Clinical outcomes and biomarker profiles of elderly pretreated NSCLC patients from the BATTLE trial

Prediction Model for Tumor Volume Nadir in EGFR -mutant NSCLC Patients Treated With EGFR Tyrosine Kinase Inhibitors

PURPOSE

In patients with advanced non-small cell lung cancer (NSCLC) and oncogenic driver mutations treated with effective targeted therapy, a characteristic pattern of tumor volume dynamics with an initial regression, nadir, and subsequent regrowth is observed on serial computed tomography (CT) scans. We developed and validated a linear model to predict the tumor volume nadir in EGFR -mutant advanced NSCLC patients treated with EGFR tyrosine kinase inhibitors (TKI).

MATERIALS AND METHODS

Patients with EGFR -mutant advanced NSCLC treated with EGFR-TKI as their first EGFR-directed therapy were studied for CT tumor volume kinetics during therapy, using a previously validated CT tumor measurement technique. A linear regression model was built to predict tumor volume nadir in a training cohort of 34 patients, and then was validated in an independent cohort of 84 patients.

RESULTS

The linear model for tumor nadir prediction was obtained in the training cohort of 34 patients, which utilizes the baseline tumor volume before initiating therapy (V 0 ) to predict the volume decrease (mm 3 ) when the nadir volume (V p ) was reached: V 0 -V p =0.717×V 0 -1347 ( P =2×10 -16 ; R2 =0.916). The model was tested in the validation cohort, resulting in the R2 value of 0.953, indicating that the prediction model generalizes well to another cohort of EGFR -mutant patients treated with EGFR-TKI. Clinical variables were not significant predictors of tumor volume nadir.

CONCLUSION

The linear model was built to predict the tumor volume nadir in EGFR -mutant advanced NSCLC patients treated with EGFR-TKIs, which provide an important metrics in treatment monitoring and therapeutic decisions at nadir such as additional local abrasive therapy.

Risk and benefit for umbrella trials in oncology: a systematic review and meta-analysis

BACKGROUND

Umbrella clinical trials in precision oncology are designed to tailor therapies to the specific genetic changes within a tumor. Little is known about the risk/benefit ratio for umbrella clinical trials. The aim of our systematic review with meta-analysis was to evaluate the efficacy and safety profiles in cancer umbrella trials testing targeted drugs or a combination of targeted therapy with chemotherapy.

METHODS

Our study was prospectively registered in PROSPERO (CRD42020171494). We searched Embase and PubMed for cancer umbrella trials testing targeted agents or a combination of targeted therapies with chemotherapy. We included solid tumor studies published between 1 January 2006 and 7 October 2019. We measured the risk using drug-related grade 3 or higher adverse events (AEs), and the benefit by objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). When possible, data were meta-analyzed.

RESULTS

Of the 6207 records identified, we included 31 sub-trials or arms of nine umbrella trials (N = 1637). The pooled overall ORR was 17.7% (95% confidence interval [CI] 9.5-25.9). The ORR for targeted therapies in the experimental arms was significantly lower than the ORR for a combination of targeted therapy drugs with chemotherapy: 13.3% vs 39.0%; p = 0.005. The median PFS was 2.4 months (95% CI 1.9-2.9), and the median OS was 7.1 months (95% CI 6.1-8.4). The overall drug-related death rate (drug-related grade 5 AEs rate) was 0.8% (95% CI 0.3-1.4), and the average drug-related grade 3/4 AE rate per person was 0.45 (95% CI 0.40-0.50).

CONCLUSIONS

Our findings suggest that, on average, one in five cancer patients in umbrella trials published between 1 January 2006 and 7 October 2019 responded to a given therapy, while one in 125 died due to drug toxicity. Our findings do not support the expectation of increased patient benefit in cancer umbrella trials. Further studies should investigate whether umbrella trial design and the precision oncology approach improve patient outcomes.

ARAP1 is an independent prognostic biomarker in older women with ovarian high-grade serous adenocarcinoma receiving first-line platinum-based antineoplastic therapy

Background: Little is known about the biological factors influencing ovarian cancer (OC) patient outcome, especially in older patients who are often underrepresented in clinical trials. We examined alterations in the transcriptomic profile of primary high-grade serous carcinoma (HGSC) samples from older OC patients (>70 years) receiving first-line platinum-based treatment to identify potential biomarkers for prediction of response to this therapy.Material and methods: Tumor samples from 50 HGSC patients were identified from a retrospective cohort, analyzed by gene expression array. The protein expression of selected biomarkers was examined using immunohistochemistry (IHC).Results: Gene expression profiling revealed 81 genes with significantly altered expression in patients experiencing progression after first-line platinum-based treatment within 6 months versus those who progressed later than 12 months. Expression of ankyrin repeat and PH domain 1 (ARAP1) was significantly lower in the group with early versus late progression (p ≤ .01). Correlation between ARAP1 expression and outcome was further confirmed by IHC staining in the discovery cohort (χ²-test, p = .004) and in independent validation cohorts. The sensitivity of ARAP1 allowed identification of 64.7% of patients with early progression in the discovery population, with a specificity of 78.6% and a negative predictive value of 78.6%. Multivariate regression analysis identified ARAP1 as an independent prognostic factor.Conclusions: This hypothesis generating study suggests that low expression of ARAP1 is an independent prognostic biomarker of shorter RFS in older patients with HGSC receiving first-line platinum-based antineoplastic therapy, which could be used to identify patients who should receive more intensive treatment and closer surveillance.

Point estimation following two-stage adaptive threshold enrichment clinical trials

Recently, several study designs incorporating treatment effect assessment in biomarker-based subpopulations have been proposed. Most statistical methodologies for such designs focus on the control of type I error rate and power. In this paper, we have developed point estimators for clinical trials that use the two-stage adaptive enrichment threshold design. The design consists of two stages, where in stage 1, patients are recruited in the full population. Stage 1 outcome data are then used to perform interim analysis to decide whether the trial continues to stage 2 with the full population or a subpopulation. The subpopulation is defined based on one of the candidate threshold values of a numerical predictive biomarker. To estimate treatment effect in the selected subpopulation, we have derived unbiased estimators, shrinkage estimators, and estimators that estimate bias and subtract it from the naive estimate. We have recommended one of the unbiased estimators. However, since none of the estimators dominated in all simulation scenarios based on both bias and mean squared error, an alternative strategy would be to use a hybrid estimator where the estimator used depends on the subpopulation selected. This would require a simulation study of plausible scenarios before the trial.

How old is "too old" for translational research?

BACKGROUND

Targeted therapies are now widely used for lung cancer management. Numerous biomarkers are performed in these patients in the diagnosis phase and have consequences on patient's management. There are some changes during elderly which can influence the biology of cancer; particularly mitochondrial dysfunction and deregulation of nutrient sensing. Elderly patients are candidate to these biological assessments, like younger ones.

METHODS

We review all the published papers based on Mesh carries with "elderly", "lung cancer", "targeted therapy".

RESULTS

After description of biological modification during elderly, the use of targeted therapies in non-small cell lung cancer (NSCLC) is presented and discussed. Tyrosine kinase inhibitors (TKIs) and antiangiogenic molecules were depicted in selected or unselected population.

CONCLUSIONS

Targeted therapies can be used in older patients with lung cancer and are sometimes an optimal choice in this particular population.

Management of elderly patients

SUMMARY

Elderly patients are often excluded from clinical trials, yet more than two-thirds of patients diagnosed with lung cancer are over 65 years old. It is therefore important to develop specific tools and trials for this specific patient population.

METHODS

This chapter first examines the management specificities of elderly patients. Randomized trials specifically involving elderly patients are then described, and likely future developments are considered.

RESULTS

Older people have several specificities. In addition to traditional criteria such as age and performance status, other important factors include the number of comorbidities and age-related changes such as cognitive deficits and depression. Specific indices taking these factors into account have been published and validated. Single-agent therapy has been widely used to treat metastatic lung cancer in the elderly, following publication of negative results from randomized phase III trials of combination chemotherapy. Recently, however, a trial of doublet therapy gave positive results, in a subgroup of independent older patients. The benefit of patient selection based on a combination of these indices has been demonstrated in open-label and randomized trials. These results must now be confirmed in phase III trials including the use of tyrosine kinase inhibitors combined with chemotherapy.

CONCLUSIONS

Indices based on a combination of age-related factors, together with judicious use of biological markers, will further improve the prognosis of elderly lung cancer patients.

Efficacy and safety of erlotinib in elderly patients in the phase IV POLARSTAR surveillance study of Japanese patients with non-small-cell lung cancer

OBJECTIVE

More tolerable treatment options are needed for the large number of elderly patients with non-small-cell lung cancer (NSCLC). An analysis of the phase IV POLARSTAR surveillance study examined the safety and efficacy of erlotinib in elderly Japanese patients with previously treated NSCLC.

MATERIALS AND METHODS

From December 2007 to October 2009, all erlotinib-treated patients with unresectable, recurrent/advanced NSCLC in Japan were enrolled. Efficacy and safety data were stratified by age (<75 years, 75-84 years, ≥85 years). Kaplan-Meier methodology was used to estimate median progression-free survival (PFS). Safety data were collected with a focus on interstitial lung disease (ILD).

RESULTS

A total of 9907 patients were eligible for safety assessment (<75 years, n=7848; 75-84 years, n=1911; ≥85 years, n=148) and 9651 for efficacy assessment (<75 years, n=7701; 75-84 years, n=1815; ≥85 years, n=135). Other baseline characteristics were balanced. The incidence of ILD (all grades) was 4.2% (<75 years), 5.1% (75-84 years), and 3.4% (≥85 years). The mortality rate due to ILD was ≤1.7% in all age groups. Other toxicities (including rash) were similar between age groups. The median PFS was 65 days (95% confidence interval [CI], 62-68) for patients aged <75 years, 74 days (95% CI, 69-82) for patients aged 75-84 years, and 72 days (95% CI, 56-93) for patients aged ≥85 years.

CONCLUSIONS

Efficacy and tolerability of erlotinib for elderly patients was not numerically inferior to that reported in younger patients. Erlotinib could be considered for elderly patients with recurrent/advanced NSCLC.

Management of elderly patients with NSCLC; updated expert's opinion paper: EORTC Elderly Task Force, Lung Cancer Group and International Society for Geriatric Oncology

Non-small-cell lung cancer (NSCLC) is a very common disease in the elderly population and its incidence in this particular population is expected to increase further, because of the ageing of the Western population. Despite this, limited data are available for the treatment of these patients and, therefore, the development of evidence-based treatment recommendations is challenging. In 2010, European Organization for Research and Treatment of Cancer (EORTC) took an initiative in collaboration with International Society of Geriatric Oncology (SIOG) and created an experts panel that provided an experts' opinion consensus paper for the management of elderly NSCLC patients. Since this publication, important new data are available and EORTC and SIOG recommended to update the 2010 recommendations. Besides recommendations for surgery, adjuvant chemotherapy and radiotherapy, treatment of locally advanced and metastatic disease, recommendations were expanded, to include data on patient preferences and geriatric assessment.

Prospective validation of quantitative NSE mRNA in pleural fluid of non-small cell lung cancer patients

Although the survival of lung cancer patients has improved significantly due to the development of early detective tools, lung cancer remains a leading cause of cancer-related death after curative surgery. So it is extremely important for cancer patients to predict early metastasis, especially pleural dissemination, the most frequent type of recurrence in patients after surgery. Based on a retrospective study of 86 curatively resected lung cancer patients (training set), we determined a cutoff value of NSE mRNA using receiver-operating characteristic curve. Then, we prospectively used this cutoff value to validate the risk of pleural recurrence in a new cohort of 81 lung cancer patients (validation set) between April 2009 and June 2010 by real-time reverse transcriptase-polymerase chain reaction. During the median 27 months of postoperative surveillance, 16 of the 81 patients died, and 9 of the 16 developed pleural metastases. Multivariate analysis with the Cox proportional hazards model showed that positive NSE mRNA was a significant independent risk factor with both overall survival and pleural recurrence-free survival (both P < 0.0001) as end points which were significantly worse in patients with positive NSE mRNA (P < 0.0001). These results indicate that quantitative NSE mRNA in pleural fluid is a reliable prognostic indicator of pleural recurrence in the clinical setting.

Potential predictive biomarkers for individualizing treatment for men with castration-resistant prostate cancer

With the surge in therapeutic options for men with castration-resistant prostate cancer (CRPC) comes increasingly complicated treatment decision making, highlighting the need for biomarkers that can identify appropriate patients for specific treatments and accurately assess disease response. Predictive biomarkers are factors related to the disease or the host that are associated with improvements in outcomes, such as survival, due to specific therapies. Such biomarkers have become of paramount importance in oncology to maximize the benefits of novel systemic agents while minimizing harm to individual patients and the costs to society. Given the number of newly approved and expensive systemic therapies, including novel hormonal therapies, chemotherapies, immunotherapies, and bone microenvironment-targeting therapies, predictive biomarkers are needed to give physicians a more rational sense of matching the right patient to the right therapy sequence at a given time. There are currently no validated predictive biomarkers in CRPC. We discuss potential predictive biomarkers in men with CRPC and how these may be developed in the context of therapeutic clinical trials.

DeMix: deconvolution for mixed cancer transcriptomes using raw measured data

MOTIVATION

Tissue samples of tumor cells mixed with stromal cells cause underdetection of gene expression signatures associated with cancer prognosis or response to treatment. In silico dissection of mixed cell samples is essential for analyzing expression data generated in cancer studies. Currently, a systematic approach is lacking to address three challenges in computational deconvolution: (i) violation of linear addition of expression levels from multiple tissues when log-transformed microarray data are used; (ii) estimation of both tumor proportion and tumor-specific expression, when neither is known a priori; and (iii) estimation of expression profiles for individual patients.

RESULTS

We have developed a statistical method for deconvolving mixed cancer transcriptomes, DeMix, which addresses the aforementioned issues in array-based expression data. We demonstrate the performance of our model in synthetic and real, publicly available, datasets. DeMix can be applied to ongoing biomarker-based clinical studies and to the vast expression datasets previously generated from mixed tumor and stromal cell samples.

AVAILABILITY

All codes are written in C and integrated into an R function, which is available at http://odin.mdacc.tmc.edu/∼wwang7/DeMix.html.

CONTACT

wwang7@mdanderson.org

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A tale of two approaches: complementary mechanisms of cytotoxic and targeted therapy resistance may inform next-generation cancer treatments

Chemotherapy and molecularly targeted approaches represent two very different modes of cancer treatment and each is associated with unique benefits and limitations. Both types of therapy share the overarching limitation of the emergence of drug resistance, which prevents these drugs from eliciting lasting clinical benefit. This review will provide an overview of the various mechanisms of resistance to each of these classes of drugs and examples of drug combinations that have been tested clinically. This analysis supports the contention that understanding modes of resistance to both chemotherapy and molecularly targeted therapies may be very useful in selecting those drugs of each class that will have complementing mechanisms of sensitivity and thereby represent reasonable combination therapies.