Mixture survival models methodology: an application to cancer immunotherapy assessment in clinical trials

Progress in immunotherapy revolutionized the treatment landscape for advanced lung cancer, raising survival expectations beyond those that were historically anticipated with this disease. In the present study, we describe the methods for the adjustment of mixture parametric models of two populations for survival analysis in the presence of long survivors. A methodology is proposed in several five steps: first, it is proposed to use the multimodality test to decide the number of subpopulations to be considered in the model, second to adjust simple parametric survival models and mixture distribution models, to estimate the parameters and to select the best model fitted the data, finally, to test the hypotheses to compare the effectiveness of immunotherapies in the context of randomized clinical trials. The methodology is illustrated with data from a clinical trial that evaluates the effectiveness of the therapeutic vaccine CIMAvaxEGF vs the best supportive care for the treatment of advanced lung cancer. The mixture survival model allows estimating the presence of a subpopulation of long survivors that is 44% for vaccinated patients. The differences between the treated and control group were significant in both subpopulations (population of short-term survival: p = 0.001, the population of long-term survival: p = 0.0002). For cancer therapies, where a proportion of patients achieves long-term control of the disease, the heterogeneity of the population must be taken into account. Mixture parametric models may be more suitable to detect the effectiveness of immunotherapies compared to standard models.

A multimodality test to guide the management of patients with a pancreatic cyst

Pancreatic cysts are common and often pose a management dilemma, because some cysts are precancerous, whereas others have little risk of developing into invasive cancers. We used supervised machine learning techniques to develop a comprehensive test, CompCyst, to guide the management of patients with pancreatic cysts. The test is based on selected clinical features, imaging characteristics, and cyst fluid genetic and biochemical markers. Using data from 436 patients with pancreatic cysts, we trained CompCyst to classify patients as those who required surgery, those who should be routinely monitored, and those who did not require further surveillance. We then tested CompCyst in an independent cohort of 426 patients, with histopathology used as the gold standard. We found that clinical management informed by the CompCyst test was more accurate than the management dictated by conventional clinical and imaging criteria alone. Application of the CompCyst test would have spared surgery in more than half of the patients who underwent unnecessary resection of their cysts. CompCyst therefore has the potential to reduce the patient morbidity and economic costs associated with current standard-of-care pancreatic cyst management practices.

Theranostic profiling of Ewing sarcoma (ES) and desmoplastic small round cell tumors (DSRCT).

e20507 Background: Ewing Sarcoma (ES)/PNET and Desmoplastic Small Round Cell Tumor (DSRCT) are sarcomas with distinct chromosomal translocations involving the EWS gene (predominately EWS-FLI1 and EWS-WT1; respectively). Their diagnosis and treatment has been difficult due to the rarity, diverse clinical presentation, overlapping histologic features and genetic complexity (Taylor BS et al, 2011). Therefore, novel approaches in clinical management are warranted. Methods: Seventeen cases (9 ES and 8 DSRCT) were analyzed using a commercial molecular profiling service (CarisTargetNow, Caris Life Sciences, Phoenix, AZ). The whole genome transcriptome analysis (29285 transcripts) was performed using HumanHT-12 beadChip (Illuminia Inc, San Diego, CA) and comparison to pooled soft tissue reference sample. Additionally, a select number of chemotherapy-predictive (theranostic) biomarkers were evaluated using immunohistochemistry, FISH, and DNA sequencing. Results: We observed 160 commonly up and 357 commonly down regulated genes between ES and DSRCT in transcriptome analysis. Cell cycle signaling, DNA replication and E2F mediated pathway genes were most commonly up regulated. In addition, higher expression of SOX-2, a recently identified cancer stem cell marker (Riggi et al, 2010), were observed in DSRCT than in ES, suggesting EWS-WT1 translocation might result in reprogramming of DSRCT to express cancer stem cells. Above threshold expression of TOP2A and TOPO1was observed in approximately 50% of all cases. Additional theranostic biomarkers (ERCC1, TS, SPARC and MGMT) showed significant inter-individual variations. No KRAS mutations or EGFR gene amplification were observed in any case. Conclusions: 1. Our transcriptome analyses might provide future therapeutic targets in cell cycle regulation, DNA replication, receptor TKI pathways and stem cell reprogramming. 2. Using multimodality test approaches, we confirmed and refined the benefits in both tumor types of individualized therapeutic assessment including predicted susceptibilities to anthracycline, irinotecan, platinum analogs, fluorouracil, and temozolomide.