Tumor Growth Rate After Nadir Is Associated With Survival in Patients With EGFR-Mutant Non-Small-Cell Lung Cancer Treated With Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitor

Assessing immunotherapy response: going beyond RECIST by integrating early tumor growth kinetics

Objective

Assess the contribution of early tumor growth dynamics modeling to predict clinical outcomes in non-small cell lung cancer patients receiving immunotherapy, alongside standard RECIST 1.1 criteria.

Methods

Our retrospective studies used data from 861 patients with advanced NSCLC enrolled in three randomized Phase III trials evaluating immunotherapy plus chemotherapy were analyzed. Tumor size measurements up to two follow-up time points were used to fit a novel Gompertz model and estimate growth rate (GR) and kinetic parameters representing depth of response (A), speed of response (B), and long-term modulation (M). Correlations between these early tumor growth parameters and clinical outcomes such as progression-free survival (PFS) and time to response (TTR) were assessed. Descriptive and discriminative analyses were performed to delineate tumor growth dynamics across various response profiles based on RECIST 1.1 criteria.

Results

The novel Gompertz model accurately described early tumor growth kinetics in 861 non-small cell lung cancer patients treated with immunotherapy. Lower growth rate (GR) and model parameter M were associated with longer progression-free survival (PFS) (HR=0.897 and 7.47x10^-7, respectively). Higher GR and parameter A correlated with shorter time to response (HR=0.575 and 0.696, respectively). Responders had significantly lower A (p=1.51e-53) and higher GR (p=0.4e-12) than non-responders. Non-durable stable disease patients had higher GR (p=0.0001) and parameter B (p=0.0002) compared to late responders. Early tumor growth parameters showed potential for predicting long-term outcomes and treatment response patterns.

Lung cancer in patients who have never smoked - an emerging disease

Lung cancer is the most common cause of cancer-related deaths globally. Although smoking-related lung cancers continue to account for the majority of diagnoses, smoking rates have been decreasing for several decades. Lung cancer in individuals who have never smoked (LCINS) is estimated to be the fifth most common cause of cancer-related deaths worldwide in 2023, preferentially occurring in women and Asian populations. As smoking rates continue to decline, understanding the aetiology and features of this disease, which necessitate unique diagnostic and treatment paradigms, will be imperative. New data have provided important insights into the molecular and genomic characteristics of LCINS, which are distinct from those of smoking-associated lung cancers and directly affect treatment decisions and outcomes. Herein, we review the emerging data regarding the aetiology and features of LCINS, particularly the genetic and environmental underpinnings of this disease as well as their implications for treatment. In addition, we outline the unique diagnostic and therapeutic paradigms of LCINS and discuss future directions in identifying individuals at high risk of this disease for potential screening efforts.

Dissecting sources of variability in patient response to targeted therapy: anti-HER2 therapies as a case study

BACKGROUND AND PURPOSE

Despite their use to treat cancers with specific genetic aberrations, targeted therapies elicit heterogeneous responses. Sources of variability are critical to targeted therapy drug development, yet there exists no method to discern their relative contribution to response heterogeneity.

EXPERIMENTAL APPROACH

We use HER2-amplified breast cancer and two agents, neratinib and lapatinib, to develop a platform for dissecting sources of variability in patient response. The platform comprises four components: pharmacokinetics, tumor burden and growth kinetics, clonal composition, and sensitivity to treatment. Pharmacokinetics are simulated using population models to capture variable systemic exposure. Tumor burden and growth kinetics are derived from clinical data comprising over 800,000 women. The fraction of sensitive and resistant tumor cells is informed by HER2 immunohistochemistry. Growth rate-corrected drug potency is used to predict response. We integrate these factors and simulate clinical outcomes for virtual patients. The relative contribution of these factors to response heterogeneity are compared.

KEY RESULTS

The platform was verified with clinical data, including response rate and progression-free survival (PFS). For both neratinib and lapatinib, the growth rate of resistant clones influenced PFS to a higher degree than systemic drug exposure. Variability in exposure at labeled doses did not significantly influence response. Sensitivity to drug strongly influenced responses to neratinib. Variability in patient HER2 immunohistochemistry scores influenced responses to lapatinib. Exploratory twice daily dosing improved PFS for neratinib but not lapatinib.

CONCLUSION AND IMPLICATIONS

The platform can dissect sources of variability in response to target therapy, which may facilitate decision-making during drug development.

Tumor Volume Nadir in Patients With ALK-Rearranged Non-Small-Cell Lung Cancer Treated With Alectinib

PURPOSE

Patients with oncogene-driven advanced non-small-cell lung cancer (NSCLC) treated with effective targeted therapy demonstrate characteristic tumor volume dynamics with initial response, nadir, and subsequent regrowth. This study investigated tumor volume nadir and time to nadir in patients with ALK-rearranged advanced NSCLC treated with alectinib.

MATERIALS AND METHODS

In patients with advanced ALK-rearranged NSCLC treated with alectinib monotherapy, tumor volume dynamics were evaluated on serial computed tomography (CT) scans using a previously validated CT tumor measurement technique. A linear regression model was built to predict tumor volume nadir. Time-to-event analyses were performed to evaluate time to nadir.

RESULTS

Among 45 patients who experienced initial volume decrease, 37 patients (25 with tumor regrowth and 12 without regrowth but >6 months follow-up) were studied for nadir volume (V_p). The linear model to predict tumor volume nadir was built using the baseline tumor volume (V₀): V₀-V_p = .696 × V₀ + 5,326 (P < 2 × 10^-16; adjusted R² = 0.86). The percent volume changes at nadir (median, -90.9%, mean, -85.3%) showed larger decrease in patients who were treated with alectinib as first-line therapy than in the ≥2nd-line group and were independent of V₀ and clinical variables. Time to nadir had a median of 11.5 months and was longer in the first-line group (P = .04).

CONCLUSION

The tumor nadir volume in patients with ALK-rearranged advanced NSCLC treated with alectinib can be predicted by the liner regression model and consists of approximately 30% of the baseline volume minus 5 cm³, providing additional insights into precision therapy monitoring and potential guides for local ablative therapy to prolong disease control.