Antibody-drug conjugates in lung and breast cancer: current evidence and future directions-a position statement from the ETOP IBCSG Partners Foundation.

Following the approval of the first antibody-drug conjugates (ADCs) in the early 2000s, development has increased dramatically, with 14 ADCs now approved and >100 in clinical development. In lung cancer, trastuzumab deruxtecan (T-DXd) is approved in human epidermal growth factor receptor 2 (HER2)-mutated, unresectable or metastatic non-small-cell lung cancer, with ADCs targeting HER3 (patritumab deruxtecan), trophoblast cell-surface antigen 2 [datopotamab deruxtecan and sacituzumab govitecan (SG)] and mesenchymal-epithelial transition factor (telisotuzumab vedotin) in late-stage clinical development. In breast cancer, several agents are already approved and widely used, including trastuzumab emtansine, T-DXd and SG, and multiple late-stage trials are ongoing. Thus, in the coming years, we are likely to see significant changes to treatment algorithms. As the number of available ADCs increases, biomarkers (of response and resistance) to better select patients are urgently needed. Biopsy sample collection at the time of treatment selection and incorporation of translational research into clinical trial designs are therefore critical. Biopsy samples taken peri- and post-ADC treatment combined with functional genomics screens could provide insights into response/resistance mechanisms as well as the impact of ADCs on tumour biology and the tumour microenvironment, which could improve understanding of the mechanisms underlying these complex molecules. Many ADCs are undergoing evaluation as combination therapy, but a high bar should be set to progress clinical evaluation of any ADC-based combination, particularly considering the high cost and potential toxicity implications. Efforts to optimise ADC dosing/duration, sequencing and the potential for ADC rechallenge are also important, especially considering sustainability aspects. The ETOP IBCSG Partners Foundation are driving strong collaborations in this field and promoting the generation/sharing of databases, repositories and registries to enable greater access to data. This will allow the most important research questions to be identified and prioritised, which will ultimately accelerate progress and help to improve patient outcomes.

A state-of-the-art review of stratified medicine in cancer: towards a future precision medicine strategy in cancer.

BACKGROUND: Building on the success of targeted therapy in certain well-defined cancer genotypes, three platform studies-NCI-MATCH, LUNG-MAP and The National Lung Matrix Trial (NLMT)-have attempted to discover new genotype-matched therapies for people with cancer. PATIENTS AND METHODS: We review the outputs from these platform studies. This review led us to propose a series of recommendations and considerations that we hope will inform future precision medicine programmes in cancer. RESULTS: The three studies collectively screened over 13 000 patients. Across 37 genotype-matched cohorts, there have been 66/875 responders, with an overall response rate of 7.5%. Targeting copy number gain yielded 5/199 responses across nine biomarker-drug matched cohorts, with a response rate of 2.5%. CONCLUSIONS: The majority of these studies used single-agent targeted therapies. Whilst preclinical data can suggest rational combination treatment to reverse adaptive resistance or block parallel activated pathways, there is an essential need for accurate modelling of the toxicity-activity trade-off of combinations. Agent selection is often suboptimal; dose expansion should only be carried out with agents with clear clinical proof of mechanism and high target selectivity. Targeting copy number change has been disappointing; it is crucial to define the drivers on shared amplicons that include the targeted aberration. Maximising outcomes with currently available targeted therapies requires moving towards a more contextualised stratified medicine acknowledging the criticality of the genomic, transcriptional and immunological context on which the targeted aberration is inscribed. Genomic complexity and instability is likely to be a leading cause of targeted therapy failure in genomically complex cancers. Preclinical models must be developed that more accurately capture the genomic complexity of human disease. The degree of attrition of studies carried out after standard-of-care therapy suggests that serious efforts be made to develop a suite of precision medicine studies in the minimal residual disease setting.

Clinical validation of a targeted methylation-based multi-cancer early detection test using an independent validation set.

BACKGROUND: A multi-cancer early detection (MCED) test used to complement existing screening could increase the number of cancers detected through population screening, potentially improving clinical outcomes. The Circulating Cell-free Genome Atlas study (CCGA; NCT02889978) was a prospective, case-controlled, observational study and demonstrated that a blood-based MCED test utilizing cell-free DNA (cfDNA) sequencing in combination with machine learning could detect cancer signals across multiple cancer types and predict cancer signal origin (CSO) with high accuracy. The objective of this third and final CCGA substudy was to validate an MCED test version further refined for use as a screening tool. PATIENTS AND METHODS: This pre-specified substudy included 4077 participants in an independent validation set (cancer: n = 2823; non-cancer: n = 1254, non-cancer status confirmed at year-one follow-up). Specificity, sensitivity, and CSO prediction accuracy were measured. RESULTS: Specificity for cancer signal detection was 99.5% [95% confidence interval (CI): 99.0% to 99.8%]. Overall sensitivity for cancer signal detection was 51.5% (49.6% to 53.3%); sensitivity increased with stage [stage I: 16.8% (14.5% to 19.5%), stage II: 40.4% (36.8% to 44.1%), stage III: 77.0% (73.4% to 80.3%), stage IV: 90.1% (87.5% to 92.2%)]. Stage I-III sensitivity was 67.6% (64.4% to 70.6%) in 12 pre-specified cancers that account for approximately two-thirds of annual USA cancer deaths and was 40.7% (38.7% to 42.9%) in all cancers. Cancer signals were detected across >50 cancer types. Overall accuracy of CSO prediction in true positives was 88.7% (87.0% to 90.2%). CONCLUSION: In this pre-specified, large-scale, clinical validation substudy, the MCED test demonstrated high specificity and accuracy of CSO prediction and detected cancer signals across a wide diversity of cancers. These results support the feasibility of this blood-based MCED test as a complement to existing single-cancer screening tests. CLINICAL TRIAL NUMBER: NCT02889978.

Collateral damage: the impact on outcomes from cancer surgery of the COVID-19 pandemic.

BACKGROUND: Cancer diagnostics and surgery have been disrupted by the response of health care services to the coronavirus disease 2019 (COVID-19) pandemic. Progression of cancers during delay will impact on patients' long-term survival. PATIENTS AND METHODS: We generated per-day hazard ratios of cancer progression from observational studies and applied these to age-specific, stage-specific cancer survival for England 2013-2017. We modelled per-patient delay of 3 and 6 months and periods of disruption of 1 and 2 years. Using health care resource costing, we contextualise attributable lives saved and life-years gained (LYGs) from cancer surgery to equivalent volumes of COVID-19 hospitalisations. RESULTS: Per year, 94 912 resections for major cancers result in 80 406 long-term survivors and 1 717 051 LYGs. Per-patient delay of 3/6 months would cause attributable death of 4755/10 760 of these individuals with loss of 92 214/208 275 life-years, respectively. For cancer surgery, average LYGs per patient are 18.1 under standard conditions and 17.1/15.9 with a delay of 3/6 months (an average loss of 0.97/2.19 LYGs per patient), respectively. Taking into account health care resource units (HCRUs), surgery results on average per patient in 2.25 resource-adjusted life-years gained (RALYGs) under standard conditions and 2.12/1.97 RALYGs following delay of 3/6 months. For 94 912 hospital COVID-19 admissions, there are 482 022 LYGs requiring 1 052 949 HCRUs. Hospitalisation of community-acquired COVID-19 patients yields on average per patient 5.08 LYG and 0.46 RALYGs. CONCLUSIONS: Modest delays in surgery for cancer incur significant impact on survival. Delay of 3/6 months in surgery for incident cancers would mitigate 19%/43% of LYGs, respectively, by hospitalisation of an equivalent volume of admissions for community-acquired COVID-19. This rises to 26%/59%, respectively, when considering RALYGs. To avoid a downstream public health crisis of avoidable cancer deaths, cancer diagnostic and surgical pathways must be maintained at normal throughput, with rapid attention to any backlog already accrued.

Extrachromosomal DNA-relieving heredity constraints, accelerating tumour evolution.

Oncogene amplification on extrachromosomal DNA (ecDNA) provides a mechanism by which cancer cells can rapidly adapt to changes in the tumour microenvironment. These circular structures contain oncogenes and their regulatory elements, and, lacking centromeres, they are subject to unequal segregation during mitosis. This non-Mendelian mechanism of inheritance results in increased tumour heterogeneity with daughter cells that can contain increasingly amplified oncogene copy number. These structures also contain favourable epigenetic modifications including transcriptionally active chromatin, further fuelling positive selection. ecDNA drives aggressive tumour behaviour, is related to poorer survival outcomes and provides mechanisms of drug resistance. Recent evidence suggests one in four solid tumours contain cells with ecDNA structures. The concept of tumour evolution is one in which cancer cells compete to survive in a diverse tumour microenvironment under the Darwinian principles of variation and fitness heritability. Unconstrained by conventional segregation constraints, ecDNA can accelerate intratumoral heterogeneity and cellular fitness. In this review, we highlight some of the recent discoveries underpinning this process.

Targeted therapy for advanced salivary gland carcinoma based on molecular profiling: results from MyPathway, a phase IIa multiple basket study.

BACKGROUND: Systemic therapy options for salivary cancers are limited. MyPathway (NCT02091141), a phase IIa study, evaluates targeted therapies in non-indicated tumor types with actionable molecular alterations. Here, we present the efficacy and safety results for a subgroup of MyPathway patients with advanced salivary gland cancer (SGC) matched to targeted therapies based on tumor molecular characteristics. PATIENTS AND METHODS: MyPathway is an ongoing, multiple basket, open-label, non-randomized, multi-center study. Patients with advanced SGC received pertuzumab + trastuzumab (HER2 alteration), vismodegib (PTCH-1/SMO mutation), vemurafenib (BRAF V600 mutation), or atezolizumab [high tumor mutational burden (TMB)]. The primary endpoint is the objective response rate (ORR). RESULTS: As of January 15, 2018, 19 patients with SGC were enrolled and treated in MyPathway (15 with HER2 amplification and/or overexpression and one each with a HER2 mutation without amplification or overexpression, PTCH-1 mutation, BRAF mutation, and high TMB). In the 15 patients with HER2 amplification/overexpression (with or without mutations) who were treated with pertuzumab + trastuzumab, 9 had an objective response (1 complete response, 8 partial responses) for an ORR of 60% (9.2 months median response duration). The clinical benefit rate (defined by patients with objective responses or stable disease >4 months) was 67% (10/15), median progression-free survival (PFS) was 8.6 months, and median overall survival was 20.4 months. Stable disease was observed in the patient with a HER2 mutation (pertuzumab + trastuzumab, n = 1/1, PFS 11.0 months), and partial responses in patients with the PTCH-1 mutation (vismodegib, n = 1/1, PFS 14.3 months), BRAF mutation (vemurafenib, n = 1/1, PFS 18.5 months), and high TMB (atezolizumab, n = 1/1, PFS 5.5+ months). No unexpected toxicity occurred. CONCLUSIONS: Overall, 12 of 19 patients (63%) with advanced SGC, treated with chemotherapy-free regimens matched to specific molecular alterations, experienced an objective response. Data from MyPathway suggest that matched targeted therapy for SGC has promising efficacy, supporting molecular profiling in treatment determination.

Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA.

BACKGROUND: Early cancer detection could identify tumors at a time when outcomes are superior and treatment is less morbid. This prospective case-control sub-study (from NCT02889978 and NCT03085888) assessed the performance of targeted methylation analysis of circulating cell-free DNA (cfDNA) to detect and localize multiple cancer types across all stages at high specificity. PARTICIPANTS AND METHODS: The 6689 participants [2482 cancer (>50 cancer types), 4207 non-cancer] were divided into training and validation sets. Plasma cfDNA underwent bisulfite sequencing targeting a panel of >100 000 informative methylation regions. A classifier was developed and validated for cancer detection and tissue of origin (TOO) localization. RESULTS: Performance was consistent in training and validation sets. In validation, specificity was 99.3% [95% confidence interval (CI): 98.3% to 99.8%; 0.7% false-positive rate (FPR)]. Stage I-III sensitivity was 67.3% (CI: 60.7% to 73.3%) in a pre-specified set of 12 cancer types (anus, bladder, colon/rectum, esophagus, head and neck, liver/bile-duct, lung, lymphoma, ovary, pancreas, plasma cell neoplasm, stomach), which account for ∼63% of US cancer deaths annually, and was 43.9% (CI: 39.4% to 48.5%) in all cancer types. Detection increased with increasing stage: in the pre-specified cancer types sensitivity was 39% (CI: 27% to 52%) in stage I, 69% (CI: 56% to 80%) in stage II, 83% (CI: 75% to 90%) in stage III, and 92% (CI: 86% to 96%) in stage IV. In all cancer types sensitivity was 18% (CI: 13% to 25%) in stage I, 43% (CI: 35% to 51%) in stage II, 81% (CI: 73% to 87%) in stage III, and 93% (CI: 87% to 96%) in stage IV. TOO was predicted in 96% of samples with cancer-like signal; of those, the TOO localization was accurate in 93%. CONCLUSIONS: cfDNA sequencing leveraging informative methylation patterns detected more than 50 cancer types across stages. Considering the potential value of early detection in deadly malignancies, further evaluation of this test is justified in prospective population-level studies.

Optimizing panel-based tumor mutational burden (TMB) measurement.

BACKGROUND: Panel sequencing based estimates of tumor mutational burden (psTMB) are increasingly replacing whole exome sequencing (WES) tumor mutational burden as predictive biomarker of immune checkpoint blockade (ICB). DESIGN: A mathematical law describing psTMB variability was derived using a random mutation model and complemented by the contributions of non-randomly mutated real-world cancer genomes and intratumoral heterogeneity through simulations in publicly available datasets. RESULTS: The coefficient of variation (CV) of psTMB decreased inversely proportional with the square root of the panel size and the square root of the TMB level. In silico simulations of all major commercially available panels in the TCGA pan-cancer cohort confirmed the validity of this mathematical law and demonstrated that the CV was 35% for TMB = 10 muts/Mbp for the largest panels of size 1.1-1.4 Mbp. Accordingly, misclassification rates (gold standard: WES) to separate 'TMBhigh' from 'TMBlow' using a cut-point of 199 mutations were 10%-12% in TCGA-LUAD and 17%-19% in TCGA-LUSC. A novel three-tier psTMB classification scheme which accounts for the likelihood of misclassification is proposed. Simulations in two WES datasets of immunotherapy treated patients revealed that small gene panels were poor predictors of ICB response. Moreover, we noted substantial intratumoral variance of psTMB scores in the TRACERx 100 cohort and identified indel burden as independent marker complementing missense mutation burden. CONCLUSIONS: A universal mathematical law describes accuracy limitations inherent to psTMB, which result in substantial misclassification rates. This scenario can be controlled by two measures: (i) a panel design that is based on the mathematical law described in this article: halving the CV requires a fourfold increase in panel size, (ii) a novel three-tier TMB classification scheme. Moreover, inclusion of indel burden can complement TMB reports. This work has substantial implications for panel design, TMB testing, clinical trials and patient management.

Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers.

INTRODUCTION: Immunotherapy is regarded as one of the major breakthroughs in cancer treatment. Despite its success, only a subset of patients responds-urging the quest for predictive biomarkers. We hypothesize that artificial intelligence (AI) algorithms can automatically quantify radiographic characteristics that are related to and may therefore act as noninvasive radiomic biomarkers for immunotherapy response. PATIENTS AND METHODS: In this study, we analyzed 1055 primary and metastatic lesions from 203 patients with advanced melanoma and non-small-cell lung cancer (NSCLC) undergoing anti-PD1 therapy. We carried out an AI-based characterization of each lesion on the pretreatment contrast-enhanced CT imaging data to develop and validate a noninvasive machine learning biomarker capable of distinguishing between immunotherapy responding and nonresponding. To define the biological basis of the radiographic biomarker, we carried out gene set enrichment analysis in an independent dataset of 262 NSCLC patients. RESULTS: The biomarker reached significant performance on NSCLC lesions (up to 0.83 AUC, P < 0.001) and borderline significant for melanoma lymph nodes (0.64 AUC, P = 0.05). Combining these lesion-wide predictions on a patient level, immunotherapy response could be predicted with an AUC of up to 0.76 for both cancer types (P < 0.001), resulting in a 1-year survival difference of 24% (P = 0.02). We found highly significant associations with pathways involved in mitosis, indicating a relationship between increased proliferative potential and preferential response to immunotherapy. CONCLUSIONS: These results indicate that radiographic characteristics of lesions on standard-of-care imaging may function as noninvasive biomarkers for response to immunotherapy, and may show utility for improved patient stratification in both neoadjuvant and palliative settings.

Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic.

Background: Treatment with immune checkpoint blockade (ICB) with agents such as anti-programmed cell death protein 1 (PD-1), anti-programmed death-ligand 1 (PD-L1), and/or anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) can result in impressive response rates and durable disease remission but only in a subset of patients with cancer. Expression of PD-L1 has demonstrated utility in selecting patients for response to ICB and has proven to be an important biomarker for patient selection. Tumor mutation burden (TMB) is emerging as a potential biomarker. However, refinement of interpretation and contextualization is required. Materials and methods: In this review, we outline the evolution of TMB as a biomarker in oncology, delineate how TMB can be applied in the clinic, discuss current limitations as a diagnostic test, and highlight mechanistic insights unveiled by the study of TMB. We review available data to date studying TMB as a biomarker for response to ICB by tumor type, focusing on studies proposing a threshold for TMB as a predictive biomarker for ICB activity. Results: High TMB consistently selects for benefit with ICB therapy. In lung, bladder and head and neck cancers, the current predictive TMB thresholds proposed approximate 200 non-synonymous somatic mutations by whole exome sequencing (WES). PD-L1 expression influences response to ICB in high TMB tumors with single agent PD-(L)1 antibodies; however, response may not be dependent on PD-L1 expression in the setting of anti-CTLA4 or anti-PD-1/CTLA-4 combination therapy. Disease-specific TMB thresholds for effective prediction of response in various other malignancies are not well established. Conclusions: TMB, in concert with PD-L1 expression, has been demonstrated to be a useful biomarker for ICB selection across some cancer types; however, further prospective validation studies are required. TMB determination by selected targeted panels has been correlated with WES. Calibration and harmonization will be required for optimal utility and alignment across all platforms currently used internationally. Key challenges will need to be addressed before broader use in different tumor types.

A framework to rank genomic alterations as targets for cancer precision medicine: the ESMO Scale for Clinical Actionability of molecular Targets (ESCAT).

Background: In order to facilitate implementation of precision medicine in clinical management of cancer, there is a need to harmonise and standardise the reporting and interpretation of clinically relevant genomics data. Methods: The European Society for Medical Oncology (ESMO) Translational Research and Precision Medicine Working Group (TR and PM WG) launched a collaborative project to propose a classification system for molecular aberrations based on the evidence available supporting their value as clinical targets. A group of experts from several institutions was assembled to review available evidence, reach a consensus on grading criteria and present a classification system. This was then reviewed, amended and finally approved by the ESMO TR and PM WG and the ESMO leadership. Results: This first version of the ESMO Scale of Clinical Actionability for molecular Targets (ESCAT) defines six levels of clinical evidence for molecular targets according to the implications for patient management: tier I, targets ready for implementation in routine clinical decisions; tier II, investigational targets that likely define a patient population that benefits from a targeted drug but additional data are needed; tier III, clinical benefit previously demonstrated in other tumour types or for similar molecular targets; tier IV, preclinical evidence of actionability; tier V, evidence supporting co-targeting approaches; and tier X, lack of evidence for actionability. Conclusions: The ESCAT defines clinical evidence-based criteria to prioritise genomic alterations as markers to select patients for targeted therapies. This classification system aims to offer a common language for all the relevant stakeholders in cancer medicine and drug development.

Differential binding affinity of mutated peptides for MHC class I is a predictor of survival in advanced lung cancer and melanoma.

Background: Cancer mutations generate novel (neo-)peptides recognised by T cells, but the determinants of recognition are not well characterised. The difference in predicted class I major histocompatibility complex (MHC-I) binding affinity between wild-type and corresponding mutant peptides (differential agretopicity index; DAI) may reflect clinically relevant cancer peptide immunogenicity. Our aim was to explore the relationship between DAI, measures of immune infiltration and patient outcomes in advanced cancer. Patients and methods: Cohorts of patients with advanced non-small-cell lung cancer (NSCLC; LUAD, n = 66) and melanoma (SKCM, n = 72) were obtained from The Cancer Genome Atlas. Three additional cohorts of immunotherapy treated patients with advanced melanoma (total n = 131) and NSCLC (n = 31) were analysed. Neopeptides and their clonal status were defined using genomic data. MHC-I binding affinity was predicted for each neopeptide and DAI values summarised as the sample mean DAI. Correlations between mean DAI and markers of immune activity were evaluated using measures of lymphocyte infiltration and immune gene expression. Results: In univariate and multivariate analyses, mean DAI significantly correlated with overall survival in 3/5 cohorts, with evidence of superiority over nonsynonymous mutational and neoantigen burden. In these cohorts, the effect was seen for mean DAI of clonal but not subclonal peptides. In SKCM, the association between mean DAI and survival bordered significance (P = 0.068), reaching significance in an immunotherapy-treated melanoma cohort (P = 0.003). Mean DAI but not mutational nor neoantigen burden was positively correlated with independently derived markers of immune infiltration in both SKCM (P = 0.027) and LUAD (P = 0.024). Conclusions: The association between mean DAI, survival and measures of immune activity support the hypothesis that DAI is a determinant of cancer peptide immunogenicity. Investigation of DAI as a marker of immunologically relevant peptides in further datasets and future clinical studies of neoantigen based immunotherapies is warranted.

Perspective: APOBEC mutagenesis in drug resistance and immune escape in HIV and cancer evolution.

The apolipoprotein B mRNA-editing enzyme, catalytic polypeptide-like (APOBEC) mutational signature has only recently been detected in a multitude of cancers through next-generation sequencing. In contrast, APOBEC has been a focus of virology research for over a decade. Many lessons learnt regarding APOBEC within virology are likely to be applicable to cancer. In this review, we explore the parallels between the role of APOBEC enzymes in HIV and cancer evolution. We discuss data supporting the role of APOBEC mutagenesis in creating HIV genome heterogeneity, drug resistance, and immune escape variants. We hypothesize similar functions of APOBEC will also hold true in cancer.

The European Society for Medical Oncology (ESMO) Precision Medicine Glossary.

Background: Precision medicine is rapidly evolving within the field of oncology and has brought many new concepts and terminologies that are often poorly defined when first introduced, which may subsequently lead to miscommunication within the oncology community. The European Society for Medical Oncology (ESMO) recognises these challenges and is committed to support the adoption of precision medicine in oncology. To add clarity to the language used by oncologists and basic scientists within the context of precision medicine, the ESMO Translational Research and Personalised Medicine Working Group has developed a standardised glossary of relevant terms. Materials and methods: Relevant terms for inclusion in the glossary were identified via an ESMO member survey conducted in Autumn 2016, and by the ESMO Translational Research and Personalised Medicine Working Group members. Each term was defined by experts in the field, discussed and, if necessary, modified by the Working Group before reaching consensus approval. A literature search was carried out to determine which of the terms, 'precision medicine' and 'personalised medicine', is most appropriate to describe this field. Results: A total of 43 terms are included in the glossary, grouped into five main themes-(i) mechanisms of decision, (ii) characteristics of molecular alterations, (iii) tumour characteristics, (iv) clinical trials and statistics and (v) new research tools. The glossary classes 'precision medicine' or 'personalised medicine' as technically interchangeable but the term 'precision medicine' is favoured as it more accurately reflects the highly precise nature of new technologies that permit base pair resolution dissection of cancer genomes and is less likely to be misinterpreted. Conclusions: The ESMO Precision Medicine Glossary provides a resource to facilitate consistent communication in this field by clarifying and raising awareness of the language employed in cancer research and oncology practice. The glossary will be a dynamic entity, undergoing expansion and refinement over the coming years.