Emerging Technologies in Therapeutics and Diagnostics

In this latest edition of The Way Ahead: The Convergence of Technology and Cancer Care, Dr. Waqas Haque shares his perspectives on the approval of lifileucel, the first individualized tumor-infiltrating lymphocyte therapy, for advanced melanoma; updates on the EV-302 trial of enfortumab vedotin, an antibody-drug conjugate technology; a new blood test for colorectal cancer screening from Guardant Health; the Model Facts label to guide decisions using machine learning; and more! 

Cell therapy updateLifileucel (Amtagvi) is the first ever individualized tumor-infiltrating lymphocyte (TIL) therapy to reach the market, obtaining FDA accelerated approval on February 16 for patients with advanced melanoma following prior treatment with a programmed cell death protein 1 (PD-1) inhibitor and a BRAF inhibitor, if the tumor carries BRAF V600 mutation. Solid tumors have been a difficult target for chimeric antigen receptor (CAR) T cells due to the lack of appropriate cell-surface biomarkers. TIL therapy solves this issue because the cells themselves are naturally wired to identify cancer biomarkers. Among 73 patients included in the study, 31.5% had an objective response (4.1% had a complete response), and after nearly 19 months of follow-up, 43.5% of responders were in remission for over a year. TIL therapy isn’t cheap: a one-time treatment has a wholesale acquisition cost of $515,000 per patient and requires a period of inpatient monitoring along with a period of intense outpatient monitoring and follow-up.

Clinical trial update: In the phase 3 EV-302 trial (published in the latest issue of the New England Journal of Medicine), researchers compared the effectiveness and safety of enfortumab vedotin combined with pembrolizumab versus platinum-based chemotherapy in patients with previously untreated advanced or metastatic urothelial carcinoma.The trial included 886 patients randomly assigned. Results showed that patients in the enfortumab vedotin/pembrolizumab group had longer progression-free survival (12.5 months vs. 6.3 months) and overall survival (31.5 months vs. 16.1 months) compared with those receiving chemotherapy, along with less frequent severe adverse events. EV-302 is being touted as a landmark trial not only because patient outcomes with platinum-based chemotherapy have a low 5-year survival rate, but because it shows the promise of antibody-drug conjugate (ADC) technology. An ADC consists of a monoclonal antibody linked to a potent chemotherapy drug, allowing targeted delivery of the drug to cancer cells while minimizing damage to normal cells. Enfortumab vedotin targets nectin-4, a tumor surface protein.

Diagnostic techGuardant Health’s Shield blood test for colorectal cancer screening showed promise in an update for the ECLIPSE trial (recently published in New England Journal of Medicine). While it effectively detected colorectal cancer at stages II and later with 100% sensitivity, its performance was less reliable detecting stage I cancer and precancerous polyps (13.5% sensitivity for advanced precancerous lesions). It works by detecting circulating tumor DNA—shed by cancer cells—in the blood. While the Shield test is convenient and potentially more comfortable than a fecal sample (such as the screening test Cologuard®), experts emphasize that it should not replace screening methods like a colonoscopy that can prevent cancer. Over 50 million eligible Americans do not get recommended colorectal cancer screenings, so the Shield test may have significant benefit particularly for these individuals. The trial also included 12% Black patients in the sample of nearly 8,000 patients, which is important given ethnic risk factors for colorectal cancer.

Safety & technology: There is tremendous enthusiasm surrounding the potential for machine learning (ML) to improve medical prognosis and diagnosis. However, there are risks to translating models into clinical care and end users are often unaware of potential harm to patients. This perspective (published in Nature) presents the “Model Facts” label, a systematic effort to ensure that front-line clinicians actually know how, when, how not, and when not to incorporate model output into clinical decisions. The “Model Facts” label was designed for clinicians who make decisions supported by a machine learning model and its purpose is to collate relevant, actionable information in one page. Practitioners and regulators must work together to standardize presentation of ML model information to clinical end users to prevent harm to patients. Efforts to integrate a model into clinical oncology practice should be accompanied by an effort to clearly articulate how a treatment regimen is selection or how a cancer screening test obtained its result.

About Dr. Haque

Waqas Haque, MD, MPH, is a third-year Internal Medicine Resident at New York University (NYU) in a Clinical Investigator Track. He recently matched to the University of Chicago for fellowship, which he will be beginning later in 2024. As a Clinical Investigator Track Resident, Dr. Haque has balanced his patient care work with a variety of research projects. During his fellowship training at the University of Chicago, he plans to further his work in innovative clinical trial design, value-based care delivery to cancer patients, and clinical investigation.

For More Information

US Food & Drug Administration (2024). FDA approves first cellular therapy to treat patients with unresectable or metastatic melanoma. Available at: https://www.fda.gov/news-events/press-announcements/fda-approves-first-cellular-therapy-treat-patients-unresectable-or-metastatic-melanoma

Phillips C (2024). First cancer TIL therapy gets FDA approval for advanced melanoma. National Cancer Institute. Available at: https://www.cancer.gov/news-events/cancer-currents-blog/2024/fda-amtagvi-til-therapy-melanoma

Powles T, Valderrama BP, Gupta S, et al (2024). Enfortumab vedotin and pembrolizumab in untreated advanced urothelial cancer. N Engl J Med, 390:875-888. DOI:10.1056/NEJMoa2312117

Chung DC, Gray DM, Singh H, et al (2024). A cell-free DNA blood-based test for colorectal cancer screening. N Engl J Med, 390:973-983. DOI:10.1056/NEJMoa2304714

Sendak MP, Gao M, Brajer N (2020). Presenting machine learning model information to clinical end users with model facts labels. NPJ Digit Med, 3 (41). DOI:10.1038/s41746-020-0253-3

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