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Chemotherapy-induced thrombocytopenia (CIT) is a known hematologic complication of oncology treatment. This single-institution study examines the degree with which CIT impacts specific pediatric solid tumor cohorts reflected by platelet transfusion burden and treatment modifications.Data regarding clinically relevant CIT were obtained via a retrospective chart review of pediatric solid tumor patients treated at Memorial Sloan Kettering Cancer Center from 2013 to 2020. Patients were stratified based on histologic diagnoses as well as chemotherapy regimen. CIT impact was assessed through platelet transfusion means, chemotherapy dose reductions, and treatment delays.A total of 150 patients were included with mean age 10.3 [0.2-21.0]. Patients receiving therapy for high-risk neuroblastoma and localized Ewing sarcoma, both of which included high-dose cyclophosphamide and doxorubicin, required the most platelet transfusions over the treatment course, with a mean of 13 and 9, respectively. Reduced relative dose intensity (RDI), due in part to CIT, was greatest for the patients receiving therapy for high-risk and intermediate-risk rhabdomyosarcoma. Fifty-six percent of high-risk patients experienced a reduced RDI during the final two cycles of treatment and 69% of intermediate-risk patients experienced one during the final four cycles of treatment.The impact of CIT varied by the administered chemotherapy regimens and dose intensity of chemotherapy agents. This study demonstrated that CIT causes both marked platelet transfusion burden as well as treatment reduction and delay within certain solid tumor cohorts. This can lend to future studies aimed at reducing the burden of CIT and targeting the most at-risk populations.

Pneumatosis intestinalis (PI) is characterized by the presence of intramural gas in the gastrointestinal (GI) tract. The overall aim of this study was to review risk factors and outcome of pediatric oncology patients at our institution who developed PI.Patients diagnosed with PI between 2007 and 2018 were identified from ICD-10 coding of radiology reports at Memorial Sloan Kettering Kids, a tertiary pediatric oncology center. Outcomes of interest were (a) resolution and time to resolution of PI, (b) surgical intervention within 2 weeks of diagnosis of PI, or (c) death secondary to PI. To capture the resolution of PI, we defined the "time to recovery (TTR)" as the time elapsed between date of PI diagnosis and the date of recovery.Forty-two patients were identified. Within 30 days of diagnosis of PI, three patients had surgical intervention for PI (7%) and two patients died (5%) due to non-PI causes. Median TTR of PI was 4.5 days (95% CI: 3-7 days). In univariable and multivariable analyses, only steroid use in the prior 30 days was significantly associated with a faster TTR of PI (HR = 2.27 [95% CI: 1.17-4.41], p = .02).This is the largest case series of patients with PI in the pediatric oncology population, which reveals significantly lower surgical and mortality rates than other published PI series. For the majority of patients, conservative medical management is indicated. A prospective study is warranted to define diagnosis and management guidelines for PI in the pediatric oncology population in a cooperative group setting.

A cancer in the contralateral breast in a woman with a previous or synchronous breast cancer is typically considered to be an independent primary tumor. Emerging evidence suggests that in a small subset of these cases the second tumor represents a metastasis. We sought to investigate the issue using massively parallel sequencing targeting 254 genes recurrently mutated in breast cancer. We examined the tumor archives at Memorial Sloan Kettering Cancer Center for the period 1995-2006 to identify cases of contralateral breast cancer where surgery for both tumors was performed at the Center. We report results from 49 patients successfully analyzed by a targeted massively parallel sequencing assay. Somatic mutations and copy number alterations were defined by state-of-the-art algorithms. Clonal relatedness was evaluated by statistical tests specifically designed for this purpose. We found evidence that the tumors in contralateral breasts were clonally related in three cases (6%) on the basis of matching mutations at codons where somatic mutations are rare. Clinical data and the presence of similar patterns of gene copy number alterations were consistent with metastasis for all three cases. In three additional cases, there was a solitary matching mutation at a common PIK3CA locus. The results suggest that a subset of contralateral breast cancers represent metastases rather than independent primary tumors. Massively parallel sequencing analysis can provide important evidence to clarify the diagnosis. However, given the inter-tumor mutational heterogeneity in breast cancer, sufficiently large gene panels need to be employed to define clonality convincingly in all cases.

Next generation sequencing panels are being used increasingly in cancer research to study tumor evolution. A specific statistical challenge is to compare the mutational profiles in different tumors from a patient to determine the strength of evidence that the tumors are clonally related, that is, derived from a single, founder clonal cell. The presence of identical mutations in each tumor provides evidence of clonal relatedness, although the strength of evidence from a match is related to how commonly the mutation is seen in the tumor type under investigation. This evidence must be weighed against the evidence in favor of independent tumors from non-matching mutations. In this article, we frame this challenge in the context of diagnosis using a novel random effects model. In this way, by analyzing a set of tumor pairs, we can estimate the proportion of cases that are clonally related in the sample as well as the individual diagnostic probabilities for each case. The method is illustrated using data from a study to determine the clonal relationship of lobular carcinoma in situ with subsequent invasive breast cancers, where each tumor in the pair was subjected to whole exome sequencing. The statistical properties of the method are evaluated using simulations, demonstrating that the key model parameters are estimated with only modest bias in small samples in most configurations.

Evaluating the prognosis of patients according to their demographic, biological, or disease characteristics is a major issue, as it may be used for guiding treatment decisions. In cancer studies, typically, more than one endpoint can be observed before death. Patients may undergo several types of events, such as local recurrences and distant metastases, with death as the terminal event. Accuracy of clinical decisions may be improved when the history of these different events is considered. Thus, it may be useful to dynamically predict patients' risk of death using recurrence history. As previously applied within the framework of joint models for longitudinal and time to event data, we propose a dynamic prediction tool based on joint frailty models. Joint modeling accounts for the dependence between recurrent events and death, by the introduction of a random effect shared by the two processes. We estimate the probability of death between the prediction time t and a horizon t + w, conditional on information available at time t. Prediction can be updated with the occurrence of a new event. We proposed and compared three prediction settings, taking into account three different information levels. The proposed tools are applied to patients diagnosed with a primary invasive breast cancer and treated with breast-conserving surgery, followed for more than 10 years in a French comprehensive cancer center.

Frailty models are gaining interest in prognostic studies, especially because of the spread of multicenter studies. However, little research has been performed to extend prognostic tools to frailty models, including discrimination measures. As previously performed for the Harrell's c-index, we extended two different discrimination measures (the model-based concordance probability estimation of Gonen and Heller and the nonparametric Uno's c-index) to take into account cluster membership. We calculate measures at three levels: between-group, where only patients with different frailties are compared, within-group, where only patients sharing the same frailty are compared, and overall. We performed simulations to study the impact of group size and the number of groups on these measures. Results showed that the two measures can be extended to frailty models while remaining independent from censoring distribution, provided that the group size is sufficient. We apply the extended measures to two real datasets, a meta-analysis and a large multicenter trial.

It is well known that the glass transition temperature of polydimethylsiloxane elastomers lies close to −120°C. According to the time temperature superposition principle, we may state that these materials keep their caoutchoutic state when they are excited by a 10 MHz mechanical wave. This is an important point particularly for acoustooptic applications. Thus, in the range of 1–10 MHz, materials with good efficiency are missed; low acoustic wave velocity is needed to give the best conditions, which means to be in a caoutchoutic state for polymeric materials. With this work, we show that three polysiloxanes, polymerized under different conditions, do not fulfill this condition. Thus the shift of Tg with frequency is very large and these polymers reach their glassy state at relatively low frequency. The acoustic wave velocity is in good agreement with mechanical predictions provided by master curves of the Young modulus.