HLA class II immunogenic mutation burden predicts response to immune checkpoint blockade.

BACKGROUND: Whereas human leukocyte antigen (HLA) class I mutation-associated neoantigen burden has been linked with response to immune checkpoint blockade (ICB), the role of HLA class II-restricted neoantigens in clinical responses to ICB is less studied. We used computational approaches to assess HLA class II immunogenic mutation (IMM) burden in patients with melanoma and lung cancer treated with ICB. PATIENTS AND METHODS: We analyzed whole-exome sequence data from four cohorts of ICB-treated patients with melanoma (n = 110) and non-small-cell lung cancer (NSCLC) (n = 123). MHCnuggets, a neural network-based model, was applied to estimate HLA class II IMM burdens and cellular fractions of IMMs were calculated to assess mutation clonality. We evaluated the combined impact of HLA class II germline genetic variation and class II IMM burden on clinical outcomes. Correlations between HLA class II IMM burden and density of tumor-infiltrating lymphocytes were computed from expression data. RESULTS: Responding tumors harbored a significantly higher HLA class II IMM burden for both melanoma and NSCLC (P ≤ 9.6e-3). HLA class II IMM burden was correlated with longer survival, particularly in the NSCLC cohort and in the context of low intratumoral IMM heterogeneity (P < 0.001). HLA class I and II IMM landscapes were largely distinct suggesting a complementary role for class II IMMs in tumor rejection. A higher HLA class II IMM burden was associated with CD4+ T-cell infiltration and programmed death-ligand 1 expression. Transcriptomic analyses revealed an inflamed tumor microenvironment for tumors harboring a high HLA class II IMM burden. CONCLUSIONS: HLA class II IMM burden identified patients with NSCLC and melanoma that attained longer survival after ICB treatment. Our findings suggest that HLA class II IMMs may impact responses to ICB in a manner that is distinct and complementary to HLA class I-mediated responses.

Genomic alterations in head and neck squamous cell carcinoma determined by cancer gene-targeted sequencing.

BACKGROUND: To determine genomic alterations in head and neck squamous cell carcinoma (HNSCC) using formalin-fixed, paraffin-embedded (FFPE) tumors obtained through routine clinical practice, selected cancer-related genes were evaluated and compared with alterations seen in frozen tumors obtained through research studies. PATIENTS AND METHODS: DNA samples obtained from 252 FFPE HNSCC were analyzed using next-generation sequencing-based (NGS) clinical assay to determine sequence and copy number variations in 236 cancer-related genes plus 47 introns from 19 genes frequently rearranged in cancer. Human papillomavirus (HPV) status was determined by presence of the HPV DNA sequence in all samples and corroborated with high-risk HPV in situ hybridization (ISH) and p16 immunohistochemical (IHC) staining in a subset of tumors. Sequencing data from 399 frozen tumors in The Cancer Genome Atlas and University of Chicago public datasets were analyzed for comparison. RESULTS: Among 252 FFPE HNSCC, 84 (33%) were HPV positive and 168 (67%) were HPV negative by sequencing. A subset of 40 tumors with HPV ISH and p16 IHC results showed complete concordance with NGS-derived HPV status. The most common genes with genomic alterations were PIK3CA and PTEN in HPV-positive tumors and TP53 and CDKN2A/B in HPV-negative tumors. In the pathway analysis, the PI3K pathway in HPV-positive tumors and DNA repair-p53 and cell cycle pathways in HPV-negative tumors were frequently altered. The HPV-positive oropharynx and HPV-positive nasal cavity/paranasal sinus carcinoma shared similar mutational profiles. CONCLUSION: The genomic profile of FFPE HNSCC tumors obtained through routine clinical practice is comparable with frozen tumors studied in research setting, demonstrating the feasibility of comprehensive genomic profiling in a clinical setting. However, the clinical significance of these genomic alterations requires further investigation through application of these genomic profiles as integral biomarkers in clinical trials.

Identification of aberrant pathway and network activity from high-throughput data.

The workshop focused on approaches to deduce changes in biological activity in cellular pathways and networks that drive phenotype from high-throughput data. Work in cancer has demonstrated conclusively that cancer etiology is driven not by single gene mutation or expression change, but by coordinated changes in multiple signaling pathways. These pathway changes involve different genes in different individuals, leading to the failure of gene-focused analysis to identify the full range of mutations or expression changes driving cancer development. There is also evidence that metabolic pathways rather than individual genes play the critical role in a number of metabolic diseases. Tools to look at pathways and networks are needed to improve our understanding of disease and to improve our ability to target therapeutics at appropriate points in these pathways.

Improving functional annotation of non-synonomous SNPs with information theory.

Automated functional annotation of nsSNPs requires that amino-acid residue changes are represented by a set of descriptive features, such as evolutionary conservation, side-chain volume change, effect on ligand-binding, and residue structural rigidity. Identifying the most informative combinations of features is critical to the success of a computational prediction method. We rank 32 features according to their mutual information with functional effects of amino-acid substitutions, as measured by in vivo assays. In addition, we use a greedy algorithm to identify a subset of highly informative features. The method is simple to implement and provides a quantitative measure for selecting the best predictive features given a set of features that a human expert believes to be informative. We demonstrate the usefulness of the selected highly informative features by cross-validated tests of a computational classifier, a support vector machine (SVM). The SVM's classification accuracy is highly correlated with the ranking of the input features by their mutual information. Two features describing the solvent accessibility of "wild-type" and "mutant" amino-acid residues and one evolutionary feature based on superfamily-level multiple alignments produce comparable overall accuracy and 6% fewer false positives than a 32-feature set that considers physiochemical properties of amino acids, protein electrostatics, amino-acid residue flexibility, and binding interactions.

What is the value added by human intervention in protein structure prediction?

This article presents results of blind predictions submitted to the CASP4 protein structure prediction experiment. We made two sets of predictions: one using the fully automated SAM-T99 server and one using the improved SAM-T2K method with human intervention. Both methods use iterative hidden Markov model-based methods for constructing protein family profiles, using only sequence information. Although the SAM-T99 method is purely sequence based, the SAM-T2K method uses the predicted secondary structure of the target sequence and the known secondary structure of the templates to improve fold recognition and alignment. In this article, we try to determine what aspects of the SAM-T2K method were responsible for its significantly better performance in the CASP4 experiment in the hopes of producing a better automatic prediction server. The use of secondary structure prediction seems to be the most valuable single improvement, though the combined total of various human interventions is probably at least as important.

Weighting hidden Markov models for maximum discrimination.

MOTIVATION: Hidden Markov models can efficiently and automatically build statistical representations of related sequences. Unfortunately, training sets are frequently biased toward one subgroup of sequences, leading to an insufficiently general model. This work evaluates sequence weighting methods based on the maximum-discrimination idea. RESULTS: One good method scales sequence weights by an exponential that ranges between 0.1 for the best scoring sequence and 1.0 for the worst. Experiments with a curated data set show that while training with one or two sequences performed worse than single-sequence Probabilistic Smith-Waterman, training with five or ten sequences reduced errors by 20% and 51%, respectively. This new version of the SAM HMM suite outperforms HMMer (17% reduction over PSW for 10 training sequences), Meta-MEME (28% reduction), and unweighted SAM (31% reduction). AVAILABILITY: A WWW server, as well as information on obtaining the Sequence Alignment and Modeling (SAM) software suite and additional data from this work, can be found at http://www.cse.ucse. edu/research/compbio/sam.html