The effectiveness of RECIST on survival in patients with NSCLC receiving chemotherapy with or without target agents as first-line treatment

Exploring Genetic Variants and Platinum Chemotherapy Response in Indonesian Non-Small Cell Lung Cancer Patients: Insights from ERCC2 rs13181

Purpose

Individual responses to platinum-based treatment for Non-Small Cell Lung Cancer (NSCLC) are influenced by genetic polymorphisms, including Single Nucleotide Polymorphisms (SNPs). This study aimed to explore the role of ERCC2 in the Nucleotide Excision Repair (NER) pathway for platinum-based chemotherapy in NSCLC. While ERCC2 is widely studied, data for Southeast Asian populations are lacking. Addressing this gap could improve personalized treatment strategies for NSCLC in this demographic.

Patients and Methods

This study recruited 82 NSCLC patients with wildtype mutations of EGFR at Dr. H.A. Rotinsulu Lung Hospital, Bandung, and Dharmais Cancer Hospital, Jakarta. Data were collected prospectively from whole blood samples and medical records, while the effectiveness of chemotherapy was assessed by evaluating the response using RECIST 1.1 criteria on fourth cycle of chemotherapy.

Results

The results of this study showed the presence of genotype variation among the subjects, with frequency distribution as follows: AA genotype (82.9%), AC genotype (15.9%), and CC genotype (1.2%). The analysis of the association between ERCC2 rs13181 CC + AC versus AA with RECIST 1.1 yielded an odds ratio (OR) of 1.042 (95% CI: 0.292-3.715; p=0.950). A multivariate analysis that included cancer stage and chemotherapy regimen as additional variables produced an adjusted odds ratio (aOR) of 0.970 (95% CI: 0.263-3.568; p=0.963).

Conclusion

This study did not find statistically significant associations between ERCC2 rs13181 polymorphisms and chemotherapy responses. However, this research highlights the presence of genetic variation within the Indonesian population, with the AA genotype being the most prevalent, which may influence chemotherapy responses. The results provided preliminary data and lay the foundation for future comprehensive cohort observational investigations.

Hybridizing mechanistic modeling and deep learning for personalized survival prediction after immune checkpoint inhibitor immunotherapy

We present a study where predictive mechanistic modeling is combined with deep learning methods to predict individual patient survival probabilities under immune checkpoint inhibitor (ICI) immunotherapy. This hybrid approach enables prediction based on both measures that are calculable from mechanistic models of key mechanisms underlying ICI therapy that may not be directly measurable in the clinic and easily measurable quantities or patient characteristics that are not always readily incorporated into predictive mechanistic models. A deep learning time-to-event predictive model trained on a hybrid mechanistic + clinical data set from 93 patients achieved higher per-patient predictive accuracy based on event-time concordance, Brier score, and negative binomial log-likelihood-based criteria than when trained on only mechanistic model-derived values or only clinical data. Feature importance analysis revealed that both clinical and model-derived parameters play prominent roles in increasing prediction accuracy, further supporting the advantage of our hybrid approach.

Hybridizing mechanistic mathematical modeling with deep learning methods to predict individual cancer patient survival after immune checkpoint inhibitor therapy

We present a study where predictive mechanistic modeling is used in combination with deep learning methods to predict individual patient survival probabilities under immune checkpoint inhibitor (ICI) therapy. This hybrid approach enables prediction based on both measures that are calculable from mechanistic models (but may not be directly measurable in the clinic) and easily measurable quantities or characteristics (that are not always readily incorporated into predictive mechanistic models). The mechanistic model we have applied here can predict tumor response from CT or MRI imaging based on key mechanisms underlying checkpoint inhibitor therapy, and in the present work, its parameters were combined with readily-available clinical measures from 93 patients into a hybrid training set for a deep learning time-to-event predictive model. Analysis revealed that training an artificial neural network with both mechanistic modeling-derived and clinical measures achieved higher per-patient predictive accuracy based on event-time concordance, Brier score, and negative binomial log-likelihood-based criteria than when only mechanistic model-derived values or only clinical data were used. Feature importance analysis revealed that both clinical and model-derived parameters play prominent roles in neural network decision making, and in increasing prediction accuracy, further supporting the advantage of our hybrid approach. We anticipate that many existing mechanistic models may be hybridized with deep learning methods in a similar manner to improve predictive accuracy through addition of additional data that may not be readily implemented in mechanistic descriptions.

Pre-Treatment Tumor Growth Rate Predicts Clinical Outcomes of Patients With Advanced Non-Small Cell Lung Cancer Undergoing Anti-PD-1/PD-L1 Therapy

Tumor growth rate (TGR; percent size change per month [%/m]) is postulated as an early radio-graphic predictor of response to anti-cancer treatment to overcome limitations of RECIST. We aimed to evaluate the predictive value of pre-treatment TGR (TGR₀) for outcomes of advanced non-small cell lung cancer (aNSCLC) patients treated with anti-PD-1/PD-L1 monotherapy. We retrospectively screened all aNSCLC patients who received PD-1 axis inhibitors in Sun Yat-Sen University Cancer Center between August 2016 and June 2018. TGR₀ was calculated as the percentage change in tumor size per month (%/m) derived from two computed tomography (CT) scans during a "wash-out" period before the initiation of PD-1 axis inhibition. Final follow-up date was August 28, 2019. The X-tile program was used to identify the cut-off value of TGR₀ based on maximum progression-free survival (PFS) stratification. Patients were divided into two groups per the selected TGR₀ cut-off. The primary outcome was the difference of PFS between the two groups. The Kaplan-Meier methods and Cox regression models were performed for survival analysis. A total of 80 eligible patients were included (54 [67.5%] male; median [range] age, 55 [30-74] years). Median (range) TGR₀ was 21.1 (-33.7-246.0)%/m. The optimal cut-off value of TGR₀ was 25.3%/m. Patients with high TGR₀ had shorter median PFS (1.8 months; 95% CI, 1.6 - 2.1 months) than those with low TGR₀ (2.7 months; 95% CI, 0.5 - 4.9 months) (P = 0.005). Multivariate Cox regression analysis revealed that higher TGR₀ independently predicted inferior PFS (hazard ratio [HR] 1.97; 95% CI, 1.08-3.60; P = 0.026). Higher TGR₀ was also significantly associated with less durable clinical benefit rate (34.8% vs. 8.8%, P = 0.007). High pre-treatment TGR was a reliable predictor of inferior PFS and clinical benefit in aNSCLC patients undergoing anti-PD-1/PD-L1 monotherapy. The findings highlight the role of TGR₀ as an early biomarker to predict benefit from immunotherapy and could allow tailoring patient's follow-up.

A Qualitative Transcriptional Signature for Predicting Prognosis and Response to Bevacizumab in Metastatic Colorectal Cancer

Bevacizumab is the molecular-targeted agent used for the antiangiogenic therapy of metastatic colorectal cancer. But some patients are resistant to bevacizumab, it needs an effective biomarker to predict the prognosis and responses of metastatic colorectal cancer (mCRC) to bevacizumab therapy. In this work, we developed a qualitative transcriptional signature to individually predict the response of bevacizumab in patients with mCRC. First, using mCRC samples treated with bevacizumab, we detected differentially expressed genes between response and nonresponse groups. Then, the gene pairs, consisting of at least one differentially expressed gene, with stable relative expression orderings in the response samples but reversal stable relative expression orderings in the nonresponse samples were identified, denoted as pairs-bevacizumab. Similarly, we screened the gene pairs significantly associated with primary tumor locations, donated as pairs-LR. Among the overlapped gene pairs between the pairs-bevacizumab and pairs-LR, we adopted a feature selection process to extract gene pairs that reached the highest F-score for predicting bevacizumab response status in mCRC as the final gene pair signature (GPS), denoted as 64-GPS. In two independent datasets, the predicted response group showed significantly better overall survival than the nonresponse group (P = 6.00e-4 in GSE72970; P = 0.04 in TCGA). Genomic analyses showed that the predicted response group was characterized by frequent copy number alternations, whereas the nonresponse group was characterized by hypermutation. In conclusion, 64-GPS was an objective and robust predictive signature for patients with mCRC treated with bevacizumab, which could effectively assist in the decision of clinical therapy.

Modification of the tumor response threshold in patients of advanced non-small cell lung cancer treated with chemotherapy plus targeted agents: a pooled study from five clinical trials in one institution

Background

Chemotherapy with targeted therapy is a promising therapeutic option for advanced non-small cell lung cancer (NSCLC) patients. Response Evaluation Criteria in Solid Tumors (RECIST) criteria were used in tumor response evaluation. We assumed an optimal threshold for this therapeutic setting and tried to seek a new tumor shrinkage cutoff with the data from five clinical trials in one institution.

Methods

The X-tile program was used to identify the optimal cut-off value of tumor shrinkage. PFS and OS were compared in the current study. Kaplan-Meier method was used to describe PFS and OS. 95% CI was calculated for PFS and OS outcomes to assess the treatment efficacy. A P value of less than 0.05 was considered statistically significant. SPSS 23.0 was used for all statistical analysis.

Results

X-tile analysis yielded -10% in the ∆SLD of the target lesions as the optimal threshold for response/non-response. The 10% tumor shrinkage could discriminate responders from non-responders in PFS (10.1 vs. 2.50 months, P=0.0007) and OS (23.00 vs. 7.66 months, P<0.0001). Univariate and multivariable analysis showed that 10% tumor shrinkage was a valid prognostic factor for PFS (P=0.018) and OS outcome (P<0.0001).

Conclusions

A 10.0% tumor shrinkage in the SLD indicated an indicative efficacy evaluation threshold for NSCLC patients treated with chemotherapy plus targeted agents.

Best Response According to RECIST During First-line EGFR-TKI Treatment Predicts Survival in EGFR Mutation-positive Non-Small-cell Lung Cancer Patients

INTRODUCTION

The association between the response to first-line epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) and survival in EGFR mutation-positive non-small-cell lung cancer (NSCLC) remains unclear. We studied the association between the response to first-line EGFR-TKIs and survival using Response Evaluation Criteria In Solid Tumors (RECIST) and maximal tumor shrinkage.

MATERIALS AND METHODS

We analyzed data from patients with advanced EGFR mutation-positive NSCLC enrolled in first-line gefitinib and afatinib trials. A total of 98 patients who achieved a response or stable disease and had ≥ 1 measurable target lesion were included. The association between the best response by RECIST or maximal tumor shrinkage and survival was analyzed in Kaplan-Meier and Cox regression models with the landmark method. The specified landmark time points were 8 weeks, the median time to maximal tumor shrinkage (16.5 weeks), and median progression-free survival (PFS; 56 weeks).

RESULTS

A total of 76 patients (77%) responded to gefitinib or afatinib. Of these 76 patients, 49 (64%) and 75 (99%) had achieved a response at 8 and 16.5 weeks, respectively. All responders had achieved a response by 56 weeks. The responders had a significantly longer PFS and overall survival (OS) compared with those with stable disease at 16.5 weeks (PFS, P = .003; OS, P < .001) and 56 weeks (PFS, P = .026; OS, P = .016) but not at 8 weeks (PFS, P = .104; OS, P = .313). Among the responders, greater tumor shrinkage was not associated with longer PFS or OS.

CONCLUSION

Those with a response to first-line gefitinib or afatinib had more favorable PFS and OS compared with those with stable disease. A sufficient observation period was required for the response to occur and predict outcomes. Greater maximal tumor shrinkage in the responders was not predictive of survival.

Pemetrexed-carboplatin with intercalated icotinib in the treatment of patient with advanced EGFR wild-type lung adenocarcinoma: A case report

RATIONALE

Tyrosine kinase inhibitors (TKIs) are known to have greater efficacy in epidermal growth factor receptor (EGFR) mutation nonsmall cell lung cancer (NSCLC). However, about 10% of EGFR wild-type (wt) patients respond to TKIs.

PATIENT CONCERNS

Several strategies to increase the efficacy of TKIs in wt NSCLC are the subjects of ongoing investigations. One of them is combining EGFR TKI with intercalated chemotherapy.

DIAGNOSES

We describe a patient with EGFR wt NSCLC, who was found with ovarian and lung metastasis, was treated with pemetrexed and intercalated icotinib.

INTERVENTIONS

In this case, we reported the successful long-term maintenance treatment of a patient with EGFR wt NSCLC with pemetrexed and Icotinib. The patient (40-year-old female) was found with ovarian masses and lung masses. Pathological, immunohistochemical, and amplification refractory mutation system (ARMS) assay examinations of ovarian specimen suggested the expression of metastatic lung adenocarcinoma with wt EGFR. After failure treatment with paclitaxel-carboplatin, the patient received 4 cycles of pemetrexed plus platinum with intercalated icotinib and then remained on pemetrexed and icotinib.

OUTCOMES

A partial response was achieved after the treatment. The patient's condition had remained stable on pemetrexed and icotinib for more than 20 months, with no evidence of progression.

LESSONS

To our knowledge, this is the first report using the long-term maintenance treatment with pemetrexed and intercalated icotinib in EGFR wt patient. The therapeutic strategies warrant further exploration in selected populations of NSCLC.