get at early detection at the stage 1 level, it may be important to have multiomic MCEDs, not just one focused on methylation or DNA sequence. But, as discussed, this may make the cost prohibitive. It may well be that enriching the population tested is the driver toward higher sensitivity, without the need for multiomic assessment, the trade-offs of the individual's genomic assessment versus more omics of the blood sample deserves consideration.

For now, MCEDs are a test for the curious affluent that only carry potential value if positive, and only about half of those will be indicative of an actual cancer diagnosis that was not otherwise apparent. It will be primarily used by the same people who get executive health physicals with all sorts of tests that have not been validated in healthy people, at exorbitant costs.

If, however, we adopt a smarter path for assessing MCEDs, they may indeed fulfill their transformative potential of being part of an annual physical—perhaps even eliminating the mass screening procedural tests as we know them today, such as mammography and colonoscopy.

In 2023, >600,000 Americans will die of cancer and >2 million will have a new diagnosis of cancer, many at late stages. We have the desperate need to improve upon these statistics, and, through generative AI and biomedical innovation, we now have the tools to achieve this big unmet goal. We have come a long way in understanding a healthy individual's risk of cancer and interpreting the molecular biological content of a blood sample. If only we can get smart about using all this information.

This article is adapted from the author's original post on his “Ground Truths” Substack, June 25, 2023.