Optimal sequencing of ibrutinib, idelalisib, and venetoclax in chronic lymphocytic leukemia: results from a multicenter study of 683 patients

Background

Ibrutinib, idelalisib, and venetoclax are approved for treating CLL patients in the United States. However, there is no guidance as to their optimal sequence.

Patients and methods

We conducted a multicenter, retrospective analysis of CLL patients treated with kinase inhibitors (KIs) or venetoclax. We examined demographics, discontinuation reasons, overall response rates (ORR), survival, and post-KI salvage strategies. Primary endpoint was progression-free survival (PFS).

Results

A total of 683 patients were identified. Baseline characteristics were similar in the ibrutinib and idelalisib groups. ORR to ibrutinib and idelalisib as first KI was 69% and 81%, respectively. With a median follow-up of 17 months (range 1-60), median PFS and OS for the entire cohort were 35 months and not reached. Patients treated with ibrutinib (versus idelalisib) as first KI had a significantly better PFS in all settings; front-line [hazard ratios (HR) 2.8, CI 1.3-6.3, P = 0.01], relapsed-refractory (HR 2.8, CI 1.9-4.1, P < 0.001), del17p (HR 2.0, CI 1.2-3.4, P = 0.008), and complex karyotype (HR 2.5, CI 1.2-5.2, P = 0.02). At the time of initial KI failure, use of an alternate KI or venetoclax had a superior PFS when compared with chemoimmunotherapy. Furthermore, patients who discontinued ibrutinib due to progression or toxicity had marginally improved outcomes if they received venetoclax (ORR 79%) versus idelalisib (ORR 46%) (PFS HR .6, CI.3-1.0, P = 0.06).

Conclusions

In the largest real-world experience of novel agents in CLL, ibrutinib appears superior to idelalisib as first KI. Furthermore, in the setting of KI failure, alternate KI or venetoclax therapy appear superior to chemoimmunotherapy combinations. The use of venetoclax upon ibrutinib failure might be superior to idelalisib. These data support the need for trials testing sequencing strategies to optimize treatment algorithms.

3D calculation of radiation-induced second cancer risk including dose and tissue response heterogeneities

PURPOSE

Tools for comparing relative induced second cancer risk, to inform choice of radiotherapy treatment plan, are becoming increasingly necessary as the availability of new treatment modalities expands. Uncertainties, in both radiobiological models and model parameters, limit the confidence of such calculations. The aim of this study was to develop and demonstrate a software tool to produce a malignant induction probability (MIP) calculation which incorporates patient-specific dose and allows for the varying responses of different tissue types to radiation.

METHODS

The tool has been used to calculate relative MIPs for four different treatment plans targeting a subtotally resected meningioma: 3D conformal radiotherapy (3DCFRT), volumetric modulated arc therapy (VMAT), intensity-modulated x-ray therapy (IMRT), and scanned protons.

RESULTS

Two plausible MIP models, with considerably different dose-response relationships, were considered. A fractionated linear-quadratic induction and cell-kill model gave a mean relative cancer risk (normalized to 3DCFRT) of 113% for VMAT, 16% for protons, and 52% for IMRT. For a linear no-threshold model, these figures were 105%, 42%, and 78%, respectively. The relative MIP between plans was shown to be significantly more robust to radiobiological parameter uncertainties compared to absolute MIP. Both models resulted in the same ranking of modalities, in terms of MIP, for this clinical case.

CONCLUSIONS

The results demonstrate that relative MIP is a useful metric with which treatment plans can be ranked, regardless of parameter- and model-based uncertainties. With further validation, this metric could be used to discriminate between plans that are equivalent with respect to other planning priorities.

Fast neutron relative biological effects and implications for charged particle therapy

In two fast neutron data sets, comprising in vitro and in vivo experiments, an inverse relationship is found between the low-linear energy transfer (LET) α/β ratio and the maximum value of relative biological effect (RBE(max)), while the minimum relative biological effect (RBE(min)) is linearly related to the square root of the low-LET α/β ratio. RBE(max) is the RBE at near zero dose and can be represented by the ratio of the α parameters at high- and low-LET radiation exposures. RBE(min) is the RBE at very high dose and can be represented by the ratio of the square roots of the β parameters at high- and low-LET radiation exposures. In principle, it may be possible to use the low-LET α/β ratio to predict RBE(max) and RBE(min, )providing that other LET-related parameters, which reflect intercept and slopes of these relationships, are used. These two limits of RBE determine the intermediate values of RBE at any dose per fraction; therefore, it is possible to find the RBE at any dose per fraction. Although these results are obtained from fast neutron experiments, there are implications for charged particle therapy using protons (when RBE is scaled downwards) and for heavier ion beams (where the magnitude of RBE is similar to that for fast neutrons). In the case of fast neutrons, late reacting normal tissue systems and very slow growing tumours, which have the smallest values of the low-LET α/β ratio, are predicted to have the highest RBE values at low fractional doses, but the lowest values of RBE at higher doses when they are compared with early reacting tissues and fast growing tumour systems that have the largest low-LET α/β ratios.

Malignant induction probability maps for radiotherapy using X-ray and proton beams

The aim of this study was to display malignant induction probability (MIP) maps alongside dose distribution maps for radiotherapy using X-ray and charged particles such as protons. Dose distributions for X-rays and protons are used in an interactive MATLAB® program (MathWorks, Natick, MA). The MIP is calculated using a published linear quadratic model, which incorporates fractionation effects, cell killing and cancer induction as a function of dose, as well as relative biological effect. Two virtual situations are modelled: (a) a tumour placed centrally in a cubic volume of normal tissue and (b) the same tumour placed closer to the skin surface. The MIP is calculated for a variety of treatment field options. The results show that, for protons, the MIP increases with field numbers. In such cases, proton MIP can be higher than that for X-rays. Protons produce the lowest MIPs for superficial targets because of the lack of exit dose. The addition of a dose bath to all normal tissues increases the MIP by up to an order of magnitude. This exploratory study shows that it is possible to achieve three-dimensional displays of carcinogenesis risk. The importance of treatment geometry, including the length and volume of tissue traversed by each beam, can all influence MIP. Reducing the volume of tissue irradiated is advantageous, as reducing the number of cells at risk reduces the total MIP. This finding lends further support to the use of treatment gantries as well as the use of simpler field arrangements for particle therapy provided normal tissue tolerances are respected.