In AEGEAN, perioperative durvalumab plus neoadjuvant chemotherapy, versus neoadjuvant chemotherapy alone, significantly improved event-free survival and pathological complete response (primary endpoints; modified ITT [mITT] population, which excluded patients with known EGFR/ALK aberrations) with a manageable safety profile in patients with resectable (R)-NSCLC. Here, we report surgical outcomes from AEGEAN. Patients with treatment-naïve R-NSCLC (stage II-IIIB[N2]) and ECOG PS 0/1 were randomized (1:1) to platinum-based chemotherapy plus durvalumab or placebo IV (Q3W, 4 cycles) before surgery, followed by durvalumab or placebo (Q4W, 12 cycles). Surgical outcomes were summarized for the mITT population using descriptive statistics. 737/740 mITT patients received treatment, 366 and 371 in the durvalumab and placebo arms, respectively; 80.6% and 80.7% underwent surgery, 77.6% and 76.7% completed surgery, and, of the 295 and 302 patients who underwent surgery, 17.3% and 22.2% had delayed surgery. Median time from last neoadjuvant dose to surgery was 34.0 days in both arms. Of patients who underwent surgery, similar proportions had open (49.2% vs 50.7%) and minimally invasive (49.2% vs 47.0%) procedures; lobectomy was the most common procedure (88.1% vs 85.4%). R0 resection rates were numerically higher in the durvalumab versus placebo arm (94.7% vs 91.3%). Median time from surgery to first adjuvant dose was 50.0 versus 52.0 days. In exploratory analyses, a numerically higher proportion of patients in the durvalumab versus placebo arm with baseline N2 nodal status had downstaging from N2 to N0 (47.3% vs 40.2%) or, with baseline N1 nodal status, from N1 to N0 (53.6% vs 46.2%) after surgery. Similar proportions had surgical complication(s) (59.1% vs 60.1%), primarily grade 1/2 (53.0% vs 51.8%, modified safety analysis set). The addition of durvalumab to neoadjuvant chemotherapy had no detrimental effect on the feasibility, approach, type, or timing of surgery and was associated with a tolerable surgical safety profile, versus neoadjuvant chemotherapy alone. NCT03800134.

Background: The anaplastic lymphoma kinase (ALK) gene fusion rearrangement is a potent oncogene, accounting for 2–7% of lung adenocarcinomas, with higher incidence (17–20%) in non-smokers. ALK-positive tumors are sensitive to ALK tyrosine kinase inhibitors (TKIs), thus ALK-positive non-small-cell lung cancer (NSCLC) is currently spearheading precision medicine in thoracic oncology, with three generations of approved ALK inhibitors in clinical practice. However, these treatments are eventually met with resistance. At the molecular level, ALK-positive NSCLC is of the lowest tumor mutational burden, which possibly accounts for the high initial response to TKIs. Nevertheless, TP53 co-mutations are relatively frequent and are associated with adverse outcome of crizotinib treatment, whereas utility of next-generation ALK inhibitors in TP53-mutant tumors is still unknown. Methods: We report the case of an ALK-positive, TP53-mutant NSCLC patient with about five years survival on ALK TKIs with continued next-generation regimens upon progression. Results: The tumor showed progression on crizotinib, but long tumor control was achieved following the incremental administration of next-generation ALK inhibitors, despite lack of evident resistance mechanisms. Conclusion: TP53 status should be taken into consideration when selecting ALK-inhibitor treatment for personalized therapies. In TP53-mutant tumors, switching TKI generations may overcome treatment exhaustion even in the absence of ALK-dependent resistance mechanisms.

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.