Chemotherapy does not affect liver regeneration following PVE or major resection. J. Surg. Oncol. 2016;113:449-455. © 2016 Wiley Periodicals, Inc.

Introduction: ARID1A is commonly mutated in colorectal cancer (CRC), frequently resulting in truncation and loss of protein expression. ARID1A recruits MSH2 for mismatch-repair during DNA replication. ARID1A deficiency promotes hypermutability and immune activation in preclinical models but its role in CRC patients is being explored. Methods:The DNA sequencing and gene expression profiling of CRC patients were extracted from TCGA and MD Anderson Cancer Center databases, with validation utilizing external databases, and correlation between ARID1A and immunologic features. Immunohistochemistry for T-cell markers was performed on a separate cohort of patients.Results: 28/417 MSS CRC patients (6.7%) had ARID1A mutation. Among 58 genes most commonly mutated in CRC, ARID1A mutation had the highest increase with frameshift mutation rates in MSS cases (8-fold, p<0.001). In MSS, ARID1A mutation was enriched in immune subtype (CMS1) and had a strong correlation with IFN-γ expression (Δz score +1.91, p<0.001). Compared with ARID1A wild-type, statistically significant higher expression for key checkpoint genes (e.g., PD-L1, CTLA4, and PDCD1) and genes sets (e.g., antigen presentation, cytotoxic T cell function, and immune checkpoints) was observed in mutant cases. This was validated by unsupervised differential expression of genes related to immune response and further, confirmed by higher infiltration of T-cells in IHC of tumors with ARID1A mutation (p=0.01). Conclusion:The immunogenicity of ARID1A mutant cases is likely due to increased level of neoantigens resulting from increased TMB and frameshift mutations. Tumors with ARID1A mutation may be more susceptible to immune therapy-based treatment strategies and should be recognized as a unique molecular subgroup in future immune therapy trials.

Defects in the peroxisomes biogenesis and/or function result in peroxisomal disorders. In this study, we describe the largest Arab cohort to date (72 families) of clinically, biochemically and molecularly characterized patients with peroxisomal disorders. At the molecular level, we identified 43 disease‐causing variants, half of which are novel. The founder nature of many of the variants allowed us to calculate the minimum disease burden for these disorders in our population ~1:30 000, which is much higher than previous estimates in other populations. Clinically, we found an interesting trend toward genotype/phenotype correlation in terms of long‐term survival. Nearly half (40/75) of our peroxisomal disorders patients had documented survival beyond 1 year of age. Most unusual among the long‐term survivors was a multiplex family in which the affected members presented as adults with non‐specific intellectual disability and epilepsy. Other unusual presentations included the very recently described peroxisomal fatty acyl‐CoA reductase 1 disorder as well as CRD, spastic paraparesis, white matter (CRSPW) syndrome. We conclude that peroxisomal disorders are highly heterogeneous in their clinical presentation. Our data also confirm the demonstration that milder forms of Zellweger spectrum disorders cannot be ruled out by the “gold standard” very long chain fatty acids assay, which highlights the value of a genomics‐first approach in these cases.

PURPOSE Acquired resistance to anti-EGFR therapy (EGFRi) in CRC has previously been explained by the model of acquiring new mutations in KRAS/NRAS/EGFR, among other MAPK-pathway members. However, this was primarily based on single-agent EGFRi trials and little is known about the resistance mechanisms of EGFRi combined with effective cytotoxic chemotherapy in previously untreated patients. METHODS We analyzed paired plasma samples from RAS/BRAF/EGFR wild-type mCRC patients enrolled in three large randomized trials evaluating EGFRi in the first-line in combination with chemotherapy and as a single-agent in third-line. The mutational signature of the alterations acquired with therapy was evaluated. CRC cell lines with resistance to cetuximab, FOLFOX, and SN38 were developed, and transcriptional changes profiled. RESULTS Patients whose tumors were treated with and responded to EGFRi alone were more likely to develop acquired mutations (46%) compared to those treated in combination with cytotoxic chemotherapy (9%). Further, contrary to the generally accepted hypothesis of the clonal evolution of acquired resistance, we demonstrate that baseline resistant subclonal mutations rarely expanded to become clonal at progression, and most remained subclonal or disappeared. Consistent with this clinical finding, preclinical models with acquired resistance to either cetuximab or chemotherapy were cross-resistant to the alternate agents, with transcriptomic profiles consistent with epithelial-to-mesenchymal transition (EMT). In contrast, commonly acquired resistance alterations in the MAPK pathway do not impact sensitivity to cytotoxic chemotherapy. CONCLUSION These findings support a model of resistance whereby transcriptomic mechanisms of resistance predominate in the presence of active cytotoxic chemotherapy combined with EGFRi, with a greater predominance of acquired MAPK mutations after single-agent EGFRi. The proposed model has implications for prospective studies evaluating EGFRi rechallenge strategies guided by acquired MAPK mutations, and highlights the need to address transcriptional mechanisms of resistance.

Sepsis is one of the leading causes of morbidity and mortality in hospitals. Early diagnosis could substantially improve the patient outcomes and reduce the mortality rate. In this paper we propose a machine learning approach for anomaly detection to aid the early detection of sepsis. Using the medical data of over 40,000 patients [1], we use both unsupervised and supervised methods to extract relevant features from the data, and then use standard classification approaches to predict sepsis six hours before clinical diagnosis occurs. To extract features, we used the reconstruction error of an autoencoding neural network trained on control patients free of sepsis, and used random forest classifiers to learn the most important features for the classification of patients. We then combined the features from both of these approaches with a variety of standard classification models. Cross-validation as well as the asymmetric utility function designed for this challenge are used to evaluate the resulting models. We obtained a utility function score for the full unseen dataset of 0.177 (Team Kriss); achieved with a logistic regression classifier. All the implementation is publicly available at https://github.com/ ineskris/SepsisChallenge-Cinc2019.

Colorectal cancer (CRC) is the second leading cause of cancer deaths worldwide. One major factor contributing to the high mortality rate of CRC is the presence of microscopic minimal residual disease (MRD) that remains radiographically undetectable and persist regardless of therapeutic interventions. In fact, many CRC patients who have received therapy with curative intent will experience recurrence years later due to MRD, which can progress to recurrent disease that results in clinical morbidity. While immune modulation is crucial for mediating progression and metastasis of primary CRC tumors, how micrometastases suppress or evade antitumor immunity and/or maintain a state of dormancy before growing into macrometastases remains poorly understood. Filling this knowledge gap requires establishing and utilizing models capable of accurately recapitulating the immune phenotype of human microsatellite stable (MSS) metastatic CRC. To this end, we generated genetically engineered, syngeneic mouse colonic organoids using CRE recombinase technology to recapitulate MSS metastatic CRC models with APC and TP53 mutations. After establishing hematologic experimental metastases, histopathological examination confirmed that those present in the liver bore a close resemblance to human colorectal adenocarcinoma liver metastases. We then leveraged our model to test the hypothesis that the activation of suppressive immune cell populations is key for enabling the establishment and progression of MRD. We performed high-plex immunofluorescent co-localization analysis to delineate the immune compartments of micro- and macrometastases. Our results showed that, overall, leukocytes (CD45+ cells) decreased as metastatic tumor foci in the liver progressed. Specifically, cytotoxic CD8+ T cell densities were markedly reduced as tumor metastases progressed to macroscopic disease. Furthermore, populations of CD4+FOXP3+ (T-reg) cells increased as tumors progressed, with an increase in the ratio of CD4+FOXP3+ to CD8+ cells during tumor progression. Additionally, M2 macrophage density (IBA-1+, CD163+) increased as the liver metastases progressed in size. Overall, our findings support our hypothesis, and initiate the first step for identifying suppressive immune infiltrates that may be exploited for therapeutic targeting. Importantly, our somatic mutation-based modeling strategy enables a highly precise recapitulation of the mutational drivers and heterogeneity that occurs in CRC patients as well as provides a valuable resource for research aimed at elucidating the immunobiology of MRD. Citation Format: Alaa M. Mohamed, Melinda Soeung, Jumanah Alshenaifi, Natalie Fowlkes, Ganiraju Manyam, Jennifer Davis, Amanda Anderson, Will Norton, Angela K. Deem, Sisi Gao, Isha Khanduri, Christopher Bistow, Federica Carbone, Stefania Napolitano, Justin Huang, Dipen M. Maru, David G. Menter, Giulio F. Draetta, Giannicola Genovese, Scott Kopetz. Recapitulating metastatic colorectal cancer in somatic mutation models for investigating the tumor immune microenvironment in minimal residual disease [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 77.

<p>Supplementary Fig S4: Top ten GO enrichment gene sets of ARID1A tumor DEGs demonstrates that most are immune processes related.</p>

<p>Supplementary Table S1</p>