The Landscape of Immunotherapy Resistance in NSCLC

Lung cancer is the leading cause of cancer mortality worldwide. Immunotherapy has demonstrated clinically significant benefit for non-small-cell lung cancer, but innate (primary) or acquired resistance remains a challenge. Criteria for a uniform clinical definition of acquired resistance have been recently proposed in order to harmonize the design of future clinical trials. Several mechanisms of resistance are now well-described, including the lack of tumor antigens, defective antigen presentation, modulation of critical cellular pathways, epigenetic changes, and changes in the tumor microenvironment. Host-related factors, such as the microbiome and the state of immunity, have also been examined. New compounds and treatment strategies are being developed to target these mechanisms with the goal of maximizing the benefit derived from immunotherapy. Here we review the definitions of resistance to immunotherapy, examine its underlying mechanisms and potential corresponding treatment strategies. We focus on recently published clinical trials and trials that are expected to deliver results soon. Finally, we gather insights from recent preclinical discoveries that may translate to clinical application in the future

Structure-based prediction of BRAF mutation classes using machine-learning approaches.

The BRAF kinase is attracting a lot of attention in oncology as alterations of its amino acid sequence can constitutively activate the MAP kinase signaling pathway, potentially contributing to the malignant transformation of the cell but at the same time rendering it sensitive to targeted therapy. Several pathologic BRAF variants were grouped in three different classes (I, II and III) based on their effects on the protein activity and pathway. Discerning the class of a BRAF mutation permits to adapt the treatment proposed to the patient. However, this information is lacking new and experimentally uncharacterized BRAF mutations detected in a patient biopsy. To overcome this issue, we developed a new in silico tool based on machine learning approaches to predict the potential class of a BRAF missense variant. As class I only involves missense mutations of Val600, we focused on the mutations of classes II and III, which are more diverse and challenging to predict. Using a logistic regression model and features including structural information, we were able to predict the classes of known mutations with an accuracy of 90%. This new and fast predictive tool will help oncologists to tackle potential pathogenic BRAF mutations and to propose the most appropriate treatment for their patients

Multilingual RECIST classification of radiology reports using supervised learning.

OBJECTIVES The objective of this study is the exploration of Artificial Intelligence and Natural Language Processing techniques to support the automatic assignment of the four Response Evaluation Criteria in Solid Tumors (RECIST) scales based on radiology reports. We also aim at evaluating how languages and institutional specificities of Swiss teaching hospitals are likely to affect the quality of the classification in French and German languages. METHODS In our approach, 7 machine learning methods were evaluated to establish a strong baseline. Then, robust models were built, fine-tuned according to the language (French and German), and compared with the expert annotation. RESULTS The best strategies yield average F1-scores of 90% and 86% respectively for the 2-classes (Progressive/Non-progressive) and the 4-classes (Progressive Disease, Stable Disease, Partial Response, Complete Response) RECIST classification tasks. CONCLUSIONS These results are competitive with the manual labeling as measured by Matthew's correlation coefficient and Cohen's Kappa (79% and 76%). On this basis, we confirm the capacity of specific models to generalize on new unseen data and we assess the impact of using Pre-trained Language Models (PLMs) on the accuracy of the classifiers

Pathway level subtyping identifies a slow-cycling biological phenotype associated with poor clinical outcomes in colorectal cancer

Molecular stratification using gene-level transcriptional data has identified subtypes with distinctive genotypic and phenotypic traits, as exemplified by the consensus molecular subtypes (CMS) in colorectal cancer (CRC). Here, rather than gene-level data, we make use of gene ontology and biological activation state information for initial molecular class discovery. In doing so, we defined three pathway-derived subtypes (PDS) in CRC: PDS1 tumors, which are canonical/LGR5+ stem-rich, highly proliferative and display good prognosis; PDS2 tumors, which are regenerative/ANXA1+ stem-rich, with elevated stromal and immune tumor microenvironmental lineages; and PDS3 tumors, which represent a previously overlooked slow-cycling subset of tumors within CMS2 with reduced stem populations and increased differentiated lineages, particularly enterocytes and enteroendocrine cells, yet display the worst prognosis in locally advanced disease. These PDS3 phenotypic traits are evident across numerous bulk and single-cell datasets, and demark a series of subtle biological states that are currently under-represented in pre-clinical models and are not identified using existing subtyping classifiers

Pathway level subtyping identifies a slow-cycling biological phenotype associated with poor clinical outcomes in colorectal cancer

Molecular stratification using gene-level transcriptional data has identified subtypes with distinctive genotypic and phenotypic traits, as exemplified by the consensus molecular subtypes (CMS) in colorectal cancer (CRC). Here, rather than gene-level data, we make use of gene ontology and biological activation state information for initial molecular class discovery. In doing so, we defined three pathway-derived subtypes (PDS) in CRC: PDS1 tumors, which are canonical/LGR5+ stem-rich, highly proliferative and display good prognosis; PDS2 tumors, which are regenerative/ANXA1+ stem-rich, with elevated stromal and immune tumor microenvironmental lineages; and PDS3 tumors, which represent a previously overlooked slow-cycling subset of tumors within CMS2 with reduced stem populations and increased differentiated lineages, particularly enterocytes and enteroendocrine cells, yet display the worst prognosis in locally advanced disease. These PDS3 phenotypic traits are evident across numerous bulk and single-cell datasets, and demark a series of subtle biological states that are currently under-represented in pre-clinical models and are not identified using existing subtyping classifiers

Oncogenic BRAF, unrestrained by TGFβ-receptor signalling, drives right-sided colonic tumorigenesis

Right-sided (proximal) colorectal cancer (CRC) has a poor prognosis and a distinct mutational profile, characterized by oncogenic BRAF mutations and aberrations in mismatch repair and TGFβ signalling. Here, we describe a mouse model of right-sided colon cancer driven by oncogenic BRAF and loss of epithelial TGFβ-receptor signalling. The proximal colonic tumours that develop in this model exhibit a foetal-like progenitor phenotype (Ly6a/Sca1+) and, importantly, lack expression of Lgr5 and its associated intestinal stem cell signature. These features are recapitulated in human BRAF-mutant, right-sided CRCs and represent fundamental differences between left- and right-sided disease. Microbial-driven inflammation supports the initiation and progression of these tumours with foetal-like characteristics, consistent with their predilection for the microbe-rich right colon and their antibiotic sensitivity. While MAPK-pathway activating mutations drive this foetal-like signature via ERK-dependent activation of the transcriptional coactivator YAP, the same foetal-like transcriptional programs are also initiated by inflammation in a MAPK-independent manner. Importantly, in both contexts, epithelial TGFβ-receptor signalling is instrumental in suppressing the tumorigenic potential of these foetal-like progenitor cells

The SIB Swiss Institute of Bioinformatics' resources: focus on curated databases

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article

Precision oncology beyond DNA mutation testing

Precision oncology emerged when genomics and new therapies converged. Genomics grew exponentially, fueled by next-generation sequencing, and gave insights into cancer physiology. This knowledge enabled the rational design of targeted therapies. Precision oncology draws from an understanding of cancer biology and an increasing collection of biomarkers and associated therapies to choose the best treatment for each patient. Despite the undeniable success of precision oncology in multiple cancers, difficulties remain. Matching a cancer mutation to a single drug has limited utility. Many drugs that succeeded in phase I/II trials failed later phase III trials. Even for effective therapies, predicting who will benefit is difficult. Some have voiced concerns that precision oncology is not feasible outside a few well-publicized examples. Here we suggest a few ways to go forward. In the first publication, we showed that it is not enough to consider pathogenic mutations of the EGFR gene to predict response to EGFR-directed treatment. Many patients present with concurrent mutations in other genes. Most of these mutations have no apparent effect on treatment response, but some can cause primary resistance via cellular pathways which bypass the drug’s action. The second publication showed that lung adenocarcinoma with a low allelic frequency of the EGFR mutation is associated with shorter progression-free survival. The low allelic frequency is an indirect sign of subclonal expansion, meaning that the tumor is heterogeneous. Cancer cells which do not carry the mutation are less likely to respond to therapy. The third work exemplified the use of copy number analysis in addition to DNA mutation testing. Based on an exceptional case, we presented a hypothesis to explain benefit from treatment with palbociclib and suggested how this can be applied in future trials. In the fourth publication, we developed a multi-cancer biomarker of liver metastasis from gene expression data. Pan-cancer biomarkers are difficult to identify but can reveal underlying biology. Gene expression biomarkers are particularly sensitive to batch effects and other sources of bias and are currently under-utilized. Several challenges remain. As we showed in the first two publications, the cancer co-mutation patterns and tumor heterogeneity are challenges for the implementation of precision oncology. We believe that the accumulation of data, the integration of algorithmic decision support, and the implementation of new assays will help overcome these difficulties. In accordance with this view, our future projects are focused on biomarker discovery and tumor classification, most recently with the integration of single-cell RNA sequencing and digital pathology.</p

A study of chromosomal breaks in carcinogenesis using polymorphic markers

Introduction. Recent studies have implicated the DNA damage response pathway in the earliest stages of carcinogenesis. It appears that fragile sites may be particularly vulnerable (for example, FRA3B, FRA9E, FRA11C) leading us and others to hypothesize that they may be preferentially targeted even in early lesions. Methods. The experimental material, human and mouse genomic DNA, was derived from two experimental models of carcinogenesis: human skin xenografts that were grown with the addition of exogenous growth factors and urothelial hyperplasia from transgenic mice overexpressing the RAS gene. The development of the material recapitulates the pathological course of preneoplastic lesions and represents its earliest stages with accuracy. In addition, we also analysed DNA from U2OS cells that were grown under replication stress, induced by the overexpression of the CDT1 gene. We employed state-of-the-art DNA microarray technology (Affymetrix SNP and Nimblegen aCGH) in order to evaluate at once up to 10204 positions on the human genome and 385000 positions on the mouse genome. The juxtaposition of normal and abnormal counterparts enabled us to precisely locate genomic lesions, especially loss-of-heterozygosity (hemizygous deletion). We analyzed the human genome project release 36.1, including the assembled chromosomes and the corresponding annotation which were obtained from NCBI. Based on a previous, well known algorithm (FLEX-STAB software, first presented by Mishmar et al, 1999), we devised and implemented a novel method to study the structural characteristics of DNA sequences. Using Repeat Masker we estimated the presence of repetitive DNA within normal and fragile regions. In total, we analysed approximately 2.5 billion bases, divided into 56 common fragile sites (aphidicolin type) and 1913 non-fragile chromosome bands. Results and conclusions. The percentage of LOH was 13.53% (28 of 189 SNPs) in fragile sites and 9.4% in non-fragile sites (logistic regression, p = 0.04). Out of 44 fragile sites that contained at least one informative marker, 12 (27.3%) had LOH in one or more SNP. The fragile sites were found to be, on average, less flexible (mean twist angle 10.742, compared with 10.787, p = 0.045). At the same time, the average GC content was higher in fragiles sites (41.7%, compared with 40.8% in normal regions, p = 0.03). In contrast with other reports, we did not find an increased percentage of retro-transposon sequences (LINE1, LINE2) within the fragile sites. As a matter of fact, their percentage appeared to be lower in fragile sequences (15.67% versus 16.82%), but the difference was not statistically significant (p = 0.055). On the other hand, we found an increased presence of SINE sequences, especially Alu, with a mean percentage of 11.66% inside the fragile sites and 10.65% in non-fragile sites (p = 0.009).Εισαγωγή. Σε πρόσφατες δημοσιεύσεις διαπιστώθηκε η ενεργοποίηση του μηχανισμού απάντησης στη γενωμική βλάβη από τα πλέον πρώιμα στάδια της καρκινογένεσης. Παράλληλα, παρατηρήθηκε η ευπάθεια ορισμένων εύθραυστων θέσεων (FRA3B, FRA9E, FRA11C) και διατυπώθηκε η υπόθεση ότι οι θέσεις αυτές είναι ευάλωτες ακόμα και σε πρώιμες αλλοιώσεις όπως είναι η υπερπλασία και η δυσπλασία. Μέθοδοι. Το πειραματικό υλικό προήλθε από δύο πειραματικά μοντέλα: ξενομοσχεύματα ανθρώπινου δέρματος που αναπτύχθηκαν υπό την επίδραση αυξητικών παραγόντων και υπερπλαστικό ουροθήλιο διαγονιδιακών ποντικών που υπερεκφράζαν το γονίδιο RAS. Το υλικό προσομοιάζει, παθολογοανατομικά, σε πρώιμες προκαρκινικές αλλοιώσεις αλλά δεν έχει δεχθεί ανεξέλεκγτες επιδράσεις. Επιπλέον, αναλύσαμε το γένωμα κυττάρων U2OS που αναπτύχθηκαν υπό την επίδραση αντιγραφικού στρες από υπερέκφραση του γονιδίου CDT1. Χρησιμοποιήσαμε την τεχνολογία των μικροσυστοιχιών DNA (Affymetrix microarray SNP, Nimblegen aCGH) για να αξιολογήσουμε ταυτόχρονα έως και 10204 θέσεις πάνω στο ανθρώπινο γένωμα και 385000 θέσεις πάνω στο γένωμα του ποντικού. Η αντιπαράθεση του φυσιολογικού και του παθολογικού δείγματος επιτρέπει τον ακριβή εντοπισμό των γενωμικών βλαβών, ιδιαίτερα της απώλειας ετεροζυγωτίας (ημίζυγη διαγραφή). Αναλύσαμε τη χαρτογράφηση 36.1 του γενώματος από το NCBI μαζί με την διαθέσιμη αλληλουχία. Βελτιώσαμε και αναπτύξαμε μια μέθοδο μελέτης των δομικών γνωρισμάτων της αλληλουχίας του DNA βασιζόμενοι στο γνωστό πρόγραμμα FLEX-STAB. Μελετήσαμε επίσης την παρουσία επαναλαμβανόμενου DNA στις φυσιολογικές και στις εύθραυστες θέσεις. Συνολικά αναλύσαμε περίπου 2.5 δισεκατομμύρια βάσεις σε 56 κοινές εύθραυστες θέσεις τύπου αφιδικολίνης και σε 1913 μη εύθραυστες χρωμοσωμικές μπάντες. Αποτελέσματα και συμπεράσματα. Στις εύθραυστες θέσεις το ποσοστό της απώλειας ετεροζυγωτίας ήταν 13.53% (28 από 189 δείκτες) ενώ στις μη εύθραυστες θέσεις ήταν 9.4% (logistic regression, p = 0.04). Από τις 44 εύθραυστες θέσεις που περιείχαν τουλάχιστον ένα πληροφοριακό πολυμορφικό δείκτη, οι 12 (27.3%) παρουσίαζαν απώλεια ετεροζυγωτίας σε έναν ή περισσότερους δείκτες. Οι εύθραυστες θέσεις ήταν κατά μέσον όρο περισσότερο δύσκαμπτες (γωνία στρέψης 10.742 σε αντίθεση με 10.787, p = 0.045). Παράλληλα, διαπιστώσαμε αυξημένο ποσοστό GC εντός των εύθραυστων θέσεων (41.7% σε σχέση με 40.8%, p = 0.03). Σε αντίθεση με ορισμένα μεμονωμένα βιβλιογραφικά ευρήματα, δεν διαπιστώσαμε αυξημένη παρουσία ρετροτρανσποζονίων μεγάλου μήκους (LINE1, LINE2) εντός των εύθραυστων θέσεων. Για την ακρίβεια, το ποσοστό ήταν οριακά χαμηλότερο (15.67% σε σχέση με 16.82%, για τις LINE1), αλλά η διαφορά δεν ήταν στατιστικά σημαντική (p = 0.055). Αντίθετα, διαπιστώσαμε αυξημένη παρουσία αλληλουχιών SINE και ειδικότερα Alu, με ποσοστό 11.66% στις εύθραυστες θέσεις και 10.65% στις μη εύθραυστες (p = 0.009)