Use specific criteria to search for up-to-date clinical trials fast /// EXPLORE NOW Tempus One: New AI Clinical Assistant Available for All Providers

Molecular Diagnostic Classification for Cancers of Unknown Primary (CUP): Post-Testing Diagnosis and Treatment Impact Analysis From Real-World Claims Data

Authors Zachary Rivers, Ryan W Huey, Rotem Ben-Shachar, Anjali Narayan Avadhani, Timothy J. Taxter, Kyle A. Beauchamp, Adam J Hockenberry, Halla Nimeiri, Joelle Allam, Aparna Kalyan, Kanwal Pratap Singh Raghav, Michael Sangmin Lee

Background:Next-generation sequencing-based molecular diagnostic classifiers can help identify the tissue of origin for CUP, and enable the selection of site-specific therapies (as opposed to empiric chemotherapy) for this vulnerable population with high unmet need. NCCN guidelines do not endorse the use of tissue of origin classifiers as standard of care for CUP patients due to limited clinical evidence. Here, we linked results of a commercial molecular diagnostic classifier with claims data to understand how this test impacted patient care.

Methods:We assessed de-identified claims data from the Komodo Healthcare Map (a database including provider visits, laboratory tests, procedures, imaging, and prescriptions) linked to the Tempus Tumor Origin (TO) test—a machine learning classifier that uses RNA-seq data to classify tumors into one of 68 histological subtypes. Eligible patients had pathologist-confirmed CUP and were classified as one of 9 subtypes (each having n>10). Impact was determined by identifying either one or more new diagnostic codes or new subtype-related medication claims following TO testing.

Results:We analyzed data from 490 patients: 483 for the diagnosis analysis and 213 for the medication analysis (206 patients appear in both, due to differences in timing of diagnosis vs. medication claims). We found that post-TO testing, 49.9% (n=241) of patients had a diagnostic code change, 63.8% (n=136) had a treatment change, and 41% (n=85) had both. In total, 59.6% (n=292) of patients were impacted by the use of this classifier, with variation according to predicted subtype (Table). Cancer types with specific treatment options were more likely to have changes in diagnostic code or treatment. For CUP predicted as lung adenocarcinomas, 85% of the cases had a new subtype-aligned medication, and 78% were specifically placed on immunotherapy (IO) compared with 24% of overall patients.

Conclusions:Using real-world claims data, we show that molecular diagnostic testing utilizing Tempus TO impacted care for a majority of CUP patients. This cohort will be followed to identify how TO testing decisions impact outcomes.

Cancer Type Diagnosis Impact Medication Impact IO Impact Total
Breast 15/24 (63%) 10/16 (63%) 3/16 (19%) 16/25 (64%)
Cholangiocarcinoma 57/107 (53%) 26/52 (50%) 4/52 (8%) 69/108 (64%)
Colorectal 16/50 (32%) 17/26 (65%) 0/26 (0%) 25/54 (46%)
Female Reproductive Tract 25/47 (53%) 19/27 (70%) 4/27 (15%) 32/48 (67%)
Gastroesophageal 13/37 (35%) 9/13 (69%) 3/13 (23%) 19/37 (51%)
Lung Adenocarcinoma 61/96 (64%) 23/27 (85%) 21/27 (78%) 67/96 (70%)
Lung Squamous Cell 19/34 (56%) 7/13 (54%) 6/13 (46%) 21/34 (62%)
Pancreatic 19/55 (35%) 15/25 (60%) 3/25 (12%) 25/55 (45%)
Urothelial 16/33 (48%) 10/14 (71%) 8/14 (57%) 18/33 (55%)