The optimal cut-off values for tumor size, number of lesions, and CEA levels in patients with surgically treated colorectal cancer liver metastases: An international, multi-institutional study

Background and Objectives Despite the long-standing consensus on the importance of tumor size, tumor number and carcinoembryonic antigen (CEA) levels as predictors of long-term outcomes among patients with colorectal liver metastases (CRLM), optimal prognostic cut-offs for these variables have not been established. Methods Patients who underwent curative-intent resection of CRLM and had available data on at least one of the three variables of interest above were selected from a multi-institutional dataset of patients with known KRAS mutational status. The resulting cohort was randomly split into training and testing datasets and recursive partitioning analysis was employed to determine optimal cut-offs. The concordance probability estimates (CPEs) for these optimal cut offs were calculated and compared to CPEs for the most widely used cut-offs in the surgical literature. Results A total of 1643 patients who met eligibility criteria were identified. Following recursive partitioning analysis in the training dataset, the following cut-offs were identified: 2.95 cm for tumor size, 1.5 for tumor number and 6.15 ng/ml for CEA levels. In the entire dataset, the calculated CPEs for the new tumor size (0.52), tumor number (0.56) and CEA (0.53) cut offs exceeded CPEs for other commonly employed cut-offs. Conclusion The current study was able to identify optimal cut-offs for the three most commonly employed prognostic factors in CRLM. While the per variable gains in discriminatory power are modest, these novel cut-offs may help produce appreciable increases in prognostic performance when combined in the context of future risk scores.publishedVersio

The optimal cut‐off values for tumor size, number of lesions, and CEA levels in patients with surgically treated colorectal cancer liver metastases: An international, multi‐institutional study

Background and Objectives: Despite the long-standing consensus on the importance of tumor size, tumor number and carcinoembryonic antigen (CEA) levels as predictors of long-term outcomes among patients with colorectal liver metastases (CRLM), optimal prognostic cut-offs for these variables have not been established. Methods: Patients who underwent curative-intent resection of CRLM and had available data on at least one of the three variables of interest above were selected from a multi-institutional dataset of patients with known KRAS mutational status. The resulting cohort was randomly split into training and testing datasets and recursive partitioning analysis was employed to determine optimal cut-offs. The concordance probability estimates (CPEs) for these optimal cut offs were calculated and compared to CPEs for the most widely used cut-offs in the surgical literature. Results: A total of 1643 patients who met eligibility criteria were identified. Following recursive partitioning analysis in the training dataset, the following cut-offs were identified: 2.95 cm for tumor size, 1.5 for tumor number and 6.15 ng/ml for CEA levels. In the entire dataset, the calculated CPEs for the new tumor size (0.52), tumor number (0.56) and CEA (0.53) cut offs exceeded CPEs for other commonly employed cut-offs. Conclusion: The current study was able to identify optimal cut-offs for the three most commonly employed prognostic factors in CRLM. While the per variable gains in discriminatory power are modest, these novel cut-offs may help produce appreciable increases in prognostic performance when combined in the context of future risk scores

The interplay of KRAS mutational status with tumor laterality in non-metastatic colorectal cancer: An international, multi-institutional study in patients with known KRAS, BRAF, and MSI status

Background: Although the prognostic relevance of KRAS status in metastatic colorectal cancer (CRC) depends on tumor laterality, this relationship is largely unknown in non-metastatic CRC. Methods: Patients who underwent resection for non-metastatic CRC between 2000 and 2018 were identified from institutional databases at six academic tertiary centers in Europe and Japan. The prognostic relevance of KRAS status in patients with right-sided (RS), left-sided (LS), and rectal cancers was assessed. Results: Of the 1093 eligible patients, 378 had right-sided tumors and 715 had left-sided tumors. Among patients with RS tumors, the 5-year overall (OS) and recurrence-free survival (RFS) for patients with KRASmut versus wild-type tumors was not shown to differ significantly (82.2% vs. 83.2% and 72.1% vs. 76.7%, respectively, all p >.05). Among those with LS tumors, KRAS mutation was associated with shorter 5-year OS and RFS on both the univariable (OS: 79.4% vs. 86.1%, p =.004; RFS: 68.8% vs. 77.3%, p =.005) and multivariable analysis (OS: HR: 1.52, p =.019; RFS: HR: 1.32, p =.05). Conclusions: KRAS mutation status was independently prognostic among patients with LS tumors, but this association failed to reach statistical significance in RS and rectal tumors. These findings confirm reports in metastatic CRC and underline the possible biologic importance of tumor location

Virtual Interviews: Assessing How Expectations Meet Reality.

ObjectiveThis study aimed to determine the post-matriculation perceptions of interns and faculty who participated in the 2020-2021 virtual interview process and how their expectations of the program and the applicants, respectively, aligned with reality.Design, setting, and participantsPublished surveys on virtual interviewing were reviewed and modified to design two surveys, for interns and for faculty who interviewed. Interns and faculty members from the Departments of Surgery and Medicine at one institution who participated in the 2020-2021 virtual interview process completed the surveys four to six months after the start of the academic year. Following survey completion, surgical interns from the same application cycle participated in one in-person focus group nine months after the start of the academic year to clarify points raised in the surveys.ResultsForty-six interns and faculty members responded to the survey (subgroup response rates ranging from 13-30%) and ten interns participated in the focus group (participation rate 100%). Most faculty and intern participants found that expectations formed during virtual interviews were accurate. However, our respondents noted limitations to virtual interviews that reduced their usefulness, including challenges with unnatural social interactions, understanding city fit, and getting a sense of resident and program culture. Participants provided possible solutions to address these challenges.ConclusionsOverall, this mixed-methods study at a single institution found that perceptions from virtual interviews were generally accurate but with some limitations. We describe several opportunities to improve the virtual interview process and optimize the application experience

Measuring the Effect of Inter-Study Variability on Estimating Prediction Error

<div><p>Background</p><p>The biomarker discovery field is replete with molecular signatures that have not translated into the clinic despite ostensibly promising performance in predicting disease phenotypes. One widely cited reason is lack of classification consistency, largely due to failure to maintain performance from study to study. This failure is widely attributed to variability in data collected for the same phenotype among disparate studies, due to technical factors unrelated to phenotypes (e.g., laboratory settings resulting in “batch-effects”) and non-phenotype-associated biological variation in the underlying populations. These sources of variability persist in new data collection technologies.</p><p>Methods</p><p>Here we quantify the impact of these combined “study-effects” on a disease signature’s predictive performance by comparing two types of validation methods: ordinary randomized cross-validation (RCV), which extracts random subsets of samples for testing, and inter-study validation (ISV), which excludes an entire study for testing. Whereas RCV hardwires an assumption of training and testing on identically distributed data, this key property is lost in ISV, yielding systematic decreases in performance estimates relative to RCV. Measuring the RCV-ISV difference as a function of number of studies quantifies influence of study-effects on performance.</p><p>Results</p><p>As a case study, we gathered publicly available gene expression data from 1,470 microarray samples of 6 lung phenotypes from 26 independent experimental studies and 769 RNA-seq samples of 2 lung phenotypes from 4 independent studies. We find that the RCV-ISV performance discrepancy is greater in phenotypes with few studies, and that the ISV performance converges toward RCV performance as data from additional studies are incorporated into classification.</p><p>Conclusions</p><p>We show that by examining how fast ISV performance approaches RCV as the number of studies is increased, one can estimate when “sufficient” diversity has been achieved for learning a molecular signature likely to translate without significant loss of accuracy to new clinical settings.</p></div

Inter-study validation performance in RNA-seq data based on SVM.

<p>(A) The colored bars report ISV sensitivities achieved by validating performance on the study designated in the horizontal axis. Dashed lines represent average ISV sensitivities for each phenotype. Solid lines report corresponding ten-fold RCV sensitivities of each phenotype. (B) The colored bars report average sensitivities from validating on studies excluded from training. Squares represent corresponding RCV sensitivities from the studies included in the training set. Results were averaged across the different combinations of training studies, and the error bars report the standard deviation of the results.</p

Multi-study Integration of Brain Cancer Transcriptomes Reveals Organ-Level Molecular Signatures

We utilized abundant transcriptomic data for the primary classes of brain cancers to study the feasibility of separating all of these diseases simultaneously based on molecular data alone. These signatures were based on a new method reported herein – Identification of Structured Signatures and Classifiers (ISSAC) – that resulted in a brain cancer marker panel of 44 unique genes. Many of these genes have established relevance to the brain cancers examined herein, with others having known roles in cancer biology. Analyses on large-scale data from multiple sources must deal with significant challenges associated with heterogeneity between different published studies, for it was observed that the variation among individual studies often had a larger effect on the transcriptome than did phenotype differences, as is typical. For this reason, we restricted ourselves to studying only cases where we had at least two independent studies performed for each phenotype, and also reprocessed all the raw data from the studies using a unified pre-processing pipeline. We found that learning signatures across multiple datasets greatly enhanced reproducibility and accuracy in predictive performance on truly independent validation sets, even when keeping the size of the training set the same. This was most likely due to the metasignature encompassing more of the heterogeneity across different sources and conditions, while amplifying signal fro

Inter-study-validation and randomized cross-validation results as function of number of studies included in analysis.

<p>Average ISV (black circles) and RCV (white squares) sensitivities as a function of the number of studies included, for ADC (A, D), SCC (B, E), and NORM (C, F), using SVM and ISSAC classifiers.</p

Summary of lung disease microarray data.

<p>The number of samples (n = 1470), number of studies (n = 26), types of platforms, and the methods of tissue extraction used to collect samples in the studies are shown. The platform labels represent: 1) Affymetrix Human Genome U133 Plus 2, and 2) Affymetrix Human Genome U133A. The sampling method labels represent: A) surgical resection, B) bronchoscopy brushing, C) bronchoalveolar lavage. See Table S2 for detailed information on the studies.</p><p>Summary of lung disease microarray data.</p