Neoadjuvant and adjuvant end-points in health technology assessment in oncology

Hepatobiliary phase MRI-guided radiofrequency ablation of small hepatocellular carcinomas invisible on precontrast MRI

PURPOSE

In cirrhotic livers reliable visualization and exact localization of small hepatocellular carcinoma (HCC) can be challenging without adequate contrast enhancement. To investigate the feasibility, technical success rate, and safety of hepatobiliary phase MRI-guided percutaneous radiofrequency ablation (RFA) of small HCCs invisible on precontrast MRI.

METHODS

53 patients (17f, 63.6 ± 16.7 years), with small HCC that were not visible in non-contrast MRI underwent MRI guided RFA. Feasibility was assessed by analyzing proper identification of the target tumor, tumor delineation during MRI-guided needle positioning and number of needle adjustments required for accurate placement. Technical success was defined as complete ablation with a safety margin of 5 mm. Safety was assessed from reports of procedure-related complications.

RESULTS

In all 53 cases, target tumors were not visible in non-contrast MRI but in the hepatobiliary phase. In 5 cases, planning imaging showed new tumors, which were either treated in the same session (n = 4) or altered the therapeutic approach (n = 1). Mean tumor diameter was 9.7 ± 1.9 mm and the number of needle adjustments was 5 ± 3. Post-ablation imaging showed a technical success rate of 98 % (51 cases, 55 tumors). No major complications occurred. Follow-up imaging (26.2 ± 22.4 month) showed no local tumor progression or recurrence.

CONCLUSIONS

Use of the hepatobiliary phase for MRI-guided ablation of otherwise MR-occult tumors is a feasible approach for an effective and safe treatment of small HCC nodules.

Neoadjuvant nivolumab plus bevacizumab therapy improves the prognosis of triple-negative breast cancer in humanized mouse models

BACKGROUND

The combination of immune checkpoint inhibitors and anti-angiogenic agents has been proposed as a promising strategy to improve the outcome of advanced triple-negative breast cancer (TNBC). However, further investigation is warranted to elucidate the specific mechanisms underlying the effects of combination therapy and its potential as neoadjuvant therapy for early-stage TNBC.

METHODS

In this study, we constructed humanized mouse models by engrafting the human immune system into severely immunodeficient mice and subsequently implanting TNBC cells into the model. The mice were treated with neoadjuvant combination therapy (bevacizumab combined with nivolumab), followed by in vivo imaging system to assess tumor recurrence and metastasis after surgery. The immune microenvironment of tumors was analyzed to investigate the potential mechanisms. Furthermore, we verified the impact of extending the interval before surgery or administering adjuvant therapy after neoadjuvant therapy on the prognosis of mice.

RESULTS

Neoadjuvant combination therapy significantly inhibited tumor growth, prevented recurrence and metastasis by normalizing tumor vessels and inducing robust CD8+ T cell infiltration and activation in primary tumors (p < 0.001). In vivo experiments demonstrated that prolonging the interval before surgery or administering adjuvant therapy after neoadjuvant therapy did not enhance its efficacy.

CONCLUSION

The preclinical study has demonstrated the therapeutic efficacy and mechanism of neoadjuvant combination therapy (nivolumab plus bevacizumab) in treating early TNBC.

Neoadjuvant chemotherapy for breast cancer: an evaluation of its efficacy and research progress

Neoadjuvant chemotherapy (NAC) for breast cancer is widely used in the clinical setting to improve the chance of surgery, breast conservation and quality of life for patients with advanced breast cancer. A more accurate efficacy evaluation system is important for the decision of surgery timing and chemotherapy regimen implementation. However, current methods, encompassing imaging techniques such as ultrasound and MRI, along with non-imaging approaches like pathological evaluations, often fall short in accurately depicting the therapeutic effects of NAC. Imaging techniques are subjective and only reflect macroscopic morphological changes, while pathological evaluation is the gold standard for efficacy assessment but has the disadvantage of delayed results. In an effort to identify assessment methods that align more closely with real-world clinical demands, this paper provides an in-depth exploration of the principles and clinical applications of various assessment approaches in the neoadjuvant chemotherapy process.

An Investigation into the Relationship Between Choice of Model Structure and How to Adjust for Subsequent Therapies Using a Case Study in Oncology

BACKGROUND

A common challenge in health technology assessments (HTAs) of cancer treatments is how subsequent therapy use within the trial follow-up may influence cost-effectiveness model outcomes. Although overall survival (OS) is often a key driver of model results, there are no guidelines to advise how to adjust for this potential confounding, with different approaches available dependent on the model structure.

OBJECTIVE

We compared a partitioned survival analysis (PartSA) with a semi-Markov multi-state model (MSM) structure, with and without attempts to adjust for the impact of subsequent therapies on OS using a case study describing outcomes for people with relapsed/refractory multiple myeloma.

METHODS

Both model structures included three health states: pre-progression, progressed disease and death. Three traditional crossover methods were considered within the context of the PartSA, whereas for the MSM, the probability of post-progression death was pooled across arms. Impacts on the model incremental cost-effectiveness ratio (ICER) were recorded.

RESULTS

The unadjusted PartSA produced an ICER of £623,563, and after adjustment yielded an ICER range of £381,340-£386,907. The unadjusted MSM produced an ICER of £1,283,780. Adjusting OS in the MSM resulted in an ICER of £345,486.

CONCLUSIONS

The simplicity of the PartSA is lost when the decision problem becomes more complex (for example, when OS data are confounded by subsequent therapies). In this setting, the MSM structure may be considered more flexible, with fewer and less restrictive assumptions required versus the PartSA. Researchers should consider important study design features that may influence the generalisability of data when undertaking model conceptualisation.

Risk-adapted modulation through de-intensification of cancer treatments: an ESMO classification

BACKGROUND

The landscape of clinical trials testing risk-adapted modulations of cancer treatments is complex. Multiple trial designs, endpoints, and thresholds for non-inferiority have been used; however, no consensus or convention has ever been agreed to categorise biomarkers useful to inform the treatment intensity modulation of cancer treatments.

METHODS

An expert subgroup under the European Society for Medical Oncology (ESMO) Precision Medicine Working Group shaped an international collaborative project to develop a classification system for biomarkers used in the cancer treatment de-intensification, based on a tiered approach. A group of disease-oriented clinical, translational, methodology and public health experts, and patients' representatives provided an analysis of the status quo, and scanned the horizon of ongoing clinical trials. The classification was developed through multiple rounds of expert revisions and inputs.

RESULTS

The working group agreed on a univocal definition of treatment de-intensification. Evidence of reduction in the dose-density, intensity, or cumulative dose, including intermittent schedules or shorter treatment duration or deletion of segment(s) of the standard regimens, compound(s), or treatment modality must be demonstrated, to define a treatment de-intensification. De-intensified regimens must also portend a positive impact on toxicity, quality of life, health system burden, or financial toxicity. ESMO classification categorises the biomarkers for treatment modulation in three tiers, based on the level of evidence. Tier A includes biomarkers validated in prospective, randomised, non-inferiority clinical trials. The working group agreed that in non-inferiority clinical trials, boundaries are highly dependent upon the disease scenario and endpoint being studied and that the absolute differences in the outcomes are the most relevant measures, rather than relative differences. Biomarkers tested in single-arm studies with a threshold of non-inferiority are classified as Tier B. Tier C is when the validation occurs in prospective-retrospective quality cohort investigations.

CONCLUSIONS

ESMO classification for the risk-guided intensity modulation of cancer treatments provides a set of evidence-based criteria to categorise biomarkers deemed to inform de-intensification of cancer treatments, in risk-defined patients. The classification aims at harmonising definitions on this matter, therefore offering a common language for all the relevant stakeholders, including clinicians, patients, decision-makers, and for clinical trials.

Treatment of small (T1mic, T1a, and T1b) node-negative HER2+ breast cancer - a review of current evidence for and against the use of anti-HER2 treatment regimens

INTRODUCTION

Since the advent of anti-HER2 therapies, evidence surrounding adjuvant treatment of small (T1mic, T1a, and T1b), node-negative, HER2-positive breast cancer (HER2+BC) has remained limited. Practices vary widely between institutions with little known regarding the added benefit of systemic therapy, including cytotoxic chemotherapy and HER2-directed treatments. Our group has set out to perform an extensive review of available literature on this topic.

AREAS COVERED

In this review, we examined HER2 biology, anti-HER therapies, outcome definitions, and available prospective and retrospective data surrounding the use of adjuvant therapy in those with small, node-negative, HER2+BC. For outcomes, we primarily explored breast cancer-specific survival (BCSS), invasive disease-free survival (iDFS), and overall survival (OS). We also investigated the incidence of adverse events with a particular focus on symptomatic and asymptomatic declines in left ventricular ejection fraction.

EXPERT OPINION

Retrospective data will likely be the main driver for future treatment decisions. Given what we know, high risk T1b and T1c subgroups derive measurable added benefit from HER2-guided combination therapies but it's not clear whether these benefits outweigh known risks associated with this combination therapy. For tumors ≤0.5cm (T1mic and T1a), treatment remains highly controversial with limited evidence available through retrospective analysis that suggest over-treatment may be occurring.

The Impasse on Overall Survival in Oncology Reimbursement Decision-Making: How Can We Resolve This?

Mature overall survival (OS) data are often unavailable at the time of regulatory and reimbursement decisions for a new cancer treatment. For patients with early-stage cancers treated with potentially curative treatments, demonstrating an OS benefit may take years and may be confounded by subsequent lines of therapy or crossover to the investigational treatment. For patients with advanced-stage cancers, mature OS data may be available but difficult to interpret for similar reasons. There are strong opinions about approval and reimbursement in the absence of mature OS data, with concerns over delay in patient access set against concerns about uncertainty in long-term benefit. This position paper reflects our individual views as patient advocate, clinician or health economist on one aspect of this debate. We look at payer decisions in the absence of mature OS data, considering when and how non-OS trial outcomes could inform decision-making and how uncertainty can be addressed beyond the trial, supporting these views with evidence from the literature. We consider when it is reasonable for payers to expect or not expect mature OS data at the initial reimbursement decision (based on criteria such as cancer stage and treatment efficacy) acknowledging that there are settings in which mature OS data are expected. We propose flexible strategies for generating and appraising patient-relevant evidence, including context-relevant endpoints and quality of life measures, when survival rates are good and mature OS data are not expected. We note that fair reimbursement is important; this means valuing patient benefit as shown through prespecified endpoints and reappraising if there is ongoing uncertainty or failure to show a sustained benefit. We suggest that reimbursement systems continue to evolve to align with scientific advances, because innovation is only meaningful if readily accessible to patients. The proposed strategies have the potential to promote thorough assessment of potential benefit to patients and lead to timely access to effective medicines.