Real-world performance analysis of a universal computational reasoning model for precision oncology in lung cancer

Abstract

Tumors harbor multiple genetic alterations, yet treatment decisions are commonly based on single biomarkers, leading to underutilization of genomic information by comprehensive molecular tests, uncertainty in clinical practice, and frequent treatment failures. Although molecular tumor boards can assist personalized treatments, this process is not scalable or standardized, resulting in highly discordant recommendations. Validated digital solutions for personalized decision support are highly needed. The Digital Drug Assignment (DDA) system is a computational reasoning model that scores treatment options based on the full tumor genomic data. We retrospectively analyzed data of 111 lung cancer patients and found that high-score MTAs (1000≦DDA score) provided significant clinical benefit over other treatments, in terms of ORR, PFS, and OS. These results demonstrate that the DDA system is predictive of relative benefit of the various agents used in lung cancer care. Digital drug assignment can potentially address challenges with complex molecular profiles in routine clinical settings.

Fig. 1. Study flowchart.

The study involved lung cancer patients who underwent precision oncology decision support. In each case, a first Molecular Tumor Board (MTB1) reviewed the medical history of the patient, determined eligibility for molecular testing and decided the appropriate molecular tests. After completion of all tests, tumor molecular profile data were processed by the Digital Drug Assignment (DDA) system, which ranked associated molecularly targeted agents (MTAs) by DDA scores. A second Molecular Tumor Board (MTB2) reviewed the test results and the DDA scores assigned by the DDA system to MTAs by processing all alterations comprising the individual tumor molecular profile. Based on the findings, MTB2 provided a written summary including possible MTA treatment recommendations. Treating clinical oncologists then considered the MTB recommendations and all other aspects of the case to make the ultimate treatment decision. In case of relapse or treatment failure, new treatment decisions were made by clinicians (dashed arrows). Precision oncology decision support was received at different points within the journey of each patient (see also Supplementary Fig. 4). All treatment and outcome data were collected from health records by treating physicians, including treatments prior to precision oncology decision support. During data processing systemic lines of treatments (LOTs) were retained and used for analysis. LOTs were stratified according to standard chemotherapies (SC) and MTAs, which were also analyzed by score stratification (high-score (1000≦DDA score) and low-score (DDA score<1000) MTA lines). Pooled LOT data were used for response and survival analyses according to stratification.

Fig. 2. Progression-free survival of MTA and SC treatment lines.

Treatment lines were stratified by administration of molecularly targeted agents (MTAs, blue) and standard chemotherapies (SC, red). Kaplan-Meier analysis by treatment types is shown for all lines of therapy (a), lines administered before precision oncology decision support (DS) (b), and lines administered after precision oncology decision support (c).

Fig. 3. Overall survival by treatment types received.

Patients were stratified by either receiving at least one line of treatment with a molecularly targeted agent (MTA, blue) or receiving exclusively standard chemotherapy treatments (SC only, red) during their treatment history. Kaplan-Meier analysis by treatment types based on all administered treatment lines is shown in (a). Patients who received treatments following precision oncology decision support (DS) were also stratified by MTA and SC treatment lines after DS (b).

Fig. 4. Progression-free survival of MTA treatments administered before and after decision support.

Treatments were stratified by administration of molecularly targeted agents (MTAs) before (red) and after (blue) precision oncology decision support (DS).

Fig. 5. Clinical outcome analyses by treatment DDA scores.

Treatments were stratified by DDA score of 1000, as high-score treatment lines (1000≦DDA score) were demonstrated to result in increased clinical benefit based on analysis of data from the SHIVA01 trial. High-score treatments are indicated with blue, the corresponding comparator sets in red in all Kaplan-Meier plots throughout (a) to (f). Progression-free survival (PFS) probability of high-score treatments was plotted against all other treatments (SC and low-score MTAs) (a) and low-score MTAs only (b). PFS of high-score treatments versus all other treatments (c) and versus low-score MTAs (d) administered following decision support (DS). Overall survival (OS) of patients who received at least one high-score MTA line of treatment (LOT) during their treatment course versus OS of patients who did not receive any high-score MTA LOT during their treatment course (e) and OS of patients who received at least one high-score MTA LOT following DS versus OS of patients who did not receive any high-score MTA LOT following DS (f).

Fig. 6. Multivariate analysis forest plots.

Multivariate analysis forest plots relative to PFS (a) and OS (b). TKI biomarker pos.: patients with driver oncogenes serving as biomarkers for FDA-approved targeted tyrosine kinase inhibitor (TKI) therapy for lung cancer; PD-L1 (overexp.): patients with molecular diagnostic results reporting positive PD-L1 expression status; any biomarker pos.: patients with any positive biomarkers for TKI or immunotherapy. SCC: squamous cell carcinoma; SCLC: small-cell lung cancer.