Deep computational image analysis of immune cell niches reveals treatment-specific outcome associations in lung cancer

The tumor immune composition influences prognosis and treatment sensitivity in lung cancer. The presence of effective adaptive immune responses is associated with increased clinical benefit after immune checkpoint blockers. Conversely, immunotherapy resistance can occur as a consequence of local T-cell exhaustion/dysfunction and upregulation of immunosuppressive signals and regulatory cells. Consequently, merely measuring the amount of tumor-infiltrating lymphocytes (TILs) may not accurately reflect the complexity of tumor-immune interactions and T-cell functional states and may not be valuable as a treatment-specific biomarker. In this work, we investigate an immune-related biomarker (PhenoTIL) and its value in associating with treatment-specific outcomes in non-small cell lung cancer (NSCLC). PhenoTIL is a novel computational pathology approach that uses machine learning to capture spatial interplay and infer functional features of immune cell niches associated with tumor rejection and patient outcomes. PhenoTIL's advantage is the computational characterization of the tumor immune microenvironment extracted from H&E-stained preparations. Association with clinical outcome and major non-small cell lung cancer (NSCLC) histology variants was studied in baseline tumor specimens from 1,774 lung cancer patients treated with immunotherapy and/or chemotherapy, including the clinical trial Checkmate 057 (NCT01673867).

Rapid detection of SARS-CoV-2 variants of concern by single nucleotide polymorphism genotpying using TaqMan assays

We evaluated the performance of SARS-CoV-2 TaqMan real-time reverse-transcription PCR (RT-qPCR) assays (ThermoFisher) for detecting 2 nonsynonymous spike protein mutations, E484K and N501Y. Assay accuracy was evaluated by whole genome sequencing (WGS). Residual nasopharyngeal SARS-CoV-2 positive samples (N = 510) from a diverse patient population in New York City submitted for routine SARS-CoV-2 testing during January-April 2020 were used. We detected 91 (18%) N501Y and 101 (20%) E484K variants. Four samples (0.8%) were positive for both variants. The assay had nearly perfect concordance with WGS in the validation subset, detecting B.1.1.7 and B.1.526 variants among others. Sensitivity and specificity ranged from 0.95 to 1.00. Positive and negative predictive values were 0.98−1.00. TaqMan genotyping successfully predicted the presence of B.1.1.7, but had significantly lower sensitivity, 62% (95% CI, 0.53, 0.71), for predicting B.1.526 sub-lineages lacking E484K. This approach is rapid and accurate for detecting SARS-CoV-2 variants and can be rapidly implemented in routine clinical setting.

Image analysis reveals molecularly distinct patterns of TILs in NSCLC associated with treatment outcome

Despite known histological, biological, and clinical differences between lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC), relatively little is known about the spatial differences in their corresponding immune contextures. Our study of over 1000 LUAD and LUSC tumors revealed that computationally derived patterns of tumor-infiltrating lymphocytes (TILs) on H&E images were different between LUAD (N = 421) and LUSC (N = 438), with TIL density being prognostic of overall survival in LUAD and spatial arrangement being more prognostically relevant in LUSC. In addition, the LUAD-specific TIL signature was associated with OS in an external validation set of 100 NSCLC treated with more than six different neoadjuvant chemotherapy regimens, and predictive of response to therapy in the clinical trial CA209-057 (n = 303). In LUAD, the prognostic TIL signature was primarily comprised of CD4⁺ T and CD8⁺ T cells, whereas in LUSC, the immune patterns were comprised of CD4⁺ T, CD8⁺ T, and CD20⁺ B cells. In both subtypes, prognostic TIL features were associated with transcriptomics-derived immune scores and biological pathways implicated in immune recognition, response, and evasion. Our results suggest the need for histologic subtype-specific TIL-based models for stratifying survival risk and predicting response to therapy. Our findings suggest that predictive models for response to therapy will need to account for the unique morphologic and molecular immune patterns as a function of histologic subtype of NSCLC.

Spatial interplay patterns of cancer nuclei and tumor-infiltrating lymphocytes (TILs) predict clinical benefit for immune checkpoint inhibitors

Immune checkpoint inhibitors (ICIs) show prominent clinical activity across multiple advanced tumors. However, less than half of patients respond even after molecule-based selection. Thus, improved biomarkers are required. In this study, we use an image analysis to capture morphologic attributes relating to the spatial interaction and architecture of tumor cells and tumor-infiltrating lymphocytes (TILs) from digitized H&E images. We evaluate the association of image features with progression-free (PFS) and overall survival in non-small cell lung cancer (NSCLC) (N = 187) and gynecological cancer (N = 39) patients treated with ICIs. We demonstrated that the classifier trained with NSCLC alone was associated with PFS in independent NSCLC cohorts and also in gynecological cancer. The classifier was also associated with clinical outcome independent of clinical factors. Moreover, the classifier was associated with PFS even with low PD-L1 expression. These findings suggest that image analysis can be used to predict clinical end points in patients receiving ICI.

System-wide transcriptome damage and tissue identity loss in COVID-19 patients

The molecular mechanisms underlying the clinical manifestations of coronavirus disease 2019 (COVID-19), and what distinguishes them from common seasonal influenza virus and other lung injury states such as acute respiratory distress syndrome, remain poorly understood. To address these challenges, we combine transcriptional profiling of 646 clinical nasopharyngeal swabs and 39 patient autopsy tissues to define body-wide transcriptome changes in response to COVID-19. We then match these data with spatial protein and expression profiling across 357 tissue sections from 16 representative patient lung samples and identify tissue-compartment-specific damage wrought by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, evident as a function of varying viral loads during the clinical course of infection and tissue-type-specific expression states. Overall, our findings reveal a systemic disruption of canonical cellular and transcriptional pathways across all tissues, which can inform subsequent studies to combat the mortality of COVID-19 and to better understand the molecular dynamics of lethal SARS-CoV-2 and other respiratory infections.

Machine Learning Highlights Downtrending of COVID-19 Patients with a Distinct Laboratory Profile

BACKGROUND

New York City (NYC) experienced an initial surge and gradual decline in the number of SARS-CoV-2-confirmed cases in 2020. A change in the pattern of laboratory test results in COVID-19 patients over this time has not been reported or correlated with patient outcome.

METHODS

We performed a retrospective study of routine laboratory and SARS-CoV-2 RT-PCR test results from 5,785 patients evaluated in a NYC hospital emergency department from March to June employing machine learning analysis.

RESULTS

A COVID-19 high-risk laboratory test result profile (COVID19-HRP), consisting of 21 routine blood tests, was identified to characterize the SARS-CoV-2 patients. Approximately half of the SARS-CoV-2 positive patients had the distinct COVID19-HRP that separated them from SARS-CoV-2 negative patients. SARS-CoV-2 patients with the COVID19-HRP had higher SARS-CoV-2 viral loads, determined by cycle threshold values from the RT-PCR, and poorer clinical outcome compared to other positive patients without the COVID12-HRP. Furthermore, the percentage of SARS-CoV-2 patients with the COVID19-HRP has significantly decreased from March/April to May/June. Notably, viral load in the SARS-CoV-2 patients declined, and their laboratory profile became less distinguishable from SARS-CoV-2 negative patients in the later phase.

CONCLUSIONS

Our longitudinal analysis illustrates the temporal change of laboratory test result profile in SARS-CoV-2 patients and the COVID-19 evolvement in a US epicenter. This analysis could become an important tool in COVID-19 population disease severity tracking and prediction. In addition, this analysis may play an important role in prioritizing high-risk patients, assisting in patient triaging and optimizing the usage of resources.

Early introductions and transmission of SARS-CoV-2 variant B.1.1.7 in the United States

The emergence and spread of SARS-CoV-2 lineage B.1.1.7, first detected in the United Kingdom, has become a global public health concern because of its increased transmissibility. Over 2,500 COVID-19 cases associated with this variant have been detected in the United States (US) since December 2020, but the extent of establishment is relatively unknown. Using travel, genomic, and diagnostic data, we highlight that the primary ports of entry for B.1.1.7 in the US were in New York, California, and Florida. Furthermore, we found evidence for many independent B.1.1.7 establishments starting in early December 2020, followed by interstate spread by the end of the month. Finally, we project that B.1.1.7 will be the dominant lineage in many states by mid- to late March. Thus, genomic surveillance for B.1.1.7 and other variants urgently needs to be enhanced to better inform the public health response.

Shotgun transcriptome, spatial omics, and isothermal profiling of SARS-CoV-2 infection reveals unique host responses, viral diversification, and drug interactions

In less than nine months, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) killed over a million people, including >25,000 in New York City (NYC) alone. The COVID-19 pandemic caused by SARS-CoV-2 highlights clinical needs to detect infection, track strain evolution, and identify biomarkers of disease course. To address these challenges, we designed a fast (30-minute) colorimetric test (LAMP) for SARS-CoV-2 infection from naso/oropharyngeal swabs and a large-scale shotgun metatranscriptomics platform (total-RNA-seq) for host, viral, and microbial profiling. We applied these methods to clinical specimens gathered from 669 patients in New York City during the first two months of the outbreak, yielding a broad molecular portrait of the emerging COVID-19 disease. We find significant enrichment of a NYC-distinctive clade of the virus (20C), as well as host responses in interferon, ACE, hematological, and olfaction pathways. In addition, we use 50,821 patient records to find that renin-angiotensin-aldosterone system inhibitors have a protective effect for severe COVID-19 outcomes, unlike similar drugs. Finally, spatial transcriptomic data from COVID-19 patient autopsy tissues reveal distinct ACE2 expression loci, with macrophage and neutrophil infiltration in the lungs. These findings can inform public health and may help develop and drive SARS-CoV-2 diagnostic, prevention, and treatment strategies.

Early introductions and community transmission of SARS-CoV-2 variant B.1.1.7 in the United States

The emergence and spread of SARS-CoV-2 lineage B.1.1.7, first detected in the United Kingdom, has become a global public health concern because of its increased transmissibility. Over 2500 COVID-19 cases associated with this variant have been detected in the US since December 2020, but the extent of establishment is relatively unknown. Using travel, genomic, and diagnostic data, we highlight the primary ports of entry for B.1.1.7 in the US and locations of possible underreporting of B.1.1.7 cases. Furthermore, we found evidence for many independent B.1.1.7 establishments starting in early December 2020, followed by interstate spread by the end of the month. Finally, we project that B.1.1.7 will be the dominant lineage in many states by mid to late March. Thus, genomic surveillance for B.1.1.7 and other variants urgently needs to be enhanced to better inform the public health response.

Systemic Tissue and Cellular Disruption from SARS-CoV-2 Infection revealed in COVID-19 Autopsies and Spatial Omics Tissue Maps

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus has infected over 115 million people and caused over 2.5 million deaths worldwide. Yet, the molecular mechanisms underlying the clinical manifestations of COVID-19, as well as what distinguishes them from common seasonal influenza virus and other lung injury states such as Acute Respiratory Distress Syndrome (ARDS), remains poorly understood. To address these challenges, we combined transcriptional profiling of 646 clinical nasopharyngeal swabs and 39 patient autopsy tissues, matched with spatial protein and expression profiling (GeoMx) across 357 tissue sections. These results define both body-wide and tissue-specific (heart, liver, lung, kidney, and lymph nodes) damage wrought by the SARS-CoV-2 infection, evident as a function of varying viral load (high vs. low) during the course of infection and specific, transcriptional dysregulation in splicing isoforms, T cell receptor expression, and cellular expression states. In particular, cardiac and lung tissues revealed the largest degree of splicing isoform switching and cell expression state loss. Overall, these findings reveal a systemic disruption of cellular and transcriptional pathways from COVID-19 across all tissues, which can inform subsequent studies to combat the mortality of COVID-19, as well to better understand the molecular dynamics of lethal SARS-CoV-2 infection and other viruses.

Rapid Implementation of Severe Acute Respiratory Syndrome Coronavirus 2 Emergency Use Authorization RT-PCR Testing and Experience at an Academic Medical Institution

An epidemic caused by an outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China in December 2019 has since rapidly spread internationally, requiring urgent response from the clinical diagnostics community. We present a detailed overview of the clinical validation and implementation of the first laboratory-developed real-time RT-PCR test offered in the NewYork-Presbyterian Hospital system following the Emergency Use Authorization issued by the US Food and Drug Administration. Nasopharyngeal and sputum specimens (n = 174) were validated using newly designed dual-target real-time RT-PCR (altona RealStar SARS-CoV-2 Reagent) for detecting SARS-CoV-2 in upper respiratory tract and lower respiratory tract specimens. Accuracy testing demonstrated excellent assay agreement between expected and observed values and comparable diagnostic performance to reference tests. The limit of detection was 2.7 and 23.0 gene copies per reaction for nasopharyngeal and sputum specimens, respectively. Retrospective analysis of 1694 upper respiratory tract specimens from 1571 patients revealed increased positivity in older patients and males compared with females, and an increasing positivity rate from approximately 20% at the start of testing to 50% at the end of testing 3 weeks later. Herein, we demonstrate that the assay accurately and sensitively identifies SARS-CoV-2 in multiple specimen types in the clinical setting and summarize clinical data from early in the epidemic in New York City.

Routine Laboratory Blood Tests Predict SARS-CoV-2 Infection Using Machine Learning

Background

Accurate diagnostic strategies to rapidly identify SARS-CoV-2 positive individuals for management of patient care and protection of health care personnel are urgently needed. The predominant diagnostic test is viral RNA detection by RT-PCR from nasopharyngeal swabs specimens, however the results are not promptly obtainable in all patient care locations. Routine laboratory testing, in contrast, is readily available with a turn-around time (TAT) usually within 1-2 hours.

Method

We developed a machine learning model incorporating patient demographic features (age, sex, race) with 27 routine laboratory tests to predict an individual’s SARS-CoV-2 infection status. Laboratory test results obtained within two days before the release of SARS-CoV-2-RT-PCR result were used to train a gradient boosted decision tree (GBDT) model from 3,356 SARS-CoV-2 RT-PCR tested patients (1,402 positive and 1,954 negative) evaluated at a metropolitan hospital.

Results

The model achieved an area under the receiver operating characteristic curve (AUC) of 0.854 (95% CI: 0.829-0.878). Application of this model to an independent patient dataset from a separate hospital resulted in a comparable AUC (0.838), validating the generalization of its use. Moreover, our model predicted initial SARS-CoV-2 RT-PCR positivity in 66% individuals whose RT-PCR result changed from negative to positive within two days.

Conclusion

This model employing routine laboratory test results offers opportunities for early and rapid identification of high-risk SARS-CoV-2 infected patients before their RT-PCR results are available. It may play an important role in assisting the identification of SARS-COV-2 infected patients in areas where RT-PCR testing is not accessible due to financial or supply constraints.

Routine Laboratory Blood Tests Predict SARS-CoV-2 Infection Using Machine Learning

BACKGROUND

Accurate diagnostic strategies to identify SARS-CoV-2 positive individuals rapidly for management of patient care and protection of health care personnel are urgently needed. The predominant diagnostic test is viral RNA detection by RT-PCR from nasopharyngeal swabs specimens, however the results are not promptly obtainable in all patient care locations. Routine laboratory testing, in contrast, is readily available with a turn-around time (TAT) usually within 1-2 hours.

METHOD

We developed a machine learning model incorporating patient demographic features (age, sex, race) with 27 routine laboratory tests to predict an individual's SARS-CoV-2 infection status. Laboratory testing results obtained within 2 days before the release of SARS-CoV-2 RT-PCR result were used to train a gradient boosting decision tree (GBDT) model from 3,356 SARS-CoV-2 RT-PCR tested patients (1,402 positive and 1,954 negative) evaluated at a metropolitan hospital.

RESULTS

The model achieved an area under the receiver operating characteristic curve (AUC) of 0.854 (95% CI: 0.829-0.878). Application of this model to an independent patient dataset from a separate hospital resulted in a comparable AUC (0.838), validating the generalization of its use. Moreover, our model predicted initial SARS-CoV-2 RT-PCR positivity in 66% individuals whose RT-PCR result changed from negative to positive within 2 days.

CONCLUSION

This model employing routine laboratory test results offers opportunities for early and rapid identification of high-risk SARS-CoV-2 infected patients before their RT-PCR results are available. It may play an important role in assisting the identification of SARS-CoV-2 infected patients in areas where RT-PCR testing is not accessible due to financial or supply constraints.

Shotgun Transcriptome and Isothermal Profiling of SARS-CoV-2 Infection Reveals Unique Host Responses, Viral Diversification, and Drug Interactions

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused thousands of deaths worldwide, including >18,000 in New York City (NYC) alone. The sudden emergence of this pandemic has highlighted a pressing clinical need for rapid, scalable diagnostics that can detect infection, interrogate strain evolution, and identify novel patient biomarkers. To address these challenges, we designed a fast (30-minute) colorimetric test (LAMP) for SARS-CoV-2 infection from naso/oropharyngeal swabs, plus a large-scale shotgun metatranscriptomics platform (total-RNA-seq) for host, bacterial, and viral profiling. We applied both technologies across 857 SARS-CoV-2 clinical specimens and 86 NYC subway samples, providing a broad molecular portrait of the COVID-19 NYC outbreak. Our results define new features of SARS-CoV-2 evolution, nominate a novel, NYC-enriched viral subclade, reveal specific host responses in interferon, ACE, hematological, and olfaction pathways, and examine risks associated with use of ACE inhibitors and angiotensin receptor blockers. Together, these findings have immediate applications to SARS-CoV-2 diagnostics, public health, and new therapeutic targets.

Stable and discriminating radiomic predictor of recurrence in early stage non-small cell lung cancer: Multi-site study

OBJECTIVES

To evaluate whether combining stability and discriminability criteria in building radiomic classifiers will improve the prognosis of cancer recurrence in early stage non-small cell lung cancer on non-contrast computer tomography (CT).

MATERIALS AND METHODS

CT scans of 610 patients with early stage (IA, IB, IIA) NSCLC from four independent cohorts were evaluated. A total of 350 patients from Cleveland Clinic Foundation and University of Pennsylvania were divided into two equal sets for training (D₁) and validation set (D₂). 80 patients from The Cancer Genome Atlas Lung Adenocarcinoma and Squamous Cell Carcinoma and 195 patients from The Cancer Imaging Archive, were used as independent second (D₃) and third (D₄) validation sets. A linear discriminant analysis (LDA) classifier was built based on the most stable and discriminate features. In addition, a radiomic risk score (RRS) was generated by using least absolute shrinkage and selection operator, Cox regression model to predict time to progression (TTP) following surgery.

RESULTS

A feature selection strategy focusing on both feature discriminability and stability resulted in the classifier having a higher discriminability on validation datasets compared to the discriminability alone criteria in discriminating cancer recurrence (D₂, AUC of 0.75 vs. 0.65; D₃, 0.74 vs. 0.62; D₄, 0.76 vs. 0.63). The RRS generated by most stable-discriminating features was significantly associated with TTP compared to discriminating alone criteria (HR = 1.66, C-index of 0.72 vs. HR = 1.04, C-index of 0.62).

CONCLUSION

Accounting for both stability and discriminability yielded a more generalizable classifier for predicting cancer recurrence and TTP in early stage NSCLC.

Intra and perinodular CT delta radiomic features associated with early response to predict overall survival (OS) in immunotherapy-treated non-small cell lung cancer (NSCLC): A multisite multi-agent study

2588

Background: None of the current biomarkers for predicting response to checkpoint inhibitors (ICIT) for advanced NSCLC are associated with long-term benefits, such as improved OS. In this multi-agent (nivolumab, pembrolizumab, or atezolizumab) multi-site study (Cleveland Clinic, Univ. of Pennsylvania), we demonstrate that changes in computer-extracted textural patterns, from within and 30mm outside the nodules, between baseline and post-treatment CT following ICIT correlate with RECIST-derived responses, and are prognostic of OS. Methods: CT scans from 139 NSCLC patients both pre-, and post 2-3 cycles of ICIT were acquired from 2 sites. Patients with objective response/stable disease per RECIST v1.1 were defined as ‘responders’, and those with progressive disease were ‘non-responders’. The cohort was divided into a discovery (D1 = 50) and two validation sets (D2 = 62, D3 = 27). 454 intranodular texture (IT) features, and 7426 perinodular features (PT) were extracted from the temporalscans, Relative differences were computed to yield a set of ‘delta-radiomic’ descriptors. In D1, 8 features that evolved the most between baseline and post-treatment CT, and performed the best in identifying responders, were determined. These were then used with a Linear Discriminant Analysis classifier to identify the responders from the non-responders. We then computed a radiomic risk score (RRS) system and tested its prognostic ability in assessing differences in OS. Results: A combination of 5 IT, 3 PT delta radiomic features yielded an AUC of 0.88 ± 0.08 in D1 and a corresponding AUC = 0.85 and 0.81 in D2 and D3, respectively. Multivariate survival metrics are shown in Table. Conclusions: Delta-radiomic features, both from inside and outside the nodules, could be used to identify patients likely to derive clinical benefit from ICIT (eg: OS) beyond anatomic response. [Table: see text]

Classes of ITD Predict Outcomes in AML Patients Treated with FLT3 Inhibitors

PURPOSE

Recurrent internal tandem duplication (ITD) mutations are observed in various cancers including acute myeloid leukemia (AML), where ITD mutations in tyrosine kinase receptor FLT3 are associated with poor prognostic outcomes. Several FLT3 inhibitors (FLT3i) are in clinical trials for high-risk FLT3-ITD-positive AML. However, the variability of survival following FLT3i treatment suggests that the mere presence of FLT3-ITD mutations might not guarantee effective clinical response. Motivated by the heterogeneity of FLT3-ITD mutations, we investigated the effects of FLT3-ITD structural features on the response of AML patients to treatment.Experimental Design: We developed the HeatITup (HEAT diffusion for Internal Tandem dUPlication) algorithm to identify and quantitate ITD structural features including nucleotide composition. Using HeatITup, we studied the impact of ITD structural features on the clinical response to FLT3i and induction chemotherapy in FLT3-ITD-positive AML patients.

RESULTS

HeatITup accurately identifies and classifies ITDs into newly defined categories of "typical" or "atypical" based on their nucleotide composition. A typical ITD's insert sequence completely matches the wild-type FLT3, whereas an atypical ITD's insert contains nucleotides exogenous to the wild-type FLT3. Our analysis shows marked divergence between typical and atypical ITD mutation features. Furthermore, our data suggest that AML patients carrying typical FLT3-ITDs benefited significantly more from both FLT3i and induction chemotherapy treatments than patients with atypical FLT3-ITDs.

CONCLUSIONS

These results underscore the importance of structural discernment of complex somatic mutations such as ITDs in progressing toward personalized treatment of AML patients, and enable researchers and clinicians to unravel ITD complexity using the provided software.See related commentary by Gallipoli and Huntly, p. 460.

Use of targeted next generation sequencing (NGS) to assess mutational load in glioblastoma (GBM)

2027

Background: Trials of immune checkpoint inhibitors in GBM are ongoing. Evidence from other tumors suggests that high mutational load predicts response to these agents. We estimated mutational load in adults with newly diagnosed GBM using targeted NGS and assessed its association with other clinicopathologic parameters. Methods: NGS was performed for all patients diagnosed with isocitrate dehydrogenase wild-type (IDH-WT) GBM at our institution since September 1, 2016. Our panel performs targeted exome sequencing of 153 cancer-related genes (+/- 10 bp at intron/exon boundaries) and detects insertions, deletions, and single nucleotide variants at an allele frequency of 2%. Polymorphisms present in > 0.1% of the population according to the ExAC database and 1000 Genomes Project were filtered to reduce germline variants in the absence of matched normals. To avoid upward skewing of mutational load due to the panel’s preferential detection of genes recurrently mutated in cancer, alterations with known or likely functional disruption per the COSMIC database and/or our internal variant knowledge base were also not counted. The number of mutations counted was divided by the coding region target territory of the NGS panel (~0.5 Mb) to extrapolate mutational load to the whole exome. The Mann-Whitney U test was used to compare mutational load according to sex, age (≥65 vs. < 65), and MGMT methylation status (positive vs. negative). Results: Of 28 patients, median age was 65, 75% were male, and 37% were MGMT-methylated. Mean mutational load was 3.6 mutations/Mb (range 0-8, standard deviation 2.3) and was higher in males (4.3, 95% CI 3.3-5.3) compared to females (1.4, 95% CI 0.03-2.8) (p = 0.004). Mutational load did not differ according to age or MGMT methylation. Conclusions: In newly diagnosed IDH-WT GBM, targeted NGS revealed an estimated mutation rate similar to that reported in whole exome sequencing studies. In addition, we detected a higher mutational load in males compared to females. The latter finding adds to recent evidence suggesting sex-specific differences in the biology of GBM, and implies that future studies should account for sex when assessing mutational load as a predictive marker of immune checkpoint inhibition.

Hydration of the folding transition state ensemble of a protein

A complete description of the mechanisms of protein folding requires knowledge of the structural and physical character of the folding transition state ensembles (TSEs). A key question concerning the role of hydration of the hydrophobic core in determining folding mechanisms remains. To address this, we probed the state of hydration of the TSE of staphylococcal nuclease (SNase) by examining the fluorescence-detected pressure-jump relaxation behavior of six SNase variants in which a residue in the hydrophobic core, Val-66, was replaced with polar or ionizable residues (Lys, Arg, His, Asp, Glu, and Asn). Because of a large positive activation volume for folding, the major effect of pressure on the wild-type protein is to decrease the folding rate. By the time wild-type SNase reaches the folding transition state, most water has already been expelled from its hydrophobic core. In contrast, the major effect of pressure on the variant proteins is an increase in the unfolding rate due to a large negative activation volume for unfolding. This results from a significant increase in the level of hydration of the TSE when an internal ionizable group is present. These data confirm that the role of water in the folding reaction can differ from protein to protein and that even a single substitution in a critical position can modulate significantly the properties of the TSE.