Predictive Performance of Cell-Free Nucleic Acid-Based Multi-Cancer Early Detection Tests: A Systematic Review

Prediagnostic evaluation of multicancer detection tests: design and analysis considerations

There is growing interest in multicancer detection tests, which identify molecular signals in the blood that indicate a potential preclinical cancer. A key stage in evaluating these tests is a prediagnostic performance study, in which investigators store specimens from asymptomatic individuals and later test stored specimens from patients with cancer and a random sample of controls to determine predictive performance. Performance metrics include rates of cancer-specific true-positive and false-positive findings and a cancer-specific positive predictive value, with the latter compared with a decision-analytic threshold. The sample size trade-off method, which trades imprecise targeting of the true-positive rate for precise targeting of a zero-false-positive rate can substantially reduce sample size while increasing the lower bound of the positive predictive value. For a 1-year follow-up, with ovarian cancer as the rarest cancer considered, the sample size trade-off method yields a sample size of 163 000 compared with a sample size of 720 000, based on standard calculations. These design and analysis recommendations should be considered in planning a specimen repository and in the prediagnostic evaluation of multicancer detection tests.

Projecting the Impact of Multi-Cancer Early Detection on Late-Stage Incidence Using Multi-State Disease Modeling

BACKGROUND

Downstaging-reduction in late-stage incidence-has been proposed as an endpoint in randomized trials of multi-cancer early detection (MCED) tests. How downstaging depends on test performance and follow-up has been studied for some cancers but is understudied for cancers without existing screening and for MCED tests that include these cancer types.

METHODS

We develop a model for cancer natural history that can be fit to registry incidence patterns under minimal inputs and can be estimated for solid cancers without existing screening. Fitted models are combined to project downstaging in MCED trials given sensitivity for early- and late-stage cancers. We fit models for 12 cancers using incidence data from the Surveillance, Epidemiology, and End Results program and project downstaging in a simulated trial under variable preclinical latencies and test sensitivities.

RESULTS

A proof-of-principle lung cancer model approximated downstaging in the National Lung Screening Trial. Given published stage-specific sensitivities for 12 cancers, we projected downstaging ranging from 21% to 43% across plausible preclinical latencies in a hypothetical 3-screen MCED trial. Late-stage incidence reductions manifest soon after screening begins. Downstaging increases with longer early-stage latency or higher early-stage test sensitivity.

CONCLUSIONS

Even short-term MCED trials could produce substantial downstaging given adequate early-stage test sensitivity.

IMPACT

Modeling the natural histories of cancers without existing screening facilitates analysis of novel MCED products and trial designs. The framework informs expectations of MCED impact on disease stage at diagnosis and could serve as a building block for designing trials with late-stage incidence as the primary endpoint.

Addressing positive multi-cancer early detection tests in head and neck Surgery: Experience with head and neck work up for high-risk referrals

OBJECTIVES

Blood-based multi-cancer early detection (MCED) tests are now commercially available. However, there are currently no consensus guidelines available for head and neck cancer (HNC) providers to direct work up or surveillance for patients with a positive MCED test. We seek to describe cases of patients with positive MCED tests suggesting HNC and provide insights for their evaluation.

METHODS

Retrospective chart review of patients referred to Otolaryngology with an MCED result suggesting HNC. Patients enrolled in prospective MCED clinical trials were excluded. Cancer diagnoses were confirmed via frozen-section pathology.

RESULTS

Five patients were included (mean age: 69.2 years, range 50-87; 4 male) with MCED-identified-high-risk for HNC or lymphoma. Only patient was symptomatic. After physical exam and follow-up head and neck imaging, circulating tumor HPV DNA testing, two patients were diagnosed with p16 + oropharyngeal squamous cell carcinomas and underwent appropriate therapy. A third patient had no evidence of head and neck cancer but was diagnosed with sarcoma of the thigh. The remaining two patients had no evidence of malignancy after in-depth workup.

CONCLUSIONS

In this retrospective study, 2 of 5 patients referred to Otolaryngology with a positive MCED result were diagnosed with HPV + oropharyngeal squamous cell carcinoma. We recommend that positive HNC MCED work up include thorough head and neck examination with flexible laryngoscopy and focused CT or MRI imaging. Given the potential for inaccurate MCED tissue of origin classification, PET/CT may be useful in specific situations. For a patient with no cancer identified, development of clear guidelines is warranted.

Cancer screening with multicancer detection tests: A translational science review

Multicancer detection (MCD) tests use a single, easily obtainable biospecimen, such as blood, to screen for more than one cancer concurrently. MCD tests can potentially be used to improve early cancer detection, including cancers that currently lack effective screening methods. However, these tests have unknown and unquantified benefits and harms. MCD tests differ from conventional cancer screening tests in that the organ responsible for a positive test is unknown, and a broad diagnostic workup may be necessary to confirm the location and type of underlying cancer. Among two prospective studies involving greater than 16,000 individuals, MCD tests identified those who had some cancers without currently recommended screening tests, including pancreas, ovary, liver, uterus, small intestine, oropharyngeal, bone, thyroid, and hematologic malignancies, at early stages. Reported MCD test sensitivities range from 27% to 95% but differ by organ and are lower for early stage cancers, for which treatment toxicity would be lowest and the potential for cure might be highest. False reassurance from a negative MCD result may reduce screening adherence, risking a loss in proven public health benefits from standard-of-care screening. Prospective clinical trials are needed to address uncertainties about MCD accuracy to detect different cancers in asymptomatic individuals, whether these tests can detect cancer sufficiently early for effective treatment and mortality reduction, the degree to which these tests may contribute to cancer overdiagnosis and overtreatment, whether MCD tests work equally well across all populations, and the appropriate diagnostic evaluation and follow-up for patients with a positive test.

Revisiting the standard blueprint for biomarker development to address emerging cancer early detection technologies

Novel liquid biopsy technologies are creating a watershed moment in cancer early detection. Evidence supporting population screening is nascent, but a rush to market the new tests is prompting cancer early detection researchers to revisit the standard blueprint that the Early Detection Research Network established to evaluate novel screening biomarkers. In this commentary, we review the Early Detection Research Network's Phases of Biomarker Development (PBD) for rigorous evaluation of novel early detection biomarkers and discuss both hazards and opportunities involved in expedited evaluation. According to the PBD, for a biomarker-based test to be considered for population screening, 1) test sensitivity in a prospective screening setting must be adequate, 2) the shift to early curable stages must be meaningful, and 3) any stage shift must translate into clinically significant mortality benefit. In the past, determining mortality benefit has required lengthy randomized screening trials, but interest is growing in expedited trial designs with shorter-term endpoints. Whether and how best to use such endpoints in a manner that retains the rigor of the PBD remains to be determined. We discuss how computational disease modeling can be harnessed to learn about screening impact and meet the needs of the moment.