Exposure-response (E-R) analyses are an integral component in the development of oncology products. Characterizing the relationship between drug exposure metrics and response allows the sponsor to use modeling and simulation to address both internal and external drug development questions (e.g., optimal dose, frequency of administration, dose adjustments for special populations). This white paper is the output of an industry-government collaboration among scientists with broad experience in E-R modeling as part of regulatory submissions. The goal of this white paper is to provide guidance on what the preferred methods for E-R analysis in oncology clinical drug development are and what metrics of exposure should be considered.

Diffuse astrocytic gliomas and their most common and aggressive representation, glioblastoma (GBM), which as per the 2021 World Health Organization (WHO) guidelines is an isocitrate dehydrogenase (IDH) wildtype without alteration in histone 3 and has glomeruloid vascular proliferation, tumor necrosis, telomerase reverse transcriptase (TERT) promoter mutation, epidermal growth factor receptor (EGFR) gene amplification, or +7/-10 chromosome copy-number changes, are fast-growing tumors with a dismal patient prognosis. Herein, we present cases of a 63-year-old male who, despite no evidence of tumor growth, developed a 6-cm tumor, histologically verified as GBM, WHO CNS grade 4, within eight months, and a 74-year-old female in whom a 1.5-cm tumor grew to 43 mm within 28 days, once again histologically confirmed as GBM, WHO CNS grade 4. Other studies using previous WHO guidelines and including up to 106 cases have shown that these tumors have a daily growth rate of 1.4% and can double their size in a period varying from two weeks to 49.6 days. These growth rates further underline the need for extensive surgical resection as disease progression is rapid, with studies reporting that resection of more than 85% of the tumor volume determined on neuroradiology improves survival compared to biopsy or limited resection and resection of more than 98% of the tumor volume statistically improves patient survival.

Tumor interface dynamics is a complex process determined by cell proliferation and invasion to neighboring tissues. Parameters extracted from the tumor interface fluctuations allow for the characterization of the particular growth model, which could be relevant for an appropriate diagnosis and the correspondent therapeutic strategy. Previous work, based on scaling analysis of the tumor interface, demonstrated that gliomas strictly behave as it is proposed by the Family-Vicsek ansatz, which corresponds to a proliferative-invasive growth model, while for meningiomas and acoustic schwannomas, a proliferative growth model is more suitable. In the present work, other morphological and dynamical descriptors are used as a complementary view, such as surface regularity, one-dimensional fluctuations represented as ordered series and bi-dimensional fluctuations of the tumor interface. These fluctuations were analyzed by Detrended Fluctuation Analysis to determine generalized fractal dimensions. Results indicate that tumor interface fractal dimension, local roughness exponent and surface regularity are parameters that discriminate between gliomas and meningiomas/schwannomas.

A major cause of chemoresistance and recurrence in tumors is the presence of dormant tumor foci that survive chemotherapy and can eventually transition to active growth to regenerate the cancer. In this paper, we propose a Quasi Birth-and-Death (QBD) model for the dynamics of tumor growth and recurrence/remission of the cancer. Starting from a discrete-state master equation that describes the time-dependent transition probabilities between states with different numbers of dormant and active tumor foci, we develop a framework based on a continuum-limit approach to determine the time-dependent probability that an undetectable residual tumor will become large enough to be detectable. We derive an exact formula for the probability of recurrence at large times and show that it displays a phase transition as a function of the ratio of the death rate μA of an active tumor focus to its doubling rate λ. We also derive forward and backward Kolmogorov equations for the transition probability density in the continuum limit and, using a first-passage time formalism, we obtain a drift-diffusion equation for the mean recurrence time and solve it analytically to leading order for a large detectable tumor size N. We show that simulations of the discrete-state model agree with the analytical results, except for O(1/N) corrections. As an example of the use of our model in a clinical setting, we show that a range of model parameters can fit Kaplan-Meier recurrence-free survival data for ovarian cancer. Finally, we show in simulations that extending the duration of chemotherapy increases both the mean recurrence time and the asymptotic (large-time) probability of no recurrence. Our results should be useful in planning optimized chemotherapy dosing and duration for cancer treatment, especially in cancer types for which no targeted therapy is available.

Immune checkpoint blockade with Programmed cell death 1 (PD-1)/PD-L1 inhibitors has been effective in various malignancies and is considered as a standard treatment modality for patients with non-small-cell lung cancer (NSCLC). However, emerging evidence show that PD-1/PD-L1 blockade can lead to hyperprogressive disease (HPD), a flair-up of tumor growth linked to dismal prognosis. This study aimed to evaluate the incidence of HPD and identify the determinants associated with HPD in patients with NSCLC treated with PD-1/PD-L1 blockade.
We enrolled patients with recurrent and/or metastatic NSCLC treated with PD-1/PD-L1 inhibitors between April 2014 and November 2018. Clinicopathologic variables, dynamics of tumor growth, and treatment outcomes were analyzed in patients with NSCLC who received PD-1/PD-L1 blockade. HPD was defined according to tumor growth kinetics (TGK), tumor growth rate (TGR), and time to treatment failure (TTF). Immunophenotyping of peripheral blood CD8+ T lymphocytes was conducted to explore the potential predictive biomarkers of HPD.
A total of 263 patients were analyzed. HPD was observed in 55 (20.9%), 54 (20.5%), and 98 (37.3%) patients according to the TGK, TGR, and TTF. HPD meeting both TGK and TGR criteria was associated with worse progression-free survival [hazard ratio (HR) 4.619; 95% confidence interval (CI) 2.868-7.440] and overall survival (HR, 5.079; 95% CI, 3.136-8.226) than progressive disease without HPD. There were no clinicopathologic variables specific for HPD. In the exploratory biomarker analysis with peripheral blood CD8+ T lymphocytes, a lower frequency of effector/memory subsets (CCR7-CD45RA- T cells among the total CD8+ T cells) and a higher frequency of severely exhausted populations (TIGIT+ T cells among PD-1+CD8+ T cells) were associated with HPD and inferior survival rate.
HPD is common in NSCLC patients treated with PD-1/PD-L1 inhibitors. Biomarkers derived from rationally designed analysis may successfully predict HPD and worse outcomes, meriting further investigation of HPD.

Healthy human tissue is highly regulated to maintain homeostasis. Secreted negative feedback factors that inhibit stem cell division and stem cell self-renewal play a fundamental role in establishing this control. The appearance of abnormal cancerous growth requires an escape from these regulatory mechanisms. In a previous study we found that for non-solid tumors if feedback inhibition on stem cell self-renewal is lost, but the feedback on the division rate is still intact, then the tumor dynamics are characterized by a relatively slow sub-exponential growth that we called inhibited growth. Here we characterize the cell dynamics of inhibited cancer growth by modeling feedback inhibition using Hill equations. We find asymptotic approximations for the growth rates of the stem cell and differentiated cell populations in terms of the strength of the inhibitory signal: stem cells grow as a power law t(1/k+1),and the differentiated cells grow as t(1/k), where k is the Hill coefficient in the feedback law regulating cell divisions. It follows that as the tumor grows, undifferentiated cells take up an increasingly large fraction of the population. Implications of these results for specific cancers including CML are discussed. Understanding how the regulatory mechanisms that continue to operate in cancer affect the rate of disease progression can provide important insights relevant to chronic or other slow progressing types of cancer.